Medicine, Faculty of
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UBCV
Trejo Vazquez, Jessica Angelina
Master of Science - MSc
University of British Columbia
Lung cancer is the leading cause of cancer deaths worldwide, largely due to metastasis. Its main subtype is lung adenocarcinoma (LUAD). Communication between cancer cells and cells within the tumor microenvironment, such as fibroblasts, can promote metastasis. This interaction can be mediated by extracellular vesicles (EVs) transferring bioactive cargo. Thus, this study aimed to identify the impact of EVs derived from LAC cells on lung fibroblasts. For EV isolation, conditioned media derived from H2073 and H1437 LAC cell lines were ultracentrifuged multiple times after debris removal. Nanoparticle Tracking Analysis was used for quantification and size characterization and EV-associated markers were assessed by western blot (WB). For lung fibroblast activation, H6013 primary adult cells were treated with 1xPBS, TGFβ (10 ng/μL) or EVs (7.13x10¹¹) every 24 hours over 3 days. Protein (for WB) and RNA (for gene expression profiling) were harvested 48 hours after treatment. RNA was extracted using the Qiagen miRNeasy kit and sequenced by Illumina NextSeq2000. Most vesicles produced by both cell lines were between 50 and 250 nm in diameter. Fibroblasts treated with TGFβ or EVs had an increase in CAF markers (mainly α-SMA), especially in the EV group compared to the PBS treatment group. The activated fibroblasts shared differentially expressed genes in the TGFβ and EV treatments; however, there was an increase in some of the genes associated with epithelial-mesenchymal transition and cell cycle only in the EV-treated group, indicating unique features of CAF activation and the same was seen for the pathway analysis. Therefore, LUAD EVs have a role in CAF activation by using both similar and unique pathways to TGFβ activation. Further understanding of the genes distinct to the EV-treated fibroblasts could lead to the identification of novel genes involved in CAF activation, and thus aid in the development of novel therapeutics that could improve patient outcomes.
EXTRACELLULAR VESICLES FROM LUNG ADENOCARCINOMA CELLS INDUCE ACTIVATION OF DIFFERENT CAF SUBTYPES by Jessica Angelina Trejo Vazquez B.Sc., National Polytechnic Institute, 2018 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Interdisciplinary Oncology) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) May 2024 © Jessica Angelina Trejo Vazquez, 2024 ii The following individuals certify that they have read and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance the thesis entitled: Extracellular vesicles from lung adenocarcinoma cells induce activation of different CAF subtypes submitted by Jessica Angelina Trejo Vazquez in partial fulfilment of the requirements for the degree of Master of Science in Interdisciplinary Oncology Examining Committee: Dr. Cathie Garnis, Associate Professor, Department of Surgery, UBC Supervisor Dr. Karla Williams, Associate Professor, Faculty of Pharmaceutical Sciences, UBC Supervisory Committee Member Dr. Tony Ng, Clinical Associate Professor, Pathology and Laboratory Medicine, UBC Supervisory Committee Member Dr. William Lockwood, Associate Professor, Pathology and Laboratory Medicine, UBC Additional Examiner Additional Supervisory Committee Members: Dr. Kevin Bennewith, Associate Professor, Pathology and Laboratory Medicine, UBC Supervisory Committee Member iii Abstract Lung cancer is the leading cause of cancer deaths worldwide, largely due to metastasis. Its main subtype is lung adenocarcinoma (LUAD). Communication between cancer cells and cells within the tumor microenvironment, such as fibroblasts, can promote metastasis. This interaction can be mediated by extracellular vesicles (EVs) transferring bioactive cargo. Thus, this study aimed to identify the impact of EVs derived from LAC cells on lung fibroblasts. For EV isolation, conditioned media derived from H2073 and H1437 LAC cell lines were ultracentrifuged multiple times after debris removal. Nanoparticle Tracking Analysis was used for quantification and size characterization and EV-associated markers were assessed by western blot (WB). For lung fibroblast activation, H6013 primary adult cells were treated with 1xPBS, TGFβ (10 ng/μL) or EVs (7.13x1011) every 24 hours over 3 days. Protein (for WB) and RNA (for gene expression profiling) were harvested 48 hours after treatment. RNA was extracted using the Qiagen miRNeasy kit and sequenced by Illumina NextSeq2000. Most vesicles produced by both cell lines were between 50 and 250 nm in diameter. Fibroblasts treated with TGFβ or EVs had an increase in CAF markers (mainly α-SMA), especially in the EV group compared to the PBS treatment group. The activated fibroblasts shared differentially expressed genes in the TGFβ and EV treatments; however, there was an increase in some of the genes associated with epithelial-mesenchymal transition and cell cycle only in the EV-treated group, indicating unique features of CAF activation and the same was seen for the pathway analysis. Therefore, LUAD EVs have a role in CAF activation by using both similar and unique pathways to TGFβ activation. Further understanding of the genes distinct to the EV-treated fibroblasts could lead to the identification of novel genes involved in CAF activation, and thus aid in the development of novel therapeutics that could improve patient outcomes. iv Lay Summary Lung cancer represents 25% of all cancer deaths. This is associated with metastasis, facilitated by the communication between cancer cells and their environment. One of these means of interaction is the secretion of small cellular containers called extracellular vesicles. Fibroblasts are a major cell type that surrounds cancer cells and are known to internalize extracellular vesicles. Upon receiving cancer-derived vesicles, the phenotype and functions of these fibroblasts can be more cancer-like, becoming irreversibly activated and promoting tumor growth and development. To better understand this transformation, we examined changes in the gene expression profile of fibroblasts activated by extracellular vesicles. Additionally, we looked at which mechanisms are switched on after fibroblast activation and their potential contribution to cancer progression. Together, these data may provide additional knowledge of lung cancer that could lead to the implementation of novel therapeutics and improve patient outcomes. v Preface Chapter 1. Figure 1.1 was adapted from Altorki et al. (2018) ‘The lung microenvironment: an important regulator of tumour growth and metastasis’ and was further designed by me. Figure 1.3 was modified from Maleki et al. (2021) ‘The role of extracellular vesicles in mediating resistance to anticancer therapies’ with permission. Table 1.1 was also modified from Maleki's (2022) “The effect of extracellular vesicles derived from lung adenocarcinoma cells on the tumor microenvironment” with permission, who adapted it from Detterback et al. (2018). Figure 1.2 was adapted from Sahai et al. (2020) ‘A framework for advancing our understanding of cancer-associated fibroblasts’. Figure 1.3 is used with permission from Maleki et al. (2021) ‘The role of extracellular vesicles in mediating resistance to anticancer therapies’ which was adapted by me. Figure 1.4 was used from Y. Liang et al. (2021) ‘Engineering exosomes for targeted drug delivery’. Figure 3.6 is used with permission from Forder et al. (2021) ‘Emerging Role of Extracellular Vesicles and Cellular Communication in Metastasis’ of which I was the illustrator. Chapter 2, Chapter 3 and Chapter 4. These sections are original, unpublished, independent research and writing carried out by me with supervision provided by Dr. Cathie Garnis. vi Table of Contents Abstract ......................................................................................................................................... iii Lay Summary ............................................................................................................................... iv Preface .............................................................................................................................................v Table of Contents ......................................................................................................................... vi List of Tables ..................................................................................................................................x List of Figures ............................................................................................................................... xi List of Abbreviations ................................................................................................................. xiii Acknowledgements ......................................................................................................................xv Dedication ................................................................................................................................... xvi Chapter 1: Introduction ................................................................................................................1 1.1 Lung cancer ..................................................................................................................... 1 1.2 Etiologies of lung cancer ................................................................................................ 2 1.3 Lung cancer classification ............................................................................................... 3 1.4 Lung cancer staging ........................................................................................................ 4 1.5 Tumor microenvironment ............................................................................................... 9 1.5.1 Cell component of the TME ................................................................................... 9 1.5.1.1 Fibroblasts ......................................................................................................... 10 1.5.1.2 CAFs ................................................................................................................. 11 1.5.2 Intercellular communication with stromal cells in cancer .................................... 13 1.6 Extracellular vesicles: Biogenesis and Function ........................................................... 13 1.6.1 EV markers and cargo ........................................................................................... 15 vii 1.6.2 Mechanisms for exosome internalization ............................................................. 16 1.7 Markers and Cancer-Associated Fibroblasts heterogeneity. ......................................... 17 1.8 Thesis outline ................................................................................................................ 18 1.8.1 Rationale, hypothesis, and overall objectives ....................................................... 18 Chapter 2: Optimizing conditions to detect differences in lung fibroblasts under different treatments, including EVs from lung adenocarcinoma cells. ..................................................21 2.1 Introduction ................................................................................................................... 21 2.2 Materials and Methods .................................................................................................. 22 2.2.1 Cell lines and cell culture conditions .................................................................... 22 2.2.2 Extracellular vesicle collection and isolation ....................................................... 22 2.2.3 Nanoparticle Tracking Analysis of EVs ............................................................... 23 2.2.4 Western Blot of EV characterization .................................................................... 23 2.2.5 Fibroblast activation assay .................................................................................... 24 2.2.6 Western Blot of CAF activation ........................................................................... 25 2.2.7 RNA extraction of H6013 ..................................................................................... 25 2.3 Results ........................................................................................................................... 26 2.3.1 Extracellular vesicle characterization ................................................................... 26 2.3.2 Optimizing EV storage: Effect of long-term storage and freeze-thaw cycles ...... 28 2.3.3 Optimizing WI-38 fibroblasts and their cell culture conditions ........................... 30 2.3.4 Optimizing the WB protocol ................................................................................. 32 2.3.5 Working with the primary fibroblasts with optimized conditions ........................ 35 2.4 Discussion ..................................................................................................................... 40 viii Chapter 3: EVs from LUAD cells induce fibroblast activation to myCAFs and dCAFs, similar to activation via TGFβ, but they also induce activation of a proliferative CAF subtype and enrichment of EMT and cell cycle-associated genes ...........................................48 3.1 Introduction ................................................................................................................... 48 3.2 Materials and Methods .................................................................................................. 49 3.2.1 RNA sequencing of H6013 ................................................................................... 49 3.2.2 RNA sequencing data analysis .............................................................................. 50 3.3 Results ........................................................................................................................... 50 3.3.1 Quantification ....................................................................................................... 50 3.3.2 Differential expression .......................................................................................... 51 3.3.3 Functional analysis ................................................................................................ 58 3.4 Discussion ..................................................................................................................... 59 Chapter 4: Conclusions and future directions ..........................................................................69 4.1 Review of Aims ............................................................................................................ 69 4.1.1 Aim 1: Induce CAF differentiation by lung adenocarcinoma EVs (four cell lines) in primary fibroblasts and acquire gene expression data (in duplicate). ............................... 69 4.1.2 Aim 2: Identify pathways involved in EV CAF activation and determine the CAF subtypes induced. .................................................................................................................. 70 4.2 Limitations .................................................................................................................... 71 4.3 Conclusions and future directions ................................................................................. 72 Bibliography .................................................................................................................................74 Appendices ....................................................................................................................................86 ix Appendix A The top 200 upregulated genes in fibroblasts cultured with TGFβ, H1437 EVs, and H2073 EVs. ........................................................................................................................ 86 Appendix B Differentially expressed genes and their fold changes were used in identifying CAF subtypes in each treatment group. [94] ............................................................................ 92 x List of Tables Table 1.1 Lung cancer staging by TMN. ........................................................................................ 6 Table 2.1 Assessment of the sample’s RNA purity, concentration, and integrity. ....................... 40 Table 2.2 Conditions to consider when culturing primary lung fibroblasts. ................................ 44 xi List of Figures Figure 1.1 The anatomy of the lung and origin of LUAD. ............................................................. 2 Figure 1.2 Sources of activation of fibroblasts into CAFs. ........................................................... 12 Figure 1.3 EV biogenesis and bioactive cargo. ............................................................................. 16 Figure 1.4 EV internalization. ....................................................................................................... 17 Figure 2.1 EV characterization. .................................................................................................... 27 Figure 2.2 Western Blot of the protein from LUAD cells and their EVs. .................................... 28 Figure 2.3 Storage time and the number of freeze-thaw cycles affect the EV number. ............... 29 Figure 2.4 Western blots with CAF activation markers to assess activation in the PBS group in WI-38 cells. ................................................................................................................................... 32 Figure 2.5 Western blots with CAF activation markers to assess activation in the PBS group in WI-38 cells after optimization. ..................................................................................................... 34 Figure 2.6 Timelines used to establish the duration of the experiment. ....................................... 36 Figure 2.7 Western blots with CAF activation markers to assess activation in the PBS group and determine experiment conditions in H6013 cells after protocol optimization. ............................. 37 Figure 2.8 CAF activation by TGFβ and H2073 EVs in WB and WB quantification. ................ 38 Figure 2.9 CAF activation by TGFβ, H1437 and H2073 EVs in WB and WB quantification. ... 39 Figure 3.1 PCA of samples after batch effect corrections. ........................................................... 51 Figure 3.2 Differentially expressed genes in fibroblasts treated with TGFβ or EVs from H1437 and H2073 cells. ............................................................................................................................ 53 Figure 3.3 Volcano plots display the most significant changes in each group. ............................ 54 Figure 3.4 Toppgene results of each group show functional enrichment based on Ontologies. .. 55 xii Figure 3.5 Heatmaps showing differentially expressed genes in the treated groups in relation to PBS. .............................................................................................................................................. 57 Figure 3.6 GSEA plots of pathways enriched for the activated fibroblasts through TGFβ and the EVs. ............................................................................................................................................... 58 Figure 3.7 Intercellular communication between cancer cells and stromal fibroblasts. ............... 68 xiii List of Abbreviations ALIX ALG-2-interacting protein X ALK anaplastic lymphoma kinase AJCC American Joint Committee on Cancer BRAF B-Raf proto-oncogene BGN Biglycan α-SMA α-smooth muscle actin CAF cancer-associated fibroblast DE differentially expressed DNA deoxyribonucleic acid ECM extracellular matrix EGFR epidermal growth factor receptor EMT epithelial-mesenchymal transition ESCRT endosomal sorting complex required for transport EVs extracellular vesicles FACS fluorescence-activated cell sorting FAP fibroblast activation protein FGF fibroblast growth factor FC fold change FSP1 fibroblast specific protein 1 GSEA Gene Set Enrichment Analysis Hsp heat shock proteins ILVs intraluminal vesicles LUAD lung adenocarcinoma LUSC lung squamous cell carcinoma LCUD lung cell undifferentiated carcinoma xiv miRNA microRNA MMPs matrix metalloproteinase mRNA messenger RNA MVBs multivesicular bodies NK natural killer NFκB Nuclear factor kappa B ncRNA non-coding RNA NSCLC non-small lung cancer ORF open reading frame PCR polymerase chain reaction PD-L1 programmed death ligand 1 PDGF platelet-derived growth factor PDGFRα/β platelet-derived growth factor receptor α/β POSTN periostin SCC squamous cell carcinoma SCLC small cell lung cancer SMADS suppressor of Mothers against Decapentaplegic SPOCK1 spock/testican-1 TAM tumor-associated macrophages TAN tumour-associated neutrophil TEC tumor endothelial cells TGFβ transforming growth factor-β TME tumor microenvironment TNM tumor, node and metastasis VEGF vascular endothelial growth factor VPS4 vacuolar protein sorting-associated protein 4 WB Western Blot xv Acknowledgements To start, I am thankful to my supervisor, Dr. Cathie Garnis, for her guidance and for the learning experience obtained during my stay in your lab with my colleagues, nurturing my mind with the expertise and our discussions. Thank you to my supervisory committee, Drs. Kevin Bennewith, Tony Ng, and Karla Williams for their invaluable input and experience, which were vital for the development of this project. I want to thank the University of British Columbia and the Interdisciplinary Oncology Program for the funding provided, which gave me many opportunities to enjoy the process of learning. Thank you to all the wonderful people in the building and their availability, they made me realize the importance of a strong community, especially in this field. Finally, a special thank you to my family for their strong foundation and support throughout my educational journey. xvi Dedication To my family, who always supports me and believes in me. To my friends, mentors, and colleagues, because if I have seen further than others, it is by standing upon the shoulders of giants. To people who have suffered from this disease. 1 Chapter 1: Introduction 1.1 Lung cancer The lungs are a pair of respiratory organs confined in a serous pleural sac in the thoracic cavity, allowing the supply of oxygen and the release of carbon dioxide from the human body [1]. The respiratory system is composed of two main areas: the conducting airways and the lung parenchyma, or respiratory compartment [2]. The conducting airways start where the trachea develops into two large lung buds (primary bronchi) that subdivide into lobar (secondary) bronchi. Two of these are introduced, respectively, into their lobes in the left lung and three in the right one, allowing the flow of air in and out of the lungs. The lobar bronchi branches into the segmental (tertiary) bronchi, which gives rise to the bronchioles and their subsequent divisions, leading to the terminal bronchioles [3]. The beginning of the parenchyma lies in the terminal bronchioles, and beyond these bifurcations is the acinus [4]. This unit is formed by a respiratory bronchiole, alveolar ducts, and alveolar sacs, where the gas exchange happens at the alveoli lining it [5]. There are two different types of cells in the alveoli: 1) Type I pneumocytes, which are the most abundant on the surface, and most of the gas exchange takes place in these simple squamous cells [6]; 2) Type II pneumocytes, which secrete pulmonary surfactant, which aids in breathing movement. Alveolar macrophages remove the external agents through phagocytosis [7]. Lung cancer involves malignancies arising from the cells of the epithelium of the respiratory tract. The importance of this neoplasm lies partly in its high incidence worldwide, being the second most common type of cancer diagnosed [8]. In addition, lung cancer holds its leading position for cancer-related deaths, surpassing the combined numbers of prostate, breast, colon, and pancreatic cancers [9]. In Canada in 2022, 13% of all new cancer cases were lung and 2 bronchus cancer, and 25% of all cancer deaths corresponded to this type of neoplasm [10]. The 5-year survival rate is approximately 22% in lung cancer patients. Importantly, the survival rate is influenced by different variables, such as the lung cancer subtype and the status of the disease when diagnosed [11], which tends to be a late stage in ~70% of cases [12]. Figure 1.1 The anatomy of the lung and origin of LUAD. Adapted from Altorki et al. (2018) [13] 1.2 Etiologies of lung cancer Different risk factors can contribute to the development of this disease. The most prevalent etiology is prolonged and frequent exposure to tobacco, thus heavy smokers are at a significantly higher risk of developing lung cancer [14]. Several hazardous substances, such as N-nitrosamines and arsenic, among other 50 carcinogenic compounds, are inhaled through mainstream smoke [15]. The body processes tobacco carcinogens for secretion but some of the 3 metabolites generated can interact with deoxyribonucleic acid (DNA) to form DNA adducts. If the damage is not repaired, acquired mutations can result in permanent driver mutations that can repress tumor suppressor genes or activate oncogenes [16]. Interestingly, 25% of all lung cancer cases worldwide occur among non-smokers. A meta-analysis by A-Sol Kim et al. suggested that non-smokers, especially women, who are exposed to environmental tobacco smoke have an increased risk of lung and other types of cancer [17]. Furthermore, occupational exposure to carcinogens constitutes the second-most significant risk factor. Some of these components include radioactive elements, like radon and uranium, or hazardous chemicals, like arsenic and asbestos, which are responsible for most cases of this type of risk [18]. Asbestos is a mineral that was widely utilized due to its versatility in products used in everyday life, like textiles, shoes, decorations, and even in construction, until around the 1980s [19]. When lung-related diseases started to be not exclusive to occupational exposure and neoplasms were also attributed to asbestos, regulations were applied for its use. Lung cancer etiology also suggests that fine respirable particles in air pollution may include various carcinogens. They can comprise organic and inorganic compounds like benzo, pyrene, chromium, and arsenic, where the combustion of fossil fuels is their primary source [20]. Furthermore, genetic risk factors comprise lung cancer family history and genetic predisposition. Certain diseases, like fibrosis and chronic obstructive pulmonary disease, seem to have a strong association with a higher lung cancer risk. 1.3 Lung cancer classification There are two main divisions of lung cancer: non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). An SCLC tumor is composed of cells with a small size and a circular to spindle-like shape with fine nuclear chromatin, minimal cytoplasm, and even absent nucleoli. 4 [21] SCLC is closely related to smoking; therefore, its current incidence is lower, partly due to the decrease in tobacco use. This classification is a very aggressive neoplasia with rapid growth and 5-year mortality of around 90%; however, it only accounts for 15% of all lung cancers [22]. On the other hand, NSCLC makes up 85% of the cases, accounting for all those types not included in SCLC. NSCLC involves malignant tumors formed from the cells lining the epithelium of the bronchi, the bronchioles, and the alveoli. There are three main types of NSCLC, where the most common is lung adenocarcinoma (LUAD), which represents 40% of all diagnosed lung cancer cases; squamous cell carcinoma (LUSCs), making up to 30% of the cases; and rarely the large cell undifferentiated carcinoma (LCUD), ~ 10-15% of cases [23]. LUSCs are normally in the lungs' central area. They can be further classified based on their histological characteristics into keratinizing, non-keratinizing, and basaloid subtypes by the World Health Organization (WHO) [24]. LCUD lacks the typical cytologic features of the other subtypes, and the cells have big nuclei and nucleoli, meaning that there is a reduced space for the cytoplasm. LUAD arises from cells that produce surfactant components in the periphery of the lung and normally serve to lubricate the lung. They present different morphological patterns, based on which they are subclassified as acinar, papillary, bronchoalveolar, and mucus-secreting by the WHO. LUAD affects smokers, but it is also the most diagnosed subtype in individuals who do not smoke [25]. 1.4 Lung cancer staging Lung cancer can include different symptoms. The most common are persistent cough, hemoptysis, dyspnea, pain in the chest or shoulders, difficulty swallowing, weight loss, hoarseness, and weakness, among others [26]. Several tools are applied to differ in sensitivity and specificity for the detection and monitoring of lung cancer. The most reliable practice 5 consists of tissue biopsy since a wide amount of information can be obtained, like the histology of the tumor, genomic mutations, and expression profile [27]. Nonetheless, whenever this type of sample collection is not feasible due to the location of the tumor or the risks involved in the collection itself, other approaches can be applied. Liquid biopsy is a non-invasive and easy-to-obtain method where different particles are analyzed from various body fluids, including peripheral blood, sputum, and cerebrospinal fluid, among others [28]. Components like circulating tumor cells and nucleic acids, in addition to circulating particles such as extracellular vesicles can provide information to detect, monitor, and determine prognosis and relapse [29]. Moreover, imaging technologies like chest X-rays, positron emission tomography, and computed tomography provide valuable information for the diagnosis, staging, and determination of the extent of the disease, allowing better management of the disease treatment [30]. However, during the early stages, lung cancer is predominantly asymptomatic; therefore, patients normally live with it for many years before it becomes apparent. To develop a 30 mm tumor arising from squamous cell carcinoma takes around 8 years, and by the time it is diagnosed with obvious physical manifestations, the risk of metastasis rises quite considerably. In addition, because of the non-specificity of the symptoms, they are often ignored, contributing to late-stage detection, which increases mortality rates [31]. Once detected, the staging system provides a reproducible scheme to obtain key information for the disease status evaluation that correlates with prognosis and patient survival prediction, which is essential for treatment selection. This enables determining the location of the cancer cells, the size of the tumor, and, if applied, the anatomical extent of its spread [32]. The TNM classification for lung cancer acts as a single system for lung cancer staging worldwide that is constantly updated and consists of features evaluating the primary tumor (T), lymph node(s) involvement (N) and distant metastasis (M) [33]. Table 1.1 6 explains in more detail the TMN stages in lung cancer. The combination of these three components determines the overall stage of lung cancer. According to the American Joint Committee on Cancer (AJCC) system, there are six stages of lung cancer [34]. In occult or hidden cancer, these cells have been identified in lung fluids, but the tumor can’t be found with other tests. Stage 0 is also denoted as carcinoma in situ, and the tumor is localized in the innermost lining of the lung without spreading to surrounding tissue. In stage I, the tumor is smaller than 4 cm, and cancer cells are only in the lung with no expansion to adjacent tissue. During stage II, the mass is 7 cm or less, but it has spread to nearby lymph nodes. In stage III, the tumor is 7 cm or more and spreads to regional lymph nodes. Finally, in stage IV, the tumor can be of any size, and it has metastasized to other parts of the body that can be distant. It has the worst 5-year survival rate since it is the most advanced form of lung cancer. Table 1.1 Lung cancer staging by TMN. Adapted from Detterback et al. (2018) [35], [36], [37] T (primary tumor) Describes size and extent of the primary tumor T0 No identification of primary tumor Tis Carcinoma in situ T1 Tumor 3 cm or less: T1a (mi) Minimally invasive T1 (ass) Superficial spreading tumor in central airways T1a Tumor 1 cm or less T1b Tumor bigger than 1cm but equal or smaller than 2 cm T1c Tumor bigger than 2 cm but equal or smaller than 3 cm T2 Tumor bigger than 3 but equal or smaller than 5 cm or tumor invasion of the visceral pleura but no the carina with the presence of atelectasis or obstructive pneumonitis: 7 T2a Tumor bigger than 3 to 4 cm or cannot be determined T2b Tumor bigger than 4 to 5 cm T3 Tumor bigger than 5 to 7 cm or spreading to chest wall, including Pancoast tumors, pericardium, phrenic nerve; or separate nodule(s) in the same lobe as primary tumor T4 Tumor bigger than 7 cm or involving: mediastinum, diaphragm, heart, great vessels, recurrent laryngeal nerve, carina, trachea, esophagus, spine; or tumor nodule(s) in a different lobe on the ipsilateral lobe T status not possible to be determined Txa Minimally invasive adenocarcinoma that can only be pathologically determined with both invasive and lepidic component Txb Tumor spreading superficially regardless size or location Txc Tumors classified as T2a if they are between 3 and 4. T2b if bigger than 4 small or the same to 5. N (regional lymph nodes) Presence of tumor in the lymph nodes around the primary tumor N0 No migration to regional node N1 Spread to ipsilateral pulmonary or hilar nodes N2 Spread to ipsilateral mediastinal or subcarinal nodes N3 Spread to contralateral mediastinal, hilar, or supraclavicular nodes M (distant metastasis) M0 No distant metastasis 8 Presence of tumor in distant sites to the primary tumor M1a Malignant pleural or pericardial effusion or pleural or pericardial nodules or separate tumor nodules(s) in a contralateral lobe M1b Single extra-thoracic metastasis M1c Multiple extra-thoracic metastases (1 or >1 organ) Many treatment options include conventional methods like surgery, radiation, chemotherapy, and other treatments like immunotherapy and targeted therapies, but the approach highly depends on the stage of diagnosis [38]. For Stage 0 or I surgery, either a lobectomy or pneumonectomy becomes the first line of action with a 5-year survival rate higher than 50% and could be complemented with other therapies. However, adjuvant or neoadjuvant chemotherapy becomes more relevant during Stage II with resection and additional radiotherapy [39]. In cases where surgery is not a viable option, radiotherapy can be used as an alternative treatment for patients. Adjuvant-targeted therapy aims at patients with specific mutations or translocations, including EGFR (Osimertinib), ALK (Alectinib), BRAF (Dabrafenib and trametinib) and ROS1(Entrectinib). In stage III, the treatment options include surgery with neoadjuvant or adjuvant therapy, chemotherapy, and radiation therapy. Finally, during stage IV, the main goal is to prolong survival and control symptoms; thus, treatments may include chemotherapy, radiotherapy-targeted therapy, and immunotherapy [34]. Many factors can affect tumor progression and can influence therapeutic outcomes in NSCLC. Among them, there is a rising interest in the tumor microenvironment (TME) since it has been shown that it plays a crucial role in these processes. 9 1.5 Tumor microenvironment Cancer cells promote a series of alterations within the tissue where the primary tumor is located to favor their survival, settlement, growth, local invasion, and metastatic dissemination [40]. The continuous and bidirectional interactions between cancer cells and their surroundings give rise to the TME. The composition of the TME can vary from one tissue to another, and it is constantly evolving, but generally, it integrates both cellular and non-cellular components. The non-cellular component refers to the extracellular matrix (ECM) constituted by water, structural proteins like collagen and fibronectin, polysaccharides, and signaling molecules [41]. Overall, the ECM normally provides the scaffold for the cellular element and promotes different biochemical and biophysical cues for tissue maintenance [42]. The cellular part includes stromal cells like fibroblasts, endothelial cells, immune cells, neuroendocrine cells, adipose cells, and stellate cells [43]. 1.5.1 Cell component of the TME Cancer cells’ signals interact with various cells of the TME to promote tumor development. One of the important cells it interacts with is the endothelial cells, which line the vascular and lymphatic endothelium and separate the circulating blood/lymph from tissues. Normal endothelial cells are crucial in the transportation of water, nutrients, metabolic waste, immune cells, gas exchange, and the formation of new blood vessels [44]. However, under carcinogenic conditions, tumor endothelial cells (TECs) allow the carcinoma to overcome acidic and hypoxic conditions by promoting angiogenesis and providing its own blood supply that provides oxygen and nutrients [45]. They participate in intravasation, where they permit the entrance of mobile cancer cells into the vessel lumen, facilitating metastasis [46]. TECs secrete cytokines that activate receptors in the tumor cells or attenuate the cytotoxic response of the immune cells [47]. 10 Additionally, they are one of the main sources of cancer-associated fibroblasts (CAF), generally related to the development of tumor progression [46]. Because of the role they play in the microenvironment, TECs seem to be a relevant target for improving cancer therapy in the early stages [47]. Another case is the immune cells in the TME, which can include innate immune cells and adaptive immune cells [48]. Immune cells can suppress tumor formation or promote tumorigenesis based on the conditions. Cytotoxic T cells and NK cells can recognize and kill cancer cells with specific antigens. However, even if a major role of infiltrating immune cells is to antagonize tumor-promoting functions, malignant cells can orchestrate changes to utilize different immune subtypes, aiding them to evade the immune response. Macrophages, for example, constitute an important population infiltrating the TME. Under tumor influence, they can be polarized to the M2 phenotype, and these tumor-associated macrophages (TAMs) then drive tumor progression, metastasis, and resistance to therapy [49] [50]. Nevertheless, a main integrant of the TME is cancer-associated fibroblasts, which are the most common type of stromal cells [51]. 1.5.1.1 Fibroblasts Fibroblasts are a major component of the connective tissue, with non-immune, non-epithelial, and non-endothelial cell characteristics and an elongated morphology [52]. They are involved in ECM deposition and remodeling by producing structural and degradative molecules that include different types of collagen, proteoglycans, fibronectin, laminins, and metalloproteinases [53]. Quiescent, non-proliferative fibroblasts maintain normal tissue homeostasis and ECM structure. Under normal conditions, they become activated to mediate tissue repair, undergo explosive replication, and differentiate into myofibroblasts during wound healing [54]. Fibroblasts can then modulate the activation and removal of immune cells, promote angiogenesis and ECM 11 remodeling [55]. Once the injury is repaired, they go back to basal levels, and surplus cells undergo apoptosis. Tumors display some features in their microenvironment that mimic some conditions that would happen during wound healing, like the inflammation reaction. However, in contrast to normal cells, they sustain this situation and develop mechanisms to grow continuously, invade surrounding tissue, avoid antitumor immunity, and turn fibroblasts into a paramount participant by activating them irreversibly [56]. 1.5.1.2 CAFs The presence of cancer cells in the vicinity can result in a chronic response to damage from the stromal cells, resulting in sustained fibrosis. These mesenchymal cells then become constitutively activated, resistant to apoptosis, and the origin of cancer-associated fibroblasts (CAFs) [57]. CAFs can arise from resident cells like fibroblasts, adipocytes, endothelial cells and pericytes or distant cells like mesenchymal stem cells [58]. Thus, CAFs are a heterogeneous population, and it has been shown that they can promote or limit tumor progression through the secretion of molecules. CAF activation and recruitment are mediated by received signals from both tumor cells and infiltrating immune cells in the form of paracrine factors that can include immune modulators [59]. In addition, CAFs can be activated by changes in the non-coding RNA expression and epigenetic and signaling pathways alterations like in the TGF-β/SMAD [60]. In particular, TGF-β is a well-established activating signal of fibroblasts, driving the expression of α-SMA [61]. Physiological and genomic stress also lead to alterations in the fibroblasts, like heat shock factor 1 and double-stranded DNA breaks. Physical changes in the ECM, including tissue stiffness, and conditions in the microenvironment, like hypoxia or metabolic reprogramming, also influence [62]. 12 Figure 1.2 Sources of activation of fibroblasts into CAFs. Adapted from Sahai et al. (2020) [62] Once activated, CAFs secrete a variety of molecules that allow ECM remodeling, induce the formation of blood and lymph vessels, and affect the immune response to enhance tumor growth and invasion [61]. For ECM remodeling, they recur to an increase in the expression of matrix metalloproteinase (MMPs) for its degradation or manipulate collagen turnover to result in desmoplasia and increased stiffness [63]. CAFs may facilitate the invasion of cancer cells through the breaching of the basement membrane using contractile forces [64]. They secrete VEGF and TGF-β, among other factors, to enhance angiogenesis. CAFs can shape both the innate and adaptive antitumor immune response, facilitating the recruitment and activity of immune cells like TAMs and tumor-associated neutrophils (TANs), and favoring their immunosuppressive role [65]. In addition, they can drive immunosuppression by blocking the maturation of dendritic cells, their antigen presentation, and their associated adaptive immune responses. 13 CAFs play a major role in drug resistance, such as targeted therapy and chemotherapy [66]. The underlying mechanisms of CAFs can vary between tumor types, but generally, they release various soluble secretory factors, regulating the hypoxic microenvironment, maintaining the stemness of cancer stem cells, inducing EMT, and enabling immune escape, among others [67], [68]. 1.5.2 Intercellular communication with stromal cells in cancer Tumors can influence their ecosystem through elaborated networks of direct cell-cell contact or by secreting extracellular factors. Some soluble intermediaries include mediators of inflammation, growth factors, cytokines, and ECM remodeling enzymes [43]. Other mediators allow horizontal genetic/biomaterial transfer, like circulating tumor cells, circulating nucleic acids, and extracellular vesicles (EVs). These emerging intercellular communication mechanisms aid in understanding how the progress of cancer (invasion, migration and drug resistance) is facilitated in the tumor microenvironment. 1.6 Extracellular vesicles: Biogenesis and Function Extracellular vesicles are small, lipid-bilayer-delimited particles secreted from most cell types that cannot replicate [69]. They are a heterogeneous group categorized based on their morphology, composition, and size, which can range from 20 nanometers to 10 microns. They carry several components from the cytoplasm and the cell membrane that are selectively loaded to mediate crosstalk. EVs can be subclassified into exosomes, microvesicles, and apoptotic bodies, among others [70]. Microvesicles form from the budding of the cell surface, and their size range goes from 100 to 1000 nm [71]. Apoptotic bodies (> 1000 nm) are remnants of the cell after it undergoes programmed cell death, and they get engulfed by phagocytes afterwards [72]. 14 Exosomes originate from the endosomal system, and they are typically 30-150 nm in diameter [73]. Their composition can be divided into membrane and lumen components. In the membrane, there are proteins such as tetraspanins and adhesion molecules, as well as lipid elements such as sphingomyelin and ceramides [74]. In the lumen, various proteins such as cytoskeletal proteins, ESCRT components, growth factors, and cytokines are present. Additionally, there are metabolites and genetic material such as DNA, mRNA, and non-coding RNA species like microRNAs [75]. During exosome formation, the plasma membrane invaginates to create an early endosome, which later matures to become a late endosome, characterized by the containment of intraluminal vesicles (ILVs). ILVs get formed by further invagination of the endosomal membrane, leading to the formation of these intracellular multivesicular bodies (MVBs). The MVBs then can undergo two different routes: either they can fuse with lysosomes for degradation or they can release their content in the form of exosomes into the extracellular space [76], [73]. Currently, multiple mechanisms for exosome biogenesis have been unveiled, including the endosomal sorting complex required for transport (ESCRT) machinery and mechanisms that aren’t dependent on it like those dependent on tetraspanins and lipids [77], [78]. ESCRT is a multi-subunit system that, as its name suggests, also has a role in sorting cargo into the MVBs. ESCRT is made up of five core complexes: ESCRT-0, -I, -II, and -III, and vacuolar protein sorting-associated protein (Vsp4) that work collaboratively to recognize cargo and form ILVs [79]. Ubiquitinated proteins are detected and sorted by a subunit of the ESCRT-0 complex. Then, ESCRTI and ESCRT-II get recruited to deform the membrane around the clusters of these ubiquitinated proteins. ALIX aids in putting ESCRT-III subunits together and recruiting this complex to the late endosome, where it completes the scission of the vesicles. Finally, Vsp4 participates in the dissociation and recycling of the ESCRT machinery. Another mechanism for 15 ILV biogenesis consists of the hydrolyzation of sphingomyelin to ceramide by type II sphingomyelinase [80]. 1.6.1 EV markers and cargo The packaging of the EV cargo can be done passively, but there is selective loading as well. Proteins are the major biomolecules contained or carried by EVs, which can determine the receptor cells to which they integrate and then modify their phenotype. ESCRT-independent sorting mechanisms for proteins include, for instance, active RAB3, which drives the entrance of EGFR into MVBs [81]. Another mechanism for protein sorting consists of lipid rafts, which are ubiquitin-based. Lipid rafts are small microdomains with high levels of cholesterol and sphingolipids found in the plasma membrane and vesicles. Tetraspanins also play a role in the exosome's biogenesis and sorting. They are highly expressed within these particles, and thus they are also used as a marker for characterization, such as CD63 [82]. The proportion of nucleic acids in the cargo content of the exosomes is high, and these vesicles can transfer them to the receiving cells without being degraded. For instance, non-coding RNAs like miRNAs can avoid degradation by RNAases and then exhibit a protumorigenic role in normal cells [83]. miRNAs are non-coding small, single-stranded nucleotides that regulate gene expression by binding to target sequences in the mRNA's 3-untranslated region (3′-UTR) or open reading frame (ORF) region [84], [85]. Considering that there is enrichment of specific types of miRNAs in exosomes when compared to the parental cell, there must be specific mechanisms for RNA sorting [86]. 16 1.6.2 Mechanisms for exosome internalization The uptake of exosomes by recipient cells is a process that can happen via different mechanisms. One of them is through the direct interaction of the surface molecules from the exosomes (i.e. tetraspanins, immunoglobulins and proteoglycans) with the plasma membrane receptors (i.e. PD-L1, TNF, FasL, and TRAIL present in tumor cells) and this can induce downstream signaling cascades in addition to the delivery of the cargo [75], [87], [88]. Another way is through fusion Figure 1.3 EV biogenesis and bioactive cargo. Adapted from Maleki et al. (2018) [79] 17 with the recipient cell membrane and the release of the content into the cytosol. However, even if it may seem like the most efficient pathway, it has been observed that the most common process is endocytosis. In this case, exosomes get internalized when the plasma membrane buds off inside the cell to form a vesicle in a rapid and temperature-sensitive manner [89]. There are different formats of endocytosis, such as phagocytosis, micropinocytosis, or other molecule-mediated endocytosis [90]. More recently, it has been proposed that the intact vesicles internalize via filopodia, which are thin projections of the cell membrane [91]. Figure 1.4 EV internalization. From Liang et al. [92] 1.7 Markers and Cancer-Associated Fibroblasts heterogeneity. A variety of markers have been used to identify fibroblasts and activated fibroblasts. Among the most common are α-smooth muscle actin (α-SMA), fibroblast activation protein (FAP), platelet-derived growth factor receptor (PDGFR)-α and -β, podoplanin, and vimentin. These markers, along with other criteria like cell shape, location, and lack of lineage markers for other types of cells, can help in identifying fibroblasts that are quiescent or activated [62]. However, considering the heterogeneous cell populations from which CAFs can arise and the diverse sources of CAF activation, it has been observed that the markers expressed can vary and may not 18 be exclusive to CAFs. Therefore, to address this heterogeneity, some studies have determined the gene expression profiles to illustrate CAF subtypes. Galbo et al. characterized it through scRNA-seq analysis, revealing CAF subtypes from three different cancer types, including lung cancer. They identified six different pan-CAF subtypes across these carcinomas with their molecular signatures, transcription factors, and genetic pathways: “Pan-myCAF, myofibroblast-like CAFs; pan-dCAF, desmoplastic CAFs; pan-iCAF and pan-iCAF-2, inflammatory-like CAFs; pan-nCAF, normal fibroblasts; pan-pCAF, proliferating CAFs” [93]. Additionally, they examined the association between poor prognosis and therapy resistance with respect to these subtypes. Thus, according to their classification, the function and contribution of different CAFs to tumor progression might be different. Several CAF classifications have been proposed, yet some common categories of CAFs in different cancer types persist, like myCAFs and iCAFs. MyCAFs are a TGFβ-activated subtype, thus they have a matrix-secreting phenotype. iCAFs can be activated by pro-inflammatory cytokines such as IL1α and Notch signaling [94]. These CAF subtypes seem to be dynamic and interconvertible between them according to conditions in the culture and signaling pathway modulation. 1.8 Thesis outline 1.8.1 Rationale, hypothesis, and overall objectives Lung adenocarcinoma holds its relevant position as a public health concern by being the most commonly diagnosed subtype of lung cancer, a disease that causes the majority of cancer deaths every year with a low 5-year survival rate [95]. Tumorigenesis can be driven in part by the dynamic communication between the tumor cells and the surrounding normal cells within the 19 TME. Signals received from tumor cells can alter the phenotype of normal cells, prompting pro-carcinogenic effects. One means of communication is through extracellular vesicles that carry bioactive cargo [76]. Fibroblasts assimilate this content, and this induces a series of changes in their morphology and functions, conferring them a cancer-associated fibroblast phenotype. There is a diversity of cells from which the CAFs can arise and, as well, plenty of stimulators thus giving rise to different RNA expression signatures which define CAF sub-types [96]. Identifying these CAF subtypes and understanding what drives their formation could allow us to determine specific therapeutic targets which can improve patient outcomes in the future. We hypothesized that EVs from lung adenocarcinoma cells promote fibroblast activation with specific gene expression compared to canonical activation such as TGFβ, favoring the generation of different CAF subtypes signatures and certain signaling pathways. To achieve this, we had the following aims: AIM I – Induce CAF activation by lung adenocarcinoma EVs in primary fibroblasts and acquire gene expression data. Chapter 2 describes the methodology and optimization process used for the isolation and co-culture of the EVs derived from LUAD cells with primary lung fibroblasts to differentiate them into CAFs. AIM II – Identify pathways involved in EV CAF activation and determine the CAF subtypes induced. Chapter 3 describes the gene expression profiles of the fibroblasts after treatment with different controls and LUAD EVs. Data was processed, then a differential gene expression analysis was done to further characterize the differences between each treatment group and to identify enriched 20 signaling pathways. In addition, recognition of activated CAF subtypes after treating them with the controls and the lung adenocarcinoma EVs was done. 21 Chapter 2: Optimizing conditions to detect differences in lung fibroblasts under different treatments, including EVs from lung adenocarcinoma cells. 2.1 Introduction Lung cancer involves malignancies arising from the cells lining the airways and air sacs. Its main subtype is lung adenocarcinoma (LUAD). The importance of this neoplasm lies partly in its high incidence worldwide and its leading in cancer-related deaths worldwide, largely due to metastasis. Research has shown that interactions between cancer cells and cells in the environment is critical in the progression of this disease [50]. The tumor microenvironment comprises various cellular and non-cellular components such as blood vessels, the extracellular matrix, immune cells and fibroblasts, that surround and interact with the tumor [97]. Extracellular vesicles serve as vehicles for intercellular communication within the TME. EVs can be secreted by both cancer cells and TME cells and they can transfer signaling cargo like proteins, lipids, nucleic acids and metabolites that can influence various processes in their recipient cells [98]. When cancer EVs interact with quiescent fibroblasts in the tumor microenvironment, they can activate them and turn them into cancer-associated fibroblasts (CAFs) [99]. Understanding the role of the components and interactions within the microenvironment is a promising approach to developing new diagnostic tools and therapeutic approaches. This may allow an intervention in the metastatic process and improve the outcomes for individuals with lung cancer. Previous work in the lab showed that LUAD-derived EVs can induce fibroblast activation and differentiation into CAFs and a gene expression profile was obtained for the different treatments [100], [101]. However, replicates were not used, and thus statistical significance could not be determined. In addition, to do a more robust study we 22 increased the number of cell lines to work with to four. Therefore, in this study, multiple replicates were generated, where the treatment was done on adult fibroblasts instead of embryonic. In the third cell line for which EVs were collected and co-cultured with the fibroblasts, activation of the fibroblasts was not observed and therefore RNA-seq was not performed. EVs from the fourth cell line were not used due to the impracticality of isolating EVs from this cell line marked with a very low secretion ratio of particles. 2.2 Materials and Methods 2.2.1 Cell lines and cell culture conditions LUAD cell lines NCI-H1437 (ATCC® CRL-5872™), NCI-H2073 (ATCC® CRL-5918™), NCI-H2228 (ATCC® CRL-5935 ™) and NCI-H1395 (CRL-5868 ™), were obtained from the American Type Culture Collection (ATCC). The cells were grown in RPMI 1640 medium which was supplemented with 10% FBS. Cancer cell lines were used within two months after being thawed to prevent genetic drift. Embryonic human lung fibroblasts WI-38 were a kind donation from Dr. Wan Lab. They were grown on a 10-cm plate in DMEM and EMEM basal media supplemented with 10% FBS. Primary lung adult fibroblasts (H-6013) were purchased from Cell Biologics; they were grown in a gelatin (Cell Biologics Cat # 6950) pre-coated tissue culture flask, and M2267 Complete Fibroblast Medium supplemented with 5% FBS was used. Primary fibroblasts were used within the 2nd and 10th passages. All the cells were cultured in an incubator at 37 °C with humidity and 5% CO2. 2.2.2 Extracellular vesicle collection and isolation LUAD cell lines H1437, H2073, H2228, and H1395 were seeded in 15-cm plates. When they achieved around 30% confluency, they were washed with 1XPBS and cultured with RPMI 1640 media complemented with bovine EV-depleted 1% FBS. This process is done by 23 ultracentrifugation of FBS (110, 000 g) for 16 hours at 4°C and then obtaining the supernatant. Before reaching 90% confluency, the media is collected to undergo a series of “slow spins” at 4°C to remove cellular debris, which consists of centrifuge cycles of 300 g for 10 min, 2,000 g for 20 min, and 10,000 g for 30 min, recovering the supernatant in each step. Then, the supernatant undergoes a series of “fast spins” to continue the isolation of EVs by precipitating them using an ultracentrifuge (Beckman Coulter, Fullerton, CA) at 110,000 g at 4°C for 90 min. After removing the supernatant, the pellet gets washed with 1XPBS to re-pellet it with another 110,000 g for a 90-min cycle. Finally, the supernatant is carefully removed, and the EVs are resuspended in 0.02 μm-filtered PBS. Before being stored at -80°C, an aliquot was done per batch for nanoparticle tracking analysis to minimize the freeze-thaw cycles. 2.2.3 Nanoparticle Tracking Analysis of EVs The aliquot is thawed, and the tube is thoroughly vortexed to later prepare a dilution of 1:2000 in 0.02 μm-filtered PBS for H1437, H2228, and H1395. A dilution of 1:10,000 in H2073 was done to yield the optimal particle range of the machine (106 -109 particles/ml) and then the solutions were loaded into Nanosight LM14C (Malvern Instruments, Amesbury, UK). Vesicle flow was analyzed for 60 seconds in triplicate per sample. 2.2.4 Western Blot of EV characterization Protein was collected from LUAD cells and resuspended EVs. Samples were lysed by using radioimmunoprecipitation assay (RIPA) buffer with 1:100 phosphatase inhibitor cocktail I & II (Sigma-Aldrich) and protease inhibitor cocktail Set III (Calbiochem). They were then incubated at 4°C, separated from other contaminants and the Pierce BCA Protein Assay Kit (Thermo Fisher Scientific) was used to quantify the protein. After separating ten μg of protein in a NuPage 4-24 12% Bis-Tris Gel (Thermo Fisher Scientific), the protein was transferred to a polyvinylidene difluoride membrane (Millipore). The membrane was blocked for 1 hour at RT in a 1X TBS solution complemented with 5% BSA and 0.1% Tween-20. Then, they were incubated at 4°C overnight with a dilution of blocking buffer and the corresponding primary antibody 1:1000 anti-Alix (System Biosciences-53540), anti-GRP 94 (sc-393402), anti-Hsp-70 (sc-24), anti-Flotillin-1 (sc-74566), and anti-CD81 (sc-166029)(Antibodies from Santa Cruz). After a series of 5-minute washes, the membranes were incubated with the secondary antibody horseradish peroxidase-conjugated, either 1:2000 Anti-Rabbit (#7074, Cell Signaling) or Anti-Mouse (NXA931, GE Healthcare). For detection, SuperSignal™ West Pico PLUS Chemiluminescent Substrate at 100% and SuperSignal™ West Femto Maximum Sensitivity Substrate (ThermoScientific) at 70% were used in a Chemidoc MP Imaging System (Bio-Rad). 2.2.5 Fibroblast activation assay H6013 cells were seeded in 24 well plates with 1 ml of media and left to reach a confluency of around 15%. After 24 hours, a quarter of the media was replaced with fresh media with 5% FBS and the application of the treatments was initiated. They consisted of adding in duplicate every 24 hours for 3 days per well: H1437 EVs (7.13x1011 particles), H2073 EVs (7.13x1011 particles), positive control TGFβ 1 (10 ng/mL) (R&D systems, reconstituted to a final concentration of 20 ug/mL) and negative control PBS. The same volumes were added to all of the wells, compensating the difference across treatments by adding PBS. The media was not replaced but PBS/TGFβ/EVs were simply added to the media each day. Protein (for WB) and RNA (for gene expression profiling) were harvested 48 hours after the last treatment, which made in total of 96 hours of co-culture. 25 2.2.6 Western Blot of CAF activation Protease inhibitors previously mentioned were added to the RIPA buffer to prevent degradation of the protein coming from the H6013 samples 24 hours after the third treatment of PBS or TGFβ or EVs in duplicate per group. The process of quantification, separation and transfer was done in the same way as mentioned for the EV markers. However, after cutting the membrane by the sizes of the markers to study, the fragments were blocked in 5% skim milk powder (instead of BSA) in 1X TBS and 0.1% Tween-20 for 30 minutes. As previously, the primary antibodies were diluted with 5% BSA blocking buffer and the membrane was incubated overnight at 4°C: 1:1,000 diluted anti-PDGFRβ (3169s, Cell Signaling Technology), anti-FAP (PA5-99313, Invitrogen), anti-α-SMA (14968, Cell Signaling Technology) and 1:5,000 anti-GAPDH rabbit (2118s, Cell Signaling Technology). The secondary antibody used was horseradish peroxidase-conjugated anti-rabbit antibody (7074S, Cell Signaling Technology) at RT for one hour in a dilution of 1:2,000 for PDGFRβ, FAP and α-SMA and 1:10,000 dilution for GAPDH in 2.5% skim milk powder in TBS-T. For detection, we used the same system described for the WB of EV characterization. 2.2.7 RNA extraction of H6013 Qiazol was added 24 hours after the last addition of each treatment and control to H6013 in duplicate. Then, the samples were stored at -80°C until they could be further processed. They were thawed, and RNA extraction was done using the Qiagen miRNeasy Mini Kit following the protocol provided in the kit. Spectrophotometry values were obtained by using the NanoDrop machine. 26 2.3 Results 2.3.1 Extracellular vesicle characterization Characterization of EVs in our lab has been done using oral and lung cancer cell lines by verifying markers in WB and using Nanoparticle Tracking Analyzer. Most of the particles secreted by LUAD cells, H2073, H1437, H2228 and H1395, have a diameter between 50-250 nm, being within the range of extracellular vesicles. The particle concentration range is within108 -109 particles/ml. A Western Blot was performed on the lysates obtained from both cell lines and their EVs, revealing expression of the EV-associated proteins Alix, HSP70, Flotillin-1, and CD81 in the EV protein samples. GRP94, also known as endoplasmin, is a protein primarily localized in the endoplasmic reticulum (ER) [102], [103], [104]. GRP94 is not particularly present in small EVs, thus it can be used to evaluate EV purity and protein contamination [105]. GRP94 could be detected in the cells but not in these particles. Taken together, small EVs seem to be mainly present in the isolated pellets. H1395 EVs were not further used due to their low EV secretion rate and since samples treated with H2228 EVs were not sent for RNA-seq, they were not included in WB characterization. A) 27 Figure 2.1 EV characterization. Nanoparticle Tracking Analysis for visualization, characterization and concentration of EVs by NanoSight A) H2073 EVs, B) H1437 EVs, C) H2228 EVs, and D) H1395 EVs. B) C) D) 28 Figure 2.2 Western Blot of the protein from LUAD cells and their EVs. We tested both samples for the vesicle markers together with GRP94 (negative marker not present in small EVs). 2.3.2 Optimizing EV storage: Effect of long-term storage and freeze-thaw cycles It has been shown that EVs can be impacted in both their concentration and RNA content by storage conditions, showing a reduction after some days [106]. EVs collected from H1395 were collected and stored at –80°C. After 10 months the EVs were thawed and analyzed by Nanosight. A reduction of 36.5% of total EVs was observed. The EVs were again stored at –80°C for 2 days, thawed and analyzed again resulting in a further 17.4% reduction of total EVs. The EVs were stored again at –80°C, thawed after 8 days, and analyzed. The final freeze-thaw showed another 5.95% reduction in total EVs. By the end of this experiment, there was a diminution of 49.2% of particles in the pellet detected by the Nanosight. Similarly, EVs were collected from H2073 cells, stored at -80°C, thawed after 1 week, then stored again at -80°C for 1 day and then thawed and analyzed. This freeze-thaw revealed a 3.26% decrease in total EVs. 29 Figure 2.3 Storage time and the number of freeze-thaw cycles affect the EV number. A-B) Storage time effect in the EVs number. There was an important reduction of 36.5% in the particles after freezing them, being stored at -80°C for ten months and doing the first two thaws. C-D) Storage time and number of thaw effects in the EVs number. Additional freeze-thaw cycles and time contributed to losing particles contributing A) F) B) C) E) F) D) E) F) 30 overall to the loss of almost half of the initial amount proving their crucial role in the preservation of EVs in storage and the size range populations. E-F) Thaw effect in the EVs number. A freeze-thaw cycle seemed to have the same reducing effect in another cell line, although it seems to affect differently each cell line. We appreciated for both cell lines that freeze-thaw cycles can decrease the total amount of EVs since we observed the loss of EV particles after every freeze-thaw applied to H1395 and H2073. Contributing to this, storage time at –80°C may impact EV concentration as well, where longer periods of storage could impact more dramatically the final concentration. In addition, EVs from different cell types may be impacted differently, observing a more pronounced loss after two days for H1395 than after one day for H2073. Therefore, once EVs were resuspended in filtered PBS and well mixed by vortexing, an aliquot was collected to be further thawed and analyzed in the Nanosight whenever this procedure couldn’t be done immediately to determine the size particle and concentration. Thus, this eliminates the need to freeze-thaw the sample, which is also stored at -80°C. In addition, collected EVs were used within a maximum of one month to reduce the effect of time in the reduction of the particles, however, it’s ideal to use them fresh or as soon as possible. 2.3.3 Optimizing WI-38 fibroblasts and their cell culture conditions WI-38 fibroblasts donated by Dr. Wan Lam were used for the evaluation of the cell culture conditions. The cells used were at passage number four after our lab started using them. Pre-warmed 37°C DMEM addition with 10%FBS was used as growth media. After the cells were thawed, the media was replaced the next day with warm fresh media. Cells were split when they reached ~80% confluency, using the split ratio 1:2 to 1:8. As mentioned in the introduction, TGF-β is a well-established activating signal of fibroblasts, driving the expression of α-SMA. Thus, we decided to use it as a positive control of fibroblast 31 activation and to use PBS as a negative control. However, after working with the fibroblasts, they showed no difference in the panel of markers used for PBS and TGFβ treated cells. This seemed to be the same case for other vials of WI-38 cells as well (Figure 2.4 A). Fibroblasts can become activated simply through culturing conditions. I hypothesized that the WI-38 cells I was using were already activated (as seen by the high signal in the CAF markers). Therefore, a new vial of WI-38 was purchased from Cedar Lanes with the lowest passage number possible across some companies of P:17. This time, more details were taken into consideration, such as their finite lifespan of approximately 50 passages and population doubling every 24 hours, which seemed to get slower as the number of passages increased [107]. Their growth also depended on the confluency of the cells, since at low concentrations they would normally take more time to grow but after reaching 20-30% confluency, the cell growth dynamics speed up. According to ATCC, the recommended media is EMEM with 10%FBS instead of DMEM and the split ratio was strictly maintained between 1:2 to 1:4. The change of media needed to be done every 2 days and the confluency would be at a maximum of 80%. Trypsin-EDTA 0.25% was used for not more than a minute. Also, everything in contact with the cells would be previously warmed up and the cell culture process was done very gently (Figure 2.4 B). With these changes some differences between the treatment groups could be seen, therefore, we proceeded to culture the fibroblasts with EVs derived from H2073, adding 4.75x10^11 particles per day. As expected slightly higher expression in α-SMA for TGFβ and the EVs treatment could be seen (Figure 2.4 C). 32 Figure 2.4 Western blots with CAF activation markers to assess activation in the PBS group in WI-38 cells. Western blots with CAF activation markers to assess activation in the PBS group in WI-38 cells. A) Western blot with 5 markers checking available WI-38 vials. B) Western blot with 4 markers checking purchased WI-38 cells. C) Western blot with 5 markers for purchased WI-38 PBS contrasted to TGFβ-induced activation and H2073 EV-based activation of fibroblasts. 2.3.4 Optimizing the WB protocol However, since the differences seen between groups were not very considerable, a new WB protocol was tried, and both WI-38 vials were checked (donated by Dr. Wan and recently purchased from the company). Changes in the new WB protocol consisted of using new 4x A) B) C) 33 Laemmli Sample Buffer (Biorad Catalog #161-0747) with NuPAGE™ Sample Reducing Agent (10X) (Invitrogen™ Catalog #NP0004) and topping the lysates with RIPA, warming the samples at 95°C for 5 minutes instead of 70°C for 10 minutes. We used a different ladder (Precision Plus Protein™ Kaleidoscope™ Prestained Protein Standards) and ran the gel at 200V-300mA for 55 minutes at RT. The materials and process for the transfer did not change (100 V for 1 hour at 4°C). The blocking was done for 30 minutes with 5% skim milk powder in TBS-T instead of 1 hour with 5% BSA. For the incubation with the primary antibodies, a quick wash with TBS-T was done before and we used the same concentrations and conditions previously described in the WB methods. Then, we washed the membranes three times for 5 minutes instead of 10 minutes and incubated them with the secondary antibodies. For this, we also switched to using 2.5% skim milk powder instead of 5% BSA under the conditions previously mentioned in the methods and doing the washes in the same way. For the imaging, we also changed the kit to the ones mentioned in the WB, using Pico at 100% and Femto at 70% with distilled water and they were exposed for no longer than 10 minutes. Then the α-SMA membrane was put back into TBS-T and it was stripped for 15 minutes. It was washed with TBS-T as mentioned and blocked with 5% BSA blocking buffer instead of using milk. The rest of the procedure was done in the same way. The experiments consisted of seeding the cells in a 24-well plate for 5 days and adding the treatments 3 times. The results seemed to show a more prominent difference for the TGFβ-treated WI-38 cells. The first two samples, PBS and TGFβ, were the WI-38 cells donated (old) and the next two PBS samples were the cells purchased from the company (new) (Figure 2.5 A). 34 In Figure 2.5 B, we can see a WB with the two treatments (PBS and TGFβ) in the WI-38 fibroblasts purchased from the company before optimization and in Figure 2.5 C we can appreciate the better activation of α-SMA in the old WI-38 cells with the optimization of both the cell culture conditions and Western Blot protocol. Figure 2.5 Western blots with CAF activation markers to assess activation in the PBS group in WI-38 cells after optimization. A) Western blot with 5 markers for both old and new WI-38 PBS samples contrasted to TGFβ-induced activation in old cells after optimization. B) Western blot with 5 markers showing no difference between PBS fibroblasts and activated cells by TGFβ and EVs before optimization. C) Western blot with 4 markers showing low expression in all the markers of PBS fibroblasts compared to TGFβ-activated cells after optimization. A) B) C) 35 Thus, after a process of optimization, the differences seen can be better appreciated in the markers, even in the vials initially obtained from the other lab when culturing the cells under the established conditions (Figure 2.5 B-C). 2.3.5 Working with the primary fibroblasts with optimized conditions In order to achieve more biologically relevant results primary adult fibroblasts (H6013) derived from adult lung tissue that tested for negative expression of α-SMA were purchased from Cell Biologics. The cells at passages 2 to 3 were harvested from flasks and cryo-preserved in vials. The media contains a minimum of 5% FBS and since all the components needed to remain at 37°C, cell culture needed to be efficient and gentle. The flasks were coated beforehand with gelatin and prewashed with PBS before the change of media every 24-48 hours to remove dead cells and replenish nutrients. Cell confluency needed to be no more than 80% and the split ratio maintained between 1:2 to 1:3. The trypsin used was five times less concentrated (Trypsin/EDTA 0.05% Solution) and the incubation period would not be higher than two minutes (versus one minute used with the Trypsin-EDTA 0.25%). In addition, a higher quantity of media was added to inactivate the trypsin and the cells were not centrifuged during splitting. The number of passages needed to be considered. Using a low passage number after expanding the cells is preferred since repeated freezing and thawing are not recommended. Finally, monitoring them every day is important since their growth can vary and their confluency exceeding 80% is not desired. To start, it was important to determine the number of days needed with treatment in order to show an increase in CAF markers (indicating CAF activation). We tried two different timelines where H6013 cells were seeded, and then 24 hours later, PBS or TGFβ were added. These 36 treatments were added three times every 24 hours to the cells. However, on the first timeline, the cells were harvested 24h after the last treatment, at day 5 as seen in Figure 2.6 A, which would make a total time of incubation of 72h with their treatments (3 days). The protein was extracted, and the WB showed that the cells treated with PBS had increased expression of only PDGFRβ and FAP. The same was observed for the TGFβ group but there wasn’t a big increase in their expression when compared to the PBS group (Figure 2.7 A). For the second timeline, the cells were harvested 48h after the last treatment, at day 6 as seen in Figure 2.6 B), making a total time of co-culture of 96h (4 days) After the protein extraction, the WB showed fainter bands for PDGFRβ and FAP in the PBS-treated groups and a considerable difference with the higher expression in the TGFβ group, thus we decided to use this second timeline for the EV treatments (Figure 2.7 A). Figure 2.6 Timelines used to establish the duration of the experiment. A) First timeline that makes a total time of 5 days with 72 hours of incubation of the H6013 cells with the corresponding treatment (PBS or TGFβ). B) The second timeline makes a total time of 6 days with 96 hours of incubation of the H6013 cells with the corresponding treatment (PBS or TGFβ). Fibroblasts were always checked for activation before each co-culture experiment (Figure 2.7 C). The first trial of adding EVs to the fibroblasts was done using H6013 cultured with EVs derived from the H2073 cell line using two different quantities of EVs (4.75x1011 and 7.13x1011) with an Day 1Seed cellsDay 2Add treatmentDay 3Add treatmentDay 4Add treatmentDay 5Harvest cells (72 hours incubation)Day 1Seed cellsDay 2Add treatmentDay 3Add treatmentDay 4Add treatmentDay 5Let incubateDay 6Harvest cells (96 hours incubation)A) B) A) 37 incubation period of 96 hours (Figure 2.6 B). We determined that the fibroblasts treated with these amounts of EVs have increased expression of the α-SMA marker, especially when adding 7.13x10^11 particles per well per day. Figure 2.7 Western blots with CAF activation markers to assess activation in the PBS group and determine experiment conditions in H6013 cells after protocol optimization. A) Western blot with 5 markers using old WI-38 cells treated with PBS (first and last lane) and TGFβ (second lane) that were previously proven positive and negative control to assess the number of days of treatment for CAF activation in primary cells. B) Western blot with 4 markers assessing different H2073EV concentrations to induce CAF activation where we added 7.13x10^11 particles, resulting number from adding 1.5 times 4.75x1011. C) Western blot with 4 markers using old WI-38 cells treated with PBS and TGFβ (first and second lane) to evaluate fibroblast activation of H6013 before their treatments. We seeded the cells and added 7.13x10^11 EVs derived from H2073 per day per well. This was done under the same conditions in duplicate so protein and RNA could be extracted in duplicate. Since noticeable differences were seen in contrast with PBS, we selected the underlined samples C) A) B) A) 38 to be sent for RNA seq (Figure 2.7 A). Protein quantification was done to validate the increase in protein expression seen in the WB using a t-test, showing a significant increase of α-SMA (p-value of 0.002) and PDGFRβ (p-value of 0.006) in the EV-treated fibroblasts when compared to the control (Figure 2.7 B). Also, a higher expression of FAP (p-value of 0.033) and PDGFRβ (p-value of 0.006) for the positive control TGFβ was seen. Figure 2.8 CAF activation by TGFβ and H2073 EVs in WB and WB quantification. Samples selected to be sent for RNA sequencing were underlined in A). Upregulation of markers in both TGFβ (FAP with a p-value of 0.033 and PDGFRβ with a p-value of 0.006) and H2073EV (α -SMA with a p-value of 0.002 and PDGFRβ with a p-value of 0.0014) groups compared to the negative control were seen and quantified. *P≤0.05, **P≤0.01 and ***P≤0.001, two-tailed t-test. We proceeded to repeat the procedure for both EVs derived from H2073 and H1437 cells, adding the same amount to determine if this would also be enough to see the activation of the CAFs with the second cell line, which was the case for α-SMA. This was also done under the same conditions in two plates to extract protein and to add Qiazol for lysis resuspension and storage at -80°C for RNA extraction in duplicates. RNA was extracted for the selected underlined samples in Figure 2.8 A). The protein quantification was applied to this WB as well using a t-test showing there is a significant difference in the cells treated with H2073 EVs for both FAP and A) B) 39 PDGFRβ with a p-value of 0.037 and 0.0032 respectively. There is a statistically significant increase in the α-SMA expression of the fibroblasts treated with TGFβ and H1437 EVs with a p-value of 0.038 and 0.004 respectively when compared to the PBS group (Figure 2.8 B). The same was done for a third cell line H2228. However, we did not observe any noticeable changes to the CAF markers and therefore did not extract RNA from them (Figure 2.8 C). Figure 2.9 CAF activation by TGFβ, H1437 and H2073 EVs in WB and WB quantification. Samples selected to be sent for RNA sequencing were underlined in A). Upregulation of markers in TGFβ (α -SMA with a p-value of 0.038), H2073EV (FAP with a p-value of 0.037 and PDGFRβ with a p-value of 0.0032) and H1437EV (α -SMA with a p-value of 0.004) groups compared to the negative control were seen and quantified. *p≤0.05, **P≤ 0.01 and ***P≤ 0.001, two-tailed t-test. C) WB of CAF activation by TGFβ, but not for H2228 EVs treated cells. C) A) B) 40 For the selected samples mentioned before, the RNA was extracted using the MiRNeasy mini kit, the results for RNA quality were checked in Nanodrop, and the RNA integrity number (RIN) value was obtained from the bioanalyzer: Table 2.1 Assessment of the sample’s RNA purity, concentration, and integrity. Samples: H6013 treated with x 260/280 260/230 ng/µl RIN PBS-1 2.12 1.48 20 9.7 PBS-2 2.02 1.66 41 10 TGFß-1 2.01 1.68 42 10 TGFß-2 1.96 1.05 38 9.9 H2073 EVS-1 1.93 1.78 80 10 H2073 EVS-2 1.98 1.72 53 10 H1437 EVs-1 1.95 1.75 30 10 H1437 EVs-2 2.05 1.76 45 10 After this evaluation, the samples were processed for RNA sequencing. 2.4 Discussion Extracellular vesicles are a very heterogeneous group of particles categorized based on different characteristics, such as origin, size and function. EVs can range from 20 nanometers to 10 microns. If they are smaller than 200 nm they are considered to be small EVs (sEVs), which include exosomes. If they are bigger than 200 nm they are considered as large EVs (lEVs), which include microvesicles. As mentioned in the introduction apoptotic bodies can range in size from (1-5 μm) [108]. In our case, we isolated the EVs by ultracentrifugation and since it is 41 increasingly clear that the isolates tend to be a heterogeneous population of EVs rather than one single type we identified them based on their size range. Therefore, we demonstrated that the samples obtained from the lung cancer cell lines had a most frequently occurring particle size within a range of 30-150 nm, specifically around 117 nm for H2073, 140 nm for H1437, 135 nm for H2228 and 108 nm for H1395. This low particle size range corresponds to small vesicles and therefore the particles isolated seem to belong mainly to this category. Different types of EVs can impact their recipient cells differently due to the diverse cargo they contain which can depend on the cell origin, biogenesis, size and composition [109]. In our case, since we are using small particles rather than just a specific subtype, the EVs might influence our recipient cells in more ways as well due to the variety of cargo that they all may contain. Thus, using more specialized EV separation techniques or a combination of them would better illustrate the impact of a group population vs individual ones. This would also likely isolate fewer EVs as a result since there is a balance between EV yield and purity and to our current knowledge, this is still a limitation in the field that needs to be overcome [110]. However, it is worth mentioning that recipient cells appear to uptake smaller EVs (< 100 nm) faster than larger EVs, thus the recipient cells might also be internalizing faster exosomes from the small EVs isolated [111]. Particle concentration from our lung cancer cell lines was 1.79x109, 1.5x109, 1.17x109 and 7.58x108 particles/ml for H2073, H1437, H2228 and H1395 respectively. Particle concentration numbers would be different for each LUAD cell line since they are different cells with different secreting ratios of EVs, even if the cellular state is similar (cancer cells secrete more EVs than normal cells) and have the same environmental conditions. However, between runs for isolation, the particle concentration would be within 108 -109 particles/ml and be very consistent when 42 isolating from the same cells with the same number of cell culture plates and using the same settings for Nanosight. In addition, it has been shown that the integrity and number of EVs can be affected by storage conditions, being reduced even after a few days. In fact, we observed a decrease in the concentration of EVs in the pellet after storage at -80°C. Extracellular vesicles are sensitive to storage conditions like temperature (even at storage at -80°C) and freeze-thaw cycles, affecting their integrity and decreasing their number after their first cycle [106]. For the H1395-derived EVs that went through long storage and freeze-thaw cycles, the concentration of particles is lower but also the EVs don’t seem to remain within the 150 nm size range, instead, there are more EVs in different sizes as if we were left with bigger particles rather than small EVs. Gelibter et al. found that freeze‐thaw cycles resulted in particle increased size which depended on the number of cycles. Additionally, they found that an increase in particle median size depended on the time storage at −80°C [112]. In addition, EVs can be adsorbed onto the tube walls during storage in solution which can lead to a significant reduction in their concentration [113]. This was mainly appreciated in H1395 EVs after 10 months of storage and after going through a series of freeze-thaw cycles, having a loss of 49.2% in the total amount of particles. Identification of CAFs can be challenging due to their heterogeneity and the lack of clear markers to identify them, but several markers have been proposed. α-SMA is a cytoskeletal protein associated with structure and a highly contractile phenotype. CAFs resembling a myofibroblast phenotype normally express α-SMA and contribute to an immunosuppressive TME by remodeling the ECM, promoting tumor progression and conferring therapeutic resistance [114], [115]. FAP is a serine protease that participates in ECM remodeling and fibrogenesis but can also be displayed by epithelial cells undergoing EMT. In the case of FAP+ 43 CAFs, they shape the immunosuppressive TME through the secretion of distinct chemokines and cytokines. There are worse outcomes for cancer patients with high expression of FAP, linked to its enzymatic activity in tissue remodeling [116]. PDGFRβ is a receptor tyrosine kinase and it has been markedly overexpressed in carcinoma tissues like pancreatic adenocarcinoma. PDGFRα/β+ CAFs have immunomodulatory effects [115]. Additionally, PDGFRα/βhigh CAFs seem to have a link to lymph node metastasis in ovarian cancer. The use of multiple markers is required since seeking one marker alone might result in excluding CAF subpopulations that do not align with that specific phenotype [117]. Thus, we used FAP, α-SMA and PDGFRβ for the evaluation of fibroblast activation into CAFs for this project. The use of these markers helped to identify that in Figure 2.9 A) H1437-derived EVs seem to induce mainly α-SMA CAFs while H2073-derived EVs increase the expression of PDGFRβ and FAP mainly but could also augment α-SMA, as seen in Figure 2.8 A). Therefore, the effect of EVs coming from different LUAD cell lines in the phenotype of lung primary fibroblasts might not be the same. It is worth mentioning that fibroblasts are sensitive to environmental conditions such as atmospheric oxygen levels, space limitations, and mechanical and physical properties [118], [119]. For example, when they sense mechanical stress, this stimulus modifies their behavior and they respond to it by remodeling the tissue [120], [121]. Therefore, to work with fibroblasts whose change of phenotype is not a result of the environment of the experiment but because of the treatments applied, we optimized a series of steps that resulted in the final conditions described in Table 2.2: 44 Table 2.2 Conditions to consider when culturing primary lung fibroblasts. Cells specifics Handling procedure Experiment setting WB protocol Primary adult cells (non-immortalized) Low split ratio Working at 37°C Changing the loading composition No expression of fibrotic markers Low to medium confluency 24 well plate Different ladder and washing periods Low concentration of serum (5% FBS) Low passage number (<10) Number of days of treatment (3 days) Using skim milk powder for blocking and 2° Abs. Coating flasks Low mechanical stimuli (tension, compression and shear stress) TGFβ dose (10 ng/ml) Different imaging reagents (Pico and Femto) Primary cells have several benefits as a model of study when compared to cell lines. They are a better representation of in vivo conditions since they maintain their genetic stability, which can be lost during prolonged culture. Therefore, they retain their original phenotype and functions which is very relevant for our purposes since we seek to study in specific the effect of extracellular vesicles on cell behavior. We decided to switch to using adult fibroblasts since there will be differences in the ability of adult vs fetal fibroblasts to express genes such as those related to fibrosis. Mateu et al. showed that α-SMA was increased in dermal neonatal fibroblasts in contrast to their adult analogues [122]. A study suggested that differences between skin fetal and adult fibroblasts could explain their opposite responses to TGF-beta stimuli, showing an 45 increase in the expression of alpha-SMA when TGFbeta was added to the medium of adult fibroblasts and no change of expression when added to fetal fibroblasts. This would likely contribute to the resultant differences in their functions such as their contractile capacity [123]. It has been shown that in vitro culture of fibroblasts, regardless of media composition, can trigger differentiation to the myofibroblast phenotype [124]. Thus, taking special care of the environment during cell culture is vital, and we considered multiple conditions. First, using a low concentration of fetal bovine serum (FBS) in the media is key since the addition of supplements can change the cell phenotype in vitro. Other setups to consider are confluency, passage number, temperature, and mechanical stimuli. Increased confluency tends to enhance some of the fibrotic markers, such as ACTA2, POSTN, COL1A1, COL1A2, etc. Temperature can influence the metabolic, gene expression, and proliferative responses of fibroblasts. We also coated the plates with a 0.1 % gelatin-based coating solution, and this appears to improve adherence. Taken together, by considering all these factors, we may help reduce the ambient stimuli affecting the fibroblast's phenotype and functions. TGF-β is a multifunctional cytokine that controls numerous cellular functions, including the promotion of the transition of epithelial cells to a mesenchymal phenotype. During wound healing, TGF-β exerts pleiotropic effects like coordinating proliferation, differentiation, ECM generation, and immune response regulation [125]. Thus, it suppresses the inflammatory response and stimulates the gene transcription of structural collagens, fibronectin, and MMPs, having a relevant role in the synthesis and remodeling of the ECM, which is vital for tissue fibrosis as well [126], [127]. TGF-β is involved in the metabolic reprogramming of fibroblasts, influencing the differentiation of fibroblasts into myofibroblasts and increasing their α-SMA levels, which contribute to the wound's contraction [128]. Therefore, TGF-β's functions on 46 fibroblasts have pronounced effects on ECM remodeling, in part through its capacity to modulate the deposition of ECM components. Cancer cells secrete various factors into the TME that can enable the recruitment and differentiation of fibroblasts, like TGFβ. This growth factor also promotes the transition of resident fibroblasts to CAFs [129]. This transition is associated with promoting tumor invasion, pulmonary metastasis, and epithelial-mesenchymal transition (EMT) in breast cancer and other types as well. Several studies have shown that cancer cells exhibit modified levels of TGFβ1, which is associated with unfavorable clinical outcomes [130] . Consistent with other studies, we observed increased levels of α-SMA after TGFβ1 and EV-treated fibroblasts, which confirms their activation. Emerging evidence suggests that cancer cells can alter fibroblast behavior by releasing extracellular vesicles. The increased attention towards EVs is linked to their mediated roles in the TME and drug resistance [76]. Focusing on their TME role, they get secreted and can, induce phenotypic changes in receptor cells. Bioactive cargo is in part responsible for these changes, involving molecules like miRNAs or proteins. For example, the RNA and protein content of EVs is internalized by endothelial cells and as a result, they stimulate tubule formation [131]. EVs derived from the body fluids of patients with tumors promoted the growth of normal fibroblasts. Interestingly, glioblastoma EVs also stimulated the growth of glioma cells, indicating a self-promoting nature [132]. Then, fibroblasts are transformed into CAFs after this stimulation which has been observed in different types of cancer like cutaneous, oral, ovarian, and bladder cancer [102], [133], [134], [135], [136]. Goulet and colleagues conducted a study where they treated healthy primary bladder fibroblasts with extracellular vesicles (EVs) sourced from bladder cancer. The research findings showed that the treated fibroblasts exhibited increased expression 47 of α-SMA and FAP, indicating their transformation into cancer-associated fibroblasts (CAFs) [137]. Afterwards, CAFs can sustain a pro-tumorigenic microenvironment as Giusti et al. saw when the activation of fibroblasts to CAFs by EVs from ovarian cancer cells increased the motility and invasiveness of cancer cells through the CAFs own secretome [138]. Therefore CAFs secretome, which includes EVs, can enhance several tumorigenic processes like ECM remodeling, angiogenesis and lymphangiogenesis and modulate the tumor immune responses in several types of cancer [58], [139], [140], [141]. In our results, we can see that TGF-β induced a myofibroblastic phenotype with an increase in α-SMA expression, as expected. Interestingly, we can see an even stronger expression of α-SMA in the fibroblasts treated with the EVs derived from H1437, suggesting the activation of myofibroblast-like CAFs. This is relevant since CAF subpopulations with this phenotype may have a potent capacity for cancer progression in types like breast and ovarian cancer. In lung cancer, they are associated with lymph node metastasis and aggressive behavior. For H1437, we appreciate a significant increase in markers like FAP and PDGFRβ. There have been multiple studies indicating that CAFs that are positive for FAP-1 have immunoadjuvant properties in cases of NSCLC. FAP-1 is therefore viewed as a molecular target for anti-CAF therapy like sibrotuzumab, an antibody to human FAP that binds to tumor sites without apparent side effects in patients with advanced NSCLC [142]. The PDGFRβ+ CAFs seem to be key in immunomodulation by promoting the migration and M2 polarization of macrophages. This interaction has been associated with immune suppression and enhanced survival of tumor cells. 48 Chapter 3: EVs from LUAD cells induce fibroblast activation to myCAFs and dCAFs, similar to activation via TGFβ, but they also induce activation of a proliferative CAF subtype and enrichment of EMT and cell cycle-associated genes 3.1 Introduction Cancer-associated fibroblasts can significantly promote tumor progression in lung cancer as a major component of the tumor microenvironment. Additionally, they can contribute to metastasis progression by remodeling the ECM and secreting factors that may facilitate the activity of motile cancer cells [143]. CAFs can influence cancer cells promoting stem cell-like properties and contributing to their resistance to chemotherapy [144]. CAFs can also inhibit the immune system’s ability to fight against cancer cells [145]. CAFs can be generated through various mechanisms. Some involve signaling molecules secreted by cells such as tumor cells or changes in the conditions of the environment. For instance, epithelial cancer cells secrete growth factors into the surrounding microenvironment, stimulating the recruitment and activation of fibroblasts [142]. Moreover, the secretion of factors like FGF2 by CAFs can directly interact with cancer cells, enhancing their invasive capabilities [63]. Therefore, CAFs are known for their wide heterogeneity. Different CAF subtypes can have distinct impacts on the tumor microenvironment and the behavior of cancer cells. In this chapter, we aimed to determine the differential expression of gene signatures associated with specific CAF subtypes that can arise when treating lung fibroblasts with EVs derived from different LUAD cells (and thus may have different bioactive molecules as cargo) and TGFβ. We wanted to compare the CAFs induced from both EVs and compare them also to our positive control 49 TGFβ) to determine if we would see results that could validate the similarities and differences seen between the three treatment groups in the Western Blots. Performing a pathway analysis also helped us to better understand the deregulated pathways in each group and determine if they are the same across treated fibroblasts. Therefore, by comprehending the heterogeneity, characteristics and functions of these CAF subtypes that arise from different stimuli we can gain a better understanding of the tumor microenvironment which could lead to novel therapeutics and potential biomarkers. 3.2 Materials and Methods 3.2.1 RNA sequencing of H6013 After RNA extraction and storage at -80°C, the samples obtained in the previous Chapter were sent to the Biomedical Research Centre at the University of British Columbia for quality control and sequencing. Approximately 100 to 200 ng were used as an input per sample and the results of the Agilent 2,100 Bioanalyzer yielded RNA integrity number (RIN) between 9.7 and 10. Samples fulfilling the quality requirements undergo the following process: “Qualifying samples were then prepped following the standard protocol for the Illumina Stranded mRNA prep (Illumina). Sequencing was performed on the Illumina NextSeq2000 with Paired-End 59bp × 59bp reads. Sequencing data was demultiplexed using Illumina’s BCL Convert. De-multiplexed read sequences were then aligned to the Homo sapiens (hg38 no Alts, with decoys) /Mus Musculous (mm10) reference sequence using DRAGEN RNA app on Basespace Sequence Hub (https://support-docs.illumina.com/SW/DRAGEN_v41/Content/SW/DRAGEN/TPipelineIntro_fDG.htm)”(SBME, 2023) [146]. 50 3.2.2 RNA sequencing data analysis RNA bulk sequencing analysis gave as a result around 40 million reads per sample. Assembly and differential expression were determined using DESeq2 (https://bioconductor.org/packages/release/bioc/html/DESeq2.html) in R. Log2 fold changes and adjusted p-values were used for the differential expression analysis comparing the treatment groups (EV or TGFβ) versus control (PBS). PCA plots, batch corrections, volcano plots, and heatmaps were done in R using prcomp, ComBat_seq, EnhancedVolcano and heatmap functions respectively while for the Venn diagrams, Venny 2.1 was used [147]. A p-adj <0.05 and a log2foldchange> 1 were considered significant for bar plots, Venn diagrams and volcano plots. A p-adj <0.05 and a log2foldchange> 2 were considered significant for the general heatmap. The heatmap of signature marker genes of specific CAF subtypes was performed using the z-scores calculated from normalized counts. Gene set enrichment analysis (GSEA) was done by using a pre-ranked list that considered the p-value and the log2foldchange in each treatment group, employing the Hallmarks gene set from MSigDB (Liberzon et al., 2011). For GSEA, pathways with an FDR q-value <0.25 and nominal p-value <0.05 were contemplated as enriched. Pathways analysis visualization given by GSEA was done with R. ToppGene Suite (https://toppgene.cchmc.org/) was used to determine the functions of different gene sets. 3.3 Results 3.3.1 Quantification BAM files were extracted from the Illumina platform, and quantification was done by using feature counts in R and the reference genome hg38 as well. The resulting matrix included the GeneID in addition to the counts per gene for each group, which were then normalized. This was 51 inputted to Combatseq to perform a batch correction since the experiments were done on separate days: Day 1 (Batch 1): Fibroblast cells were treated with PBS, TGFβ and EVs from H1437. For RNA-seq, one replicate of PBS and TGFβ and two RNA replicates from cells treated with EV1437 were sent. Day 2 (Batch 2): Fibroblast cells were treated with PBS, TGFβ and EVs from H2073. For RNA-seq, one replicate of PBS and TGFβ and two RNA replicates from cells treated with EV2073 were sent. We then accounted for the differences between batches using DeSeq2. Results from the PCA showed a diminution in the differences between the PBS and TGFβ controls (Figure 3.1). Figure 3.1 PCA of samples after batch effect corrections. A better grouping of the replicates of the positive and negative samples can be seen after using Combatseq and DeSeq2. 3.3.2 Differential expression The differentially expressed genes were determined by selecting those with less than 0.05 of padj and a log2foldchange of 1. We determined the significant upregulated and downregulated genes 52 in different treatments in relation to the PBS group. We observed that the fibroblasts treated with H1437 EV seem to have more differentially expressed genes (both upregulated and downregulated genes) than the rest of the groups. Then, the cells treated with H2073 EVs and finally, those cells treated with TGFβ had the least amount of differentially expressed genes. In addition, for those genes differentially expressed in the H1437 EV group, there seem to be more upregulated genes than downregulated genes, which seems to be the same pattern for the rest of the groups as well (Figure 3.2 A). We determined the number of significantly differentially expressed genes in common between treatments. There were more upregulated genes in common between the EV treatment groups but there was also a big overlap of the H1437 group with the TGFβ treated group as well. A percentage of 15.7 % out of 100% of the total amount of the significantly upregulated genes suggested a considerable overlap between the three treatments. For the downregulated genes there were more similar genes between H1437 and both the TGFβ and H2073 groups but not as many in common between the three or H2073 vs TGFβ (Figure 3.2 B). 53 Figure 3.2 Differentially expressed genes in fibroblasts treated with TGFβ or EVs from H1437 and H2073 cells. Bar graphs show the amount of upregulated and downregulated genes in each group. B) Venn diagrams showing the numbers and percentages of upregulated and downregulated genes in common between each group. Volcano plots were done to visually identify differences in the genes that are significantly differentially expressed by plotting the -log10 (p-adjusted) in the y-axis and the log2foldchange into the x-axis of the graph. The top-upregulated genes that are both differentially expressed and statistically significant across the different groups are very similar (Figure 3.3 A-C). TGFβ EV1437 A) B) A) B) 54 Figure 3.3 Volcano plots display the most significant changes in each group. Most of these relevant genes are in common between the three treatment groups, especially for the upregulated ones. Several significant and upregulated genes are similar across groups such as SERPINE2, SPOCK1, AMIGO2 and ELN which corroborates the similarities observed between groups. Fewer downregulated genes are observed across groups, such as CEMIP. Then in Toppgene, we submitted the lists of the significant upregulated genes of each group to determine the associated functions and pathways per treatment. For all the significantly upregulated genes in the activated fibroblasts, we found that the molecular function that all the groups share is extracellular matrix structural constituent. The EV groups shared microtubule and tubulin binding while they had other functions that were unique to each EV group. For the biological process, TGFβ is more involved in circulatory system development, cell adhesion, and response to growth factors while the EVs share the mitotic cell cycle, cell cycle progress and chromosome segregation. The pathways of TGFβ-treated cells are linked with extracellular matrix organization, while both EV groups are mainly for cell cycle mitotic and cell cycle checkpoints. EV2073 C) 55 Figure 3.4 Toppgene results of each group show functional enrichment based on Ontologies. Most of the functions are similar between treatment groups, except for the cell cycle, present only in the EV groups. We saw more similarities between EV groups than with the TGFβ-treated group for the significantly upregulated genes. However, all the groups had some similarities, like the extracellular matrix structural constituents. A heatmap was plotted for all the significant differentially expressed genes in the groups with a log2foldchange higher than 2 and padj values lower than 0.01. This was to visually see how these genes were expressed across the different groups, which were similar or different and therefore we could better understand the functional commonalities between the groups. There are more genes in common between the EV groups than with the PBS group as was expected. However, there are also differences between the 2 EV groups, shown in the red boxes. This TGFβ EV1437 EV2073 56 could be due to the differences between the cell lines and their cargo (Figure 3.4 A). EV cargo consists of diverse molecules that include nucleic acids and interestingly, Lawson et al. determined that EVs derived from LUAD cells showed higher expression of a subset of miRNAs in comparison to the cells themselves [148]. This indicates a selective packaging of cargo into the EVs, which could also differ across cell lines and thus, it is worth further study. In conclusion, observations in the RNA seq data align with the WB results where we could see the activation of the EV-treated fibroblasts when compared to the PBS but also could see that LUAD-derived EVs induced a higher expression of either different markers or to a different extent. To determine the type of CAFs that were induced in our different groups we used the gene signatures of CAF subtypes described by Galbo, et.al. and visualized our data in a heatmap showing the expression for those specific genes to identify whether the CAF subtypes were similar or different across treatments. The levels of expression of most of the genes of this panel seemed to be more similar between both EV groups and they also appear to have more in common with the TGFβ group than with the PBS-treated group (Figure 3.5 B). Based on this heatmap we could see in our treatment groups most of the CAF subtypes. In contrast to the PBS-treated cells, in the TGFβ-treated group, there is a higher expression of dCAFs and especially myCAF phenotype. For the H1437 EVs, these phenotypes are also increased (even more than in TGFβ) but there are also pCAFs. Finally, for H2073 we had pCAFs as well and to a lesser extent the presence of dCAFs and myCAFs. We also observed the downregulation of inflammation-associated genes in our activated fibroblasts when compared to the PBS group as seen in the blue box. 57 Z-score A) B) Figure 3.5 Heatmaps showing differentially expressed genes in the treated groups in relation to PBS. A) A heatmap displaying genes that are similarly expressed to PBS but also differently expressed. B) A heatmap showing the different CAF subtypes activated by TGFβ and the EVs. 58 3.3.3 Functional analysis For GSEA analysis a prerank was done to the data input to GSEA, which consisted of using a p-value less than 0.05 and FDR of <0.25 and Hallmarks gene set from MsigDB. Pathways in common between the three groups were shown in bright yellow, which consisted mainly of upregulated pathways such as hypoxia, myogenesis and glycolysis pathways. The groups in common between TGFβ and H1437 were marked in light yellow. Groups in common between EV groups are shown in green, which again consisted of upregulated pathways mainly associated with tumorigenic functions such as cell cycle, EMT and lung cancer-related oncogenic pathways. Figure 3.6 GSEA plots of pathways enriched for the activated fibroblasts through TGFβ and the EVs. TGFβ EV1437 EV2073 59 3.4 Discussion The tumor microenvironment can importantly harbor cancer-associated fibroblasts (CAFs), which are extremely versatile cells that are actively involved in cancer advancement by engaging in intricate interactions with other cell types in the tumor and its surroundings. Therefore, the functions of CAFs are dynamic and context-dependent, with evidence suggesting that they can have both tumor-promoting and tumor-restraining effects [149]. Research has shown that the composition of CAFs changes with cancer progression, and the ratio of specific CAF subpopulations has been linked to clinical outcomes [150]. Thus, it is important to first identify the CAF subtypes arising in diverse malignancies along with the deregulated signaling pathways that accompany them. Importantly, the rise of certain subtypes is also highly influenced by the mechanism of activation of the fibroblasts, in this case, we studied through the EVs. Therefore, in our study after receiving and processing the information from RNA seq, we saw more upregulated than downregulated genes in the different treatments when compared to PBS. Interestingly, many of these upregulated genes showed a high overlap between the TGFβ and the EV groups and therefore, their functions are also similar according to ToppGene Suite. According to this tool, for all the significantly upregulated genes in the cells treated, we found that the molecular function that all the groups have in common is the extracellular matrix structural constituents. However, the TGFβ group seems to be more specific with the structurally associated functions. Fibronectin, integrin, and collagen are all components of the extracellular matrix and are involved in the regulation of cell adhesion, migration, and tissue organization. In addition, TGFβ treated fibroblasts seem to have increased expression of some genes that are associated with the functions of TGFβ itself, like the regulation of angiogenesis, and destabilization of E-cadherin-mediated cell-cell adhesion. 60 Interestingly, the functions more specific to the EVs, microtubule and tubulin binding could impact cancer progression since the microtubules contribute to maintaining cell structure and shape, having an important role during cell division. Both EV groups seem to have a higher disposition for cell cycle progression and mitosis [151]. Therefore, the pathways of TGFβ-treated cells are linked with extracellular matrix organization, while both EV groups are mainly for cell cycle mitotic and cell cycle checkpoints. In particular, for the volcano plots, as previously mentioned, the genes that were upregulated in the three different groups were very similar. In fact, they were almost the same when considering the top 20 significantly upregulated genes in all the groups. These top genes had in common some functions and processes for all the groups, like extracellular matrix structural constituents, collagen-containing extracellular matrix, structural molecule activity, cell adhesion, and growth factor binding. In particular, TGFβ treatment was associated with the response to wounding and the cellular response to the transforming growth factor beta stimulus. For H2073 with circulatory system development and response to wounding. For H1437 with chromosome separation and extracellular matrix organization. These functions are relevant since the remodeled ECM generates physical barriers since it is denser, more rigid than normal tissue, and more resistant to drug penetration. This can also lead to hypoxia and an effect on blood vessels [152]. In the general heatmap, there are similarities and differences between the PBS and the EV groups, which have been covered in the previous diagrams. However, even if we found that most of the time the genes of the two EV treatments would group and show the same pattern, opposite to PBS, there were also some particularities. For example, in the first red square, we can see a higher expression of certain genes in H1437 than in H2073 and appreciate an even lower expression in the PBS-treated cells. The functions of these genes are associated with lung 61 fibrosis. For the second square in red, the genes seem to have a higher expression in H2073 and PBS, with a very low expression in H1437. Interestingly, their functions are related to inflammatory response and fatty acid transport and binding. We confirmed through RNA-seq that we induced a CAF phenotype in the fibroblasts treated with both TGFβ and the EV treatment groups as shown previously in the WB by seeing an increase in the log2foldchange in all PDGFRβ, FAP and ACTA2. A previous study identified different CAF subtypes that were present in three cancer types, expressing distinct immune regulation factors and transcriptional drivers. Each CAF subtype was associated with specific molecular pathways and different clinical outcomes [93]. Figure 3.5 B) illustrates different CAF subtypes in the co-cultured fibroblasts based on their gene expression profile. Details on differentially expressed genes used for identifying CAF subtypes in treatment groups are provided in Appendix B. Genes with a fold change of ³ 2 are considered significantly differentially expressed in this analysis. Based on this we could see in our treatment groups most of the previously described CAF subtypes (Figure 3.5 B). Contrary to the PBS-treated cells, in the TGFβ-treated we can observe a myCAF phenotype and also a high expression of dCAFs, confirming their strong contractile ability as well as their neo-angiogenesis and ECM remodeling potential. For the EVs, we had a strong phenotype of both myCAFs and pCAFs and, to a lesser extent, dCAFs in H1437. Finally, for H2073 we had a strong pCAF phenotype and the lower presence of dCAFs and myCAFs as well with fewer genes upregulated. This corroborated the WB results, where α-SMA had a higher expression in H1437-treated fibroblasts than in H2073-treated fibroblasts, which had an increase in the expression of mainly the other markers, PDGFRβ and FAP in Figure 2.9 A). 62 In detail, myofibroblast CAFs are a TGFβ-activated subtype, they express high levels of α-SMA and FAP and are found proximal to malignant cells, where they enhance tumor migration and metastasis [129], [153]. MyCAFs can be identified by the increase of genes involved mainly in the extracellular matrix (ECM) organization and smooth muscle contraction [154]. In our study, while the three treatment groups showed an increase in ACTA2 and FAP, H1437 treated group showed the highest expression levels. This suggests enrichment of myCAFs, especially in the H1437 group even when compared to the TGFβ positive control. In addition, H2073-treated cells induce also a myCAF phenotype but to a lesser extent when compared to the others. It has also been observed that myCAFs highly express genes related to angiogenesis. Both EV-treated groups and the TGFβ treated group showed positive enrichment of PDGFA, MEF2C which is related to neo-angiogenesis, in addition to high levels of VEGF [93]. This suggests an increase of angiogenesis-related genes in the three treatment groups but especially in the TGFβ one, aligning with the circulatory system development biological processes seen previously for the significantly upregulated genes in TGFβ. Desmoplastic CAFs are defined by increased expression of genes that code for collagen, MMPs and the deposition of extracellular matrix components. Thus, dCAFs are associated with higher matrix reorganizing ability and the formation of high-density fibrosis around cancer cells, leading to increased tumor stiffness and promoting cancer cell invasion [155]. In addition, dCAFs express TGFβ-signaling molecules such as SERPINE1, CTHRC1, THBS2, TGFBI, COMP and INHBA, which were expressed in the three groups but especially in TGFβ, as could be expected [93]. It has been shown that both dCAFs and pCAFs are associated with ECM remodeling and enhanced EMT. 63 EV-treated fibroblasts presented populations of dCAFs and myCAFs signatures, demonstrating their similarity with the TGFβ-treated group. However, as mentioned before, the proportion of the phenotype for these two subtypes would differ between H1437, H2073 and TGFβ. Thus, this also shows the peculiarities of the EV-induced CAF activation by different LUAD cell lines and TGFβ that we previously saw in the unique genes enriched per specific treatment and thus this could be the reason for the difference in some of their functions as well. Additionally, EV-induced CAFs were both enriched in proliferative CAFs, suggesting their increased replication potential. pCAFs, as the name suggests, have increased proliferative and metabolic abilities and thus they have higher expression of genes related to the cell cycle [155]. Additionally, fibroblasts treated with EVs showed the highest levels of genes associated with cell cycle like FOXM1 [93]. Tumors with high amount of dCAFs and pCAFs showed increased gene expression associated with cell cycle, proving an enhanced replication potential in the TME. This suggests the progression to mitosis in the EV-treated group which was consistent with our pathway analysis. It is noteworthy that in certain cancer types where dCAFs are associated with poor clinical outcomes, there was an increase in the gene sets related to cell cycle, ECM and collagen. Interestingly, Galbo et al. also analyzed individually the proportion of different CAF subtypes in lung cancer based on the study made by Lambrechts et al., identifying four subtypes for this specific type of cancer [156]. The CAFs with the highest proportion were the dCAFs, representing 44.8% of the population, followed by myCAFs with 31.7%, iCAFs with 13.4%, and metabolic CAF (mCAF) with 10.1%. Importantly, Galbo and his group had similar results when compared to the type of fibroblasts derived from the tumor found by Lambrechts, since they validated the presence of gene expression associated with dCAFs, myCAFs and mCAFs in Lambrecht’s data. 64 Additionally, they believed that the iCAFs detected in Lambrecht’s study could be from fibroblasts that were not tumorigenic due to the lack of expression of additional CAF-related genes. This is interesting since our results show in common the phenotype of dCAFs and myCAFs in our positive control and the EV-treated fibroblasts (each treatment with their corresponding population proportions) while observing the absence or decrease of inflammation-related genes in the three treatments as well. In contrast, when compared to studies made in other cancer types where CAF activation by EVs seems to lead predominantly to a pro-inflammatory phenotype, we seemed to obtain a more proliferative/myofibroblast type that may induce immunosuppression [102]. These could have to do with the type of cancer cells from where the EVs are derived and the cargo they transfer but also might be highly influenced by the cell culture conditions. Thus, including details regarding the experimental setting might be relevant for CAF activation studies. CAFs play a crucial role in the progression and spread of cancer cells by altering the tumor microenvironment through the secretion of various growth factors and cytokines [63]. Hence, since we wanted to identify pathways and processes associated with the CAF phenotype obtained in the different treatments and therefore to determine which substances could be secreted from these CAFs for tumor progression, we performed a GSEA analysis. As a result, we could determine that there was an interesting upregulation of certain pathways in the EV treatment groups such as G2M checkpoint, E2F targets and epithelial to mesenchymal transition, among others (Figure 3.6). In the G2M checkpoint, genes involved in cell division cycle progression are present [157]. In the E2F targets, E2F transcription factors regulate the cell cycle and this pathway includes some of their encoded target genes. Finally, the EMT transition pathway includes genes involved in this 65 process that are present during wound healing, fibrosis and/or metastasis. From these pathways, we determined the log2folchange of their most relevant genes. Then, to focus on the most important differences appreciated in relation to the PBS-treated fibroblasts we determined to follow up on some of the genes involved in the EMT transition pathway. They corresponded to the top six genes present in both EV-treated groups and had the highest log2foldchange values as well as the lowest p-adjusted values. In this way, we are considering both biologically relevant and statistically significant genes and therefore, we followed up on SPOCK1, BGN and POSTN. SPOCK1 is a proteoglycan that can eventually be secreted into the extracellular space and once there it regulates the equilibrium of the ECM. Transcription of SPOCK1 is increased in multiple cancer types, including lung cancer [158] [159]. SPOCK1 is involved in facilitating EMT in lung cancer cells in response to stimulation by TGFβ-1 and its high expression has been correlated with malignant invasive characteristics, the formation of an immunosuppressive TME and poor prognosis [160]. Biglycan is a protein that is encoded by BGN and secreted outside the cell where it interacts with various components of the ECM. The TGFβ signaling pathway regulates BGN and then this protein can induce EMT of diverse malignancies [161]. BGN expression is associated with a mesenchymal gene signature, making it a potential EMT biomarker [162]. Periostin is a protein encoded by POSTN. It is a secreted extracellular matrix protein that interacts with fibronectin. It has been shown that POSTN is highly expressed in malignant stroma and it is primarily produced by CAFs in head and neck cancer. Research also showed that TGF-β3 triggered the induction of POSTN in CAFs [163]. Yamato. et al. demonstrated that activated fibroblasts in pulmonary fibrosis secreted periostin and promoted the progression of NSCLC contributing to poor prognosis [164]. Similarly, they found that POSTN secreted by 66 CAFs participates in EMT induction, and drug resistance in NSCLC [165]. It also promotes the cancer stem cell (CSC)-like phenotype of tumor cells [166]. Recent research has revealed that CAFs in the TME are the primary source of EMT-related gene expression in the tumor parenchyma and that lower CAF signature scores are generally linked to longer survival periods [167] [168]. As mentioned, the expression of EMT-related genes in the TME can affect cancer cells in several ways, inducing cancer growth and metastasis. The acquisition of stem-like characteristics by cancer cells has been linked with CAF-induced EMT [169]. Interestingly, in addition, there was an association between an EMT signature and the immune activity in the TME and its impact on the prognosis of patients with HNSCC [170]. Despite SPOCK1, BGN, and POSTN not directly interacting with each other, Anastassiou et al. identified a gene expression signature across cancer types, with an upregulated subset of "EMT core signature" genes. These EMT markers include the EMT-inducing proteins, different collagens, and our three proteins, among others. Thus, this gives evidence of their joint role in the EMT process of cancer cells, where they can undergo mesenchymal transition to different extents, ranging from no expression to high co-expression of the signature genes [171], [172]. Interestingly, in this signature, we can also appreciate the presence of the markers we used to determine CAF activation: FAP, PDGFRβ and ACTA2. Curiously, a study found that CAF-derived FAP promotes EMT transition and, thus, gastric cancer progression [173]. Additionally, SPOCK1 and POSTN appear in the list of the most upregulated genes in the three groups (TGFβ and two EV groups), as shown in Appendix A. CAFs have clinical and therapeutic relevance since there is documented evidence of the relationship between them and patient outcomes. In fact, CAF number or function has been linked with tumor progression and therapy responses. Therefore, targeting CAFs in combination 67 with other therapies can be considered a great addition to anticancer regimens. However, they have a heterogeneous population, and thus, targeting specific CAF subtypes could greatly benefit treatments [143]. In addition, patients could benefit from inhibiting signals coming from CAFs and elements they use to interact with cancer cells, like ECM components and their downstream signaling pathways. Based on this, the results suggest that EVs derived from LUAD cells induce CAF activation of fibroblasts. Furthermore, they seem to induce a higher expression of proliferative, ECM remodeling and EMT-associated genes in these CAFs. Thus, the secretion of these genes from CAFs in the TME could promote ECM remodeling and stiffness while inducing mesenchymal transformation of cancer cells. Both processes can facilitate the migration and invasion of lung cancer cells. However, these findings need further validation by analyzing the expression of SPOCK, BGN and POSTN EMT genes in the cell culture media coming from CAFs, shown in the literature previously but not for specifically EV-activated CAFs. In addition, cancer cells could be treated with this media and analysis of their migration and invasion abilities (which have metastatic potential) would be relevant to prove what has been previously found for CAFs as summarized in Figure 3.4) [50]. 68 Figure 3.7 Intercellular communication between cancer cells and stromal fibroblasts. However, in this case, this would be specifically to validate the pro-tumorigenic effect and activated signaling pathways that LUAD EV-activated CAFs have in the TME to come up with CAF-targeted therapies. 69 Chapter 4: Conclusions and future directions 4.1 Review of Aims 4.1.1 Aim 1: Induce CAF activation by lung adenocarcinoma EVs in primary fibroblasts and acquire gene expression data. In Chapter 2 we proceeded to optimize the conditions necessary to culture fibroblasts without inducing a higher expression of the markers we used to determine the inductions into cancer-associated fibroblasts. The modifications included changing the source from where the fibroblasts were obtained regarding both adult and primary fibroblasts to allow us to have a model biologically relevant and without a high expression of the markers used for CAF activation. The handling of the cells became more specific with the confluency and the split ratio. Some of the physical cell culture conditions were modified as well, like the temperature, the media composition, coating vessels, and caring for any physical stimuli given. Finally, an important element was the changes applied to the WB procedure. As one of the main cell constituents of the TME, the phenotype and functions of the fibroblasts can be changed with different stimuli, which can include receiving and internalizing EV cargo. To have a good understanding of the effect of EVs in fibroblasts we decided to utilize EVs coming from four different LUAD cell lines. However, we had to discard two due to the low EV secretion of one and the lack of CAF activation seen in the WB for the other one. We then saw higher expression of the CAF markers for the cells treated with TGF β and the EVs derived from two different LUAD cell lines when compared to the cells treated with PBS. These changes included seeing an increase of α-SMA in TGF β, EVH1437 and EVH2073 treated cells. A higher expression of FAP in TGFβ and EVH2073 treated cells. And an increase of PDGFRβ in 70 both TGFβ and EVH2073-treated cells. We then extracted RNA and sent it for sequencing in duplicates as planned. 4.1.2 Aim 2: Identify pathways involved in EV CAF activation and determine the CAF subtypes induced. During Chapter 3 we processed the RNA seq data received, starting from the BAM files containing the mapping results output from the read aligner. We input it for Featurecounts obtaining as a result the counts of reads assigned to each gene per sample. Then, we eliminated the genes that didn’t have any expression in any group. We did a batch correction by using Combatseq since we used control duplicates from different days and then we visualized the replicates in a PCA plot. We then accounted for the differences between batches using DeSeq2, obtained the differentially expressed genes in relation to the PBS group and did a comparison of all our treatment groups. We then further visualized these differences using Venn diagrams, volcano plots and heatmaps (a general and another one for only CAF genes). For the first heatmap, we identified all significant and differentially expressed genes of the EVs when compared to the PBS. For the second we only saw the expression of specific genes that comprise the gene signatures of different CAF subtypes. They all lead us to conclude that there are a lot of similarities between TGFβ and the EV groups, which is consistent with what was seen in the Western Blot. They all have functions associated with ECM constitution and organization. However, EV groups seemed to have a higher expression of genes responsible for cell cycle progress. CAF phenotypes were also very similar, myCAFs and dCAFs, but EV treatments had additional pCAFs which are also involved with enhanced proliferative capacities. In addition, pathway analysis unveiled that there is enrichment for EMT and cell cycle-associated gene sets, 71 among other interesting findings. When focused on the genes with the biggest difference in their expression we decided to focus on three associated with EMT. Thus, we observed that when treating primary lung fibroblasts with LUAD-derived EVs, they mainly induce the activation of proliferative, desmoplastic, and myofibroblast CAF subtypes. Therefore, analyzing the expression of the proteins whose expression is increased in these EV-activated CAFs and the functions they are associated with could help to elucidate how EVs impact tumor progression in the TME and help to develop novel therapeutic strategies against CAFs. 4.2 Limitations For a most robust study of the effect of EVs in fibroblasts, it would have been desirable to analyze the treatment with the EVs from additional LUAD cells. However, the number of particles needed to induce a change in the third cell line was not enough and the secretion ratio of the fourth was considerably low to isolate a reasonable amount for the experiments. It is worth mentioning that the EV isolation procedure unfortunately still needs optimization and the long processing times are a barrier to adequately analyzing these cell lines. Additionally, the 2D cell culture of fibroblasts has important limitations when considering the study of the TME. Settings like physical conditions and ECM interactions cannot fully be represented in this setup, which could impact the behavior of the cells, like proliferation, growth, and functions. This model seems very simplistic and inefficient for the study of complex cell interactions and functions when plated on a rigid base of polystyrene. A 3D culture would better mimic the environment and therefore give a more accurate representation. It would also allow the modeling of cell-matrix interactions, allowing us to better study the ECM dynamics and 72 remodeling after treatment with the EVs. However, the best way to study and consider the usual biological conditions and interactions would be to use an in-vivo model. 4.3 Conclusions and future directions As a recompilation, we confirmed that special care needs to be taken to reduce freeze-thaw cycles and limit the time before usage of extracellular vesicles to reduce their loss during storage. EVs secreted from lung adenocarcinoma cancer cells transfer bioactive molecular cargo. We obtained CAF activation as a result of culturing LUAD EVs with primary adult lung fibroblasts, highlighting their vital role in the crosstalk between tumor cells and the fibroblasts of the TME. EV cargo induces phenotypical and functional changes in the stromal cells that could assist cancer cells in their processes of tumorigenesis and metastasis. To prove this, we determined that CAF activation through TGFβ induced a phenotype that is very similar to the one obtained for the LUAD EV-treated groups. Interestingly, the EV groups seemed to have enhanced proliferative capabilities in relation to TGFβ. Additionally, we analyzed the differentially expressed genes and did a pathway analysis, resulting in three genes that participate in the EMT pathway and are also related to ECM remodeling: SPOCK1, BGN, and POSTN. In order to confirm the results of our study, it is required to detect the differential expression of the genes of interest using another technique, such as polymerase chain reaction (PCR). As proposed, we would also need to determine their expression in the media to validate the EMT gene's presence in the TME and be able to execute their pro-tumorigenic functions. Additionally, testing the expression of downstream effectors through methods like western blot to validate the signaling of the pathway analysis in each treatment group would be interesting. It would be relevant to verify the CAF markers and the subtypes distinguished in each treatment 73 using techniques like fluorescence-activated cell sorting (FACS). Another option is to use immunohistochemistry, which could be used to validate the protein expression of these markers in tissue sections. In addition, it would be important to measure the protein levels of key proliferation markers such as Ki-67 to confirm what was seen both in the CAF subtypes and the cycle progression-associated pathways of the EV treatments. To validate the applicability of these findings in relevant animal models or human clinical samples EVs can be isolated from broncho-alveolar lavage fluid (BALF) or tissue collected from mice or humans with lung adenocarcinoma and be added to 3D cultured primary lung fibroblasts or mice to determine the effect they have and its similarity with our results. We could also use human LUAD formalin fixed paraffin embedded sections and stain for known CAF subtype markers to see if there are differences in type and number of CAF subtypes that may correlate with our obtained phenotype or clinical data. Immuno-labelled electron microscopy would aid in identifying the localization, quantification and visualization of our protein candidates and their cellular context in tissues and cells. In the same specimens, in situ hybridization would enable also visualizing and localizing the RNA of our candidates. Therefore, by identifying differentially expressed genes in lung fibroblasts cultured with EVs we could determine the activated CAF-phenotypes in each treatment group. We can then understand how these phenotypes interact with other cells in the TME through specific pathways. This knowledge will aid in developing more effective CAF-targeted therapies that can improve the survival rate of patients with LUAD. 74 Bibliography [1] M. Haddad and S. Sharma, “Physiology, Lung,” in StatPearls, Treasure Island (FL): StatPearls Publishing, 2023. Accessed: Sep. 17, 2023. [Online]. Available: http://www.ncbi.nlm.nih.gov/books/NBK545177/ [2] A. Patwa and A. 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Anastassiou, “A multi-cancer mesenchymal transition gene expression signature is associated with prolonged time to recurrence in glioblastoma,” PLoS One, vol. 7, no. 4, p. e34705, 2012, doi: 10.1371/journal.pone.0034705. [172] D. Anastassiou et al., “Human cancer cells express Slug-based epithelial-mesenchymal transition gene expression signature obtained in vivo,” BMC Cancer, vol. 11, p. 529, Dec. 2011, doi: 10.1186/1471-2407-11-529. [173] J. Liu et al., “Stromal fibroblast activation protein alpha promotes gastric cancer progression via epithelial-mesenchymal transition through Wnt/ β-catenin pathway,” BMC Cancer, vol. 18, no. 1, p. 1099, Dec. 2018, doi: 10.1186/s12885-018-5035-9. 86 Appendices Appendix A The top 200 upregulated genes in fibroblasts cultured with TGFβ, H1437 EVs, and H2073 EVs. Gene List TGFβ Gene List2 H1437 Gene List3 H2073 LINC01583 7.224834 PI16 9.316105 DPT 9.353165 PI16 7.106557 KANK4 8.744841 COL15A1 8.050061 LNCAROD 6.822573 ELN 8.459065 LINC00881 7.809138 ELN 6.685845 LINC01583 8.233716 CNTN1 7.731387 TRPM2 6.624008 OR7E14P 8.18901 LINC00319 7.612428 HTR1D 6.582705 LNCAROD 8.071041 LINC01583 7.329444 OR7E14P 6.202388 TRPM2 7.945179 C17orf78 7.248118 LOC105370068 6.110699 LOC105370068 7.879023 TRIM9 7.12471 LOC102724849 5.958517 COL10A1 7.850161 LINC01936 7.109637 PCDH1 5.952176 GRAP2 7.532318 LINC01013 6.989299 COL10A1 5.919748 CAPN6 7.489231 SEPTIN5 6.881019 IGFBP3 5.873467 NEFM 7.488259 LOC284950 6.762708 CADM1 5.843535 VAV3 7.484094 ANXA8L1 6.749829 INHBB 5.793857 HTR1D 7.351575 FZD3 6.708108 LOC105372582 5.790212 PLPP4 7.298514 KLF17 6.670443 WDR17 5.681544 SNORD45C 7.191911 LOC102724404 6.651883 PDZK1IP1 5.588183 MAP3K9 7.168665 THEMIS 6.625376 CATSPERB 5.543739 EPCAM 7.080858 LINC02547 6.563875 MAP3K9 5.542356 PIFO 7.050611 POSTN 6.561367 CGB3 5.497801 CGB3 6.96006 ZAP70 6.483861 MDFI 5.491538 HCN4 6.892654 GRASP-AS1 6.447674 TSKS 5.453246 CCNYL6 6.815367 PCDHB6 6.430217 PLN 5.422485 ESRP2 6.810179 MLLT10P1 6.400288 SORBS2 5.367092 CATSPERB 6.809754 GPR179 6.365414 LINC01714 5.338612 UCA1 6.706564 CADM3 6.34689 DPY19L2P1 5.32502 PCDH1 6.679142 HTR1D 6.333245 TMEM252 5.30959 BHMT 6.665295 CBFA2T3 6.303807 NSUN7 5.309325 IGFL2-AS1 6.664979 C15orf54 6.302785 GNMT 5.295343 MEIG1 6.625514 PKLR 6.299176 EGLN3 5.294249 MDFI 6.619459 SMIM6 6.266827 RSAD2 5.290434 LHX6 6.474149 LOC102724467 6.234183 87 LOC105376353 5.290301 MIR483 6.452859 LINC02324 6.225636 MADD-AS1 5.290254 TBC1D22A-AS1 6.452533 PLET1 6.202378 THEMIS 5.283714 NPY4R 6.451827 OR7E14P 6.191268 ATRNL1 5.2542 EFNA2 6.3919 PAK3 6.191009 LINC01629 5.253088 RSPO3 6.390831 PLP1 6.126245 SMO 5.242172 ACTC1 6.337219 TCEAL6 6.122834 MIR23B 5.241748 CCN5 6.285506 TTC9 6.118312 VAT1L 5.235137 IGFBP3 6.269576 CYP2C8 6.06649 NFATC2 5.225565 LINC01629 6.262305 C2orf72 6.019944 POSTN 5.214489 SLIT3 6.261997 DOK2 6.004819 C11orf42 5.180083 CSMD1 6.22211 RHBDL3 6.004514 NLRP3 5.177695 LINC00319 6.22178 SLIT3 5.997982 LINC00880 5.169465 MIR181B1 6.198949 SHCBP1L 5.988372 SLIT3 5.164689 POSTN 6.161872 FGA 5.957037 A1CF 5.141487 TCEAL5 6.154078 SLC38A3 5.910347 SLC15A1 5.1312 SORBS2 6.143897 TRPM2 5.881044 NIPAL4 5.117124 OR1L3 6.137625 LOC105374344 5.853529 LINC01592 5.054253 BCL2 6.129163 RASSF9 5.843496 LHX6 5.048568 LINC01619 6.126133 HPR 5.833027 MYO7A 5.046303 NIPAL4 6.042455 PADI3 5.828093 ITGB6 5.024394 GPR183 6.001775 MKX 5.797477 HSD17B13 5.019305 KRT83 5.995532 LINC00271 5.748801 MYOC 5.018831 GRM4 5.994205 LDLRAD4 5.74129 ACTC1 5.00374 KIRREL3-AS3 5.972238 GPRASP2 5.687985 SPOCK1 4.961816 IL21R-AS1 5.961504 IL20RA 5.68144 PLPP4 4.956828 SLC15A1 5.935068 H3C7 5.674204 CIDEA 4.952387 DACH2 5.901081 HSD17B13 5.669798 ANXA8L1 4.949122 TCTEX1D1 5.859894 OR56B4 5.643985 PDZRN3-AS1 4.947225 SNORA80E 5.852577 FGG 5.616023 SUSD4 4.928964 SUSD4 5.849599 BRINP1 5.614423 SCARNA18 4.906779 SNORD33 5.846435 LOC101929679 5.592557 PTPRB 4.890927 PCDH10 5.842415 DENND1C 5.582032 LINC01770 4.885486 PTTG3P 5.836505 KRT83 5.578749 C9orf152 4.8577 LOC101930421 5.834724 PI16 5.570983 LOC101927914 4.834049 LINC01979 5.834242 PCP4L1 5.567971 LINC00222 4.832582 ARHGAP31-AS1 5.834181 SHISA9 5.564876 NTF3 4.831055 ANKFN1 5.833942 BTBD11 5.559194 EGF 4.829893 LINC02657 5.831807 RASGRP4 5.55099 88 SLITRK6 4.827669 C1orf189 5.829979 RGS22 5.540255 TPO 4.820892 CNTN1 5.818728 GPR45 5.530338 CCDC144NL-AS1 4.785461 PRH2 5.810983 LOC101927164 5.511691 GRIN2C 4.777423 CADM3 5.810966 NRTN 5.508099 GPR160 4.746837 ATRNL1 5.806806 MYO7A 5.507571 FIGNL2 4.738821 ST6GALNAC3 5.805946 FLJ12825 5.493024 KCNG2 4.728122 KCNJ16 5.805638 AGR2 5.452229 OTOA 4.724669 BTBD11 5.800413 LOC101593348 5.450503 LIMD1-AS1 4.724669 SPOCK1 5.793549 BICDL2 5.44821 LINC01613 4.724669 STK32A 5.773759 PWAR1 5.440098 PCAT19 4.724626 ZNF385C 5.755661 CASP12 5.436153 SNORD116-13 4.724564 SPATA22 5.755091 OR5E1P 5.426981 MUC5B 4.724519 PDZD9 5.740104 FYB1 5.417606 CELF3 4.72444 SLC7A14 5.694599 H2BC17 5.416078 NRTN 4.724365 MIR4513 5.689069 CNBD2 5.414424 NUDT9P1 4.713032 TNFRSF6B 5.68798 WDR17 5.379483 KIRREL1-IT1 4.70381 KCNIP4-IT1 5.684263 SH2D3C 5.379303 SUN3 4.701983 SLC25A2 5.677127 NKAPP1 5.320089 PADI2 4.691939 ART4 5.676331 MRC1 5.297432 SGCZ 4.691821 SCN2A 5.668338 FNDC5 5.289408 NALCN-AS1 4.691041 CST2 5.667989 MYO3B 5.271617 CLDN14 4.690497 ENTPD3 5.666117 ABCG4 5.259409 LOC101929460 4.690094 PIEZO2 5.652632 CRNDE 5.247533 ATP8A2 4.683438 SYN3 5.623421 CD69 5.23294 MYH16 4.683385 NTF4 5.621065 SLITRK6 5.231979 TLR5 4.683336 LINC01711 5.613797 PTPRC 5.227257 MERTK 4.683297 OMG 5.597736 USP2-AS1 5.2224 SNORD3K 4.683255 CDIPTOSP 5.590873 HOXC11 5.218099 GDNF-AS1 4.669741 OR10A3 5.586705 LRRC19 5.201747 KLF17 4.646682 PDZK1IP1 5.584067 OR5C1 5.182509 LOC729732 4.64139 TMSB15A 5.582495 IGFBP3 5.158511 ART4 4.605681 SMO 5.579224 CCL7 5.152547 CPE 4.601269 CLDN14 5.568773 PCCA-AS1 5.151611 LOC105374454 4.594114 NRTN 5.549133 MAATS1 5.14624 SNORD45C 4.591647 LINC01698 5.546718 LOC102723704 5.120881 RASSF9 4.57074 HRNR 5.523786 C8orf86 5.120319 SORCS2 4.570211 LOC100505915 5.51852 RAB39B 5.105898 FAM27B 4.55291 TUSC1 5.500127 IHH 5.099719 89 HPCA 4.55208 MIR23B 5.470254 HPCA 5.09597 FLJ12825 4.551408 TMEM191B 5.453881 PEG3 5.09269 DCSTAMP 4.523412 NCKAP1L 5.45242 PDZK1IP1 5.080796 METTL14-DT 4.523412 C5orf46 5.450839 PROC 5.062579 ANGPTL5 4.5231 NKAPP1 5.442069 RPL23AP87 5.041187 HECW1-IT1 4.523099 PRG2 5.429936 LOC105376353 5.023941 C20orf85 4.523021 NALCN-AS1 5.428397 LINC01940 5.012104 CD28 4.522259 PDZRN3-AS1 5.41471 NUDT9P1 5.007986 RBPMS2 4.522119 LINC02547 5.361643 CPLX3 4.997833 GRM2 4.522083 LOC100132686 5.357586 SCG3 4.997833 PCBP3-AS1 4.521855 MYO7A 5.356283 TLL1 4.993736 NRN1L 4.52179 ADRA1A 5.337036 LINC00705 4.993584 WFIKKN2 4.51773 ITGB6 5.323158 TAS2R42 4.990275 IFNW1 4.517711 OPCML 5.296753 LSP1 4.989503 PSMB11 4.517711 MIR4496 5.294451 ARRDC5 4.988863 USP17L2 4.51771 OR5B21 5.290104 ATP13A4 4.988863 SNORD70 4.517706 TOLLIP-AS1 5.279062 STRA6 4.986051 TMEM125 4.517692 USP43 5.263492 TMIGD3 4.985571 ENKUR 4.461421 KRT81 5.260292 CDC20B 4.984464 LOC283045 4.427929 GOLGA6L7 5.258339 BGLAP 4.984464 PLXNA4 4.42684 LINC00623 5.254344 H4C1 4.984464 PCCA-AS1 4.40944 FAM81A 5.254236 AWAT2 4.984464 ZNF385C 4.408135 LOC101928008 5.243065 HAPLN1 4.972446 PCDH10 4.407136 LOC101929460 5.239052 LRRD1 4.962855 LINC01711 4.396179 TNKS2-AS1 5.238671 CMTM2 4.961521 NOX4 4.395993 PROC 5.236127 SNORD1B 4.960552 GBX2 4.323006 VWA5B1 5.234238 ELAVL4 4.95658 TMEM95 4.309639 LINC02135 5.210212 APOBEC4 4.955989 C1orf146 4.309639 VAT1L 5.187706 LOC105372582 4.945493 ZDHHC8P1 4.309623 TAGAP 5.179822 LINC01931 4.944446 SNORD91A 4.309623 CRLF1 5.16978 MROH2A 4.94408 GDAP1L1 4.309613 EGLN3 5.169543 CCDC73 4.94408 PSORS1C3 4.309559 GRIN2C 5.167405 FAM27B 4.931805 ENPEP 4.309541 LINC01592 5.154535 SLC35F4 4.923213 MIR6881 4.309503 HAL 5.145869 SUPT20HL1 4.923213 DPP6 4.309492 RUNDC3A 5.14531 BRINP2 4.923213 BTBD11 4.309237 MIG7 5.131055 CLEC18C 4.923213 TYRP1 4.306234 LOC101928557 5.127216 LCE2B 4.923213 90 LOC100132686 4.295489 KRT85 5.120219 STAU2-AS1 4.89996 TUSC1 4.279273 CLECL1 5.118947 CDK18 4.899574 SAMD3 4.269662 DERL3 5.107068 UNC13A 4.899217 CD69 4.265499 MIR1249 5.100199 MUCL1 4.896226 LINC00211 4.253601 VWA5B2 5.091691 BCL6B 4.868659 ITIH4-AS1 4.251229 SMIM32 5.09029 PNLDC1 4.863386 SMIM34A 4.251229 ATP2B3 5.09029 SNORD33 4.859468 MIR5008 4.250984 LINC00398 5.088795 KIAA0040 4.857966 MIR3124 4.249615 MOGAT3 5.086616 GPR160 4.850573 LIMS3-LOC440895 4.249153 FIGNL2 5.073627 EVPL 4.828089 LOC100507412 4.248474 LINC02649 5.067983 CDX1 4.824482 ABLIM2 4.246934 NBAT1 5.054576 LOC100129129 4.820524 FOXG1 4.235465 EGF 5.028401 VN1R5 4.82051 CARNMT1-AS1 4.22668 NUDT9P1 5.025534 DPY19L2P1 4.816054 POU5F1P3 4.193406 NTF3 5.018301 TMPRSS7 4.810478 SNORA75 4.161015 SORCS2 5.010444 SNORA21B 4.808222 MEIG1 4.160238 LOC101927815 5.004142 RASL10A 4.808012 LINC01348 4.144751 AARSD1 5.000891 LINC00167 4.80583 LOC105378047 4.138608 NRN1L 5.000581 KLRC4-KLRK1 4.802398 NFE4 4.135996 KCNN3 4.999362 TMEM61 4.802398 OR5E1P 4.134094 RPL34-AS1 4.997261 CELF3 4.793709 SERPINE2 4.100022 H3C2 4.996825 RSPH6A 4.779329 LOC100506281 4.096885 NT5DC4 4.996744 LINC00677 4.777928 MUC4 4.087114 TMCO5A 4.994191 MYOC 4.771506 GRAP2 4.064389 MYOM3 4.992334 NSUN7 4.76836 GRIA4 4.0509 NUDT10 4.987891 LINC01152 4.763007 LINC02450 4.045696 A1CF 4.975577 DHRS2 4.760601 NTF4 4.0351 MYLK2 4.92367 FAM90A25P 4.758593 CFAP52 4.029628 GALNT9 4.915859 BRWD1-AS1 4.753595 ZNF503-AS1 4.028238 SNORA75 4.901053 PRRG2 4.75339 DGCR5 4.028238 CADM1 4.85936 ATRNL1 4.753168 TXNDC8 4.028238 P2RY4 4.858954 XKR5 4.752025 SPAG17 4.027891 CMPK2 4.858824 SNORD160 4.749399 MIR4530 4.027527 LOC100128006 4.857313 NRN1L 4.747556 KCTD8 4.027447 ADGRB3 4.855582 SPOCK1 4.74675 DLG3-AS1 4.026716 FCN3 4.85195 MUC4 4.74467 XKR5 4.02635 MAP7D2 4.846989 LINC02749 4.737101 NBAT1 4.026276 LOC100507412 4.841828 MIR6806 4.733154 91 TTTY10 4.024059 FAM106B 4.840422 MIR3960 4.718226 LOC101929217 4.02023 LOC643387 4.829955 SYK 4.709713 MIR302CHG 4.020225 CCDC144NL-AS1 4.823353 OTC 4.709713 SCARNA26B 4.020202 MYOCD 4.819378 SLC52A1 4.699308 SNORD144 4.020058 ANXA8L1 4.817763 ZSWIM2 4.687734 COL21A1 4.010802 ALDH1L1 4.810356 PLAC4 4.687734 LRRN4 4.00483 CAMK1G 4.801277 CPE 4.672251 TEPP 3.991094 LRRIQ4 4.774154 ZNF705E 4.67051 PAK3 3.982395 MIR3181 4.770076 C11orf72 4.662533 GPR18 3.981694 CAMSAP3 4.768268 SNORD45C 4.661137 MRC1 3.980916 LOC101929470 4.767641 LINC01290 4.655806 NPAS4 3.963046 ENKUR 4.765248 ANKRD34B 4.648662 MAP7D2 3.962732 FAM124B 4.764639 LINC02587 4.646934 NCAM2 3.927836 GPC3 4.762323 AGAP7P 4.64318 CADM3 3.922942 OR5K2 4.762189 GNRH2 4.643107 LOC101927402 3.904891 SLC6A17 4.759639 ADAM20 4.64074 92 Appendix B Differentially expressed genes and their fold changes were used in identifying CAF subtypes in each treatment group. [94] Genes + TGFβ + H1437 + H2073 COL1A1 1.984972 2.27056648 2.40981842 COL1A2 1.05824 2.61424009 2.57708811 COL3A1 0.4365188 1.35990606 1.26087128 COL4A1 2.536938 3.52102598 2.30221134 COL4A2 2.238996 2.19836357 1.63210514 COL5A1 1.75679 2.60817378 3.04202076 COL6A1 0.0601273 1.30325555 1.15185411 COL7A1 1.482524 1.917152 1.396917 COL8A1 0.9662036 1.039858 0.261242 COL10A1 5.919748 7.850161 3.439542 COL11A1 1.723218 1.510626 1.753085 COL13A1 -0.4529341 -0.8795464 -1.059152 COL16A1 0.809145 1.300332 1.217435 TAGLN 1.083503 1.26201 0.394071 ELN 6.685845 8.459065 2.727863 BGN 2.668528 2.954891 2.789988 DCN -0.2025599 0.3056628 0.3559463 LUM 1.017964 1.903446 1.97411 MMP1 -0.7716303 -0.02342055 -0.4774471 MMP2 1.41875 1.788453 1.369747 MMP3 -1.368668 -0.3193145 -0.828159 MMP10 2.166584 -0.5664714 2.083315 MMP11 0.6436781 -0.4686112 0.5810455 93 MMP14 0.4210739 0.5617309 0.1280949 MMP19 0.8046223 0.4990434 0.8022309 SERPINE1 1.062816 1.37822 0.4015017 CTHRC1 2.06441 2.169629 1.892386 THBS2 0.7184171 1.012665 0.03363845 SULF1 0.4970074 0.5575006 -0.1098644 COMP 3.609376 2.199152 3.13549 INHBA 1.538429 1.78008 0.1090486 TGFBI 1.560684 2.428255 2.077116 PDGFA 1.325293 1.313007 0.0570899 PDGFC 1.144782 1.005279 0.7333522 VEGFA 0.407112 0.2661423 0.500008 ANGPT2 0.07314019 -1.277105 -1.466967 ACTA2 1.523654 1.852385 1.619459 FAP 2.026615 2.083832 1.838245 POSTN 5.214489 6.161872 6.561367 MYL6 0.1130064 0.3480255 -0.2740828 MYH9 0.429519 0.1947501 0.2077094 MYH11 0.9367244 0.7176796 -0.4151792 TPM1 2.012866 2.46378 1.067951 SORBS2 5.367092 6.143897 3.380998 RRAS -0.3487326 -0.198476 -0.1944841 RASL12 -3.61085 -8.51681 1.118845 RASGRP2 0.2692156 -0.02964939 -0.08662524 C3 -1.005004 -0.4595693 -0.6817785 C7 -0.4093883 -1.974599 0.8872372 94 CXCL12 -1.975763 0.5142238 0.2022188 CXCL14 -2.167696 -1.141681 0.5510989 CFD -0.7591522 -0.8380013 -0.1544546 CXCL2 0.2863275 1.374595 1.32271 CXCL3 -0.9783645 0.8099728 0.3111157 CCL2 -2.520309 -3.966479 -1.27174 IL6 -0.4350259 0.7781007 -1.280133 PDPN 1.222588 2.18004 2.194101 S100A4 -0.5134241 -0.466096 -0.6291213
Thesis/Dissertation
eng
Interdisciplinary Oncology
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Garnis, Cathie
Extracellular vesicles from lung adenocarcinoma cells induce activation of different CAF subtypes
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