Medicine, Faculty of
Pathology and Laboratory Medicine, Department of
DSpace
UBCV
Collinge, Brett
Doctor of Philosophy - PhD
University of British Columbia
Aggressive B-cell lymphomas encompass a heterogeneous group of neoplasms unified by their rapid clinical course and potential for cure with appropriate therapy. Traditional classification systems, grounded in morphology, often fail to capture the molecular diversity that shapes prognosis and therapeutic response. While molecular profiling has refined classification in diffuse large B-cell lymphoma (DLBCL), other entities remain poorly defined. Among these, high-grade B-cell lymphoma with MYC and BCL2 and/or BCL6 rearrangements (HGBCL-DH/TH) and high-grade B-cell lymphoma, not otherwise specified (HGBCL-NOS), represent challenging diagnostic groups with uncertain biological underpinnings. This thesis aimed to define the molecular landscape of HGBCL-DH/TH and HGBCL-NOS to refine their classification and support a biologically informed taxonomy of aggressive B-cell lymphomas. To achieve this, we assembled large study cohorts with detailed clinicopathological data and performed extensive molecular profiling. In HGBCL-DH/TH, tumors with MYC and BCL6 rearrangements exhibited marked heterogeneity, indicating that they do not constitute a single diagnostic group. In contrast, tumors with MYC and BCL2 rearrangements (HGBCL-DH-BCL2) were biologically uniform and exhibited a characteristic gene expression signature. Molecular profiling demonstrated that in HGBCL-DH-BCL2, cooperative genetic alterations converge to dysregulate pathways active in dark zone germinal center B-cells, and that its characteristic gene expression signature closely aligns with transcriptomic profiles of dark zone B-cells. Expression of this signature, termed the dark zone signature (DZsig), extends beyond HGBCL-DH-BCL2 to identify a broader group of lymphomas unified by a dark zone phenotype, providing a biologically informed framework for classification and highlighting potential therapeutic vulnerabilities. Analysis of HGBCL-NOS confirmed that this category is not biologically cohesive but instead comprises tumors with features aligning with established molecular subgroups. The DZsig resolves a substantial fraction of HGBCL-NOS as dark zone lymphomas, while additional cases can be reclassified through genetics-based subtyping of DLBCL, underscoring the limited utility of HGBCL-NOS as a standalone category. Collectively, these findings demonstrate that morphology-based systems alone obscure meaningful biological distinctions. The addition of molecular profiling clarifies the taxonomy of aggressive B-cell lymphomas, enabling refinement of HGBCL-DH/TH and resolution of HGBCL-NOS into molecularly defined subgroups. This framework provides greater diagnostic precision and lays the foundation for developing targeted therapeutic strategies.
INTEGRATING MOLECULAR APPROACHES TO ADVANCE THE DIAGNOSTIC AND THERAPEUTIC FRAMEWORK OF AGGRESSIVE B-CELL LYMPHOMAS by Brett Collinge A DISSERTATION SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (PATHOLOGY AND LABORATORY MEDICINE) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) October 2025 © Brett Collinge, 2025 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: Integrating Molecular Approaches to Advance the Diagnostic and Therapeutic Framework of Aggressive B-Cell Lymphomas submitted by Brett Collinge in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Pathology and Laboratory Medicine Examining Committee: David W. Scott, Professor, Department of Medicine, UBC Supervisor Christian Steidl, Professor, Department of Pathology and Laboratory Medicine, UBC Co-supervisor Stephen Yip, Associate Professor, Department of Pathology and Laboratory Medicine, UBC Supervisory committee member Brad Nelson, Professor, Department of Medical Genetics, UBC University Examiner Alexander Wyatt, Assistant Professor, Department of Urologic Sciences, UBC University Examiner Pierre Milpied, Director, Centre of Immunology of Marseille-Luminy, Aix-Marseille University External Examiner Additional Supervisory Committee Members: Martin Hirst, Professor, Department of Microbiology & Immunology, UBC Supervisory Committee Member Louis M. Staudt, Distinguished Investigator, Center for Cancer Research, National Cancer Institute Supervisory Committee Member iii Abstract Aggressive B-cell lymphomas encompass a heterogeneous group of neoplasms unified by their rapid clinical course and potential for cure with appropriate therapy. Traditional classification systems, grounded in morphology, often fail to capture the molecular diversity that shapes prognosis and therapeutic response. While molecular profiling has refined classification in diffuse large B-cell lymphoma (DLBCL), other entities remain poorly defined. Among these, high-grade B-cell lymphoma with MYC and BCL2 and/or BCL6 rearrangements (HGBCL-DH/TH) and high-grade B-cell lymphoma, not otherwise specified (HGBCL-NOS), represent challenging diagnostic groups with uncertain biological underpinnings. This thesis aimed to define the molecular landscape of HGBCL-DH/TH and HGBCL-NOS to refine their classification and support a biologically informed taxonomy of aggressive B-cell lymphomas. To achieve this, we assembled large study cohorts with detailed clinicopathological data and performed extensive molecular profiling. In HGBCL-DH/TH, tumors with MYC and BCL6 rearrangements exhibited marked heterogeneity, indicating that they do not constitute a single diagnostic group. In contrast, tumors with MYC and BCL2 rearrangements (HGBCL-DH-BCL2) were biologically uniform and exhibited a characteristic gene expression signature. Molecular profiling demonstrated that in HGBCL-DH-BCL2, cooperative genetic alterations converge to dysregulate pathways active in dark zone germinal center B-cells, and that its characteristic gene expression signature closely aligns with transcriptomic profiles of dark zone B-cells. Expression of this signature, termed the dark zone signature (DZsig), extends beyond HGBCL-DH-BCL2 to identify a broader group of lymphomas unified by a dark zone phenotype, providing a biologically informed framework for classification and highlighting potential therapeutic vulnerabilities. iv Analysis of HGBCL-NOS confirmed that this category is not biologically cohesive but instead comprises tumors with features aligning with established molecular subgroups. The DZsig resolves a substantial fraction of HGBCL-NOS as dark zone lymphomas, while additional cases can be reclassified through genetics-based subtyping of DLBCL, underscoring the limited utility of HGBCL-NOS as a standalone category. Collectively, these findings demonstrate that morphology-based systems alone obscure meaningful biological distinctions. The addition of molecular profiling clarifies the taxonomy of aggressive B-cell lymphomas, enabling refinement of HGBCL-DH/TH and resolution of HGBCL-NOS into molecularly defined subgroups. This framework provides greater diagnostic precision and lays the foundation for developing targeted therapeutic strategies. v Lay Summary Aggressive B-cell lymphomas are fast-growing blood cancers that are usually treated with chemotherapy. The type of chemotherapy a patient receives is largely determined by how their cancer looks under the microscope, which oversimplifies the true complexity of the disease. Advances now make it possible to study cancers at the molecular level, such as by examining DNA mutations, revealing that tumors that look the same under the microscope can have very different molecular features. Grouping tumors by these features allows for more tailored, targeted treatments rather than a “one-size-fits-all” chemotherapy approach. This study examined two poorly understood and clinically challenging types of lymphoma by analyzing patient tumors at the molecular level. The findings highlight the limitations of the current classification system and provide a more accurate molecular-based way to group these cancers. This system can guide the use of more effective therapies to ultimately improve patient outcomes. vi Preface All work detailed in this thesis was conducted in the Department of Lymphoid Cancer Research, at the BC Cancer Research Centre, and was approved by the University of British Columbia’s Research Ethics Board (UBC BC Cancer REB certificate numbers H17-02079 and H23-01810). Generative artificial intelligence (GenAI) tools were used in the preparation of this thesis. Specifically, ChatGPT (OpenAI, GPT-4 and GPT-5) was used to edit written sections to improve clarity and conciseness. GenAI was not used to design research, generate or analyze data, or derive scientific conclusions. All experimental work, data analysis, interpretation, and substantive content were carried out by the author, B Collinge, or collaborators. To the best of my knowledge, this dissertation is entirely original, except for material that has been published or accepted for publication. Chapter 1: Components of this chapter contain material from a review article that has been accepted for publication, in which I was co-first author together with Dr. Laura Hilton. Specifically, Section 1.3, selected parts of Sections 1.2 and 1.4, as well as Table 1.1, Figure 1.3, and Figure 1.4, are derived from this publication. The remainder of this chapter represents my own original work. Hilton LK*, Collinge B*, Scott DW. Genomic Taxonomy of Aggressive B-cell Lymphoid Neoplasms. Annu Rev Pathol Mech Dis. In Press. I was the primary contributor to Figure 1.3 and the text included in Section 1.3. DWS was the primary contributor to Table 1.1 and the text included in Sections 1.2. LKH was the primary contributor to Figure 1.5 and the text included in Section 1.4. vii Chapter 2: This chapter includes work that is intended for publication but has not yet been submitted. I was the lead investigator for this work, and under the supervision of Dr. David W Scott, I was primarily responsible for designing the research, analysis and interpretation of data, writing this chapter, and further contributed to data collection. This section contains contributions from the individuals listed below. Collinge B, Hilton LK, Wong JA, Ben-Neriah S, Alduaij W, Boyle M, Meisner B, Miyata-Takata T, Craig JW, Lytle A, Farinha P, Slack GW, Gerrie AS, Villa D, Freeman C, Savage KJ, Sehn LH, Mungall AJ, Steidl C, Morin RD, Scott DW. BC and DWS conceived and designed the study, analyzed and interpreted data, and wrote this chapter. BC, LKH, and JAW performed bioinformatics analyses; BC performed statistical analyses; BC, LKH, WA, MB, BM, T-MT, JWC, AL, PF, GWS, and DWS contributed to assembly of the study cohort; SB-N performed preformed fluorescent in situ hybridization (FISH); SB-N, BC, WA, and DWS performed FISH scoring; JWC, AL, PF, and GWS performed immunohistochemistry (IHC) scoring. WA, ASG, DV, CF, KJS, LHS, and DWS participated in the collection and analysis of clinical data. MB and BC performed digital gene expression profiling; MB, BM, LKH, AJM, CS, RDM, and DWS contributed to the provision of study material and sequencing. Chapter 3: This chapter is derived from two published manuscripts. I was the primary author of the first study and co-first author with SB-N for the second study. For both studies, under the supervision of Dr. David W Scott, I was primarily responsible for designing the research, interpretation and analysis of data, and writing the manuscript, and further contributed to data collection. The contributions of each author are outlined below. viii Collinge B, Ben-Neriah S, L. Chong, Boyle M, Jiang A, T. Miyata-Takata, Farinha P, Craig JW, Slack GW, D. Ennishi, A. Mottok, Meissner B, Chavez EA, Gerrie AS, Villa D, Freeman C, Savage KJ, Sehn LH, Morin RD, Mungall AJ, Gascoyne RD, Marra MA, Connors JM, Steidl C, Scott DW. The Impact of MYC and BCL2 Structural Variants in Tumors of DLBCL Morphology and Mechanisms of False-Negative MYC IHC. Blood. 2021;137(16):2196-2208. BC and DWS conceived and designed this study, analyzed and interpreted data, and wrote the manuscript; BC and LC performed bioinformatics analysis; BC and AJ performed statistical analyses; BC performed in vitro experiments; BC, MB, BM, T-MT, PF, JWC, GWS, DE, and DWS contributed to assembly of the study cohort; SB-N performed FISH; SB-N, BC, AM, and DWS performed FISH scoring; AM, PF, JWC, and GWS performed IHC scoring; ASG, DV, CF, KJS, LHS, JMC, and DWS participated in the collection and analysis of clinical data. MB and BC performed digital gene expression profiling; MB, BM, EAC, RDM, AJM, RDG, MAM, JMC, CS, and DWS contributed to the provision of study material and sequencing. All authors edited and approved the final version of the manuscript. Collinge B*, Ben-Neriah S*, Hilton LK, Alduaij W, Tucker T, Slack GW, Farinha P, Craig JW, Boyle M, Meissner B, Villa D, Gerrie AS, Sehn LH, Savage KJ, Morin RD, Mungall AJ, Steidl C, and Scott DW (2024). Unbalanced MYC break-apart FISH patterns indicate the presence of a MYC rearrangement in HGBCL-DH-BCL2. Blood. 2024;144(15):1611–1616. BC, SB-N, and DWS conceived and designed the study; BC, SB-N, LKH, and DWS analyzed and interpreted data; BC and DWS wrote the manuscript; BC and LKH performed bioinformatics and statistical analyses; BC, WA, GWS, PF, JWC, MB, BM, and DWS contributed to assembly of the study cohort; SB-N and TT performed FISH; SB-N, TT, BC, WA, and DWS performed FISH scoring; WA, DV, ASG, LHS, KJS, and DWS participated in the collection and analysis of clinical data; MB and BC performed digital gene expression profiling; MB, BM, RDM, AJM, CS, and DWS ix contributed to provision of study material and sequencing; all authors edited and approved the final version of the manuscript. Chapter 4: This chapter is derived from a published manuscript in which I was co-first author, together with Dr. Waleed Alduaij. For this study, I was primarily responsible for assembling the cohort, analyzing and interpreting molecular data, and contributed to the writing of related sections and data collection. WA was primarily responsible for analyzing and interpreting clinical data, writing the manuscript, and also made significant contributions to assembling the study cohort. The contributions of each author are outlined below. Alduaij W*, Collinge B*, Ben-Neriah S, Jiang A, Hilton LK, Boyle M, Meissner B, Chong LC, Miyata-Takata T, Slack GW, Farinha P, Craig JW, Lytle A, Savage KJ, Villa D, Gerrie AS, Freeman CL, Gascoyne RD, Connors JM, Morin RD, Sehn LH, Mungall AJ, Steidl C, Scott DW. Molecular Determinants of Clinical Outcomes in a Real-World Diffuse Large B-cell Lymphoma Population. Blood. 2023;141(20):2493-2507. WA, BC, and DWS conceived and designed the study and wrote the manuscript; WA, BC, LKH, and DWS analyzed and interpreted data; AJ, WA, BC, and LKH performed statistical analyses; BC, WA, MB, BM, T-MT, GWS, PF, JWC, AL, and DWS contributed to assembly of the study cohort; SB-N performed FISH; SB-N, BC, WA, and DWS performed FISH scoring; GWS, PF, JWC, and AL performed IHC scoring; WA, KJS, DV, ASG, CLF, JMC, and DWS participated in the collection and analysis of clinical data; MB and BC performed digital gene expression profiling; MB, BM, RDM, AJM, CS, and DWS contributed to provision of study material and sequencing; All authors edited and approved the final version of the manuscript. x Chapter 5: This chapter is derived from a published manuscript in which I was the primary author. Under the supervision of Dr. David W. Scott, I was primarily responsible for designing the research, interpreting and analyzing the data, and writing the manuscript, while also contributing to data collection. The contributions of each author are outlined below. Collinge B, Hilton LK, Wong J, Alduaij W, Ben-Neriah S, Slack GW, Farinha P, Boyle M, Meissner B, Cook JR, Ott G, Rosenwald A, Campo E, Amador C, Greiner TC, Raess PW, Song JY, Inghirami G, Ondrejka SL, Jaffe ES, Weisenburger DD, Chan WC, Holte H, Beiske K, Fu K, Delabie J, Pittaluga S, Iqbal J, Wright G, Savage KJ, Mungall AJ, Staudt LM, Steidl C, Feldman AL, Morin RD, Rimsza LM, and Scott DW. High-grade B-cell lymphoma, not otherwise specified: an LLMPP study. Blood Adv. First Edition. BC, JRC, GO, RDM, LMR, and DWS conceived and designed the study; BC and DWS interpreted data and wrote the manuscript; BC, LKH, and JW analyzed data; BC, SB-N, WA, GWS, PF, MB, BM, JRC, GO, AR, EC, CA, TCG, PWR, JYS, GI, SLO, ESJ, DDW, WCC, HH, KB, KF, JD, SP, GW, ALM, AJM, ALF, LRM, and DWS performed research; WA, GWS, PF, JRC, GO, AR, EC, CA, TCG, PWR, JYS, GI, SLO, JI, KJS, CS, LMR, DWS provided study materials and data; All authors edited and approved the manuscript. Chapter 6: Section 6.3 incorporates text adapted from the review article featured in Chapter 1, for which DWS and LKH were the primary contributors to the relevant portions of text. The remainder of the text and figures in this chapter represent my own original contributions. xi Table of Contents Abstract .................................................................................................................................... iii Lay Summary ............................................................................................................................ v Preface ...................................................................................................................................... vi Table of Contents ..................................................................................................................... xi List of Tables .......................................................................................................................... xvi List of Figures ....................................................................................................................... xvii List of Abbreviations .............................................................................................................. xx Acknowledgements .............................................................................................................. xxiv Chapter 1: Introduction ............................................................................................................. 1 1.1 Non-Hodgkin Lymphoma ....................................................................................................... 1 1.1.1 Epidemiology and Etiology ............................................................................................... 1 1.1.2 History of Lymphoma Classification ................................................................................. 2 1.1.3 Classification of Non-Hodgkin Lymphomas ..................................................................... 3 1.2 Classification of Aggressive B-cell Lymphomas .................................................................... 4 1.2.1 2017 WHO Classification System .................................................................................... 6 1.2.2 Limitations of Aggressive B-cell Lymphoma Classification .............................................. 9 1.3 B-Cell Development and Lymphomagenesis ....................................................................... 11 1.3.1 Early Development in the Bone Marrow ......................................................................... 11 1.3.2 B-Cell Maturation and Activation in Lymphoid Tissue .................................................... 12 1.3.3 The Germinal Center Reaction ...................................................................................... 13 1.4 Molecular Classification of Aggressive B-Cell Lymphomas ................................................. 17 1.4.1 Burkitt Lymphoma .......................................................................................................... 18 xii 1.4.2 Diffuse Large B-cell Lymphoma, Not Otherwise Specified ............................................ 21 1.4.3 Molecular Stratification in Clinical Trials ......................................................................... 24 1.4.4 Completing the Spectrum ............................................................................................... 26 1.5 Thesis Outline and Aims ...................................................................................................... 28 Chapter 2: The Molecular Landscape of HGBCL-DH/TH ....................................................... 37 2.1 Introduction ........................................................................................................................... 37 2.2 Materials and Methods .......................................................................................................... 38 2.2.1 Study cohort .................................................................................................................... 38 2.2.2 FISH and IHC .................................................................................................................. 39 2.2.3 Digital gene expression profiling ..................................................................................... 40 2.2.4 GCB DLBCL comparison cohort for mutation analysis ................................................... 40 2.2.5 GEP extension cohort ..................................................................................................... 40 2.2.6 DNA library construction and sequencing ....................................................................... 41 2.2.7 Detection of somatic mutation and structural variants .................................................... 41 2.3 Results .................................................................................................................................. 42 2.3.1 The molecular landscape of HGBCL-DH-BCL6 .............................................................. 42 2.3.2 The molecular landscape of HGBCL-DH-BCL2 .............................................................. 44 2.3.3 DHITsig expression extends beyond HGBCL-DH-BCL2 to identify DZ lymphomas ....... 46 2.4 Discussion ............................................................................................................................. 47 Chapter 3: Refining FISH-Based Diagnostic Approaches and Criteria for HGBCL-DH-BCL2 ................................................................................................................ 63 3.1 Introduction ........................................................................................................................... 63 3.2 Materials and Methods .......................................................................................................... 65 xiii 3.2.1 DLBCL cohort .................................................................................................................. 65 3.2.2 HGBCL-DH-BCL2 cohort ................................................................................................ 65 3.2.3 FISH and IHC analyses ................................................................................................... 66 3.2.4 Digital gene expression profiling ..................................................................................... 67 3.2.5 MYC mutation calling ...................................................................................................... 67 3.2.6 Modeling MYC-N11S ...................................................................................................... 68 3.2.7 Digital droplet PCR .......................................................................................................... 69 3.2.8 Western blot analysis ...................................................................................................... 70 3.2.9 Cell line IHC .................................................................................................................... 71 3.2.10 Identification of MYC structural variants ........................................................................ 71 3.2.11 Statistical analyses ........................................................................................................ 73 3.3 Results .................................................................................................................................. 73 3.3.1 Association of MYC and BCL2 SVs with mRNA and protein expression ........................ 73 3.3.2 Mechanisms of MYC and BCL2 deregulation within molecular subtypes ....................... 74 3.3.3 Role of MYC and BCL2 CNAs in contributing to HGBCL-DH-BCL2 biology ................... 76 3.3.4 Mechanisms of discordance between MYC mRNA and detectable protein .................... 76 3.3.5 N11S mediated Y69 epitope disruption leads to false-negative MYC IHC staining ........ 77 3.3.6 The significance of unbalanced MYC break-apart FISH patterns ................................... 78 3.4 Discussion ............................................................................................................................. 80 Chapter 4: Gene Expression Profiling Identifies a Poor-Prognosis Subgroup that Extends Beyond FISH-defined HGBCL-DH-BCL2 ................................................................ 102 4.1 Introduction ......................................................................................................................... 102 4.2 Materials and Methods ........................................................................................................ 104 4.2.1 Study cohort .................................................................................................................. 104 xiv 4.2.2 FISH and IHC ................................................................................................................ 105 4.2.3 Digital gene expression profiling ................................................................................... 106 4.2.4 Rearrangement partner definition and identification by sequencing ............................. 107 4.2.5 Treatment categories and survival endpoints ............................................................... 108 4.2.6 Diagnosis-to-treatment interval analysis ....................................................................... 109 4.2.7 Statistical analysis ......................................................................................................... 109 4.3 Results ................................................................................................................................ 109 4.3.1 Characteristics of the study population ......................................................................... 109 4.3.2 Real-word prevalence and outcomes of GEP-defined subgroups ................................ 110 4.3.3 Comparison of FISH- and GEP-defined subgroups ...................................................... 111 4.3.4 Association of GEP molecular subgroups with diagnosis to treatment interval ............ 113 4.4 Discussion ........................................................................................................................... 113 Chapter 5: Molecular Characterization and Classification of HGBCL-NOS ...................... 131 5.1 Introduction ......................................................................................................................... 131 5.2 Materials and Methods ........................................................................................................ 132 5.2.1 Study cohort .................................................................................................................. 132 5.2.2 FISH and IHC ................................................................................................................ 133 5.2.3 Digital gene expression profiling ................................................................................... 134 5.2.4 Comparison of molecular features in HGBCL-NOS to BL and DLBCL ......................... 134 5.2.5 DNA Library construction and sequencing .................................................................... 135 5.2.6 DTarget sequencing of the DLBCL-NOS cohort ........................................................... 136 5.2.7 Alignment and variant calling ........................................................................................ 136 5.2.8 RNAseq analysis ........................................................................................................... 137 xv 5.2.9 Centralized pathology review ........................................................................................ 137 5.1.10 Statistical analysis ....................................................................................................... 137 5.3 Results ................................................................................................................................ 138 5.3.1 Clinical and molecular characteristics of the study cohort ............................................ 138 5.3.2 The genetic landscape of HGBCL-NOS ........................................................................ 139 5.3.3 Molecular classification of HGBCL-NOS ....................................................................... 141 5.3.4 Centralized pathology review ........................................................................................ 143 5.4 Discussion ........................................................................................................................... 144 Chapter 6: Conclusion ............................................................................................................ 165 6.1 Summary ............................................................................................................................. 165 6.1.1 Impact on Current Aggressive B-Cell Lymphoma Classification ................................... 165 6.1.2 Toward Molecular Classification of Aggressive B-Cell Lymphomas ............................. 167 6.2 Limitations and Future Directions ....................................................................................... 171 6.3 Integration into Classification and Clinical Practice ............................................................ 174 Bibliography ............................................................................................................................ 177 xvi List of Tables Table 1.1. Aggressive B-cell lymphoma entities in the 2017 Revised 4th Edition of the WHO Classification of Tumors of Haematopoietic and Lymphoid Tissues .......................... 31 Table 2.1. Clinical and pathological characteristics of the study cohort .................................... 52 Table 2.2. Clinical and pathological characteristics of HGBCL-DH-BCL2 by transformation status .............................................................................................................. 54 Table 2.3. Clinical and pathological characteristics of HGBCL-DH-BCL2 by morphology ......... 56 Table 3.1. CRISPR gRNA and N11S ssODN repair template ................................................... 85 Table 3.2. Primers for Sanger sequencing, cloning, and ddPCR .............................................. 86 Table 3.3. Demographic and Clinical Characteristics of Study Cohorts .................................... 87 Table 4.1. Comparison of baseline clinical characteristics between the entire study population and patients with evaluable gene expression profiling data ................................... 117 Table 4.2. Baseline clinical and pathological characteristics of R-CHOP-treated patients according to refined COO subgroups .......................................................................... 119 Table 4.3. Clinical outcomes in R-CHOP-treated patients according to GEP-defined molecular subgroups after adjusting for IPI risk factors by multivariable Cox regression ......... 121 Table 4.4. Clinical characteristics of R-CHOP-treated patients with DZsig+ tumors by HGBCL-DH-BCL2 status ......................................................................................................... 122 Table 5.1. Clinical and pathological characteristics of tumors submitted as HGBCL-NOS by local pathology review .......................................................................................................... 147 Table 5.2. Comparison of clinical characteristics in HGBCL-NOS and DLBCL-NOS ............... 149 Table 5.3. Significant CNAs identified by GISTIC2 in HGBCL-NOS ....................................... 151 Table 5.4. Clinical characteristics of tumors by central pathology review diagnosis ............... 152 xvii List of Figures Figure 1.1. Overview of lymphoma subtype distribution by lineage and clinical behavior ......... 32 Figure 1.2. Diagnostic methods and features used to classify aggressive B-cell lymphomas in the 2017 WHO Revised 4th Edition ..................................................................... 33 Figure 1.3. Normal B-cell development ...................................................................................... 34 Figure 1.4. Mechanisms of Sustained Proliferative Signaling in Aggressive B-cell Lymphomas ................................................................................................................................ 35 Figure 1.5. Classification of aggressive B-cell lymphomas ........................................................ 36 Figure 2.1. The molecular landscape of HGBCL-DH/TH ........................................................... 58 Figure 2.2. Similar molecular features between de novo and transformed HGBCL-DH-BCL2 ....................................................................................................................... 59 Figure 2.3. Similar molecular features between HGBCL-DH-BCL2 tumors with DLBCL or high-grade morphology ............................................................................................................... 60 Figure 2.4. Comparison of the mutational landscape between HGBCL-DH-BCL2 and GCB-DLBCL ............................................................................................................................... 61 Figure 2.5. DHITsig expression extends beyond HGBCL-DH-BCL2 and identifies tumors sharing features of dark zone germinal center B cells ................................................................ 62 Figure 3.1. The role of MYC and BCL2 SVs in deregulating mRNA and protein expression .... 88 Figure 3.2. Association of MYC amplification and high-level copy number gains with MYC mRNA expression ....................................................................................................................... 89 Figure 3.3. The role of MYC and BCL2 SVs in deregulation of mRNA and protein expression within molecular subtypes ........................................................................................ 90 Figure 3.4. MYC and BCL2 mRNA expression by GEP subtype .............................................. 92 Figure 3.5. Atypical double-hit tumors do not share a similar GEP as HGBCL-DH-BCL2 ......... 93 Figure 3.6. Association of MYC and BCL2 mRNA expression with IHC status ......................... 95 xviii Figure 3.7. MYC-N11S polymorphism is associated with decreased MYC protein expression as detected by IHC using the Y69 antibody ............................................................. 96 Figure 3.8. MYC-N11S polymorphism disrupts Y69 binding ...................................................... 97 Figure 3.9. MYC-N11S polymorphism confers false negative MYC IHC staining when present on the translocated allele ............................................................................................... 98 Figure 3.10. Patterns of MYC break-apart FISH probe signals and their proportions in HGBCL-DH-BCL2 ....................................................................................................................... 99 Figure 3.11. MYC partner gene and rearrangement architecture in HGBCL-DH-BCL2 according to MYC break-apart FISH pattern ............................................................................. 100 Figure 3.12. Impact of MYC break-apart FISH patterns on MYC and DZsig expression ......... 101 Figure 4.1. Overview of the study population and biopsy availability for research analyses ... 123 Figure 4.2. FFP in the entire study population compared to patients with or without GEP data using the DLBCL90 assay ................................................................................................ 124 Figure 4.3. Clinical outcomes according to gene expression-defined molecular subgroups ... 125 Figure 4.4. Clinical outcomes in patients treated with R-CHOP according to structural variants in MYC and BCL2 detected by FISH ........................................................................... 126 Figure 4.5. Clinical and molecular characteristics of DZsig+ tumors in relation to HGBCL-DH-BCL2 ..................................................................................................................... 127 Figure 4.6. Distribution of MYC and BCL2 rearrangement status in DZsig+ tumors and associated outcomes ......................................................................................................... 128 Figure 4.7. Refined COO subgroups are associated with different diagnosis-to-treatment intervals (DTI) in R-CHOP treated patients ............................................................................... 129 Figure 5.1. Clinical and molecular features of HGBCL-NOS ................................................... 154 Figure 5.2. The genetic landscape of HGBCL-NOS ................................................................ 155 Figure 5.3. Comparison of the genetic landscape of HGBCL-NOS tumors with intermediate or blastoid morphology ......................................................................................... 157 xix Figure 5.4. Comparison of the mutational landscape of HGBCL-NOS to DLBCL-NOS and EBV-negative BL ................................................................................................................ 158 Figure 5.5. Molecular features of GCB tumors ......................................................................... 160 Figure 5.6. Molecular features of ABC HGBCL-NOS ............................................................... 161 Figure 5.7. Outcomes in HGBCL-NOS by refined COO subgroups ......................................... 162 Figure 5.8. Comparison of tumors by centralized pathology review assignment ..................... 162 xx List of Abbreviations A53: A LymphGen genetic subtype defined by aneuploidy and TP53 mutations ABC: Activated B-cell-like ALK: Anaplastic Lymphoma Kinase B-ALL: B-cell acute lymphoblastic leukemia/lymphoma BCR: B-cell receptor BL: Burkitt lymphoma BL11q: Burkitt-like lymphoma with 11q aberration BLGSP: Burkitt Lymphoma Genome Sequencing Project BN2: A LymphGen genetic subtype characterized by BCL6 rearrangement and NOTCH2 mutations CAR: Chimeric antigen receptor CD40L: CD40 ligand CI: Confidence interval CNA: Copy number alteration CNS: Central nervous system CODOX-MR: Cyclophosphamide, vincristine, doxorubicin, methotrexate, and rituximab COO: Cell-of-origin CPR: Centralized pathology review CSR: Class-switch recombination DA-EPOCH-R: Dose adjusted etoposide, doxorubicin, vincristine, cyclophosphamide, prednisone and rituximab ddPCR: Digital droplet PCR DHITsig: Double-hit signature DLBCL: Diffuse large B-cell lymphoma DLBCL-NOS: Diffuse large B-cell lymphoma, not otherwise specified DPE: Dual protein expressor DTI: Diagnosis-to-treatment interval DZ: Dark zone DZsig: Dark zone signature EBER: Epstein-Barr virus-encoded RNA EBV: Epstein-Barr virus xxi ECOG PS: Eastern Cooperative Oncology Group performance status EZB: A LymphGen genetic subtype characterized by BCL2 rearrangement and mutations in epigenetic modifiers FDA: Food and Drug Administration FDC: Follicular dendritic cell FFP: Freedom from progression FFPE: Formalin-fixed, paraffin-embedded FISH: Fluorescence in situ hybridization FL: Follicular lymphoma GC: Germinal center GCB: Germinal center B-cell-like GEP: Gene expression profiling HAEM5: Fifth edition of the WHO classification of haematolymphoid neoplasms HDMTX: High-dose methotrexate HGBCL-DH-BCL2: High-grade B-cell lymphoma with MYC and BCL2 rearrangements HGBCL-DH-BCL6: High-grade B-cell lymphoma with MYC and BCL6 rearrangements HGBCL-DH/TH: High-grade B-cell lymphoma with MYC and BCL2 and/or BCL6 rearrangements HGBCL-NOS: High-grade B-cell lymphoma, not otherwise specified HGBCL-TH: High-grade B-cell lymphoma with MYC, BCL2, and BCL6 rearrangement HHV8: Human herpesvirus 8 HIV: Human immunodeficiency virus HL: Hodgkin lymphoma HR: Hazard ratio HyperCVAD: Hyperfractionated cyclophosphamide, vincristine, doxorubicin, and dexamethasone ICC: International Consensus Classification IG: Immunoglobulin IGH: Immunoglobulin heavy chain IgV: Immunoglobulin variable region IHC: Immunohistochemistry IPI: International Prognostic Index ISH: In situ hybridization xxii IVAC-R: Ifosfamide, etoposide, cytarabine, and rituximab LBCL: Large B-cell lymphoma LDH: Lactate dehydrogenase LG: Lost green signal LLBC: Lunenburg Lymphoma Biomarker Consortium LLMPP: Lymphoma/Leukemia Molecular Profiling Project LR: Lost red signal LZ: Light zone MBC: Memory B cell mBL: Molecular Burkitt lymphoma MCD: A LymphGen genetic subtype characterized by MYD88 L265P and CD79B mutations MHC: Major histocompatibility complex MHG: Molecular high-grade N1: A LymphGen genetic subtype defined by NOTCH1 mutations NA: Not available NBC: Naive B cell NHL: Non-Hodgkin lymphoma NK: Natural killer OS: Overall survival PBL: Plasmablastic lymphoma PC: Plasma cell PCA: Principal component analysis PFS: Progression-free survival PMBCL: Primary mediastinal large B-cell lymphoma pola-R-CHP: Polatuzumab vedotin, rituximab, cyclophosphamide, doxorubicin, and prednisone R-CEOP: Rituximab, cyclophosphamide, etoposide, vincristine, and prednisone R-CHOP: Rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone RAG: Recombination-activating gene RB-CHOP: Rituximab, bortezomib, cyclophosphamide, doxorubicin, vincristine, and prednisone REAL: Revised European-American Lymphoma RNP: Ribonucleoprotein RS: Reed-Sternberg xxiii SD: Standard deviation SHM: Somatic hypermutation SLO: Secondary lymphoid organ ssODN: Single-stranded oligodeoxynucleotide ST2: A LymphGen genetic subtype enriched in SOCS1 and TET2 mutations SV: Structural variant TFH: T follicular helper TH: T helper TLR: Toll-like receptor TMA: Tissue microarray TNA: Total nucleic acids TSCA: Truseq Custom Amplicon ULN: Upper limit of normal VAF: Variant allele frequency VST: Variance stabilizing transformation WES: Whole exome sequencing WGS: Whole genome sequencing WHO: World Health Organization WT: Wildtype xxiv Acknowledgements I would first like to express my deepest gratitude to my supervisor, David Scott, for his guidance, support, and encouragement throughout my PhD. His mentorship has been invaluable both scientifically and personally. I am also grateful to my co-supervisor, Christian Steidl, your support throughout my studies is greatly appreciated. I would like to thank the current and past members of the Centre for Lymphoid Cancer for their collaboration and camaraderie, particularly Laura Hilton, Jasper Wong, Waleed Alduaij, Susana Ben-Neriah, Adèle Telenius, Merrill Boyle, and Barbara Meissner. During my time at the NIH, I was fortunate to be supervised by Lou Staudt, who welcomed me into his lab and provided invaluable mentorship, and Jim Phelan, whose supervision and support I am grateful for. I also thank the members of the NCI Lymphoid Malignancies Branch, especially Smriti Kanangat and Vivian Morris, for their friendship, assistance, and advice during my time in Bethesda. I am grateful to my supervisory committee members, Stephen Yip and Martin Hirst, for their guidance, thoughtful feedback, and encouragement throughout my graduate studies. To my friends and colleagues in the UBC Department of Pathology and Laboratory Medicine, and especially Elyn Rowe, thank you for your friendship and many thoughtful conversations along the way. Finally, I would like to extend my deepest thanks to my friends and family, whose love and support made this possible. In particular, Tyrone Robertson, Christina Robertson, Shawn Collinge, Simon Collinge, and Christine Dehner – thank you, this would not have been possible without you. 1 Chapter 1: Introduction 1.1 Non-Hodgkin lymphoma 1.1.1 Epidemiology and Etiology Non-Hodgkin lymphoma (NHL) encompasses a markedly diverse group of hematological malignancies that originate from lymphocytes at various stages of development. Lymphocytes include B cells, which produce antibodies; T cells, which coordinate immune responses and kill infected or abnormal cells; and natural killer (NK) cells, which target and destroy virally infected or transformed cells. NHLs typically present in lymphoid organs such as lymph nodes, the spleen, or bone marrow, but can also involve extranodal tissues. While NHLs are primarily thought to originate from errors during normal lymphocyte development (as discussed in section 1.3), several other factors can contribute to lymphomagenesis.1 These include immunodeficiencies (both congenital and acquired, as well as iatrogenic immunosuppression such as post-organ transplantation), and infectious agents, such as Epstein-Barr virus (EBV), which is strongly linked to certain NHL subtypes. Autoimmune conditions, such as Sjögren's syndrome, also elevate NHL susceptibility, likely due to chronic inflammation and persistent antigenic stimulation. Collectively, NHLs are the sixth most common cancer in Canada, with approximately 10,900 new cases diagnosed annually.2 NHLs exhibit a slight male predominance, with a male-to-female ratio of approximately 1.3:1. The incidence of NHL increases with age, with a median age at diagnosis of approximately 67 years. Globally, NHLs are more common in developed countries, particularly in North America and Europe. However, demographic patterns vary widely across NHL subtypes, with some forms more common in children and young adults, and others endemic to specific geographic regions. 2 1.1.2 History of Lymphoma Classification The heterogeneity of NHLs presents as a wide spectrum of clinical behaviors, from slow-growing diseases to highly aggressive tumors. Consequently, prognosis and clinical management vary substantially among NHL subtypes, necessitating an accurate, universally accepted classification system. Historically, NHL classification began with its distinction from Hodgkin lymphoma (HL), the other major branch of mature lymphoid malignancies (Figure 1.1A). In Thomas Hodgkin’s 1832 report, “On Some Morbid Appearances of the Absorbent Glands and Spleen,” he detailed specific nodal and splenic abnormalities.3 This initial description, later termed "Hodgkin's disease" by Samuel Wilks, laid the groundwork for recognizing HL as a distinct entity. The defining feature of HL is the presence of Reed-Sternberg (RS) cells: large, multinucleated cells within a prominent inflammatory background. Before the formal introduction of the term “non-Hodgkin lymphoma” in 1956,4 lymphoid malignancies lacking the characteristic RS cells of Hodgkin lymphoma were referred to by various overlapping and imprecise terms, including lymphosarcoma, pseudoleukemia, reticulosarcoma, and aleukemia. These early designations encompassed a wide range of diseases – most of which would now fall under the umbrella of NHL, but also likely included other conditions, including some non-malignant abnormalities.5 As such, NHLs initially emerged as a diagnosis of exclusion, defined not by the features they possessed, but rather by what they were not. While the binary distinction between HL and NHL was a pivotal first step in lymphoma classification, it overlooked the significant biological and clinical heterogeneity among NHLs. This early limitation highlights a recurring challenge in lymphoma classification: initial systems, though necessary and useful, often oversimplify complex biological realities. As scientific understanding and diagnostic tools have advanced, early classification frameworks have required continual refinement to more accurately represent the true diversity of NHLs. Consequently, this early binary distinction has evolved into a modern classification system that now recognizes over 60 distinct NHL subtypes.6,7 3 The foundation for the current NHL classification schema was established by the 1994 Revised European-American Lymphoma (REAL) classification.8 This system proposed that lymphomas should be categorized based on their presumed normal cellular counterparts whenever possible, as this offers the most meaningful insights into disease biology, prognosis, and treatment response. However, the REAL classification also recognized the limitations of existing knowledge about lymphocyte differentiation and lymphoma pathogenesis, acknowledging that a purely biological classification was not yet feasible. As a result, it adopted a pragmatic framework that defined identifiable disease entities using a combination of morphologic, immunophenotypic, and genetic criteria. Importantly, the REAL classification emphasized the need for regular updates, recognizing that future advances in immunology and molecular pathology may lead to the discovery of new subtypes or reveal that some currently defined entities do not represent distinct biological diseases. The task of updating and expanding the REAL classification was taken on by the World Health Organization (WHO) as part of its broader effort to standardize tumor classification across all organ systems. Building on the REAL framework, the WHO formally introduced its classification of lymphoid neoplasms in the 3rd edition of the Classification of Tumours of Haematopoietic and Lymphoid Tissues in 2001. This integrative approach—combining morphology, immunophenotype, genetic features, and clinical behavior—has remained central to successive editions, including the 4th edition (2008)9, revised 4th edition (2017)1, and 5th edition (2022)7. In the sections that follow, the discussion will be focused on the 2017 WHO classification, with updates from the 2022 5th edition addressed separately in Section 2.4. 4 Figure 1.1. Overview of lymphoma subtype distribution by lineage and clinical behavior. (A) Relative frequency of Hodgkin lymphoma (HL) and non-Hodgkin lymphoma (NHL) among all lymphomas. (B) Distribution of NHLs by lineage and clinical behavior. Approximately 90% of NHLs are of B-cell origin, which are further characterized as aggressive (50%) or indolent (40%) based on their clinical presentation. The remaining ~10% of NHLs are derived from T or NK cells. NHL subtype frequencies reflect estimates from the 2017 WHO Classification of Tumours of Haematopoietic and Lymphoid Tissues.1 HodgkinLymphoma10%Non−HodgkinLymphoma90%LymphomaAT− andNK−cell10%IndolentB−cell40%AggressiveB−cell50%Non−HodgkinLymphoma (NHL)B 5 1.1.3 Classification of Non-Hodgkin Lymphomas The 2017 revised 4th edition WHO Classification of Tumours of Haematopoietic and Lymphoid Tissues recognizes over 60 NHL subtypes, which are broadly categorized into two groups based on their presumed normal cellular counterparts: mature B-cell neoplasms (comprising B-cell lymphomas and plasma cell neoplasms), and mature T- and NK-cell neoplasms (Figure 1.1B).1 Mature B-cell lymphomas make up ~85–90% of all NHLs and can present with either indolent or aggressive clinical behavior. Indolent lymphomas make up 30-40% of mature B-cell lymphomas and are slower-growing malignancies that are typically not curable but, when required, are highly responsive to palliative treatment, with many patients living for years or even decades with good quality of life. A major challenge in their clinical management is histological transformation to an aggressive B-cell lymphoma. Among indolent lymphomas, follicular lymphoma (FL) is the most common, comprising approximately 65–70% of cases. FL carries an estimated annual transformation risk of ~3%,10 and more than half of FL-related deaths are attributable to transformation.11 Despite this, histologic transformation accounts for only a minority of aggressive B-cell lymphomas, with the vast majority (85–90%) arising de novo, without a prior history of indolent disease. Aggressive B-cell lymphomas comprise the remaining 60–70% of mature B-cell lymphomas and thus constitute over half of all NHLs. The term aggressive reflects the rapid clinical progression driven by high proliferative activity, typically necessitating prompt clinical intervention, with the corollary that, even when widely disseminated, they retain the potential for cure with appropriate treatment strategies. As such, a biologically informed classification of aggressive B-cell lymphomas is essential to guide treatment decisions. 6 1.2 Classification of Aggressive B-cell Lymphomas Although unified by their B-cell origin and aggressive clinical behavior, aggressive B-cell lymphomas encompass a biologically diverse group of neoplasms. The 2017 revised 4th edition of the WHO Classification of Tumours of Haematopoietic and Lymphoid Tissues builds on the pragmatic framework established by the 1994 Revised European-American Lymphoma (REAL) classification, which aimed to define a list of “real” disease entities that could be reliably identified using routine diagnostic tools. This approach emphasized practical definitions based on a combination of histological appearance, immunophenotype, and a limited set of clinical and cytogenetic features. Using this framework, the 2017 WHO classification recognizes 20 distinct aggressive B-cell lymphoma entities (Table 1.1), representing a significant expansion from the two entities originally described in the REAL classification: diffuse large B-cell lymphoma (DLBCL) and Burkitt lymphoma (BL). While histology remains foundational to diagnosis, the expanded classification reflects growing integration of clinical, immunophenotypic, and genetic data to more accurately capture the biological diversity of aggressive B-cell lymphomas. Understanding the principles of the REAL classification is essential to appreciating how current lymphoma taxonomy has evolved. Although BL was defined in part by MYC rearrangement and a characteristic immunophenotype, these features were also reported, though less commonly, in DLBCL, limiting their diagnostic specificity. As a result, morphology served as the key distinguishing feature between these two entities. BL is characterized by a diffuse proliferation of monomorphic, medium-sized B cells with round nuclei, finely clumped chromatin, multiple paracentrally located nucleoli, and deeply basophilic cytoplasm. Mitotic figures are numerous, and the uniform size and density of the tumor cells often result in squared-off cytoplasmic borders. In contrast, DLBCL exhibits considerable morphological heterogeneity, both within and between cases. It is defined by a diffuse proliferation of medium to large B cells with nuclei more than twice the size of those of normal lymphocytes. Three main morphological 7 variants are recognized—centroblastic, immunoblastic, and anaplastic—along with several rarer forms, each associated with distinct nuclear and cytoplasmic features. While some tumors appear monomorphic, most cases are polymorphic and contain an admixture of centroblasts and immunoblasts. Due to poor reproducibility and lack of clear clinical or biological correlates, DLBCL is not subclassified by morphological variant. Under the 2017 WHO classification, aggressive B-cell lymphomas are broadly categorized based on tumor cell appearance (Table 1.1). This includes cases with classic BL or DLBCL cytology and further recognizes a category of “high-grade” tumors. This group encompasses cases with either intermediate features between BL and DLBCL or blastoid cytology. Intermediate cases typically consist of medium-sized cells with greater variability in nuclear size and shape than is seen in classic BL, while blastoid cytology is characterized by cells with finely dispersed chromatin, scant cytoplasm, and inconspicuous nucleoli – features reminiscent of lymphoblastic lymphoma. The majority of aggressive B-cell lymphomas now fall within the LBCL group, which includes newly defined entities that, in earlier classifications, would likely have been diagnosed as DLBCL. As a result, DLBCL is now designated as DLBCL, not otherwise specified (DLBCL-NOS), underscoring its role as a default category for cases that do not meet criteria for a more specific subtype. This shift reflects the ongoing refinement of lymphoma classification, driven by the integration of clinical, immunophenotypic, and cytogenetic data to more precisely define biologically distinct entities that were previously grouped based largely on morphology. 1.2.1 2017 WHO Classification System Aggressive B-cell lymphomas typically present with rapidly progressive disease, often accompanied by systemic symptoms, reflecting their high proliferative rate. Common presenting features include rapidly enlarging lymphadenopathy and B symptoms, which includes fever, weight loss, and drenching night sweats. Most patients are diagnosed at an advanced stage, with 8 involvement of both nodal and extranodal sites. Clinical features such as age, sex, immune status, and site of involvement can provide initial diagnostic clues that help narrow the diagnosis. A small subset of aggressive B-cell lymphoma entities are further defined in part by clinical features, particularly the primary site of disease involvement. For example, a diagnostic requirement of primary DLBCL of the central nervous system (CNS) is disease confinement to the brain parenchyma, spinal cord, leptomeninges, or eyes. Similarly, primary mediastinal large B-cell lymphoma (PMBCL) diagnosis requires the presence of a large anterior mediastinal mass, which typically presents in young female patients, reflecting the role of clinical features as both required and supporting elements in diagnosis. Anatomical site can also support diagnosis in aggressive B-cell lymphomas that, while not defined by location, show strong predilections for specific regions. For example, plasmablastic lymphoma (PBL) commonly arises in the oral cavity or other mucosal sites, particularly in patients with HIV. Recognition of these distinctive patterns, when integrated with histologic, immunophenotypic, and genetic findings, is essential for defining the differential diagnosis and guiding appropriate ancillary testing. Histology remains a cornerstone of aggressive B-cell lymphoma classification and is essential for identifying the morphological features that help distinguish between subtypes. Key aspects of morphology relevant to classification include tumor cell cytology, the composition of non-neoplastic cells in the tumor microenvironment, and the overall tumor growth pattern. Cytology refers to the appearance of individual cells, including their size, shape, and nuclear features, and helps distinguish between subtypes (Table 1.1). The surrounding non-neoplastic cell populations, including reactive T cells, histiocytes, and stromal elements, can also provide diagnostic insights. For example, in T-cell/histiocyte-rich large B-cell lymphoma, scattered malignant B cells are found within a background densely populated by reactive immune cells. The spatial arrangement of both malignant and non-malignant cells determines the tumor’s growth pattern. While aggressive B-cell lymphomas can exhibit a range of growth patterns, most display 9 a diffuse pattern, characterized by complete effacement of the normal lymphoid architecture by tumor cells. This contrasts with many indolent lymphomas, which often partially preserve normal tissue architecture. Some growth patterns are more specific and diagnostically informative. For example, intravascular large B-cell lymphoma is defined by the selective proliferation of neoplastic cells within the lumina of small blood vessels. In addition to histology, immunophenotyping plays a critical role in the classification of aggressive B-cell lymphomas. Immunophenotype refers to the pattern of protein expression on the surface and within cells, which is reflective of their lineage and differentiation stage. In routine diagnostic practice, immunophenotyping is most commonly assessed by immunohistochemistry (IHC), which uses antibodies to detect specific marker proteins of interest. This approach is essential for confirming B-cell lineage and helps further refine classification by linking tumors to their closest normal cellular counterparts (Figure 1.2). For instance, IHC can distinguish between morphologically similar entities by identifying unique marker expression patterns. Plasmablastic lymphoma (PBL), which can histologically resemble DLBCL, displays strong expression of plasma cell-associated markers, such as CD138, while showing little or no expression of pan B-cell markers, such as CD20 and PAX5 (Figure 1.2). Other aggressive B-cell lymphoma subtypes are defined by distinct immunophenotypic features, such as ALK-positive large B-cell lymphoma, which displays strong cytoplasmic ALK protein expression, and HHV8-positive DLBCL-NOS, which is associated with infection by human herpesvirus 8 (HHV8) and identified by nuclear expression of the viral LANA-1 protein using IHC. EBV-positive DLBCL-NOS is identified through a related approach, using EBER in situ hybridization (ISH), which detects virally encoded RNA transcripts and results in strong nuclear staining in EBV-infected tumor cells. These ancillary techniques are essential for distinguishing morphologically similar but biologically distinct entities. 10 Table 1.1: Aggressive B-cell lymphoma entities in the 2017 Revised 4th Edition of the WHO Classification of Tumors of Haematopoietic and Lymphoid Tissues. Revised 4th Edition Cytology Proportion Estimate† No. WGS/WES No. Targeted Diffuse large B-cell lymphoma, not otherwise specified LBCL 75% ~3000 ~4,400 Primary diffuse large B-cell lymphoma of the central nervous system LBCL 5% 200-300 200-300 High-grade B-cell lymphoma with MYC and BCL2 and/or BCL6 rearrangements High-grade/LBCL 4-5% ~50 50-100 Primary mediastinal (thymic) large B-cell lymphoma LBCL 4% 200-300 300-500 High-grade B-cell lymphoma, not otherwise specified High-grade 1-4% ~10 50-100 Burkitt lymphoma Medium 2-3% 400-500 200-300 EBV-positive diffuse large B-cell lymphoma, not otherwise specified LBCL 1-2% 10-20 20-50 T-cell/histiocyte-rich large B-cell lymphoma LBCL 1-2% 0 20-50 Plasmablastic lymphoma LBCL <1% 150-200 100-150 Intravascular large B-cell lymphoma LBCL <1% 20-50 50-100 Primary cutaneous diffuse large B-cell lymphoma, leg type LBCL <1% 20-50 20-50 B-cell lymphoma, unclassifiable, with features intermediate between diffuse large B-cell lymphoma and classic Hodgkin lymphoma LBCL <1% 20-50 20-50 Burkitt-like lymphoma with 11q aberration Medium/high-grade <1% 10-20 10-20 Large B-cell lymphoma with IRF4 rearrangement LBCL <1% 0 10-20 Fibrin-associated large B-cell lymphoma LBCL <1% 0 10-20 Primary effusion lymphoma LBCL <1% 0 10-20 ALK-positive large B-cell lymphoma LBCL <1% 0 <10 HHV8-positive diffuse large B-cell lymphoma, not otherwise specified LBCL <1% 0 <10 Lymphomatoid granulomatosis LBCL <1% 0 0 Diffuse large B-cell lymphoma associated with chronic inflammation LBCL <1% 0 0 †Estimated proportion of aggressive B-cell lymphoma subtypes among adult patients. Estimates are based on data from the BC Cancer registry and Abro et al.103 LBCL: large B-cell lymphoma; high-grade: blastoid or intermediate between BL/DLBCL 11 Knowledge of the genetic aberrations contributing to lymphomagenesis, beginning with the discovery of MYC rearrangements in BL over 40 years ago,12,13 has led to the establishment of a small number of aggressive B-cell lymphoma subtypes that are defined by specific structural alterations. These genetic alterations are typically detected using fluorescence in situ hybridization (FISH), a molecular cytogenetic technique that uses fluorescently labeled DNA probes to detect and locate specific DNA sequences on chromosomes. FISH is most commonly performed using break-apart probes, which flank the gene of interest and separate when a chromosomal rearrangement is present (Figure 1.2). In some settings, dual-fusion probes may be used to identify specific translocation partners, such as the t(8:14) rearrangement involving MYC and the IGH locus. However, dual fusion probes are less commonly used in routine practice because they will not detect rearrangements involving alternative partner genes outside the specific fusion being targeted. MYC rearrangement is a hallmark feature of BL but is not specific to this diagnostic entity, as it is also frequently observed in other aggressive B-cell lymphomas, including half of PBLs. MYC rearrangement is also a defining feature of high-grade B-cell lymphoma with MYC and BCL2 and/or BCL6 rearrangements (HGBCL-DH/TH), more commonly referred to as “double-hit” or “triple-hit” lymphoma. The majority of HGBCL-DH/TH cases morphologically resemble DLBCL-NOS, necessitating cytogenetic testing for their distinction. Large B-cell lymphoma with IRF4 rearrangement also resembles DLBCL morphologically but is defined by an IRF4 gene rearrangement and is associated with distinct clinical features, most often affecting children and young adults and typically presenting in Waldeyer’s ring or other head and neck sites. Burkitt-like lymphoma with 11q aberration (BL11q) closely mimics BL in terms of morphology but lacks MYC rearrangement and is instead characterized by a distinctive pattern of 11q gain and loss. These overlapping histologic features underscore the importance of genetic testing as an essential component of the diagnostic workup. 12 Figure 1.2. Diagnostic methods and features used to classify aggressive B-cell lymphomas in the 2017 WHO Revised 4th Edition. Classification incorporates histology, immunophenotype defined by immunohistochemistry or flow cytometry, chromosomal rearrangements typically detected by fluorescence in situ hybridization, and select clinical features. ImmunophenotypeDLBCLImmunohistochemistry (IHC)Markers of B-cell differentiationHistology ImmunophenotypeFluorescent in situ hybridization (FISH)Immunohistochemistry (IHC)Markers of B-cell differentiationChromosomal rearrangementsDer14Der8Clinical features• Site of involvement• Immune status• Viral status• Age• Sex• Cytology• TME composition• Growth PatternLBCLBurkitt Blastoid Intermediate“High-grade” 13 1.2.2 Limitations of Aggressive B-cell Lymphoma Classification A driving principle for developing a taxonomy is grouping neoplasms into categories based on shared biology. While greater integration of clinical, immunophenotypic, and cytogenetic data has facilitated biological refinement of aggressive B-cell lymphoma subtypes, overall classification remains heavily dependent on histological assessment. However, morphology does not reliably capture underlying biological heterogeneity, as demonstrated by overlapping morphological features among many established diagnostic entities. Furthermore, classification continues to rely on the same core diagnostic features used in the 1994 REAL classification. As a result, important biological distinctions may be missed, limiting the ability to consistently identify clinically relevant subgroups. These limitations highlight the need to reassess the current framework in light of emerging molecular data that more accurately reflect disease biology and may support more precise diagnostic approaches. Advances in high-throughput sequencing have greatly expanded our understanding of the genetic alterations and dysregulated cellular pathways that drive lymphomagenesis. This knowledge has provided biological support for a limited number of established aggressive B-cell lymphoma entities – for example, gene expression profiling has confirmed PMBCL as a distinct biological entity. At the same time, these insights have revealed substantial molecular heterogeneity within other diagnostic categories. In parallel, B-cell development has been characterized in increasing detail, highlighting the intrinsic programs and cellular interactions that govern B-cell differentiation and function. Although molecular characterization of aggressive B-cell lymphomas remains incomplete, current data indicate that malignant B cells often adopt phenotypes that mirror specific stages of normal B-cell differentiation and become dependent on dysregulation of associated differentiation stage-specific signaling pathways. This developmental framework enables genetic alterations to be contextualized within defined points in B-cell maturation, clarifying their impact on cellular survival and proliferation. 14 Importantly, an expanding arsenal of targeted therapies now offers the potential to exploit these vulnerabilities. To improve patient outcomes and move beyond reliance on chemotherapy, classification systems must evolve to identify biologically defined subgroups that are more likely to respond to targeted agents. Such an approach will require a shift toward a taxonomy grounded in molecular mechanisms, moving beyond the morphology-centric framework that has defined lymphoma classification for more than a century. 1.3 B-Cell Development and Lymphomagenesis To overcome the limitations of a finite coding genome, B-cell development follows a dynamic, non-linear trajectory, enabling the generation of antibodies that recognize a nearly unlimited array of antigens.14 This process requires B cells to undergo somatic DNA alterations and transiently adopt programs that, at multiple stages of development, mirror key characteristics of various cancer hallmarks.15 Although these programs are essential for creating an extensive antibody repertoire, the underlying physiological mechanisms can be co-opted to drive lymphomagenesis.16 Understanding these parallels provides crucial insights into aggressive B-cell lymphoma pathogenesis and highlights potential therapeutic vulnerabilities. 1.3.1 Early Development in the Bone Marrow B-cell development begins in the bone marrow (Figure 1.3A), where cells progress through distinct stages—pro-B, pre-B, and immature B cells—before migrating to secondary lymphoid organs (SLO) for further maturation. Reflecting this normal trajectory, B-cell malignancies are broadly classified as precursor or mature neoplasms, depending on their resemblance to early or differentiated B cells. While aggressive B-cell lymphomas are classified as mature neoplasms, some acquire early genetic lesions during bone marrow development.17 15 Figure 1.3. Normal B-cell development. (A) B-cell development begins in the bone marrow, where hemopoietic stem cells are signaled to differentiate into Pro-B cells through interactions with bone marrow stromal cells. Pro-B cells undergo RAG-mediated VDJ recombination of their heavy chain genes, while VJ recombination of light chain genes occurs at the Pre-B cell stage. Immature B cells leave the bone marrow and migrate to secondary lymphoid organs, where they mature into NBCs. (B) Schematic of antigen-dependent normal B-cell development and the GC reaction. (C) Survival of NBCs depends on antigen-independent tonic BCR signalling in NBCs. (D) Antigen-dependent BCR signalling in NBCs provides the first activation signal. (E) Antigen-dependent BCR signalling in GC B cells, which promotes the survival of cells with moderate to high affinity for antigen. NBC: naïve B cell, MBC: memory B cell, CSR: class switch recombination, TLR: toll-like receptor, PC: plasma cell, BCR: B-cell receptor. CCND3Innertial cyclesBTKLYNSYKBLNKPTENPIP2PI3KP PAKTT308 S473PI3K/AKTPathwayNF-κBPathwayPIP3PIP3PIP3PIP3PLCγ2NBC antigen-dependent BCR signalingLYNSYKBLNKPTENPIP2PIP2PIP2PIP2 PIP3AKTSHP-1HPK1CSKPI3KP PT308 S473PI3K/AKTPathwayCD40CD40LMHC-IITCRNF-κBPathwayGCB antigen-dependent BCR signalingLYNSYKBLNKPTENPIP2PI3KP PAKTT308 S473PI3K/AKTPathwayPIP3PIP3PIP3PIP3NBC antigen-independent ”tonic” BCR signalingShortenedG1 phaseApoptosis(damaged BCR)G2/MphaseS phase“Signal 1”BCR-antigen“Signal 2”CD40 (or TLR) Apoptosis(low affinity BCR)Extrafollicular reactionLow-affinityMBCShort-lived PCCSRNF-κBmTORMYCS phaseNBCBCL6 MYCDark Zone Light ZoneHHEXBACH2IRF4PRDM1FOXO1FOXO1BC D EMBCPCAVDJrecombinationof the heavychainVJrecombinationof the lightchainImmature BPre-BPro-BBM HSCB cell development in the bone marrowBone marrow(BM)MALTLymph nodesSpleen 16 The primary purpose of B-cell development in the bone marrow is to generate a functional B-cell receptor (BCR) and establish a diverse BCR repertoire. Although early antigen binding is often low-affinity, this diversity allows for later refinement. BCR formation involves somatic recombination of heavy and light chain immunoglobulin (IG) loci by recombination-activating genes (RAGs). Heavy chain (IGH) recombination begins at the pro-B cell stage, followed by light chain recombination at the pre-B cell stage. Successful assembly yields a complete BCR, allowing immature B cells to migrate to SLOs for further maturation. Errors in recombination of IG loci can result in chromosomal translocations, such as t(14;18), which places the BCL2 oncogene under the transcriptional control of IG regulatory elements, leading to constitutive expression of the anti-apoptotic protein BCL2.18,19 1.3.2 B-Cell Maturation and Activation in Lymphoid Tissue B-cell maturation continues in SLOs – such as lymph nodes, the spleen, and mucosa-associated lymphoid tissues (Figure 1.3B). SLOs are inherently inflammatory environments, enriched with supportive cells that sustain low-level cytokine production to maintain B-cell homeostasis, even in the absence of an immunogenic response. Upon antigen encounter, SLOs coordinate the development of plasma cells (PCs) and memory B cells (MBCs). This process occurs through two primary pathways: the extrafollicular reaction and the germinal center (GC) reaction. Both pathways require precise coordination between mutational processes that fine-tune the BCR and rapid proliferation for clonal expansion. BCR tuning is primarily achieved through somatic hypermutation (SHM), which introduces mutations in the IG variable region (IgV) to alter affinity. Additionally, class-switch recombination (CSR) enables switching of BCR isotypes to produce antibodies with different effector functions. Although these processes are tightly regulated, errors can result in oncogenic mutations and chromosomal translocations. 17 B cells originating from the bone marrow first undergo maturation into naïve B cells (NBCs) within SLOs. NBCs predominantly localize within primary lymphoid follicles and express an unswitched BCR. These cells can persist for several months without antigenic stimulation, relying on antigen-independent "tonic" BCR signaling for survival through activation of the PI3K/AKT pathway (Figure 1.3C).20 Activation of NBCs is initiated upon BCR antigen engagement (Figure 1.3B). In contrast to tonic BCR signaling, antigen-dependent BCR signaling leads to receptor crosslinking and activation of multiple downstream pathways, including both PI3K/AKT and canonical NF-κB (Figure 1.3D).21 However, complete activation is dependent on receipt of a second signal, and NBCs that fail to receive a timely second signal undergo apoptosis, preventing inappropriate immune activation.22,23 This second signal can be T cell-dependent, mediated by CD40 engagement with CD40 ligand (CD40L) on T helper (TH) cells, which occurs at the T:B border where NBCs present processed antigen via major histocompatibility complex (MHC) class II molecules to cognate TH cells, or T cell-independent, driven by pattern recognition receptors such as Toll-like receptors (TLRs). Receipt of a second signal initiates a bifurcated response (Figure 1.3B), where activated B cells either rapidly differentiate into short-lived PCs and low-affinity MBCs through the extrafollicular pathway (favored by T cell-independent responses)24 or enter the GC reaction to undergo affinity maturation - the process in which antibodies progressively increase their affinity through iterative mutation and selection.25–27 While somatic hypermutation (SHM) of the IG loci, which alters antigen affinity of the BCR, predominantly occurs during GC affinity maturation, it can also occur to a lesser extent extrafollicularly.28 Cells in both pathways can also undergo class switch recombination (CSR), which creates BCR isotypes with different effector functions. The extrafollicular and GC reactions can be seeded by both activated NBCs and MBCs, but activated NBCs constitute the majority of cells entering the GC reaction.29 Notably, some aggressive B-cell lymphomas are proposed to 18 acquire alterations that favor MBC differentiation and GC re-entry, creating a reservoir of long-lived MBCs that can undergo iterative cycles of GC entry to acquire additional genetic lesions, ultimately leading to malignant transformation.30–33 1.3.3 The Germinal Center Reaction To rapidly generate high-affinity antibodies, GC B cells (GCBCs) must activate programs that mirror canonical cancer hallmarks, increasing their susceptibility to malignant transformation, including active induction of somatic mutations, dampened DNA damage sensing, metabolic reprogramming, rapid proliferation, and suppressed terminal differentiation.34 To coordinate these processes, GCs employ multiple regulatory mechanisms, such as compartmentalization of processes into spatially distinct regions, epigenetic reprogramming to enable transient and reversible activation of transcriptional programs, and rewiring of BCR signaling such that apoptosis becomes the default fate of GCBCs in the absence of extrinsic survival signals. 14 The GC reaction is anatomically divided into dark (DZ) and light zones (LZ), with SHM and clonal expansion occurring in the DZ, and affinity-driven selection occurring in the LZ.35,36 The spatial separation of these processes necessitates dynamic migration between these zones. Affinity selection in the LZ is dependent on BCR interactions with antigen-presenting follicular dendritic cells (FDCs) and CD40-CD40L interactions with T follicular helper (TFH) cells.35,37,38 Positively selected cells either differentiate into PCs or MBCs or recycle to the DZ for additional rounds of proliferation and BCR diversification. To facilitate rapid and reversible transitioning between cell states in response to environmental cues, GCBCs undergo dramatic epigenetic changes. Entry into the GC is accompanied by a global reduction in DNA methylation39 and genome-wide reorganization of chromatin into a more open, active state.40 Within the GC, BCL6 represses key regulatory genes that are held in a poised bivalent state, enabling rapid activation upon receipt of appropriate 19 signals.41 The transcriptional programs repressed by BCL6 include genes involved in cell growth and metabolism (MYC), cell cycle regulation (CDKN1A, CDKN1B), DNA damage response (TP53, ATR), and terminal differentiation (IRF4, PRDM1). In addition, BCL6 suppresses BCL2 expression in GCBCs, rendering them more dependent on external survival signals. BCL6 exerts these effects through the recruitment of corepressor complexes and histone-modifying enzymes.42 Notably, components of these epigenetic regulatory pathways are frequently mutated in aggressive B-cell lymphomas. To facilitate affinity selection and safeguard against mutational processes, BCR signaling in GCBCs is rewired, such that their default cellular fate is apoptosis, with up to half of all GCBCs dying every 6 hours.43 Attenuated BCR signaling is in part due to elevated phosphatase activities (e.g., SHP-1 and SHIP-1) and altered signaling molecule expression, such as increased PTEN, which reduces the PIP3/PIP2 ratio and redirects AKT activity toward negative regulators of BCR signaling (Figure 1.3E).44–46 As a result, the tonic BCR signaling observed in NBCs is suppressed, coupling antigen recognition to cell survival. In addition to prolonging survival, BCR signaling further serves to prime GCBCs for selection.47 Affinity of the BCR is a critical determinant of B-cell fate in the LZ. High-affinity BCRs produce greater priming and further capture of antigen from FDCs and presentation to TFHs, favoring recycling to the DZ or plasma cell differentiation. Meanwhile, lower-affinity BCRs favor MBC differentiation, and B cells that fail to receive appropriate signals undergo apoptosis.14 The key regulator of PC differentiation is PRDM1, which is actively repressed by BCL6 in GCBCs. Induction of IRF4, downstream of NF-κB signaling, suppresses BCL6 and thereby permits PRDM1 expression.48 In cells destined for the MBC fate, higher levels of BACH2 maintain PRDM1 repression.49 During MBC differentiation, BCL2 is derepressed and tonic BCR signaling is re-established, supporting long-term survival of MBCs. 20 Positive selection in the LZ is critically dependent on MYC.50,51 In NBCs, either BCR or CD40 activation is sufficient for activation of both the PI3K/AKT and NF-kB pathways, leading to induction of MYC.45 While antigen engagement in GCBs initiates strong proximal BCR signaling and downstream PI3K/AKT activation, there is poor signal propagation through PKC-β, resulting in inefficient NF-κB activation.52 Instead, CD40 signaling in GCBs serves to preferentially drive NF-kB activation. As such, both BCR and CD40 signals are required to highly induce MYC.45 Dual BCR or CD40 activation in GCBs further serves to activate mTOR.45 For cells recycling to the DZ, the induction of MYC and mTOR, is proportional to the degree of BCR priming and T-cell help, and together serve as indicators of the number of subsequent cell divisions within the DZ.53,54 Induction of mTOR promotes anabolic growth, while transient MYC expression serves as an initial proliferative “push”, promoting transition from G1 to S phase in recycling cells.55 Upon DZ re-entry, MYC is suppressed by BCL6, and DZ B cells undergo multiple rounds of division via “inertial cycling,” which is promoted by CCND3 and occurs in the absence of additional external signals (Figure 1.3B).56 The two major processes occurring in the DZ, cell division and SHM, are temporally and spatially separated.57 SHM occurs during early G1 phase,58–60 and DZ inertial cycles are characterized by a shortened G1 phase, limiting the extent of mutation.61,62 As such, the majority of SHM accumulates after multiple rounds of proliferation have been completed, before cells return to the LZ. This sequential ordering enables the expansion of high-affinity clones that have already undergone positive selection before additional mutations are introduced. Such a mechanism helps preserve functional BCRs, as most new mutations are either neutral or deleterious, reducing antigen affinity or producing non-functional receptors.63 Notably, apoptosis in the DZ predominantly occurs in late G1 phase, before either cell cycle re-entry or LZ migration.64 In contrast to the LZ, where apoptosis is primarily observed in low-affinity clones that fail to receive appropriate extrinsic signals, the majority of cells undergoing 21 apoptosis in the DZ have acquired deleterious mutations leading to a non-functional BCR (e.g., missense mutations and deletions).43,64 This implicates the existence of a mechanism for BCR sensing within the DZ, despite the restriction of antigen-presenting FDCs to the LZ. Although the exact mechanism is unknown, it may involve binding to soluble antigen65 or represent an antigen-independent form of BCR signaling, analogous to tonic signaling observed in NBCs. In addition to quality control, BCR signaling in the DZ GCBCs plays a role in regulating the transition back to the LZ. This transition is governed by FOXO1, the master transcriptional regulator of the DZ program, which resides in the nucleus during the DZ phase.66,67 Activation of the PI3K/AKT pathway downstream of BCR signalling leads to phosphorylation and nuclear export of FOXO1, terminating the DZ program and facilitating re-entry into the LZ. Notably, genes that regulate GCBC fate decisions and coordinate DZ/LZ transitions are recurrently mutated in aggressive B-cell lymphomas, highlighting the clinicopathological relevance of a more granular understanding of GC biology. 1.4 Molecular Classification of Aggressive B-Cell Lymphomas Our understanding of the molecular spectrum of aggressive B-cell lymphomas has been shaped largely by the availability of genomic and transcriptomic data, which in turn has been influenced by the relative frequency of each entity and the timing of their formal recognition. Common subtypes such as DLBCL-NOS have been extensively characterized, benefiting from large patient cohorts and early inclusion in molecular studies. In contrast, rarer entities like BL have required international collaborative efforts to assemble sufficient sample sizes for meaningful analysis, while many other rare and/or recently established subtypes remain largely uncharacterized (Table 1.1). Molecular studies of DLBCL-NOS and BL illustrate two contrasting outcomes: in BL, molecular profiling has confirmed its biological distinctiveness and uniformity, whereas in DLBCL-NOS, it has revealed substantial molecular heterogeneity. Importantly, in 22 DLBCL-NOS, molecular profiling has helped reveal underlying molecular substructure, allowing tumors to be subclassified into groups with shared genetic and transcriptional features. These molecular subgroups are associated with distinct clinical outcomes and therapeutic vulnerabilities, underscoring the potential of molecular classification to inform prognosis and guide personalized treatment strategies. In the two sections that follow, a general description of BL and DLBCL-NOS will be given, followed by a discussion of progress in molecular characterization and classification in each entity. 1.4.1 Burkitt Lymphoma BL is a highly aggressive malignancy with a remarkably short malignant cell doubling time, necessitating urgent clinical intervention. The corollary of this rapid proliferation is that BL is extremely chemosensitive, making it highly curable when treated appropriately. Management of BL relies on dose-intensive, multi-agent immunochemotherapy regimens. These protocols have demonstrated excellent outcomes, especially in pediatric and younger adult populations. Commonly used regimens include CODOX-M/IVAC, and HyperCVAD, which both incorporate CNS prophylaxis or therapy due to the high risk of CNS involvement. The addition of rituximab, a monoclonal antibody targeting CD20, to these regimens has further improved event-free and overall survival.68 With intensive treatment, long-term overall survival rates of 70–90% can be achieved.69 Despite these favorable outcomes, the intensity of therapy can lead to significant treatment-related toxicity, particularly in older adults or patients with comorbidities. In such cases, less-intensive regimens, such as dose-adjusted EPOCH-R, may be considered to maintain a curative approach while minimizing toxicity. BL is classified into three epidemiologic variants with different geographic distributions and clinical presentations: immunodeficiency-associated, endemic, and sporadic. Immunodeficiency-associated BL primarily arises in the setting of HIV infection but can also occur 23 in other immunosuppressive states, including post-transplantation. Endemic BL occurs predominantly in equatorial Africa and most often presents in children as tumors involving the jaw or other facial bones. In these regions, BL is the most prevalent childhood cancer. The geographic distribution of endemic BL closely overlaps with regions of holoendemic malaria, and EBV infection is detected in over 90% of cases, strongly implicating both EBV and malaria in its pathogenesis. Sporadic BL occurs globally and typically affects children and young adults, with a median age of 30 years in adults. However, its age distribution is bimodal, with a second incidence peak in elderly patients. Among adult aggressive B-cell lymphomas, sporadic BL accounts for only 2-3% of cases but represents 30-50% of childhood lymphomas in non-endemic regions. Unlike endemic BL, facial involvement is uncommon; sporadic cases more frequently present with abdominal tumors, particularly involving the ileocecal region. EBV is detected in only 20-30% of sporadic cases. EBV status, not epidemiological variant, is the main driver of molecular differences among BL.70 The immunophenotypic profile of BL is consistent with a GC B cell origin. Tumor cells are typically positive for pan–B-cell markers, including CD19, CD20, CD79a, and PAX5, as well as GC-associated markers CD10 and BCL6 (Figure 1.2A). The proliferation index is exceedingly high, with nearly all tumor cells (>95%) showing nuclear positivity for Ki-67, reflecting the rapid growth kinetics of the neoplasm. In contrast to many other aggressive B-cell lymphomas, BL cells are negative or only weakly positive for BCL2. The molecular hallmark of BL is a chromosomal translocation involving the MYC oncogene located at 8q24. In the vast majority of cases, MYC is translocated to the IGH locus on chromosome 14q32, resulting in the t(8;14) translocation. Less commonly, MYC is translocated to the kappa or lambda light chain loci, leading to t(2;8) or t(8;22), respectively. These translocations place MYC under the control of immunoglobulin enhancer elements, resulting in its constitutive expression and contributing to uncontrolled cellular proliferation. MYC 24 rearrangements in BL arise predominantly from aberrant somatic hypermutation. This implicates the activity of AID and suggests that MYC translocation occurs during the GC stage of B-cell maturation. The 2017 WHO classification does not mandate the presence of a MYC rearrangement for diagnosis, citing limitations in current diagnostic technologies, which may fail to detect cryptic or complex rearrangements involving MYC. Nonetheless, MYC rearrangement is reported to be detected in >90% of BL cases. Apart from MYC rearrangement, BL typically exhibits a relatively simple karyotype with few additional structural abnormalities or copy number alterations, underscoring its molecular distinctiveness among aggressive B-cell lymphomas. Next-generation sequencing has uncovered key pathways involved in BL pathogenesis and revealed distinct molecular differences based on EBV status.70,71 Although MYC rearrangement drives rapid proliferation, it also induces pro-apoptotic stress.72 To support tumor cell survival, BL must therefore acquire anti-apoptotic mechanisms. In EBV-negative BL, this is frequently achieved through inactivating mutations in TP53, which occur in approximately 60% of cases. In contrast, TP53 mutations are much less frequent in EBV-positive BL, suggesting that EBV contributes to alternative mechanisms to suppress apoptosis. In addition to other differences in the mutational frequencies of specific genes, there is an inverse correlation between EBV status and the total number of driver mutations. Sequencing studies have also highlighted the importance of BCR signaling in BL. In about 60% of EBV-negative cases, mutations in TCF3 or its negative regulator ID3 contribute to constitutive, antigen-independent activation of the PI3K pathway, supporting cell survival (Figure 1.4A).70,71,73 Notably, this signaling does not engage the NF-κB pathway, resembling a malignant form of tonic BCR signaling observed in normal NBCs (Figure 1.3C). Beyond MYC, whose transient expression marks GC B cells recycling to the DZ,50,51 other genes essential to DZ biology, such as CCND3 and FOXO1 (Figure 1.3B), are also recurrently mutated in BL.70,71 The association of BL with a DZ phenotype has also been supported by GEP 25 studies. Two landmark studies demonstrated that BL can be reliably distinguished from DLBCL based on a unique transcriptional signature, now known as the molecular BL (mBL) signature.74,75 This classifier consistently separates BL from DLBCL-NOS and aligns with transcriptional profiles derived from sorted DZ and LZ GCBCs, with mBL tumors showing a strong resemblance to DZ GCBCs.36 These findings reinforce the view that BL represents a malignant counterpart of DZ GCBCs, both genetically and transcriptionally. The homogeneous molecular landscape of BL has enabled the identification of potential therapeutic targets, including PI3K and SYK (Figure 1.4A), which may be exploited to disrupt the co-opted tonic BCR signaling that drives tumor survival.73 However, the rarity of BL, along with the high cure rates achieved using intensive multi-agent chemotherapy, presents significant challenges to conducting frontline clinical trials of targeted agents. Moreover, the disease’s rapid progression limits the feasibility of evaluating such therapies in relapsed or refractory settings, where patients often deteriorate too quickly to be eligible for clinical trials. In principle, targeted therapies may hold the greatest clinical utility in endemic regions, where limitations in supportive care infrastructure preclude the use of intensive regimens. In these settings, reliance on resource-adapted protocols and frequent late-stage presentation results in overall survival rates of only 30–50%, underscoring the need for more accessible and effective treatment strategies.76 1.4.2 Diffuse Large B-cell Lymphoma, Not Otherwise Specified DLBCL-NOS accounts for approximately 75% of adult aggressive B-cell lymphomas (Table 1.1). The median age at diagnosis for DLBCL-NOS is 67 years, with a slight male predominance.77 Most cases arise de novo, although a small subset represents transformation from a previously diagnosed or occult indolent B-cell lymphoma.78 Patients may present with nodal or extranodal disease, with up to 40% of cases confined to extranodal sites at diagnosis.1 The most common extranodal site is the gastrointestinal tract, although nearly any extranodal location 26 can be involved. About 70% of patients present with advanced-stage disease, as defined by the Ann Arbor staging system,79 while the remainder have limited-stage disease, characterized as stage I or II, non-bulky (largest mass ≤ 10 cm), localized, and without B symptoms (unexplained fever, weight loss >10% in less than 6 months, or drenching night sweats). Risk stratification at diagnosis is guided by the International Prognostic Index (IPI), which incorporates five clinical variables: age >60 years, elevated serum lactate dehydrogenase (LDH), Eastern Cooperative Oncology Group performance status (ECOG PS) ≥2, Ann Arbor stage III/IV, and involvement of more than one extranodal site.80 While the IPI remains clinically valuable for prognostication, it has demonstrated limited utility in guiding treatment decisions for high-risk patients, largely because it does not capture the underlying biological diversity of DLBCL-NOS. Despite the advanced stage at presentation in the majority of patients, DLBCL-NOS is potentially curable in most patients. Standard frontline treatment consists of R-CHOP immunochemotherapy, comprising rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone. Although this regimen cures 60-70% of patients, patients with relapsed or refractory disease after frontline treatment have dismal outcomes, with limited benefit from salvage chemotherapy and autologous stem cell transplantation.78,81 In recent years, chimeric antigen receptor (CAR) T-cell therapy has emerged as a promising option for patients with relapsed or refractory disease, offering improved response rates and durable remissions in a subset of patients, although overall outcomes remain suboptimal. Heterogeneity in treatment response to frontline R-CHOP therapy has provoked intense efforts over the past two decades to identify prognostic and predictive biomarkers to refine therapeutic strategies. Cell-of-origin (COO) classification created the foundations for this endeavor, which relates tumor cells to normal B-cell counterparts using gene expression profiling (GEP). This system dichotomizes the majority of DLBCL-NOS tumors into the germinal center B-cell-like (GCB) and activated B-cell-like (ABC) subgroups,82 with an approximately 15% higher 2- 27 year progression-free survival (PFS) in GCB-DLBCL versus ABC-DLBCL following R-CHOP.83–85 COO subtyping of DLBCL-NOS is recommended under the 2017 WHO classification, with the use of immunohistochemistry algorithms being considered acceptable surrogates for GEP. COO classification laid the foundation for numerous subsequent studies demonstrating that ABC- and GCB-DLBCLs rely on distinct B-cell signaling pathways to sustain malignant growth and survival.16 These differences reflect varied underlying mutational landscapes that activate subtype-specific oncogenic programs and define the distinct biology of each molecular subtype.70 Building on these studies, multiple clinical trials were designed to evaluate therapies specifically targeting ABC-DLBCL biology,86–88 particularly its dependence on NF-κB activation driven by chronic active BCR signaling (Figure 1.5B).89 Although none of these trials met their primary endpoints, long-term follow-up and retrospective analyses in these trials and others have reinforced the potential utility of COO classification for guiding treatment.87,90,91 Results from clinical trials informed by molecular classification are discussed in the following section (1.4.3). Subsequent studies shifted focus from GEP to genetic alterations, uncovering recurrent mutation patterns that further refine the molecular subtypes of DLBCL and offer additional opportunities for biology-driven therapy. Three such classifiers have been developed, each defining distinct molecular subtypes that show strong concordance with COO and exhibit remarkable similarity in the subgroups they identify. Two of these classifications, HMRN92 and DLBclass93,94, were originally discovered by naïve approaches without a priori information about mutations that would likely co-cluster, while LymphGen95,96 leveraged “seed” classes of known co-occurring mutations alongside COO assignments. Despite these methodological differences, the convergence of subtypes across these algorithms underscores the robustness of the underlying genetic structure in DLBCL. Given the substantial overlap among these classifiers, further discussion will focus on the LymphGen model, which defines six molecular subtypes 28 (MCD, N1, BN2, EZB, ST2, A53) and designates tumors that lack sufficient features for confident classification as “Other”. MCD tumors have a high frequency of mutations affecting genes in the BCR signaling/canonical NF-kB pathway, including MYD88L265P and CD79B (Figure 1.4B), and are strongly ABC by COO. The rare N1 subgroup is defined primarily by NOTCH1 truncating mutations. This group is strongly ABC and generally lacks additional mutations. Hallmark events in the BN2 class are BCL6 rearrangement and truncating mutations affecting NOTCH2. Although these hallmarks and ABC COO were used as seed features for LymphGen, many of these tumors are COO-unclassified, and some are GCB. The EZB genetic subgroup consists of GCB tumors with BCL2-rearrangement and a high frequency of mutations affecting chromatin modifier genes, including KMT2D, CREBBP, and EZH2. This genetic profile shows considerable overlap with the indolent FL, suggesting shared early mechanisms of pathogenesis. The ST2 subtype is enriched in SOCS1 and TET2 mutations. These ST2 tumors are typically GCB and are enriched for mutations in genes in the JAK/STAT pathway (Figure 1.4C). The A53 group is defined by a high burden of copy number alterations, including arm-level aneuploidy and focal gains and deletions, in combination with TP53 mutation or deletion. These tumors span the full range of COO subtypes and frequently harbor sufficient mutations to be placed into a mutation-driven subgroup. Correspondingly, TP53 mutations are frequently found in other subgroups. Thus, the presence of aneuploidy/CNA events to drive membership in this class may simply be a progression phenotype. Additionally, technical challenges associated with accurate CNV calling may contribute to inaccurate classification. The relationship of the DLBCL genetic subgroups to outcomes and therapy has been explored in several studies, as discussed below. 29 Figure 1.4. Mechanisms of Sustained Proliferative Signaling in Aggressive B-cell Lymphomas. Selected signaling pathways commonly co-opted by specific subgroups of aggressive B-cell lymphomas. Frequently mutated genes within each pathway are marked with a star: green indicates activating mutations, and red indicates inactivating mutations. (A) Tonic BCR signaling, characteristic of BL. (B) Chronic active BCR signaling, typical of ABC DLBCL, particularly the MCD subtype. (C) JAK/STAT signaling, frequently observed in ST2 DLBCL and PMBCL. Chronic Active BCRSFKSYKAntigenBCRP PPPPPPP CD79ACD79BP PPPPPPPIRAK1/4MYD88BCL10MALT1CARD11TRAF6IκBαIKKγIKKβ IKKα NF-κBPLC-γBTKPKC-βNF-κB PathwayBCAPPI3KAKTmTORPI3K PathwayTonic BCR LYNSYKCD19PI3KPActive BCRMALT1PPPPPI3KAKTmTORPI3K PathwayBTKPKC-βBCL10CARD11NF-κB PathwayTFHMΦDC-SIGNAtypical GlycanCD40MHCIITCRTonic BCR LYNSYKPPPPCD19PI3KAKTmTORPI3K PathwayPIP2PIP3MYCPTENSHP-1TCF3ID3MΦDC-SIGNtypical GlycanJAK/STAT JAK JAKPPPPP PPPCytokineCytokine Receptor IL4RSTAT6STAT3STATTranscriptionAutocrine SignallingPTPN1/6SOCS1IL10MAPKPP RASRAFMEKERKGrowth FactorRTKGrowth and ProliferationChronic Active BCRSFKSYKAntigenBCRP PPPPPPP CD79ACD79BP PPPPPPPIRAK1/4MYD88BCL10MALT1CARD11TRAF6IκBαIKKγIKKβ IKKα NF-κBPLC-γBTKPKC-βNF-κB PathwayBCAPPI3KAKTmTORPI3K PathwayTonic BCR LYNSYKPPPPCD19PI3KAKTmTORPI3K PathwayPIP2PIP3MYCPTENSHP-1TCF3ID3Active BCRMALT1PPPPPI3KAKTmTORPI3K athwayBTKPKC-βBCL 0CARD11NF-κB PathwayTFHMΦDC-SIGNAtypical GlycanCD40MHCIITCJAK/STAT JAK JAKPPPPP PPPCytokineCytokine Receptor IL4RSTAT6STAT3STATTranscriptionAutocrine SignallingPTPN1/6SOCS1IL10A B C 30 1.4.3 Molecular Stratification in Clinical Trials Since the introduction of the anti-CD20 monoclonal antibody rituximab to CHOP chemotherapy in the early 2000s,97 there has been limited progress in improving frontline treatment options for DLBCL. Efforts to improve frontline therapy have included dose intensification and the addition of single agents to R-CHOP, but results have been largely disappointing. The POLARIX trial represents the first randomized, blinded trial to demonstrate a significant improvement in progression-free survival (PFS) over R-CHOP in 20 years, despite enrollment of over 10,000 patients across nearly 20 trials over this period.91,98 The POLARIX trial evaluated pola-R-CHP, which replaces vincristine in R-CHOP with polatuzumab vedotin – an antibody-drug conjugate that targets CD79B, a component of the BCR. In this trial, pola-R-CHP led to a modest improvement in 2-year PFS compared to R-CHOP (76.7% versus 70.2%). However, subsequent analyses revealed that this benefit was primarily driven by improved outcomes in ABC tumors, with no benefit among the GCB subtype.91 Although polatuzumab vedotin has received FDA approval for use in DLBCL regardless of subtype, Canada’s Drug Agency has recommended reimbursement for only the ABC subtype. Other earlier trials incorporating COO subtyping into their design include PHOENIX, REMoDL-B, and ROBUST phase III trials.86–88 These studies evaluated the addition of single-agent therapies to R-CHOP, aiming for enhanced efficacy in the ABC subtype. In the REMoDL-B trial, all patients with DLBCL were randomized to receive either R-CHOP or RB-CHOP (R-CHOP plus the proteasome inhibitor bortezomib), with COO determined in parallel by GEP. The primary endpoint was 30-month PFS within the GCB and ABC subgroups. At a median follow-up of 30 months, no significant PFS benefit was observed with the addition of bortezomib in either subgroup or the overall cohort. However, with extended follow-up, improved 5-year PFS was seen in the ABC group (54.4% with R-CHOP vs. 69.4% with RB-CHOP), while no benefit was observed in the GCB group.90 31 The PHOENIX trial evaluated the addition of the BTK inhibitor ibrutinib to R-CHOP. COO was assigned using an IHC-based algorithm that classified patients as GCB or non-GCB, with the non-GCB group largely corresponding to ABC or Unclassified subtypes by GEP.99 Only non-GCB patients were included in the study. While no overall difference in PFS was observed between treatment arms, a significant interaction with age was identified, as part of a pre-specified analysis. Among patients aged 60 years or older, ibrutinib plus R-CHOP was associated with increased toxicity, leading to dose reductions and/or treatment discontinuation in many patients and a non-significant trend toward worse PFS in the experimental arm. In contrast, patients under 60 experienced improved 36-month PFS with ibrutinib plus R-CHOP (77.4% vs. 66.3%). A retrospective subset analysis restricted to GEP-defined ABC cases showed an even greater benefit, with a 36-month PFS of 80.5% vs. 64.5%. Newer, more selective BTK inhibitors such as acalabrutinib and zanubrutinib have been developed to reduce off-target toxicities and may offer improved tolerability in future combination strategies.100 Retrospective analysis of the PHOENIX trial incorporating genetic-based subtypes demonstrated an even greater benefit to patients with tumors classified as MCD or N1, but not BN2.101 The more recent GUIDANCE-01 study was a randomized controlled trial evaluating the addition of different targeted agents (X) to R-CHOP, selected based on an approximated genetic subgrouping algorithm that used BCL2 and BCL6 structural variants (SVs) and mutation status of 18 genes.102 Overall, R-CHOP-X demonstrated superior PFS over R-CHOP, though, while the study was underpowered to detect differences in outcomes within each genetic subgroup, it appears the MCD-like group receiving ibrutinib was the major driver of this result. Collectively, these studies highlight the clinical relevance of molecular classification in identifying biologically distinct subgroups, enabling more precise prediction of therapeutic response and informing the design of new subtype-directed treatment strategies. 32 1.4.4 Completing the Spectrum While targeted therapies in molecularly defined DLBCL subgroups show promise, replacing the current morphology-based system requires comprehensive molecular characterization across the spectrum of aggressive B-cell lymphomas. While other more common entities, such as PMBCL and primary DLBCL of the CNS, as well as some rarer entities, like BL and PBL, have undergone molecular characterization, little sequencing data is available for the remaining entities. The majority of uncharacterized entities each account for less than 1% of total aggressive B-cell lymphomas, with most marked by very specific clinical presentations and features. Among those representing more than 1% of total aggressive B-cell lymphomas and having limited molecular characterization, high-grade B-cell lymphoma, not otherwise specified (HGBCL-NOS), and HGBCL-DH/TH are the most common (Table 1.1). HGBCL-NOS accounts for approximately 1-4% of aggressive B-cell lymphomas, with a range in reported frequency due to inconsistent diagnostic practices across institutions. Defined by high-grade morphology and the absence of concurrent MYC and BCL2 and/or BCL6 rearrangements (Figure 1.5), HGBCL-NOS remains a poorly characterized and diagnostically challenging category. Originating from a provisional entity first introduced in the 1994 REAL classification to accommodate cases that could not be definitively classified as BL or DLBCL, it was acknowledged from the outset that this group lacked reproducibility and likely encompassed biologically diverse tumors. Despite its limitations, the category persists largely unchanged in the 2017 WHO classifications. Ongoing efforts to better delineate the boundary between BL and DLBCL have resulted in minimal material changes to the definition of HGBCL-NOS, yet the evolving criteria have nonetheless hindered its characterization, compounded by the rarity and subjectivity of diagnosis. Although often treated with intensified chemotherapy regimens, there is limited evidence to guide this approach, underscoring the need for molecular profiling to refine classification and inform clinical management. 33 Figure 1.5. Classification of aggressive B-cell lymphomas. 2017 WHO diagnostic criteria for Burkitt lymphoma (BL), high-grade B-cell lymphoma, not otherwise specified (HGBCL-NOS), high-grade B-cell lymphoma with MYC and BCL2 and/or BCL6 rearrangements (HGBCL-DH/TH), and diffuse large B-cell lymphoma, not otherwise specified (DLBCL-NOS). *Under the 2017 WHO, MYC rearrangement is not required for the diagnosis of BL, although it is present in >90% of BL. FISH Testing MYC and BCL2 and/or BCL6 rearrangement Burkitt High-grade DLBCLMorphologyWHO Revised4th Edition BL HGBCL-NOS DLBCL-NOSHGBL-DH-BCL2 HGBL-DH-BCL6HGBCL-DH/THMYCrearrangement* 34 HGBCL-DH/TH represents a newly established diagnostic entity in the 2017 WHO classification. This group includes tumors with either DLBCL or high-grade morphology that harbor MYC and BCL2 and/or BCL6 rearrangements (Figure 1.5). The establishment of this entity was primarily driven by poor clinical outcomes, with limited biological justification. Subsequent studies have provided support for its distinction, particularly among tumors with MYC and BCL2 rearrangements (HGBCL-DH-BCL2), which display immunophenotypic uniformity and share a characteristic gene expression signature (DHITsig). In contrast, those with MYC and BCL6 rearrangements (HGBCL-DH-BCL6) display greater immunophenotypic heterogeneity. Despite these advances, the genetic landscape of HGBCL-DH/TH remains poorly defined. Comprehensive molecular profiling could help overcome the poor outcomes associated with this group by refining classification to guide more optimal therapeutic approaches. In addition to the controversy surrounding the formation of HGBCL-DH/TH as a diagnostic entity, there are significant challenges and questions surrounding the diagnosis of HGBCL-DH/TH. While this group comprises ~5% of aggressive B-cell lymphomas, accurate diagnosis requires routine screening for rearrangements by FISH in all tumors. As routine FISH testing is challenging in resource-constrained clinical labs, detection of MYC protein by IHC has been proposed to screen tumors for subsequent FISH testing. However, ∼25% of MYC rearranged tumors are negative for MYC protein expression, and it is unknown if the lack of detectable protein expression is due to non-functional rearrangements or other mechanisms specific to IHC. Further challenges associated with FISH relate to the interpretation of “unbalanced” break-apart FISH patterns, as well as whether the definition of HGBCL-DH/TH should be expanded to include other SVs, such as copy number alterations (CNAs). 35 1.5 Thesis Outline and Aims Large-scale sequencing studies have revealed substantial molecular heterogeneity among aggressive B-cell lymphomas, prompting a shift toward molecular-based classification systems that identify biologically and clinically relevant subgroups with shared targetable features. In DLBCL, the most common aggressive B-cell lymphoma, the integration of molecular profiling has already led to the development of targeted therapies with promising results, laying the foundation for broader molecular-based classification across the spectrum of aggressive B-cell lymphomas. However, the evolution or replacement of the current morphology-centric system requires comprehensive molecular characterization of all major aggressive B-cell lymphoma subtypes. HGBCL-DH/TH and HGBCL-NOS are among the most clinically challenging and poorly characterized subtypes. The overall objective of this thesis is to define the molecular landscape of these two entities in order to contribute to a more complete, biologically informed taxonomy of aggressive B-cell lymphomas. This work aims to improve diagnostic precision, guide clinical decision-making, and support the rational selection of targeted therapies. Aim 1: Refine current diagnostic approaches and criteria for HGBCL-DH/TH The classification of HGBCL-DH/TH was introduced in the 2017 WHO classification based on the presence of MYC and BCL2 and/or BCL6 rearrangements, largely driven by poor clinical outcomes rather than underlying biology. While tumors with MYC and BCL2 rearrangements (HGBCL-DH-BCL2) show relative homogeneity, those with MYC and BCL6 rearrangements (HGBCL-DH-BCL6) are more heterogeneous and less well characterized. Furthermore, the diagnosis of HGBCL-DH/TH relies on routine FISH, which is not consistently performed across institutions and presents practical challenges. This aim seeks to refine the biological and technical definitions of HGBCL-DH/TH to improve diagnostic clarity and clinical relevance. 36 Specific Aims 1.1 Characterize the molecular landscape of HGBCL-DH/TH to determine if it represents a biologically homogeneous entity (Chapter 2) 1.2 Assess whether the definition of HGBCL-DH/TH should be expanded to include copy number alterations (Chapter 3) 1.3 Evaluate MYC IHC as a screening tool for subsequent routine FISH testing (Chapter 3) 1.4 Investigate the clinical and biological significance of unbalanced break-apart FISH patterns in the context of HGBCL-DH/TH diagnosis (Chapter 3) Aim 2: Support a biologically informed taxonomy of aggressive B-cell lymphomas by classifying HGBCL-DH/TH and HGBCL-NOS according to molecular features The current classification of HGBCL-DH/TH and HGBCL-NOS is limited by reliance on morphology and cytogenetics, contributing to diagnostic ambiguity and poor biological resolution. This aim seeks to classify these tumors based on robust molecular features to clarify their relationship to established subtypes and enable their integration into a more biologically grounded taxonomy of aggressive B-cell lymphomas. Specific Aims 2.1 Refine classification of HGBCL-DH/TH using molecular-based approaches (Chapter 4) 2.2 Refine classification of HGBCL-NOS using molecular-based approaches (Chapter 5) 37 Chapter 2: The Molecular Landscape of HGBCL-DH/TH 2.1 Introduction HGBCL-DH/TH represents a newly introduced diagnostic entity in the 2017 WHO classification and encompasses tumors with DLBCL or high-grade morphology and MYC and BCL2 and/or BCL6 rearrangements.1 The creation of this category was largely driven by the poor outcomes experienced by these patients following stand-of-care regimens,104,105 leading to many centers using dose-intensive regimens for treatment of HGBCL-DH/TH, although randomized data supporting their benefit are lacking. As patient outcomes are intrinsically linked to current treatment strategies, to remain clinically relevant as treatment strategies evolve, optimal classification should define tumors based on shared biological features, thereby increasing the likelihood of predicting responses to emerging targeted therapies. However, the establishment of HGBCL-DH/TH had limited biological justification beyond the defining rearrangements. Subsequent studies have shown that the specific combination of rearrangements is biologically meaningful, with tumors harboring MYC and BCL2 rearrangements (HGBCL-DH-BCL2; inclusive of HGBCL-TH) exhibiting consistent molecular features indicative of biological uniformity.106,107 This subgroup accounts for approximately 80% of all HGBCL-DH/TH and consistently exhibits a GCB COO.106 In contrast, MYC and BCL6 rearranged tumors (HGBCL-DH-BCL6), which represent the remaining ~20% of HGBCL-DH/TH, are more heterogeneous and span all COO subtypes.106 These distinctions suggest that the definition of HGBCL-DH/TH may group biologically distinct tumors into a single diagnostic entity, potentially obscuring important differences with implications for patient management. Additional evidence for biological uniformity among HGBCL-DH-BCL2 comes from the identification of a gene expression signature (DHITsig) that reliably distinguishes these tumors from other GCB DLBCL, reflecting a unifying biology that extends beyond their common COO.107 38 Notably, DHITsig also identifies a small subset of GCB DLBCL-NOS tumors that lack concurrent MYC and BCL2 rearrangements by FISH. Approximately 20% of these tumors harbor “cryptic” rearrangements detectable only through higher-resolution or more targeted methods, underscoring the limitations of standard FISH.108 However, in the remaining 80% of DLBCL-NOS tumors with DHITsig expression, the basis for this expression is unknown. This implies that DHITsig may reflect a broader biological phenotype not exclusively defined by MYC and BCL2 rearrangements, and that other molecular aberrations may converge on a similar transcriptional program. As the genetic landscape of HGBCL-DH/TH beyond the defining rearrangements remains poorly characterized, this study aims to use comprehensive genomic profiling to better define the biological boundaries of this group. Specifically, we seek to determine whether HGBCL-DH/TH should remain a single diagnostic entity or be redefined—for example, by narrowing the classification to the more biologically uniform HGBCL-DH-BCL2 subgroup. We also aim to identify additional core genetic features beyond MYC and BCL2 rearrangements that drive disease biology, thereby clarifying the molecular basis of DHITsig expression. By addressing these questions, we aim to advance a more biologically informed classification system and uncover therapeutic vulnerabilities that could guide the development of more effective treatments. 2.2 Materials and Methods 2.2.1 Study cohort Primary diagnostic biopsies from adult patients (age ³ 18 years) with DLBCL or high-grade morphology harboring MYC and BCL2 and/or BCL6 rearrangements, as detected by break-apart FISH, were identified in the Lymphoid Cancer Database, BC Cancer. All biopsies were reviewed by BC Cancer hematopathologists to confirm a HGBCL-DH/TH diagnosis and to assign a cytological subtype (blastoid or intermediate) to tumors with high-grade morphology. Patients with 39 a positive HIV serology or post-transplant lymphoproliferative disorder were excluded. Relapsed biopsies were excluded, but patients with a prior indolent lymphoma or composite lymphoma (where the tumor comprises both an aggressive and indolent lymphoma) at the time of diagnosis were included. The final study cohort was restricted to biopsies with available whole-exome or whole-genome sequencing. 2.2.2 FISH and IHC FISH was performed on Formalin-Fixed Paraffin-Embedded (FFPE) material on whole tissue sections or tissue microarray (TMA) sections using commercially available Vysis LSI (Abbott) or Metasystems XL break apart probes for MYC (Abbott 01N63-020; Metasystems D-6030-100-TC), BCL2 (Abbott 05N51-020; Metasystems D-6018-100-OG), and BCL6 (Abbott 01N23-020; Metasystems D-6016-100-OG). TMAs were constructed from duplicate 0.6 mm cores. Images were captured using a Metafer CoolCube 1 camera and Metafer software (version 3.11.8). Two independent observers scored 100 nuclei per biopsy, except for small or poor-quality biopsies, where only 50-100 nuclei could be scored. For biopsies with discrepant scores, consensus was reached by a third observer. Rearrangement was defined as a break-apart signal in ≥5% of cells. IHC was performed on TMA sections for MYC (Y69, Epitomics; or EP121 [formally Y69], Biocare), BCL2 (E17, Abcam; and BCL2/124, Dako), CD10 (DAK-CD10, Dako), BCL6 (PG-B6p, Dako), and MUM1 (MUM1p, Dako). IHC was independently scored by two expert hematopathologists, and biopsies with discordant scores were further evaluated by a third hematopathologist to reach consensus. IHC positivity was defined as ≥30% (CD10, BCL6, and MUM1),99 ≥40% (MYC), and ≥50% (BCL2) staining of tumor cells.109 For biopsies not included on TMAs, clinical IHC and FISH data were used when available. 40 2.2.3 Digital gene expression profiling RNA was extracted from FFPE biopsies using the Qiagen AllPrep DNA/RNA FFPE Kit (80234) or isolated from DNAse treatment of total nucleic acids (TNA) extracted using the ALINE FFPE GenePure Purification Kit (FP-2001-BC-384). Digital GEP was performed on RNA using the DLBCL90 assay on the nCounter platform (nanoString) to assign COO and DHITsig status, as previously described.107,108 2.2.4 GCB DLBCL comparison cohort for mutation analysis For comparison of mutational features to GCB DLBCL, 110 biopsies were drawn from a previously described cohort with available WES.95,96 As FISH data were unavailable for this cohort, these tumors further represent those that were negative for DHITsig expression to minimize inclusion of undetected HGBCL-DH/TH cases. Variant calling for this cohort was performed using the same pipeline as described for the main study cohort, allowing for direct comparison of mutation frequencies between groups. 2.2.5 GEP extension cohort To assess the expression of DHITsig in other aggressive B-cell lymphoma entities and investigate its underlying biology, 55 BL and 184 GCB tumors with DLBCL morphology were included in the analysis. The BL cases, obtained through the Burkitt Lymphoma Genome Sequencing Project (BLGSP),71 consisted of 27 adult and 28 pediatric biopsies with available DLBCL90 GEP data. The GCB-DLBCL tumors were drawn from the original cohort in which DHITsig was defined107, and included 158 DLBCL-NOS and 26 HGBCL-DH-BCL2 cases, all with available DLBCL90 data. RNAseq data were available for 171 of these GCB-DLBCL biopsies. To evaluate the relationship between DHITsig expression and previously reported mBL74 and DZ GCBC36 signatures, RNAseq data were pseudoaligned to the Gencode version 33 41 transcriptome (hg38) using Salmon110 and normalized with DESeq2111 and variance stabilizing transformation (VST). For each signature, scores were calculated by averaging the normalized expression of the signature genes for each sample and converting the resulting values to z-scores. 2.2.6 DNA library construction and sequencing For FFPE samples, TNA was extracted using the ALINE FFPE GenePure Purification Kit. When ≥300 ng of FFPE extracted TNA was available, TNA was also treated with S1 nuclease to reduce the rate of chimeric fragments derived from single-stranded DNA and overhangs.112 Paired-end DNA libraries for sequencing were generated using a PCR-based protocol as previously described.113 For fresh frozen tissues, DNA was extracted and subjected to library construction as previously described.114 Whole exome capture was performed using the Agilent SureSelectXT Human All Exon V6 + UTR kit or IDT xGen Exome Hybridization Panel. For targeted capture sequencing, hybridization capture was performed with a panel created by IDT or Twist Biosciences, as previously described.115 This panel was designed to capture the regions around MYC (Chr8:126262754-129664254), BCL2 (Chr18:63077345-63128767), BCL6 (Chr3:187721376-188032725), and recurrent rearrangement partner loci: IGH (Chr14:105546276-106879844), IGK (Chr2:88686728-89330586), IGL (Chr22:22329967-23061520), and PAX5 (Chr9:37034478-37465410). Libraries were sequenced on an Illumina HiSeq2500, HiSeqX, or NovaSeq with 150 bp paired-end reads. 2.2.7 Detection of somatic mutations and structural variants Simple somatic mutations were identified from WES (n = 176) or previously published WGS data (n = 22). WGS and WES were aligned to the GRCh37 reference genome using bwa-mem (v0.7.17)116. Somatic variants were identified with the SLMS-3 pipeline, an ensemble variant 42 calling approach that uses Strelka2117, LoFreq118, Mutect2119, and SAGE. Only variants called by three or more variant callers were passed. Somatic variants were annotated with VCF2MAF and the Ensembl Variant Effect Predictor120. The GnomAD reference was used to remove germline variants with a population allele frequency > 1e-4 and further filtered against a custom blacklist of variants that recur when tumors are run without, but not with, a matched normal. Significantly mutated genes were identified using dNdScv.121 LymphGen classification96 was performed on variant calls using somatic mutations and BCL2/BCL6-rearrangement data. Targeted capture sequencing data for identification of SVs was aligned to the hg38 reference genome using bwa-mem (v0.7.17). SVs involving MYC and BCL2 were identified by an ensemble approach using GRIDSS2122 and Manta123, with VCF files output by these tools intersected using the BioConductor package StructuralVariantAnnotation124. SVs were filtered out unless they met a minimum variant allele frequency of 0.05 and/or passed manual curation in IGV125. WGS data was analyzed using the same pipeline as capture data, and mapped to the hg38 genome build with CrossMap.126 Circular plots were generated with the R package Circlize.127 Bioinformatic pipelines are available as part of the LCR Modules repository. 2.3 Results The study cohort included 198 patients with HGBCL-DH/TH: 31 with HGBCL-DH-BCL6 and 167 with HGBCL-DH-BCL2. Cases with rearrangements of all three genes (HGBCL-TH), representing 25% of the cohort, were included in the HGBCL-DH-BCL2 group. The clinical and pathological characteristics of the study cohort are presented in Table 2.1. 2.3.1 The molecular landscape of HGBCL-DH-BCL6 Among HGBCL-DH-BCL6 tumors, DLBCL morphology was observed in 77% of tumors, with 13% showing intermediate and 10% blastoid morphology (Table 2.1). For the 21 tumors with 43 available MYC rearrangement partner data, 13 (62%) involved an immunoglobulin (IG) partner, 6 (29%) a non-IG partner, while a partner could not be identified in 2 (10%) tumors (Table 2.1). In 4 cases (19%), MYC was rearranged to the BCL6 locus, representing so-called "pseudo double-hit" events.128 A cryptic BCL2 rearrangement involving the IGH locus was identified in one case, which was not detectable by conventional break-apart FISH. This tumor was retained in the HGBCL-DH-BCL6 group, as identifying rearrangements that are cryptic to FISH is not currently feasible in routine clinical practice. By the IHC-based Hans algorithm, 79% of cases were classified as GCB and 21% as non-GCB. BCL6 protein expression was detected in 96% of evaluable cases, with CD10 and MUM1 positivity in 55% and 38%, respectively. GEP-based COO classification identified 64% of cases as GCB, 25% as ABC, and 11% as unclassified (Figure 2.1A). DHITsig was positive in 46% of cases, negative in 43%, and indeterminate in 11%. LymphGen classification revealed substantial genetic heterogeneity (Figure 2.1B). BN2 was the most common subtype (35%), followed by ST2 (16%), EZB (6%), and MCD (6%). One case was classified as a BN2/ST2 composite, and 32% of cases were unclassified (“Other”). Correspondingly, several features commonly associated with the BN2 subtype were among the most recurrently mutated genes in HGBCL-DH-BCL6 (Figure 2.1D), including mutations in CCND3 (32%), CD70 (23%), HIST1H1D (23%), and DTX1 (16%). Notably, while NOTCH2 truncating mutations are a hallmark feature of BN2 lymphomas, only a single case harbored this feature. Additionally, despite BN2 being the most common LymphGen subtype, mutations characteristic of other genetic subtypes occurred at frequencies comparable to the top BN2-associated alterations. These included epigenetic regulators commonly altered in EZB, such as CREBBP (26%), KMT2D (23%), and EZH2 (16%), as well as SOCS1 (19%), SGK1 (16%), and TET2 (13%), which are frequently mutated in ST2. Overall, HGBCL-DH-BCL6 demonstrates a heterogeneous molecular profile across immunophenotypic, transcriptional, and genetic features. 44 2.3.2 The molecular landscape of HGBCL-DH-BCL2 Among HGBCL-DH-BCL2 tumors, DLBCL morphology was observed in 75% of tumors, with 12% showing intermediate and 13% blastoid morphology (Table 2.1). Of the 113 cases with available MYC rearrangement partner data, 45 (40%) involved an IG partner, 56 (50%) a non-IG partner, and for 12 cases (11%), a rearrangement partner could not be identified (Table 2.1). As previously described, HGBCL-DH-BCL2 was associated with a more homogeneous immunophenotype compared to HGBCL-DH-BCL6 (Table 2.1). CD10 and BCL6 IHC were positive in 92% and 88% of tumors, respectively, while MUM1 was positive in only 12%. Based on these markers, 97% of tumors were classified as GCB by the Hans algorithm. Consistently, GEP identified a GCB COO in 93% of cases, with only 2% classified as ABC and 5% as Unclassified (Figure 2.1A). DHITsig expression was observed in 78% of cases, with 16% indeterminate and only 6% negative (Figure 2.1A). LymphGen classification further supported the molecular uniformity of this group (Figure 2.1B). The majority of cases (86%) were classified as EZB, while Composite subtypes involving EZB (EZB-COMP) were observed in an additional 8% of cases. The BN2 and ST2 groups each accounted for 1% of cases, while the remaining tumors were classified as Other (5%). Consistently, the most frequently mutated genes in HGBCL-DH-BCL2 were in chromatin modifiers KMT2D (52%), CREBBP (46%), and EZH2 (40%), along with BCL2 (63%) and TNFRSF14 (37%) – representing prototypical EZB features as well as genes commonly mutated in FL, an indolent lymphoma that often transforms into aggressive B-cell lymphoma. Notably, only 14% of cases represented a transformation from a prior indolent lymphoma. Comparing the clinical and molecular features between transformed and de novo HGBCL-DH-BCL2 revealed similar features across groups (Figure 2.2A; Table 2.2), with only the proportion of patients with two or more extranodal sites being significantly different between de novo (56%) and transformed (30%) cases (P = 0.04). Consistently, there was no significant difference in the mutational frequency of any 45 recurrently mutated genes (Figure 2.2B). Together, these findings highlight the molecular homogeneity of HGBCL-DH-BCL2, with no apparent molecular differences between de novo and transformed cases. Although the distinction between DLBCL and high-grade morphology does not affect the diagnosis of HGBCL-DH/TH, as both fall under the same entity, the 2017 WHO classification includes a reporting recommendation for morphological subtype. To evaluate whether morphology correlates with underlying biology, molecular and clinical features were compared between tumors with DLBCL morphology (n = 125) and those with high-grade morphology (n = 42), which included 20 intermediate and 22 blastoid cases (Table 2.1). Among clinical variables (Figure 2.3A; Table 2.3), high-grade morphology was associated with a higher proportion of patients with an elevated LDH ratio (P = 0.04) and an ECOG performance status ³2 (P = 0.01). The distribution of DHITsig also differed significantly by morphology (P = 0.005), with expression observed in 97% of high-grade tumors compared to 72% of DLBCL morphology tumors (Figure 2.3A; Table 2.3). Among recurrently mutated genes, ARID1A mutations were more frequent in high-grade morphology tumors (21% vs. 7%; P = 0.018). In contrast, several genes were also more frequently mutated in DLBCL morphology tumors, including LTB (18% vs. 0%; P = 0.001), CARD11 (14% vs. 0%; P = 0.007), and BTG1 (14% vs. 2%; P = 0.046). Interestingly, TP53 mutation, a well-described adverse prognostic feature, was also more frequent in DLBCL morphology tumors (35% vs. 15%; P = 0.011; Figure 2.3B). However, after multiple test correction (Holm-Bonferroni), only LTB remained statistically significant (Q < 0.1). While these results highlight some potential differences in clinical and molecular features between morphological subtypes, the overall features remain broadly consistent across groups. HGBCL-DH-BCL2 tumors almost uniformly fall into the GCB and EZB molecular categories, both of which are associated with a favorable prognosis among DLBCL-NOS. However, despite this classification, HGBCL-DH-BCL2 is consistently linked to poor clinical 46 outcomes, suggesting the presence of additional biological features that distinguish these tumors from other GCB lymphomas. Transcriptomic comparisons with GCB DLBCL previously led to the identification of DHITsig.107 To determine whether these differences extend to the genetic level, mutation frequencies of recurrently mutated genes were compared between HGBCL-DH-BCL2 and GCB DLBCL. Given the absence of FISH data in the comparison cohort, the analysis was restricted to GCB DLBCL that were negative for DHITsig expression to minimize inclusion of undetected HGBCL-DH-BCL2. Comparison of mutation frequencies between HGBCL-DH-BCL2 and DHITsig-negative GCB DLBCL identified 26 differentially mutated genes (Q < 0.1; Figure 2.4). Beyond canonical EZB-associated alterations (BCL2, KMT2D, CREBBP, EZH2), genes more frequently mutated in HGBCL-DH-BCL2 included regulators of cell cycle progression and genome stability (TP53, RB1, and RBM38), as well as DZ regulatory genes (MYC, FOXO1, and CCND3), the latter of which are also more frequently mutated in BL. In contrast, GCB DLBCL more commonly harbored mutations in genes involved in NF-κB signaling (BCL10, NFKBIA, MYD88, STAT3, and TNFAIP3) and immune surveillance (CD70 and CD58). These findings highlight distinct genetic programs between HGBCL-DH-BCL2 and GCB DLBCL, with HGBCL-DH-BCL2 enriched for mutations associated with DZ biology and proliferation, and GCB DLBCL showing a greater frequency of alterations linked to LZ-associated NF-κB signaling and immune interactions. 2.3.3 DHITsig expression extends beyond HGBCL-DH-BCL2 to identify DZ lymphomas The mBL signature, originally derived by comparing gene expression between BL and DLBCL, has been shown to substantially overlap with the transcriptional program of DZ GCBCs.36,74 In prior studies, when DZ and LZ signatures were constructed using sorted GCBC populations, most DLBCLs were classified as LZ-like, whereas the majority of mBL cases aligned 47 with the DZ compartment.36 These findings suggest that the mBL signature serves as a proxy for DZ GCBC biology and support the concept of BL as a prototypical DZ lymphoma. Given that HGBCL-DH-BCL2 is also enriched for mutations in DZ-associated genes, we aimed to determine whether DHITsig reflects a similar DZ phenotype. We applied the DLBCL90 assay to assess DHITsig expression across an expanded cohort of aggressive B-cell lymphomas, including 55 BL tumors and 184 DLBCL-morphology tumors from the original DHITsig discover cohort (DLC380), which comprised both DLBCL-NOS and HGBCL-DH-BCL2 cases. Strikingly, all 55 BL cases were classified as DHITsig+ (Figure 2.5A). To further investigate whether DHITsig captures DZ biology, we calculated mBL and DZ GCBC signature scores across RNAseq data from the DLC380 cohort. DHITsig scores were strongly correlated with both the DZ GCBC signature (R2 = 0.37, P < 0.0001; Figure 2.5B) and the mBL signature (R2 = 0.45, P < 0.0001; Figure 2.5C), demonstrating substantial concordance with established DZ-associated transcriptional programs. Together, these findings indicate that DHITsig identifies a transcriptionally and genetically coherent subset of aggressive B-cell lymphomas of germinal center origin characterized by DZ biology. This group extends beyond HGBCL-DH-BCL2 to include BL and a subset of GCB DLBCL-NOS that share similar transcriptomic profiles. 2.4 Discussion Through characterizing the genetic landscape of HGBCL-DH/TH, this study aimed to refine the biological boundaries of this entity and clarify the molecular basis of DHITsig expression. While HGBCL-DH-BCL2 exhibits a homogeneous and biologically distinct genetic profile, HGBCL-DH-BCL6 displays marked heterogeneity and shares negligible genetic similarities with HGBCL-DH-BCL2. The mutational profile of HGBCL-DH-BCL2 suggests evolution from an FL-like precursor, with subsequent divergence driven by hijacking the intrinsic survival and proliferative programs of DZ GCBCs to enable transformation into an aggressive B- 48 cell lymphoma. We further demonstrate that DHITsig expression extends beyond HGBCL-DH-BCL2 to identify a broader group of "DZ lymphomas", which includes BL and a subset of GCB DLBCL. Collectively, these findings support redefining HGBCL-DH/TH to include only HGBCL-DH-BCL2 and underscore the value of molecular profiling in identifying a biologically defined phenotype with potential diagnostic and therapeutic relevance that is not fully captured by current diagnostic practices. Building on previous reports,106 our analysis confirmed the transcriptional heterogeneity of HGBCL-DH-BCL6, with tumors spanning all COO subgroups, and further demonstrated that this variability extends to the genetic level. LymphGen classification distributed tumors across the BN2, ST2, EZB, and MCD subtypes, with approximately one-third unclassified. Although BN2 was the most common subtype (35%), BN2-associated mutations were limited, and alterations linked to other subtypes occurred at comparable frequencies. Notably, NOTCH2 truncating mutations, reported in approximately 40% of BN2 tumors,96 were conspicuously absent in HGBCL-DH-BCL6. Given that BCL6 rearrangement is a key feature of the BN2 subtype, it is likely that BN2 classification in some tumors is driven primarily by rearrangement status rather than a broader BN2-associated genetic program. This suggests that, despite the broad LymphGen distribution, HGBCL-DH-BCL6 may be even more genetically diverse than these subtype assignments imply. In contrast, HGBCL-DH-BCL2 showed remarkable molecular uniformity, with nearly all cases having a GCB COO and classified as EZB by LymphGen, with mutational profiles dominated by chromatin-modifying genes, which are also typically mutated in FL. Importantly, molecular features were indistinguishable between de novo and transformed cases. This suggests that both groups arise from an FL-like common progenitor cell carrying early founder mutations such as BCL2 translocation and mutations in chromatin-modifying genes. From this precursor, divergent evolutionary paths can produce either clinically apparent FL or aggressive lymphoma without an overt indolent phase. This model is supported by phylogenetic analyses of 49 paired FL biopsies taken at diagnosis and at relapse or transformation, which show that sequential samples usually share only a limited set of early founding mutations, while most genetic changes are unique to each timepoint.125,129,130 Although HGBCL-DH-BCL2 falls within favorable prognostic GCB/EZB subgroups, it is associated with poor outcomes. Comparison with GCB DLBCL revealed key genetic distinctions that could be mapped to normal DZ/LZ GC biology, supporting a biological basis for this adverse prognosis. HGBCL-DH-BCL2 was enriched for alterations in genes linked to dark zone entry (MYC) and regulation (CCND3, FOXO1), and showed a relative paucity of mutations that promote NF-κB activation and immune evasion compared with GCB DLBCL. In normal GCs, interactions between GCBCs and other immune cells occur predominantly in the LZ, where NF-κB signaling is activated through positive selection by TFH cells. In contrast, the DZ is relatively immune-cold, and GCBCs downregulate genes involved in immune interactions, potentially reducing the selective pressure to acquire mutations that promote immune evasion. Consistent with this biology, DHITsig+ tumors are more often MHC I/II negative and contain fewer infiltrating T cells than GCB DLBCL.107,131 Genes more frequently mutated in HGBCL-DH-BCL2 include MYC, FOXO1, and CCND3, all integral to dark zone (DZ) biology. In the germinal center, MYC marks a small subset of B cells recycling to the DZ, providing an initial proliferative push by promoting G1–S phase transition before being rapidly suppressed by BCL6. In HGBCL-DH-BCL2, MYC rearrangement uncouples its expression from normal regulation, enabling constitutive expression in the DZ, where most tumors also express BCL6. MYC mutations, which often occur when the rearrangement involves an IG locus due to somatic hypermutation, are largely passenger events, though some, affecting phosphorylation sites, can stabilize MYC and enhance oncogenic potential. Within the DZ, CCND3 promotes “inertial cycling” through repeated divisions without external signals, and mutations affecting C-terminal phosphorylation residues can prevent its degradation. FOXO1 50 maintains the DZ transcriptional program, and its nuclear ejection via PI3K/AKT-mediated phosphorylation signals transition back to the light zone. Most FOXO1 mutations impair this regulation, potentially locking the cell in a DZ state. Collectively, these alterations highlight a recurring theme in HGBCL-DH-BCL2 of co-opting normal DZ pathways. The recently released International Consensus Classification (ICC) and fifth edition of the WHO classification of haematolymphoid neoplasms (WHO HAEM5) have refined the categorization of aggressive B-cell lymphomas. The homogeneous molecular landscape of HGBCL-DH-BCL2 has led to its recognition as a distinct entity in both classification systems. In contrast, reflecting the substantial biological heterogeneity of HGBCL-DH-BCL6, the ICC has relegated this group to provisional entity status, while WHO HAEM5 classifies these tumors within the broader categories of DLBCL-NOS or HGBCL-NOS, depending on morphology. Further studies are needed to clarify the optimal classification of HGBCL-DH-BCL6. Under both the ICC and WHO HAEM5, HGBCL-DH-BCL2 includes tumors with either DLBCL or high-grade morphology, a distinction largely based on tumor cell size, which is smaller in high-grade tumors. Notably, introduction of CCND3 mutations into normal GCBCs leads to a reduction in cell size through cyclin D3-mediated G1 compression, enabling repeated inertial DZ divisions without full regrowth between cycles. While genetic differences between morphological subtypes are limited, high-grade tumors were more often DHITsig+ and associated with elevated LDH ratios, suggesting that cell size variation may reflect underlying proliferative or metabolic imbalances influenced by non-genetic factors. Given the shared molecular features across subtypes, the inherent subjectivity of histological classification, and the potential for tissue processing artifacts to confound interpretation, current evidence supports classifying both morphologies within a single diagnostic entity. DHITsig is also expressed by a subset of GCB DLBCL lacking concurrent MYC and BCL2 rearrangements, demonstrating that it is not merely a surrogate for these events. Limitations of 51 histology-based classification and the inability of FISH to detect cryptic rearrangements—present in roughly 20% of DHITsig+ DLBCL-NOS—highlight the potential of molecular profiling to define a broader, biologically coherent phenotype. Our data clarify the biological significance of DHITsig, showing a strong correlation with established DZ transcriptional signatures and near-universal expression in BL. This supports the concept that DHITsig identifies a biologically unified group of aggressive lymphomas with features of DZ GCBCs, encompassing HGBCL-DH-BCL2, BL, and a subset of GCB DLBCL. Based on these findings, we refer to DHITsig hereafter as the “dark zone signature” (DZsig). Collectively, these results support “dark zone lymphoma” as an umbrella term encompassing HGBCL-DH-BCL2, BL, and a subset of GCB DLBCL-NOS, all of which share characteristic features with DZ GCBCs. While BL and HGBCL-DH-BCL2 are routinely identified in clinical practice, the DZsig+ subset of GCB DLBCL-NOS is currently only recognized through gene expression profiling and requires further genetic and biological characterization. Despite clinical diversity within this group, including features unique to BL such as tumor lysis syndrome, targeting shared molecular vulnerabilities across the broader DZ lymphoma spectrum offers a promising avenue to improve patient outcomes. 52 Table 2.1. Clinical and pathological characteristics of the study cohort. Characteristic HGBCL-DH-BCL6 HGBCL-DH-BCL2 n = 31 n = 167 Age, median (range) 69 (37-93) 65 (28-93) Age > 60, n (%) 22 (71%) 115 (69%) Male sex, n (%) 18 (58%) 105 (63%) Ann Arbor stage, n (%) I-II 6 (21%) 42 (25%) III-IV 22 (79%) 124 (75%) Missing 3 1 LDH > ULN, n (%) No 10 (42%) 59 (37%) Yes 14 (58%) 100 (63%) Missing 7 8 Extranodal sites, n (%) 0-1 11 (46%) 55 (49%) >1 13 (54%) 57 (51%) Missing 7 55 ECOG PS, n (%) 0-1 16 (57%) 92 (56%) 2-4 12 (43%) 72 (44%) Missing 3 3 IPI, n (%) Low (0-2) 8 (31%) 50 (34%) High (3-5) 18 (69%) 96 (66%) Missing 5 21 Transformed, n (%) No 30 (97%) 143 (86%) Yes 1 (3%) 24 (14%) Cytology, n (%) DLBCL 24 (77%) 125 (75%) Intermediate 4 (13%) 20 (12%) Blastoid 3 (10%) 22 (13%) HGBCL-TH - 47/160 (29%) Cryptic HGBCL-DH-BCL2 1/21 (5%) - MYC rearrangement partner IG 13 (62%) 45 (40%) non-IG 6 (29%) 56 (50%) not found 2 (10%) 12 (11%) Missing 10 54 Immunohistochemistry MYC 21/27 (78%) 118/140 (84%) BCL2 17/30 (57%) 149/162 (92%) CD10 17/31 (55%) 148/160 (92%) BCL6 27/28 (96%) 125/142 (88%) MUM1 10/26 (38%) 14/114 (12%) 53 Hans COO, n (%) GCB 23 (79%) 155 (97%) non-GCB 6 (21%) 4 (3%) Missing 2 8 COO, n (%) GCB 18 (64%) 132 (93%) UNCLASS 3 (11%) 7 (5%) ABC 7 (25%) 3 (2%) Missing 3 25 DHITsig, n (%) Positive 13 (46%) 111 (78%) Indeterminant 3 (11%) 23 (16%) Negative 12 (43%) 8 (6%) Missing 3 25 LymphGen, n (%) EZB 2 (6%) 144 (86%) ST2 5 (16%) 1 (1%) BN2 11 (35%) 1 (1%) MCD 2 (6%) 0 (0%) Composite 1 (3%) 13 (8%) Other 10 (32%) 8 (5%) LDH: lactate dehydrogenase; ULN: upper limit of normal; ECOG PS: Eastern Cooperative Oncology Group Performance Status; IPI: International Prognostic Index; HGBCL-TH: high-grade B-cell lymphoma with MYC, BCL2, and BCL6 rearrangement; HGBCL-DH-BCL2: high-grade B-cell lymphoma with MYC and BCL2 rearrangement; IG: immunoglobulin; COO: cell-of-origin; GCB: germinal center B-cell-like; ABC: activated B-cell-like; DHITsig: double-hit signature. 54 Table 2.2. Clinical and pathological characteristics of HGBCL-DH-BCL2 by transformation status Characteristic De novo Transformed P n = 143 n = 24 Age, median (range) 65 [28, 93] 65.5 [40, 83] 0.22 Age > 60, n (%) 101 (71%) 14 (58%) 0.24 Male sex, n (%) 92 (64%) 13 (54%) 0.37 Ann Arbor stage, n (%) 0.45 I-II 38 (27%) 4 (17%) III-IV 104 (73%) 20 (83%) Missing 1 0 LDH > ULN, n (%) 0.49 No 49 (36%) 10 (43%) Yes 87 (64%) 13 (57%) Missing 7 1 Extranodal sites, n (%) 0.04 0-1 39 (44%) 16 (70%) >1 50 (56%) 7 (30%) Missing 54 1 ECOG PS, n (%) 0.50 0-1 78 (55%) 14 (64%) 2-4 64 (45%) 8 (36%) Missing 1 2 IPI, n (%) 0.48 Low (0-2) 83 (67%) 13 (59%) High (3-5) 41 (33%) 9 (41%) Missing 19 2 HGBCL-TH 41/139 (29%) 6/21 (29%) 1 MYC rearrangement partner 0.50 IG 40 (41%) 5 (31%) non-IG 48 (49%) 8 (50%) not found 9 (9%) 3 (19%) Missing 46 8 Immunohistochemistry MYC 105/123 (85%) 13/17 (76%) 0.31 BCL2 128/138 (93%) 21/24 (88%) 0.41 CD10 127/139 (91%) 21/21 (100%) 0.37 BCL6 114/127 (90%) 11/15 (73%) 0.08 MUM1 11/105 (10%) 3/9 (33%) 0.08 55 Hans COO, n (%) 1 GCB 134 (97%) 21 (100%) non-GCB 4 (3%) 0 (0%) Missing 5 3 COO, n (%) 0.07 GCB 119 (94%) 13 (81%) Unclassified 4 (3%) 3 (19%) ABC 3 (2%) 0 (0%) Missing 17 8 DHITsig, n (%) 0.18 Positive 101 (80%) 10 (62%) Indeterminant 18 (14%) 5 (31%) Negative 7 (6%) 1 (6%) Missing 17 8 LymphGen, n (%) 0.26 EZB 123 (86%) 21 (88%) ST2 0 (0%) 1 (4%) BN2 1 (1%) 0 (0%) Composite 11 (8%) 2 (8%) Other 8 (6%) 0 (0%) LDH: lactate dehydrogenase; ULN: upper limit of normal; ECOG PS: Eastern Cooperative Oncology Group Performance Status; IPI: International Prognostic Index; HGBCL-TH: high-grade B-cell lymphoma with MYC, BCL2, and BCL6 rearrangement; HGBCL-DH-BCL2: high-grade B-cell lymphoma with MYC and BCL2 rearrangement; IG: immunoglobulin; COO: cell-of-origin; GCB: germinal center B-cell-like; ABC: activated B-cell-like; DHITsig: double-hit signature. 56 Table 2.3. Clinical and pathological characteristics of HGBCL-DH-BCL2 by morphology Characteristic DLBCL Morphology High-grade Morphology P n = 125 n = 42 Age, median (range) 66 [28, 93] 65 [40, 91] 0.85 Age > 60, n (%) 85 (68%) 30 (71%) 0.85 Male sex, n (%) 82 (66%) 23 (55%) 0.27 Ann Arbor stage, n (%) 0.31 I-II 34 (27%) 8 (19%) III-IV 90 (73%) 34 (81%) Missing 1 0 LDH > ULN, n (%) 0.04 No 50 (42%) 9 (23%) Yes 70 (58%) 30 (77%) Missing 5 3 Extranodal sites, n (%) 0.83 0-1 39 (48%) 16 (52%) >1 42 (52%) 15 (48%) Missing 44 11 ECOG PS, n (%) 0.01 0-1 77 (63%) 15 (37%) 2-4 46 (37%) 26 (63%) Missing 2 1 IPI, n (%) 0.08 Low (0-2) 65 (61%) 31 (78%) High (3-5) 41 (39%) 9 (22%) Missing 19 2 Transformed, n (%) 0.62 No 108 (86%) 35 (83%) Yes 17 (14%) 7 (17%) HGBCL-TH 34/121 (28%) 13/39 (33%) 0.55 MYC rearrangement partner 0.35 IG 30 (36%) 15 (52%) non-IG 44 (52%) 12 (41%) not found 10 (12%) 2 (7%) Missing 41 13 Immunohistochemistry MYC 93/109 (85%) 25/31 (81%) 0.58 BCL2 109/120 (91%) 40/42 (95%) 0.52 CD10 107/118 (91%) 41/42 (98%) 0.19 BCL6 100/107 (93%) 25/35 (71%) 0.001 MUM1 11/87 (13%) 3/27 (11%) 1 57 Hans COO, n (%) 1 GCB 114 (97%) 41 (98%) non-GCB 3 (3%) 1 (2%) Missing 8 0 COO, n (%) 0.72 GCB 100 (93%) 32 (91%) Unclassified 5 (5%) 2 (6%) ABC 2 (2%) 1 (3%) Missing 18 7 DHITsig, n (%) 0.005 Positive 77 (72%) 34 (97%) Indeterminant 22 (21%) 1 (3%) Negative 8 (7%) 0 (0%) Missing 18 7 LymphGen, n (%) 0.45 EZB 106 (85%) 38 (90%) ST2 1 (1%) 0 (0%) BN2 1 (1%) 0 (0%) Composite 12 (10%) 1 (2%) Other 5 (4%) 3 (7%) LDH: lactate dehydrogenase; ULN: upper limit of normal; ECOG PS: Eastern Cooperative Oncology Group Performance Status; IPI: International Prognostic Index; HGBCL-TH: high-grade B-cell lymphoma with MYC, BCL2, and BCL6 rearrangement; HGBCL-DH-BCL2: high-grade B-cell lymphoma with MYC and BCL2 rearrangement; IG: immunoglobulin; COO: cell-of-origin; GCB: germinal center B-cell-like; ABC: activated B-cell-like; DHITsig: double-hit signature. 58 Figure 2.1. The molecular landscape of HGBCL-DH/TH. (A) Distribution of COO and DHITsig status in HGBCL-DH-BCL6 (left panel) and HGBCL-DH-BCL2 (right panel). (B) Distribution of LymphGen subtypes in HGBCL-DH-BCL6 (left panel) and HGBCL-DH-BCL2 (right panel). (C–D) Oncoplots showing the mutation status of the most frequently mutated genes in (C) HGBCL-DH-BCL6 and (D) HGBCL-DH-BCL2. Genes displayed in each oncoplot were mutated in at least 10% of tumors in the respective group and were identified as significantly mutated in HGBCL-DH/TH. The set of significantly mutated genes includes the union of those found significant in HGBCL-DH-BCL2, HGBCL-DH-BCL6, or in the combined HGBCL-DH/TH cohort. Gene mutation frequencies for each group are shown in the bar plots on the right, with odds ratios and 95% confidence intervals for each gene comparison shown in the forest plots. Tumor morphology, COO subtype, DHITsig status, and LymphGen subtype are shown in the bottom annotation track. 93 %5 %78 %16 %6 %COO DHITsigHGBCL-DH-BCL264 %11 %25 %46 %11 %43 %COO DHITsigHGBCL-DH-BCL66%16%38%6%0%32%EZB ST2 BN2 MCD N1 OtherHGBCL-DH-BCL6C [binary]%42%32%26%26%26%26%26%23%23%23%19%16%16%16%16%16%16%16%16%16%16%16%16%16%16%13%13%13%13%13%13%13%13%13%13%13%HGBCL-DH-BCL60255075100Percent (%)0255075100Percent (%)A B94%1% 1% 0% 0%5%EZB ST2 BN2 MCD N1 OtherHGBCL-DH-BCL2GCBUnclassCOOABCNAPOSINDDHITsigNEGNAHigh-gradeDLBCLMorphologyEZBBN2LymphGenEZB-COMPBN2-COMPST2MCDOtherBCL2KMT2DCREBBPEZH2TNFRSF14MYCIGLL5BCL7ATP53FOXO1SOCS1MEF2BGNA13IRF8CCND3HIST1H1ESGK1B2MDTX1ACTBC10orf12HNRNPUKLHL6PIM1HLA−DMBLTBP2RY8DDX3XBTG1ARID1ACARD11EBF1HIST1H1BHVCN1MFHAS1OSBPL10POU2F2RB1RBM38ZFP36L1MorphologyCOODHITsigLymphgen63%52%46%40%37%34%31%31%30%29%28%25%23%23%20%20%17%16%15%14%14%14%14%14%13%13%13%12%11%11%11%10%10%10%10%10%10%10%10%10%HGBCL-DH-BCL2Missense Mutation Nonstop Mutation In Frame INDEL Nonsense MutationSplice Site Translation Start Site Frame Shift INDEL Multi HitAlteration 59 Figure 2.2. Similar molecular features between de novo and transformed HGBCL-DH-BCL2. (A) Distribution of clinical characteristics, immunophenotype, COO, DHITsig, LymphGen, and MYC rearrangement partner among HGBCL-DH-BCL2 tumors with DLBCL or high-grade morphology. Comparisons of clinical and IHC data were performed using Fisher’s exact tests, while comparisons of COO, DHITsig, LymphGen, and MYC rearrangement partner were performed using chi-square tests. (B) Comparison of the mutational landscape of HGBCL-DH-BCL2 tumors with DLBCL or high-grade morphology. Genes included in the oncoplot were mutated in at least 10% of either group and identified as significantly mutated in HGBCL-DH-BCL2. Mutation frequencies for each group are shown in the bar plots on the right, with corresponding odds ratios and 95% confidence intervals shown in the forest plots. *P < 0.05; **P < 0.01. 67%45%56%64%73%71%64%97%93%85%10%90%91%2%3%94%6%14%80%1%0%6%8%86%9%41%50%255075100IPI High (3−5)ECOG PS > 1Extranodal Sites > 1LDH Ratio > 1Stage III−IVAge > 60Male SexHans GCBMUM1 IHCBCL6 IHCCD10 IHCBCL2 IHCMYC IHCABCUNCLASSGCBDHITsig-DHITsigIndDHITsig+OtherBN2ST2EZB−COMPEZBPartner not foundNon−IG partnerIG partner59%36%30%56%83%58%54%100%88%76%33%73%100%0%19%81%6%31%62%0%4%0%8%88%19%31%50%0 25 50 75100Percent (%)*De novo TransformedBCL2KMT2DCREBBPEZH2TNFRSF14MYCIGLL5BCL7ATP53FOXO1SOCS1MEF2BGNA13IRF8CCND3HIST1H1ESGK1B2MDTX1ACTBC10orf12HNRNPUKLHL6PIM1HLA−DMBLTBP2RY8DDX3XBTG1ARID1ACARD11EBF1HIST1H1BHVCN1MFHAS1OSBPL10POU2F2RB1RBM38ZFP36L10 20 40 60Mutated (%)-2 0 2ln(Odds Ratio)De Novo TransformedA BMissense MutationSplice SiteNonstop MutationTranslation Start SiteIn Frame INDELFrame Shift INDELNonsense MutationMulti HitAlteration-4 60 Figure 2.3. Similar molecular features between HGBCL-DH-BCL2 tumors with DLBCL or high-grade morphology. (A) Distribution of clinical characteristics, immunophenotype, COO, DHITsig, LymphGen, and MYC rearrangement partner among HGBCL-DH-BCL2 tumors with DLBCL or high-grade morphology. Comparisons of clinical and IHC data were performed using Fisher’s exact tests, while comparisons of COO, DHITsig, LymphGen, and MYC rearrangement partner were performed using chi-square tests. (B) Comparison of the mutational landscape of HGBCL-DH-BCL2 tumors with DLBCL or high-grade morphology. Genes included in the oncoplot were mutated in at least 10% of either group and identified as significantly mutated in HGBCL-DH-BCL2. Mutation frequencies for each group are shown in the bar plots on the right, with corresponding odds ratios and 95% confidence intervals shown in the forest plots. *P < 0.05; **P < 0.01. 61%37%52%58%73%68%66%97%91%85%13%94%91%2%5%94%8%21%72%1%1%4%10%85%12%36%52%DLBCL255075100IPI High (3−5)ECOG PS > 1Extranodal Sites > 1LDH Ratio > 1Stage III−IVAge > 60Male SexHans GCBMUM1 IHCBCL6 IHCCD10 IHCBCL2 IHCMYC IHCABCUNCLASSGCBDHITsig-DHITsigIndDHITsig+OtherBN2ST2EZB−COMPEZBPartner not foundNon−IG partnerIG partner78%63%48%77%81%71%55%98%95%81%11%71%98%3%6%91%0%3%97%0%0%7%2%90%7%52%41%High−grade0 25 50 75100Percent (%)A BBCL2KMT2DCREBBPEZH2TNFRSF14MYCIGLL5BCL7ATP53FOXO1SOCS1MEF2BGNA13IRF8CCND3HIST1H1ESGK1B2MDTX1ACTBC10orf12HNRNPUKLHL6PIM1HLA−DMBLTBP2RY8DDX3XBTG1ARID1ACARD11EBF1HIST1H1BHVCN1MFHAS1OSBPL10POU2F2RB1RBM38ZFP36L10 20 40 60Mutated (%)−6 −4 −2 0 2ln(Odds Ratio)DLBCL High−gradeMissense MutationSplice SiteNonstop MutationTranslation Start SiteIn Frame INDELFrame Shift INDELNonsense MutationMulti HitAlteration******* 61 Figure 2.4. Comparison of the mutational landscape between HGBCL-DH-BCL2 and GCB-DLBCL. Bar plots showing the frequency of gene mutations in HGBCL-DH-BCL2 (n = 167) compared to GCB-DLBCL (n = 110). The HGBCL-DH-BCL2 group includes both de novo and transformed tumors with either DLBCL or high-grade morphology. The GCB-DLBCL group is drawn from a previously described cohort with available WES (Schmitz et al) and is restricted to DHITsig-negative tumors. Genes included for comparison were identified as significantly mutated in either HGBCL-DH-BCL2 or GCB-DLBCL, with the genes shown representing those with significantly different mutation frequencies between the two groups. P values were calculated using Fisher’s exact test and adjusted for multiple comparisons with the Benjamini-Hochberg method. *Q < 0.1; **Q < 0.01; ***Q < 0.001; ****Q < 0.0001. **** **** **** ** ** ** * *** ** ** * *020406080BCL2KMT2DCREBBPEZH2 MYCBCL7A TP53FOXO1MEF2BCCND3ACTBHNRNPUC10orf12RBM38HVCN1RB1EEF1A1Percent Mutated******** ** **ELAVL1BCL10NFKBIACD70MYD88CD58TET2STAT3TNFAIP3HGBCL-DH-BCL2 GCB DLBCL 62 Figure 2.5. DHITsig expression extends beyond HGBCL-DH-BCL2 and identifies tumors sharing features of dark zone germinal center B cells. (A) Heatmap showing normalized expression of DHITsig genes across GCB-DLBCL (n = 158), HGBCL-DH-BCL2 (n = 26), and BL (n = 55) tumors. Gene expression was determined using the DLBCL90 assay. The GCB-DLBCL and HGBCL-DH-BCL2 tumors shown represent cases from the original DLBCL90 DHITsig discovery cohort (Ennishi et al). (B) Scatter plot showing the correlation between DHITsig score and a dark-zone germinal-center B-cell (DZ GCB) gene expression signature. (C) Scatter plot showing the correlation between DHITsig score and the molecular Burkitt lymphoma (mBL) gene expression signature. DHITsig scores were determined using the DLBCL90 assay, while DZ GCB and mBL scores were derived from RNA-seq data. The plotted tumors include GCB-DLBCL (n = 147) and HGBCL-DH-BCL2 (n = 24) tumors from panel A with available RNA-seq data. Pearson correlation coefficients (R2) and corresponding p-values are shown on each plot. IL21REBI3SEMA7ACD80SGPP2IRF4STAT3BATFVOPP1MIR155HGRFFLVASPLY75CDK5R1GPR137BSYBUSMIM14AFMIDCD24JCHAINRGCCQRSL1SNHG19GAMTPEG10TNFSF8ALOX5SLC25A27MYCOR13A1DHITsigCOOPathologyNormalized Expression−4 −2 0 2 4 GCBCOOPOS NEGDHITsigIND DLBCL-NOS HGBCL-DH-BCL2 BLPathology −202−40 −20 0 20DHITsig ScoreDZ GCB scoreR2 = 0.37, p < 0.0001 −1012−40 −20 0 20DHITsig ScoremBL scoreR2 = 0.45, p < 0.0001INDNEGPOSDHITsigAB C 63 Chapter 3: Refining FISH-Based Diagnostic Approaches and Criteria for HGBCL-DH-BCL2 3.1 Introduction While the prior results support the classification of HGBCL-DH-BCL2 as a distinct entity, there are questions that remain surrounding its current diagnosis. Initially defined by rearrangements alone, HGBCL-DH/TH has since prompted proposals for an “atypical double-hit” category, encompassing tumors with other concurrent structural variants, specifically CNAs involving increased copy number (i.e., gains and amplifications).132 These suggestions are primarily based on conflicting outcome data, which is also influenced by current treatment regimens.132–138 As treatment strategies evolve, classification systems should focus on identifying tumors with shared molecular features. While HGBCL-DH-BCL2 represents a molecularly homogeneous group, it remains uncertain whether CNAs are sufficient to confer a comparable molecular phenotype. Furthermore, while the association of MYC and BCL2 rearrangements with increased protein expression is well described106,109,139,140, the impact of MYC and BCL2 CNAs on expression is less well known. As the diagnosis of HGBCL-DH/TH requires FISH testing for MYC rearrangement in all tumors of DLBCL morphology, a method that can also detect CNAs, such a classification could be integrated into clinical practice. However, whether atypical double-hit tumors share similar biology to HGBCL-DH-BCL2 requires further investigation. Furthermore, an atypical double-hit entity would substantially increase the number of tumors where subsequent FISH for BCL2 SVs is required, where a reflexive testing strategy is being used. As routine FISH testing is already challenging where clinical pathology resources are constrained, the association of MYC rearrangement with increased protein expression has provided a potential avenue to screen tumors for subsequent FISH testing. However, ~25% of tumors with MYC rearrangement are negative for MYC protein expression by IHC (universally performed using the Y69 antibody)141 using a threshold of ≥40% tumor cell staining.106 As the study of MYC rearrangement with increased expression has been largely limited to IHC, it is 64 unknown if the lack of detectable protein expression is due to low MYC expression or other mechanisms specific to IHC. Finally, diagnosing HGBCL-DH-BCL2 requires accurate detection of MYC rearrangements. In approximately half of HGBCL-DH-BCL2 tumors, MYC is rearranged to an IG locus (IGH, IGL, or IGK), while a range of non-IG rearrangement partners are observed in the remaining tumors.140,142 Rearrangements with IG or recurrent non-IG partners (BCL6 and PAX5) lead to elevated MYC expression, while rearrangements involving other non-IG partners have less clear biological consequences.115,143 Due to the diversity of potential MYC rearrangement partners, FISH using break-apart probes is recommended for detecting MYC rearrangements.144 Typical MYC break-apart probes include a red and green probe, which hybridize to regions centromeric and telomeric to MYC, respectively. When the MYC locus is intact, fused signals are observed, while a rearrangement of the MYC locus results in the separation of probe signals. Although balanced MYC break-apart patterns (equal numbers of break-apart probe signals) are widely interpreted as evidence of MYC rearrangement, the significance of unbalanced patterns is less clear. This is particularly true when a break-apart signal is lost, as such patterns raise the possibility that a region of the MYC locus is deleted rather than rearranged. A recent survey found that over half of laboratories reported unbalanced FISH patterns as being equivocal for MYC rearrangement, and a small proportion reported them as being negative.145 Given this uncertainty, along with its impact on diagnostic accuracy and clinical decision making, the significance of these unbalanced FISH patterns requires clarification. Here, we performed a comprehensive analysis of the association of MYC and BCL2 SVs (rearrangement and CNAs), as determined by FISH, with expression at the mRNA and protein level in a cohort of 802 de novo tumors with DLBCL morphology. Using this approach, we evaluated the biological impact of MYC and BCL2 CNAs and investigated potential mechanisms underlying IHC negativity in tumors with MYC rearrangement. In a separate but partially 65 overlapping cohort of 297 HGBCL-DH-BCL2 biopsies, we report the incidence of unbalanced MYC break-apart FISH patterns, the architecture of these rearrangements, and the biological consequences, as measured by MYC mRNA expression, MYC protein expression, and DZsig status. 3.2 Materials and Methods 3.2.1 DLBCL cohort The study cohort consisted of 802 tumors with DLBCL morphology, as determined by central pathology review. The tumors represent all those with available GEP data in three cohorts drawn from a population-based registry of adult patients (age ³ 18 years) diagnosed and treated in British Columbia, Canada. One cohort (GPH), gathered prospectively, was inclusive of incisional/excisional biopsies from patients diagnosed between 2014 and 2017 with a priority for patients treated with curative intent. The second cohort (DLC347), previously described,142 included patients diagnosed between 2001 and 2012 who were treated with curative intent and for whom a matched fresh-frozen biopsy was available. The third cohort (CCSRI), aiming to be more broadly representative, represents patients diagnosed between 2005 and 2010, including those not treated with curative intent, where adequate FFPE tumor material was available, inclusive of core needle biopsies. Patients with post-transplant lymphoproliferative disorders, primary mediastinal large B-cell lymphoma, primary central nervous system involvement, as well as those known to have a history of indolent lymphoma or HIV infection, were excluded. 3.2.2 HGBCL-DH-BCL2 cohort Tumors with DLBCL or high-grade morphology harboring MYC rearrangements, detected by break-apart FISH, were identified in the Lymphoid Cancer Database, BC Cancer. Patterns of MYC rearrangements were defined as: (a) balanced: equal number of break-apart probe signals; 66 (b) unbalanced-gain: red and green break-apart signals both present but not in equal numbers; (c) lost red (LR): only green break-apart signal(s) present, and (d) lost green (LG): only red break-apart signal(s) present. Further analysis was restricted to 297 HGBCL-DH-BCL2 tumors, defined as having one of the above MYC break-apart patterns and a concurrent BCL2 rearrangement detected by FISH. A comparison group of 333 GCB-DLBCL that were negative for MYC rearrangement by break-apart FISH was drawn from the CCSRI cohort described above. MYC immunohistochemistry (IHC) and nanoString gene expression profiling were performed for all biopsies with sufficient material. 3.2.3 FISH and IHC analyses FISH was performed on FFPE material on whole tissue sections or TMAs using commercially available Vysis LSI (Abbott) or Metasystems XL break-apart probes for MYC (Abbott 01N63-020; Metasystems D-6030-100-TC) and BCL2 (Abbott 05N51-020; Metasystems D-6018-100-OG). TMAs were constructed from duplicate 0.6 mm cores. Images were captured using a Metafer CoolCube 1 camera and Metafer software (version 3.11.8). Two independent observers scored 100 nuclei per biopsy, except for small or poor-quality biopsies, where only 50-100 nuclei could be scored. Tumors in which <50 nuclei could be scored were considered a failed FISH result. For biopsies with discrepant scores, consensus was reached by a third observer. Rearrangement was defined as a break-apart signal in ≥5% of cells. Gain was defined as 3-4 fused signals in ≥25% of cells, while high-level gain was defined as 5+ signals in ≥10% of cells.132 Cases with a break-apart FISH pattern and increased signal number were assigned to the rearranged category. Tumors with amplification, defined by uncountable signals, were assigned to their own category. 67 IHC staining was performed for MYC (Y69, Epitomics) and BCL2 (BCL2/124, Dako; and E17, Epitomics) on the Benchmark XT platform (Ventana). Thresholds of ≥40% and ≥50% of tumor cell staining were used to define positivity for MYC and BCL2, respectively.109 3.2.4 Digital gene expression profiling Digital GEP was performed on total RNA extracted from FFPE material using the DLBCL90 assay on the nCounter platform (nanoString) to determine MYC and BCL2 mRNA expression levels and assign COO and DZsig status, as previously described.107 For the DLBCL cohort, tumors were classified into molecular subtypes with DZsig+ taking precedence over COO, and with both DZsig-Indeterminant and DZsig+ tumors included in the DZsig+ group. These analyses were performed before the implementation of the refined COO algorithm. For the HGBCL-DH-BCL2 cohort, tumors were assigned using this algorithm into refined COO subgroups, prioritizing COO over DZsig status for ABC tumors. DZsig+ GCB and unclassified tumors were grouped as DZsig+, while DZsig- and DZsig-indeterminant tumors remained in their respective COO subgroups. 3.2.5 MYC mutation calling To determine potential mechanisms for unexplained MYC IHC negativity, MYC sequencing data from 446 tumors were analyzed. Mutational data for 325 tumors were generated based on deep targeted sequencing using a TruSeq Custom Amplicon assay targeting recurrently mutated DLBCL genes.146 Library construction was performed using TruSeq Custom Amplicon kits (TSCA; Illumina, San Diego, CA). TSCA-based libraries were constructed according to the manufacturer’s protocol (Illumina) for end-repair, A-tailing, and adaptor ligation. For hybridization of the custom-designed oligo pool, 250 ng of input DNA from fresh frozen samples were used. Samples were individually indexed using dual indexing, and the pooled samples were sequenced on an Illumina 68 MiSeq instrument, using MiSeq V2 cycle reagent kits and paired-end 2x150-bp reads. Coding exons of 57 genes, including MYC, were sequenced. An additional 121 tumors were sequenced using two Agilent SureSelect custom hybrid capture assays.143,147 For 92 tumors, a capture assay was used to enrich for DNA in the regions of a 7.8 Mb target space that interrogated commonly rearranged lymphoma genes, including the MYC locus.143 Libraries were pooled, and capture enrichment was performed, followed by sequencing on an Illumina HiSeq 2500 (v4 chemistry, 125 bp paired-end reads). For the remaining 29 tumors, a capture assay targeting actionable lymphoma mutations was used to assess a total of 614 regions from 43 genes, including MYC, for a final capture size of 385Kb.147 Adapter ligated libraries were constructed following the 100ng SureSelect XT2 Target Enrichment System for Illumina Multiplexed Sequencing protocol Version D.2. Indexed libraries from 16 different cases were pooled and sequenced on an Illumina MiSeq using 150-bp paired-end reads. For all three sequencing data sets, sequencing reads were aligned to the human reference genome (hg19) using BWA-mem version 0.7.17116 and variants for individual cases were identified using VarScan v2.4.3.148 Variants were further manually reviewed in the Integrated Genome Viewer version 2.3.93.149 3.2.6 Modeling MYC-N11S The N11S variant was engineered into the native MYC gene of the DOHH-2 (DSMZ) DLBCL cell line using CRISPR/Cas9. Cells were maintained in RPMI-1640 (ThermoFisher) containing 10% FBS. The CRISPOR webtool150 was used to design a gRNA targeting a site adjacent to the region of MYC encoding for the 11th amino acid (Table 3.1). Stocks of 200μM Alt-R crRNA (IDT) and 200uM Alt-R tracrRNA (IDT) were mixed in equimolar concentrations to a final concentration of 100μM and heated to 95°C for 2 minutes, then cooled to room temperature. For each transfection, 1.7μl of crRNA:tracrRNA complex was mixed with 2.1μl PBS and 1.7 μl of stock 61μM Alt-R Cas9 (IDT) for the formation of the ribonucleoprotein (RNP) complex. The RNP 69 complex solution was incubated at room temperature for 20 minutes. For each transfection, 5μl RNP complex solution was mixed with 1μl electroporation enhancer (IDT) and 4μl ssODN repair template (100 μM; IDT), and solutions were made up to 100μl in Cell Line Nucleofector™ Solution T + supplement (Lonza). The ssODN repair template was designed to incorporate the N11S variant as well as a synonymous mutation to the PAM site to prevent retargeting of Cas9 to edited alleles. One million cells were resuspended in transfection solutions and transferred to an electroporation cuvette. Cells were electroporated using the Nucleofector™ 2b Device (Lonza) and program A-032. Post-transfected cells were transferred to a 12-well plate in 1ml of RPMI + FBS and incubated for 24 hours before replacing media with fresh RPMI + FBS. Cells were incubated for an additional 24 hours, then diluted in RPMI + FBS to a concentration of 0.5 cells per 100μl. A volume of 100μl of diluted cells was transferred to each well of a 96-well plate. Cells were incubated at 37°C to allow for expansion of single-cell clones and subsequent downstream analyses. Incorporation of the N11S variant was validated by Sanger sequencing, and allelic frequencies were determined using a custom ddPCR assay, described below (Table 3.2). Wildtype (WT) controls were expanded from single cells under the same experimental conditions. Western blots were performed on cell lysates using the Y69 and 9E10 antibodies. For IHC, cell lines were formalin-fixed and paraffin-embedded, and IHC was performed using the Y69 antibody. 3.2.7 Digital droplet PCR For the determination of the allelic frequency of the N11S variant in edited cell lines, a ddPCR mutation detection assay was designed. Primers were designed to amplify a region encompassing the N11S variant region from DNA or cDNA (Table 3.2). Competitive locked nucleic acid probes conjugated with a FAM (5'6-FAM-CA+AC+CG+CA+G+CTAT-3'IABkFQ) or HEX (5'HEX-CCA+AC+A+G+GA+A+CTATG-3'IABkFQ) fluorophore were designed to target the N11S or WT allele, respectively. DNA and RNA were extracted from 10x10μm FFPE scrolls with 70 the AllPrep DNA/RNA FFPE Kit (Qiagen), and cDNA was generated from 400ng of RNA using the SuperScript™ III First-Strand Synthesis System (Invitrogen) per the manufacturer’s protocol. Each ddPCR reaction was prepared to a final volume of 20 μL, containing 10 μL of 2X ddPCR Supermix for Probes (no dUTP), 900nM of forward and reverse primer, 250nM of each probe, 5 units of Hind III restriction enzyme diluted in NEBuffer 2.1, and either 50ng of DNA or 1ng RNA equivalents of cDNA. Oil droplets were generated using a QX200 droplet generator (Bio-Rad) and transferred to a C1000 TouchTM thermal cycler. Thermocycling conditions were set to an initial 10-minute incubation period at 95°C, followed by 40 cycles of 30 seconds at 94°C and 1 minute at 60°C. Reactions were then analyzed on a QX200 droplet reader, and the resultant data were interpreted using QuantSoftTM software, Regulatory Edition (Bio-Rad). 3.2.8 Western blot analysis Cells were pelleted by centrifugation at 1200x g for 5 minutes and lysed in radioimmunoprecipitation assay (RIPA) buffer supplemented with protease and phosphate inhibitor cocktail (1mM, Thermo Scientific) for 15 minutes on ice. Cellular debris was cleared from lysate solutions by centrifugation at 14,000 x g for 15 minutes. Protein concentrations of lysates were quantified using the Micro BCA™ Protein Assay Kit (ThermoFisher). Cell lysates were heated at 70°C for 10 minutes in 1X NuPAGE® LDS Sample Buffer with 5mM dithiothreitol (DTT) and separated by sodium dodecyl sulfate polyacrylamide gel electrophoresis using a pre-cast 10-well NuPAGE® 4-12% Bis-Tris Gel (Invitrogen). For Western blots performed on DOHH-2 cell lines lysates, 40μg of lysate in 1X NuPAGE® LDS Sample Buffer and 5mM DTT was loaded in each lane. Gel electrophoresis was performed at 120V for 1.5 hours. Following electrophoresis, protein was transferred to a nitrocellulose Turbo-Blot® membrane by semi-dry electrophoretic transfer at 25V for 15 minutes. Membranes were blocked with 5% skim milk in 1x PBS +0.05% Tween20 for 1 hour at room temperature. Immunoblotting for MYC was 71 performed with the Y69 (Epitomics) and 9E10 (Abcam) antibodies (1:1000 dilutions). Immunoblotting for histone H3 was performed with the ab1791 (Abcam) antibody (1:10,000 dilution). Membranes were incubated with primary antibodies diluted in 5% skim milk in 1x PBS +0.05% Tween20 for 1 hour at room temperature. Membranes were washed 3x10 minutes with 1x PBS +0.05% Tween20 and then incubated for 1 hour at room temperature with either anti-mouse IgG HRP (Promega W4021; 9E10 antibody only) or anti-rabbit IgG HRP (Promega W4011) secondary antibody (1:1000 for MYC and 1:10000 for Histone H3). Membranes were washed 3 x 10 minutes with 1x PBS +0.05% Tween20 and visualized by enhanced chemiluminescence using a ChemiDoc™ MP Imaging System (Bio-Rad). 3.2.9 Cell line IHC Approximately 20 million cells were pelleted and resuspended in 10% buffered formalin for 24 hours at room temperature, then washed with 95% ethanol. Cells were pelleted by centrifugation, and supernatant was removed and replaced with an approximately equal amount of liquefied Histogel (ThermoFisher). Histogel was gently mixed and set before removing the pellet and submitting it for routine clinical FFPE processing. IHC staining was performed for MYC using the EP121 clone (previously Y69, Inter Medico) on the Dako Omnis platform (Agilent Technologies). IHC was scored on a 10% increment scale of percent positively stained cells. IHC for DOHH-2 cells was performed on FFPE whole sections, while IHC for WSU-DLCL2 and SU-DHL-10 was performed on TMA (duplicate 0.6mm cores). 3.2.10 Identification of MYC structural variants MYC rearrangement architecture and partner gene were predicted from previously published whole-genome sequencing and/or capture sequencing data.108,115,129,143,151–153 The preservation of each tumor sample was either FFPE or fresh frozen. Total nucleic acids (TNA) 72 were extracted from FFPE samples using the ALINE FFPE GenePure purification kit, automated on the Hamilton Nimbus robot. Samples with at least 300 ng of TNA were subjected to S1 nuclease treatment to reduce the rate of chimeric fragments leading to SV artifacts.112 A PCR-based protocol was used to construct paired-end DNA libraries from FFPE samples for sequencing as previously described.113 For fresh frozen tissues, DNA was extracted and subjected to library construction as previously described.114 For targeted sequencing, constructed DNA libraries were pooled and subjected to hybridization capture with a panel created by IDT or Twist Biosciences. The Twist panel covers the same regions as the IDT panel, but several regions had 2X or 4X probe coverage to improve capture and coverage uniformity. Capture libraries were sequenced on an Illumina HiSeq2500, HiSeqX, or NovaSeq with 150 bp paired-end reads, targeting between 50-100M reads per library. Targeted capture sequencing or WGS data were aligned to either hg38 or GRCh37 reference genome builds with bwa-mem.116 SVs derived from data aligned to GRCh37 were mapped to the hg38 genome build with CrossMap.126 SVs were identified in sequencing data by an ensemble approach using GRIDSS2122 and Manta123, intersecting SVs from both callers with the BioConductor package StructuralVariantAnnotation.124 SVs with variant allele frequency (VAF) of less than 0.05 were removed, and a subset of SVs were subjected to manual review in IGV149 and either rescued or removed. Bioinformatic pipelines are available as part of the LCR Modules repository. Rearrangement partners were grouped as IG (IGH, IGK, or IGL), recurrent non-IG (BCL6 or PAX5), other non-IG (all other partners), intrachromosomal, or not found. Intrachromosomal rearrangements were defined as structural events involving the MYC locus and another region of chromosome 8 more than 20kb away. As intrachromosomal events involving MYC and TOX (8q12.1) have been identified in multiple patient biopsies115, these events were included in the other non-IG group. As the significance of all other identified intrachromosomal events is unknown, this group was treated similarly to cases where a rearrangement was not found. 73 3.2.11 Statistical analyses Comparisons between mRNA expression levels were performed using two-sided t-tests with adjustment for multiple testing using the Holm-Bonferroni method. Odds ratios and associated significance of protein expression and DZsig+ expression within subgroups were determined by univariable logistic regression. All statistical analyses were performed using R-4.0.0 or R 4.2.2 software. This study was approved by the University of British Columbia/BC Cancer Research Ethics Board, in accordance with the Declaration of Helsinki. 3.3 Results 3.3.1 Association of MYC and BCL2 SVs with mRNA and protein expression Genetic and IHC analysis of MYC and BCL2 was performed in 802 de novo tumors of DLBCL morphology drawn from three population-based cohorts. Clinical characteristics are shown in Table 3.3. Identification of MYC and BCL2 SVs by FISH was interpretable in 771 and 740 tumors, respectively. MYC gain (MYC-G; 3-4 copies) was the most frequent MYC SV, detected in 20% (152/771) of tumors, while MYC rearrangement (MYCTr) and high-level MYC gain (MYC-G+; 5+ copies) were detected in 13% (104/771) and 4% (30/771) of tumors, respectively. In contrast, BCL2Tr was the most frequent BCL2 SV, detected in 28% (206/740) of tumors, while BCL2Gain and BCL2Gain+ were detected in 21% (155/740) and 8% (56/740) of tumors, respectively. To determine the significance of MYC and BCL2 SVs, the association of these alterations with mRNA and protein expression was examined in comparison to expression levels in tumors lacking SVs (Figure 3.1). MYC and BCL2 mRNA expression was assessed by GEP in all tumors, while dichotomous levels of MYC and BCL2 protein expression were determined by IHC in 709 and 769 tumors, using thresholds of ≥40% and ≥50% cell staining to define positivity, respectively. Both MYCTr and BCL2Tr were strongly associated with increased mRNA and protein expression (P<0.0001). In contrast, MYC and BCL2 CNAs were observed to have different effects. Both 74 BCL2Gain and BCL2Gain+ were strongly associated with increased mRNA and protein expression (P<0.0001), with BCL2Gain+ conferring similar mRNA levels as BCL2Tr. In contrast, MYCGain and MYCGain+ were not associated with increased mRNA (P=0.95 and P=0.78) or protein (P=0.21 and P=0.18) expression. Notably, no increase in mRNA expression was seen even in tumors with >7 copies of MYC (P=0.79; Figure 3.2). Taken together, while rearrangements of both MYC and BCL2 are associated with deregulated expression, only CNAs of BCL2 appear to produce aberrant expression. Amplification of MYC (MYC-Amp; defined by uncountable signals) was observed in four tumors and was associated with elevated MYC mRNA expression levels (P=0.04), with all tumors displaying levels of expression comparable to MYCTr (Figure 3.2). While only one tumor displayed amplification without a break-apart pattern, due to the rarity and limited understanding of MYCAmp, these tumors were excluded from other FISH subgroups.154 3.3.2 Mechanisms of MYC and BCL2 deregulation within molecular subtypes The increase in expression associated with MYC and BCL2 rearrangements and BCL2 CNAs was observed across all molecular subtypes (Figure 3.3). However, some alterations failed to reach significance due to low incidence within specific subtypes. To determine the role of SVs within each molecular subtype, the subtype-specific frequency of MYC and BCL2 SVs was explored. In total, 155 tumors were classified as DZsig+, including 88% (46/52) of HGBCL-DH-BCL2 tumors. Highest expression levels of both MYC and BCL2 were observed in DZsig+ tumors (Figure 3.4). MYCTr and BCL2Tr were the only SVs frequently observed in the DZsig+ subtype, detected in 49% (74/151) and 67% (97/144) of tumors, and were both associated with increased levels of expression (Figure 3.3A-B). 75 HGBCL-DH-BCL2 tumors are primarily of GCB COO.106 In agreement, 89% (138/155) of DZsig+ tumors had a GCB COO. After the exclusion of DZsig+ tumors from the GCB subgroup (in the following, we refer to GCB-DLBCL as DZsig- by definition), MYCTr was observed in only 4% (11/268) of GCB-DLBCLs and was not significantly associated with increased expression (Figure 3.3A). In general, MYC expression levels were lowest in GCB-DLBCL, with 77% (206/268) of GCB-DLBCLs expressing MYC at levels below the cohort mean (Figure 3.4). In contrast, BCL2Tr was observed in 30% (79/261) of GCB-DLBCLs and was strongly associated with increased expression (Figure 3.3B). Thus, while MYC overexpression was not a common feature of GCB-DLBCL, inappropriate BCL2 expression, achieved through translocation, is a feature of a subset of these tumors. Although ABC-DLBCLs expressed higher levels of MYC compared to GCB-DLBCL, no MYC SVs associated with increased expression were observed at an appreciable frequency in ABC tumors (Figure 3.3A), suggesting that alternative mechanisms drive MYC expression in ABC-DLBCL. In contrast, BCL2 CNAs were frequently observed in ABC tumors and were further associated with increased expression (Figure 3.3B). This increase was apparent for both BCL2Gain and BCL2Gain+, which were detected in 38% (96/256) and 13% (34/256) of ABC-DLBCLs, respectively. Therefore, while both MYC and BCL2 appear to be elevated in ABC tumors by SV-independent mechanisms, BCL2 CNAs further contribute to deregulated expression, while MYC CNAs do not. 3.3.3 Role of MYC and BCL2 CNAs in contributing to HGBCL-DH-BCL2 biology To assess whether MYC and BCL2 CNAs confer a similar biological phenotype as rearrangements, we used DZsig expression—a characteristic feature of HGBCL-DH-BCL2 tumors—as a surrogate marker of HGBCL-DH-BCL2 biology. 76 Concurrent MYC and BCL2 CNAs (MYCCNA/BCL2CNA) were observed in 12% (88/733) of tumors, while MYCCNA/BCL2Tr and BCL2CNA/MYCTr were observed in 8% (55/733) and 2% (14/733) of tumors, respectively. Only 3% (3/88) of MYCCNA/BCL2CNA tumors were DZsig+ (Figure 3.5A), suggesting that CNAs alone are insufficient to produce HGBCL-DH-BCL2 biology. Although CNAs in the context of a co-existing translocation were more likely to belong to the DZsig+ subtype, the rarity of these events prevented discernment of whether this observation could be attributed to the rearranged locus alone. While 67% (6/9) of MYCTr/BCL2Gain tumors and 80% (4/5) of MYCTr/BCL2Gain+ tumors were DZsig+, these tumors were not significantly more likely to belong to the DZsig+ subtype than tumors harboring MYCTr alone (MYCTr/BCL2Normal), wherein 45% (15/33) were DZsig+ (P=0.27 and P=0.18; Figure 3.5B). In agreement with the lack of an mRNA correlate for MYCGain, BCL2Tr/MYCGain was not associated with DZsig+. While 37% (34/93) of tumors with BCL2Tr alone (BCL2Tr/MYCNormal) were DZsig+, only 20% (9/45) of BCL2Tr/MYCGain tumors belonged to the DZsig+ subtype. Although BCL2Tr/MYCGain+ tumors were more frequently DZsig+ (50%; 5/10), they were not significantly more likely to be classified as DZsig+ than tumors harboring BCL2Tr alone (P=0.41; Figure 3.5C). Of note, all tumors with amplification of MYC were DZsig+. 3.3.4 Mechanisms of discordance between MYC mRNA and detectable protein Similar to previous reports106, 30% (29/98) of MYCTr tumors were negative for MYC protein by IHC (≤40% cell staining). IHC positivity was strongly associated with increased levels of MYC mRNA expression (P<0.0001; Figure 3.6). Notably, of the 11 MYCTr tumors with mRNA expression levels below the cohort mean, 82% (9/11) were negative for MYC IHC, providing one mechanism of MYC IHC negativity in MYCTr tumors. To determine a potential mechanism for IHC negativity in MYCTr tumors with high mRNA expression levels, MYC sequencing data for 446 tumors were analyzed. Four of the five highest 77 mRNA expressing MYCTr tumors that were negative for MYC IHC harbored mutations in exon two of MYC. Interestingly, three of these tumors carried a single nucleotide polymorphism resulting in an asparagine to serine substitution at the 11th amino acid residue of MYC (MYC-N11S), while the fourth tumor had mutations affecting nearby residues (T8I and L14V). In total, the MYC-N11S variant was observed in five MYCTr tumors, with 4/5 negative for MYC protein expression (Figure 3.7A). Among all sequenced tumors, 7.6% (34/446) harbored the N11S variant, and 91% (30/33) were negative for MYC protein expression, as detected by IHC using the Y69 antibody (Figure 3.7A). Analysis of MYC mRNA expression revealed no significant difference between N11S positive and negative tumors (P=0.33, Figure 3.7B). In contrast, tumors positive for the N11S variant showed significantly reduced MYC IHC staining (P<0.0001, Figure 3.7C). Interestingly, the Y69 antibody is known to bind within the N-terminus of MYC155, suggesting that the association of the N11S variant with reduced protein detection may be due to disrupted binding of the Y69 antibody. 3.3.5 N11S mediated Y69 epitope disruption leads to false-negative MYC IHC staining To determine if the N11S variant disrupts binding of the Y69 antibody, MYC-N11S was modelled in the MYC rearranged DLBCL cell line DOHH-2 using CRISPR/Cas9. Both heterozygous and homozygous mutants were produced in replicate. Western blots using the Y69 antibody detected significantly reduced levels of MYC in N11S cell lines compared to WT (Figure 3.8A-B). In contrast, nearly equivalent levels of MYC were detected between N11S and WT cell lines using the 9E10 antibody, which binds to a different region of MYC.156 Taken together, these results suggest that the N11S variant disrupts Y69 antibody binding, leading to reduced detection of the protein rather than a true decrease in protein levels. 78 To model the impact of MYC-N11S in a routine clinical setting, MYC IHC was performed on FFPE sections of N11S cell lines. A significant reduction in the proportion of stained cells was observed in N11S cell lines (30-50% staining) compared to WT cell lines (70-90% staining), with heterozygous and homozygous cell lines showing comparable levels of staining (Figure 3.8C). The absence of a difference in staining between heterozygous and homozygous cell lines may be explained by predominant expression of the N11S variant in heterozygous cells, potentially reflecting incorporation of the variant on the translocated allele. Interestingly, MYC-N11S was also observed in the MYCTr DLBCL cell lines WSU-DLCL2 and SU-DHL-10. Expression of the N11S variant was only seen in WSU-DLCL2, which was correspondingly MYC IHC negative, while SU-DHL-10 expressed the WT allele and was positive for MYC IHC (Figure 3.9). The presence of the N11S variant on the non-translocated allele of SU-DHL-10 was confirmed by linked-read sequencing (data not shown). 3.3.6 The significance of unbalanced MYC break-apart FISH patterns To explore the significance of unbalanced MYC break-apart FISH patterns in HGBCL-DH-BCL2 tumors, a total of 297 HGBCL-DH-BCL2 tumors were identified by MYC and BCL2 break-apart FISH. Patterns of MYC rearrangements were categorized into balanced, unbalanced-gain, LR, or LG gt (Figure 3.10A). Balanced MYC break-apart FISH patterns were observed in 65% of tumors, while unbalanced patterns were observed in 35% of tumors, comprising 22% unbalanced-gain, 11% LG, and 2% LR (Figure 3.10B). The combined incidence of 13% of tumors with unbalanced-loss patterns (LR or LG) is consistent with the previously reported incidence of 12% in a study that additionally included other MYC rearranged aggressive B-cell lymphomas.157 DNA sequencing was available for 130/297 (44%) HGBCL-DH-BCL2 tumors. A MYC rearrangement partner was identified in 80/87 (91%) tumors with a balanced pattern, compared to 23/26 (88%) tumors with an unbalanced-gain pattern (P = 0.69), and 12/17 (71%) tumors with 79 an unbalanced-loss pattern (P = 0.025), including 3/3 (100%) LR and 9/14 (64%) LG patterns (Figure 3.11A). Notably, for unbalanced-loss patterns where a MYC rearrangement partner was identified, 9/12 (75%) involved an IG or recurrent non-IG partner (BCL6 or PAX5). In comparison, IG and recurrent non-IG partners constituted 62/87 (71%) identified partners for balanced patterns, and 12/23 (52%) unbalanced-gain patterns. In summary, while the proportion of identified breakpoints is lower for the unbalanced-loss patterns relative to the balanced group, a partner locus known to drive MYC expression was identified in the majority of these tumors. Among tumors with an identified rearrangement partner, the breakpoint was centromeric to MYC in 2/3 (67%) tumors with an LR pattern, while 9/9 (100%) tumors with a LG pattern had a breakpoint telomeric to MYC (Figure 3.11A-B). For a centromeric break, the resulting rearrangement places MYC on the derivative chromosome containing the green signal, while for a telomeric break, MYC is placed on the derivative chromosome containing the red signal (Figure 3.11B). As such, based on the breakpoint locations and observed FISH signals, the lost signal is expected to be from the derivative chromosome that does not contain MYC for 13/14 (93%) tumors with an unbalanced-loss pattern. Thus, the rearrangement architecture of unbalanced-loss patterns preserves the derivative chromosome that contains MYC. We next determined if unbalanced patterns were associated with elevated MYC protein and MYC mRNA expression. Among tumors with a balanced pattern, 117/137 (85%) were positive for MYC protein expression by IHC (Figure 3.12A). There was no significant difference in the proportion of tumors positive by MYC IHC among LR (4/5 [80%]; P = 0.56), LG (23/29 [79%]; P = 0.41), and unbalanced-gain (34/45 [76%]; P = 0.17) patterns compared to tumors with a balanced pattern. In contrast, the proportion of tumors positive by MYC IHC was significantly greater for all four break-apart patterns (balanced P < 0.0001; unbalanced-gain P < 0.0001; LR P = 0.006; LG P < 0.0001) compared to MYC break-apart FISH negative (48/262 [18%]). The observation that 15% of tumors with balanced MYC rearrangements are negative by MYC IHC suggests that IHC 80 is not a reliable ancillary method for confirming a productive MYC rearrangement, due to the risk of false-negative results from mechanisms described above. There was similarly no significant difference in the level of MYC mRNA expression between tumors with balanced and unbalanced patterns (unbalanced-gain P = 0.97; LR P = 0.76; LG P = 0.43). In contrast, all four break-apart patterns were associated with significantly higher MYC mRNA expression compared to GCB-DLBCL (balanced P < 0.0001; unbalanced-gain P < 0.0001; LR P = 0.02; LG P < 0.0001) (Figure 3.12B). Additionally, the proportion of tumors that were DZsig+ was not significantly different in tumors with balanced FISH patterns (93/120 [78%]) compared to those with unbalanced-gain (34/42 [81%]; P = 0.83), LR (3/5 [60%]; P = 0.33) and LG (14/20 [70%]; P = 0.57), while a significantly higher proportion of tumors were DZsig+ in all four break-apart groups compared to GCB-DLBCL (36/333 [11%]; balanced P < 0.0001; unbalanced-gain P < 0.0001; LR P = 0.01; LG P < 0.0001) (Figure 3.12C). 3.4 Discussion This study aimed to address critical outstanding questions related to the diagnosis of HGBCL-DH-BCL2. Specifically, three key issues were investigated: (1) whether the definition of HGBCL-DH-BCL2 should be expanded to include MYC and BCL2 CNAs, (2) whether MYC IHC can serve as an effective screening strategy to reduce the burden of routine FISH testing, and (3) whether unbalanced MYC break-apart FISH patterns should be interpreted as a positive MYC FISH result. The first two questions were investigated using a cohort of 802 de novo tumors with DLBCL morphology, in which the effects of MYC and BCL2 structural variants on gene and protein expression were comprehensively evaluated. Consistent with prior reports, MYCTr and BCL2Tr were both strongly associated with elevated expression. In contrast, while BCL2 copy number alterations (CNAs) correlated with increased expression, MYC CNAs did not. MYC expression in 81 normal B-cell differentiation is tightly regulated, with transient expression observed immediately before GC formation following interaction with cognate T-cells and in a small subset of centrocytes in the LZ undergoing cyclic reentry into the DZ.50,51 Most DLBCL tumors display a GEP corresponding to an LZ COO, a cell not expected to express MYC.36 Tumors with MYCTr circumvent this regulation by placing MYC under the control of regulatory elements of genes that are transcriptionally active in the GC, such as immunoglobulin loci.12,13,158 In contrast, while focal MYC CNAs have been reported, most affect a larger region, likely retaining the native MYC regulatory elements.108,159,160 Therefore, we postulate that transcriptional regulation of MYC is maintained in the majority of DLBCL tumors harboring MYC CNAs, explaining the lack of impact on expression. Of note, while this study was restricted to FISH due to its use in clinical practice, this observation highlights the need for further studies using methodologies with greater resolution to fully elucidate the spectrum of MYC CNAs and their associated significance. Such methods could additionally account for the confounding effects of cryptic translocations.108,161,162 Distinct features of MYC expression were observed across molecular subtypes. MYCTr was the only SV associated with increased expression and was almost exclusively observed in DZsig+ tumors. With the removal of DZsig+ tumors from the GCB subtype, deregulation of MYC was absent from the majority of remaining GCB tumors, suggesting that MYC deregulation is not a fundamental feature of GCB-DLBCL biology. In contrast, MYC expression was slightly elevated in ABC-DLBCL in the absence of SVs, suggesting that alternative mechanisms may contribute to MYC deregulation, such as NF-kB activation.45,163 Distinct mechanisms of BCL2 deregulation were also observed across molecular subtypes. Rearrangement of BCL2 was predominantly a feature of DZsig+ tumors, although it was also observed in a portion of GCB-DLBCLs. In contrast, deregulation of BCL2 expression by 82 CNAs was predominantly a feature of ABC-DLBCL. Of note, BCL2 expression in ABC-DLBCL was also elevated in the absence of BCL2 SVs, likely also due to NF-kB activation.139,164 The definition of HGBCL-DH-BCL2 is limited to tumors with concurrent rearrangements of MYC and BCL2, excluding those with CNAs, despite uncertainty about whether CNAs may have comparable biological or clinical significance. Studies exploring the impact of CNAs have introduced the concept of an “atypical double-hit” category. While clinical outcomes are important for identifying high-risk patients, they are inherently influenced by existing treatment regimens. As treatment strategies for DLBCL continue to evolve, classification frameworks should prioritize underlying biology over treatment-dependent prognostic associations. The DZsig gene expression signature was highly sensitive in identifying tumors with concurrent MYC and BCL2 rearrangements, supporting their biological uniformity. However, DZsig expression was also detected in 108 tumors lacking concurrent MYC and BCL2 rearrangements by FISH. Importantly, tumors with MYC and BCL2 CNAs did not exhibit gene expression profiles similar to those of HGBCL-DH-BCL2. As such, these findings support the exclusion of CNAs from the diagnostic definition of HGBCL-DH-BCL2, irrespective of their potential prognostic relevance. While MYC IHC has been proposed as a screening method for subsequent FISH165, MYC IHC negativity was observed in 30% of MYCTr tumors, consistent with previous reports.106 Two major mechanisms were seen that uncoupled MYCTr from MYC IHC positivity, namely low resultant MYC mRNA levels and the presence of the MYC-N11S variant. The N11S variant was detected in 7.5% of all sequenced tumors, comparable with the global minor allele frequency of 3.4% (rs4645959).166 Modeling the N11S variant in a cell line harboring MYCTr led to a decrease in detectable protein. Interestingly, while this effect only appears to be active when the N11S variant is present on the translocated allele, 4/5 patient tumors harboring the N11S variant with concomitant MYCTr were negative for MYC IHC. 83 It was previously reported that the MYC-N11S variant results in lower protein levels.167 Here, we showed that the introduction of the variant did not alter mRNA levels or protein levels when an antibody (9E10) to the C-terminus of the protein was used. The discrepancy between the levels of protein detected by the 9E10 and Y69 antibodies in manipulated cell lines suggests that, rather than a true decrease in MYC expression, an apparent decrease in MYC protein is observed due to N11S-mediated disruption of the Y69 epitope. While the use of an IHC-suitable antibody that binds to the C-terminus would be ideal for accurate detection of MYC protein, the development of such an antibody has proven challenging.155 The third key question addressed in this study was whether unbalanced MYC break-apart FISH patterns should be interpreted as indicative of a MYC rearrangement. Our analysis demonstrated that the majority of HGBCL-DH-BCL2 tumors with unbalanced MYC break-apart FISH patterns preserve the MYC coding region and involve known partner loci capable of driving constitutive MYC expression. These unbalanced rearrangements were associated with similar biological consequences to those seen in balanced rearrangements, including elevated MYC mRNA and protein levels and DZsig positivity. While dual-fusion FISH for MYC and the IG loci could be performed to identify the presence of a MYC::IG rearrangement, which additionally has prognostic signficance142, a negative result would not exclude rearrangement with a non-IG partner, which is common in HGBCL-DH-BCL2. Ideally, corroboration could be provided by a GCB COO, DZsig positivity, and elevated MYC mRNA levels, but methods to provide these molecular results are not currently in widespread use. Therefore, in the routine practice setting, unbalanced MYC break-apart FISH patterns should be reported as positive for MYC rearrangement in the context of diagnosing HGBCL-DH-BCL2. In summary, this study strongly supports the current exclusion of CNAs of MYC and BCL2 from the definition of HGBCL-DH-BCL2, acknowledging that this study was limited to DLBCL morphology and excluded transformation from indolent lymphoma. It also highlights mechanisms 84 that uncouple MYC rearrangement from MYC protein expression, including those reflecting true low mRNA and protein levels, possibly related to the MYC partner gene140, and others that are spurious, related to disruption of an antibody epitope. Finally, these findings demonstrate that unbalanced MYC break-apart FISH patterns reflect biologically active rearrangements and should be interpreted as positive in the diagnostic evaluation of HGBCL-DH-BCL2. Collectively, these results underscore the value of molecular profiling and support redefining HGBCL-DH-BCL2 through a GEP-based framework, moving beyond reliance on FISH alone. 85 Table 3.1. CRISPR gRNA and N11S ssODN repair template Name Type Manufacturer Sequence N11S ssODN repair template Ultramer® DNA Oligonucleotide IDT TCCAGCAGCCTCCCGCGACGATGCCCCTCAACGTTAGCTTCACCAACCGCAGCTATGACCTCGACTACGACTCGGTGCAGCCGTATTTCTACTGCGACGA MYC gRNA Custom Alt-R® CRISPR-Cas9 guide RNA IDT CACCGTCAACGTTAGCTTCACCAAC 86 Table 3.2. Primers for Sanger sequencing, cloning, and ddPCR Name Type Manufacturer Purpose Sequence MYC F DNA Oligo IDT Forward Sanger sequencing of MYC GCACCAAGACCCCTTTAACT MYC R DNA Oligo IDT Reverse Sanger sequencing of MYC CAGAGTCGCTGCTGGTGG MYC ddPCR DNA F DNA Oligo IDT ddPCR CTGCCTCCCGCTTTGTG MYC ddPCR cDNA F DNA Oligo IDT ddPCR GCTGCTTAGACGCTGGATTT MYC ddPCR R DNA Oligo IDT ddPCR CCGAGTCGTAGTCGAGGT 87 Table 3.3. Demographic and Clinical Characteristics of Study Cohorts Characteristic Total cohort CCSRI cohort DLC347 cohort GPH cohort n = 802 n = 497* n = 325* n = 175 Treatment R-CHOP/R-CHOP–like 722 (90%) 435 (88%) 325 (100%) 157 (90%) Intensive 11 (1%) 1 (0%) — 10 (6%) Palliative 23 (3%) 17 (3%) — 6 (3%) Other 13 (2%) 13 (3%) — — Unknown 29 (4%) 29 (6%) — — Refused 4 (0%) 2 (0%) — 2 (1%) Age, y Median (range) 66 (19-96) 68 (20-96) 64 (19-92) 65 (19-93) ≤60 272 (34%) 161 (32%) 133 (41%) 56 (32%) >60 530 (66%) 336 (68%) 192 (59%) 119 (68%) Sex, no. (%) Male 485 (60%) 288 (58%) 207 (64%) 110 (63%) Female 317 (40%) 209 (42%) 118 (36%) 65 (37%) Stage, no. (%) I/II 181 (24%) 118 (25%) 81 (25%) 38 (22%) III/IV 585 (76%) 345 (75%) 244 (75%) 135 (78%) Missing 36 34 — 2 LDH, no. (%) Normal 375 (52%) 217 (52%) 146 (47%) 100 (57%) Above the ULN 342 (48%) 200 (48%) 162 (53%) 75 (43%) Missing 85 80 17 — ECOG PS, no. (%) 0-1 476 (63%) 290 (64%) 217 (67%) 102 (58%) 2-4 281 (37%) 162 (36%) 108 (33%) 73 (42%) Missing 45 45 — — Extranodal sites, no. (%) 0-1 655 (82%) 413 (83%) 278 (86%) 134 (77%) ≥2 147 (18%) 84 (17%) 47 (14%) 41 (23%) IPI score, no. (%) Low (0-1) 230 (31%) 141 (33%) 103 (33%) 51 (29%) Intermediate (2-3) 351 (48%) 209 (48%) 153 (48%) 79 (45%) High (4-5) 152 (21%) 81 (19%) 60 (19%) 45 (26%) Missing 68 66 9 — *Overlap between CCSRI and DLC347 cohorts (n = 195). Abbreviations: ECOG PS, Eastern Cooperative Oncology Group performance status; IPI, International Prognostic Index; LDH, Lactate dehydrogenase; ULN, Upper level of normal. 88 Figure 3.1. The role of MYC and BCL2 SVs in deregulating mRNA and protein expression. (A) Association of MYC SVs with MYC mRNA and protein expression. (B) Association of BCL2 SVs with BCL2 mRNA and protein expression. The violin plots on the left show the association of mRNA expression levels with each SV. Error bars show the mean and 95% confidence intervals. On the right, the proportions of tumors that are positive or negative by IHC are shown along with forest plots indicating the association of each SV with IHC positivity (odds ratios with 95% confidence intervals). All comparisons were made in reference to the normal group. Tr: translocation. MYCNormalMYCGainMYCGain+MYCTr NEGPOSNEGPOSNEGPOSNEGPOSBCL2NormalBCL2GainBCL2Gain+BCL2Tr NEGPOSNEGPOSNEGPOSNEGPOS0.950.78<0.00012969151386572741411.3 (0.9−1.9)1.7 (0.8−3.6)4.6 (2.9−7.5)0.210.18<0.0001<0.0001<0.0001<0.000117186452271211331773.4 (2.1−5.4)9.8 (3.4−28)8.2 (4.8−14)<0.0001<0.0001<0.00018 10 12 14 16 18log2 (MYC mRNA)0 100 200 300MYC IHC (count)0 0.5 1 1.5Log10 (odds ratio)8 10 12 14 16 18log2 (BCL2 mRNA)0 100 200 300BCL2 IHC (count)0 0.5 1 1.5Log10 (odds ratio)ABmRNAmRNAIHCIHCOR (95% CI) POR (95% CI) P 89 Figure 3.2. Association of MYC amplification and high-level copy number gains with MYC mRNA expression. The violin plots show MYC mRNA expression levels for tumors within each category. Tumors with MYC high-level gains are separated into subgroups with either 5-7 copies or >7 copies. Amplification (Amp) refers to tumors with uncountable copies of MYC. All comparisons were made in reference to the normal group. MYCTr is shown for reference. Error bars show the mean and 95% confidence intervals. 0.79 0.79 0.04810121416Normal 5−7 copies >7 copies Amp Trlog2 (MYC mRNA) 90 DHITsig+MYCNormalMYCGainMYCGain+MYCTr NEGPOSNEGPOSNEGPOSNEGPOSGCBMYCNormalMYCGainMYCGain+MYCTr NEGPOSNEGPOSNEGPOSNEGPOSABCMYCNormalMYCGainMYCGain+MYCTr NEGPOSNEGPOSNEGPOSNEGPOS****0.810.551851428428170.8 (0.2−3.2)0.8 (0.1−5)4.7 (2.1−10)0.780.83***0.131147824914133261.5 (0.7−3)1.3 (0.3−6.4)9 (2.4−33)0.310.76*****0.270.814927203076851.3 (0.7−2.6)3.1 (0.6−16)2 (0.6−6.8)0.370.160.268 10 12 14 16log2 (MYC mRNA)0 50 100 150MYC IHC (count)−1 0 1 2Log10 (odds ratio)DHITsig+BCL2NormalBCL2GainBCL2Gain+BCL2Tr NEGPOSNEGPOSNEGPOSNEGPOSGCBBCL2NormalBCL2GainBCL2Gain+BCL2Tr NEGPOSNEGPOSNEGPOSNEGPOSABCBCL2NormalBCL2GainBCL2Gain+BCL2Tr NEGPOSNEGPOSNEGPOSNEGPOS********0.4559072720107 (1.2−40)16 (1.7−146)36 (11−117)**********0.120.3687035171677591.2 (0.6−2.6)2.2 (0.5−9.5)11 (5.1−26)0.60.3****0.1*****21313348722835.8 (1.9−17)8.7 (1.1−68)1.7 (0.4−8.2)***0.498 10 12 14 16log2 (BCL2 mRNA)0 50 100 150BCL2 IHC (count)−1 0 1 2Log10 (odds ratio)mRNAmRNAABIHCIHCOR (95% CI) POR (95% CI) P 91 Figure 3.3. The role of MYC and BCL2 SVs in deregulation of mRNA and protein expression within molecular subtypes. (A) Association of MYC SVs with MYC mRNA and protein expression. (B) Association of BCL2 SVs with BCL2 mRNA and protein expression. The violin plots on the left show the association of mRNA expression levels with each SV. Error bars show the mean and 95% confidence intervals. On the right, the proportions of tumors that are positive or negative by IHC are shown along with forest plots indicating the association of each SV with IHC positivity (odds ratios with 95% confidence intervals). * P ≤ 0.05, ** P ≤ 0.01, *** P ≤ 0.001, **** P ≤ 0.0001. Tr: translocation. 92 Figure 3.4. MYC and BCL2 mRNA expression by GEP subtype. (A) Expression levels of MYC mRNA within molecular subtypes. (B) Expression levels of BCL2 mRNA within molecular subtypes. Error bars show the mean and 95% confidence intervals. 0.001< 0.0001< 0.000181012141618DHITsig+ GCB ABClog2 (MYC mRNA)A0.02< 0.0001< 0.000181012141618DHITsig+ GCB ABClog2 (BCL2 mRNA)B 93 MYCTr / BCL2NormalMYCTr / BCL2GainMYCTr / BCL2Gain+MYCTr / BCL2TrMYCNormal / BCL2TrMYCGain / BCL2TrMYCGain+ / BCL2TrMYCTr / BCL2Tr15 (45)6 (67)4 (80)46 (88)18 (55)3 (33)1 (20) 6 (12)2.4 (0.5−11)4.8 (0.5−48)9.2 (3.1−27)0.270.18<0.000134 (37) 9 (20)5 (50)46 (88)59 (63)36 (80)5 (50) 6 (12)0.4 (0.2−1)1.7 (0.5−6.4)13 (5.1−34)0.050.41<0.0001−0.5 0 0.5 1 1.5Log10 (odds ratio)MYCTr / BCL2 Tr−40−2002040MYCCNV / BCL2 CNV MYCTr / BCL2 CNV MYCCNV / BCL2 Tr−40−2002040Normal Single CNV Single Rearrangement0 1500 3000 0 1500 3000 0 1500 3000−40−2002040ABCDHITsig+ (%) DHITsig- (%) OR (95% CI) PHGBL-DH/TH-BCL2 Atypical Double-Hit Not Otherwise Specified DHITsig+GCB ABCCOO LPSDHITsig ScoreMYCTr / BCL2TrMYCNormal / BCL2NormalMYCCNV / BCL2CNVMYCCNV / BCL2NormalMYCNormal / BCL2CNVMYCTr / BCL2GainMYCTr / BCL2Gain+MYCTr / BCL2NormalMYCNormal / BCL2TrMYCGain / BCL2TrMYCGain+ / BCL2Tr 94 Figure 3.5. Atypical double-hit tumors do not share a similar GEP as HGBCL-DH-BCL2. (A) DZsig score and COO LPS are shown for HGBCL-DH-BCL2, atypical double-hit, and not otherwise specified tumors. Dotted lines indicate cutoffs between molecular subtype classifications as shown in the figure legend. (B) Forest plots show the odds ratios with 95% confidence intervals of belonging to the DZsig+ subtype for each indicated subgroup of atypical double-hit tumors in comparison to tumors with MYC rearrangement alone (MYCTr/BCL2Normal) or (C) BCL2 rearrangement alone (MYCNormal/BCL2Tr). Tr: translocation. 95 Figure 3.6. Association of MYC and BCL2 mRNA expression with IHC status. (A) Expression of MYC mRNA in MYC IHC positive and negative tumors. (B) Expression of BCL2 mRNA in BCL2 IHC positive and negative tumors. Error bars show the mean and 95% confidence intervals. <0.0001810121416NEG POSlog2 (MYC mRNA)A<0.0001810121416NEG POSlog2 (BCL2 mRNA)BMYC IHC BCL2 IHC 96 Figure 3.7. MYC-N11S polymorphism is associated with decreased MYC protein expression as detected by IHC using the Y69 antibody. (A) MYC mRNA expression is shown in the bottom track as a heatmap for the 446 tumors in which MYC sequencing data was available. Tumors are linearly arranged from lowest to highest expression, and the corresponding N11S status, MYC IHC status, and MYC rearrangement status for each tumor are shown in the tracks above the heatmap. Break-apart positive (BA POS) refers to tumors positive for MYC rearrangement (MYCTr). (B) Expression of MYC mRNA in N11S positive and negative tumors. P-value was determined by an unpaired two-sided t-test. Error bars represent the mean and 95% confidence intervals. (C) Percentage of tumor cells positive by MYC IHC staining in N11S positive and negative tumors. The red dotted line indicates the ≥40% threshold used to identify MYC IHC positivity. P-value was determined by the Mann-Whitney U test. Only tumors with IHC scores on a 10% increment scale of percent positively stained cells are included in panels B and C (n = 323). 0.33FISHIHCN11SmRNA810121416log2 (MYC mRNA)020406080100MYC IHC Score (% positive cells)< 0.0001NEG POSN11SNEG POSN11SAB CMYC mRNAlog2 Zscore-202MYC FISHBA POS BA NEGMYC IHCPOS NEGNAMYC N11SPOS NEGNA 97 Figure 3.8. MYC-N11S polymorphism disrupts Y69 binding. The N11S variant was modeled in DOHH-2 cells using CRISPR/Cas9. N11S-/- refers to WT cell lines while N11S+/- and N11S+/+ refer to cell lines heterozygous and homozygous for the N11S variant, respectively. (A) Western blotting for MYC was performed on whole cell extracts of N11S and WT DOHH-2 cells using the Y69 and 9E10 antibodies. Histone H3 was used as a loading control. (B) Ratio of protein detected by Western blotting using the Y69 and 9E10 antibodies, normalized for loading. Error bars indicate standard error. (C) MYC IHC using the Y69 antibody on FFPE section from N11S and WT DOHH-2 cell lines (40X objective lens). The percentage of positively stained cells for each whole section is shown in the upper right corner of the images. Replicate 1Replicate 2N11S VariantAllele 1Allele 2 - - - - ++- - + + ++- - - - ++- - + + ++9E10 AntibodyY69 AntibodyMYCHistone H3A BC90%70% 50%40%40%30%N11S-/-(Wild Type)N11S+/-(Heterozygous)N11S+/+(Homozygous)N11S-/- N11S+/- N11S+/+Y69/9E10 Intensity Ratio0.000.250.500.751.00 98 Figure 3.9. MYC-N11S polymorphism confers false-negative MYC IHC staining when present on the translocated allele. SU-DHL-10 and WSU-DLCL2 both harbor MYCTR and the N11S variant. (A) RNA sequencing reads mapping to the position of the single-nucleotide polymorphism resulting in the N11S variant. The WT allele is encoded by an A, while an A to G substitution results in MYC-N11S. (B) MYC IHC using the Y69 antibody (40X objective lens). 099.800.11000.1 000255075100A C G TNucleotideAligning reads (%)Cell LineSU−DHL−10WSU−DLCL2BWSU-DLCL2SU-DHL-10A 99 Figure 3.10. Patterns of MYC break-apart FISH probe signals and their proportions in HGBCL-DH-BCL2. (A) Representative MYC break-apart FISH patterns. When the MYC locus is intact, the red (centromeric) and green (telomeric) signals are in close proximity and produce a fused signal. An example of a rearrangement producing a balanced break-apart pattern is depicted using MYC and IGH as example loci. (B) Frequency of break-apart FISH patterns observed in 297 HGBCL-DH-BCL2 tumors. Balanced: equal number of break-apart probe signals. Unbalanced-gain: red and green break-apart signals are both present but not equal in numbers. LR: loss of red break-apart signal. LG: loss of green break-apart signal. 65%22%11%n=2970255075100HGBCL−DH−BCL2Percent (%)MYC break-apartFISH patternLRLGUnbalanced-gainBalanced2%Normal FISH patternBalanced FISH patternLoss of red signal(LR)Loss of green signal(LG)Gain of red or green signal(unbalanced-gain)Unbalanced FISH patterns2 Fused1 Fused 1 Red 1 GreenMYC8q24.21Chr8 (x2)Chr14 (x2) IGH14q32.33LocusIGH MYCDer14Chr14Chr8Der8LocusA Bcentromeric probe (red) telomeric probe (green)1 Fused 2 Red 1 Green 1 Fused 1 Green 1 Fused 1 Redcentromeric probe (red)telomeric probe (green) 100 Figure 3.11. MYC partner gene and rearrangement architecture in HGBCL-DH-BCL2 according to MYC break-apart FISH pattern. (A) Frequency of MYC rearrangement partners and breakpoints relative to the MYC gene determined in whole genome and/or capture sequencing. Rearrangement partners are grouped as IG (IGH, IGK, or IGL), recurrent non-IG (BCL6 or PAX5), other non-IG (all other partners), intrachromosomal, or not found. Intrachromosomal rearrangements were defined as structural events involving the MYC locus and another region of chromosome 8 more than 20kb away. Intragenic breaks occurring in intron 1 of MYC are included in the centromeric group. (B) MYC breakpoint location in tumors with LR and LG patterns, showing that the MYC gene is predicted to be on the derivative chromosome, with the signal that is still present in almost all tumors. AMYCMetasystemsVysis127.5 mb 129 mbMYC128.5 mb 129 mbMYCDer(8)127.5 mbMYCDer(8)Centromeric Probe (Red)Der(other)130.5 mb129.5 mbMYCDer(other)129.5 mbChr8 breakpointMYC break-apartFISH patternLR pattern and centromeric break MYC retained LG pattern and telomeric break MYC retainedLR33%33%33%33%67%n=3 n=30255075100Balanced Unbalanced-gainPercent (%)MYC rearrangement partnerNot foundIntrachromosomalOther non−IGRecurrent non−IGIG52%20%21%n=87 n=8019%27%42%n=26 n=23LG21%29%14%29% 100%n=14 n=9Breakpoint relative to MYCCentromericTelomericBChr84%8%7%7%60%40%83%17%127.5 mb 128.5 mb 129.5 mbTelomeric Probe (Green)AbsentPresentLoss of red signal (LR) Loss of green signal (LG)AbsentPresent 101 Figure 3.12. Impact of MYC break-apart FISH patterns on MYC and DZsig expression. (A) Proportion of tumors positive for MYC protein expression by immunohistochemistry. P values are from Fisher’s exact tests. (B) MYC mRNA expression, as determined by digital gene expression profiling. P values are from t-tests. (C) Proportion of tumors positive for DZsig, as determined by the DLBCL90 assay. P values are from Chi-square tests. Ref: reference group. *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001. 85%15%76%24%80%20%79%21%18%82%n=137 n=262n=45 n=29n=5**** ref**** ******ref ****ns nsns0255075100Balanced UnbalancedGainLR LG GCB DLBCLPercent (%)MYC ImmunohistochemistryPositive NegativeAn=120 n=333n=42 n=20n=5**** ref**** *****ref ****ns nsns0255075100Balanced UnbalancedGainLR LG GCB DLBCLPercent (%)Refined COOUnclassGCBDHITsig+ ABCC**** **** ***** refns nsns ****refn=20n=42n=120 n=5 n=333810121416Balanced UnbalancedGainLR LG GCB DLBCLlog2(MYC mRNA)MYC mRNABNo No sequencing data YesPartner found78%18%81%17%60%20%20%70%30%11%89% 102 Chapter 4: Gene Expression Profiling Identifies a Poor-Prognosis Subgroup that Extends Beyond FISH-defined HGBCL-DH-BCL2 4.1 Introduction The initial establishment of HGBCL-DH/TH as a diagnostic entity was largely driven by early reports of poor clinical outcomes, with historical 2-year PFS estimates of 20-40%.104,105 These early series were subject to substantial selection bias due to selective FISH testing, which was most often performed in tumors with high-grade morphology, high proliferation rate, or at the request of the treating physician, often prompted by an aggressive clinical presentation. As a result, initial reports suggested that 50-70% of these tumors exhibited high-grade morphology.104,105 Routine FISH testing has since revealed that only about 15% of HGBCL-DH/TH cases have high-grade morphology, with the vast majority exhibiting DLBCL morphology.168 When FISH is applied broadly to all tumors with DLBCL morphology, about 8% are reclassified as HGBCL-DH/TH, of which approximately 80% are HGBCL-DH-BCL2 and 20% are HGBCL-DH-BCL6.106 Building on these efforts, other studies have sought to clarify the prognosis of HGBCL-DH/TH using less selective testing approaches. The Lunenburg Lymphoma Biomarker Consortium (LLBC) applied systematic FISH testing to DLBCL morphology cases from large prospective trials and population-based registries in Europe and North America, thereby reducing the selection bias inherent in earlier studies.142 In this study, patients with HGBCL-DH/TH had a 2-year PFS of approximately 60% with R-CHOP-based therapy, with similar outcomes in HGBCL-DH-BCL2 and HGBCL-DH-BCL6. While these outcomes are substantially better than early estimates, they remained significantly worse than those for other DLBCL-NOS cases without these rearrangements in the same study population. As the data were derived largely from clinical trial populations and more than half of the eligible cases lacked material for FISH testing, the true population-level outcomes are likely less favorable. In light of the updated ICC and WHO HAEM5 103 classification systems and the recognition that HGBCL-DH-BCL6 represents a biologically heterogeneous group, accurately defining population-level outcomes for the more uniform HGBCL-DH-BCL2 subgroup remains a pressing need. On a similar note, most studies examining clinical outcomes of molecular subgroups in DLBCL have used cohorts selected from clinical trials or population registries that were restricted to cases with frozen tissue or large incisional or excisional biopsies suitable for TMA construction,85,108,142 which excludes the increasingly common core needle biopsies.169,170 Notably, such limitations apply to the original DZsig discovery cohort.107 As a result, real-world, population-level outcomes of GEP-based subgroups have not been systematically assessed. Given the limitations of FISH, a GEP-based definition of HGBCL-DH-BCL2 has the potential to identify a broader, clinically and biologically relevant population. DZsig expression captures about 80% of HGBCL-DH-BCL2 and extends to a subset of DLBCL-NOS lacking MYC and/or BCL2 rearrangements by FISH. In the original DZsig description, about half of DZsig+ tumors were not double-hit by FISH, with approximately 20% harboring cryptic rearrangements that escape detection.107,108 While these tumors showed similarly poor outcomes to HGBCL-DH-BCL2 in the discovery cohort, interpretation is limited by the constraints of that cohort, and both the true prevalence of DZsig expression and the associated outcomes among DLBCL-NOS remain to be clarified. Conversely, approximately 20% of HGBCL-DH-BCL2 tumors are DZsig-, meaning they would be missed by a purely GEP-based approach. As such, these cases warrant further study to understand their biology and clinical behavior. Here, we assess the clinical outcomes of GEP- and FISH-defined subgroups in a population-based DLBCL cohort from British Columbia, Canada, treated under uniform provincial guidelines, by profiling all evaluable diagnostic FFPE biopsies to determine whether DZsig provides a better definition than FISH for identifying a biologically and clinically meaningful subgroup. Furthermore, we examined the association between GEP-defined molecular 104 subgroups and diagnosis-to-treatment interval (DTI), a metric that impacts patient outcomes and influences clinical trial eligibility in DLBCL.171,172 By evaluating these subgroups in an unselected, real-world population, our goal was to understand how reliance on FISH or GEP may have introduced selection bias into existing clinical trial datasets and to inform the design of future trials targeting poor-prognosis molecular subgroups. 4.2 Materials and Methods 4.2.1 Study Cohort The study cohort included all adults (age ³ 18 years) diagnosed with de novo DLBCL-NOS or HGBCL-DH/TH with DLBCL morphology in BC, Canada, between 2005 and 2010. Biopsies with the appropriate gene rearrangements were reclassified into HGBCL-DH/TH after FISH testing. Other large B-cell lymphoma entities that do not fall under DLBCL-NOS, as defined by the WHO Classification of Tumors of Haematopoietic and Lymphoid Tissues revised 4th edition, were excluded.1 Pathological diagnosis was established using province-wide review by lymphoma pathologists, which is routine for all lymphoma biopsies in BC, and was confirmed in the tissue blocks acquired for research analysis by expert lymphoma pathologists. Patients with positive HIV serology, post-transplant lymphoproliferative disorder, prior history of indolent lymphoma, and composite lymphoma (where the tumor comprises both an aggressive and indolent lymphoma) were excluded. Patients with discordant lymphomas (where two lymphoma diagnoses are present in different tissues) were generally excluded, except for ones with discordant low-grade B-cell lymphoma in the bone marrow. Limited-stage disease was defined as Ann Arbor Stage I or II in the absence of B symptoms, bulky disease (³ 10 cm), and disease that could not be encompassed safely within a single radiation field. According to provincial guidelines, patients with limited-stage disease were eligible to receive 3 cycles of R- 105 CHOP and involved-field radiation therapy or 4 cycles of R-CHOP if the fluorodeoxyglucose positron emission tomography (FDG-PET) scan was negative after 3 cycles of R-CHOP. All other patients were defined as having advanced-stage disease. The 2005-2010 era provided mature patient follow-up (12.2 years by reverse Kaplan-Meier censoring), yet included only patients in the post-rituximab era, as rituximab was introduced for the frontline treatment of DLBCL in BC in 2001. The 2005-2010 era predated the currently implemented guideline of routine FISH testing in all biopsies of DLBCL morphology and treatment intensification with dose-adjusted etoposide, doxorubicin, vincristine, cyclophosphamide, prednisone, and rituximab (DA-EPOCH-R) in appropriately fit patients diagnosed with HGBCL-DH/TH aged 75 years or younger. Therefore, outcomes of HGBCL-DH/TH patients in this cohort were not impacted by DA-EPOCH-R. Of note, a small number of HGBCL-DH/TH patients received intensified regimens after being diagnosed by selective FISH testing (n = 4). No specific intensive regimen was recommended, but regimens that were used are described in the ‘treatment category definitions’ subsection. 4.2.2 FISH and IHC All available diagnostic biopsies underwent central pathology review to confirm the diagnosis and select a representative area for TMA construction. TMAs were constructed from duplicate 0.6 mm cores. Bone/bone marrow (BM) biopsies and biopsies with a tumor surface area of <2mm2 were excluded from research analysis. FISH was performed on TMA sections, or whole tissue sections for small biopsies that were unsuitable for inclusion on TMA, using commercially available Vysis LSI (Abbott) or Metasystems XL break apart probes for MYC (Abbott 01N63-020; Metasystems D-6030-100-TC), BCL2 (Abbott 05N51-020; Metasystems D-6018-100-OG), and BCL6 (Abbott 01N23-020; Metasystems D-6016-100-OG). Images were captured using a Metafer CoolCube 1 camera and Metafer software (version 3.11.8). Two independent observers scored 106 100 nuclei per biopsy, except for small or poor-quality biopsies, where only 50-100 nuclei could be scored. Tumors in which <50 nuclei could be scored were considered a failed FISH result. For biopsies with discrepant scores, consensus was reached by a third observer. Rearrangement was defined as a break-apart signal in ≥5% of cells. IHC staining was performed on TMA sections by the BC Cancer clinical pathology lab on the Ventana Benchmark XT or Dako Omnis platform for MYC (Y69, Epitomics; or EP121, Inter Medico [previously Y69]), BCL2 (E17, Abcam; and BCL2/124, Dako), CD10 (56C6, Leica; or 56C6, Dako), BCL6 (LN22, Leica; or PG-B6p, Dako), and MUM1 (MUM1p, Dako). IHC positivity was defined as ≥30% (CD10, BCL6, and MUM1),99 ≥40% (MYC), and ≥50% (BCL2) staining of tumor cells.109 IHC was independently scored by two expert hematopathologists, and biopsies with discordant scores were further evaluated by a third hematopathologist to reach consensus. For biopsies with missing or failed IHC or FISH results, clinical data were used when available. Clinical IHC and FISH were performed on whole tissue sections, as described above. 4.2.3 Digital gene expression profiling RNA was extracted from FFPE biopsies using the Qiagen AllPrep DNA/RNA FFPE Kit (80234) or isolated from DNAse treatment of total nucleic acids (TNA) extracted using the ALINE FFPE GenePure Purification Kit (FP-2001-BC-384), automated on the Hamilton Nimbus robot. Digital gene expression profiling (GEP) was performed on RNA using the DLBCL90 assay on the nCounter platform (nanoString) to assign COO, DZsig status, and determine MYC and BCL2 mRNA expression levels, as previously described.107,108 Gene expression subgroups were assigned hierarchically, with COO taking precedence over DZsig status for ABC tumors. GCB and Unclassified tumors that were DZsig+ were assigned to the DZsig+ group, while DZsigind and DZsigneg tumors were assigned to their respective COO subgroups. 107 4.2.4 Rearrangement partner definition and identification by sequencing Capture sequencing (n = 62) or WGS (n = 12) was performed on 74 biopsies, including all DZsig+ and HGBCL-DH-BCL2 cases with sufficient material, to identify MYC and BCL2 rearrangement partners. Sequencing was performed on DNA or TNA isolated from FFPE tissue. DNA was extracted using the Qiagen AllPrep DNA/RNA FFPE Kit (80234), while TNA was extracted using the ALINE FFPE GenePure Purification Kit (FP-2001-BC-384), automated on the Hamilton Nimbus robot. When ≥300 ng of FFPE extracted TNA was available, TNA was also treated with S1 nuclease to remove single-stranded DNA.9 Paired-end DNA libraries for sequencing were generated as previously described.8 For capture sequencing, hybridization capture was performed with a panel created by IDT or Twist Biosciences, as previously described.115 This panel was designed to capture the regions around MYC (Chr8:126262754-129664254), BCL2 (Chr18:63077345-63128767), and recurrent rearrangement partner loci: IGH (Chr14:105546276-106879844), IGK (Chr2:88686728-89330586), IGL (Chr22:22329967-23061520), BCL6 (Chr3:187721376-188032725), and PAX5 (Chr9:37034478-37465410). Libraries were sequenced on an Illumina HiSeq2500, HiSeqX, or NovaSeq with 150 bp paired-end reads. Reads were aligned to the hg38 reference genome using BWA-MEM (v0.7.17)10, and duplicate reads were marked with Picard (v2.22.3; http://broadinstitute.github.io/picard/). Rearrangement partners were identified by an ensemble approach using GRIDSS211 and Manta,12 with VCF files generated by these tools intersected using the BioConductor package StructuralVariantAnnotation.13 SVs were filtered out unless they met a minimum variant allele frequency of 0.05 and/or passed manual curation in IGV.14 Partner loci were annotated based on proximity to known GC B cell super-enhancer loci15 or to known rearrangement partner genes.8 MYC rearrangement partners were classified as IG (IGH, IGK, and IGL) or non-IG (all other partners, including intrachromosomal events). 108 4.2.5 Treatment categories and survival endpoints Patients were categorized into one of eight treatment groups based on the therapy they received: (1) R-CHOP, which included all patients who received at least one cycle of R-CHOP (n=629); (2) R-CHOP-HDMTX, comprising patients who received high-dose intravenous methotrexate in conjunction with R-CHOP for treatment of confirmed or strongly suspected central nervous system (CNS) involvement at diagnosis (n = 6). These patients were excluded from the R-CHOP category. All patients in this group were treated during an era that preceded the routine use of high-dose methotrexate for CNS relapse prophylaxis; (3) R-CEOP, defined as patients who were deemed unsuitable to receive anthracycline chemotherapy on the basis of cardiac dysfunction and were intended to receive R-CEOP at diagnosis, with etoposide substituting for doxorubicin in the regimen (n=54); (4) Intensive, which included patients who received either cyclophosphamide, vincristine, doxorubicin, methotrexate, leucovorin, and rituximab (CODOX-MR) followed by ifosfamide, mesna, etoposide, cytarabine, and rituximab (IVAC-R), with or without consolidative autologous hematopoietic cell transplantation (n=5), or R-CHOP followed by consolidative autologous transplantation (n=2); (5) CEOP/CHOP, which included patients treated with CEOP/CHOP where rituximab was intentionally withheld due to CD20 negativity (n=2); (6) Palliative, including patients treated with palliative-intent immunochemotherapy (n=68) or other palliative measures such as radiation, systemic glucocorticoids, or best supportive care (n=90); (7) Patient refused, referring to patients who declined treatment (n=10); and (8) Unknown, for whom no treatment data were available (n=46). For survival analyses, freedom from progression (FFP) was defined from the date of diagnosis with definitive DLBCL biopsy to disease progression, relapse, or death caused by lymphoma/acute treatment toxicity, with death from unrelated causes censored. Overall survival (OS) was determined from the date of definitive diagnosis to the date of last follow-up or death from any cause. 109 4.2.6 Diagnosis-to-treatment interval analysis Diagnosis-to-treatment interval (DTI) was defined as the interval between the date of the definitive DLBCL diagnostic biopsy and the date on which the first immunochemotherapy cycle was administered, identified using the BC Cancer pharmacy database. 4.2.7 Statistical analysis Baseline patient clinical and tumor pathological characteristics were compared using chi-squared or Fisher’s exact tests. Kaplan-Meier analysis was used to estimate survival rates, and groups were compared using the log-rank test. Cox proportional hazard models were used to assess the association between GEP-defined molecular subgroups and clinical outcomes after adjusting for known prognostic factors. Statistical analyses were performed using GraphPad Prism (Version 9.3.1, GraphPad Software, San Diego, CA, USA) or R statistical computing software (Version 4.2.0; R Core Team 2022). 4.3 Results 4.3.1 Characteristics of the study population Of the 1149 patients in the study population, 902 had an evaluable clinical diagnostic biopsy (Figure 4.1), 649 of which were included on TMA. Biopsies were unevaluable if they were not available (n = 34), had insufficient remaining material for research analysis (n = 159), or were obtained from bone/BM (n = 54; where sample processing precludes the molecular profiling performed in this study). Clinical FISH and IHC data were used for unevaluable biopsies when available. GEP data were available for 804 patients in the study population, including 629 of the 866 patients treated with R-CHOP (Figure 4.1A-B). Comparison of the baseline clinical characteristics between patients with available GEP data and the entire study population showed that patients 110 with GEP data were broadly representative of the study population, except for a significantly lower proportion of patients with stage III/IV disease and >1 extranodal site (Table 4.1). In R-CHOP-treated patients, the proportion of patients with >1 extranodal site was the only significant difference (Table 4.1). Patients with missing GEP data had significantly inferior outcomes than patients with available GEP data (Figure 4.2A). Among the reasons for missing GEP data, patients with insufficient material or with a bone/BM biopsy were associated with inferior outcomes compared to patients with available GEP data (Figure 4.2B). 4.3.2 Real-world prevalence and outcomes of GEP-defined subgroups The distribution of COO subtypes was 54% GCB, 11% Unclassified, and 36% ABC (Figure 4.3A). Using the refined COO classification, 93 (12%) tumors were subsumed by the DZsig+ subgroup, of which 90 (97%) were GCB and 3 (3%) were Unclassified by COO. DZsig positivity was observed in 8% of DLBCL-NOS tumors and 75% of HGBCL-DH-BCL2 tumors (Figure 4.3B). In R-CHOP-treated patients, baseline clinical and pathological features differed significantly among molecular subgroups, with notable differences in stage and LDH levels (Table 4.2). To assess the prognostic impact of molecular signatures, FFP and OS were evaluated according to standard COO in R-CHOP-treated patients. GCB tumors had a 14% higher 2-year FFP (81% vs 67%; P < 0.0001) and a 12% higher 2-year OS (83% vs 71%; P < 0.001) compared to ABC tumors (Figure 4.3C-D). Using the refined COO algorithm, DZsig+ tumors had the poorest 2-year outcomes (FFP: 51%, OS: 57%; Figure 4.3E–F). When this poor-prognosis DZsig+ group was pulled out of the GCB subgroup, the remaining GCB tumors had excellent 2-year outcomes (FFP: 87%, OS: 89%). In multivariable Cox regression adjusting for individual IPI risk factors, DZsig positivity was independently associated with inferior FFP (HR = 3.9, 95% CI 2.5–6.0) and OS (HR = 2.7, 95% CI 1.9–3.9) compared to the GCB group (Table 4.3). Taken together, DZsig 111 identifies a distinct poor-prognosis subgroup following R-CHOP and further identifies a refined subgroup of GCB tumors lacking DZsig expression that are associated with excellent outcomes. 4.3.3 Comparison of FISH- and GEP-defined subgroups As nearly half of all DZsig+ tumors in the original DZsig discovery cohort lacked detectable MYC and/or BCL2 rearrangements, we next examined the relationship between clinical outcomes and GEP- versus FISH-defined subgroups. Across the full cohort, 849 patients had sufficient FISH data to determine HGBCL-DH-BCL2 status, of which 53 (6%) were classified as HGBCL-DH-BCL2. Among R-CHOP–treated patients, 42 (6%) were HGBCL-DH-BCL2, while the remaining 615 (92%) lacked concurrent MYC and BCL2 and/or BCL6 rearrangements and were therefore classified as DLBCL-NOS (Table 4.1). By FISH criteria alone, HGBCL-DH-BCL2 was associated with significantly inferior 2-year FFP (52% vs 76%; P < 0.001) and 2-year OS (55% vs 78%; P < 0.0001) compared with DLBCL-NOS (Figure 4.4A-B). In total, 38% of DZsig+ tumors were HGBCL-DH-BCL2, while the remaining 62% were classified as DLBCL-NOS by FISH (Table 4.2). Among R-CHOP-treated DZsig+ patients, baseline clinical characteristics did not differ significantly between HGBCL-DH-BCL2 and DLBCL-NOS (Table 4.4), and both groups had similar outcomes, with 2-year FFP of 48% vs 54% (P = 0.46) and 2-year OS of 52% vs 59% (P = 0.36), respectively (Figure 4.4C-D). In contrast, the HGBCL-DH-BCL2 tumors lacking DZsig expression (DZsig-) had more favorable 2-year outcomes (FFP 73%; OS 73%), comparable to those of DZsig- DLBCL-NOS (FFP: 78%, OS: 81%; Figure 4.4C-D). Compared to DZsig+ HGBCL-DH-BCL2, DZsig- cases showed lower BCL2 mRNA expression (P = 0.009) and were more often CD10 negative by IHC (P = 0.0008), with 100% of DZsig+ HGBCL-DH-BCL2 tumors being CD10 IHC positive, compared to 50% of DZsig- HGBCL-DH-BCL2 (Figure 4.5A-C). 112 DZsig+ DLBCL-NOS tumors, which accounted for 62% of all DZsig+ cases (Figure 4.6A), showed CD10 IHC positivity in 48/52 tumors (92%) but more variable MYC and BCL2 expression levels (Figure 4.5A). Although by definition, these tumors lack concurrent MYC and BCL2 rearrangements, 86% carried either a sole MYC or sole BCL2 rearrangement (Figure 4.6B). Among DZsig+ DLBCL-NOS with available sequencing data, FISH-cryptic rearrangements that would otherwise render a HGBCL-DH-BCL2 diagnosis were detected in 5/36 (14%) tumors (Figure 4.6C). Accounting for these cryptic rearrangements reduced the proportion of DZsig+ DLBCL-NOS lacking a MYC and/or BCL2 rearrangement from 14% to 8% (Figure 4.6B-C). To assess whether rearrangement status influenced clinical outcomes among DZsig+ DLBCL-NOS tumors, R-CHOP-treated patients were stratified by the presence of sole MYC (n = 12), sole BCL2 (n = 19), or neither rearrangement (n = 6), with DZsig+ HGBCL-DH-BCL2 (n = 25) included for reference (Figure 4.6D-E). As identification of cryptic rearrangements is not currently feasible in routine diagnostic practice, tumors with cryptic rearrangements remained in their original FISH-defined categories. Although limited by sample size, those with sole MYC rearrangement demonstrated a trend toward superior 2-year FFP (75% vs 48%; P = 0.07) and OS (75% vs 52%; P = 0.06) compared with HGBCL-DH-BCL2 (Figure 4.6D-E). In contrast, 2-year outcomes for cases with sole BCL2 rearrangement (FFP: 53%, OS: 63%) or no detectable rearrangements (FFP: 50%, OS: 48%) were similar to HGBCL-DH-BCL2. While the prognostic and biological significance of sole MYC rearrangement among DZsig+ tumors remains to be determined, collectively, these findings demonstrate that a GEP-based definition identifies more than twice as many cases as FISH alone. The additional cases detected by GEP show clinical, molecular, and outcome profiles comparable to FISH-defined HGBCL-DH-BCL2, whereas FISH-defined HGBCL-DH-BCL2 lacking DZsig expression exhibit atypical molecular features and more favorable outcomes. 113 4.3.4 Association of GEP molecular subgroups with diagnosis to treatment interval We confirmed the relationship between clinical outcomes and DTI in our study population using the same weekly intervals defined by Maurer et al.171 DTI was strongly associated with FFP and OS in R-CHOP-treated patients (P < 0.00001, Figure 4.7A-B). Using patients with available GEP data, we analyzed the relationship between GEP-defined molecular subgroups and DTI. Frequency distributions of DTI for each molecular subgroup were generated (Figure 4.7C), and subgroups were compared using negative binomial regression. Relative to GCB-DLBCL, DZsig+ and ABC tumors had significantly shorter DTI, with expected DTI ratios (95% CI) of 0.79 (0.65-0.96, P < 0.01) for DZsig+ and 0.85 (0.75-0.97, P = 0.02) for ABC (Figure 4.7D). There was no significant difference in DTI distribution between DZsig+ HGBCL-DH-BCL2 and DZsig+ DLBCL-NOS (Figure 4.7E). In conclusion, DZsig+ status identified patients with significantly shorter DTI, regardless of whether tumors met FISH-based criteria for HGBCL-DH-BCL2 or were classified as DLBCL-NOS. 4.4 Discussion Drawing from more than 1,100 consecutively diagnosed patients treated under uniform provincial guidelines, we integrated clinical and molecular data to quantify the prevalence, characteristics, and outcomes of GEP- and FISH-defined subgroups in tumors of DLBCL morphology. We demonstrate that DZsig identifies a population twice the size of FISH-defined HGBCL-DH-BCL2, with two-thirds lacking concurrent MYC and BCL2 rearrangements but showing similarly poor outcomes. In contrast, the approximately 25% of HGBCL-DH-BCL2 tumors that were “missed” by GEP (i.e., DZsig-) were associated with more favorable outcomes. These findings highlight the ability of GEP to identify a biologically cohesive and poor-prognosis subgroup that extends beyond the current FISH-defined criteria. 114 Our analysis leveraged a large, unselected real-world cohort with a high rate of evaluable biopsies for GEP and FISH. Patients with available GEP data were broadly representative of the full study population, although a slight underrepresentation of those with advanced-stage disease and >1 extranodal site suggests some selection bias toward cases with better tissue availability. Importantly, missing GEP data was associated with inferior outcomes, particularly when absence was due to insufficient material or bone/bone marrow biopsies. This indicates that patients may be excluded from real-world molecular testing because specific clinical scenarios influence the nature of tumor sampling. Such scenarios include the need for urgent treatment, which can preclude obtaining sizeable biopsies, and certain disease presentations, such as those restricted to bone, bone marrow, and/or surgically inaccessible sites. These findings underscore the need to optimize tissue collection and processing protocols to ensure equitable molecular testing across clinical subgroups. Consistent with previous studies, using the standard COO classification, GCB tumors had better outcomes than ABC tumors, although the difference between groups was much smaller than in other rituximab-era series,83,85 further emphasizing that COO alone does not account for heterogeneity in outcomes following R-CHOP. Here, we show that refining COO by separating out the poor-prognosis DZsig+ group, which are uniformly GCB by standard COO, leaves a residual GCB subgroup with excellent outcomes, suggesting R-CHOP may be sufficient for these patients. FISH-defined HGBCL-DH-BCL2 accounted for 6% of DLBCL morphology tumors, consistent with previous reports.106 Outcomes were poorer than in the more recent LLBC series142 but remained better than historical estimates.104,105 This result highlights potential bias in the LLBC series toward more favorable outcomes, likely a result of drawing from selected populations and the exclusion of over half of patients who lacked sufficient material for analysis, a factor that in our cohort was associated with poorer outcomes. DZsig expression was observed in 75% of 115 HGBCL-DH-BCL2. Although limited by sample size, the remaining HGBCL-DH-BCL2 tumors that lacked DZsig expression displayed atypical features, including frequent CD10 negativity, and were associated with outcomes comparable to DZsig- DLBCL-NOS following R-CHOP. Notably, a similar pattern was reported for HGBCL-DH-BCL2 tumors lacking expression of the “molecular high-grade” (MHG) signature in a separate series.173 This signature was directly derived from the mBL signature,74,75 which we have previously demonstrated is strongly correlated with DZsig expression (Section 2.3.3). We confirmed that DZsig identifies a poor-prognosis population within GCB tumors of DLBCL morphology following R-CHOP, consistent with the original description of DZsig.107 Comparison of GEP and FISH classifications revealed that only about one-third of DZsig+ tumors met current FISH-based criteria for HGBCL-DH-BCL2. Therefore, the majority of DZsig+ tumors would not be detected by standard pathology workflows, yet outcomes were equivalent regardless of HGBCL-DH-BCL2 status. Sequencing of DZsig+ DLBCL-NOS revealed that 14% harbored cryptic MYC and BCL2 rearrangements that would otherwise classify them as HGBCL-DH-BCL2, comparable to previous reports.108 Among DZsig+ tumors lacking both rearrangements, nearly all had a sole rearrangement of either MYC or BCL2. Stratifying DZsig+ tumors by rearrangement status suggested that those with sole MYC rearrangement may be associated with more favorable outcomes following R-CHOP, an observation that warrants further exploration. Collectively, these findings indicate that GEP captures a biologically cohesive poor-risk group that is more than twice the size of the population defined by current FISH criteria. Given that many centers use intensified chemotherapy regimens for HGBCL-DH-BCL2, these observations raise the question of whether FISH is the optimal method to select patients for treatment intensification, or whether selection should be based on DZsig expression. In a representative patient sample selected from two phase II trials of young, high-risk patients with DLBCL morphology tumors treated with dose-dense immunochemotherapy, DZsig+ was not 116 associated with inferior outcomes.174 Although confirmatory studies are needed, this suggests that chemotherapy intensification in appropriately selected patients may overcome the negative prognostic effect conferred by DZsig+. Alternatively, DZsig+ patients could be considered for trials that incorporate novel therapeutic approaches in the frontline treatment of high-risk large B-cell lymphoma patients.175 The use of DZsig would lead to broader, but more refined, patient selection for these strategies than FISH. We established the association between DTI and outcomes in this cohort and extended these findings by showing that poor-prognosis molecular subgroups have shorter DTI in patients treated with curative and palliative intent.171 The shorter DTI seen in high-risk molecular subgroups likely contributes to their underrepresentation in DLBCL clinical trials because typical delays associated with trial enrolment select against these patients. In the Alliance/CALGB 50303 trial, only 1.2% of DLBCL patients with evaluable FISH data were HGBCL-DH,176 compared to 8% in our population. In the GOYA trial, where a nanoString-based assay was used for COO assignment similar to our study, the proportion of ABC patients was 26%,84 6% lower than in our cohort. This discrepant proportion of high-risk molecular subgroups between real-world and clinical trial populations further highlights the need to adjust trial design to facilitate the inclusion of high-risk patients who require urgent treatment initiation. In conclusion, this large, real-world study of tumors with DLBCL morphology shows that incorporating the DZ signature into COO classification identifies a subset of GCB patients with clinically relevant outcomes and DTI. Recognizing this subgroup may help refine patient selection for treatment intensification and inform clinical trial design. 117 Table 4.1: Comparison of baseline clinical characteristics between the entire study population and patients with evaluable gene expression profiling data Entire cohort R-CHOP-treated patients With GEP data P With GEP data P All (DLBCL90) All (DLBCL90) Characteristic n = 1149 n = 804 n = 866 n = 629 Age, Median (range, years) 69 (19-96) 69 (20-96) 0.36 66 (19-93) 66 (20-93) 0.72 Age > 60 years (n, %) 824 (72%) 565 (70%) 0.51 573 (66%) 409 (65%) 0.66 Female sex (n, %) 500 (44%) 351 (44%) 0.96 381 (44%) 274 (44%) 0.87 Ann Arbor stage, n (%) 0.02 0.08 I-II 436 (41%) 345 (46%) 369 (44%) 296 (49%) III-IV 633 (59%) 402 (54%) 471 (56%) 313 (51%) Missing 80 57 26 20 ECOG PS, n (%) 0.07 0.15 0-1 593 (57%) 447 (61%) 523 (64%) 403 (68%) 2-4 445 (43%) 280 (39%) 289 (36%) 188 (32%) Missing 111 77 54 38 LDH > ULN, n (%) 0.28 0.23 No 467 (50%) 348 (53%) 386 (52%) 299 (55%) Yes 459 (50%) 305 (47%) 357 (48%) 240 (45%) Missing 223 151 123 90 Extranodal sites, n (%) 0.04 0.04 0-1 800 (75%) 591 (79%) 633 (75%) 487 (80%) >1 269 (25%) 156 (21%) 207 (25%) 122 (20%) Missing 80 57 26 20 B symptoms (n, %) 0.16 0.21 No 677 (64%) 502 (68%) 555 (66%) 422 (70%) Yes 375 (36%) 240 (32%) 280 (34%) 184 (30%) Missing 97 62 31 23 Bulky disease ≥ 10 cm (n, %) 0.28 0.34 No 727 (71%) 531 (73%) 567 (71%) 425 (73%) Yes 296 (29%) 192 (27%) 237 (29%) 158 (27%) Missing 126 81 62 46 IPI (n, %) 0.07 0.13 Low (0-1) 272 (29%) 225 (34%) 247 (33%) 207 (38%) Low-Intermediate (2) 215 (23%) 157 (24%) 180 (24%) 134 (25%) High-Intermediate (3) 216 (23%) 141 (21%) 170 (23%) 113 (21%) High (4,5) 235 (25%) 136 (21%) 158 (21%) 92 (17%) Missing 211 145 111 83 Pathology 0.75 0.82 DLBCL-NOS 797 (94%) 714 (94%) 615 (94%) 561 (94%) HGBCL-DH-BCL2 53 (6%) 44 (6%) 42 (6%) 36 (6%) Unknown 299 46 209 32 Treatment regimen (n, %) 0.74 1 R-CHOP 866 (79%) 629 (82%) 866 (100%) 629 (100%) R-CHOP-HDMTX 6 (0.5%) 4 (0.5%) - - R-CEOP 54 (5%) 38 (5%) - - CEOP/CHOP 2 (0.2%) 0 - - Palliative 158 (14%) 89 (12%) - - Intensive 7 (0.6%) 4 (0.5%) - - Patient Refused 10 (0.9%) 7 (0.9%) - - Unknown 46 33 - - 118 Categorical variables were compared using Fisher’s exact test, and age distributions were compared using the Mann-Whitney test. Abbreviations: GEP, gene expression profiling; ECOG PS, Eastern Cooperative Oncology Group performance status; LDH, lactate dehydrogenase; ULN, upper limit of normal; DLBCL-NOS, diffuse large B-cell lymphoma, not otherwise specified; HGBCL-DH-BCL2, high-grade B-cell lymphoma with MYC and BCL2 rearrangements; R-CHOP, rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone; HDMTX, high-dose methotrexate; R-CEOP, rituximab, cyclophosphamide, etoposide, vincristine, and prednisone. 119 Table 4.2. Baseline clinical and pathological characteristics of R-CHOP-treated patients according to refined COO subgroups DZsig+ GCB Unclassified ABC Characteristic n = 70 n = 279 n = 67 n = 213 P Age, Median (range, years) 64 (35-88) 67 (22-92) 64 (20-92) 67 (31-93) 0.18 Age > 60 years (n, %) 45 (64%) 181 (65%) 40 (60%) 143 (67%) 0.74 Female sex (n, %) 26 (37%) 122 (44%) 24 (36%) 102 (48%) 0.23 Ann Arbor stage, n (%) 0.01 I-II 30 (43%) 151 (55%) 30 (46%) 85 (42%) III-IV 39 (57%) 120 (44%) 35 (54%) 119 (58%) Missing 1 8 2 9 ECOG PS, n (%) 0.29 0-1 45 (65%) 190 (72%) 40 (65%) 128 (65%) 2-4 24 (35%) 73 (28%) 22 (35%) 69 (35%) Missing 1 16 5 16 LDH > ULN, n (%) <0.01 No 24 (37%) 144 (61%) 37 (61%) 94 (53%) Yes 40 (63%) 93 (39%) 24 (39%) 83 (47%) Missing 6 42 6 36 Extranodal sites, n (%) 0.47 0-1 53 (77%) 221 (82%) 55 (85%) 158 (77%) >1 16 (23%) 50 (18%) 10 (15%) 46 (23%) Missing 1 12 2 9 B symptoms (n, %) 0.34 No 46 (67%) 197 (74%) 43 (66%) 136 (67%) Yes 23 (33%) 71 (26%) 22 (34%) 68 (33%) Missing 1 7 2 9 Bulky disease ≥ 10 cm (n, %) 0.34 No 43 (67%) 186 (71%) 50 (79%) 146 (75%) Yes 21 (33%) 76 (29%) 13 (21%) 48 (24%) Missing 6 17 4 19 IPI (n, %) 0.09 Low (0-1) 20 (30%) 105 (44%) 22 (37%) 60 (33%) Low-Intermediate (2) 16 (24%) 64 (27%) 16 (27%) 38 (21%) High-Intermediate (3) 17 (26%) 42 (18%) 12 (20%) 42 (23%) High (4,5) 13 (20%) 29 (12%) 10 (17%) 40 (22%) Missing 4 39 7 33 Pathology <0.0001 DLBCL-NOS 41 (62%) 251 (97%) 64 (98%) 205 (100%) HGBCL-DH-BCL2 25 (38%) 9 (3%) 1 (2%) 1 (0%) Unknown 4 19 2 7 Rearrangement (FISH) MYC 38/67 (57%) 15/262 (6%) 4/66 (6%) 16/207 (8%) <0.001 BCL2 44/62 (71%) 81/221 (37%) 15/58 (26%) 3/178 (2%) <0.001 BCL6 11/61 (18%) 43/213 (20%) 27/53 (51%) 55/167 (33%) <0.001 Mean log2(mRNA) count (SD) MYC 11.9 (1.4) 10.7 (0.81) 10.8 (0.8) 11.5 (0.9) <0.0001 BCL2 12.4 (1.9) 11.2 (1.6) 11.7 (1.3) 12.0 (1.3) <0.0001 120 Immunohistochemistry MYC/BCL2 DPE 32/58 (55%) 16/238 (7%) 12/57 (21%) 79/173 (46%) <0.001 MYC 36/53 (68%) 37/215 (17%) 13/52 (25%) 85/165 (52%) <0.001 BCL2 53/66 (80%) 145/255 (57%) 52/64 (81%) 178/201 (89%) <0.001 BCL6 54/60 (90%) 216/235 (92%) 45/58 (78%) 108/165 (65%) <0.001 CD10 62/65 (95%) 161/243 (66%) 13/59 (22%) 9/179 (5%) <0.001 MUM1 2/56 (4%) 44/224 (20%) 28/53 (53%) 134/163 (82%) <0.001 GCB by Hans 64/65 (99%) 207/232 (89%) 29/54 (54%) 23/159 (14%) <0.001 Categorical variables were compared using Fisher’s exact test, and continuous variables using one-way ANOVA. Abbreviations: DZsig+, dark zone signature positive; GCB, germinal center B-cell-like; ABC, activated B-cell-like; ECOG PS, Eastern Cooperative Oncology Group performance status; LDH, lactate dehydrogenase; ULN, upper limit of normal; DLBCL-NOS, diffuse large B-cell lymphoma, not otherwise specified; HGBCL-DH-BCL2, high-grade B-cell lymphoma with MYC and BCL2 rearrangements; FISH, fluorescence in situ hybridization; SD, standard deviation; DPE, dual protein expressor. 121 Table 4.3. Clinical outcomes in R-CHOP-treated patients according to GEP-defined molecular subgroups after adjusting for IPI risk factors by multivariable Cox regression FFP OS Variable HR (95% CI) P HR (95% CI) P GEP-defined subgroups DZsigpos 3.9 (2.5-6.0) < 0.0001 2.7 (1.9-3.9) < 0.0001 ABC 2.5 (1.7-3.5) < 0.0001 1.6 (1.2-2.1) < 0.001 Unclassified 2.2 (1.4-3.6) < 0.01 1.7 (1.1-2.5) 0.01 GCB (reference group) - - - - IPI risk factors Age > 60 years 1.3 (0.9-1.7) 0.13 2.5 (1.9-3.3) < 0.0001 Stage III/IV 1.3 (0.9-1.9) 0.11 1.3 (1.0-1.8) 0.03 PS >1 1.8 (1.3-2.4) < 0.001 1.8 (1.4-2.3) < 0.0001 Elevated LDH 2.4 (1.7-3.3) < 0.0001 1.6 (1.2-2.1) < 0.001 Extranodal >1 1.5 (1.0-2.1) 0.03 1.2 (0.9-1.6) 0.27 Abbreviations: R-CHOP: rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone, DLBCL: diffuse large B-cell lymphoma, GEP: gene expression profiling, FFP: freedom from progression, OS; overall survival, HR: hazard ratio, CI: confidence interval, IPI: international prognostic index, DZsig+: dark zone signature positive, GCB: germinal centre B-cell-like, ABC: activated B-cell-like, PS: performance status, LDH: lactate dehydrogenase. 122 Table 4.4. Clinical characteristics of R-CHOP-treated patients with DZsig+ tumors by HGBCL-DH-BCL2 status DZsig+ DZsig+ HGBCL-DH-BCL2 DLBCL-NOS Characteristic (n = 25) (n = 41) P Age, n (%) 0.43 > 60 years 18 (72%) 25 (61%) ≤ 60 years Sex, n (%) 0.61 Male 14 (56%) 26 (63%) Female 11 (44%) 15 (37%) Stage, n (%) 0.99 I/II 11 (44%) 17 (43%) III/IV 14 (56%) 23 (57%) NA 0 1 PS, n (%) 0.79 0-1 16 (64%) 27 (67%) 2-4 9 (36%) 13 (33%) NA 0 1 LDH, n (%) 0.58 Normal 7 (32%) 16 (42%) Elevated 15 (68%) 22 (58%) NA 3 3 Extranodal sites, n (%) 0.25 0-1 16 (64%) 32 (80%) ≥ 2 9 (36%) 8 (20%) NA 0 1 B symptoms, n (%) 0.61 No 17 (68%) 27 (67%) Yes 8 (32%) 13 (33%) NA 0 1 Bulky disease ≥ 10 cm, n (%) 0.38 No 14 (64%) 29 (76%) Yes 8(36%) 9 (24%) NA 3 3 IPI, n (%) 0.74 Low (0-1) 8 (35%) 12 (31%) Low-Intermediate (2) 5 (22%) 9 (23%) High-Intermediate (3) 4 (17%) 11 (28%) High (4,5) 6 (26%) 7 (18%) NA 2 2 P values derived from Fisher’s exact test. Abbreviations: DZsig+: dark zone signature positive, FISH: fluorescence in situ hybridization, NA: not available, PS: performance status, LDH: lactate dehydrogenase level, IPI: International prognostic index. 123 Figure 4.1. Overview of the study population and biopsy availability for research analyses. (A) All patients; (B) patients treated with curative-intent R-CHOP. The number of patients with an adequate biopsy for research analyses is shown, along with reasons for inadequate biopsies. FISH was attempted on all biopsies deemed adequate for research. Digital gene expression profiling with the DLBCL90 assay was attempted for all adequate biopsies with sufficient RNA. Patients with a de novo tumors of DLBCL morphology, diagnosed in BC between 2005 and 2010n = 11491. Biopsy not available (n = 34)2. Insufficient material (n = 159)3. Bone/BM (n = 54)1. R-CHOP-HDMTX (n = 6)2. R-CEOP (n = 54)3. Palliative (n = 158)4. Intensive (n = 7)5. Refused treatment (n = 10)6. CEOP/CHOP (n = 2)7. Unknown (n = 46)1. Biopsy not available (n = 23)2. Insufficient material (n = 110)3. Bone/BM (n = 43)R-CHOP treated patientsn = 866R-CHOP treated patients with an adequate biopsy for research analysesn = 690R-CHOP treated patients with GEP data (DLBCL90)n = 6291. Insufficient RNA (n = 23)2. DLBCL90 failed (n = 110)Patients with an adequate biopsy for research analysesn = 902Patients with GEP data (DLBCL90)n = 9021. Insufficient RNA (n = 36)2. DLBCL90 failed (n = 62)Patients with a de novo tumors of DLBCL morphology, diagnosed in BC between 2005 and 2010n = 1149AFull cohort R-CHOP cohortB 124 Figure 4.2. FFP in the entire study population compared to patients with or without GEP data using the DLBCL90 assay. (A) Freedom from progression and (B) overall survival in R-CHOP-treated patients in the study population, comparing patients with or without GEP data. (C) Freedom from progression and (D) overall survival in R-CHOP-treated patients stratified by reasons for missing GEP data. The insufficient material/RNA group includes cases with inadequate diagnostic tissue, insufficient extracted RNA, or DLBCL90 assay failure – most commonly due to low RNA yield. Survival differences were assessed using the log-rank test. *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001. 628 493 458 434 420 399 374 352 325 297237 156 140 134 131 125 115 109 104 1000%25%50%75%100%Freedom from progression−−Number at risk| | ||||| || | ||| | |||| | | ||| | ||| |||||||||||| || || |||||||||| |||||||| |||||||||||||| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| |||||||||||||||||||||| ||||||||||||||| | | | | | || | | ||| | || | | | | | | | || | | | | |||||| || |||||||||| | ||||| || ||||| | ||| | | || | | ||||| || ||| || |||| || | |GEP availableGEP not available0 1 2 3 4 5 6 7 8 9YearsA0%25%50%75%100%Freedom from progression−−−−Number at risk| | ||||| || | ||| | |||| | | ||| | ||| |||||||||||| || || |||||||||| |||||||| |||||||||||||| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| |||||||||||||||||||||| |||||||||||||| | | | | || | | ||| | | | | | | || | | | | |||||| || ||||||||| | |||| | ||||| | ||| | | || | | ||| | ||| || ||| || | ||| || | | | | | | | | || | || | | | || | || | | ||GEP availableInsufficientBiopsy not availableBone/BM628 493 458 434 420 399 374 352 325 297171 117 105 101 99 94 86 83 78 7523 15 14 13 12 11 9 7 7 743 24 21 20 20 20 20 19 19 180 1 2 3 4 5 6 7 8 9YearsC2−year FFP (%)756565492−year FFP (%)7562 P = 0.006P = 0.001628 538 486 464 447 428 405 379 354 326237 181 156 145 144 137 128 120 116 110628 538 486 464 447 428 405 379 354 32623 17 16 14 14 12 10 7 7 7171 132 114 107 107 102 95 90 86 8143 32 26 24 23 23 23 23 23 220%25%50%75%100%Overall survival−−Number at risk| | | | | | || |||||||| | | | | | | | || ||| | ||| |||||||| || | | | | | | | | |GEP availableGEP not available0 1 2 3 4 5 6 7 8 9YearsB0%25%50%75%100%Overall survival−−−−Number at risk| | | | | | || |||||||| | | | | | | | || ||| | ||| ||||||||| | |||| || | ||GEP availableBiopsy not availableInsufficientBone/BM0 1 2 3 4 5 6 7 8 9YearsD2−year OS (%)78672−year OS (%)78736862P = 0.047P = 0.066 125 Figure 4.3. Clinical outcomes according to gene expression-defined molecular subgroups. (A) Alluvial plot showing the distribution of COO and refined COO subtypes within the study cohort. Each ribbon links a tumor’s COO subtype to its refined COO assignment, which incorporates DZsig status. In the refined COO algorithm, COO classification takes priority for ABC tumors, while GCB and Unclassified tumors with DZsig positivity are assigned to the DZsig+ subgroup. (B) Bar plot showing the distribution of refined COO in DLBCL-NOS and HGBCL-DH-BCL2 tumors. (C) FFP and (D) OS among R-CHOP-treated patients stratified by COO. (E) FFP and (F) OS among R-CHOP-treated patients stratified by refined COO. Survival differences were assessed using the log-rank test. *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001. | | | | || | ||| | || | | | ||| || || || | |||| | | | | | | |||| | | |278 257 246 239 231 223 211 200 185 17267 60 49 47 45 43 41 37 33 32213 177 151 142 135 127 120 112 106 9870 44 40 36 36 35 33 30 30 240%25%50%75%100%Overall survival−−−−Number at riskGCBUnclassABCDZsig+0 1 2 3 4 5 6 7 8 9YearsF2−year OS (%)89737157Unclass345 300 286 275 267 258 244 230 215 1960%25%50%75%100%Overall survival−Number at risk| | | | | |||| ||| | || | | | | ||| || ||||| | | | | | | |||| || | |GCBABC0 1 2 3 4 5 6 7 8 9YearsD70 61 49 47 45 43 41 37 33 32−213 177 151 142 135 127 120 112 106 98−2−year OS (%)837170 ******** ****| |||| | | ||| | | | | ||||||||||| | ||| ||||| | |||||| | | ||| ||||||||||| |||||| ||||||||||||||||||||||||||| |||||||||||||||||||||||||||||||||||||| ||||| |||| | | | | | | | || | | | | || || ||| | || | | ||| || | | | | ||| || || | | | ||| || || ||| | | || | | ||||| || ||||| | || |||| |||||||||||||| || ||| || ||| |||| || |||| | || || | || | | | | | || | || | | |||| | || | || || | || |0%25%50%75%100%Freedom from progression−−−Number at risk| | |||| | | ||| | | | | ||||||||||| | ||| |||||| | |||||| | || ||||| ||||||||||||| ||||||| ||||||||||||||||||||||||||| |||||||||||||||||||||||||||||||||||||||||||| ||||| |||| | | | | | | | || | | | | || || ||| | || | | ||| || | | | | ||| || || | | | ||| || || ||| | | || | | ||||| || ||||| | || |||| |||||||||||||| || ||| || ||| |||| || |||| | || || | |GCBUnclassABC345 283 271 261 253 244 228 217 201 18270 55 48 47 45 42 40 36 31 29213 155 139 126 122 113 106 99 93 860 1 2 3 4 5 6 7 8 9YearsC2−year FFP (%)816967278 247 237 230 223 214 200 190 174 16067 55 48 47 45 42 40 36 31 29213 155 139 126 122 113 106 99 93 8670 36 34 31 30 30 28 27 27 220%25%50%75%100%Freedom from progression−−−−Number at riskGCBUnclassABCDZsig+0 1 2 3 4 5 6 7 8 9YearsE2−year FFP (%)87726751* *********** *****ACOO Refined COO12%42%10%36%54%11%36%B43%38%75%20%n=713 n=440255075100DLBCL-NOSHGBCL-DH-BCL2Percent (%)8%11%DZsig+GCBUnclassABCRefined COODZsig+GCBUnclassABCRefined COO 126 Figure 4.4. Clinical outcomes in patients treated with R-CHOP according to structural variants in MYC and BCL2 detected by FISH. (A) Freedom from progression (FFP) and (B) overall survival (OS) between HGBCL-DH-BCL2 and DLBCL-NOS. (C) FFP and (D) OS in R-CHOP–treated patients stratified by DZsig and FISH-defined HGBCL-DH-BCL2 status. Survival differences were assessed using the log-rank test. *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001. | ||||| || | ||| | |||| | | ||| | || |||||||||||| || || |||| ||||| |||||||| ||||||| ||||| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| |||||||||||||||||||| ||||||| ||| | | | | || | | | || | | | | || | || | | | || | | | | | | ||| | | ||||| || | ||| | |||| | | ||| | |||||||||||||||| || ||| ||||| ||||| |||||||||||||||| ||||| |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| ||||||||||||||||||||||| ||||||| ||| | | | | | | | | | | | | | | ||0%25%50%75%100%Freedom from progression−−Number at risk614 485 453 431 417 395 367 343 316 28942 23 22 20 19 18 15 14 14 110 1 2 3 4 5 6 7 8 9YearsA0%25%50%75%100%Freedom from progression−−−−Number at riskDZsig− DLBCL-NOSDZsig− HGBCL-DH-BCL2DZsig+ DLBCL-NOSDZsig+ HGBCL-DH-BCL2519 427 396 377 364 344 323 302 276 25311 8 8 7 7 6 4 4 4 441 21 20 18 18 18 16 16 16 1425 12 12 11 10 10 10 9 9 60 1 2 3 4 5 6 7 8 9YearsC2-year FFP (%)78735448DLBCL-NOSHGBCL-DH-BCL22-year FFP (%)7652 ***** ***| | | | | | || || ||||| | | | | | | | || || | ||| ||||||| | | || | | | | | | || ||||||||| | ||| | | | | || ||| | ||| ||||||||614 529 480 460 444 424 398 369 345 31842 27 23 21 21 20 18 16 16 12519 459 417 400 384 367 348 325 301 27911 9 8 7 7 6 4 4 4 441 27 24 22 22 21 19 18 18 1625 13 13 12 12 12 12 10 10 60%25%50%75%100%Overall survival−−Number at riskDLBCL-NOSHGBCL-DH-BCL20 1 2 3 4 5 6 7 8 9YearsB0%25%50%75%100%Overall survival−−−−Number at riskDZsig− DLBCL-NOSDZsig− HGBCL-DH-BCL2DZsig+ DLBCL-NOSDZsig+ HGBCL-DH-BCL20 1 2 3 4 5 6 7 8 9YearsD2-year OS (%)817359522-year OS (%)7855 ***** *** 127 Figure 4.5. Clinical and molecular characteristics of DZsig+ tumors in relation to HGBCL-DH-BCL2. (A) Heatmap showing DZsig status, COO subtype, MYC and BCL2 rearrangement status, MYC rearrangement partner, IHC for CD10, MYC and BCL2, and MYC/BCL2 mRNA expression. A separate ABC track (n=2) flags rare ABC tumors that were DZsig+ or HGBCL-DH-BCL2 and were excluded from further analysis. (B) Box plots showing MYC (left) and BCL2 (right) log2 mRNA expression in HGBCL-DH-BCL2 tumors, stratified by DZsig status. Expression levels were determined by digital gene expression profiling using the DLBCL90 assay, and groups were compared using a t-test. (C) Proportion of CD10-positive cases among HGBCL-DH-BCL2 tumors, stratified by DZsig status. Groups were compared using Fisher’s exact test. COOABCUNCLASSGCBDZsigPOSINDNEGFISHPOSCrypticNEGNAIHCPOSNEGNAMYC partnerIGNon-IGNot foundNAmRNA (Zscore)−4 −2 0 2 4BA0255075100Percent (%)100%50%50%n = 32 n = 8P = 0.0008DZsigCOOMYC FISHBCL2 FISHMYC partnerCD10 IHCMYC IHCBCL2 IHCMYC mRNABCL2 mRNA**PathologyPathologyHGBCL-DH-BCL2DLBCL-NOSNo break1011121314log2 (MYC mRNA)P = 0.31POS NEG/INDDZsigC1112131415log2 (BCL2 mRNA)P = 0.009POS NEG/INDDZsigPOS NEG/INDDZsigCD10 IHC POS NEG 128 Figure 4.6. Distribution of MYC and BCL2 rearrangement status in DZsig+ tumors and associated outcomes. (A) Proportion of DLBCL morphology tumors that were HGBCL-DH-BCL2 or DLBCL-NOS. (B) MYC and BCL2 rearrangement status among DLBCL-NOS tumors. (C) MYC and BCL2 rearrangement status among DLBCL-NOS tumors with available sequencing data to detect cryptic rearrangements. (D) Freedom from progression and (E) overall survival among DZsig+ tumors according to MYC and BCL2 rearrangement status. Survival differences were assessed using the log-rank test. *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001. 38%62%n=880255075100DZsig+Percent (%)A14%50%36%n=500255075100DZsig+DLBCL-NOSPercent (%)B8%39%39%14%n=360255075100Percent (%)CDZsig+DLBCL-NOSwith sequencingHGBCL-DH-BCL2 DLBCL-NOSPathologyMYC−BCL2− MYC−BCL2+MYC and BCL2 rearrangement statusMYC+BCL2− Cryptic HGBCL-DH-BCL2MYC+BCL2+ 52MYC−BCL2− 5012 11 9 9 9 9 8 8 8 619 12 12 10 10 9 8 7 7 70%25%50%75%100%Overall survival−−Number at risk| |||MYC+BCL2−MYC−BCL2+0 1 2 3 4 5 6 7 8 9YearsE2-year OS (%)75636 4 3 3 3 3 3 3 3 3−25 13 13 12 12 12 12 10 10 6−P = 0.0580%25%50%75%100%Freedom from progression−−−−Number at risk| | || | | | | ||| | | | | || | || | | | | | | ||MYC+BCL2−MYC−BCL2+MYC−BCL2−MYC+BCL2+12 9 8 8 8 8 7 7 7 519 9 9 7 7 7 6 6 6 66 3 3 3 3 3 3 3 3 325 12 12 11 10 10 10 9 9 60 1 2 3 4 5 6 7 8 9Years2-year FFP (%)75535048DP = 0.066 129 C0 20 40 60 80 10003691215DTI (days)FrequencyDZsig+0 20 40 60 80 100051015202530354045DTI (days)FrequencyGCBMedian (IQR): 30 (16-43)0 20 40 60 80 100051015202530DTI (days)FrequencyABCMedian (IQR): 28 (16-41)Frequency0 20 40 60 80 100036912DTI (days)UnclassMedian (IQR): 33 (27-44)Median (IQR): 25 (14-38)A0 1 2 3 4 5 6 7020406080100YearsFreedom from prgoressionDiagnosis to Treatment Interval (days)0-614-2028-347-1321-2735+Log-rank testfor trendP < .00001No. at risk:0-6 60 33 30 30 2828 28 267-13 123 76 68 63 62 58 54 5214-20 139 93 88 82 78 74 70 6521-27 124 96 89 84 81 77 75 7228-34 113 94 84 82 78 74 66 6235+ 311 270 250 238 234 221 205 191BDiagnosis to Treatment Interval (days)0-614-2028-347-1321-2735+Log-rank testfor trendP < .000010 1 2 3 4 5 6 7020406080100YearsOverall survival60 39 33 31 31 29 29 27123 86 78 74 71 69 65 62139 108 92 88 85 82 78 71124 105 91 86 85 83 80 78113 105 94 89 85 79 73 68No. at risk:0-67-1314-2021-2728-3435+ 311 286 265 252 244 233 217 201D0.5 1.0 1.5ABCUnclassDZsig+Expected DTI ratio relative to GCBP-value< .01.89.02Frequency0 20 40 60 80012345678DTI (days)DLBCL-NOSDZsig+Frequency0 20 40 60 800123456DTI (days)HGBCL-DH-BCL2DZsig+E 130 Figure 4.7. Refined COO subgroups are associated with different diagnosis-to-treatment intervals (DTI) in R-CHOP-treated patients. (A) Freedom from progression and (B) overall survival in the study population according to DTI intervals previously defined by Maurer et al. (C) DTI frequency distribution according to molecular subgroup. Six outliers with DTI >100 days are not shown but were included for statistical comparisons (1 DZsig+, 4 GCB, and 1 ABC). (D) Comparison of DTI ratios of molecular subgroups by negative binomial regression using the GCB subgroup as a reference. DTI ratios and 95% confidence intervals (95% CI) and P values are shown, as determined by negative binomial regression. (E) Comparison of DTI distribution in DZsig+ tumors based on HGBCL-DH-BCL2 status. DTI ratio (95% CI) of DZsig+ HGBCL-DH-BCL2 relative to DZsig+ DLBCL-NOS was 0.94 (0.67-1.34, P=0.74) by negative binomial regression. 131 Chapter 5: Molecular Characterization and Classification of HGBCL-NOS 5.1 Introduction HGBCL-NOS is a diagnostic entity in which membership is defined by malignant cell morphology (i.e., blastoid chromatin resembling lymphoblastic lymphoma or cytologic features intermediate between BL and DLBCL) and the absence of concurrent MYC- and BCL2- and/or BCL6-rearrangements.1,6,7,144 This group represents an evolving diagnostic category formally introduced in the 1994 REAL classification when it was recognized that some tumors could not be confidently classified as BL or DLBCL, giving rise to the provisional entity “High-grade B-cell lymphoma, Burkitt-like”.8 The most recent refinement in the evolution of this category has been the establishment1, and subsequent refinement, of HGBCL-DH-BCL2, noting that the latest classifications (ICC and WHO-HAEM5) are at variance with tumors with MYC- and BCL6-rearrangements6,7,177. Other efforts to refine HGBCL-NOS have primarily focused on restricting its classification by more clearly defining BL and DLBCL, resulting in minimal material changes to the overall classification of this group74,75. Consequently, HGBCL-NOS remains a diagnosis of exclusion that largely relies on morphological assessment. A recent large retrospective multi-institutional study of HGBCL-NOS highlights the unmet needs and challenges of this group.178 Most patients had high-risk features, including stage IV disease and elevated lactate dehydrogenase. Reflecting the lack of standard treatment for this group, patients were treated with a range of regimens, from the DLBCL standard R-CHOP to more intensive regimens typically reserved for BL and HGBCL-DH-BCL2. However, outcomes were not improved with intensified treatment, with progression-free survival (PFS) of 55% at 2 years, emphasizing the need for more effective treatment strategies. Characterization of the tumors by IHC and FISH revealed considerable heterogeneity beyond the majority having a GCB immunophenotype. This heterogeneity has also been observed in other studies that employed targeted sequencing approaches.179–183 132 Optimal classification identifies tumors with common biological, clinical, and molecular features, while maintaining accurate tumor categorization and minimizing the number of unclassified cases. BL and DLBCL-NOS have been extensively characterized at the genomic level, revealing their core genetic aberrations and underlying molecular substructure.70,71,92,93,95,96 In contrast, HGBCL-NOS remains poorly understood, and improving its clinical management requires a better understanding of this group. However, progress has been hindered by its rarity and the challenges associated with membership being contingent on identifying high-grade morphology, which is affected by pre-analytic variables and inter-observer variability. To overcome these barriers, we collected HGBCL-NOS tumors from affiliated sites of the Lymphoma/Leukemia Molecular Profiling Project (LLMPP) consortium. Leveraging the framework provided by our understanding of the BL and DLBCL, we conducted a comprehensive molecular analysis of HGBCL-NOS to determine whether these tumors share common molecular features or if they can be more accurately classified using molecular approaches. To reflect the real-world setting, analyses were initially performed on tumors based on their submitted local diagnosis. All tumors further underwent centralized pathology review by the LLMPP pathology panel and were reanalyzed to assess whether this process could identify a more cohesive group. 5.2 Materials and Methods 5.2.1 Study cohort Participating institutions submitted paraffin blocks, immunophenotypic and genetic results, and, when available, clinical records for cases diagnosed as HGBCL-NOS, according to the WHO revised 4th edition criteria.1 Bone and bone marrow biopsies, as well as fine needle aspirates, were excluded. FISH was performed on all biopsies to exclude HGBCL-DH-BCL2 and HGBCL-DH-BCL6. Other exclusion criteria included patient age under 18 years, known HIV infection, post-transplant lymphoproliferative disorders, and primary central nervous system lymphomas. In 133 instances where a primary biopsy was unavailable, a relapse biopsy was analyzed for seven patients. Additionally, five patients had a prior diagnosis of indolent lymphoma. Each biopsy was locally reviewed by an LLMPP hematopathologist to confirm HGBCL-NOS diagnosis and to assign a cytological subtype. Cytological subtypes included: (1) Intermediate between DLBCL and BL (“intermediate cytology”): defined as a predominance of medium-sized cells with round to oval nuclei, small basophilic nucleoli, but more irregularity in shape and/size than those of classic BL; (2) Blastoid: defined as a predominance of cells with finely dispersed chromatin, sparse cytoplasm, with inconspicuous nucleoli as may be seen in lymphoblastic lymphoma; (3) Other: used for rare cases not fitting intermediate or blastoid cytology (e.g. cases resembling small immunoblasts). The final cohort consisted of 92 patients with an available HGBCL-NOS biopsy. 5.2.2 FISH and IHC TMAs were constructed from duplicate 0.6 mm cores from FFPE blocks. FISH was performed on TMA sections using commercially available Metasystems XL break-apart probes for MYC (D-6030-100-TC), BCL2 (D-6018-100-OG), and BCL6 (D-6016-100-OG). Images were captured using a Metafer CoolCube 1 camera and Metafer software (version 3.11.8). Two independent observers scored 100 nuclei per biopsy, except for small or poor-quality biopsies, where only 50-100 nuclei could be scored. Tumors in which <50 nuclei could be scored were considered a failed FISH result. For biopsies with discrepant scores, consensus was reached by a third observer. Rearrangement was defined as a break-apart signal in ≥5% of cells. IHC was performed on TMA sections using the Dako Omnis platform for MYC (EP121, Biocare), BCL2 (E17, Abcam; and BCL2/124, Dako), CD10 (DAK-CD10, Dako), BCL6 (PG-B6p, Dako), MUM1 (MUM1p, Dako), CD20 (L26, Dako), TDT (EP266, Dako), and KI67 (MIB-1, Dako). EBER status was determined by EBER ISH (Dako). IHC positivity was defined as ≥30% (CD10, 134 BCL6, and MUM1),99 ≥40% (MYC), and ≥50% (BCL2) staining of tumor cells.109 IHC was independently scored by two expert hematopathologists, and biopsies with discordant scores were further evaluated by a third hematopathologist to reach consensus. For biopsies not included on TMAs, clinical IHC and FISH data provided by the submitting institution were used when available. 5.2.3 Digital gene expression profiling TNA was extracted from FFPE biopsies using the ALINE FFPE GenePure Purification Kit, automated on the Hamilton Nimbus robot. RNA was isolated by DNase treatment of TNA. COO and DZsig status were determined using the DLBCL90 assay on the nCounter platform (nanoString), as previously described.107,184 Tumors were assigned to refined COO subgroups, prioritizing COO over DZsig status for ABC tumors. DZsig+ GCB and unclassified tumors were grouped as DZsig+, while DZsig- and DZsig-indeterminant tumors remained in their respective COO subgroups.184 5.2.4 Comparison of molecular features in HGBCL-NOS to BL and DLBCL Data from two previously described cohorts were used to compare the molecular features of HGBCL-NOS with BL and DLBCL. The first cohort included 63 BL biopsies with WGS data from the BLGSP.71 This cohort was limited to patients aged 18 or older with EBV-negative tumors to align with the HGBCL-NOS cohort inclusion criteria and avoid EBV status as a confounding factor, as EBV-positive BL has distinct molecular features70,71. The second cohort consisted of 781 DLBCL-NOS biopsies, drawn from a population-based cohort of adult patients diagnosed with a de novo tumor of DLBCL morphology between 2005 and 2010 in British Columbia, Canada.184 Data available for this cohort included FISH, IHC, and DLBCL90 gene expression profiling. All biopsies had sufficient FISH data to exclude a diagnosis of HGBCL-DH/TH. Targeted 135 capture sequencing of 125 genes that are recurrently mutated in aggressive B-cell lymphomas was performed on 341 FFPE biopsies with adequate nucleic acids. 5.2.5 DNA Library construction and sequencing For FFPE samples, TNA was extracted using the ALINE FFPE GenePure Purification Kit. When ≥300 ng of FFPE extracted TNA was available, TNA was also treated with S1 nuclease to reduce the rate of chimeric fragments derived from single-stranded DNA and overhangs.112 Paired-end DNA libraries for sequencing were generated using a PCR-based protocol as previously described.113 For fresh frozen tissues, DNA was extracted and subjected to library construction as previously described.114 HGBCL-NOS biopsies with available fresh frozen tissue underwent WGS, while FFPE biopsies were analyzed using WES, targeted capture sequencing, and low-pass WGS, prioritized in this order based on nucleic acid availability. Sequencing data availability for FFPE biopsies varied due to sequencing failures and/or insufficient nucleic acids. Whole exome capture was performed using the SureSelectXT Human All Exon V6 + UTR (Agilent) kit. For targeted capture sequencing, hybridization capture was performed with a panel created by IDT or Twist Biosciences, as previously described.115 This panel was designed to capture the regions around MYC (Chr8:126262754-129664254), BCL2 (Chr18:63077345-63128767), BCL6 (Chr3:187721376-188032725), and recurrent rearrangement partner loci: IGH (Chr14:105546276-106879844), IGK (Chr2:88686728-89330586), IGL (Chr22:22329967-23061520), PAX5 (Chr9:37034478-37465410). Libraries were sequenced on an Illumina HiSeq2500, HiSeqX, or NovaSeq with 150 bp paired-end reads. 136 5.2.6 Target sequencing of the DLBCL-NOS cohort Targeted capture sequencing of 125 genes was performed for 341 DLBCL-NOS biopsies with adequate nucleic acids. The panel (“LySeqST”) was designed to cover genes that are recurrently mutated in aggressive B-cell lymphomas.153 DNA was isolated from FFPE material, and libraries were constructed and sequenced as described above. 5.2.7 Alignment and variant calling WGS, WES, LySeqST targeted sequencing, and low-pass WGS were aligned to the GRCh37 reference genome using bwa-mem (v0.7.17)116. Somatic variants were identified with the SLMS-3 pipeline, an ensemble variant calling approach that uses Strelka2117, LoFreq118, Mutect2119, and SAGE. Only variants called by three or more variant callers were passed. Somatic variants were annotated with VCF2MAF and the Ensembl Variant Effect Predictor120. The GnomAD reference was used to remove germline variants with a population allele frequency > 1e-4, and further filtered against a custom blacklist of variants that recur when tumors are run without, but not with, a matched normal. Significantly mutated genes were identified using dNdScv121, MutSig2CV185, and OncodriveFML186. LymphGen classification96 was performed on variant calls using somatic mutations and BCL2/BCL6-rearrangement data. Variant calls were further used to assign tumors into BL-like and DLBCL-like genetic subtypes using a previously described random forest classifier.71 Tumors classified to the IC-BL and DGG-BL subgroups were collapsed into a single BL-like group. Copy number alterations (CNAs) were identified from WGS or low-pass WGS using Control-FREEC187, and recurrent CNAs were identified using GISTIC2188. Targeted capture sequencing data for identification of SVs was aligned to the hg38 reference genome using bwa-mem (v0.7.17). SVs involving MYC and BCL2 were identified by an ensemble approach using GRIDSS2122 and Manta123, with VCF files output by these tools intersected using the BioConductor package StructuralVariantAnnotation124. SVs were filtered out unless they met 137 a minimum variant allele frequency of 0.05 and/or passed manual curation in IGV125. WGS data was analyzed using the same pipeline as capture data, and mapped to the hg38 genome build with CrossMap.126 Circular plots were generated with the R package Circlize.127 Bioinformatic pipelines are available as part of the LCR Modules repository. 5.2.8 RNAseq analysis RNA was isolated by DNase treatment of TNA extracted from FFPE material, as described above. RNAseq libraries were constructed with a ribodepletion method and sequenced with a target of 100M reads per library. RNAseq data were pseudoaligned to the Gencode version 33 transcriptome reference (hg38) with Salmon110 and normalized with DESeq2111 and variance stabilizing transformation (VST). 5.2.9 Centralized pathology review All tumors included in this study underwent subsequent centralized pathology review (CPR), conducted by a panel of LLMPP hematopathologists. Each tumor was independently assessed by four pathologists, and a diagnosis was finalized if at least three agreed. If fewer than three agreed, the case was reviewed by the entire panel (10-15 pathologists) during an in-person or virtual meeting, with the final diagnosis determined by majority vote. Analyses are interpreted according to the initial local submitting diagnosis unless otherwise stated. 5.2.10 Statistical Analysis Statistical analyses were performed with R version 4.4.0. This study was approved by the University of British Columbia/BC Cancer Research Ethics Board in accordance with the Declaration of Helsinki. All patient and sample identifiers have been replaced with research-specific identifiers. 138 5.3 Results 5.3.1 Clinical and molecular characteristics of the study cohort This study included 92 biopsies submitted as HGBCL-NOS from eight LLMPP sites. For comparisons of HGBCL-NOS with other related aggressive B-cell lymphoma entities, 63 BL and 781 DLBCL-NOS biopsies were drawn from previously described cohorts20,40 and included in analyses when indicated. HGBCL-NOS tumors in the main study cohort were identified by the submitting pathologists as showing the following morphologic features: 55% with features intermediate between BL and DLBCL (henceforth referred to as “intermediate” morphology), 34% blastoid cytology, and 11% unspecified (Figure 5.1A). Clinical characteristics of the study cohort are included in Table 5.1. There was no significant difference between the clinical characteristics of blastoid and intermediate tumors. To characterize the molecular features of HGBCL-NOS, tumors were analyzed using FISH, IHC, gene expression profiling, and sequencing. FISH analysis identified a MYC-rearrangement in 43/92 (47%) biopsies, BCL2-rearrangement in 7/86 (8%), and BCL6-rearrangement in 11/86 (13%) (Figure 5.1A-B). The frequency of MYC-rearrangement in HGBCL-NOS (47%) was significantly higher than in DLBCL-NOS (6%, P < 0.0001) (Figure 5.1B). In contrast, the frequency of BCL2- and BCL6-rearrangement was lower in HGBCL-NOS compared to DLBCL-NOS. Routine IHC stains were available for the majority of biopsies included in the study (Table 5.1). MYC protein expression was observed in 34/54 (63%) biopsies, and 59/86 (57%) were positive for BCL2 protein expression. Only 1/49 biopsies with available data were positive for TDT expression. EBV positivity was observed in 1/49 biopsies with available EBER ISH. The median Ki67 proliferation index was 90%. By the Hans algorithm, 68/83 (82%) biopsies had a GCB immunophenotype. By digital gene expression profiling, the distribution of COO subtypes was 59% GCB, 25% ABC, and 16% Unclassified (Table 5.1). Using the refined COO algorithm, 26/41 139 (63%) GCB and 5/11 (45%) Unclassified biopsies were reassigned to the DZsig+ subgroup (Figure 5.1C), totaling 45% of all biopsies, significantly higher than the frequency of DZsig+ tumors observed in DLBCL-NOS (7%; P < 0.0001). 5.3.2 The genetic landscape of HGBCL-NOS Using WGS or capture sequencing data available for 66 biopsies, a “cryptic” MYC-rearrangement was detected in 4/35 (11%) tumors that were negative for MYC break-apart FISH, and a BCL2-rearrangement was detected in 3/57 (5%) BCL2 FISH-negative biopsies (Figure 5.1A). Accounting for these “cryptic” rearrangements would render a diagnosis of HGBCL-DH-BCL2 in 6% (4/66) of the sequenced tumors. These biopsies were not excluded from the study, as identifying rearrangements that are cryptic to FISH is not currently feasible in routine clinical practice. In addition to the four cryptic MYC-rearrangements, a MYC-rearrangement partner was identified by sequencing in 87% (23/27) of tumors that were MYC FISH-positive. Among identified MYC-rearrangements, 80% (25/31) involved an IG locus (22 IGH, 2 IGL, 1 IGK) (Figure 5.2 A). Half of IGH::MYC rearrangements had a breakpoint in the IGHV or Eµ region, while the remaining half were in a downstream switch acceptor region (Figure 5.2B). The breakpoint architecture observed in HGBCL-NOS is similar to that of BL and sole MYC-rearranged DLBCL-NOS. In contrast, MYC-rearrangements in HGBCL-DH-BCL2 more frequently involve non-IG partners, and for those with IGH::MYC rearrangements, the breakpoint is more commonly in a downstream switch acceptor region.71,115 CNAs were identified from WGS or low-pass WGS available for 58 biopsies. Analysis of CNAs using GISTIC2 identified 26 recurrent focal or arm-level CNAs. Included among focal CNAs were amplifications of 2p16.1 (REL) and 13q31.3 (MIR17HG), and deletions of 1p36.32 (TNFRSF14), 6q21 (PRDM1), and 13q14.2 (RB1) (Figure 5.2C). The most frequent arm-level 140 CNA was amplification of 18q, which includes BCL2. Notably, dysregulation of BCL2 expression by amplification is also commonly observed in ABC-DLBCL.189 Mutations were identified from WGS or WES sequencing available for 61 biopsies. In total, 24 genes were found to be significantly mutated among HGBCL-NOS biopsies. All significantly mutated genes that were present in at least 10% of samples have been previously reported to be recurrently mutated in DLBCL-NOS and/or BL.71 The most frequently mutated genes across HGBCL-NOS tumors were KMT2D (39%), TP53 (38%), IGLL5 (30%), MYC (26%), CCND3 (25%), and CREBBP (20%), with all other genes mutated in less than 20% of biopsies (Figure 5.2D). Together, these findings highlight the molecular heterogeneity of HGBCL-NOS, which spans features characteristic of both DLBCL and BL. Notably, subclassifying HGBCL-NOS into blastoid and intermediate morphologies did not clarify the underlying molecular heterogeneity. Tumors with intermediate and blastoid morphology showed no significant differences in clinical or molecular characteristics (Figure 5.3A), including similar distributions of immunophenotypic markers, FISH results, and gene expression subtypes. They also harbored similar frequencies of recurrent gene mutations (Figure 5.3B) and displayed comparable copy number profiles (Figure 5.3C). Consistently, principal component analysis (PCA) revealed no clustering based on morphological subtype (Figure 5.3D). To determine if HGBCL-NOS shares similarities with related aggressive B-cell lymphoma entities, comparisons of gene mutational frequencies were made to DLBCL-NOS and BL (Figure 5.4A). Interestingly, KMT2D was the most frequently mutated gene among both HGBCL-NOS (39%) and DLBCL-NOS (30%) tumors, observed at similar frequencies in both groups, while being significantly less common in BL (11%; Q < 0.01) (Figure 5.4B). Although comparisons between BL and HGBCL-NOS were limited by sample size, seven genes were more frequently mutated in HGBCL-NOS compared to BL (Q ≤ 0.1), with these mutations almost entirely absent in BL but occurring at similar frequencies in DLBCL-NOS and HGBCL-NOS (Figure 5.4B). 141 Despite more power in comparisons with the DLBCL-NOS cohort, only three genes were mutated at significantly higher frequency in DLBCL-NOS than in HGBCL-NOS (Figure 5.4C). In comparison, 24 genes were significantly more frequently mutated in DLBCL-NOS than in BL (Figure 5.4D). These findings indicate that HGBCL-NOS has a complex genomic landscape overlapping with DLBCL-NOS, suggesting that its heterogeneity, analogous to that of DLBCL-NOS, may be resolved by identifying molecular substructure. 5.3.3 Molecular classification of HGBCL-NOS Genetic classification has been an approach to resolve the heterogeneity of DLBCL-NOS.92,93,95,96 Given the similarities between HGBCL-NOS and DLBCL-NOS, we sought to determine if the DLBCL-derived LymphGen genetic classification system96 could also identify molecular substructure within HGBCL-NOS. Interestingly, all LymphGen subtypes other than the rare N1 group were represented among HGBCL-NOS tumors (Figure 5.1B), with the most frequent subtypes being EZB (15%) and MCD (10%). Consistent with MCD being the second most common LymphGen subgroup, mutations in MYD88 and CD79B were each observed in 15% of biopsies, with 6/9 MYD88-mutated tumors harboring an L265P hotspot mutation (Figure 5.2D). Although a subset of HGBCL-NOS tumors could be assigned to a LymphGen subgroup, the majority of tumors (66%) remained unclassified (Other) – a significantly higher proportion than in DLBCL-NOS (34%; P < 0.00001) (Figure 5.1B). Notably, 89% of BL could also not be assigned to a LymphGen subgroup, consistent with BL having molecular features that are distinct from DLBCL-NOS. Of interest, the four most frequently mutated genes in BL (MYC, TP53, CCND3, ID3) also represented mutational features that distinguished HGBCL-NOS from DLBCL-NOS, with all four genes significantly more frequently mutated in HGBCL-NOS than DLBCL-NOS (Figure 5.4C). However, their frequency did not reach the level seen in BL, with all but TP53 significantly less frequently mutated in HGBCL-NOS than in BL (Figure 5.4B). These findings are 142 consistent with HGBCL-NOS comprising multiple tumor groups, with a subset aligning to defined DLBCL-NOS genetic subtypes, and a further subset sharing features with BL. The latter subset may either resemble BL at a genetic level or represent an intermediate group, such as in HGBCL-DH-BCL2, where BL-associated mutations are common among an EZB DLBCL background.96,190 Although originally described in HGBCL-DH-BCL2, DZsig expression extends to BL and a subset of GCB DLBCL-NOS.40 BLs are universally DZsig+ and have a GCB COO, with other defining features including MYC-rearrangement and a lack of high BCL2 expression. In contrast, only ~15% of GCB DLBCL-NOS are DZsig+, and two-thirds of such cases harbor a BCL2-rearrangement. Exploring gene expression of HGBCL-NOS using PCA, the top two PCAs clustered samples by refined COO subgroups (Figure 5.2E), highlighting the utility of refined COO as a framework for organizing the observed genomic heterogeneity. As such, given the high frequency of DZsig expression and MYC-rearrangement in HGBCL-NOS, we leveraged the refined COO framework to explore if any HGBCL-NOS tumors bear molecular resemblance to BL. Among GCB tumors that harbored a MYC-rearrangement, DZsig expression was observed in 19/20 (95%) HGBCL-NOS, compared to 12/19 (63%) DLBCL-NOS (P = 0.02) (Figure 5.5A-B). In total, 19/26 (73%) of DZsig+ GCB HGBCL-NOS tumors harbored a MYC-rearrangement, compared to only 12/48 (25%) of GCB DZsig+ DLBCL-NOS (P = 0.0001) (Figure 5.5B). Using a previously described random forest classifier, GCB HGBCL-NOS tumors were assigned into BL-like or DLBCL-like genetic subgroups based on their mutational patterns.20 Among the 15 DZsig+ GCB tumors with available sequencing, 53% were assigned to the BL-like subgroup, compared to 1/12 (8%) DZsig- GCB tumors (P = 0.02; Figure 5.5C). Within the group of DZsig+ tumors classified as BL-like, 6/8 harbored an IG::MYC rearrangement, and all but one tumor had BCL2 mRNA expression below the cohort median. Similar to BL, all the BL-like tumors were unclassified by LymphGen. In contrast, among the DZsig+ GCB tumors assigned to the 143 DLBCL-like subgroup, 6/7 displayed high levels of BCL2 mRNA expression, with all six tumors correspondingly positive for BCL2 protein expression by IHC (Figure 5.5C-D). Five of these tumors harbored BCL2-rearrangements, two of which were cryptic to FISH. By LymphGen classification, 5/7 DLBCL-like tumors were classified as EZB, one as ST2, and one as Other (Figure 5.5D). Of note, only 3/12 (25%) GCB DZsig- tumors were classified into a LymphGen subgroup. Among ABC HGBCL-NOS tumors, 4/13 (31%) were MCD, while the remainder were unclassified by LymphGen (Figure 5.6A). Interestingly, DZsig expression was observed in 5/17 (29%) ABC HGBCL-NOS tumors, which was rarely observed in ABC DLBCL-NOS tumors (1/267; P < 0.00001) (Figure 5.6B). DZsig expression was only observed among non-MCD ABC tumors, and collectively, 69% of ABC tumors with available digital gene expression and sequencing data were either DZsig+ or MCD (Figure 5.6A). Although outcome data was limited in this cohort, the ABC subgroup displayed the worst outcomes, with a 2-year overall survival (OS) of 21% (Figure 5.7A). In contrast to DLBCL-NOS40, OS was similar in GCB (2y-OS 61%) and DZsig+ (2y-OS 69%) HGBCL-NOS. However, despite not having DZsig information at the time of treatment, all GCB tumors received an R-CHOP-like regimen, while 62% of DZsig+ tumors were treated with a chemotherapy dose-intensified regimen (Figure 5.7B). In aggregate, these results highlight that HGBCL-NOS represents a heterogeneous collection of tumors, and although a subset of tumors have molecular features that align with existing taxonomies, a further subset cannot be confidently classified. 5.3.4 Centralized pathology review Recognizing the challenges of identifying high-grade morphology, all HGBCL-NOS tumors underwent CPR by the LLMPP panel of hematopathologists. Upon CPR, only 42% of biopsies were confirmed as HGBCL-NOS, and 45% were reclassified as DLBCL-NOS (Figure 5.8A). The 144 remaining biopsies were reclassified as Burkitt-like lymphoma with 11q aberration (BL11q; n=1), B-cell acute lymphoblastic leukemia/lymphoma (B-ALL; n=1), or BL (n=5), and an additional five biopsies were deemed insufficient for definitive diagnosis. The proportion of tumors confirmed as HGBCL-NOS was similar between those submitted as intermediate (20/51; 39%) and blastoid (16/31; 52%) (P = 0.36; Figure 5.8B). There was no significant difference in the clinical or molecular features between tumors that were either confirmed as HGBCL-NOS or reclassified as DLBCL-NOS (Figure 5.8C). Furthermore, there was no significant difference in the frequency of any recurrently mutated genes (Figure 5.8D) or CNA profiles (Figure 5.8E). Consistently, PCA analysis of RNAseq data revealed substantial overlap between CPR diagnostic groups (HGBCL-NOS and DLBCL-NOS) (Figure 5.8F). 5.4 Discussion Our characterization of a sizeable cohort of HGBCL-NOS tumors gathered across the LLMPP consortium, incorporating centralized pathology review and comprehensive molecular profiling, aimed to define common and distinct biological features of this disease. Although the frequency of MYC-rearrangement and DZsig-positivity, both observed in approximately half of tumors, was distinct from both BL and DLBCL-NOS, the study revealed HGCBL-NOS to be a highly heterogeneous disease. Centralized review highlighted the challenge of using morphology as a defining feature, with over half of HGBCL-NOS tumors reassigned to other categories, predominantly DLBCL-NOS. However, this reclassification did not reduce the molecular heterogeneity observed within HGBCL-NOS, with no differences in molecular features between confirmed and reclassified tumors. HGBCL-DH-BCL2 encompasses tumors with a spectrum of morphologies.6,7 In an analogous fashion, given the rarity of HGBCL-NOS and the absence of a unified set of molecular features, one way of considering this category is as comprising tumors that represent 145 morphological extremes of established categories. At the gene expression level, the refined COO groupings encapsulated the majority of variation seen across HGBCL-NOS, marking this system as a useful method for parsing tumors. Using gene expression profiling to assign refined COO subtypes, 45% were DZsig+, 22% GCB, and 25% ABC, with 9% being unclassified. Within the DZsig+ group, most of the tumors had molecular features strongly aligned with either BL or HGBCL-DH-BCL2, a minority of which harbored cryptic rearrangements that would have led to definitive membership. These results are consistent with those seen in pediatric MYC-rearranged lymphoma, where most were DZsig+ and had mutational profiles consistent with BL, irrespective of high-grade or DLBCL morphology.191 Further, approximately one-third of ABC HGBCL-NOS tumors were assigned to the MCD genetic subtype. However, many tumors cannot be resolved using this approach, representing likely rare biology not observed in established categories. One such example is the high proportion of ABC HGBCL-NOS expressing DZsig, a phenotype rarely seen in DLBCL-NOS, which requires further exploration. Our study was not designed or powered to determine the outcomes of HGBCL-NOS. The absence of a standard treatment and the inclusion of tumors from across the LLMPP consortium led to variability in treatment regimens. Nonetheless, consistent with Zayac et al178, the poorest outcomes were observed among ABC tumors. Although treatment escalation, as used in BL and HGBCL-DH-BCL2, may be appropriate for DZsig+ HGBCL-NOS, agents with efficacy in ABC DLBCL-NOS, such as polatuzumab vedotin and BTK inhibitors,101,192 may be the route to improving outcomes in ABC HGBCL-NOS. The challenges associated with morphological classification and the rarity of these tumors supports the inclusion of these patients in DLBCL-NOS trials, particularly those incorporating biomarker-guided treatment arms and/or retrospective molecular characterization.178 In conclusion, HGBCL-NOS represents a heterogeneous collection of aggressive B-cell lymphomas. Beyond detecting recurrent rearrangements to exclude HGBCL-DH-BCL2, more 146 extensive molecular profiling of these high-grade morphology tumors is encouraged, as the majority can be aligned to established pathology categories and/or genetic groups using this information. Finally, further work is needed to understand the origins of the morphological variance, along with the biology of the remaining tumors that cannot currently be molecularly realigned. 147 Table 5.1. Clinical and pathological characteristics of tumors submitted as HGBCL-NOS by local pathology review. Intermediate Blastoid Unspecified Total cohort morphology morphology morphology Characteristic n = 92 n = 51 n = 31 n = 10 P* Age, median (range) 66.5 (19 – 90) 66 (30 – 90) 66 (22 – 89) 73.5 (19 – 84) 0.48 Age > 60, n (%) 58 (63%) 33 (65%) 17 (55%) 8 (80%) 0.49 Male sex, n (%) 56 (61%) 32 (63%) 21 (68%) 3 (30%) 0.81 Ann Arbor stage, n (%) 0.72 I/II 10 (20%) 7 (25%) 3 (16%) 0 (0%) III/IV 40 (80%) 21 (75%) 16 (84%) 3 (100%) Missing 42 23 12 7 LDH > ULN, n (%) 0.19 No 16 (31%) 11 (38%) 3 (17%) 2 (50%) Yes 35 (69%) 18 (62%) 15 (83%) 2 (50%) Missing 41 22 13 6 Extranodal sites, n (%) 0.79 0-1 33 (55%) 21 (57%) 12 (52%) - >1 27 (45%) 16 (43%) 11 (48%) - Missing 32 14 8 10 ECOG PS, n (%) 0.54 0-1 26 (55%) 16 (62%) 9 (50%) 1 (33%) 2-4 21 (45%) 10 (38%) 9 (50%) 2 (67%) Missing 45 25 13 7 IPI, n (%) 0.32 Low (0 – 2) 12 (27%) 9 (33%) 3 (18%) - High (3 – 5) 33 (73%) 18 (67%) 14 (82%) 1 (100%) Missing 47 24 14 9 Transformed, n (%) 0.29 No 53 (91%) 29 (97%) 18 (86%) 6 (86%) Yes 5 (9%) 1 (3%) 3 (14%) 1 (14%) Unknown 34 21 10 3 Relapse biopsy, n (%) 0.38 No 53 (90%) 26 (84%) 20 (95%) 7 (100%) Yes 6 (10%) 5 (16%) 1 (5%) - Unknown 33 20 10 3 Rearrangement (FISH) MYC 43/92 (47%) 22/51 (43%) 19/31 (61%) 2/10 (20%) 0.17 BCL2 7/86 (8%) 4/48 (8%) 1/28 (4%) 2/10 (20%) 0.65 BCL6 11/86 (13%) 8/48 (17%) 1/28 (4%) 2/10 (20%) 0.14 Cryptic HGBCL-DH-BCL2 4/66 (6%) 3/39 (8%) 1/19 (5%) 0/8 (0%) 1 Immunohistochemistry CD10 65/88 (74%) 37/48 (77%) 24/31 (77%) 4/9 (44%) 1 BCL6 55/71 (77%) 33/41 (80%) 14/21 (67%) 8/9 (89%) 0.35 MUM1 25/53 (48%) 13/30 (43%) 8/14 (57%) 4/9 (44%) 0.52 MYC 34/54 (63%) 20/34 (59%) 10/14 (71%) 4/6 (67%) 0.52 BCL2 49/86 (57%) 25/50 (50%) 16/27 (59%) 8/9 (89%) 0.48 TDT 1/49 (2%) 0/25 (0%) 1/15 (7%) 0/9 (0%) 0.38 Ki67, median (range) 90 (40 – 100) 90 (40 – 100) 100 (70 – 100) 90 (50 – 100) 0.35 EBER ISH 1/49 (2%) 0/33 (0%) 1/13 (8%) 0/3 (0%) 0.28 Hans COO, n (%) 1 GCB 68 (82%) 39 (87%) 25 (86%) 4 (44%) Non-GCB 15 (18%) 6 (13%) 4 (14%) 5 (56%) Missing 9 6 2 1 148 COO, n (%) 0.75 ABC 17 (25%) 7 (18%) 5 (25%) 5 (50%) Unclassified 11 (16%) 7 (18%) 4 (20%) - GCB 41 (59%) 25 (64%) 11 (55%) 5 (50%) Missing 23 12 11 - Refined COO, n (%) 0.69 ABC 17 (25%) 7 (18%) 5 (25%) 5 (50%) Unclassified 6 (9%) 4 (10%) 2 (10%) - GCB 15 (22%) 11 (28%) 3 (15%) 1 (10%) DZsig+ 31 (45%) 17 (44%) 10 (50%) 4 (40%) Missing 23 12 11 - LymphGen, n (%) 0.69 EZB 9 (15%) 4 (12%) 3 (15%) 2 (29%) ST2 3 (5%) 1 (3%) 2 (10%) - BN2 2 (3%) 2 (6%) - - MCD 6 (10%) 3 (9%) 3 (15%) - Composite 40 (66%) 23 (68%) 12 (60%) 5 (71%) Other 1 (2%) 1 (3%) - - Missing 31 17 11 3 *Comparison between intermediate and blastoid morphology groups LDH: lactate dehydrogenase; ULN: upper limit of normal; ECOG PS: Eastern Cooperative Oncology Group Performance Status; IPI: International Prognostic Index; FISH: fluorescence in situ hybridization; ISH: in situ hybridization; COO: cell-of-origin; GCB: germinal center B-cell-like; ABC: activated B-cell-like. 149 Table 5.2. Comparison of clinical characteristics in HGBCL-NOS and DLBCL-NOS. HGBCL-NOS DLBCL-NOS Characteristic n = 92 n = 781 P Age, median (range) 66.5 (19, 90) 69 (20, 96) 0.04 Age > 60, n (%) 58 (63%) 547 (70%) 0.19 Male sex, n (%) 56 (61%) 438 (56%) 0.44 Ann Arbor stage, n (%) I/II 10 (20%) 323 (44%) < 0.001 III/IV 40 (80%) 408 (56%) Missing 42 50 LDH > ULN, n (%) No 16 (31%) 351 (55%) < 0.001 Yes 35 (69%) 287 (45%) Missing 41 143 Extranodal sites, n (%) 0-1 33 (55%) 573 (78%) < 0.001 >1 27 (45%) 158 (22%) Missing 32 50 ECOG PS, n (%) 0-1 26 (55%) 428 (60%) 0.54 2-4 21 (45%) 282 (40%) Missing 45 71 IPI, n (%) Low (0 – 2) 12 (27%) 383 (56%) < 0.001 High (3 – 5) 33 (73%) 301 (44%) Missing 47 97 Rearrangement (FISH) MYC 43/92 (47%) 45/780 (6%) < 0.0001 BCL2 7/86 (8%) 139/642 (22%) < 0.01 BCL6 11/86 (13%) 162/591 (27%) < 0.01 Immunohistochemistry MYC 34/54 (63%) 181/579 (31%) < 0.0001 BCL2 49/86 (57%) 513/727 (71%) 0.01 CD10 65/88 (74%) 264/659 (40%) < 0.0001 BCL6 55/71 (77%) 490/630 (78%) 1 MUM1 25/53 (47%) 277/596 (46%) 1 Hans COO, n (%) 68/83 (82%) 351/612 (57%) < 0.0001 GCB 68 (82%) 351 (57%) Non-GCB 15 (18%) 261 (43%) Missing 6 169 COO, n (%) ABC 17 (25%) 267 (38%) 0.07 Unclassified 11 (16%) 81 (12%) GCB 41 (59%) 351 (50%) Missing 23 82 Refined COO, n (%) ABC 17 (25%) 267 (38%) < 0.001 Unclassified 6 (9%) 79 (11%) GCB 15 (22%) 303 (43%) DZsig+ 31 (45%) 50 (7%) Missing 23 82 150 LymphGen, n (%) EZB 9 (15%) 58 (17%) < 0.001 ST2 3 (5%) 48 (14%) BN2 2 (3%) 29 (9%) N1 0 (0%) 3 (1%) MCD 6 (10%) 48 (14%) Composite 1 (2%) 40 (12%) Other 40 (66%) 115 (34%) Missing 31 441 LDH: lactate dehydrogenase; ULN: upper limit of normal; ECOG PS: Eastern Cooperative Oncology Group Performance Status; IPI: International Prognostic Index; FISH: fluorescence in situ hybridization; ISH: in situ hybridization; COO: cell-of-origin; GCB: germinal center B-cell-like; ABC: activated B-cell-like. 151 Table 5.3. Significant CNAs identified by GISTIC2 in HGBCL-NOS. Region Broad or focal CNA type Frequency Q value 1q21.2 focal amplification 0.26 < 0.01 1q25.1 focal amplification 0.36 < 0.01 2p16.1 focal amplification 0.14 < 0.01 13q31.3 focal amplification 0.29 < 0.001 18q23 focal amplification 0.43 0.07 1p36.32 focal deletion 0.10 < 0.01 1p36.11 focal deletion 0.12 < 0.01 2q21.1 focal deletion 0.07 0.09 6q21 focal deletion 0.33 < 0.01 13q14.2 focal deletion 0.12 < 0.01 14q32.33 focal deletion 0.17 < 0.01 18q broad amplification 0.34 < 0.0001 7p broad amplification 0.34 < 0.0001 7q broad amplification 0.34 < 0.0001 1q broad amplification 0.29 < 0.0001 3q broad amplification 0.28 < 0.0001 3p broad amplification 0.26 < 0.0001 18p broad amplification 0.22 < 0.001 6p broad amplification 0.24 < 0.001 1p broad amplification 0.22 < 0.01 12p broad amplification 0.19 0.02 12q broad amplification 0.19 0.04 8q broad amplification 0.17 0.06 13q broad amplification 0.16 0.07 14q broad amplification 0.16 0.09 15q broad amplification 0.16 0.09 152 Table 5.4. Clinical characteristics of tumors by central pathology review diagnosis. HGBCL-NOS DLBCL-NOS Characteristic n = 39 n = 41 P Age, median (range) 69 (22, 90) 67 (19, 89) 1 Age > 60, n (%) 25 (64%) 27 (66%) 1 Male sex, n (%) 24 (62%) 25 (61%) 1 Ann Arbor stage, n (%) I/II 2 (10%) 7 (29%) 0.14 III/IV 19 (90%) 17 (71%) Missing 18 17 LDH > ULN, n (%) No 6 (32%) 8 (30%) 1 Yes 13 (68%) 19 (70%) Missing 20 14 Extranodal sites, n (%) 0-1 14 (54%) 13 (50%) 1 >1 12 (46%) 13 (50%) Missing 13 15 ECOG PS, n (%) 0-1 11 (58%) 13 (57%) 1 2-4 8 (42%) 10 (43%) Missing 20 18 IPI, n (%) Low (0 – 2) 5 (26%) 5 (24%) 1 High (3 – 5) 14 (74%) 16 (76%) Missing 20 20 Transformed, n (%) No 21 (84%) 27 (96%) 0.18 Yes 4 (16%) 1 (4%) Unknown 14 13 Relapse biopsy, n (%) No 24 (92%) 24 (86%) 0.67 Yes 2 (8%) 4 (14%) Unknown 13 13 Submitting cytology, n (%) Blastoid 16 (41%) 10 (24%) 0.20 Intermediate 20 (51%) 24 (59%) Unspecified 3 (8%) 7 (17%) Rearrangement (FISH) MYC 20/39 (51%) 17/41 (41%) 0.50 BCL2 2/37 (5%) 4/38 (11%) 0.67 BCL6 5/38 (13%) 5/37 (14%) 1 Cryptic HGBCL-DH-BCL2 1/26 (4%) 1/32 (3%) 1 Immunohistochemistry TDT 0/18 (0%) 0/26 (0%) 1 MYC 13/22 (59%) 17/27 (63%) 1 BCL2 25/37 (68%) 21/38 (55%) 0.35 CD10 24/38 (63%) 30/39 (77%) 0.22 BCL6 20/29 (69%) 31/37 (84%) 0.24 MUM1 11/23 (48%) 13/25 (52%) 1 Ki67, median (range) 90 (50, 100) 90 (40, 100) 0.87 EBER ISH 0/21 (0%) 0/21 (0%) 1 Hans COO, n (%) 0.78 GCB 27 (77%) 30 (81%) Non-GCB 8 (23%) 7 (19%) Missing 4 4 153 COO, n (%) 0.52 ABC 10 (34%) 7 (21%) Unclassified 5 (17%) 6 (18%) GCB 14 (48%) 21 (62%) Missing 10 7 Refined COO, n (%) ABC 10 (34%) 7 (21%) 0.65 Unclassified 3 (10%) 3 (9%) GCB 5 (17%) 8 (24%) DZsig+ 11 (38%) 16 (47%) Missing 10 7 LymphGen, n (%) EZB 3 (11%) 5 (16%) 0.89 ST2 2 (7%) 1 (3%) BN2 1 (4%) 1 (3%) MCD 2 (7%) 4 (13%) Composite 1 (4%) 0 (0%) Other 19 (68%) 20 (65%) Missing 11 10 LDH: lactate dehydrogenase; ULN: upper limit of normal; ECOG PS: Eastern Cooperative Oncology Group Performance Status; IPI: International Prognostic Index; FISH: fluorescence in situ hybridization; ISH: in situ hybridization; COO: cell-of-origin; GCB: germinal center B-cell-like; ABC: activated B-cell-like. 154 Figure 5.1. Clinical and molecular features of HGBCL-NOS. (A) Heatmap of molecular features of the 92 HGBCL-NOS tumors included in the study, grouped by morphological subtype. MYC, BCL2, and BCL6-rearrangement status were determined by FISH. Tumors with a negative (or missing) MYC or BCL2 FISH result where a rearrangement was identified by sequencing are indicated with a red dot. (B) Comparison of clinical and molecular features in HGBCL-NOS to DLBCL-NOS. P values were calculated by Fisher’s exact test for comparisons of clinical, FISH, and IHC data, and by χ2 test for Refined COO and LymphGen comparisons. (C) Distribution of COO and refined COO subtypes in HGBCL-NOS. ∗P < 0.05; ∗∗P < 0.01; ∗∗∗P < 0.001; ∗∗∗∗P < 0.0001. 18 1214 1417347345456980636113847825763 47787445925220310251566Clinical FISH IHC Refined COO LymphGen0255075100Percent44402245567056272265771314678407 113843Male SexAge > 60Stage III−IVLDH Ratio > 1Extranodal Sites > 1ECOG PS > 1IPI High (3−5)MYCBCL2BCL6 MYCBCL2CD10BCL6MUM1Hans GCBDZsig+GCBUNCLASSABCEZBST2BN2MCD N1COMPOther255075100DLBCL-NOSHGBCL-NOS**************************** ********* ***Intermediate Blastoid MorphologyMYC rearrangementBCL2 rearrangementBCL6 rearrangementRefined COOLymphGenUnspecifiedEZB ST2 BN2LymphGenMCD COMP OtherRefined COODZsig+ GCB Unclass ABC NAPOS Cryptic NARearrangementNEGAABCUNCGCBABCUNCGCBDZsig+COO Refined COOCB 155 −20−1001020−20−10 0 10 20 30PC1: 13% variancePC2: 10% varianceRefined COOKMT2DTP53IGLL5MYCCCND3CREBBPGNA13ID3P2RY8ARID1ACD79BMYD88OSBPL10HLA−BTBL1XR1IRF8BCL2BCL6DDX3XEBF1FOXO1H1-5H1-3KLF2PIM1TET2MorphologyRefined COOLymphGen39%38%30%26%25%20%18%18%18%15%15%15%15%13%13%11%10%10%10%10%10%10%10%10%10%10%EZBST2BN2LymphGenMCDCOMPOtherDCA B814127.3MB127.5MB127.7MB 127.9MB128.1MB105.6MB105.7MB105.8MBCASC8PVT1MYC2AE4G2G1A1G3GDM0 10 20 30Percent2389142289MB89.4MB188.1MB188.6MB127.3MB127.8MB128.3MB36.9MBBM3.73105.5MB106.5MB22.4MB22.9MBKGI6LCBLPPCASC8PVT1MYCPAX5ZCCHC7CHGIVHGILGIIGHIGKIGLNon−IG0 20 40 60PercentMissense MutationSplice SiteNonstop MutationTranslation Start SiteIn Frame INDELFrame Shift INDELNonsense MutationMulti HitAlterationDZsig+GCBUnclassRefined COOABCNAEIntermediateBlastoidUnspecifiedMorphologyVEmuMDG3G1A1RRG2G4EA2MYD88 L265P1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22-0.50-0.250.000.250.50ChromosomeProportionDZsig+GCBUnclassRefined COOABCNA 156 Figure 5.2. The genetic landscape of HGBCL-NOS. (A) The architecture of MYC-rearrangements and the observed frequency of IGH, IGK, IGL, and non-IG rearrangement partners. The outermost ring shows the chromosome ideogram, followed by a genomic coordinate scale and gene coordinate track. The innermost lines show the linkages between MYC and the corresponding rearrangement partner for each sample. (B) The architecture of IGH::MYC rearrangements and the frequency of breakpoints occurring at the indicated regions of the IGH locus. Eμ and 3' regulatory region (RR) enhancers are included in the gene coordinate track (red). (C) Copy number profile of HGBCL-NOS. The proportion of tumors with a copy number gain (red) or deletion (blue) is plotted across each chromosome. (D) Oncoplot showing the mutation status of the most frequently mutated genes in HGBCL-NOS for each patient’s tumor. Genes included in the plot were mutated in at least 10% of HGBCL-NOS samples and identified as a significantly mutated gene in HGBCL-NOS, DLBCL-NOS, or BL. (E) Principal component analysis of RNA sequencing data. 157 Figure 5.3. Comparison of the genetic landscape of HGBCL-NOS tumors with intermediate or blastoid morphology. (A) Clinical and molecular features in HGBCL-NOS tumors with intermediate or blastoid morphology. Comparisons of clinical, FISH, and IHC data were performed using Fisher’s exact tests, while refined COO and LymphGen classifications were compared by χ² tests. No comparisons were statistically significant. (B) Comparison between the mutational landscape of HGBCL-NOS tumors with intermediate or blastoid morphology. Genes included in the oncoplot were mutated in at least 10% of HGBCL-NOS samples and identified as a significantly mutated gene in HGBCL-NOS, DLBCL-NOS, or BL. Gene mutation frequencies for each group are shown in the bar plots on the right, with odds ratios and 95% confidence intervals for each gene comparison shown in the forest plots. (C) Copy number profiles of HGBCL-NOS tumors with intermediate or blastoid morphology. The proportion of tumors with a copy number gain (red) or deletion (blue) is plotted across each chromosome. (D) Principal component analysis of RNA sequencing data. −20−1001020PC2: 10% variance-0.50-0.250.000.250.501 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22-0.50-0.250.000.250.50ChromosomeBlastoidIntermediateProportionProportionKMT2DTP53MYCIGLL5CCND3GNA13P2RY8CREBBPID3CD79BHLA−BMYD88OSBPL10ARID1ABCL6DDX3XPIM1TBL1XR1TET2LymphGenRefined COO0 10203040Mutated (%)−3 0 3 6ln(Odds Ratio)Intermediate Blastoid38436275656317867438750594380771018284436391268Intermediate0255075100ECOG PS > 1Extranodal Sites > 1LDH Ratio > 1Stage III−IVAge > 60Male SexBCL6 FISHBCL2 FISHMYC::IG partnerMYC FISHHans GCBMUM1 IHCBCL6 IHCCD10 IHCBCL2 IHCMYC IHCABCUNCLASSGCBDZsig+OtherCOMPMCDBN2ST2EZBPercent504883845568448361865971576777102515500010151560Blastoid25 50 75 100Missense MutationSplice SiteNonstop MutationTranslation Start SiteIn Frame INDELFrame Shift INDELNonsense MutationMulti HitAlterationNADZsig+GCBUNCLASSRefined COOABC ST2BN2MCD OtherEZBLymphGenCompA BC DIntermediateBlastoidUnspecifiedMorphology 158 *** *** *** **ID3 MYCCCND3TP53020406080Percent Mutated** * * * * * *KMT2DCD79BMYD88CREBBP IRF8OSBPL10 BCL2*** *** * *DDX3XMYC ID3CCND3HGBCL-NOS > BL HGBCL-NOS < BLB C* * *CARD11SOCS1PIM1HGBCL-NOS > DLBCL-NOSHGBCL-NOS < DLBCL-NOS020406080Percent MutatedKMT2DTP53PIM1MYD88MYCCREBBPBCL2BTG2SOCS1CARD11SGK1CCND3GNA13HLA−BP2RY8TMSB4XDUSP2B2MCD79BBTG1TBL1XR1FOXO1OSBPL10EZH2DDX3XMEF2BARID1AID3TET2EP300KLHL6TNFRSF14HLA−AKLF2ZFP36L1H1−3BCL6IRF8H1−5TCF30 20 40 60DLBCL−NOS HGBCL−NOS BLMutated (%)AMissense MutationSplice SiteNonstop MutationTranslation Start SiteIn Frame INDELFrame Shift INDELNonsense MutationMulti HitAlterationDHGBCL-NOS DLBCL-NOS BLHGBCL-NOS DLBCL-NOS BL HGBCL-NOS DLBCL-NOS BL**** *** *** *** ** ** ** ** ** ** ** ** ** * * * * * * * * * * *020406080MYD88BCL2PIM1BTG1CD79BDUSP2TMSB4XBTG2SOCS1KMT2DCARD11SGK1CREBBPMEF2BOSBPL10NOTCH2HLA−AIRF8PRDM1ZFP36L1EZH2 B2MTNFRSF14CD58Percent Mutated**** **** **** **** **** *** * *MYC ID3DDX3XCCND3TP53ARID1ATCF3GNA13DLBCL-NOS < BLDLBCL-NOS > BL 159 Figure 5.4. Comparison of the mutational landscape of HGBCL-NOS to DLBCL-NOS and EBV-negative BL. (A) Oncoplot showing the mutation status of recurrently mutated genes for 61 HGBCL-NOS, 341 DLBCL-NOS, and 63 BL tumors. HGBCL-NOS and BL tumors were sequenced by WGS or WES, and DLBCL-NOS tumors were sequenced using a targeted capture panel covering 125 genes. Genes included in the plot represent the intersection between 1) genes covered by the targeted capture panel and 2) genes that were identified as significantly mutated in HGBCL-NOS, DLBCL-NOS, or BL and mutated at a frequency of ≥10% in at least one of these groups. Nine genes that fulfilled the second criterion were not covered by the capture panel (EBF1, FBXO11, IGLL5, KIAA1671, PCBP1, PHF6, RFX7, SMARCA4, TFAP4). Genes are ordered by their mutational frequency across all groups, and gene mutation frequencies for each group are shown in the bar plots on the right. (B) Comparison between HGBCL-NOS and BL, with DLBCL shown in faded color for reference. Genes with significantly different mutation frequencies between HGBCL-NOS and BL are displayed. (C) Comparison between HGBCL-NOS and DLBCL, with BL shown in faded color for reference. (D) Comparison between HGBCL-NOS and DLBCL, with HGBCL-NOS shown in faded color for reference. Genes with significantly different mutation frequencies between HGBCL-NOS and DLBCL are displayed. P values were calculated using Fisher’s exact test and adjusted for multiple comparisons using the Benjamini-Hochberg method. ∗Q < 0.1; ∗∗Q < 0.01; ∗∗∗Q < 0.001; ∗∗∗∗Q < 0.0001. 160 Figure 5.5. Molecular features of GCB tumors. All analyses are restricted to GCB tumors. For refined COO, the GCB group includes tumors that were negative or indeterminate for DZsig expression. MYC- and BCL2-rearrangement status was determined by FISH, and MYC-rearrangement partners were identified by sequencing. (A-B) The relationship between DZsig expression and MYC-rearrangement status in (A) HGBCL-NOS and (B) DLBCL-NOS. Each ribbon in the alluvial plot represents a single biopsy, linking its MYC-rearrangement and DZsig expression status (left). Bar plots show the percentage of DZsig+ biopsies stratified by MYC-rearrangement status (top right) and the frequency of MYC-rearrangement stratified by DZsig expression (bottom right). (C) HGBCL-NOS biopsies were assigned into BL-like and DLBCL-like groups using a random forest classifier trained on mutational data to distinguish BL from DLBCL-NOS. The bar plot shows the percentage of tumors classified into DLBCL-like and BL-like groups stratified by DZsig expression status. Box plots display MYC and BCL2 mRNA expression levels in DZsig+ HGBCL-NOS tumors classified as DLBCL-like or BL-like. P values for comparisons between groups were calculated using a t-test. (D) Molecular features of BL-like and DLBCL-like DZsig+ HGBCL-NOS tumors. Each column of the heatmap corresponds to a single biopsy. MYC and BCL2 mRNA expression levels are shown as a Z-score relative to all HGBCL-NOS biopsies with available digital gene expression profiling data. 0% 25% 50% 75% 100%0% 25% 50% 75% 100%GCBDZsig+MYC-MYC+LymphGenMYC rearrangementMYC partnerMYC mRNABCL2 rearrangmentBCL2 mRNACD10 IHCBCL6 IHCMUM1 IHCBCL2 IHCMorphologymRNA (Zscore)−2 −1 0 1 2IntermediateBlastoidUnspecifiedMorphologyPOSCrypticNEGRearrangementPOSNEGNAIHCIGnon-IGNo breakMYC PartnerNADLBCL-likeBL-likeEZBST2OtherLymphGenAC DBDLBCL-NOSMYC−MYC+0% 25% 50% 75% 100%Refined COO by MYC FISH statusGCBDZsig+0% 25% 50% 75% 100%MYC FISH status by refined COOGCBDZsig+MYC−MYC+RefinedCOOMYCFISHHGBCL-NOSMYC−MYC+Refined COO by MYC FISH statusGCBDZsig+MYC FISH status by refined COORefinedCOOMYCFISH111870255075100GCB DZsig+Percent8101214log2 mRNA expressionMYC BCL20.26 < 0.001DLBCL-likeBL-likeBL genetic subgroupPOSCrypticNEGRearrangement 161 Figure 5.6. Molecular features of ABC HGBCL-NOS. (A) LymphGen subtype and DZsig expression status for ABC HGBCL-NOS tumor with available sequencing data. (B) The percentage of HGBCL-NOS (left) and DLBCL-NOS (right) tumors that are positive for DZsig expression within each COO subgroup. 26/415/115/1748/3512/81 1/267GCB UNC ABC GCB UNC ABC0255075100COOPercent DZsig+HGBCL-NOS DLBCL-NOSA BLymphGenMCD OtherDZsigPOS IND NEGLymphGenDZsig38% 31% 31% 162 Figure 5.7. Outcomes in HGBCL-NOS by refined COO subgroups. (A) Overall survival (OS) by refined COO subgroups. OS was determined from the date of diagnosis to the date of last follow-up or death from any cause. (B) Treatment regimen stratified by refined COO subgroups. The intensive treatment category refers to high-intensity multiagent combinations such as CODOX-M/IVAC variants and other similarly aggressive schedules. auto-HCT: autologous hematopoietic stem cell transplantation. A B0.000.250.500.751.00OS (proportion)2y OS (%)100UNCLASS69DZsig+61GCB21ABC2 1 1 0 0 0 0 0 0 013 9 8 8 7 4 2 2 2 29 5 4 3 2 2 2 2 2 19 4 1 1 1 0 0 0 0 00 1 2 3 4 5 6 7 8 9Time (Years)|| | || | | | ||||| ||| |100% 100%38%23%23%15%67%22%11%n=2 n=13n=9 n=90255075100UNCLASS GCB DZsig+ ABCRefined COOPercent (%)TreatmentIntensive + auto−HCTIntensiveEPOCH−RR−CHOP−like 163 BlastoidUnspIntInsufficient B-ALLBL11qBLDLBCL-NOSHGBCL−NOSSubmitting CPR-0.50-0.250.000.250.50Proportion1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22-0.50-0.250.000.250.50ChromosomeProportionHGBCL-NOSDLBCL-NOS42466890646213571517768594869631017343844771168Confirmed asHGBCL-NOS0255075100ECOG PS > 1Extranodal Sites > 1LDH Ratio > 1Stage III−IVAge > 60Male SexBCL6 FISHBCL2 FISHMYC IG partnerMYC FISHHans GCBMUM1 IHCBCL6 IHCCD10 IHCBCL2 IHCMYC IHCABCUNCLASSGCBDZsig+OtherCOMPMCDBN2ST2EZB445070716661141075428155635284779242147033131664Reclassified asDLBCL-NOS25 50 75 100PercentKMT2DTP53IGLL5CCND3MYCRPTNCREBBPGNA13ARID1AID3P2RY8CD79BMYD88OSBPL10TBL1XR1HLA−BEBF1IRF8KLF2PAX5DDX3XFBXO11FOXO1H1-5H1-3PIM1POU2F2LymphGenRefined COO0 20 40Mutated (%)−3 0 3 6ln(Odds Ratio)Confirmed as HGBCL−NOS Reclassified as DLBCL-NOSMissense MutationSplice SiteNonstop MutationTranslation Start SiteIn Frame INDELFrame Shift INDELNonsense MutationMulti HitAlterationNADZsig+GCBUNCLASSRefined COOABC ST2BN2MCD OtherEZBLymphGenComp92HGBCL-NOSLLMPP SitesLocal Pathology Review (Submitting)Centralized Pathology Review (CPR)DLBCL-NOSn = 41 45%BLn = 5 5%HGBCL-NOSn = 39 42%BL11qn = 1 1%B-ALLn = 1 1%Insufficientn = 5 5%CPR DiagnosisHGBCL-NOSDLBCL-NOSCPR Diagnosis-20-1001020-20 -10 0 10 20 30PC1: 14% variancePC2: 10% varianceA BC DE F 164 Figure 5.8. Comparison of tumors by centralized pathology review assignment. (A) Biopsies were initially reviewed locally by LLMPP hematopathologists at each site to confirm HGBCL-NOS diagnosis and assign a morphological subtype. All biopsies then underwent centralized pathology review by a panel of LLMPP hematopathologists. Biopsies were either confirmed as HGBCL-NOS, reclassified as another lymphoma entity, or deemed insufficient for diagnosis. (B) Concordance of submitted morphological subtype and centralized pathology review diagnosis. Each ribbon in the alluvial plot represents a single biopsy, linking its morphological subtype assigned by the submitting site to the centralized review diagnosis. (C) Clinical and molecular features in tumors confirmed as HGBCL-NOS or reclassified as DLBCL-NOS. Comparisons of clinical, FISH, and IHC data were performed using Fisher’s exact test, while refined COO and LymphGen classifications were compared by χ² test. No comparisons were statistically significant. (D) Comparison between the mutational landscape of tumors confirmed as HGBCL-NOS or reclassified as DLBCL-NOS. Genes included in the oncoplot were mutated in at least 10% of HGBCL-NOS samples and identified as a significantly mutated gene in HGBCL-NOS, DLBCL-NOS, or BL. Gene mutation frequencies for each group are shown in the bar plots on the right, with odds ratios and 95% confidence intervals for each gene comparison shown in the forest plots. (E) Copy number profiles of tumors confirmed as HGBCL-NOS or reclassified as DLBCL-NOS. The proportion of tumors with a copy number gain (red) or deletion (blue) is plotted across each chromosome. (F) Principal component analysis of RNA sequencing data. 165 Chapter 6: Conclusion The research presented in this dissertation, and the accompanying published manuscripts,184,189,193,194 provides a comprehensive molecular and clinical characterization of HGBCL-DH/TH and HGBCL-NOS, with the overarching aim of advancing a more biologically informed classification system for aggressive B-cell lymphomas. Broadly, the work can be divided into two components: refining the current classification system (Chapters 2-3) and moving beyond it toward a molecularly defined framework (Chapters 4-5). The first component included characterizing the molecular landscape of HGBCL-DH/TH to assess whether it represents a biologically homogeneous entity, evaluating whether its definition should be expanded to include copy number alterations, investigating the utility of MYC IHC as a screening tool for FISH testing, and examining the significance of unbalanced break-apart MYC FISH patterns in diagnosis. The second component focused on exploring the molecular framework of aggressive B-cell lymphomas to support a classification system rooted in underlying biology rather than morphology and limited cytogenetic analysis. This chapter will synthesize these findings and their impact on classification (Section 6.1), discuss the limitations of the study and outline future directions (Section 6.2), and touch on practical considerations for integrating molecular classification into clinical practice (Section 6.3). 6.1 Summary 6.1.1 Impact on Current Aggressive B-Cell Lymphoma Classification Molecular profiling of HGBCL-DH/TH demonstrated that HGBCL-DH-BCL2 represents a biologically uniform subgroup, characterized by recurrent genetic alterations and a distinctive DZ-associated expression profile. By contrast, HGBCL-DH-BCL6 showed marked heterogeneity, challenging the validity of grouping both entities together. These findings support restricting the HGBCL-DH/TH category to the biologically coherent subgroup of HGBCL-DH-BCL2, a change 166 reflected in recent updates to both the ICC and WHO HAEM5.6,7 In these classifications, HGBCL-DH-BCL2 is recognized as a distinct entity, whereas HGBCL-DH-BCL6 is designated a provisional entity in the ICC and reclassified into DLBCL-NOS or HGBCL-NOS in WHO HAEM5. Regarding the diagnostic criteria for HGBCL-DH-BCL2, the presented work supports the decision not to include CNAs in the definition of this group. MYC CNAs were not associated with increased gene or protein expression and thus lacked the biological impact observed with MYC rearrangements. Although BCL2 CNAs were linked to higher expression, this was largely confined to the ABC subtype. Furthermore, tumors classified as “atypical double-hit” were rarely DZsig+, underscoring that CNAs cannot substitute for rearrangements. Both the ICC and WHO HAEM5 now specifically exclude CNAs from the definition of HGBCL-DH-BCL2, citing work from this dissertation in support of this decision. Both classification systems also provide guidance on how tumors should be screened for FISH testing, with the ICC and WHO HAEM5 explicitly recommending against the use of MYC IHC as a triage tool. This reflects evidence that roughly 30% of MYC rearranged biopsies are negative by IHC, with a subset of these demonstrated to be false-negative results due to a common germline SNP (MYC-N11S) that disrupts the binding site of the universally used Y69 antibody. Given that no alternative antibodies are currently suitable for clinical use, MYC IHC cannot form the basis of a reliable screening strategy. Instead, universal FISH testing is advised, ensuring that biologically and clinically meaningful rearrangements are not missed. Both classifications reference the associated manuscript from this work in support of this recommendation, further emphasizing the influence of the presented work on current diagnostic practice. This dissertation further addressed the interpretation of unbalanced MYC break-apart FISH patterns, a source of persistent diagnostic uncertainty, with many laboratories reporting them as equivocal or negative for rearrangement. The findings demonstrate that these patterns 167 typically preserve the MYC coding region and involve recurrent partner loci known to drive constitutive MYC expression. Notably, unbalanced patterns were present in 13% of HGBCL-DH-BCL2 cases, meaning a substantial subset of patients would be misdiagnosed if these patterns were not considered positive. These findings provide clear evidence that unbalanced patterns should be interpreted as positive for rearrangement. While much of this work focused on clarifying the diagnosis of HGBCL-DH-BCL2, it also had implications for the current diagnosis of HGBCL-NOS. Preliminary results from the HGBCL-NOS study, presented at the International Conference on Malignant Lymphomas and referenced in both the ICC and WHO HAEM5, underscored the lack of unifying molecular or clinical features in this group. Reflecting this, both classifications acknowledge the heterogeneity of HGBCL-NOS and further emphasize that this diagnosis should be applied sparingly, only when a more definitive diagnosis cannot be made. Taken together, this work refines key aspects of the current diagnostic framework for aggressive B-cell lymphomas, with immediate clinical relevance as many centers now escalate treatment for HGBCL-DH-BCL2, making accurate diagnosis essential. At the same time, it underscores the limitations of a system rooted in morphology and highlights the value of molecular profiling in defining subgroups with shared clinical and biological features. These insights point beyond incremental refinements to existing categories, laying the groundwork for a more comprehensive, molecularly based framework for classification. 6.1.2 Toward Molecular Classification of Aggressive B-Cell Lymphomas Molecular profiling of HGBCL-DH-BCL2 revealed a highly uniform mutational landscape marked by frequent alterations in regulators of DZ biology, including MYC, FOXO1, and CCND3. In keeping with DZ physiology, these tumors lack canonical and non-canonical NF-κB pathway mutations, show reduced expression of MHC class I and II, and are characterized by a relatively 168 sparse microenvironment.107 Collectively, these findings indicate that HGBCL-DH-BCL2 depends on dysregulation of programs normally active in the DZ, a relationship further supported by gene expression profiling showing consistent alignment with transcriptional profiles of DZ GC B cells. These findings prompted the redefinition of the previously described “double-hit signature” as the dark zone signature (DZsig), reflecting its ability to capture tumors unified by a DZ phenotype rather than by cytogenetic status alone. DZsig identifies nearly all HGBCL-DH-BCL2 cases, but its scope is broader, encompassing BL and a subset of GCB DLBCL-NOS. BL similarly harbors recurrent mutations in DZ-associated genes,70,71 and DZsig overlaps substantially with the BL-derived mBL signature.74,75 Collectively, these observations argue that HGBCL-DH-BCL2, BL, and DZsig+ GCB DLBCL represent a broader family of DZ lymphomas, unified by shared transcriptional programs, recurrent mutations in DZ regulators, and a tumor microenvironment defined by low immune infiltration. In this way, DZ lymphomas emerge as a cohesive molecularly defined umbrella group that transcends conventional morphological and cytogenetic categories. The recognition of DZ lymphomas as a molecularly coherent group also has important clinical implications. Across entities, DZ lymphomas are enriched for advanced-stage disease, bone marrow involvement, and elevated lactate dehydrogenase, and they carry a higher risk of CNS involvement at diagnosis and relapse, particularly in BL.168,195 Patients with DZ lymphomas that present with DLBCL morphology have poor outcomes following standard R-CHOP immunochemotherapy, regardless of HGBCL-DH-BCL2 status. While treatment escalation improves outcomes in BL and HGBCL-DH-BCL2, the optimal approach for the broader spectrum of DZ lymphomas remains uncertain. Despite their clinical diversity, including features unique to BL such as tumor lysis syndrome, their shared molecular dependencies suggest that targeting convergent vulnerabilities may offer a strategy to improve outcomes across the group. In the broader context of lymphoma classification, the recognition of DZ lymphomas represents a continuation of long-standing principles. The REAL system emphasized that linking 169 tumors to their normal physiological counterparts provides insight into biology, clinical behavior, and treatment response. This idea is exemplified by the COO framework, which divides DLBCL-NOS into GCB and ABC subgroups.82 These categories have proven clinically meaningful by connecting tumor biology with disease behavior and patient outcomes, while also identifying distinct pathways that can be targeted therapeutically. Importantly, despite its name, COO does not specify the exact normal cell from which a lymphoma originates but instead describes how malignant cells co-opt developmental programs and pathways active at particular stages of B-cell differentiation to support their survival and growth. Evidence suggests that lymphomagenesis is commonly a prolonged, stepwise process driven by successive genetic and epigenetic alterations that can occur across different stages of B-cell development. This is best illustrated through the multi-hit model of FL, where the initiating BCL2 translocation arises in the bone marrow and creates a population of B cells with altered biology.196,197 These cells can persist for years, repeatedly re-enter GCs, and acquire additional mutations, particularly in chromatin-modifying genes, before ultimately progressing to overt lymphoma. Supporting this model, BCL2-rearranged B cells carrying tumor-confirmed FL mutations have been detected in the peripheral blood many years prior to clinical diagnosis.198 The recognition of cancer precursor cells (CPCs) provides a broader framework for understanding how such early lesions contribute to aggressive lymphomas. CPCs are pre-malignant populations carrying a limited number of founding mutations that constrain the spectrum of subsequent alterations and ultimately shape tumor biology. Analyses of serial biopsies in DLBCL demonstrate that patients with long diagnostic–relapse intervals often share only a few ancestral mutations between tumors, yet both diagnostic and relapsed samples typically fall into the same genetic and transcriptional subtype.153 These data suggest that limited early CPC mutations channel disease evolution toward defined biological endpoints. Moreover, each genetic subgroup of DLBCL tends to share hallmark mutations with a particular indolent 170 lymphoma prone to transformation, underscoring how CPC lineages underpin both indolent and aggressive entities.96 Within this framework, mutational profiling can refine physiological cell counterpart assignments by distinguishing between tumors that otherwise share overlapping transcriptional programs. For example, HGBCL-DH-BCL2 can be readily separated from BL by the presence of FL-like mutations, which are essentially absent in BL.70,71 These FL-like lesions, including BCL2 rearrangements and recurrent mutations in chromatin-modifying genes, define the EZB genetic subtype, into which nearly all HGBCL-DH-BCL2 cases fall. At the same time, interpreting mutations in the correct biological context is critical. Many of these mutations play important roles in early disease development,199–205 but whether they remain functionally relevant in the fully transformed disease is less certain. Chromatin-modifying mutations, for instance, may initially drive lymphomagenesis by altering GC dynamics or reducing the requirement for external signals such as T-cell help,197 but in established tumors, they could act through entirely different mechanisms or even represent residual markers of pathogenesis rather than active drivers of tumor biology or therapeutic vulnerabilities. The case of EZH2 illustrates this complexity. EZH2 mutations have been proposed to remodel GCs and alter B-cell dependencies during early FL development,205 while in DLBCL cell lines, EZH2 inhibition has been shown both to relieve PC differentiation blocks204 and to reactivate MHC I/II expression.131 These findings suggest that the functional impact of such mutations may shift depending on disease stage and cellular context. For instance, in DZsig+ tumors, EZH2 inhibition may preferentially restore MHC I/II expression, whereas in EZB tumors lacking the DZsig, the dominant role of EZH2 may be to maintain a plasma cell differentiation block – a possibility consistent with the higher frequency of NF-κB pathway mutations observed in EZB DZsig- tumors.96,107 More broadly, integrating genetic lesions with transcription-based classification systems can help interpret mutations in the correct biological context, enabling more 171 precise pinpointing of the pathways that sustain the fully transformed tumor and may represent effective therapeutic targets. The integration of gene expression and genetic data also served to resolve much of the heterogeneity within HGBCL-NOS, allowing most tumors to be reassigned to established biological groups defined by refined COO and LymphGen frameworks. Around half of HGBCL-NOS tumors were DZsig+, and these separated into two mutational groups, with one showing only MYC rearrangement and a BL-like spectrum of mutations, and the other showing BCL2 rearrangement aligning with HGBCL-DH-BCL2. Among the remaining HGBCL-NOS tumors, which lacked DZsig expression, a subset could still be placed into recognized genetic subgroups, including many ABC tumors assigned to the MCD subtype. These results demonstrate how integrating gene expression and genetic data reduces reliance on subjective morphology, resolves apparent heterogeneity, and enables more accurate assignment of tumors to biologically and clinically meaningful categories. Taken together, these findings illustrate how integrative molecular approaches can redefine aggressive B-cell lymphomas by uniting transcriptional programs, genetic lesions, and clinical features into a coherent framework. The recognition of DZ lymphomas, the delineation of CPC-driven trajectories, and the reclassification of HGBCL-NOS all demonstrate the value of moving beyond morphology and cytogenetics to biologically grounded categories. This molecular perspective not only clarifies disease origins and evolutionary pathways but also provides insight into prognosis and therapeutic vulnerabilities, laying the groundwork for a classification system that is both biologically informed and clinically meaningful. 6.2 Limitations and Future Directions Although this dissertation advances a biologically informed framework for the classification of aggressive B-cell lymphomas, several limitations remain. First, the ultimate purpose of 172 classification is to inform clinical management, particularly treatment selection, yet the work presented here primarily refines taxonomy rather than identifying specific therapeutic strategies. While defining DZ lymphomas as a coherent molecular group is a critical step, the optimal means of targeting their shared biology remains unclear. However, preliminary signals suggest therapeutic relevance. Retrospective analysis of the REMoDL-B trial, for example, showed significant improvement in PFS with the addition of bortezomib to R-CHOP among patients expressing the MHG signature, which is directly derived from the mBL signature and substantially overlaps with DZsig.90,173,206 The “immune-cold” nature of DZ lymphomas also appears to be clinically relevant, particularly in the context of immunotherapies. Evaluation of CAR-T in HGBCL-DH-BCL2 has been limited by small sample sizes, leading some studies to conclude that outcomes are comparable to other subgroups.207 Gene expression profiling has helped address this limitation by identifying a broader group of patients with shared biology, and analyses from the TRANSCEND trial showed that DZsig+ tumors had inferior outcomes after CAR-T treatment.208 Consistent with this, CAR-T has also been associated with poor outcomes in relapsed/refractory BL,209 and CD20 x CD3 bispecific antibodies have shown little activity in DZsig+ tumors.210 Emerging evidence suggests that ATR-dependent mechanisms may allow DZ lymphomas to actively exclude T cells, raising the possibility that immune evasion, rather than simply a lack of immune infiltration, underlies their resistance to immunotherapies.211 Together, these observations highlight the need for further work to explain the ineffectiveness of immunotherapies in DZ lymphomas, and more broadly, to identify novel therapeutic targets that address the unique vulnerabilities of this biologically coherent but clinically challenging group. From a biological standpoint, additional work is needed to fully characterize the spectrum of DZ lymphomas. Among DZsig+ DLBCL-NOS, approximately 10-20% harbor cryptic MYC and/or BCL2 rearrangements not detectable by standard FISH,108 and most of the remainder carry either sole MYC or BCL2 rearrangement. The mechanisms driving DZsig expression in cases 173 lacking these events remain unknown, raising fundamental questions about how DZ programs can be activated in the absence of canonical genetic lesions. Similarly, it remains unresolved whether MYC-rearranged tumors without BL morphology represent true biological equivalents of BL or a distinct disease subset. In HGBCL-NOS, tumors with MYC rearrangements showed mutational profiles similar to BL, but interpretation was limited by small cohort size and sparse clinical data. Within DLBCL-NOS, R-CHOP-treated DZsig+ tumors with sole MYC rearrangement were associated with more favorable outcomes compared to other DZsig+ subgroups. Notably, while BL achieves excellent response rates with intensified chemotherapy regimens, there is little data on the efficacy of R-CHOP in this setting.212 Driven by the historically poor outcomes seen with CHOP alone, use of more intensive regimens began prior to the advent of rituximab.213 More recent studies suggest that treatment de-escalation may be feasible, with regimens such as DA-EPOCH-R showing efficacy in BL,214,215 though these remain considerably more intensive than R-CHOP. Taken together, these findings highlight that, while BL and HGBCL-DH-BCL2 fall clearly within the DZ spectrum, further work is needed to define the biology, clinical behavior, and optimal treatment of the remaining DZsig+ lymphomas that do not align neatly with these established categories. The limitations of the HGBCL-NOS cohort mentioned above, including cohort size and incomplete clinical annotations, more broadly constrain the ability to fully resolve this group. While integrative profiling substantially reduced heterogeneity, unusual subgroups persisted. Notably, nearly one-third of ABC HGBCL-NOS tumors were DZsig+, a finding rarely seen in tumors with DLBCL morphology, suggesting underlying biological differences that remain to be explained. In addition, a proportion of HGBCL-NOS cases could not be confidently assigned to a recognized genetic subtype, echoing a broader challenge also seen in genetics-based classification of DLBCL, where roughly one-third of cases remain unclassified.96 These observations highlight that although molecular profiling can substantially refine taxonomy, it does not yet fully resolve NOS 174 categories. This limitation must be reconciled when considering how molecular profiling is deployed in clinical practice and incorporated into clinical trial design. Taken together, future directions should prioritize extending molecular characterization to capture the full spectrum of DZ lymphomas while simultaneously establishing the therapeutic relevance of these findings through pre-clinical studies and clinical trials. 6.3 Integration into Classification and Clinical Practice The findings presented in this dissertation underscore both the value and the challenges of integrating molecular data into lymphoma classification. While the results support the delineation of biologically coherent entities such as HGBCL-DH-BCL2 and the broader family of DZ lymphomas, the work also highlights unresolved heterogeneity within HGBCL-NOS and emphasizes the importance of interpreting genetic alterations in their biological context. Moving forward, the central task is to ensure that molecularly informed categories not only reflect shared biology but also provide actionable insights for patient management and clinical trial design. With almost 4,000 available WGS/WES spanning much of the aggressive B-cell lymphoma spectrum (Table 1.1), the field is approaching a point at which a purely genomics-defined taxonomy could be proposed. However, the presented work supports an approach of ongoing evolution rather than wholesale replacement of existing classifications. Such an approach builds on the pragmatic philosophy of the REAL and later WHO systems, which emphasized that linking tumors to their normal cell counterparts provides critical biological insight. In this context, categories like BL and HGBCL-DH-BCL2 stand out as genomically and transcriptionally homogeneous, while others, particularly DLBCL-NOS and HGBCL-NOS, remain molecularly heterogeneous and therefore central targets for refinement. A genomics-informed classification should therefore emphasize categories that are strongly supported by multiple layers of evidence, including transcriptional programs, recurrent 175 genetic lesions, and consistent clinical features. Where such evidence is lacking, provisional designations remain important to encourage further investigation. Importantly, categories should be forward-looking: while ideally defined by genomic assays, they must also remain accessible worldwide through the development of surrogate markers such as IHC or limited-panel sequencing. This dual structure would promote global applicability while maintaining biological accuracy, ensuring that advances are not restricted to centers with comprehensive genomic infrastructure. Practical considerations are central to successful integration. Increasing reliance on small biopsies in routine clinical practice can limit the availability of nucleic acids for molecular assays, making it essential to adapt workflows to preserve material for genomic testing. Dedicated cores for sequencing, streamlined integration of assays into diagnostic pipelines, and improved turnaround times are all critical. Where tissue is limited or unavailable, circulating tumor DNA offers a potential alternative for assigning tumors to molecular categories.216 These logistical elements will be especially important for clinical trial design, where the short diagnosis-to-treatment intervals characteristic of DZ lymphomas necessitate rapid diagnostic turnaround if patients are to be prospectively enrolled into biologically stratified trials. 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Thesis/Dissertation
eng
Pathology and Laboratory Medicine
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Scott, David W.
Steidl, Christian
Integrating molecular approaches to advance the diagnostic and therapeutic framework of aggressive B-cell lymphomas
Text