Medicine, Faculty of
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UBCV
Chu, Ken H.
Master of Science - MSc
University of British Columbia
Pancreatic ductal adenocarcinoma (PDAC) is the most common form of pancreatic cancer, with one of the lowest 5-year survival rates at 10% across all cancers. In mouse models, PDAC arises from acinar or ductal cells, and previous transcriptomic analyses have identified specific markers of acinar- and ductal-cell-derived PDAC. However, although we have developed a more comprehensive understanding of the cellular origin of PDAC from mouse models, this knowledge has not been effectively utilized to classify patient tumors. Here, we generated mouse models where acinar or ductal cells, using Ptf1aCreER or Sox9CreER, respectively, expressed oncogenic Kras in the absence of Trp53 or Pten and formed PDAC tumors. Using both in vivo and in vitro methods, we reveal that acinar- and ductal-cell-derived tumors are largely similar to one another with small differences in gene expression. We also found variability in the expression levels of previously predicted markers of cellular origin and propose a more comprehensive set of transcriptomic markers. Moreover, our acinar-cell-derived PDAC samples showed enrichment in the classical subtype, consistent with previous studies, while basal subtype genes were expressed at similar levels across both cellular origins. Overall, our findings demonstrate that acinar and ductal tumors have similar expression profiles and require further research to elucidate the reliability of markers for diagnostic use.
TRANSCRIPTIONAL ANALYSES OF ACINAR- AND DUCTAL-CELL-DERIVED PANCREATIC CANCER by Ken H. Chu B.Sc., The University of British Columbia, 2021 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Cell and Developmental Biology) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) July 2024 © Ken H. Chu, 2024 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, a thesis entitled: Transcriptional Analyses of Acinar- And Ductal-Cell-Derived Pancreatic Cancer. submitted by Ken H. Chu in partial fulfillment of the requirements for the degree of Master of Science in Cell and Developmental Biology Examining Committee: Dr. Janel Kopp, Cell and Developmental Biology, UBC Supervisor Dr. Michael Underhill, Cell and Developmental Biology, UBC Supervisory Committee Member Dr. Kevin Bennewith, Department of Pathology and Laboratory Medicine, UBC Supervisory Committee Member Dr. Catherine Van Raamsdonk, Department of Medical Genetics, UBC Additional Examiner iii Abstract Pancreatic ductal adenocarcinoma (PDAC) is the most common form of pancreatic cancer, with one of the lowest 5-year survival rates at 10% across all cancers. In mouse models, PDAC arises from acinar or ductal cells, and previous transcriptomic analyses have identified specific markers of acinar- and ductal-cell-derived PDAC. However, although we have developed a more comprehensive understanding of the cellular origin of PDAC from mouse models, this knowledge has not been effectively utilized to classify patient tumors. Here, we generated mouse models where acinar or ductal cells, using Ptf1aCreER or Sox9CreER, respectively, expressed oncogenic Kras in the absence of Trp53 or Pten and formed PDAC tumors. Using both in vivo and in vitro methods, we reveal that acinar- and ductal-cell-derived tumors are largely similar to one another with small differences in gene expression. We also found variability in the expression levels of previously predicted markers of cellular origin and propose a more comprehensive set of transcriptomic markers. Moreover, our acinar-cell-derived PDAC samples showed enrichment in the classical subtype, consistent with previous studies, while basal subtype genes were expressed at similar levels across both cellular origins. Overall, our findings demonstrate that acinar and ductal tumors have similar expression profiles and require further research to elucidate the reliability of markers for diagnostic use. iv Lay Summary In 2023, pancreatic cancer was the 4th leading cause of cancer-related deaths in Canada, with pancreatic ductal adenocarcinoma (PDAC) being the most common type, accounting for over 90% of cases. This is because there are no early signs to detect PDAC and it responds poorly to medications. The pancreas contains two cell types: one releases digestive substances, and the other carries them to the food. When these cells get damaged, they can grow uncontrollably leading to the formation of PDAC. Currently, doctors cannot determine the cellular origin of cancer when they first see the patient. To address this, we used special mice to develop PDAC from either cell type. While the tumors were very similar, we did find some differences that may help tell them apart. Identifying these markers could guide the use of specific anti-cancer drugs based on the tumor's cellular origin. v Preface Conceptualization, formal analysis, investigation, visualization, and writing of the original draft were completed by Ken H. Chu in collaboration with Dr. Janel L. Kopp, Dr. Kevin Bennewith, Dr. T. Michael Underhill, and Dr. Catherine Van Raamsdonk, with assistance as outlined below. The mice used in this study were bred in the University of California, San Diego, by Dr. Janel L. Kopp in full compliance of the San Diego Animal Care and Use Committee. KTrp53 and KPten mice were bred and maintained at the Modified Barrier Facility (MBF) by Brian Daly, Wesley Hunt, Atefeh Samani, Farnaz Taghizadeh, Alex Lee, and Yu Cao. These latter experiments were approved by the University of British Columbia Animal Care and Use Committees (certificate # A22-0179, # A22-0155). All PDAC cell lines used in this study were generated by Dr. Janel L. Kopp, Ken H. Chu, Brian Daly, Tiffany Chen, Angela Dou, Wesley Hunt, Atefeh Samani, Farnaz Taghizadeh, Wesley Hunt, Kayla Bolduc, and Karnjit Sarai. Endotoxin and morphological change in DU:KPten cell lines in dropout image was completed by Angela Dou. The data used in this thesis is also present in the theses of Angela Dou, Alex Lee, and Soheila Zarei. However, my thesis differs in that it specifically compares acinar and ductal tumors at the transcriptome level for both the AC & DU:KTrp53 and AC & DU:KPten model. In contrast, previous theses focused on examining the histology and progression of tumors in mice. Sectioning of the slides was done by Ken Chu, Angela Dou, Justin Chhuor, Wesley Hunt, Farnaz Taghizadeh, Alex Lee, Kayla Bolduc, and Yu Cao. The immunohistochemistry and immunofluorescence staining were conducted and analyzed by Ken H. Chu with guidance from the LSI Imaging Facility, Dr. Guang Gao and Dr. Janel L. Kopp. All the samples were sequenced by the BRC sequencing core and analysis of the data was performed by Ken H. Chu with guidance from the UBC/LSI Bioinformatics Facility, and Dr. Janel L. Kopp. The work is intended for publication but has not yet been submitted. vi Table of Contents Abstract ....................................................................................................................................iii Lay Summary ...........................................................................................................................iv Preface ...................................................................................................................................... v Table of Contents .....................................................................................................................vi List of Tables ............................................................................................................................ix List of Figures .......................................................................................................................... x List of Abbreviations ..............................................................................................................xii Acknowledgements ................................................................................................................xv Dedication ............................................................................................................................. xvii Chapter 1: Introduction ........................................................................................................... 1 1.1 Overview of Pancreas Anatomy and Development ........................................................... 1 1.2 Pancreatic Ductal Adenocarcinoma (PDAC) ..................................................................... 2 1.3 Progression of PDAC Morphology from Acinar and Ductal Cells ...................................... 3 1.4 Molecular Drivers of PDAC ............................................................................................... 4 1.5 History of GEMMs for Studying PDAC Progression .......................................................... 6 1.6 Exploring PDAC Cellular Origins through GEMMs ...........................................................13 1.7 Molecular Markers for PDAC Cellular Origins ..................................................................14 1.8 Clinical Subtypes of PDAC ..............................................................................................15 1.9 Objective..........................................................................................................................16 Chapter 2: Material and Methods ...........................................................................................18 2.1 Mice .................................................................................................................................18 2.2 Primary PDAC Cell Culture ..............................................................................................18 2.3 Proliferation Assay ...........................................................................................................20 vii 2.4 Gemcitabine Assay ..........................................................................................................20 2.5 Histology and Immunohistochemistry ..............................................................................21 2.6 RNA-Extraction and analyses ..........................................................................................22 2.7 Statistical Analysis ...........................................................................................................23 Chapter 3: Results ..................................................................................................................24 3.1 Acinar- and Ductal-Cell-Derived PDAC Tumors can be Transcriptionally Different ..........24 3.2 Experimentally introduced heterogeneity in bulk tumor composition can influence differentially expressed genes. ..............................................................................................30 3.3 Differentially Expressed Genes in Bulk RNA Sequencing Could be Associated with Non-tumor Epithelial Cell Types. ...................................................................................................32 3.4 Transcriptional Differences Between Acinar- and Ductal-Cell-Derived PDAC Tumors persist in In-vitro conditions ...................................................................................................36 3.5 Enrichment of EMT Genes in Ductal-Cell-Derived PDAC Tumors by Media Composition 45 3.6 Transcriptional Differences in Acinar- vs. Ductal-Derived PDAC Tumors in Dropout Media. ..............................................................................................................................................48 3.7 Reported markers of PDAC cellular origin are not reliable markers of cellular origin across genotypes and contexts. ........................................................................................................50 3.8 Proposing New Markers for Cellular Origin Classification in PDAC. .................................52 3.9 Identification of Conserved Cellular Origin-Specific Genes in PDAC from In-vitro analysis ..............................................................................................................................................52 3.10 Translation of Mouse-Derived Transcriptomic Markers into Clinical PDAC Subtypes .....58 3.11 Clinical Acinar and Ductal Cell Markers show no enrichment in Acinar- or Ductal-Cell-Derived Mouse Tumors .........................................................................................................62 viii 3.12 Both Acinar- and Ductal-Cell-Derived tumors classified as P-EMT Subtype over C-EMT Subtype .................................................................................................................................64 3.13 Cellular Origin and Relationship to Clinical PDAC Subtypes ..........................................66 3.14 Ductal-cell-derived PDAC Exhibits Greater Sensitivity to Gemcitabine Treatment .........71 Chapter 4: Discussion ............................................................................................................73 4.1 The cellular origin of PDAC .............................................................................................73 4.2 Variability in Expression Levels of Markers of Cellular Origin...........................................74 4.3 Clinical PDAC subtypes on Cellular origin .......................................................................76 4.4 Limitation of our models and future direction ...................................................................77 Bibliography ............................................................................................................................79 Appendix .................................................................................................................................89 ix List of Tables Table 1.1. GEMMs of PDAC to study the cellular origin. ...........................................................12 x List of Figures Figure 3.1. Schematic of Acinar and Ductal: KTrp53 PDAC Mouse Models ..........................27 Figure 3.2. Histological staining of Representative Acinar and Ductal: KTrp53 Bulk Tumors 28 Figure 3.3. Trp53 loss with oncogenic Kras Acinar- and Ductal-Cell-Derived PDAC Bulk Tumors. .................................................................................................................................29 Figure 3.4. Bisque and CIBERSORT analysis on Acinar- and Ductal-Cell-Derived PDAC Bulk Tumors. .................................................................................................................................31 Figure 3.5. Trp53 loss with oncogenic Kras Acinar- and Ductal-Cell-Derived PDAC Bulk Tumors. .................................................................................................................................34 Figure 3.6. Tabula Maris Analysis on Agr2 Expression. ........................................................35 Figure 3.7. Schematic of Acinar and Ductal: KPten PDAC Mouse Models. ...........................40 Figure 3.8. Acinar and Ductal: Trp53 and KPten Complete Cell Lines. .................................41 Figure 3.9. Loss of Trp53 or Pten with the Activation of Oncogenic KrasG12D in Acinar- and Ductal-Cell-Derived PDAC Cell lines in Complete media. ......................................................42 Figure 3.10. Comparison of Read Depth between AC and DU Samples. ..............................44 Figure 3.11. Comparisons of GO Biological Process between Acinar and Ductal Cells Under Complete and Dropout Conditions. ........................................................................................47 Figure 3.12. Loss of Trp53 or Pten with the Activation of oncogenic KrasG12D in Acinar- and Ductal-Cell-Derived PDAC Cell Lines in Dropout Media. .......................................................49 Figure 3.13. Z-Score of Selected Markers Across All Conditions and Datasets. ...................51 Figure 3.14. Comparative Transcriptomic of Acinar- and Ductal-Cell-Derived Tumors. .........54 Figure 3.15. Protein Profiling of Acinar- and Ductal-Cell-Derived Tumors Markers. ..............55 Figure 3.16. Z-Score of Our Markers Across All Conditions and Datasets. ...........................57 xi Figure 3.17. Comparisons of Our Markers with Clinical Epithelial PDAC Dataset. ................60 Figure 3.18. Acinar- and Ductal-Cell-Derived PDAC markers from Xu et al. .........................63 Figure 3.19. PCA analysis of C-EMT and P-EMT subtypes from Aiello et al. ........................65 Figure 3.20. Meta-analysis Displaying All Markers and Clinical PDAC Subtypes. .................68 Figure 3.21. Z-Score of Clinical PDAC Subtypes Across All Conditions and Datasets. .........70 Figure 3.22. Acinar- and Ductal-Cell-Derived PDAC Complete Cell Lines and Response to Gemcitabine Treatment. ........................................................................................................72 Figure A.1. Acinar and Ductal KTrp53 Complete Cell Lines PCA plot ...................................89 xii List of Abbreviations AC – Acinar Cell AC:KPten – Ptf1aCreER;KrasG12D; Ptenflox; R26RYFP mice AC:KTrp53 – Ptf1aCreER; KrasG12D; Trp53; R26RYFP mice AC:KPten and DU:KPten – Ptf1aCreER; KrasG12D; Ptenflox; R26RYFP & Sox9CreER; KrasG12D; Ptenflox; R26RYFP mice AC:KTrp53 and DU:KTrp53 – Ptf1aCreER; KrasG12D; Trp53flox; R26RYFP & Sox9CreER; KrasG12D; Trp53flox; R26RYFP mice ADEX – Aberrantly Differentiated Endocrine Exocrine Agr2 – anterior gradient 2 BS-Seq – Bisulfite Sequencing CDKN2A – Cyclin Dependent Kinase Inhibitor 2A Cre (CreER) - Cre Recombinase, estrogen receptor conjugated C-EMT – Complete EMT DMEM – Dulbecco’s Modified Eagle Medium DU – Ductal Cell DU:KPten – Sox9CreER; KrasG12D; Ptenflox; R26RYFP mice DU:KTrp53 – Sox9CreER; KrasG12D; Trp53flox; R26RYFP mice ER – Estrogen Receptor FBS – Fetal Bovine Serum GO – Gene Ontology HBSS – Hanks’ Balanced Salt Solution H&E – Hematoxylin And Eosin IC – Intermediate Co-expressing cells IC50 – Half-maximal inhibitory concentration IF – Immunofluorescence xiii IFN_Sign – interferon signature IHC – Immunohistochemistry Kras – Kirsten Rat Sarcoma Viral Oncogene Homolog Krt20 – keratin 20 KPten – KrasG12D; Ptenflox; R26RYFP mice KTrp53 – KrasG12D; Trp53flox, R26RYFP mice LSL – LoxP-Stop-LoxP MGA – Mouse Gene Atlas mRNA – Messenger RNA NF-Κb – Nuclear Factor Kappa B NGS – Next-Generation Sequencing Nt5e – 5' nucleotidase, ecto PBS – Phosphate-Buffered Saline P-EMT – Partial EMT PC# - Principal Component # PDAC - Pancreatic Ductal Adenocarcinoma PDX1 – Pancreatic and Duodenal Homeobox 1 PI3K – Phosphoinositide 3-kinase Ptf1a – Pancreas Transcription Factor 1 Subunit Alpha Pten – phosphatase and tensin homolog QM-PDA – Quasi-Mesenchymal PDAC RNA-seq – RNA-Sequencing scBasal – Single-Cell Basal scClassical – Single-Cell Classical scRNA-seq – Single-Cell RNA-Sequencing SMAD4 – SMAD Family Member 4 xiv Sox9 – SRY-box transcription factor 9 Trp53 – tumor protein p53 TF – Transcription Factor TM – Tamoxifen YFP – Yellow Fluorescence Protein xv Acknowledgements I would like to express my gratitude to Dr. Janel L. Kopp for providing me with an exceptional foundation to continue building my knowledge in science. Her kindness and patience as a teacher have been invaluable, guiding me through confusion and fostering critical thinking during my independent research throughout this project. I am also extremely grateful to have many opportunities to work with many different individuals from other labs to learn more outside of cancer as well. Overall, I am delighted to have progressed a lot in the field of science and to have gained more insights about the worlds of academic and industry opportunities. Next, I would like to thank my committee members, Dr. Michael Underhill and Dr. Kevin Bennewith, for their support throughout my masters and helpful advice to direct my project. From our initial committee meeting, their feedback has been instrumental in shaping my learning and guiding my research direction. I am grateful for their different perspectives on research in academia and industry, which have influenced my own future scientific endeavors. I would also like to acknowledge Dr. Stephane Flibotte for his patience and guidance in the realm of bioinformatics. His support has been invaluable as I started to self-teach and analyze data. I have learned a lot over the past two years and will continue to learn more about bioinformatics to be able to sufficiently code and analyze sequencing data in the future. And also thank you to the BRC Sequencing Core for their support. My heartfelt thanks goes out to everyone in my lab for their assistance and thoughtfulness during my time in the lab. Special gratitude to our manager, Brian, for his help in organizing and efficiently moving my project forward. I am also grateful to: Jeff (C. Elegan expert) for his invaluable assistance in experimental design and in helping me streamline my work; Liam for his extensive knowledge in the field of science and to be always testing me about topics outside of PDAC; Kevin for his incredible work ethic and assistance throughout all my projects without any hesitation for which I am very thankful for; Ian for his great advice throughout my experiments and his great clinical background in medicine to learn from; Anni for giving me an opportunity to work xvi with her to complete her project and learn more about diabetes with its complex relationship to PDAC; Angela for her helpful guidance when I first joined the lab to her ongoing support as I complete this masters project; Shilpa for her incredible feedback and suggestions when I first established this project with little knowledge of how to begin. I would also like to extend my appreciation to past and current members of the lab, including Grace, Niki, Tiffany, Angelina, Cynthia, Andy, and Justin. Outside of our lab, I would like to thank Jacky, Natasha, Evan, Victoria, Bal, Alex, George, Mikky, Howard, Yuchen, and everyone else for all the helpful advice throughout all my work! Lastly, I want to extend my deepest gratitude to my family and friends for their unwavering support throughout this whole project. Without their encouragement, I would not have been able to pursue or even complete my degree. I am thankful for the opportunity to be able to learn more about the field of cancer and bioinformatics to potentially make a positive difference in human lives. xvii Dedication This thesis is dedicated to Mom, Dad, Brother, my loving advisor Saki, and my cat Mikan. 1 Chapter 1: Introduction 1.1 Overview of Pancreas Anatomy and Development The pancreas is located behind the stomach and extends from the duodenum consisting of 3 parts: the head, body, and tail (Talathi et al., 2024). The two functions of the pancreas are: aiding in digestion as part of the exocrine system and secreting hormones, such as insulin and glucagon, as part of the endocrine system (A-Kader & Ghishan, 2012). In particular, my project focused on the exocrine part, which secretes enzymes that pass through the main and accessory ducts to the duodenum for the digestion of food (A-Kader & Ghishan, 2012). Exocrine cells make up 80% of the pancreas and are comprised of acinar and ductal cells (Talathi et al., 2024). The acini are pyramidal-like cells that contain zymogen granules that release many digestive enzymes into the pancreatic duct (A-Kader & Ghishan, 2012; Talathi et al., 2024). The main enzymes being endopeptidases (trypsin, chymotrypsin, and elastase), lipase, and amylase (A-Kader & Ghishan, 2012). Ductal cells, on the other hand, secrete fluid and bicarbonate to transport the enzymes at an alkaline pH to function effectively in the small intestine (A-Kader & Ghishan, 2012). Initially, these enzymes are transported in their inactive form to prevent auto-digestion and upon reaching the duodenum, they are activated by intestinal brush borders, leading to enterokinase cleaving trypsin, which initiates a cascade of enzyme activations (A-Kader & Ghishan, 2012). Altogether, the exocrine cells work together through stimulation of the vagus nerve to assist in the digestion of food (A-Kader & Ghishan, 2012). The development of the pancreas also involves many specific genes, such as Hnf1b, Ptf1a (Pancreas-specific transcription factor 1a) and Sox9 (Sex determining region Y Box 9) (Dassaye et al., 2016). After pancreatic progenitors cells form, Sox9 is co-expressed with Pdx-1 and becomes restricted to the trunk domain to become ductal cells (Dassaye et al., 2016). If the pancreas loses Sox9, this causes pancreatic hypoplasia and thus Sox9 has a crucial role in the development of ductal cells in the pancreas (Dassaye et al., 2016). For the development of acinar cells, Ptf1a is co-expressed with Pdx-1 and is crucial for the growth of the dorsal pancreatic 2 endoderm and exocrine gene transcription (Dassaye et al., 2016). Loss of Ptf1a leads to a truncated pancreas without the formation of acini or islets leading to impaired pancreatic function (Dassaye et al., 2016; Masui et al., 2007). Hnf1b (Hepatocyte nuclear factor-1 beta) plays an important role in the formation of the pancreatic progenitor cells and has a direct regulatory relationship with the Ptf1a promoter (Dassaye et al., 2016; Haumaitre et al., 2005). In the mature pancreas, Ptf1a and Sox9/Hnf1b continue to play an important role in maintaining the function and homeostasis of acinar and ductal cells, respectively (Masui et al., 2007; Seymour, 2014). Overall, mutations in the crucial developmental genes, Ptf1a, Sox9, and Hnf1b can lead to pancreatic cancer in both acinar and ductal cells of the mature pancreas (Jakubison et al., 2018; L. Kim et al., 2008; Kopp, von Figura, et al., 2012). 1.2 Pancreatic Ductal Adenocarcinoma (PDAC) Pancreatic Ductal Adenocarcinoma (PDAC) is the most prevalent form of pancreatic cancer and has a low survival rate in Canada and the USA (Siegel et al., 2023). The complexity of treating this disease in vivo stems from challenges such as late-stage diagnosis, drug resistance through the dense tumor microenvironment (TME), genetic mutations, and the absence of actionable biomarkers for early detection (Sarantis et al., 2020). In vitro, pancreatic cancer cells showed gemcitabine sensitivity, whereas pancreatic stellate cells (PSC) were resistant, highlighting the challenges of treating PDAC (Amrutkar et al., 2020). The resistance of PSCs to gemcitabine highlights a major challenge because these stromal cells are a key component of the TME which can act as a physical barrier, thereby preventing the drug from reaching the cancer cells effectively and contributing to the overall resistance of the tumor to treatment. In particular, the TME is characterized by a dense desmoplasia and is often hypoxic and rich in cancer-associated fibroblasts, regulatory T cells, and extracellular matrix components (Oberstein & Olive, 2013; Sarantis et al., 2020). This dense barrier reduces the accessibility of drugs, enabling cancer cells to survive and continue interacting with cells in the TME (Sarantis et 3 al., 2020). Interestingly, research has explored reducing stromal formation, but researchers found that stromal ablation leads to a more aggressive PDAC phenotype (Rhim et al., 2014). Thus, further work is still needed to identify methods of characterizing the stromal tissue for therapeutics. As most diagnoses of PDAC are also at a late stage, the disease is near fatal, preventing the option of surgical intervention and leaving patients with limited treatment options (Sarantis et al., 2020). While frontline treatments like FOLFIRINOX and gemcitabine/paclitaxel have emerged, they pose risks to patient health and show limited effectiveness, leading to eventual resistance (Oberstein & Olive, 2013; Sarantis et al., 2020). Specifically, gemcitabine is an antimetabolite drug, functioning as a nucleoside analog of cytidine (Oberstein & Olive, 2013). Upon cellular uptake through hENT1 (human equilibrative nucleoside transporter), gemcitabine becomes phosphorylated to the active state to become incorporated into the DNA disrupting replication (Oberstein & Olive, 2013). The purpose of identifying molecular subtypes is important, as in theory this can lead to targeted therapies to provide more effective treatments for patients diagnosed with PDAC. Recent studies have shown that the molecular subtype of PDAC can respond differently to gemcitabine. For instance, basal-like PDAC tumors exhibit greater response than classical subtypes (O’Kane et al., 2020; Tiriac et al., 2018). However, this does not mean all basal tumors respond uniformly, suggesting that other factors, such as the cellular origin of the tumor, could provide additional insights into the treatment response. Currently, PDAC subtypes are not used in clinical practice, highlighting a significant opportunity to enhance treatment outcomes if addressed. By identifying and understanding these molecular subtypes in relation to the cellular origin, we can develop targeted therapies tailored to the genetic features of each subtype. 1.3 Progression of PDAC Morphology from Acinar and Ductal Cells PDAC commonly arises from the head of the pancreas from either acinar or ductal cells (Hank & Liss, 2018; Hezel et al., 2006). Ductal cells were traditionally considered the origin of PDAC, but recent research has highlighted the plasticity of acinar cells to transform into precursor 4 lesions (Kopp, von Figura, et al., 2012). Acinar cells first undergo acinar-to-ductal metaplasia (ADM) and further progress to pancreatic intraepithelial neoplasia (PanIN) (Hank & Liss, 2018). During ADM, acinar cells lose their cell shape and gain a duct-like morphology due to changes in gene expression (Marstrand-Daucé et al., 2023). However, ADM is a reversible process that can regenerate back into acinar cells unless there is consistent inflammation or oncogene expression leading to the complete transformation into PanIN (Marstrand-Daucé et al., 2023). In PanIN, there are three stages: PanIN1A/PanIN1B is characterized by columnar cells exhibiting hyperplasia, which is an increase in tissue size without dysplasia or abnormal cell growth (Jonckheere et al., 2010). PanIN2 then exhibits dysplasia and loss of cellular polarity, progressing to PanIN3, where the cells resemble in situ carcinoma, the precursor stage to PDAC (Jonckheere et al., 2010). While acinar cells exhibit plasticity and can readily transform into various types of precursor lesions, ductal cells are more resistant to this transformation and tend to develop into intraductal papillary mucinous neoplasms (IPMN) or mucinous cystic neoplasm (MCN), which are less common than pancreatic intraepithelial neoplasia (PanIN) (Hezel et al., 2006; Kopp, von Figura, et al., 2012; Marstrand-Daucé et al., 2023). IPMN arise from the main pancreatic duct or the branches and are mucin-producing neoplasms. MCN are large columnar epithelium that have an ovarian-type stroma (Marstrand-Daucé et al., 2023). As PDAC progresses from PanIN, IPMN, or MCN, it may undergo further differentiation characterized by histological features, such as varying degrees of differentiation with glandular duct-like structures (Hezel et al., 2006). However, spatial heterogeneity can also occur within a pancreatic lesion, where a certain region exhibits different precursor lesions (Hezel et al., 2006). 1.4 Molecular Drivers of PDAC Most pancreatic cancer cases have an accumulation of mutations that lead to the formation of PDAC (Cicenas et al., 2017). One of the most commonly mutated genes in over 90% of cases is the KRAS (Kirsten ras) gene (Cicenas et al., 2017). In particular, the G12 modification 5 accounts for over 99% of cases, encompassing specific mutations like G12D, G12V, and G12C (Bamford et al., 2004; Shen et al., 2022). Normal KRAS regulates many cellular processes, such as proliferation, differentiation, and apoptosis (Downward, 2003). KRAS has intrinsic GTPase activity, which catalyzes bound GTP to GDP to communicate to downstream pathways, such as RAF-MEK-ERK, PI3K-AKT-mTOR, and RAL-TBK-NF-kB pathways, which are directly affected during RAS transformation (C. Zhu et al., 2022). Mutations in the KRAS gene can lead to increased activity of the KRAS protein, which is often considered the initiating mutation (Cicenas et al., 2017). Oncogenic Kras also changes the expression of Ptf1a and Sox9. In the presence of oncogenic Kras, acinar cells transform into ADM, leading to the silencing of Ptf1a and the absence in PDAC expression (Jakubison et al., 2018). Sox9 is upregulated in PDAC, and alongside oncogenic Kras, Sox9 accelerates the formation of precursor lesions (Kopp, von Figura, et al., 2012). Sox9 is found in all stages of PDAC from ductal cells, precursor lesions, and PDAC (Shroff et al., 2014). To transition from a precursor lesion to PDAC, requires the accumulation of additional mutations, such as the loss of tumor suppressor genes, SMAD4, TP53 (P53), and CDKN2A (P16), during the transformation process (Hank & Liss, 2018). In particular, TP53 (tumor protein 53) is the most commonly mutated tumor suppressor gene (Cicenas et al., 2017). Normal TP53 responds to cellular stress, such as DNA damage, leading to growth arrest or apoptosis (Levy et al., 1993). However, loss or mutant TP53 prevents the protein from binding to the DNA, and performing cellular responses leading to uncontrolled cell growth and proliferation that can lead to tumorigenesis (Kern et al., 1992). Mutant TP53 can also alter the immunosuppressive properties of the TME accelerating PDAC progression and metastasis (Cui & Guo, 2016). As mutations in TP53 are common in all cancers, the development of drugs is a significant area of interest (Hassin & Oren, 2023). Particularly, targeting TP53 for treatment has been difficult due to the challenge of developing a drug to selectively target mutant p53 proteins while sparing wild-type p53 and the lack of knowledge in off-target effects (Amrutkar et al., 2020; Hassin & Oren, 2023; Levine, 2022). Current methods of inhibiting TP53 exist, such 6 as using viruses to deliver WT p53 complementary DNA to tumor cells to restore normal TP53 function or the inhibition of proteins associated with TP53, such as the MDM2-TP53 complex in tumor cells, to reactivate MDM2-mediated degradation of p53 (Levine, 2022). Our current understanding of how different tumor suppressor genes contribute to the overall heterogeneity of PDAC tumors is still unknown. Additionally, our study also focuses on the phosphatase and tensin homolog (PTEN) gene, a tumor suppressor gene involved in regulating cell growth (Ying et al., 2011). The loss of one PTEN allele is frequent in clinical patients, leading to dysregulation of the PI3K/AKT signaling pathway (Asano et al., 2004). In the active state, PTEN is dephosphorylated and converts PIP3 to PIP2 (Saiki et al., 2021). As the PI3K-AKT pathway is downstream of KRASG12D, the loss of a Pten allele results in continuous activation of this pathway, leading to increased cell growth (Ying et al., 2011). In the PI3K pathway, the catalytic and regulatory PI3K subunit could also become mutated leading to uncontrolled cell proliferation (Aguirre et al., 2004; Schönhuber et al., 2014). In PDAC, the loss of Pten with KrasG12D from ductal cells leads to the formation of IPMN and PDAC (Kopp et al., 2018). Overall, understanding the roles of these different mutational drivers has significantly improved our comprehension of PDAC development. 1.5 History of GEMMs for Studying PDAC Progression The use of clinical models has revolutionized our understanding of PDAC tumorigenesis (Schönhuber et al., 2014). Using mutations prevalent in humans, previous researchers have created genetically engineered mouse models (GEMM) to study invasive PDAC (Guerra & Barbacid, 2013; Mota Reyes et al., 2021; Saiki et al., 2021). Using GEMMs, we can further study how oncogenes and tumor suppressor genes in humans contribute to PDAC development at different stages. This can also help us test the effectiveness of new treatments before administrating them to patients (Mazur & Siveke, 2012). 7 The initial exploration of the cellular origin used Ela-KrasG12D mouse models, where acinar cell-specific promoters drove the expression of KrasG12D (Grippo et al., 2003). The study’s primary focus was to elucidate the association between Kras and pancreatic acinar cells, establishing that the Kras oncogene is required for the initiation of pancreatic neoplasia (Grippo et al., 2003). However, a limitation of this model was that KrasG12D is activated in acinar cells from birth so there is no control over the timing of KrasG12D activation (Grippo et al., 2003). To address cell type specificity, researchers developed a Lox-STOP-Lox (LSL)-KrasG12D, inserted upstream of exon 1 in the endogenous Kras locus (Hingorani et al., 2003). The Cre/loxP-based system allows for the precise control over the activation and silencing of genes. This system allows us to control excisions and activation of target DNA through site-specific recombination, involving a Cre recombinase and a short DNA recognition site (LoxP, locus of x-over, P1) (H. Kim et al., 2018). While other systems, such as Flp and Dre exist, this thesis will prioritize discussion of the Cre recombinase as this was the system used for our experiments, but all work in a similar principle for gene editing (J. Wu et al., 2017). The LSL-KrasG12D model with Ptf1a-Cre or Pdx1-Cre specifically targets acinar, CAC, ductal, and endocrine cells, allowing KrasG12D activation to occur in multiple cell types from E8.5 (Guerra & Barbacid, 2013; Hingorani et al., 2003). Activation of Pdx1 & Ptf1a with KrasG12D resulted in the development of mainly PanIN, with a small subset of mice developing PDAC after being left alive longer (Hingorani et al., 2003). Moreover, to specifically induce acinar cells, researchers incorporated the CreER protein to Pdx1, Elastase, Mist1, and proCPA1 (Gidekel Friedlander et al., 2009; Habbe et al., 2008). By using CreER, the activation of KrasG12D could be controlled by the administration of tamoxifen. Once tamoxifen is injected and processed in the liver, 4-hydroxytamoxifen (4-OHT) binds to the Cre that is fused to a mutated hormone-binding domain of the estrogen receptor (CreER) in a specific cell (M.-Y. Chen et al., 2023). The 4-OHT binds to the estrogen receptor (ER), causing the release of heat shock protein 90 (HSP90) from the CreER complex (Feil et al., 2009; H. Kim et al., 2018). This enables the CreER complex to enter the nucleus, where it can cleave LoxP sites and initiate 8 recombination events (Feil et al., 2009; H. Kim et al., 2018). The results showed that after tamoxifen injection, acinar cells could undergo transformation into PanIN lesions in the absence of pancreatitis (Gidekel Friedlander et al., 2009; Habbe et al., 2008). While many mouse models have focused on acinar cells as the cellular origin of PDAC, studies have also shown that ductal cells can initiate PDAC (Kopp, von Figura, et al., 2012; Ray et al., 2011). The use of Ck19CreERT; LSL-KrasG12D mice led to the formation of pancreatic lesions originating from normal pancreatic ducts rather than acinar cells (Ray et al., 2011). However, a limitation of this study is that Ck19CreERT; LSL-KrasG12D is not expressed in smaller ducts (Ray et al., 2011). The use of Sox9CreER mice with KrasG12D allowed for the activation of Kras in smaller ducts and centroacinar cells as well as larger ducts, providing a more comprehensive view (Kopp, von Figura, et al., 2012). When Sox9CreER mice activated KrasG12D, this led to the low frequency of PanIN formation around large pancreatic ducts and not distributed across the pancreas as seen in Ptf1aCreER; KrasG12D mice (Kopp, von Figura, et al., 2012). As acinar cells are more susceptible to PanIN formation compared to ductal and centroacinar cells (CAC), this suggested that ductal cells are more resistant to the formation of PDAC precursor lesions (Kopp, von Figura, et al., 2012). As these mouse models mainly developed precursor lesions, to further study tumor progression to PDAC, many GEMMs with mutations to tumor suppressor genes found in humans, such as TP53, SMAD4, and CDKN2A/p16 were developed (Mota Reyes et al., 2021; Saiki et al., 2021). The combination of activating point mutations in the Kras oncogene with tumor suppressor inactivation led to the formation of precursor lesions and PDAC at a higher frequency (Mota Reyes et al., 2021; Saiki et al., 2021). For instance, models incorporating the deletion of Trp53f/f, Ptenf/f, or Smad4f/f showed rapid development of precursor lesions and PDAC (Bardeesy et al., 2006; Kopp et al., 2018; Lee et al., 2019). In relation to the cellular origin, different genetic mutations are required to promote Kras-induced PDAC tumorigenesis (Y. Xu et al., 2019). Kopp et al. used transgenic Sox9CreER with loss of Pten and LSL-KrasG12D in ductal cells and observed the 9 formation of PanIN, IPMN, and progression to PDAC in some cases (Kopp et al., 2018). Furthermore, Lee et al. demonstrated that both acinar- (Ptf1aCreER) and ductal- (Sox9CreER) cells with oncogenic LSL-KrasG12D and Trp53 loss led to the formation of low/high grade PanIN and PDAC (Lee et al., 2019). This was consistent with and later supported by additional studies using similar mouse models (J. M. Bailey et al., 2016; Faraoni et al., 2023; Ferreira et al., 2017; Flowers et al., 2021). Overall, the loss of tumor suppressor genes along with the oncogene Kras in acinar cells primarily lead to the formation of PanIN lesions, while ductal cells lead to the formation of PanIN or IPMN lesions around or within the duct structures (Aguirre et al., 2003; Bardeesy et al., 2006; Flowers et al., 2021; Izeradjene et al., 2007; Kopp, von Figura, et al., 2012; Lee et al., 2019; Von Figura et al., 2014). Interestingly, different tumor suppressor genes can also either promote or inhibit acinar or ductal cell transformation into precursor lesions and PDAC (Von Figura et al., 2014). Von Figura et al. using Ptf1aCreER; LSL-KrasG12D; Brgf/f (Acinar origin) and Hnf1bCreERT2; KrasG12D; Brgf/f (Ductal origin) found that Brg1 is crucial for acinar-derived PanIN formation, but inhibits the formation of IPMN (Von Figura et al., 2014). As future mouse models continue to be developed, we can better understand the interactions of each tumor suppressor in the context of cellular origin and the progression to PDAC. Our study used both floxed and heterozygous loss of tumor suppressor genes, as these mice were previously established. However, the copy number of tumor suppressor is known to influence and change the progression of precursor lesions to PDAC (Dou et al., 2022; Flowers et al., 2021; Kopp, von Figura, et al., 2012). In our unpublished manuscript, we found Ptf1aCreER; LSL-KrasG12D; Ptenf/+ mainly formed ADM and PanIN formation, while Ptf1aCreER; LSL-KrasG12D; Ptenf/f developed PDAC in all mice (Dou et al., 2022). Similarly, Sox9CreER; LSL-KrasG12D; Ptenf/+ and Sox9CreER; LSL-KrasG12D; Ptenf/f had differences in the types of IPMN formed, demonstrating different morphologies based on the copy number of tumor suppressors (Kopp, von Figura, et al., 2012). To elaborate further with Trp53, in acinar cells, the copy status of Trp53 led to different grades of tumors, with well-differentiated tumors for Trp53+/+, and a mix of well-10 /poorly-differentiated tumors for Trp53f/+ and Trp53f/f (Flowers et al., 2021). For ductal cells, homozygous loss of Trp53 is necessary for the formation of both well-/poorly-differentiated PDAC (Flowers et al., 2021). In this thesis, we will focus on the overall differences in the cellular origin between Trp53 and Pten loss, regardless of copy status. Ultimately, for our investigation, we used the following mouse models: Ptf1aCreER or Sox9CreER with LSL-KrasG12D, R26RYFP, and Trp53f/f or Trp53f/+ (AC:KTrp53 or DU:KTrp53) and Ptf1aCreER or Sox9CreER with LSL-KrasG12D, R26RYFP, and Ptenf/f or Ptenf/+ (AC:KPten or DU:KPten) (Dou et al., 2022; Kopp, von Figura, et al., 2012; Lee et al., 2019). Specifically, Ptf1aCreER is a genetically modified allele of the endogenous Ptf1a gene (Kopp, von Figura, et al., 2012). Whereas, Sox9CreER is a transgenic mouse with exogenous Sox9CreER sequences inserted in exon 1 (Kopp et al., 2011). Next, the Trp53f/f or Trp53f/+ models denote the conditional knockout of the Trp53 gene in one or both alleles in the presence of Cre recombinase, due to the flanking loxP sites around exon 2-10 (Lee et al., 2019). Similarly, Ptenf/f or Ptenf/+ represents a conditional knockout of the Pten gene, involving one or both alleles, in the presence of Cre recombinase. This knockout targets exon 5, which contains the phosphatase domain critical for the gene's function (Lesche et al., 2002). Lastly, a R26RYFP reporter allele is also present using a LSL to ensure successful visualization of Cre recombination (Lee et al., 2019). We also incorporated existing models from the literature to study the cellular origin of PDAC (Table 1.1) (Faraoni et al., 2023; Ferreira et al., 2017; Flowers et al., 2021). Ferreira et al. generated acinar-cell-derived (Ela1-CreERT2; LSL-KrasG12D/+; Trp53f/f; R26RYFP) and ductal-cell-derived (Ck19-CreER; LSL-KrasG12D/+; Trp53f/f; R26RYFP) PDAC mouse models (Ferreira et al., 2017). Faraoni et al. focused on acinar-cell-derived (Mist1CreERT2; LSL-KrasG12D; LSL-Trp53R172H/+; LSL-Rosa26mTmG) and ductal-cell-derived (Hnf1bCreERT2; LSL-KrasG12D; LSL-Trp53R172H/+ or R172H/R172H; LSL-Rosa26mTmG) PDAC mouse models, with a key difference being that Trp53 expression is absent in all tissues (Faraoni et al., 2023). Additionally, Flowers et al. also examined the acinar-cell-derived (Ptf1aCreER; LSL-KrasG12D; Trp53f/f; R26RtdTomato) and ductal-cell-11 derived (Sox9CreER; LSL-KrasG12D; Trp53f/f; R26RtdTomato) PDAC models (Flowers et al., 2021). By incorporating these previously studied models, we provided a comprehensive analysis of PDAC from different cellular origins. 12 Abbreviation Genotype Reference AC:KTrp53 Ptf1aCreER; LSL-KrasG12D; Trp53flox; R26RYFP (Lee et al., 2019) DU:KTrp53 Sox9CreER; LSL-KrasG12D; Trp53flox; R26RYFP (Lee et al., 2019) AC:KPten Ptf1aCreER; LSL-KrasG12D; Ptenflox; R26RYFP (Kopp et al., 2018) DU:KPten Sox9CreER; LSL-KrasG12D; Ptenflox; R26RYFP (Kopp et al., 2018) Ferreira et al. AC Ela1-CreERT2; LSL-KrasG12D/+; Trp53f/f; R26RYFP (Ferreira et al., 2017) Ferreira et al. DU Ck19-CreER; LSL-KrasG12D/+; Trp53f/f; R26RYFP (Ferreira et al., 2017) Faraoni et al. AC Mist1CreERT2; LSL-KrasG12D; LSL-Trp53R172H/+; LSL-Rosa26mTmG (Faraoni et al., 2023) Faraoni et al. DU Hnf1bCreERT2; LSL-KrasG12D; LSL-Trp53R172H/R172H; LSL-Rosa26mTmG (Faraoni et al., 2023) Flowers et al. AC Ptf1aCreER; LSL-KrasG12D; Trp53f/f; R26RtdTomato (Flowers et al., 2021) Flowers et al. DU Sox9CreER; LSL-KrasG12D; Trp53f/f; R26RtdTomato (Flowers et al., 2021) Table 1.1. GEMMs of PDAC to study the cellular origin. 13 1.6 Exploring PDAC Cellular Origins through GEMMs The cellular origin of PDAC was thought to be ductal cells, but recent work using GEMMs has shown that acinar cells can become duct-like (Busnardo et al., 1983; Guerra et al., 2007; Kopp et al., 2011). With a PDAC mutation, both acinar and ductal cells have the potential to transition to precursor lesions but do not fully develop into PDAC (Hank & Liss, 2018; Kopp, von Figura, et al., 2012; Lee et al., 2019). Interestingly, acinar cells become duct-like, which leads to down-regulation of acinar cell genes and reactivation of ductal-like transcription programs (Kopp, von Figura, et al., 2012). As we currently cannot track the progression of human acinar and ductal cells into PDAC, GEMMs and in vitro systems provide a valuable method to study how mutations drive this transformation. Recently, a research group cultured human acinar and ductal cells and demonstrated that inducing specific mutations in these cells can lead to a transition to PDAC in vivo (Y. Xu et al., 2024). GEMMs have shown that PDAC tumors derived from acinar or ductal cells with Trp53 loss and Kras activation exhibit different histology and phenotypes (Lee et al., 2019). In particular, acinar-cell-derived PDAC showed more frequent high-grade mucinous PanIN formation and delayed progression to PDAC, whereas ductal-cell-derived PDAC showed less frequent formation and reached endpoints earlier (Lee et al., 2019). These observations demonstrate that acinar- and ductal-cell-derived tumors have different phenotypic traits that could be further explored. However, the ability of both acinar and ductal cells to progress to PDAC adds complexity to identifying a single cellular origin in late-stage PDAC tumors (Backx et al., 2022). Ongoing research aims to identify transcriptomic markers, phenotypes, and methylation differences that could identify the cellular origin of PDAC tumors (Espinet et al., 2021; Faraoni et al., 2023; Ferreira et al., 2017; Flowers et al., 2021; Lee et al., 2019). Below, we will further discuss using transcriptomic markers to determine the cellular origin of PDAC tumors. 14 1.7 Molecular Markers for PDAC Cellular Origins To identify molecular markers, advances in next-generation sequencing (NGS) technologies have allowed researchers to further look into the genome of PDAC tumors. NGS is a high-throughput method to sequence small fragments of DNA or RNA, which collectively cover the enter genome or transcriptome, to understand the genes or mutations present (Behjati & Tarpey, 2013). NGS allows researchers to look into the genome of many cancers and identify mutations for further study in developing therapies (Harris & McCormick, 2010). As NGS continues to improve, RNA-Seq and scRNA-seq have become more accessible for everyone to define the transcriptome of PDAC and many cancers. Thus, the identification of genes at the transcriptomic level in PDAC has been an area of interest for enhancing diagnosis and treatment. However, the challenge lies in identifying a robust gene or set of genes that can definitively determine if the tumor originated from acinar or ductal cells in patients. The heterogeneous nature of PDAC tumors prevents many markers from being clinically useable (Taherian et al., 2022). Previous studies have identified several potential markers of cellular origin in PDAC, including Agr2 (Ferreira et al., 2017), Krt20 (Lee et al., 2019), Nt5e (Faraoni et al., 2023), and the IFN_Signature (IFN_Sign) (Espinet et al., 2021). Research findings from GEMMs and human microarray data indicate that higher expression of Agr2 (anterior gradient 2) in PDAC is suggestive of a ductal cellular origin (Ferreira et al., 2017). Next, Krt20 (Keratin 20), an intermediate filament protein, is strongly expressed in acinar-cell-derived PDAC based on immunohistochemistry (Lee et al., 2019). Furthermore, Nt5e (CD73, 5'-nucleotidase ecto), from GEMMs and RNA-Seq, was shown to be a ductal-cell-derived PDAC marker that contributes to the immunosuppressive environment of PDAC tumors (Faraoni et al., 2023). Lastly, IFN_Sign, an interferon signature, refers to a set of genes upregulated in interferon signaling, particularly in ductal-cell-derived PDAC using RNA-Seq and Bisfulfite Sequencing (BS-Seq) (Espinet et al., 2021). Collectively, these markers provide insight into the cells that could contribute as a cellular origin of PDAC. Given the high tumor interpatient heterogeneity in PDAC, it is essential to ensure 15 potential marker genes are effective across larger datasets, different genotypes, mutations, and sex. This crucial aspect is often overlooked in previous literature to ensure the reliability of identified markers for clinical use. 1.8 Clinical Subtypes of PDAC Through the use of NGS, molecular classifications of PDAC into potential clinical subtypes have been identified (Aiello et al., 2018, 2018; P. Bailey et al., 2016; Collisson et al., 2011; Moffitt et al., 2015; Raghavan et al., 2021). Many groups have contributed to identifying transcriptional subtypes, with the most key ones being Moffit, Collisson, Bailey, and most recently Raghavan, with each group using different methods, from the earliest being by Collisson using microarray to Raghavan using scRNA-seq on patient samples. However, the consenus subtypes for the past decade has been Moffitt’s Classical and Basal subtype from microarray data (Moffitt et al., 2015). The Classical subtype is associated with epithelial genes and has a more favorable prognosis, while the Basal subtype is associated with a more aggressive clinical course and poor differentiation, leading to a worse clinical prognosis (Z. Xu et al., 2021). However, with the limitations of microarray data, recent studies have shown the contribution of the stroma to earlier subtype classifications (Maurer et al., 2019). Although tumor purity is an important consideration, most resected tumors have low cellularity in a clinical setting (Z. Xu et al., 2021). The most recent study by Raghavan using scRNA-seq showed that the classical and basal do co-exist with an intermediate subtype known as Intermediate-Coexpressor State (IC) (Raghavan et al., 2021). Moreover, the IC state could act as a transition between Basal and Classical states, showcasing that tumors collected from patients could have more than one subtype based on where the tumor piece was collected (Raghavan et al., 2021). In fact, when multiple pieces of the same tumor were assessed for PDAC subtype transcriptionally or by multiplex staining, the basal and classical signature genes were found to co-exist in the same tumor (Hayashi et al., 2020; Pitter et al., 2022; Raghavan et al., 2021). These observations show 16 that intratumoral heterogeneity poses a challenge for treatment as different regions of tumors could respond differently to treatments. Consequently, this could suggest that identifying PDAC subtypes could require multiple sampling from different regions, but further work is required to understand the mechanisms in how cells can transition between states. Recent research has shown how cellular origin is associated with different clinical PDAC subtypes (Faraoni et al., 2023; Flowers et al., 2021; Lee et al., 2019). As acinar-cell-derived PDAC was more closely enriched in the classical subtype and ductal-cell-derived PDAC was more associated with the basal subtype (Flowers et al., 2021). Although these cellular origins have been associated with clinical subtypes, a limitation of this research is the reliance on small sample sizes and only using pieces of bulk tumor tissue. As PDAC is a heterogeneous, a smaller dataset would not capture the full spectrum of PDAC variability. A more comprehensive dataset would be required to fully capture the molecular landscape of PDAC and identify if the cellular origin is associated with clinical PDAC subtypes. 1.9 Objective My main objective was to identify molecular profiles underlying PDAC cells derived from an acinar or ductal cell of origin. Additionally, I assessed if acinar- or ductal-cell-derived PDAC exhibit differences in proliferation and response to chemotherapeutics. I hypothesize that PDAC tumor cells display an acinar- or ductal-cell-derived molecular profile that also influences their response to chemotherapy. We utilized four previously generated mouse models in our study, which were Ptf1aCreER or Sox9CreER mice, each with activation of LSL-KrasG12D and loss of either tumor suppressor genes Trp53flox or Ptenflox, along with a R26RYFP reporter gene. We then sequenced the RNA from bulk tumor samples, and cell lines grown in different media formulations to identify genes specific to an acinar or ductal cellular origin. Additionally, we also examined if there were differences in tumor growth and response to gemcitabine in vitro. This study used mouse models, in vitro cell culture assays, and bioinformatics to identify potential transcriptomic 17 markers that could be used clinically to provide more targeted therapeutic approaches for patients with PDAC. 18 Chapter 2: Material and Methods 2.1 Mice All animal experiments used in this study followed regulations set by the University of British Columbia and University of California, San Diego Animal Care Committee. Sox9CreER (Font-Burgada et al., 2015; Kopp et al., 2011; Lee et al., 2019; Srinivas et al., 2001), Ptf1aCreER (Hingorani et al., 2003; Kopp et al., 2018; Lee et al., 2019), KrasLSL-G12D (Tuveson et al., 2004), Trp53flox (Tuveson et al., 2004), Ptenflox (Lesche et al., 2002), and R26RLSL-YFP (Marino et al., 2000) mice have been previously described and were maintained using a mixed C57BL6 and CD1 background (Charles River Laboratories). We induced recombination by injecting tamoxifen (5 mg/40g body weight, Sigma-Aldrich) subcutaneously. Mice were monitored until humane endpoint unless otherwise specified. We used both male and female mice in this study, but as their sexes did not exhibit substantial differences this was not considered a factor in this study (Figure A.1). 2.2 Primary PDAC Cell Culture Primary mouse cell lines were established from PDAC tumors harvested by previous students between 2015 and 2021. These included AC:KTrp53, DU:KTrp53, AC:KPten and DU:KPten mice, as previously described with slight modifications (Reichert et al., 2013). Briefly, a portion of the tumor sample was minced into small portions using a scalpel or scissor and then incubated with Dulbecco’s modified Eagle’s medium/Ham’s F-12 with Phenol Red (Corning) with 2.22 mg/mL collagenase B (Roche), 0.05 mM CaCl2, and 0.02 mg/ml DNase I (Sigma-Aldrich) at 37°C for 20 minutes. After dissociation, the cells were collected and further dissociated using 0.05% trypsin-EDTA (Corning) at room temperature for 5 minutes before adding trypsin inhibitors, 2 mg/ml soybean trypsin inhibitor (Sigma-Aldrich) and 2% Fetal Bovine Serum (FBS, HyClone) in 1X PBS (Lonza). Cells were washed with Hank’s Balanced salt solution (HBSS) (Lonza) and plated on 3.33, 3.71, or 3.96 rat tail collagen type I (Corning) 2 mm thick gel and grown in PDAC 19 cell medium (Hereafter known as Complete Ductal Media) containing 0.25 mg/ml bovine serum albumin (BSA) (VWR), 5 mg/ml D-glucose (VWR), 0.1mg/ml soybean trypsin inhibitor type I (STI) (Sigma-Aldrich), 5 μl/ml insulin-transferrin-selenium (ITS) (Gibco), 25 μg/ml bovine pituitary extract (BPE) (Gibco), 20 ng/ml epidermal growth factor (EGF) (Sigma-Aldrich), 5 nM 3, 3′, 5-triiodo-L-thyronine (TLT) (Sigma-Aldrich), 1 μM dexamethasone (Sigma-Aldrich), 100 ng/ml cholera toxin (Sigma-Aldrich), 1.22 mg/ml nicotinamide (Sigma-Aldrich), 5% Nu-serum IV culture supplement (Corning), 0.5X penicillin/streptomycin (VWR), and 2.5 mg/ml fungizone (Life Technologies) in DMEM:Ham’s F12 medium, and maintained at 37°C in 5% CO2. We passaged the cell lines every two to four days, allowing them to reach approximately 80-90% confluency before splitting them to freeze and continue passaging (Split cells at 1:3 or 1:4 depending on growth rate). To generate Dropout media to produce low endotoxin levels, the following components were removed from the Complete Ductal Media: BSA, STI, ITS, BPE, cholera toxin, and fungizone. To compensate for the loss of BPE, a serum-replacement supplement, we increased the concentration of Nu-serum IV from 5% to 10% and Nu-serum IV lots with low levels of endotoxin were used. To passage the cells, the media was aspirated and collagen was digested in warmed DMEM: Ham’s F12 with 2.22 mg/mL collagenase type XI (Sigma-Aldrich) solution and 0.05 mM CaCl2. The gels were then placed in a 37°C water or bead bath and digested for 8-20 minutes until the collagen gel was dissolved. The cells were collected and dissociated using 0.05% trypsin-EDTA at room temperature for 5 minutes, followed by trypsin inhibition with 2 mg/ml soybean trypsin inhibitor in 2% FBS in 1X PBS. Cells were washed 4 times with HBSS before replating in Complete or Dropout media for continued propagation or experiments. Cell line images were taken at 40X using AE2000 Inverted Microscope Series (Motic). 20 2.3 Proliferation Assay When the cells reached approximately 80-90% confluency, they were passaged and resuspended in 4-5 ml of Complete Ductal Media. Subsequently, 100 μl of the cell suspension was aliquoted into 2 Eppendorf tubes for cell counting using the Via1-Cassette (ChemoMetec) to determine the cell concentration. For the proliferation assay, the Complete Ductal Media was modified by replacing Dulbecco’s modified Eagle’s medium/Ham’s F-12 with Phenol Red (Corning) to DMEM/F-12, HEPES, without phenol red (Gibco). The cells were plated on 96-well plates (VWR) with type 1 collagen gels at a density of 750 cells for AC:KTrp53 and DU:KTrp53 complete cell lines and 2500 cells for AC:KPten and DU:KPten complete cell lines. After plating cells, for AC:KPten and DU:KPten complete cell lines, we added RealTime-Glo MT Cell Viability Assay (Promega, G9713) with complete media according to the manufacturer's instructions. For AC:KTrp53 and DU:KTrp53 complete cell lines, CellTiter-Glo Luminescent Cell Viability assay (Promega, G7570) was added in media at selected time points following manufacturer's instructions. The luminescence in the wells was measured at 24, 48, and 72 hours after seeding to evaluate cell proliferation using the Tecan Infinite M1000 plate reader. For RealTime-Glo, the integration time was set to the default at 1000 ms with no attenuation. For CellTiter-Glo, live-cell fluorescence and dead-cell fluorescence were measured at 400Ex nm/ 505Em nm and 485Ex nm/ 520Em with excitation and emission bandwidths at 5 nm. A gain of 60 was chosen for all fluorescence measurements with a total of 50 flashes, a flash frequency of 400 Hz, and an integration time of 20 µs. There is a total of 5 biological replicates for each AC:KTrp53 and DU:KTrp53, and 3 for both AC:KPten and DU:KPten complete cell lines, with a minimum of 3 technical replicates each. 2.4 Gemcitabine Assay When the cell concentration reached the same confluency as described in section 2.3, the cell number was determined using the Via1-Cassette (ChemoMetec). The cells were then plated 21 onto 96-well plates (VWR) with type 1 collagen gels at a density of 5000 cells for AC:KTrp53 and DU:KTrp53 complete cell lines, and 2500 cells for AC:KPten and DU:KPten complete cell lines. The cells were seeded and allowed to adhere for 24 hours at 37°C in a 5% CO2 incubator. After 24 hours, the complete ductal media was replaced with complete ductal media containing Gemcitabine (Sigma-Aldrich) at concentrations ranging from 2000 nM to 0.6 nM for AC:KPten and DU:KPten and 10000 nM to 3.2 nM for AC:KTrp53 and DU:KTrp53 complete cell lines. Additionally, RealTime-Glo MT Cell Viability Assay (Promega, G9713) was added to the media for AC:KPten and DU:KPten complete cell lines, while CellTiter-Glo Luminescent Cell Viability assay (Promega, G7570) was used for AC:KTrp53 and DU:KTrp53 complete cell lines, following manufacturer protocol. The luminescence was measured using the Tecan Infinite M1000 plate reader 72 hours after addition of gemcitabine. The settings for RealTime-Glo and CellTiter-Glo were the same as described in section 2.3 (Proliferation Assay). There is a total of 5 biological replicates for each AC:KTrp53 and DU:KTrp53, and 2 for AC:KPten and 3 for DU:KPten complete cell lines, with a minimum of 3 technical replicates each. 2.5 Histology and Immunohistochemistry Paraffin-embedded sections were prepared and underwent immunohistochemical (IHC) stains, hematoxylin and eosin (H&E), anti-YFP immunohistochemistry, or Alcian blue, as described previously (Lee et al., 2019; Zhang et al., 2023). The stained slides were scanned with a 20x objective using a 3DHISTECH Panoramic MIDI slide scanner (Quorum Technologies Inc. Guelph, Canada) or the 60x oil objective using the Leica THUNDER 3D Cell Imager (Leica Microsystems, Inc., Wetzlar, Germany). All histopathological analyses were conducted on sections containing PDAC unless otherwise specified. Immunohistochemistry and immunofluorescence staining were conducted as previously described (Kopp et al., 2018; Lee et al., 2019; Zhang et al., 2023). Antigen retrieval for GATM, SOX9, YFP, SH3TC2, and LDHD was completed using a low pH buffer at pH 6.0 made from 10 mM citrate buffer. Primary antibodies 22 were rabbit anti-SOX9 (Fisher Scientific, AB5535MI, 1:300), rabbit anti-LDHD (Invitrogen, MA5-36153, 1:100), rabbit anti-SH3TC2 (Abcam, ab204334, 1:100), rabbit anti-GATM (Invitrogen, PA5-109756, 1:500), or goat anti-GFP (Abcam, ab6673, 1:300). Secondary antibodies were Cy3 conjugated AffiniPure Donkey Anti-Rabbit IgG (Jackson ImmunoResearch, 711-165-152, 1:500), Cy5 conjugated Donkey Anti-Rat IgG Antibody (Jackson ImmunoResearch, 712-175-153, 1:500), Horse anti-Goat IgG ImmPRESS Secondary Antibody (Vector Laboratories, MP-7405-15, 1:2), or Horse Anti-Rabbit IgG ImmPRESS Polymer Detection Kit (Vector Laboratories, MP-7401, 1:2). LDHD, SH3TC2, and GATM, were quantified in Adobe Photoshop 2022 using the Black & White function to filter and highlight the brown area (blue filter), and their percentages was calculated relative to the tumor lesion area using ImageJ. SOX9 intensity mean per nuclei was counted using the Imaris Microscopy Image Analysis Software. 2.6 RNA-Extraction and analyses RNA was extracted from cell lines and bulk tissues using the RNeasy Mini Kit (QIAGEN) and the RNA concentration was initially measured using the NanoDrop ND-1000 UV-Vis Spectrophotometer (Thermo Scientific). RNA quality was re-assessed again using the Agilent 2100 BioAnalyzer and RNA with a RNA integrity value >8 was further analyzed. Libraries were prepared using the standard protocol for Illumina Stranded mRNA Prep (Illumina). Samples were sequenced by Illumina NextSeq2000 with Paired-End 61bp x 61bp reads to a depth of approximately 50 million pair end reads per sample. To demultiplex the sequencing data, Illumina’s bcl2fastq2 was used. RNA-sequencing data have been deposited at the GEO under dataset number GSE271004. RNA-sequencing analysis was performed using a previously described method except the read-sequence was aligned to the Mus Musculus (mm39) reference sequence with the addition of a Cre and YFP sequence (Vuilleumier et al., 2019). To identify differentially expressed genes (DEG) across all conditions, we independently analyzed the gene expression profiles of each 23 condition to determine overlapping genes. Gene Ontology analysis was conducted with ClusterProfiler or enrichr and plotted using ggplot2 (Hadley, 2016; Kolde, 2019; Kuleshov et al., 2016; T. Wu et al., 2021; Yu et al., 2012). To calculate the Z-scores, variance stabilizing transformation (VST) was applied to the DESeq2 dataset (dds), and scaled to produce a matrix of Z-scores of samples and gene IDs. Volcano plots and heatmaps we utilized the kalisto-DESeq2 pipeline (v3.18) using VolcaNoseR and pheatmap, respectively (Bray et al., 2016; Goedhart & Luijsterburg, 2020; Kolde, 2019; Love et al., 2014). We applied the reference-based decomposition dataset from Bisque to our bulk samples using scRNA-Seq data from mouse pancreatic cancer (Jew et al., 2020; Schlesinger et al., 2020). We utilized CIBERSORTx to estimate cell type populations, leveraging the ImmuCC signature matrix, which defines the relative compositions of 25 immune cell types in mouse tissues based on microarray-based mRNA expression data and was validated across diverse tissues using flow cytometry (B. Chen et al., 2018; Z. Chen et al., 2017). CIBERSORTx was run with enabled batch correction, disabled quantile normalization, and 100 permutations. Faraoni et al., (GSE189130) and Flower et al., (GSE164180) datasets were processed using the kalisto-DESeq2 pipeline (v3.18) (Faraoni et al., 2023; Flowers et al., 2021). 2.7 Statistical Analysis GraphPad Prism and R were used for analysis and graphical representations. Statistical parameters included the sample size N (number of animals), mean ± SD, mean ± SEM, and statistical significance are reported in the Figures and Figure legends. For statistical tests, the Shapiro-Wilk test was used to assess data normality. If the data was normally distributed, we used an F-test to determine whether to apply the unpaired t-test with or without Welch's correction. For non-normally distributed data, we used the Mann-Whitney U-test. Significance was determined with a threshold of P < 0.05, denoted by an asterisk (*p<0.05, **p<0.01, ***p<0.001, and ****p<0.0001). 24 Chapter 3: Results 3.1 Acinar- and Ductal-Cell-Derived PDAC Tumors can be Transcriptionally Different To determine the influence of cellular origin on the PDAC transcriptome, we used our previously generated mouse models with LSL-KrasG12D and R26RYFP reporter allele expression and loss of p53 (Trp53flox) specifically in acinar- (Ptf1aCreER;LSL-KrasG12DTrp53flox/flox;R26RYFP- hereafter referred to as AC:KTrp53) or ductal- (Sox9CreER;LSL-KrasG12DTrp53flox/flox;R26RYFP- hereafter referred to as DU:KTrp53) cells to collect PDAC derived from different cells of origin (Lee et al., 2019) (Fig. 3.1A). For most AC:KTrp53 and DU:KTrp53 mice, we dissected 1 or 2 distinct tumors and split each of them three ways: one for obtaining fresh frozen sections, another for fixed paraffin embedded sections, and the third portion for culturing cells on collagen (Fig. 3.1B) (Reichert et al., 2013). Histological examination of the paraffin sections was used to validate that the tissue collected contained PDAC derived from YFP+ lineage traced cells (Lee et al., 2019) (Fig. 3.2). First, to further understand the influence of the cell of origin on the transcriptional tumor characteristics, we isolated RNA from the fresh frozen sections and performed bulk RNA-seq analysis of AC:KTrp53 and DU:KTrp53 tumors (Fig. 3.3A). The principal component analysis (PCA) based on the 500 top genes showed distinct clustering based on the cellular origin. We next performed differential gene expression analysis between acinar- and ductal-cell derived tumors (Fig. 3.3B). We found 208 genes preferentially expressed in bulk AC:KTrp53 PDAC and 220 genes enriched in bulk DU:KTrp53 PDAC (Fig. 3.3C). Consistent with the design of our mouse models and Sox9, but not Ptf1a expression in PDAC (Kopp, von Figura, et al., 2012), we found that Cre expression was maintained in DU:KTrp53 derived PDAC, but not AC:KTrp53 PDAC (Fig. 3.3D). As acinar cells dedifferentiate into a more duct-like state, this leads to the silencing or downregulation of Ptf1a leading to loss of Cre expression (L. Zhu et al., 2007). In ductal-cell-derived PDAC, Esr1 is also a significant DEG, likely due to the mapping of a portion of the ER mRNA from the CreER mRNA to the Esr1 locus. The proper segregation of Cre expression 25 based on the model provides supportive evidence that our results are reliable and free from contamination. To determine whether these differentially expressed genes (DEGs) were enough to segregate the acinar- and ductal-cell-derived tumors, we performed hierarchical clustering. This analysis shows that these DEG could segregate AC:KTrp53 and DU:KTrp53 bulk tumors (Fig. 3.3B). These data are consistent with previous studies showing that gene expression differences can distinguish PDAC from different cellular origins (Faraoni et al., 2023; Ferreira et al., 2017; Flowers et al., 2021; Lee et al., 2019). We then performed gene ontology (GO) analysis to measure differences in biological processes between bulk AC:KTrp53 and DU:KTrp53 PDAC genes. Overall, the GO analysis showed that the transcriptomic profiles of bulk AC:KTrp53 and DU:KTrp53 tumors have different enrichment in Biological Processes (Fig. 3.3E). Specifically, we observed using the Mouse Gene Atlas gene set library (Enrichr) that DEGs enriched in bulk AC:KTrp53 tumors were predominantly associated with gastrointestinal cell types (Fig. 3.3F). This is consistent with Cldn18 expression being more highly enriched in acinar-cell-derived PDAC (Fig. 3.3D). Cldn18 expression, like Krt20 positivity and Alcian blue positivity, are known markers for precancerous lesions, including pancreatic intraepithelial neoplasia (PanIN) (Tanaka et al., 2011), and have been previously associated with an acinar cell of origin (Lee et al., 2019). Further, GO analyses with the GO:Biological Process gene set library found genes associated with epithelial maintenance and biosynthesis of various compounds in AC:KTrp53 PDAC but these categories did not reach significance likely due to the low number of DEGs (Fig. 3.3E). GO analysis of pathways enriched in bulk DU:KTrp53 PDAC tumors revealed categories associated with defense response against biotic stimuli, symbiont, and viruses indicating potential interactions between the tumor and viral processes (Fig. 3.3E). These findings are consistent with our previous finding that interferon-response associated genes are enriched in these DU:KTrp53 PDAC (Espinet et al., 2021). However, when looking at the Mouse Gene Atlas gene set library, we found genes associated with the pancreas (Fig. 3.3F), like Pnlip (encodes for pancreatic 26 lipases to digest fat), Ctrl (expressed in acinar cells for lipid digestion), and Cpa1 (encodes for pancreatic enzymes for protein digestion), which are expressed in normal acinar cells. This suggests that differences in the extent of normal or even tumor microenvironmental cells incorporated into the piece of bulk tumor used for RNA sequencing could artifactually influence the genes identified as differentially enriched in the compared groups. 27 Figure 3.1. Schematic of Acinar and Ductal: KTrp53 PDAC Mouse Models A. Schematic of the alleles in AC:KTrp53: Ptf1aCreER; Trp53flox; LSL-KrasG12D; LSL-R26RYFP and DU:KTrp53: Sox9CreER; Trp53flox; LSL-KrasG12D; LSL-R26RYFP. B. Overview of Bulk AC:KTrp53 and DU:KTrp53 tumor collection procedure. 28 Figure 3.2. Histological staining of Representative Acinar and Ductal: KTrp53 Bulk Tumors H&E and YFP staining of AC:KTrp53 and DU:KTrp53 bulk PDAC tumors. Black scale bars are 80 μm. 29 Figure 3.3. Trp53 loss with oncogenic Kras Acinar- and Ductal-Cell-Derived PDAC Bulk Tumors. A. Schematic of bulk tumor sample collection from the AC:KTrp53 and DU:KTrp53 mouse model for RNA sequencing. B. Principal component analysis (PCA) on the transcriptome of AC:KTrp53 and DU:KTrp53 bulk tumors. C. Heatmap of DEG in AC:KTrp53 and DU:KTrp53 PDAC bulk tumors (N=9) (p<0.05, log2FC>1). D. Volcano plot showing fold changes for DEG in AC:KTrp53 and DU:KTrp53 bulk tumors. E. Gene ontology showing the biological process of AC:KTrp53 and DU:KTrp53 bulk tumors. The bubble size reflects the Gene Ratio, the proportions of genes for an ontology term and the color reflects the adjusted p-value. F. Cell Types enriched in AC:KTrp53 and DU:KTrp53 bulk tumors. Legend as described in E. 30 3.2 Experimentally introduced heterogeneity in bulk tumor composition can influence differentially expressed genes. To determine whether there was differential incorporation of non-tumor cell types into each tumor analyzed in our data, we conducted Bisque and CIBERSORT analysis (Fig. 3.4A and 3.4B) (B. Chen et al., 2018; Jew et al., 2020). Both Bisque and CIBERSORT are tools used for deconvolution of bulk RNA-seq data using a reference gene expression matrix (B. Chen et al., 2018; Jew et al., 2020). We also included bulk tumors analyzed in Flower et al. AC (Ptf1aCreER; LSL-KrasG12D; Trp53f/f; R26RtdTomato), Flower et al. DU (Sox9CreER; LSL-KrasG12D; Trp53f/f; R26RtdTomato), Faraoni et al. AC (Mist1CreERT2; LSL-KrasG12D; LSL-Trp53R172H/+; LSL-Rosa26mTmG) and Faraoni et al. DU (Hnf1bCreERT2; LSL-KrasG12D; LSL-Trp53R172H/R172H; LSL-Rosa26mTmG) to increase our sample size and also examine the different cell types in various bulk PDAC datasets derived from tumors of different cellular origins (Faraoni et al., 2023; Flowers et al., 2021). As expected from the enrichment of the “Pancreas” gene set in our GO analyses (Fig. 3.3F), 3 of the 5 DU:KTrp53 tumors had a transcriptional program consistent with a portion of the bulk “tumor” being comprised of acinar cells. However, these particular AC:KTrp53 tumors did not have an expression pattern for acinar cells. Interestingly, acinar-cell-derived tumors isolated by Flowers et al. and ductal-cell-derived tumors from Faraoni et al. also had signs of acinar cell signatures being captured. These data illustrate the significant influence that small changes in cellular composition can have on transcriptomic analyses. 31 Figure 3.4. Bisque and CIBERSORT analysis on Acinar- and Ductal-Cell-Derived PDAC Bulk Tumors. A. Tumor composition of AC:KTrp53 (N=4) and DU:KTrp53 (N=5) bulk tumors, Flower et al.’s (AC, N=4 and DU, N=3), and Faraoni et al.’s (AC, N=3 and DU, N=3) bulk PDAC dataset using the tool Bisque. B. CIBERSORT algorithm for immune cell populations on the same samples used in panel A. 32 3.3 Differentially Expressed Genes in Bulk RNA Sequencing Could be Associated with Non-tumor Epithelial Cell Types. To test whether previously identified markers of PDAC cellular origin (Faraoni et al., 2023; Flowers et al., 2021) may or may not be independent of tumor composition, we examined the expression of acinar cell of origin markers Krt20 (Lee et al., 2019), Gata4 (Flowers et al., 2021), and Cldn18 (Flowers et al., 2021); as well as ductal cell of origin markers Agr2 (Ferreira et al., 2017) Nt5e (Faraoni et al., 2023), and the IFN Signature genes (Espinet et al., 2021) in our bulk acinar- and ductal-cell-derived PDAC data. We observed that genes known to be expressed in epithelial cells, such as Krt20, Gata4, and Cldn18 had a gene expression pattern that matched previously published markers of acinar-cell-derived PDAC (Fig. 3.5A-C). IFNSign gene expression, which was originally identified using sorted PDAC cells, tended to be more ductal-cell-derived PDAC enriched, as previously reported (Espinet et al., 2021; Flowers et al., 2021; Lee et al., 2019). In contrast, the expression of Nt5e and Agr2 showed higher expression in the acinar-cell-derived tumors compared to ductal-cell-derived tumors, which is different than their previously reported expression pattern (Fig. 3.5E and 3.5F) (Faraoni et al., 2023; Ferreira et al., 2017). Nt5e expression has been reported to be very high in endothelial cells, as well as expressed in Muc5b-positive ducts in chronic pancreatitis (Faraoni et al., 2023; O’Brien et al., 2023). In the Faraoni et al’s dataset, our Bisque analysis shows that 2 out of 3 samples have enrichment for a pericyte population (Fig. 3.4A). However, in our dataset, only 2 out of 5 samples contain pericyte enrichment, with all 4 of our acinar-cell-derived PDAC contained endothelial cells (Fig. 3.4A). Together these data suggest that Nt5e may have been identified in Faroani et al. as a differentially expressed gene based on the bias in endothelial contribution in the ductal-cell-derived tumors. Moreover, as Nt5e is also involved in the inflammatory responses driven by the TME (Faraoni et al., 2023), we performed CIBERSORT analysis to assess any differences in immune cell types present in acinar- and ductal-cell-derived PDAC (Fig. 3.4B). We found that the amount of many immune cells populations fluctuated from sample to sample in the three analyzed 33 data sets, however none of these differences seemed to have a ductal- or acinar-cell-derived PDAC bias (Fig. 3.4B). Nonetheless, these fluctuations, in combinations with changes in other cell types could contribute to changes in Nt5e expression (Faraoni et al., 2023). Agr2 expression was also different in our datasets as compared to previous publications (Fig. 3.5F). To assess whether the discrepancies in Agr2 expression might also be due to differences in cellular composition, we examined which mouse cell types have high expression of Agr2 using the Tabula-muris resource (Fig. 3.6A) (The Tabula Muris Consortium et al., 2018). We found that cells of the intestinal epithelium have the highest expression of Agr2 (Fig. 3.6B). Since our acinar-cell-derived tumors have high enrichment of the large intestine and stomach gene set libraries (Fig. 3.3F), our finding that Agr2 expression tends to be higher in our acinar-cell-derived PDAC supports the conclusion that Agr2 expression might be a reflection of the presence of the mucinous/gut endoderm phenotype typically associated with PanIN lesions. This observation could be due to the fact that organoid cells in the Ferreira et al. dataset, established from YFP-positive cells from both models, could have maintained a mucinous phenotype compared to the bulk PDAC RNA-Seq datasets. Additionally, these YFP-positive cells could potentially include precursor cells rather than exclusively PDAC tumor cells. In summary, there are differences between acinar- and ductal-cell-derived tumors in AC:KTrp53 and DU:KTrp53 bulk tumors. The small number of differentially expressed genes could be due to significant variation in tumor composition within our bulk PDAC samples. Additionally, the presence of normal acinar and ductal cells in bulk tumors could also obscure the identification of cellular origin markers (Fig. 3.4A). Therefore, a microenvironment-free approach would be beneficial for robustly identifying markers of tumor cellular origin. 34 Figure 3.5. Trp53 loss with oncogenic Kras Acinar- and Ductal-Cell-Derived PDAC Bulk Tumors. Dot plot of z-scores for acinar markers A. Agr2, B. Gata4, and C. Cldn18, and ductal markers, D. IFNSign E. Nt5e, and F. Agr2 between AC:KTrp53 (N=4) and DU:KTrp53 (N=5) bulk samples. 35 Figure 3.6. Tabula Maris Analysis on Agr2 Expression. A. tSNE plot showing FACS-filtered gene expression analysis of Agr2 across all tissues, showing log-transformed CPM expression specifically in goblet and epithelial cells. B. Cumulative distribution of log-transformed CPM in top 6 tissue class. 36 3.4 Transcriptional Differences Between Acinar- and Ductal-Cell-Derived PDAC Tumors persist in In-vitro conditions To minimize the influence of the TME, we chose to culture the tumor cells in vitro to isolate the tumor cell expression profile. To accomplish this, we established primary PDAC cell lines in complete media (see Materials and Methods) using a protocol known for supporting normal ductal cells (Reichert et al., 2013) that does not require complete dissociation and we hypothesized would reduce the amount of selective pressures that the tumor cells might be subjected to. This protocol was also designed to negatively select against fibroblast cell populations, resulting in a tumor epithelial culture within 1-2 passages. While we do not have quantitative data such as flow cytometry to support tumor cell purity, we observed that the cells displayed a more epithelial-like characteristics rather than fibroblastic ones. As indicated above, we utilized a one-third portion of the tumor identified by necropsy when AC:KTrp53 and DU:KTrp53 mice near or at humane endpoint (Section 3.1) to establish primary PDAC cell cultures (Fig. 3.8), as well as from additional AC:KTrp53 and DU:KTrp53 tumors that were not subjected to bulk RNA sequencing. In addition, to begin to understand whether gene expression differences identified between cellular origins in one genetic background would also be present in another genetic background, we also established primary PDAC cell cultures from mice with LSL-KrasG12D and R26RYFP reporter alleles and reduced Pten (Ptenflox) in acinar- (Ptf1aCreER;LSL-KrasG12D;Ptenflox/+;R26RYFP mice – AC:KPten mice) or ductal- (Sox9CreER;LSL-KrasG12D;Ptenflox/+ or flox/flox;R26RYFP mice – DU:KPten mice) cells (Dou et al., 2022; Hill et al., 2010; Kopp et al., 2018) (Fig. 3.7 and 3.8). These mouse models both form PDAC, albeit with different latencies and precursor lesions (Flowers et al., 2021; Lee et al., 2019). Tumors in AC:KPten mice were associated with PanIN precursor lesions; and tumors in DU:KPten mice were isolated from IPMN-associated PDAC (Cell line 409) or microscopic preIPMN or PanIN-associated PDAC (Cell line 367 and 746A). The addition of the AC:KPten and DU:KPten mouse models alongside the AC:KTrp53 and DU:KTrp53 mouse model in vitro allows 37 us to explore the RNA-sequencing based transcriptional differences in PDAC across cellular origin and genotypes (Fig. 3.9A). Consistent with our bulk tumor analysis, our RNA sequencing results based on PDAC cell lines grown in complete media indicate that tumor cells derived from an acinar cell or ductal cell origin exhibited different transcriptomic profiles (Fig. 3.9B and 3.9C). Interestingly, the primary 2 axes on a PCA plot based on 500 most variable genes for our large dataset of AC:KTrp53 (N=15) and DU:KTrp53 (N=20) cell lines did not reveal distinct clusters based on the cellular origin (Fig. 3.9B). PC1 and PC2 were also not clearly separating by biological sex (Fig. A.1). Principal component analysis of the top 500 most variable genes expressed in AC:KPten (N=3, female) and DU:KPten (N=3, female) cell lines showed that cellular origin could explain the majority of the differences between samples (Fig. 3.9C). Analysis of the DEGs between an acinar and ductal cell of origin showed a total of 1468 genes enriched in AC:KTrp53 cell lines and 1023 genes enriched in DU:KTrp53 cell lines (Fig. 3.9F). The AC:KPten and DU:KPten complete cell lines had fewer DEG at 582 with 318 genes enriched in AC:KPten cell lines and 264 genes enriched in DU:KPten cell lines (Fig. 3.9G). As before, these results identified our internal control reference marker, Cre, as a ductal-cell-derived PDAC differentially expressed gene (Fig. 3.9G-F). We again identified Krt20 and Gata4 as an acinar-cell-derived tumor marker in the AC:KTrp53 vs DU:KTrp53 cell line and AC:KPten vs DU:KPten cell line comparisons. To test whether the genes differentially expressed between AC:KTrp53 and DU:KTrp53 cell lines or AC:KPten vs DU:KPten cell lines could reliably separate cellular origin within the same genotype, we performed hierarchical clustering of all the Trp53 samples (Fig. 3.9D) or all the KPten samples (Fig. 3.9E). These results demonstrate that DEGs for the AC:KPten vs DU:KPten cell line comparison accurately clustered based on cellular origin (Fig. 3.9E). Although the AC:KTrp53 and DU:KTrp53 complete cell lines primarily separated by their DEG into two distinct cellular origins, a few DU:KTrp53 complete cell lines did not cluster with their experimentally defined cellular origin (Fig. 3.9D). In fact, it appeared that many of the AC:KTrp53 cell lines clustered into a separate group from the other AC:KTrp53 38 cell lines (Fig. 3.9D) maintained Trp53 expression due to incomplete recombination of the flox allele or incorrect genotyping of this allele in AC:KTrp53 mice (Fig. 3.10A). However, the other group of AC:KTrp53 cell lines was enriched in glyoproteins, thus it is unclear which gene program may be more significant in driving the separation. In contrast to the Trp53flox allele, which removes a large portion of the Trp53 gene, the Pten gene only floxes exon 5 (Fig. 3.10B). This was consistent with previous results, which found that 2 DU:KPten cell lines retained one of the wildtype allele (Dou et al., 2022). KPten cell lines also universally had undetectable reads from nucleotide positions 4500 to 6000 (Fig. 3.10B). However, upon closer inspection, this region contained a repetitive ATAT sequence that is likely responsible for the alignment dropout. We then analyzed the GO profiles associated with genes enriched in different cells of origin for the KTrp53 and KPten cell line comparisons (Fig. 3.9H and 3.9I). Consistent with our previous analysis of the bulk Trp53 tumors, the AC:KTrp53 cell lines s enrichment expression of genes associated with the stomach and intestine (Fig. 3.9H and 3.9I), while the DU:KTrp53 cell lines had enrichment of terms associated with interferon response, such as Response to Virus and Response to Type 2 Interferon (Fig. 3.9H). AC:KPten cell lines were enriched in ontology categories associated with GPCR signaling and endoderm formation, while DU:KPten complete cell lines were enriched in genes associated with cell cycle processes and response to endogenous as well as genes non-specifically expressed across a variety of cell types (Fig. 3.9I). In sum, acinar-cell-derived PDAC appears to be associated with a gastric/intestinal epithelial gene expression program that have reduced expression in the ductal-cell-derived PDAC. The GO analysis suggested that there may be differences in cellular proliferation between PDAC from different cellular origin (Fig. 3.9). We further investigated whether we could detect differences in proliferation cell culture. To do this, we performed a RealTime-Glo proliferation assay and observed that DU:KTrp53 cell lines compared to AC:KTrp53; however, AC:KPten cell lines tended to be more proliferative than DU:KPten cell lines (Fig. 3.9J and 3.9K). This suggested 39 that the proliferative rate was more dependent upon the cell line in question, rather than whether it was derived from an acinar or ductal cell. 40 Figure 3.7. Schematic of Acinar and Ductal: KPten PDAC Mouse Models. Schematic of the alleles in A. AC:KPten: Ptf1aCreER; Ptenflox; LSL-KrasG12D; LSL-R26RYFP and B. DU:KPten: Sox9CreER; Ptenflox; LSL-KrasG12D; LSL-R26RYFP 41 Figure 3.8. Acinar and Ductal: Trp53 and KPten Complete Cell Lines. Representative images of AC:KTrp53, DU:KTrp53, AC:KPten and DU:KPten cell lines in Complete media. Scale bar are 100μm taken at 40X magnification. 42 Figure 3.9. Loss of Trp53 or Pten with the Activation of Oncogenic KrasG12D in Acinar- and Ductal-Cell-Derived PDAC Cell lines in Complete media. A. Schematic of PDAC mouse models in Complete media collected for RNA sequencing. B. Principal component analysis (PCA) on the transcriptome of AC:KTrp53 and DU:KTrp53 and C. AC:KPten and DU:KPten PDAC cell lines. D. Heatmap of DEG and Trp53 expression in AC:KTrp53 (N=34) and DU:KTrp53 (N=14) (padj.<0.05, log2FC>1) and E. AC:KPten (N=3) and 43 DU:KPten (N=3) (padj.<0.05, log2FC>1) PDAC cell lines. F. Volcano plot showing DEG in AC:KTrp53 and DU:KTrp53 PDAC cell lines. G. Volcano plot showing DEG in AC:KPten and DU:KPten PDAC cell lines. H. Gene Ontology and Cell Types for AC:KTrp53 and DU:KTrp53 and I. AC:KPten and DU:KPten cell lines. Legend as described in Figure 3.3E. Fold increase in cell number for J. AC:KTrp53 and DU:KTrp53 cell lines and K. AC:KPten and DU:KPten cell lines. The center line represents the median, and the top and bottom of the box represent the first and third quartiles, while the whiskers extend to 1.5 times the interquartile range (IQR) from the edges of the box. 44 Figure 3.10. Comparison of Read Depth between AC and DU Samples. The plot shows read depth and position in transcripts for A. the Trp53 gene with exon 2-10 boxed in green with AC:KTrp53 cell lines in red and DU:KTrp53 cell lines in blue. The top 3 acinar cell lines with Trp53 expression are shown. B. shows the Pten gene with exon 5 boxed in green with AC:KPten in red and DU:KPten in blue. 45 3.5 Enrichment of EMT Genes in Ductal-Cell-Derived PDAC Tumors by Media Composition Previous publications have shown that the media formulation can affect the cell state of PDAC cells (Raghavan et al., 2021). Our research group had previously noted that certain components of our complete ductal media could trigger a strong pro-inflammatory response in macrophages (Laskin et al., 1994), which led to the identification and removal of 6 media factors and created a “Dropout” media formulation that, among other things, removed high levels of insulin and endotoxin from the PDAC cell “microenvironment” (Dou et al, 2022). We then grew our AC:KTrp53, DU:KTrp53, AC:KPten, DU:KPten cell lines in this media to examine whether an reducing these media components affected gene expression similarly for acinar- and ductal-cell-derived PDAC. We found that DU:KPten cell lines increased their growth and changed morphology, appearing to lose cell contact inhibition, by passage 5 (Fig. 3.11A). Moreover, we found that GO categories associated with “response to other organisms” and antimicrobial genes, such as Tnip3, Cxcl3, and Slpi were reduced in cell lines cultured for >3 passages in dropout media compared to cell lines grown in complete media, regardless of genotype (Fig. 3.11B-F). Particularly for DU:KPten cell lines, consistent with these cells transitioning towards a less epithelial phenotype, there was downregulation of Cdh1 and upregulation of Cdh2, Snai1, Vim, Zeb1, and Zeb2, suggestive of an epithelial-to-mesenchymal-like transition. These changes suggest a shift towards a different cellular identity. However, no noticeable difference in growth during passaging was present for AC:KTrp53, DU:KTrp53, AC:KPten, and DU:KPten cell lines. Interestingly, AC:KTrp53 and DU:KTrp53 demonstrated better survival and recovery from single-cell growth conditions, while AC:KPten and DU:KPten failed to recover. The comparisons between dropout and complete gene expression revealed that transitioning to dropout conditions resulted in decreased expression of gastric gene, and also led to an enrichment of EMT genes in DU:KPten, which aligned with the observed morphological changes. As Tp63 is crucial for the 46 development of epithelial tissue, the change is EMT genes could suggest changes in Tp63 related genes, despite Tp63 not being expressed in mice (Martens et al., 2022). 47 Figure 3.11. Comparisons of GO Biological Process between Acinar and Ductal Cells Under Complete and Dropout Conditions. Gene Ontology (GO) analysis comparing enriched biological processes for A. AC:KTrp53 complete and AC:KTrp53 dropout media B. DU:KTrp53 complete and DU:KTrp53 dropout media, C. AC:KPten complete and AC:KPten dropout media and D. DU:KPten complete and DU:KPten dropout media using ClusterProfiler. 48 3.6 Transcriptional Differences in Acinar- vs. Ductal-Derived PDAC Tumors in Dropout Media. We then examined whether removing the shared inflammatory response program present in the complete media uncovered gene expression differences more specific to an acinar- or ductal-cell of origin (Fig. 3.12A). We found that AC:KTrp53 and DU:KTrp53 cell lines or AC:KPten and DU:KPten cell lines cultured in “dropout” media were separated in the principal component analysis by cell of origin (Fig. 3.12B-C). This suggested again that differential gene expression analysis can identify differences based on PDAC cell of origin. When DEG were used to perform a hierarchical cluster analysis of either the KTrp53 or KPten cell lines, they clustered based on cell of origin. The 708 genes enriched in the AC:KTrp53 dropout cell lines and the 1976 genes enriched in AC:KPten dropout cell lines were still enriched for genes expressed in the small intestine and stomach, while the 680 genes enriched in DU:KTrp53 dropout cell lines and the 2320 genes enriched in DU:KPten dropout cell lines were enriched in cell growth and proliferation (Fig. 3.12F-I). Additionally, our internal control, Cre, was again expressed specifically in the ductal-cell-derived PDAC cell lines. 49 Figure 3.12. Loss of Trp53 or Pten with the Activation of oncogenic KrasG12D in Acinar- and Ductal-Cell-Derived PDAC Cell Lines in Dropout Media. A. Schematic of PDAC mouse models in dropout media collected for RNA sequencing. B. Principal component analysis (PCA) on the transcriptome of AC:KTrp53 and DU:KTrp53 and C. AC:KPten and DU:KPten PDAC cell lines in dropout media. D. Heatmap of DEG in AC:KTrp53 (N=2) and DU:KTrp53 (N=2) (padj.<0.05, log2FC>1) and E. AC:KPten (N=3) and DU:KPten (N=2) (padj.<0.05, log2FC>1) PDAC cell lines in dropout media. F. Volcano plot showing DEG in AC:KTrp53 and DU:KTrp53 PDAC cell lines in dropout media. H. Gene Ontology and Cell Types for AC:KTrp53 and DU:KTrp53 and I. AC:KPten and DU:KPten cell lines in dropout media. Legend as described in Figure 3.3E. 50 3.7 Reported markers of PDAC cellular origin are not reliable markers of cellular origin across genotypes and contexts. We then revisited the 6 previously reported markers of cellular origin (Espinet et al., 2021; Faraoni et al., 2023; Ferreira et al., 2017; Flowers et al., 2021; Lee et al., 2019), and observed varying expression patterns per genotype and condition. We found that acinar markers reached statistical significance in predicting cellular origin under more conditions compared to ductal markers (Fig. 3.13A-C). Specifically, Krt20, Cldn18, and Gata4 were generally significant in bulk and complete media conditions (Fig. 3.13A-C). However, the dropout condition did not show significance, likely due to the small sample size. In contrast, the ductal markers, Agr2, IFNSign, and Nt5e, showed no significance across all in vitro conditions, consistent with our bulk tumor results (Fig. 3.13D-F). In sum, re-evaluating the six previous markers in vitro, we found acinar markers to be more consistent across conditions, whereas ductal markers failed to differentiate datasets by cellular origin (Fig. 3.4). 51 Figure 3.13. Z-Score of Selected Markers Across All Conditions and Datasets. Mean Z-Score for Acinar markers: A. Krt20 (Lee et al., 2019), B. Cldn18 (Flowers et al., 2021), C. Gata4 (Flowers et al., 2021), and Ductal markers: D. Agr2 (Ferreira et al., 2017), E. IFNSign (Espinet et al., 2021), and F. Nt5e (Faraoni et al., 2023), across all conditions (Bulk, Complete, and Dropout cell lines) including Faraoni et al., and Flower et al.’s datasets. 52 3.8 Proposing New Markers for Cellular Origin Classification in PDAC. We next examined whether we could use our own datasets to identify genes highly conserved and specific to PDAC derived from acinar or ductal cells across all conditions. We first examined the similarity and clustering of the transcriptomic profiles from all the sequenced PDAC samples from our datasets, and found that our in vitro samples clustered separately from our bulk samples (Fig. 3.14A). This findings aligns with the percentage of gene expression that is predicted to be derived from non-tumor cells in each bulk sample (Fig. 3.5). We also observed that all the PDAC cell lines clustered together and showed high similarity (Fig 3.14A). Finally, we note that neither the bulk tumors or cell lines were further separated by cell of origin nor consistently by cell culture condition, suggesting that the variability depicted in this clustering analysis is not driven by cell origin. 3.9 Identification of Conserved Cellular Origin-Specific Genes in PDAC from In-vitro analysis We next excluded the bulk samples from our efforts to identify markers of cellular origin due to variable RNA contribution from different tumor microenvironmental cell types. Using all the ductal- and acinar-cell-derived PDAC cell line comparisons, we overlapped the DEG based on the cellular origin and found 17 genes that were conserved across all conditions (Fig. 3.14B). Despite identifying these genes as consistently differentially expressed, a heatmap of the 17 genes expression across all of our samples, including the bulk samples, show that these genes tend to be differentially expressed, but there is inconsistent expression across samples from a given cellular origin (Fig. 3.14B). To examine the variability, we calculated the coefficient of variation (CV) for each group. We found that AC markers on AC samples had a CV of 167%, and DU markers on DU samples had a CV of 114%, suggesting that gene expression variability exists within each group. For example, the expression of Sox9 is higher in ductal-cell-derived PDAC cell lines compared to the acinar cell counterparts, but Sox9 expression increases in acinar-cell-53 derived bulk tumors (Fig. 3.14B and Fig. 3.15F) suggesting its expression would not be a good readout in clinical samples of the cellular origin of PDAC. We also tried to identify candidate genes among the 17 genes for further validation using protein expression analysis via immunohistochemistry or immunofluorescence (Fig. 3.15A and 3.15B). However, these results indicated no significant difference in protein expression, suggesting that these markers are again unsuitable for identifying PDAC of different cellular origins in clinical samples (Fig. 3.15C-F). These findings emphasize the need for larger sample sizes to capture the heterogeneity between samples and highlights the limitation of relying solely on any one transcriptional marker to classify the cellular origin of PDAC. We then examined whether an index of markers might be able to consistently identify the cellular origin of PDAC. To do this, we used the group of 7 acinar-cell-derived PDAC markers and the 10 ductal-cell-derived PDAC markers to determine whether the combined expression of these genes could reproducibly separate ductal- from -acinar-cell-derived tumors. From these analyses, the acinar-cell-derived markers are good at differentiating the PDAC of acinar cell of origin from all of our datasets, but many of the differences do not reach statistical significance (Fig. 3.16A). Additionally, these differences are, at best, trending in comparable datasets from other animal models (Fig. 3.4A). Similarly, our “Ductal” cell marker group effectively distinguished ductal-cell-derived tumors from acinar-cell-derived tumors in previous mouse models (Fig. 3.16B). Together these analyses suggest that our acinar cell of origin markers may have some value in distinguishing differences in vitro or in isolated tumor cells, but their use across modalities/contexts/genotypes remains to be determined. 54 Figure 3.14. Comparative Transcriptomic of Acinar- and Ductal-Cell-Derived Tumors. A. Spearman correlation plot displaying all the samples across all three conditions (Bulk, Complete, and Dropout cell lines). B. Heat map of conserved differentially expressed genes (DEG) between all samples: 7 acinar genes and 10 ductal genes (padj.<0.05, log2FC>1). 55 Figure 3.15. Protein Profiling of Acinar- and Ductal-Cell-Derived Tumors Markers. A. LDHD, SH3TC2, GATM, and SOX9 staining on AC:KTrp53 (N=4) and DU:KTrp53 bulk tumors (N=4). B. LDHD, SH3TC2, GATM, and SOX9 staining on AC:KPten (N=4) and DU:KPten bulk 56 tumors (N=4). White scale bars are 50 μm and black scale bars are 80 μm. Positive Area % comparing C. GATM, D. LDHD, and E. SH3TC2 expression between AC:KTrp53 (N=4) and DU:KTrp53 (N=4) (Square) and AC:KPten (N=4) and DU:KPten (N=4) PDAC sections (Circles). F. Intensity Mean of SOX9 expression between AC:KTrp53 (N=4) and DU:KTrp53 (N=4) (Square) and AC:KPten (N=4) and DU:KPten (N=4) PDAC sections (Circles). 57 Figure 3.16. Z-Score of Our Markers Across All Conditions and Datasets. Mean Z-Score for our A. Acinar Markers and B. Ductal Markers across all conditions (Bulk, Complete, and Dropout cell lines) including Faraoni et al. and Flower et al.’s datasets. 58 3.10 Translation of Mouse-Derived Transcriptomic Markers into Clinical PDAC Subtypes We next aimed to assess the applicability of our mouse-derived transcriptomic markers to clinical datasets to explore the potential in identifying the cellular origin in clinical PDAC patients. Before exploring all publicly available PDAC datasets, we initially compared our dataset with Maurer et al.'s laser captured microdissected epithelial PDAC tissue from clinical PDAC samples (Maurer et al., 2019). Because the tissue is microdissected to purify the tumor cells, we felt this might be the closest to our in vitro isolated tumor cells. Through analyzing the average expression of our 7 acinar-cell-derived PDAC and 10 ductal-cell-derived PDAC markers in each of these samples, we aimed to see if the patient samples were separated into two distinctive groups: 1 with high “Acinar” and low “Ductal” markers and one with the opposite pattern. However, most samples did not follow either of these patterns, suggesting that they do not distinguish groups of patients that might have tumors arising from different cellular origins (Fig. 3.17A). We initially attempted to categorize Maurer et al. clinical epithelial samples based on our set of 17 markers as either acinar or ductal (Fig. 3.17B). However, many samples showed no significant expression differences between our acinar and ductal markers. Therefore, we incorporated the ductal IFNSign gene set into our ductal signature (Fig. 3.17C) (Espinet et al., 2021). Despite these efforts, while some samples could be distinguished, the majority remained indistinguishable. Subsequently, instead of using individual markers, we calculated the difference between AC and DU samples, with AC as positive and DU as negative, separated into quartiles (Q1:DU, Q2:Unspecified, and Q3:AC). We observed partial separation using our own markers, but many clinical samples remained unclassified (Fig. 3.17D). So, we further explored combining our ductal-cell-derived markers with the IFNSign ductal markers once again, but this did not improve classification results (Figure 3.17E) (Espinet et al., 2021). From the samples that separated, we wanted to visualize if they would separate into distinct clusters, but when visualized using a PCA plot the samples showed no distinct separation (Fig. 3.17F). Even with the expanded set of ductal markers, the samples in the clinical dataset were not separated into clear groups (Fig. 3.17G). 59 These results collectively suggest that our transcriptomic markers, even when combined with other datasets, are not able to accurately categorize clinical epithelial PDAC samples to their cellular origin. Therefore, working with bulk tumors where different cell types are also present would pose an even greater challenge to accurately identify the cellular origin. 60 Figure 3.17. Comparisons of Our Markers with Clinical Epithelial PDAC Dataset. A. Hierarchical clustering on our acinar- and ductal-cell-derived PDAC markers on Maurer et al. (2019) epithelial PDAC clinical samples. B. Our acinar- and ductal-cell-derived PDAC markers on Maurer et al. (2019) epithelial PDAC clinical samples. C. Our acinar- and ductal-cell-derived PDAC Markers with IFNSign dataset on Maurer et al. (2019) epithelial PDAC dataset. D. The differences in expression of our acinar- and ductal-cell markers applied to Maurer et al. (2019) epithelial PDAC clinical samples, separated into quartiles (Q1:DU, Q2:Unspecified, and Q3:AC). E. The difference in expression between our acinar- and ductal-cell markers with IFNSign included in the ductal gene set, following the same quartiles as in panel B. F. PCA plot visualizing the samples separated into quartiles on Maurer et al.’s (2019) epithelial dataset. G. PCA plot 61 visualizing the samples separated into the same quartiles as panel D, but with IFNSign included in the ductal gene set as shown in panel C. 62 3.11 Clinical Acinar and Ductal Cell Markers show no enrichment in Acinar- or Ductal-Cell-Derived Mouse Tumors Recent studies have utilized CRISPR-mediated in vitro transformation of normal ductal or acinar cells from human islet isolations, to determine whether human acinar or ductal cells can form tumors after induction of common PDAC-associated genetic changes (Y. Xu et al., 2024). Through these analyses, they identified genes differentially expressed between ductal- and acinar-cell-derived PDAC in a human background (Y. Xu et al., 2024). To determine whether these human-based cell of origin markers could distinguish cellular origin in experimental models of spontaneous tumors from mice, we examined the expression of the conserved human genes in our mouse datasets (Fig. 3.18), as well as those of other mouse models with different cellular origins (Faraoni et al., 2023; Flowers et al., 2021). We found that these genes were generally uniformly expressed across each dataset regardless of cellular origin (Fig. 3.18). This suggests that these markers will not be accurate of cellular origin in tumors spontaneously arising from genetically engineered mouse models. 63 Figure 3.18. Acinar- and Ductal-Cell-Derived PDAC markers from Xu et al. A heatmap displaying the expression levels of KRASG12V (K) expressing organoids with knocked out p16/CDLN2A (P), TP53 (T) and SMAD4 (S) genes in acinar organoids and ductal cell lines using the CRISPR/Cas9 system, A. 32 Acinar-KPTS high genes. B. 342 Ductal-KPTS high genes, as identified in Xu et al. study across all conditions (Bulk, Complete, and Dropout cell lines) including Faraoni et al., and Flower et al.’s datasets. 64 3.12 Both Acinar- and Ductal-Cell-Derived tumors classified as P-EMT Subtype over C-EMT Subtype Previous studies have suggested that the classical PDAC subtype is representative of an acinar cell of origin, while the basal PDAC subtype is associated with a ductal-cell-of-origin (Flowers et al., 2021; Lee et al., 2019). To begin to ask whether basal and classical PDAC subtypes were similarly associated with cell of origin in our datasets, we first used an established dataset where classical and basal subtype tumors had been identified in FACS-isolated mouse PDAC tumors (Aiello et al., 2018). Using principal component analysis, these tumors were separated into two groups (Fig. 3.19A and 3.19B). From previous data, these two groups were identified as those with partial epithelial to mesenchymal transition (P-EMT), with characteristics of classical subtype PDAC, and those with complete EMT (C-EMT) that were similar to basal subtype PDAC (Aiello et al., 2018). We then used the first PCA1 from these data to determine if the tumors from our acinar- and ductal-cell-derived tumor datasets group more closely with the C-EMT or P-EMT samples. We found that all of our complete and dropout cell lines clustered more closely with the P-EMT or the classical subtype (Fig. 3.19C). These data suggest that none of the tumors in our datasets were similar to the C-EMT, and potentially basal subtype PDAC, that had been previously identified. 65 Figure 3.19. PCA analysis of C-EMT and P-EMT subtypes from Aiello et al. PCA plot showing C-EMT, P-EMT, in vitro cell lines grown in complete media for A. AC:KTrp53, and DU:KTrp53, and also B. AC:KPten, and DU:KPten samples. C. PC1 of Aiello et al. (2018) C-EMT and P-EMT markers, and all the in vitro samples. D. PC1 of Classical and Basal markers from Moffitt et al. applied on all the in vitro samples, and epithelial PDAC clinical samples from Maurer et al (2019). 66 3.13 Cellular Origin and Relationship to Clinical PDAC Subtypes To further investigate the PDAC subtype of our mouse tumors of different cellular origins, we applied the Moffitt et al. (2015) classical and basal subtype classification onto the Maurer et al. (2019) epithelial dataset as a reference and then compared these to our samples (Fig. 3.19D). As a reminder, the human tumor epithelial samples from the Maurer et al. (2019) dataset were isolated by laser capture microdissection. Although there is a great degree of overlap in the expression of genes defining the basal and classical subtypes in these patient tumors (Fig. 3.19D), our mouse tumors of different cellular origins were closer to the basal subtype tumors from the Maurer et al (2019) dataset (Fig. 3.19D). Surprisingly, this is the opposite conclusion of what we found using the mouse EMT classification from Aiello et al. (2018) (Fig. 3.19C). Overall, these findings suggest that PDAC derived from different cellular origins in mice are not consistently associated with one PDAC subtype over another. To further examine the inconsistent results in subtype classification using Aiello et al. and Maurer et al.’s dataset, we further compared our samples with other established subtypes to investigate whether acinar- or ductal-cell-derived PDAC is enriched in a clinical PDAC subtype (Aiello et al., 2018; P. Bailey et al., 2016; Collisson et al., 2011; Moffitt et al., 2015; Raghavan et al., 2021). Taken together, we observed similar patterns to previously published data that acinar-cell-derived PDAC, regardless of condition and dataset, showed enrichment in the classical subtype (Fig. 3.20C) (Flowers et al., 2021; Lee et al., 2019). Interestingly, in all conditions for the classical subtype, the bulk datasets had higher overall expression compared to the corresponding in vitro conditions (Fig. 3.20C). We next analyzed the basal subtypes and found consistent expression of the QM-PDA, Squamous, and Basal gene sets across both cellular origins (Fig. 3.20D). Finally, when assessing the Exocrine-like (Collisson et al., 2011), ADEX (P. Bailey et al., 2016), and Immunogenic (P. Bailey et al., 2016) subtypes, we also observed no direct correlation between acinar- and ductal-cell-derived tumors (Fig. 3.20E). When analyzing the ADEX, 67 Immunogenic, Exocrine-like, and IC subtypes, we observed that both bulk datasets generally had higher levels of expression compared to the in vitro samples (Fig. 3.20E). This finding is consistent with our finding that bulk tumors are enriched for the genes indicating the inclusion of immune cells and normal acinar cells, which are also hallmarks of these PDAC tumor subtypes. This is also consistent with previous findings that these subtypes are more indicative of the TME than the tumor cells themselves. (Maurer et al., 2019; Moffitt et al., 2015). There are multiple publications describing PDAC subtypes in clinical data, and there is a general overlap in the finding of 2 subtypes, but some inconsistencies in the precise gene expression differences defining these subtypes (Aung et al., 2018; Connor et al., 2019; Juiz et al., 2020; Suurmeijer et al., 2022). Therefore, we next sought to synthesize or diagram the expression of multiple gene lists or individual genes representing cell of origin markers or clinical PDAC subtypes in each tumor from our dataset (Fig. 3.20). In other words, Figure 3.20 summarizes all of the individual comparisons and resulting averages that can be found in Figure 3.21, Figure 3.4, and Figure 3.13, but reports them on a per-sample basis. This meta-analysis illustrates several points: First, a large number of samples is needed to accurately identify the consistency of a particular marker when assessed across the inherent heterogeneity in gene expression in PDAC more generally. Second, our “Acinar” and “Ductal” cell markers of cellular origin are generally predictable but there are still individual samples that do not strongly express these markers that are known to be derived from a particular cell type. Thirdly, on average acinar-cell-derived tumors are enriched in Classical PDAC subtype markers, but some exceptions have low expression of these markers. Finally, at least in our dataset, genes associated with a “basal” subtype are fairly uniformly expressed. 68 Figure 3.20. Meta-analysis Displaying All Markers and Clinical PDAC Subtypes. A. The heatmap illustrates the expression of previous markers, Cldn18, Gata4, Nt5e, Krt20, Agr2, IFNSign, and B. our identified acinar and ductal markers. The heatmap also shows previously established C. Classical, D. Basal, and E. Non-tumor subtypes in PDAC clinical subtypes across our samples and the Flower et al. dataset. 69 70 Figure 3.21. Z-Score of Clinical PDAC Subtypes Across All Conditions and Datasets. Mean Z-Score ± SD for A. Moffitt et al., B. Bailey et al., C. Collisson et al., and D. Raghavan et al., clinical PDAC subtype markers across all conditions (In-vitro and bulk), Faraoni et al., and Flower et al.'s datasets. 71 3.14 Ductal-cell-derived PDAC Exhibits Greater Sensitivity to Gemcitabine Treatment QM-PDA or basal PDAC tumors are known to be more sensitive to gemcitabine treatment compared to classical PDAC tumors (Collisson et al., 2011). Therefore, we next examined whether a functional readout of classical or basal subtype was present in cells of different cellular origins. Given that our acinar-cell-derived PDAC showed enrichment in the classical subtype, we aimed to investigate whether there were any differences in the response to gemcitabine between acinar- and ductal-cell-derived PDAC. To study this, AC:KTrp53, DU:KTrp53, AC:KPten and DU:KPten cell lines were treated with gemcitabine to assess their sensitivity (Fig. 3.22A-D). These data show that DU:KTrp53 and DU:KPten cell lines grown in complete media have slightly, but not significantly, greater sensitivity to gemcitabine (Fig. 3.22E and 3.22F). This suggests that similar to the transcriptional analyses, the gemcitabine sensitivity does not robustly or accurately reflect a characteristic of a distinct PDAC cell of origin or PDAC subtype. 72 Figure 3.22. Acinar- and Ductal-Cell-Derived PDAC Complete Cell Lines and Response to Gemcitabine Treatment. Gemcitabine cell viability curves for A. AC:KTrp53 cell (N=5), B. DU:KTrp53 (N=5), C. AC:KPten (N=2), and D. DU:KPten (N=3). Dose-response curves on PDAC cell lines treated with different concentrations of gemcitabine the bars are mean ± SEM. Different colors indicate biological replicates. Gemcitabine LogIC50 for E. AC:KTrp53 and DU:KTrp53 cell lines and F. AC:KPten and DU:KPten cell lines. Graph is shown as mean ± SEM. 73 Chapter 4: Discussion 4.1 The cellular origin of PDAC Our study demonstrates that acinar- and ductal-cell-derived PDAC tumors do have DEG, but these DEGs account for only a small proportion of the transcriptome. This finding was surprising as acinar and ductal cells have distinct functions, we expected to see tumors with a consistent acinar or ductal cellular origin. Instead, the progression of PDAC appears to lead acinar and ductal cells to similar transcriptomic states, resulting in the loss of their distinct molecular signatures. Additionally, when analyzing bulk tumors, the presence of various cell types alongside tumor cells can introduce variability in gene expression data. This variability can increase the number of DEGs, creating noise that could obscure true differences in cellular origin. Together, these observations suggest that acinar and ductal cells adopt a similar gene expression profile during PDAC progression complicating the identification of robust markers. To further study the cellular origin of PDAC comprehensively, we cultured only the tumor cells in vitro. After observing clear separation based on the cellular origin for our AC:KPten and DU:KPten, the results from our larger AC:KTrp53 and DU:KTrp53 showed no differences. Instead, our AC:KTrp53 and DU:KTrp53 separated into 3 groups based loosely on Trp53 expression, suggesting that tumor suppressor expression also plays a role in defining transcriptional heterogeneity. Specifically, some AC:KTrp53 complete cell lines maintained Trp53 expression compared to our DU:KTrp53 complete cell lines, which could be due to acinar cells transitioning from PanIN prior to PDAC (Fig. 3.10). As PanIN adjacent have been described in PDAC to retain a WT Trp53 copy, this observation could explain the expression of Trp53 in some acinar-cell-derived cell lines (Tonelli et al., 2022). Another possibility is that acinar-cell-derived PDAC, requiring extensive alterations in their transcriptional programs, also maintain Trp53 expression due to gain-of-function alterations linked to changes in methylation and acetylation processes (J. Zhu et al., 2015). Our study underscores the need for a larger number of biological replicates to effectively capture the true variability in gene expression across tumors. While current 74 understanding of tumor suppressor gene expression in patient subtypes remains limited, our findings suggest this could be also considered a potential option for patient stratification. 4.2 Variability in Expression Levels of Markers of Cellular Origin Our studies show that the previously established markers of cellular origin show significant variability in gene expression. We found that acinar markers showed a slightly better trend at identifying acinar-cell-derived PDAC compared to ductal markers. This consistency could be due to acinar-cell-derived PDAC requiring more distinct gene changes to develop into PDAC, leading to a more consistent set of alterations in gene expression (Lee et al., 2019). Moreover, the presence of mucinous and intestinal markers, such as Krt20, consistently indicates progression from precursor lesions, such as ADM or PanIN transformation, which persist through to PDAC. Whereas, ductal tumors tended to demonstrate greater variability in gene expression across tumors, which suggests that ductal tumors have less consistency in key epigenetic changes, such as acinar cells expressing Sox9 to transform into ADM (Kopp, Figura, et al., 2012). Therefore, ductal cells can use various pathways to progress to PDAC, while acinar cells require more conserved gene activation to progress towards PDAC. Regarding Ferreira et al.’s findings, the difference in Agr2 expression could be attributed to the fact the tumor cells were cultured in organoids 20 days prior to qPCR analysis (Ferreira et al., 2017). As media formulation have been observed to influence the PDAC transcriptional landscape, the tumor cells may have preferentially selected for a more mucinous phenotype compared to the bulk RNA-seq datasets (Raghavan et al., 2021). Therefore, these findings highlight the need for careful consideration of experimental conditions when identifying and interpreting markers of cellular origin. To further explore the impact of our experimental conditions, we found that our complete media contained high levels of endotoxin (lipopolysaccharides, LPS) that are found in gram-negative bacteria, leading to GO terms enriched in immune and interferon signatures. LPS is known to stimulate the NF-kB signaling pathway, which is a key regulator of immune and 75 inflammatory responses (Pfeffer, 2011). In tumors, NF-kB is constitutively active promoting cell proliferation and pro-inflammatory genes leading to inflammation (Pfeffer, 2011). As endotoxin can mimic aspects of the TME in vitro, this can stimulate responses similar to those caused by intratumoral bacteria, showing how cancer cells respond in vivo (Nejman et al., 2020). However, higher concentrations of endotoxins compared to those found in vivo could potentially alter gene expression in cancer cells. As we are not specifically studying how inflammation or bacteria influences cancer cells, this could complicate the interpretation of gene expression. Additionally, the NF-kB transcription factor is known to regulate cellular responses to IFN-processes (Pfeffer, 2011). Therefore, higher levels of LPS could increase NF-kB activity, stimulating IFN gene expression and potentially driving a stronger IFNSign signature. However, we did not observe a strong expression of IFNSign genes in ductal-cell-derived PDAC in vitro, suggesting the presence of endotoxins may not be as influential in our media. On the other hand, the observed GO: Biological Process in our complete media for DU:KTrp53 showed enrichment in inflammation and interferons aligning with those found in the bulk datasets which could be attributed to NF-kB-mediated amplification of interferon transcription factors. The GO categories appear to provide clearer insights into similarities in gene expression changes compared to analyzing lists or individual genes alone. This observation suggests that a broader regulatory program is altered, potentially influenced by variations in transcription factors. However, there is variability in which specific gene regions are activated or accessible for gene expression. Comparing across all the conditions, our own acinar and ductal cell markers were more effective at distinguishing cellular origin across all datasets, although there remains considerable variation in expression within and between conditions. The samples in our dataset were derived from Ptf1aCreER and Sox9CreER, whereas Faraoni et al. samples came from Mist1CreERT2 and Hnf1bCreERT2, which introduces biological variability. Moreover, the use of LSL-Trp53R172H in a heterozygous or homozygous knockout results in the loss of wild-type Trp53 in all cells, which impacts both tumor progression and the tumor microenvironment. Additionally, Flowers et al., also 76 observed different precursor lesions prior to formation to acinar- and none for ductal-cell-derived tumors, which could have led to changes in PDAC gene expression. Another consideration is that Flower’s Sox9CreER samples contained 2/3 degraded RNA, which could complicate the interpretation of our results when comparing to their dataset. Given that the gene expression of mature tumors is largely similar from both origins, any differences in markers may be subtle or limited to early stages of tumor development. To further investigate these differences, future studies could employ ATAC-seq and chromatin marker analyses to identify epigenetic changes and chromatin accessibility patterns that might distinguish the tumors based on their cellular origins. 4.3 Clinical PDAC subtypes on Cellular origin In the clinical context of PDAC, our findings show that mouse models following C-EMT and P-EMT showed our samples to be classified as P-EMT (Classical Subtype). However, when classifying C-EMT and P-EMT in the context of clinical PDAC subtypes, P-EMT was found to be enriched in the classical subtypes associated with acinar cellular origin. In contrast, genes related to C-EMT were expressed in both cellular origins and were not limited to the basal subtype or ductal cellular origin. Thus, rather than the absence of basal samples, the genes that drive C-EMT might not be as expressed in our dataset. Interestingly, when aligning our samples with Moffitt’s subtypes alongside Maurer’s samples, our samples were classified as basal. This suggests that classifications of C-EMT and P-EMT observed in mouse models may not directly correlate to human classifications due to species-specific variation. These differences include a lack of conservation for many genes between mice and humans, changing how these subtypes are interpreted across species. Additionally, as the tumors are grown in a controlled laboratory setting, mutations in mice are induced all at one time to stimulate the genetic alterations observed in clinical PDAC cases. However, human PDAC tumors have greater variability due to genetic background and environmental factors that could influence the initiation and progression of PDAC 77 differently in humans than mice, impacting the interactions that occur with the TME. Consequently, mouse models may not fully capture the gene expression seen in human basal PDAC tumors. When analyzing across all conditions and datasets, our initial expectation was to see an enrichment of the classical subtype in acinar-cell-derived PDAC and the basal subtype in ductal-cell-derived PDAC. However, we found that only acinar-cell-derived showed classical expression, albeit not uniformly across all samples. Notably, we found that the basal subtype is expressed in both cellular origins. This observation could be attributed to the transcription factor TP63 found in squamous and basal subtypes clinically (Somerville et al., 2018). In normal pancreatic tissue and PDAC tumors from mouse models, Tp63 expression is absent, unlike in human pancreatic ducts where basal cells express TP63 (Martens et al., 2022). This discrepancy in expression could lead to differences in down-stream signaling genes and explain the absence of enrichment of a basal subtype. To address the limitations of single-criterion classifications from previous studies, these classifications overlook that samples do not neatly fit into the classical or basal categories (Faraoni et al., 2023; Flowers et al., 2021). Moreover, applying human PDAC subtypes across studies reveals a challenge, that these subtypes may primarily reflect intratumoral heterogeneity and not intertumoral heterogeneity that would be expected if tumors in different patients were arising from different cells of origin. This heterogeneous environment shows that there is a lot of variability in the expression of these markers. Therefore, instead of comparing classical and basal subtypes 1:1, establishing clear standards to define a “true” classical and “true” basal phenotype will ensure more accurate classifications of tumors. 4.4 Limitation of our models and future direction As we used the Sox9CreER transgene and Ptf1aCreER to study PDAC with Trp53 or Pten loss this study has limitations in generalizability to the broader landscape of PDAC. As there are also other tumor suppressors associated with acinar or ductal cellular origin, such as CDKN2A 78 and SMAD4, further studies with different mouse models could explore their influence on PDAC initiation and progression. This would provide a more holistic approach to understand the development of the PDAC, encompassing both the influence of cellular origin and the effects of tumor suppressor loss. Moreover, as tumors evolve through the progressive accumulations of genetic alterations, studying how PDAC progresses with induced loss of tumor suppressors at different time points could provide insights into the heterogeneous nature of tumors. As studying only heterozygous or homozygous loss of tumor suppressors may overlook intermediate states and the dynamic process of tumor evolution with the TME. While we focused predominantly on the tumor cells, it will be important for future studies to also include the TME. Given the differences found in vivo and in vitro settings, these findings carry important implications to understand how interactions between the cellular origin of PDAC and the TME influence disease progression and therapeutic responses. Another interesting topic is the variability in the gut microbiota influenced by different mouse housing conditions (Kartal et al., 2022). For instance, the AC:KTrp53 and DU:KTrp53 mice showed that ductal-cell-derived PDAC had slower progression (Lee et al., 2019). Whereas, Flower’s et al. DU tumors grew faster than Flower’s et al. AC tumors, within the same mouse model (Aside from reporter gene), these findings showing differences in tumor growth dynamics that could be attributed to factors such as environmental or experimental conditions in different facilities (Flowers et al., 2021). As we also only used RNA-sequencing for this study, future investigations involving a larger sample size could explore the use of single-cell RNA-seq and spatial transcriptomics to unveil markers of cellular origin. Using spatial transcriptomics to visualize the gene expression within the tissue could help elucidate how different cell populations contribute to tumor development in the context of the cellular origin. 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Thesis/Dissertation
eng
Cell and Developmental Biology
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Kopp, Janel
Transcriptional analyses of acinar- and ductal-cell-derived pancreatic cancer
Text