Autophagy Machinery Contributes to Cell Survival and Small Extracellular Vesicle Composition in Triple- Negative Breast Cancer Cells

Autophagy machinery contributes to cell survival and small extracellular vesicle composition in triple- negative breast cancer cells

by Jing Xu

BSc, University of British Columbia, 2012

Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy

in the Department of Molecular Biology and Biochemistry Faculty of Science

Copyright in this work rests with the author. Please ensure that any reproduction or re-use is done in accordance with the relevant national copyright legislation. Approval

Name: Jing Xu Degree: Doctor of Philosophy (Molecular Biology and Biochemistry) Title: Autophagy machinery contributes to cell survival and small extracellular vesicle composition in triple-negative breast cancer cells Examining Committee: Chair: Jack Chen Professor Sharon Gorski Senior Supervisor Professor Nancy Hawkins Supervisor Associate professor Nicholas Harden Supervisor Professor Christopher Beh Internal Examiner Professor John Brumell External Examiner Professor Department of Molecular Genetics University of Toronto

Date Defended/Approved: Dec 5, 2019

ii Abstract

Macroautophagy (hereafter autophagy) is a catabolic cellular process where double- membraned autophagosomes capture cytoplasmic cargos and fuse with lysosomes for content degradation. Basal autophagy maintains cellular homeostasis by removing long- lived proteins and damaged organelles. Autophagy can also be upregulated to promote cell survival in the presence of stressors such as starvation and oxidative stress. Autophagy can suppress tumorigenesis by maintaining genome stability in normal cells, or enable cancer cell survival during nutrient limitation, hypoxia or chemotherapy treatment. Therefore, inhibiting autophagy may improve chemotherapy efficacy. Triple- negative breast cancers (TNBC) are a subtype of breast cancers that do not overexpress hormone receptors. Chemotherapy remains one of the few systemic treatment options for TNBC, making the development of chemotherapy resistance particularly problematic in disease management. This thesis describes cell-intrinsic and cell-extrinsic functions of autophagy machinery in cultured TNBC cells, and explores the potential utility of autophagy inhibition to enhance treatment response. Cytoprotective autophagy was induced in response to epirubicin treatment in TNBC cells. Autophagy inhibition reduced cell viability and improved efficacy of epirubicin in both drug-naïve and drug- resistant cells. Further investigation revealed cell-extrinsic roles of autophagy, in the form of its contribution to the composition of small extracellular vesicles (sEV), nano- sized vesicular entities with known roles in cell-cell communication. Lysosomal inhibition by chloroquine (CQ) induced co-localization of mammalian autophagy-related (ATG) 8 homologs with endolysosomal tetraspanins, and introduced significantly higher levels of ATG8s in TNBC-derived sEV. The concurrent increase in poly-ubiquitinated proteins and autophagy adaptors in sEV suggested a potential mechanism where degradative cargos are loaded into sEV by autophagy machinery and then expelled. CQ-induced enrichment of ATGs was limited to a subpopulation of sEV, highlighting the heterogeneity and context-dependency of sEV composition. Finally, CQ-mediated lysosomal inhibition was found to dampen the growth-promoting effects of sEV in recipient cells. Taken together, this work demonstrated cytoprotective roles of autophagy in TNBC cells, and the dynamic contribution of autophagy machinery to sEV composition, warranting further examination of autophagy inhibition as a potential therapeutic avenue in TNBC.

iii Keywords: autophagy; extracellular vesicle; lysosome; chloroquine; triple-negative breast cancer

iv Dedication

To my grandparents, my uncle, and the kindness of strangers.

v Acknowledgements

I’m greatly indebted to my supervisor, Dr. Sharon Gorski, for her continued support and guidance throughout the years. Her encouragement and trust made this project possible. I’d also like to acknowledge my committee members, Dr. Nancy Hawkins and Dr. Nicholas Harden, for their comments and suggestions that helped to shape and improve this project. I’d also like to acknowledge my examiners, Dr. John Brumell and Dr. Christopher Beh, for their comments and suggestions.

I’d like to thank my colleagues in the Gorski lab, especially Dr. Suganthi Chittaranjan, Nancy Go and Dr. Mario Jardon for their generous support and insight. I’m grateful to my former supervisors, Dr. Catharine Rankin and Dr. Tiffany Timbers whose guidance during my undergraduate initiated my official quest after scientific research. I want to thank Dr. Rik Blok for his mentorship. I also want to thank Garnet Martens, Bradford Ross, and Derrick Horne at the UBC Bioimaging Facility for training and assistance with TEM microscopy, Dr. Emma Guns, Dr. Elham Hosseini-Beheshti, and Mei Yieng Chin for sharing their expertise in EV research, Dr. Gregg Morin for comments and suggestions on our manuscript, Dr. Cathie Garnis and James Lawson for sharing reagents and protocols, Dr. Andrew Leidal for the protease protection assay protocol, Wenbo Xu, Vincent Chui and the Terry Fox Laboratory core for flow cytometry and confocal microscopy assistance. I also want to thank CIHR and SFU for providing the funds for this and additional projects.

I would like to give special thanks to my life companion and best friend, Bian Tu, for her unwavering support and prompt delivery of constructive criticism. I’m grateful for my sister and my cousins, who safeguarded my sanity over the years. And last but not least, I’d like to acknowledge my significant other, Kevin C Yang, for his support over the course of my graduate studies.

vi Table of Contents

Approval ...... ii Abstract ...... iii Dedication ...... v Acknowledgements ...... vi Table of Contents ...... vii List of Tables ...... x List of Figures...... xi List of Acronyms ...... xiii Image ...... xvi

Chapter 1. Introduction ...... 1 1.1. Autophagy ...... 1 1.1.1. Autophagy overview ...... 1 1.1.2. Mammalian autophagy machinery ...... 2 1.1.3. ATG8s and selective autophagy ...... 5 1.1.4. Measuring autophagy flux ...... 6 1.1.5. Non-degradative functions of autophagy machinery ...... 8 1.1.6. LC3-associated phagocytosis (LAP) ...... 9 1.2. Extracellular Vesicles ...... 9 1.2.1. Extracellular vesicle overview ...... 9 1.2.2. Exosome biology ...... 11 1.2.3. Functions of exosomes ...... 12 1.2.4. Exosome and EV nomenclature ...... 13 1.3. Crosstalk between autophagy and exosome biogenesis ...... 14 1.3.1. Amphisomes: crossroad of autophagy and endocytosis ...... 14 1.3.2. Amphisomes: novel secretory organelles? ...... 15 1.3.3. Autophagy and exosome biogenesis share molecular machinery ...... 16 1.3.4. Additional insight of autophagy-exosome crosstalk from viral infections ...... 17 1.4. Autophagy and exosomes in cancer ...... 18 1.4.1. Autophagy in cancer ...... 18 1.4.2. Exosomes in cancer ...... 19 1.4.3. Cancer stress responses involve autophagy and exosome release ...... 20 1.4.4. Autophagy and exosomes in chemotherapy resistance ...... 22 1.5. Triple-negative breast cancer: an unmet medical need ...... 23 1.5.1. Breast cancer ...... 23 1.5.2. Triple-negative breast cancer ...... 24 1.5.3. Chloroquine and hydroxychloroquine ...... 25 1.6. Rationale, hypotheses and specific aims ...... 26

Chapter 2. Materials and Methods...... 29 2.1. Key Reagents ...... 29

vii 2.2. Cell lines and tissue culture conditions ...... 30 2.3. Genetic manipulation of cells ...... 31 2.3.1. siRNA-mediated knockdown ...... 31 2.3.2. CRISPR-mediated gene knockout ...... 31 2.4. sEV isolation ...... 32 2.4.1. Pre-clearing of conditioned media...... 32 2.4.2. sEV isolation using ExoQuick ...... 32 2.4.3. sEV isolation using immunoaffinity capture ...... 33 2.5. Characterization of sEV ...... 33 2.5.1. Nanoparticle tracking analysis (NTA) ...... 33 2.5.2. Transmission electron microscopy ...... 33 2.6. Tandem mass spectrometry ...... 34 2.6.1. Sample preparation ...... 34 2.6.2. SP3 clean up and digestion ...... 34 2.6.3. TMT labeling ...... 35 2.6.4. High Performance Liquid Chromatography (HPLC) Fractionation ...... 35 2.6.5. Mass spectrometry analysis ...... 35 2.6.6. Mass spectrometry data analysis ...... 36 2.7. Western blot analyses ...... 36 2.8. Cell growth and viability assays ...... 36 2.8.1. Trypan blue exclusion assay ...... 36 2.8.2. Incucyte growth and viability assay ...... 37 2.8.3. Clonogenic assay ...... 37 2.8.4. Endothelial tube formation assay ...... 38 2.9. Immunofluorescence microscopy ...... 38 2.10. Autophagic flux assays ...... 39 2.10.1. Flux analysis with western blot ...... 39 2.10.2. Flux analysis with fluorescence microscopy ...... 39 2.11. Caspase activity assay ...... 40 2.12. Flow cytometry analysis of DQ-BSA ...... 40 2.13. Trypsin protection assay ...... 40 2.14. Bioinformatics analyses...... 41 2.14.1. Analysis of quantitative proteomics data ...... 41 2.14.2. Enrichment analyses ...... 41 2.15. Data repositories ...... 41 2.16. Statistics ...... 42

Chapter 3. Autophagy supports TNBC cell survival and chemotherapy resistance 43 3.1. Introduction ...... 43 3.2. TNBC cells upregulate autophagy in response to epirubicin treatment ...... 44 3.3. Anthracycline-resistant TNBC cells show increased basal autophagy flux ...... 47 3.4. Autophagy inhibition improves anthracycline efficacy in TNBC cells ...... 49 3.5. Autophagy inhibition re-sensitizes drug-resistant TNBC cells to chemotherapy ... 52

viii 3.6. Discussion ...... 54

Chapter 4. Autophagy machinery contributes to small extracellular vesicle composition during lysosomal stress ...... 57 4.1. Introduction ...... 57 4.2. Chloroquine inhibits lysosomal function and autophagy ...... 58 4.3. Chloroquine induces co-localization of autophagy and endolysosomal markers .. 62 4.4. Lysosomal inhibition with chloroquine does not substantially alter bulk sEV profile 66 4.5. Chloroquine alters sEV proteomic profile ...... 69 4.6. Chloroquine induces accumulation of mammalian ATG8 homologs in the cytoplasm and sEV ...... 72 4.7. Chloroquine enriches ATG8 homologs in sEV lumen ...... 74 4.8. LC3B incorporation in sEV requires lipidation ...... 76 4.9. Chloroquine induces accumulation of ATG8 homologs in specific sEV subtype .. 78 4.10. Chloroquine induces accumulation of poly-ubiquitinated proteins in sEV ...... 80 4.11. CQ alters biological function of TNBC-derived sEV ...... 81 4.12. Discussion...... 84

Chapter 5. General Discussion ...... 87 5.1. Cell-intrinsic roles of autophagy in TNBC cells ...... 87 5.1.1. Study summary and significance ...... 87 5.1.2. Limitations and future directions ...... 88 5.2. Cell-extrinsic roles of autophagy in TNBC cells ...... 89 5.2.1. Study summary and significance ...... 89 5.2.2. Limitations and future directions: sEV biology ...... 92 5.2.3. Limitations and future directions: autophagy-sEV crosstalk ...... 94

References ...... 97

Appendix A. Considerations in sEV collection ...... 116 Ultracentrifugation ...... 116 Ultrafiltration ...... 118 ExoQuick ...... 119 FBS in conditioning media ...... 120

Appendix B. Supplementary Figures and Tables ...... 122

ix List of Tables

Table 1.1. Molecular functions of mammalian core ATG machinery ...... 3 Table 1.2. Human ATG8 proteins ...... 5 Table 1.3. List of selective autophagy adaptor proteins ...... 6 Table 1.4. Breast cancer molecular subtypes ...... 24 Table 2.1. List of key reagents...... 29 Table 3.1. Epirubicin-resistant TNBC cell lines have markedly increased anthracycline IC50 ...... 47

x List of Figures

Figure 1.1. Types of autophagy pathways...... 2 Figure 1.2. Molecular machinery of autophagy ...... 4 Figure 1.3. Methods to measure autophagy flux...... 8 Figure 1.4. Known types of extracellular vesicles...... 10 Figure 1.5. Autophagosomes and endosomes can fuse and form hybrid organelles termed amphisomes ...... 15 Figure 1.6. Cellular stresses activate autophagy and exosome release...... 21 Figure 1.7. Cancer-derived exosomes upregulates autophagy in recipient epithelial cells and induce secretion of pro-tumour factors...... 22 Figure 1.8. Cancer-derived exosomes upregulate autophagy and promote drug resistance in recipient cancer cells...... 23 Figure 1.9. Structures of chloroquine and hydroxychloroquine ...... 26 Figure 3.1. Epirubicin treatment induces autophagy in TNBC cells as indicated by immunofluorescence assay ...... 45 Figure 3.2. Epirubicin treatment induces autophagy flux in TNBC cells as indicated by western blot flux assay ...... 46 Figure 3.3. Epirubicin-resistant TNBC cells have higher basal autophagy flux...... 48 Figure 3.4. siRNA treatment reduces protein levels of ATG5 and ATG7...... 49 Figure 3.5. siRNA knockdown of ATG5 or ATG7 further reduced viability in epirubicin- treated cells ...... 50 Figure 3.6. Chloroquine improves efficacy of chemotherapy against MDA-MB-231 cells...... 51 Figure 3.7. Autophagy inhibition induces apoptosis in R8 and R75 cells ...... 53 Figure 3.8. Chloroquine re-sensitizes chemotherapy-resistant cells to epirubicin...... 54 Figure 4.1. 10µM CQ does not reduce TNBC cell viability below 90% at 48 hours...... 59 Figure 4.2. Chloroquine inhibits lysosomal degradation...... 60 Figure 4.3. Chloroquine inhibits autophagy ...... 61 Figure 4.4. CQ-induced punctate localization of LC3B on endosomes...... 64 Figure 4.5. Chloroquine induces co-localization of autophagy proteins with endolysosomal markers...... 66 Figure 4.6. Immunoblot characterization of TNBC-derived sEV ...... 67 Figure 4.7. CQ does not alter bulk sEV profile...... 68 Figure 4.8. Gene ontology term analyses of MDA-MB-231 sEV ...... 70 Figure 4.9. CQ treatment affects a greater proportion of EV proteome compared with whole cell proteome ...... 71 Figure 4.10. CQ treatment differentially affects the levels of sEV-associated proteins in whole cell and sEV fraction ...... 72 Figure 4.11. CQ enriches ATG8 homologs and adaptor proteins in sEV ...... 73

xi Figure 4.12. MAP1LC3 family proteins were present in sEV ...... 74 Figure 4.13. Schematic representation of a trypsin protection assay...... 75 Figure 4.14. Chloroquine enriches sEV luminal ATG8s, Syntenin-1 and p62...... 76 Figure 4.15. ATG4B is required for LC3B lipidation...... 77 Figure 4.16. LC3B lipidation is required for inclusion into sEV ...... 78 Figure 4.17. CQ treatment selectively enriches LC3B and GABARAP in CD63-high sEV ...... 79 Figure 4.18. CQ induces accumulation of K48 linkage-specific poly-ubiquitination in sEV ...... 80 Figure 4.19. TNBC-derived sEVs are taken up by HMEC-1 cells...... 81 Figure 4.20. Dose and context dependent effects of MDA-MB-231 derived sEV...... 82 Figure 4.21. CQ alters growth effects of sEV from SUM159 and Hs578T cells...... 83 Figure 4.22. CQ treatment alters angiogenic ability of MDA-MB-231 sEV...... 84 Figure 5.1. Potential models of autophagy-exosome crosstalk...... 91

xii List of Acronyms

ALIX ALG-2-interacting protein X AMBRA1 Autophagy and Beclin 1 regulator 1 AMP adenosine monophosphate AMPK AMP-activated protein kinase ANOVA Analysis of variance ARMM ARRDC1-mediated microvesicles ARRDC1 Arrestin-domain-containing protein 1 ATG Autophagy-related ATP adenosine triphosphate Baf Bafilomycin A1 BODIPY Boron-dipyrromethene CAF Cancer-associated fibroblasts Cas9 CRISPR associated protein 9 Caspase Cysteine-dependent aspartate specific protease CD Cluster of differentiation CFTR Cystic fibrosis transmembrane conductance regulator CMA Chaperone-mediated autophagy CQ Chloroquine CRISPR Clustered regularly interspaced short palindromic repeats DAPI 4’6-diamidino-2-phenylindole ER Endoplasmic reticulum ESCRT Endosomal sorting complex required for transport EV Extracellular vesicle FBS Fetal bovine serum GABA gamma-aminobutyric acid GABARAP Gamma-aminobutyric acid receptor-associated protein GABARAPL Gamma-aminobutyric acid receptor-associated protein-like GFP Green fluorescence protein GRASP Golgi reassembly stacking protein HCQ Hydroxychloroquine

xiii HPLC High Performance Liquid Chromatography HSP Heat shock protein HSP7C Heat shock cognate 71 kDa protein (HSPA8; HSC70)

IC50 Half maximal inhibitory concentration IF Immunofluorescence IHC Immunohistochemistry IP Immunoprecipitation LAMP2 Lysosome associated membrane protein type 2 LAP LC3-associated phagocytosis LAR Luminal androgen receptor LC3 Microtubule-associated protein 1A/B light chain 3 LECA Last eukaryotic common ancestor LIR LC3 interacting region MAPK Mitogen-activated protein kinase MHC Major histocompatibility complex miR Micro-RNA MS Mass spectrometry mTOR Mechanistic target of rapamycin NBF Neutral buffered formalin NTA Nanoparticle tracking analysis PAM50 Prediction analysis of microarray 50 PARP Poly [ADP-ribose] polymerase PBS Phosphate buffered saline PDAC Pancreatic ductal adenocarcinoma PE Phosphatidylethanolamine PECA Probe-level expression change average PFA Paraformaldehyde PI Phosphatidylinositol PI(3)P Phosphatidylinositol 3-phosphate PI3KC3 Phosphatidylinositol 3-kinase catalytic subunit type 3 PS Phosphatidylserine PM Plasma membrane RFP Red fluorescent protein

xiv sEV Small extracellular vesicles siRNA Small interfering RNA SNARE SNAP Receptor SP3 Solid-phase enhanced sample preparation TEM Transmission electron microscopy TMT Tandem mass tag TNBC Triple-negative breast cancers ULK Unc-51 like kinase UPR Unfolded protein response UVRAG UV Radiation Resistance Associated VAMP Vesicle-associated membrane protein VPS Vacuolar protein sorting

xv Image

xvi Chapter 1. Introduction

Portions of the text in this chapter were published as a review article: The interplay between exosomes and autophagy – partners in crime. J. Cell Sci. 131, jcs2152101. I surveyed the literature and wrote the manuscript.

1.1. Autophagy

1.1.1. Autophagy overview

Macroautophagy (hereafter referred to as autophagy) is a process ubiquitous among almost all eukaryotes, in which cytosolic proteins and organelles are captured by double-membrane vesicles, termed autophagosomes, and degraded through fusion with lysosomes (Figure 1.1A)2. The prefix “macro” serves to differentiate macroautophagy from other types of lysosome-dependent self-digestion, namely chaperone-mediated autophagy and microautophagy. Chaperone-mediated autophagy transports target proteins directly across the lysosomal membrane for degradation (Figure 1.1B), whereas microautophagy facilitates the lysosomal degradation of proteins through inward budding of lysosomes (Figure 1.1C).

Autophagy serves to remove proteins, protein aggregates, and damaged organelles; the amino acids, lipids, and sugars recycled from autophagic degradation can be used to sustain cell survival, especially under stress conditions such as starvation3,4. Autophagy is also crucial in normal development, where changes in cellular architecture or breakdown of cellular components are required5. Consequently, defects in autophagy can result in the pathogenesis of a variety of diseases, including but not limited to lysosomal storage, inflammatory, and neurodegenerative diseases6.

The term “autophagy” was coined in 1963 to describe lysosome-mediated self- digestion by Christian de Duve, who also discovered and named lysosomes7. In the 1990s, Yoshinori Ohsumi identified essential autophagy genes and pioneered the functional characterizations of autophagy-related proteins in yeast8,9. Around the same time, Daniel J Klionsky described the cytoplasm to vacuole targeting (Cvt) pathway in

1 yeast, which was later found to be a type of selective autophagy10. Amid the rapid expansion of the autophagy field, Ohsumi received a Nobel Prize in Medicine in 2016 for his pioneering work.

Figure 1.1. Types of autophagy pathways. A) Macroautophagy relies on the formation of double-membraned autophagosomes in the cytoplasm to sequester cargos for degradation, followed by subsequent fusion with the lysosome to create autolysosomes where contents are degraded. B) Chaperone-mediated autophagy involves the heat shock cognate 71 kDa protein (HSC70/HSP7C)-dependent recognition of substrates that contain a KFERQ-motif, and the translocation of substrates into lysosomal lumen via LAMP2A. C) Microautophagy is the invagination of the lysosomal membrane and direct sequestration of cytoplasm for lysosomal degradation.

1.1.2. Mammalian autophagy machinery

To date, more than 30 ATG genes have now been identified that have well- conserved homologs across eukaryotes11. A subset of ATG proteins are essential to the formation of autophagosomes and hence termed core autophagy machinery (Table 1.1). Upon induction of autophagy, a complex comprised of ATG13 and Unc-51 like kinase 1 (ULK1) initiates nucleation of the nascent phagophore and recruits the Class III phosphatidylinositol 3-kinase (PI3K) complex, which includes Beclin-1, ATG14, and PI3K

2 catalytic subunit type 3 (PIK3C3/VPS34). VPS34 catalyzes the conversion of phosphatidylinositol (PI) to phosphatidylinositol 3-phosphate (PI(3)P) to promote phagophore expansion12.

Two ubiquitin-like conjugation systems are then deployed to covalently link microtubule-associated protein 1 light chain 3 (MAP1LC3B/LC3B) to phosphatidylethanolamine (PE). Nascent LC3B protein is first cleaved by ATG4B into its mature, cytosolic form (LC3B-I)13. ATG7, a ubiquitin-activating (E1)-like enzyme, catalyzes the conjugation of ATG12 to ATG5, activates LC3B-I, and transfers LC3B to the ubiquitin-conjugating protein (E2)-like ATG314,15. Guided by ATG16L1 to target membranes16, the ATG5-ATG12 complex assumes ubiquitin protein ligase (E3)-like functions and catalyzes the transfer of LC3B-I to PE, thereby anchoring LC3B to the growing phagophore membrane17 (Figure 1.2). Lipidated LC3B is referred to as LC3B-II.

Table 1.1. Molecular functions of mammalian core ATG machinery. Group Proteins Function ULK-1 complex ULK-1 Phosphorylates ATG13 and FIP200 ATG13 Regulatory Class III PI3K complex BECN1 Regulatory VPS34 (PIK3C3) Catalyzes production of PI(3)P from PI ATG14 Regulatory; membrane curvature sensing LC3B conjugation ATG3 E2-like enzyme; conjugates LC3B to PE ATG4B Cysteine protease; Priming and recycling of LC3B ATG5 Acts as E3-like enzyme in complex with ATG12 ATG7 E1-like enzyme; activates ATG12 and LC3B ATG12 Ubiquitin-like; can be conjugated to ATG5 ATG16L1 Dimerizes and forms complex with ATG5-ATG12; specifies sites of LC3B lipidation Other ATG9 Transmembrane protein; membrane delivery Adapted from 18,19.

Figure 1.2. Molecular machinery of autophagy. Key autophagy-related (ATG) proteins are shown in their locations in the autophagy pathway. ULK1 complex (green) governs the initiation of the autophagy process, and recruits the Class III PI3K complex (blue) to promote phagophore expansion. Two ubiquitin-like conjugation systems catalyze the formation of ATG5-ATG12-ATG16L complex and the covalent conjugation of LC3B to PE on the membrane of the growing autophagosome.

Closed autophagosomes can fuse with lysosomes to form autolysosomes. Although relatively less well known compared to the rest of the autophagy machinery, autophagosome-lysosome fusion requires Ras-related protein (Rab) GTPases20, SNAP Receptors (SNAREs), and tethering factors21, similar to other cellular membrane fusion processes. Gamma-aminobutyric acid receptor-associated protein (GABARAP), a mammalian ATG8 homologue, also contributes to autophagosome-lysosome fusion22,23.

Autophagy is regulated by multiple signaling pathways. Stressors such as starvation, reactive oxygen species (ROS), and hypoxia are known to induce autophagy24. The best-known regulator of autophagy is nutrient availability, mediated through the mechanistic target of rapamycin (mTOR) pathway. In the presence of abundant nutrients and growth factors, the mTOR complex 1 (mTORC1) phosphorylates and inactivates the ULK1 complex, thereby inhibiting autophagy. Starvation depletes cellular adenosine triphosphate (ATP) and accumulates adenosine monophosphate (AMP), which activates AMP-activated protein kinase (AMPK) to inhibit mTORC125. AMPK also directly phosphorylate ULK1 and ATG14 to stimulate autophagy26,27. Signals

4 such as ER stress or oxidative stress can also activates autophagy to support cell survival28,29.

1.1.3. ATG8s and selective autophagy

ATG8s are small ubiquitin like proteins that can be covalently linked to PE. Human ATG8s consist of seven proteins divided into two subfamilies: the MAP1LC3 (LC3) and GABARAP subfamilies (Table 1.2). Division of labour between the subfamilies is evident, as silencing of all LC3 family proteins reduces the size of autophagosomes, whereas knockdown of GABARAP family proteins enlarges autophagosomes and reduces autophagosome-lysosome fusion30. However, ATG8 homologs are individually dispensable for autophagosome biogenesis, which suggests some degree of functional redundancy22,31.

Table 1.2. Human ATG8 proteins. MAP1LC3 subfamily Roles in autophagy LC3A Selective autophagy; autophagosome-lysosome fusion LC3B Selective autophagy; autophagosome elongation, autophagosome- lysosome fusion LC3B2 Not reported due to similarity with LC3B (all but 1 amino acid) LC3C Selective autophagy; autophagosome-lysosome fusion

GABARAP subfamily Roles in autophagy GABARAP Selective autophagy; autophagy initiation; autophagosome-lysosome fusion GABARAPL1 Selective autophagy; autophagy initiation; autophagosome-lysosome fusion GABARAPL2 Selective autophagy; autophagosome closure; autophagosome-lysosome fusion Adapted from 32.

Bulk autophagy of the cytoplasm requires GABARAPs but not LC3s31. In contrast, selective autophagy that degrades specific substrates utilizes both LC3 and GABARAP family proteins. Specific cargo adaptor proteins interact with ATG8s through an LC3-interacting region (LIR) motif, which facilitates incorporation of specific adaptors

5 and their cargos into autophagosomes33,34 (Table 1.3). Notable examples of selective autophagy include the selective degradation of mitochondria or protein aggregates, termed mitophagy and aggrephagy, respectively35,36.

Table 1.3. List of selective autophagy adaptor proteins Cargo Adaptor proteins Mitochondria SQSTM1 (p62), BNIP3L (Nix), OPTN, FUNDC1, PHB2 Peroxisomes SQSTM1 (p62), NBR1, TOLLIP, Endoplasmic reticulum RETREG1 (FAM134B) Bacteria/viruses SQSTM1/p62, CALCOCO2/NDP52, OPTN Ferritin NCOA4 Glycogen STBD1 Ubiquitinated substrate SQSTM1/p62, NBR1, CALCOCO2 (NDP52), OPTN Adapted from 33,37

1.1.4. Measuring autophagy flux

Autophagy flux is defined as entirety of the autophagy process, from the capture of substrates to their eventual degradation and recycling. Direct measurement of autophagy flux remains difficult, as it requires monitoring the sequestration, degradation and recycling of substrates over time. Therefore the number or ratios of autophagic structures are often used as surrogate measurements of flux, usually in combination with artificial lysosomal inhibition that causes autophagosome buildup. Still, a large number of autophagic structures may represent either an increase in their rate of formation, or a block in their rate of clearance. Therefore multiple types of assays are often used in conjunction to measure autophagy flux.

In mammalian cells, LC3B is the most widely used marker for autophagosomes. In an immunoblot-based flux assay, saturating levels of bafilomycin A1 (Baf) are used to inhibit vacuolar ATPase (V-ATPase) proton pumps and prevent acidification of lysosomes. The levels of LC3B-positive autophagic structures that accumulate would be proportional to the rate of autophagy turnover38 (Figure 1.3A).

Alternatively, cells lines stably expressing the mRFP-GFP-LC3B (tandem fluorescent LC3B or tfLC3B) reporter may be used to visualize autophagy flux39.

6 Autophagosomal membrane containing tfLC3B appear as puncta that are positive for GFP and RFP using fluorescence microscopy. The formation of autolysosomes exposes the tfLC3B on the inner autophagosomal membrane to the low pH of lysosomal lumen, thereby quenching the GFP fluorescence. RFP, being more resistant to low pH, continues to fluoresce, resulting in RFP-only puncta that represent autolysosomes (Figure 1.3B). The number of RFP+ puncta, as well as ratio between GFP+RFP+ and RFP+ puncta can be used for quantitative flux measurement.

The levels of known autophagic cargo, usually LC3B adaptors, can also be used to complement flux measurement, as they are degraded by autophagy. Therefore, decreased levels of adaptor proteins may indicate an increase in autophagy flux. Known adaptors such as p62/SQSTM1 and NBR1 are used in this respect38. However, since the levels of autophagy adaptors may be regulated by other processes, care must be taken when interpreting the changes in the levels of autophagy adaptors.

Figure 1.3. Methods of measuring autophagy flux. Multiple methods can be used in conjunction to determine the levels of autophagy flux. A) Western blot based flux assay utilizes a saturating concentration of bafilomycin A1 (Baf) to inhibit autophagosome-lysosome fusion, and measures the levels of LC3B accumulation in a set period of time. The amount of membrane-associated LC3B (LC3B-II) reflects the quantities of accumulated autophagosomes, which can be quantitated using western blotting. Actin was shown as loading control. B) mRFP-GFP-LC3B (tfLC3B)-based flux assay uses fluorescent microscopy. Incorporation of tfLC3B protein into autophagosomes results in RFP and GFP-positive puncta. Fusion of autophagosomes with lysosomes leads to drastic decrease of luminal pH and quenching of GFP fluorescence inside autolysosomes, which appear as RFP-only puncta.

1.1.5. Non-degradative functions of autophagy machinery

Autophagy is, in general, regarded as a degradative and catabolic process. Yet, several non-degradative roles linked to autophagy machinery have been discovered, ranging from immune modulation to unconventional protein secretion, which appears to require subsets of ATGs instead of the entire autophagy process40. For instance, autophagy machinery enables the loading of both self and foreign antigens onto the class II major histocompatibility complex (MHC II), which mediates adaptive immunity41– 43. Autophagy machinery also contributes to the unconventional secretion of immune

8 modulators. Conventional protein secretion requires signal peptide sequences that enable the proteins to transit from ER to Golgi, and then to the plasma membrane (PM)44. An LC3B-positive carrier can sequester the cytokine interleukin 1 beta (IL-1β) directly from the cytosol and subsequently fuse with the plasma membrane to release the IL-1β into extracellular space45,46. Autophagy machinery can also facilitate the unconventional secretion of membrane proteins. Mutant cystic fibrosis transmembrane conductance regulator (CFTR) proteins that fail to undergo conventional exocytosis can instead utilize Golgi reassembly stacking protein (GRASP) and autophagy machinery to reach the plasma membrane47. In this case, only the proteins required for autophagosome formation and LC3B lipidation (ATG1, 5, 7, 8), but not lysosomal fusion (vesicle-associated membrane protein 7; VAMP7), are required47.

1.1.6. LC3-associated phagocytosis (LAP)

In addition to facilitating autophagy degradation as discussed above, LC3B was found to be recruited to single-membraned phagosomes and macropinosomes— cytosolic vesicles containing phagocytosed or macropinocytosed extracellular content— in a process termed LC3-associated phagocytosis (LAP). LAP requires the LC3 lipidation machinery but not the formation of autophagosomes48,49. The ATG5-ATG12- ATG16L1 complex plays a significant role in targeting LC3B to the phagosome membrane16,50. LAP has been speculated to expedite the degradation of phagosome content by mediating fusion with lysosomes. Recent observations of LC3B lipidation occurring at single-membrane endosomes, even in the presence of lysosomal inhibition, raises exciting possibilities of non-degradative functions of a LAP-like machinery51.

1.2. Extracellular Vesicles

1.2.1. Extracellular vesicle overview

Extracellular vesicles (EV) is an umbrella term that describe diverse vesicular entities that exist outside of cells and lack nuclei52. To date, all known cell types release

9 some kinds of EV. EV may be categorized based on size, donor cell type, molecular markers or presumed site of origin. For example, cancer-derived EV are sometimes collectively referred to as oncosomes. EV include larger vesicles, such as microvesicles and apoptotic bodies that range from a few hundred nanometers to a few microns in size, and smaller vesicles (50-150nm) such as ectosomes, which originate from the plasma membrane, and exosomes, which are products of the endocytic pathway53 (Figure 1.4).

Figure 1.4. Known types of extracellular vesicles. Extracellular vesicles (EV) are classified based on origin and size. Exosomes are defined as small EV of endosomal origin, produced when multivesicular bodies (MVB) fuse with the plasma membrane and release their intraluminal vesicles (ILV) to the extracellular space. Key trafficking components involved in exosome biogenesis are shown54 (purple: small GTPases; blue: tethering factors). Plasma membrane blebbing can also produce EV, which include ectosomes, microvesicles and apoptotic bodies. Arrows in diagram represent vesicular trafficking events that occur during the endosome maturation process.

10 1.2.2. Exosome biology

Endocytosis is the process by which cells internalize fluids, macromolecules, membranes and receptors via invaginations of the plasma membrane. These membrane invaginations, sometimes coated with clathrin or caveolin, become intracellular vesicles following membrane scission55,56. Primary endocytic vesicles fuse with early endosomes, where cargo sorting is initiated. Through a process known as endosome maturation, early endosomes undergo a series of biochemical changes that give rise to late endosomes, which ultimately fuse with lysosomes55,56.

Exosomes are small extracellular vesicles originating from the endocytic pathway. During endosome maturation, some endosomes undergo another membrane invagination and fission event that produces intermediate organelles characterized by numerous intraluminal vesicles (ILVs)56. These intermediate organelles are termed multivesicular bodies (MVB) because of this morphology. MVB may fuse with the plasma membrane to release the ILVs to the extracellular space, creating exosomes, which are typically identified in the nanometer size range (50–150nm)57–59(Figure 1.4).

Exosomes contain combinations of membrane-associated and soluble proteins, DNA, mRNAs, and species of small RNAs such as microRNAs58. While non-selective (bulk) loading of exosomes is likely, there is evidence in some instances for selective loading, the mechanisms of which vary depending on cell type and stimulus60. The endosomal sorting complex required for transport (ESCRT) components and associated proteins make up four complexes that are involved in a wide range of membrane budding and scission events: ESCRT-0 sequesters ubiquitinated membrane proteins; ESCRT-I and -II induce membrane deformation; while ESCRT-III induces membrane scission61. Naturally, ESCRT complexes and accessory proteins have been shown to play a major role in ILV biogenesis and exosome loading62,63. Oligonucleotides can enter into exosomes by association with RNA-binding proteins, which localize to lipid rafts in MVBs64,65. Ubiquitination plays a role in the selective incorporation of exosomal proteins66–68. Furthermore, membrane microdomains enriched in tetraspanins (Cluster of differentiation 63/CD63, CD9, CD81) also participate in the recruitment of protein and oligonucleotide cargo into exosomes69,70. One mechanism of exosomal loading utilizes the electrostatic association of HSP7C (HSC70) with the MVB membrane to facilitate exosomal loading of proteins possessing KFERQ motifs in mammals71. The

11 heterogeneity of exosome composition and function suggests that other mechanisms of exosome loading exist; however, the extent of their individual contribution remains to be demonstrated60.

The fate of exosomes after release is thus far poorly understood. In circulation, exosomes are thought to have longer half-life compared to engineered liposomes72, owning partially to the CD47 “do not eat me” signal on exosomal membrane, which prevents phagocytosis by monocyte and macrophages73. Available evidence suggests that exosomes may interact with recipient cells through various means. Binding of exosomes with cell surface receptors such as proteoglycans or integrins—which can confer some degree int of specificity in exosome signaling74—may initiate downstream signaling cascades or the internalization of exosomes through the endocytic pathway54. How exosome cargos escape the eventual lysosomal degradation and retain signaling capacity remains unknown.

1.2.3. Functions of exosomes

Exosomes and exosome-like EV have been identified in species ranging from protozoa75,76, fungi77,78, slime mold79, and plants80 to fruit flies81 and mammals82. Releasing exosomes is a multi-step and energy-dependent process, the prevalence of which suggests that exosome signaling to be a well-conserved and valuable adaptation for cell-cell communication. Exosomes and exosome-like EVs have been implicated in growth83, development84, host-pathogen interactions85 and viral infections86. For instance, taking advantage of the stability of exosomes in the extracellular environment, Dictyostelium can shed chemoattractant-containing exosomes to direct collective migration79, further highlighting the functional versatility of exosomes. In mammals, exosomes produced by various cell types can be found in multiple body fluids53. Prevailing theories attribute the functions of exosomes to either excretion of intracellular waste product or the mediation of cell-cell signaling. However, these two functions may not be mutually exclusive.

Exosomes are of special interest in the study of diseases. In neurodegenerative disorders, neuronal cells release amyloids-containing sEV to reduce intracellular

12 proteotoxicity, which inadvertently contributes to the spread of amyloids and Alzheimer pathology in the brain87,88. The roles of exosomes in cancers will be elaborated later in this chapter.

1.2.4. Exosome and EV nomenclature

Due to the small size of exosomes, their cellular origins are difficult to ascertain once exosomes are released. While methodologies of single-vesicle analysis are being developed, various exosome markers have been proposed to evaluate the quality of bulk exosome isolations. Typically, multiple protein markers are recommended, which include a combination of membrane (CD63, CD81, CD9) and luminal (tumor susceptibility gene 101/TSG101, Syntenin-1) proteins89. Other proteins commonly identified in proteomic analyses of exosome isolation, such as ALG-2-interacting protein X (ALIX), heat shock protein 90 (HSP90), HSP7C (HSC70), MHCs and annexins90, are also used . However, questions remain on whether widely used markers are sufficient and accurate. For example, recent data suggest that DNA and HSP90 were not identified within exosomes; TSG101 was also associated with exosomes as well as Arrestin-domain-containing protein 1 (ARRDC1)-mediated microvesicles (ARMMs)91,92. For these reasons, EV researchers have adopted a more rigorous approach in EV terminology, preferring to define EV based on broad physical characteristics unless substantial evidence of EV subtype can be shown. Furthermore, efforts to improve standardization and rigor in EV research have been made that require careful experimental design and data reporting, including the metadata associated with EV collection93. Therefore, in this thesis, the term “exosome” will be used to describe original research that used the same term and “EV” in all other situations.

13 1.3. Crosstalk between autophagy and exosome biogenesis

1.3.1. Amphisomes: crossroad of autophagy and endocytosis

Autophagosomes are known to fuse with endosomes to form hybrid organelles termed amphisomes. Amphisomes can them fuse with lysosomes for content degradation94,95 (Figure 1.5). Antagonistic interactions between autophagy and exosome release in the form of amphisome degradation have been well documented. In the erythroleukemic cell line K562, starvation or rapamycin treatment induces autophagy, increases autophagosome-MVB fusion and decreases exosome release96, perhaps as cells attempt to recycle MVB for energy instead. Failure to release exosomes can also lead to the redirection of MVB to autophagic degradation. In cell and mouse models, aggregation and degradation of TSG101 through conjugation with ubiquitin-like protein ISG15 (ISGylation) was sufficient to impair exosome biogenesis. Induced ISG15 expression promoted protein aggregation and degradation, reduced the number of MVB, and blocked exosome release97. Prevention of endosome-lysosome fusion through the use of bafilomycin A1, a dominant-negative mutant form of RAB7, or ATG5 knockdown all rescued exosome release, which suggests that the lysosomal degradation of aggregate-containing MVBs might have involved autophagy machinery97. Another report demonstrated autophagic clearance of aberrant endocytic vacuoles following knockout of CD63, where inhibition of autophagy with chloroquine partially rescued exosome biogenesis in the CD63-null cells98. These studies illustrate the prevalence of autophagic degradation of MVBs in diverse contexts. However, as discussed below, additional evidence indicates that MVB and autophagosme fusion may perform additional functions.

Figure 1.5. Autophagosomes and endosomes can fuse and form hybrid organelles termed amphisomes. Amphisomes can fuse with lysosomes for content degradation.

1.3.2. Amphisomes: novel secretory organelles?

Recent studies suggest that amphisomes may serve additional secretory functions. LC3B was found to co-localize with the endosomal markers EEA1, RAB7 and RAB11 on amphisome-like organelles, which are vital to the production of reactive oxygen species (ROS) that regulate the secretion of mucin granules in lung epithelial cells99. Another report demonstrated interferon gamma (IFN-) induced autophagy- dependent exosome secretion of annexin A2 (ANXA2) in lung epithelial cells, which the authors argue took place through amphisomes100. IFN- treatment induced the co- localization of LC3B, CD63, and ANXA2 on what could be amphisomes. This co- localization and subsequent exosome release were dependent upon ATG5, RAB11, and RAB27A, suggesting that the formation of autophagosomes, MVB and the fusion of amphisomes with the plasma membrane were vital to the process100.

15 Care must be taken to differentiate the autophagy-dependent unconventional secretion from exosomal secretion. For example, while functional MVBs are required for optimal autophagy-dependent secretion of IL-146, autophagosome-lysosome fusion is dispensable101, suggesting that LC3B-positive IL-1 carrier vesicles fuse directly with the plasma membrane. The reliance on MVB functionality could be due to the extensive crosstalk between autophagy and endocytosis102. Curiously, IFN- induced exosomal secretion of ANXA2 requires RAB8A100, a known mediator of autophagy-dependent IL- 1 secretion in macrophages103. These observations suggest a potential overlap between autophagy-mediated unconventional secretion and exosome release, but further studies are required to delineate the possible connections between these processes.

1.3.3. Autophagy and exosome biogenesis share molecular machinery

Autophagy and endocytosis are known to interact extensively and share molecular machinery104. Subsets of the autophagy machinery have been shown to contribute to exosome biogenesis, while the completion of the autophagic process itself appears dispensable105,106. A recent report highlighted crucial non-autophagic functions of ATG5 and ATG16L1 in exosome biogenesis105. ATG5 was shown to dissociate vacuolar proton pumps (V1V0-ATPase) from MVB, which prevented acidification of the MVB lumen and allowed MVB-PM fusion and exosome release. Accordingly, knockout of ATG5 or ATG16L1 significantly reduced exosome release and attenuated the exosomal enrichment of lipidated LC3B. Moreover, treatment with lysosomal or V-ATPase inhibitors rescued exosome release in ATG5 knockout cells, which further supported the role of luminal pH in controlling whether MVBs undergo lysosomal degradation or plasma membrane fusion. Importantly, ATG7 knockout did not affect exosome release, suggesting that the formation of autophagosomes or LC3B lipidation was not required. This study thus provides a mechanism where autophagy-related proteins directly regulate the fate of MVBs and subsequent exosome biogenesis. While the biological function of LC3B in exosomes remains unclear, its localization on the lumen side of ILVs as shown in this study suggests a LAP-like lipidation event either at the MVB membrane or at membrane invaginations that subsequently become ILVs. The eventual release of

16 intact LC3B-positive exosomes points to non-degradative functions of the LAP-like mechanism105.

The ATG12-ATG3 complex was also found to regulate exosome biogenesis in addition to its role in autophagy, through its interaction with ALG-2-interacting protein X (ALIX), an ESCRT-associated protein crucial to exosome biogenesis106. Here, loss of ATG12-ATG3 altered MVB morphology, impeded late endosome trafficking and reduced exosome biogenesis. ALIX knockdown also reduced basal autophagy flux, demonstrating a reciprocal regulation between autophagy and exosome biogenesis. Importantly, starvation-induced autophagy remained intact despite loss of ALIX or disruption of the ATG12-ATG3 complex, implying the existence of different regulatory machinery that controls basal and stress-induced autophagy, as well as its interactions with endocytic compartments (Murrow et al., 2015). As elaborated below, context dependency is a recurring theme in exosome-autophagy crosstalk, with both processes responsive to various forms of cellular stress.

A past study has also implicated ATG9, the only transmembrane ATG, in the formation of ILVs in Drosophila. Under basal conditions, loss of ATG9 impaired autophagy flux and reduced the number of ILVs in amphisomes and autolysosomes107. However, whether the ILVs, in this case, were released as exosomes remains unknown.

1.3.4. Additional insight of autophagy-exosome crosstalk from viral infections

Studies of viral infections provide a fascinating perspective on interactions between autophagy and exosome production. Viruses are known to hijack the exosomal pathway to evade the host immune system and increase infectivity59. Increasing evidence suggests that viruses may also take advantage of the autophagy-exosome crosstalk to facilitate their replication and release. The hepatitis C virus (HCV) offers a unique model to delineate the links between autophagy and exosome biogenesis. HCV infection has been shown to lead to the upregulation of autophagy, as well as the release of virus-containing exosomes86,108. Knockdown of Beclin1 or ATG7 decreased the level of extracellular exosome-associated HCVs109, suggesting that the core autophagy machinery plays a role in the packaging of HCV particles into exosomes.

17 Indeed, increased autophagosome-lysosome fusion reduced the release of HCV particles, suggesting that a portion of HCV particles or its replication machinery may reside within autophagosomes110. Curiously, HCV infection differentially regulates autophagy at different time points. In the early stages of HCV infection, upregulation of Rubicon, a negative regulator of autophagosome-lysosome fusion, suppresses autophagy flux, indicating that HCV viruses may exploit the build-up of autophagosomes for replication111. Later on during the infection, UV Radiation Resistance Associated (UVRAG) expression is induced111. Given the role of UVRAG in enhancing endosomal transport and endosome maturation112, its delayed induction in HCV infection may reflect altered endosomal trafficking, which potentially facilitates virus escape via exosomes. Delineating the egress route of HCV particles may inform molecular links between autophagy and endocytic pathways in the context of infection. In a similar manner, studying the altered or perturbed state of cellular processes may offer valuable insight.

1.4. Autophagy and exosomes in cancer

1.4.1. Autophagy in cancer

Cell-autonomous autophagy is recognized as having a dual role in cancer, performing a tumour-suppressing role in normal cells and acting as tumour-promoting in transformed cells. Autophagy contributes to cellular homeostasis in normal cells by removing potentially damaging proteins and organelles and therefore maintaining genome stability and preventing tumorigenesis113. In established tumours, however, autophagy supports tumour progression, for example, by maintaining mitochondrial integrity in cancer cells with oncogenic Ras mutations114,115. Because chemotherapies and stressor like hypoxia induce autophagy in surviving cancer cells, autophagy inhibition being investigated as a potential therapeutic avenue116,117. For instance, studies of autophagy inhibition in various types of cancers, including but not limited to lymphoma cells118, estrogen receptor (ER)-positive breast cancer cells119, leukemia cell lines and xenografts120, and pancreatic ductal adenocarcinoma (PDAC) cells and xenografts121, have all shown therapeutic benefits of autophagy inhibition, ranging from growth suppression to the enhancement of chemotherapy efficacy. However, a thorough

18 understanding of the context-dependent roles of autophagy in cancer is needed for more precise and efficient autophagy modulation for therapeutic benefits122.

In comparison, the non-degradative roles of autophagy in cancer are less explored, although autophagy-dependent secretion was shown to modulate the tumor microenvironment and drive more aggressive cancer cell behaviour123. For instance, breast cancer cells were shown to induce oxidative stress and autophagy in co-cultured fibroblasts, which in turn switched to glycolytic metabolism and released pro-cancer factors such as lactate and fatty acids124. Here, inhibiting autophagy would serve both to weaken cancer cells and to prevent pro-cancer activities of cancer-associated fibroblasts (CAF). However, autophagy is required for the release of the immune modulator high- mobility group B1 (HMGB1) from dying cells, making autophagy inhibition counterproductive to immune surveillance125. Therefore, the roles of autophagy, especially cell-extrinsic ones, need to be better understood.

1.4.2. Exosomes in cancer

Exosomes have been recognized as powerful mediators of cell-cell communication in cancer. Many recent reviews have discussed the roles of exosomes in the tumour microenvironment126–129 and anti-tumour immune responses130–132. Overall, tumour-derived exosomes are thought to alter the tumor microenvironment, facilitate immunosurveillance evasion and promote local invasion and distant metastases. Exosomes from metastatic melanoma cells altered the phenotype of bone marrow progenitor cells, leading to an increase in the size of the primary tumour, as well as in the size and number of metastases133. In a metastatic breast cancer model, exosomes were shown to influence organotropic metastasis: exosomes bearing distinct combinations of surface integrins home in to different organs and prepare the resident cells for incoming metastases74.

Due to their presence in bodily fluids, including blood and urine, circulating exosomes have the potential to serve as biomarkers for cancer progression or treatment response. Serum exosomes were shown to carry DNA with a mutation profile that is almost identical to the primary tumour134. Thus the detection of biomarkers in exosomes

19 may reveal small, hidden tumours at early stages of the disease135. It may be possible in the future to survey the change in exosome composition as the disease progresses or responds to treatment, and so obtain crucial insight into the change in disease status without the need for invasive sampling. Furthermore, engineered exosomes are promising carriers for drugs and nucleic acids72. Tumours that actively scavenge nutrients through macropinocytosis, such as Ras-transformed pancreatic cancer cells136, may be especially responsive to such an exosome-mediated drug delivery.

1.4.3. Cancer stress responses involve autophagy and exosome release

Cellular stress responses are crucial to cancer cell survival, due to the frequent onslaught of chemotherapy, radiation, and attacks from the host immune system. Stressors such as hypoxia were found to increase both exosome secretion137 and autophagy flux138 in breast cancer cells, although the extent to which these two pathways are coordinated in cancer remains mostly unexplored. ER stress is known to upregulate autophagy in multiple types of normal and cancerous cells139. Recently, ER stress was shown to increase MVB formation and exosome release in HeLa cells, whereas loss of IRE1a and PERK, two arms of unfolded protein response (UPR) signaling, abolished the increase in exosome production140. In breast and prostate cancer cell lines, rotenone-induced mitochondrial damage led to increased levels of endosomal tetraspanins (CD9, CD63, and CD81) and ATG7 in the cytoplasm, which coincided with an up-regulation of autophagy and increased exosome release141. A study of regulatory proteins in vesicular trafficking pathways identified that GAIP interacting protein C-terminus (GIPC) simultaneously regulated autophagy and exosome production in pancreatic cancer cells. Knockdown of GIPC led to decreased mTOR activity, increased autophagy flux as well as increased exosome production142. Together, these studies suggest that under certain conditions, autophagy and exosome biogenesis may constitute part of a coordinated stress response (Figure 1.6).

Figure 1.6. Cellular stresses activate autophagy and exosome release. Stresses such as hypoxia, ER stress or chemotherapy simultaneously activate autophagy and exosome release, in what is possibly a coordinated stress response.

Breast cancer cell-derived exosomes was found to alter autophagy flux in recipient breast epithelial cells143. Upon uptake of cancer exosomes, ROS production, DNA damage response and autophagy were induced in breast epithelial cells. Subsequently, epithelial cells secreted unknown factors that promoted cancer cell growth143 (Figure 1.7). Since secretory autophagy has been reported to mediate the release of nutrients and growth factors from stromal cells to promote cancer cell growth144,145, the induction of autophagy in breast epithelial cells may have served the same purpose.

Figure 1.7. Cancer-derived exosomes upregulates autophagy in recipient epithelial cells and induce secretion of pro-tumour factors. Cancer cells release exosomes that elevate intracellular ROS levels and induce DNA-damage responses in recipient epithelial cells, as well as upregulate autophagy and secretion of pro- tumour factors.

1.4.4. Autophagy and exosomes in chemotherapy resistance

Acquired resistance to chemotherapies and targeted therapies is one of the major obstacles in combating cancers. Understanding how cancer cells survive chemotherapy and develop resistance is crucial to successful cancer control. Induction of autophagy and exosome release have been documented following drug treatments146,147, indicating that they may be involved in the development of chemotherapy resistance. In support of this possibility, increased levels of autophagy flux and exosome production in various types of chemotherapy-resistant cancer cells have been reported148,149. For example, increased release of exosomes was observed in platinum-resistant ovarian cancer cell lines, as well as in serum from patients with cisplatin-resistant tumours150, while increased autophagy flux was found in platinum- resistant ovarian cancer cells151. Although it is unknown whether these changes are part of the resistance mechanism or merely a consequence of the shifting cellular phenotype, these observations provide a rationale to investigate the relationship between autophagy, exosome and drug resistance.

The development of chemotherapy resistance in turn affects exosome signaling. Exosomes were proposed to propagate drug-resistant phenotypes through the transfer of miRNA or multidrug-resistant transporter (MDR) proteins between cancer cells127,152,153. Additionally, exosomes from gefitinib-treated EGFR-mutant PC-9 cells

22 were shown to increase autophagy flux in recipient cancer cells, which became less responsive to cisplatin treatment154 (Figure 1.8). These studies illustrate the potential capacity of the crosstalk between tumour-derived exosomes and autophagy to influence tumour behavior and its interactions with the microenvironment.

Figure 1.8. Cancer-derived exosomes upregulate autophagy and promote drug resistance in recipient cancer cells. Drug-resistant cancer cells release exosomes that promote drug resistance in the recipient cancer cells. This effect may be facilitated by exosome-mediated transfer of multi-drug resistance (MDR) transporters and micro-RNAs (miRs), or exosome-mediated induction of cytoprotective autophagy in the recipient cells through unknown mechanisms.

1.5. Triple-negative breast cancer: an unmet medical need

1.5.1. Breast cancer

In 2019, 220,400 Canadians will be diagnosed with cancer. Female breast cancers accounts for 25% of all new cancer cases in women, and are the second most commonly diagnosed cancer overall in Canada. The incidence of breast cancer has been relatively stable since 2004, while the age-standard mortality rate has been decreasing from 41.7 per 100,000 in 1988 to a projected 22.4 in 2019, likely due to an increase in mammography screening and advancement in therapy155. Despite progress, breast cancers rank the first in cancer-related deaths in populations between 30 and 49 years of age155, highlighting a still unmet medical need.

23 Suspected breast cancers are usually biopsied to confirm malignant diagnosis, during which time histopathological examinations are performed. Traditionally, breast cancers are divided into biological subtypes based on hormone receptor status: luminal- like breast cancers that overexpress one or both of estrogen and progesterone receptors (ER, PR), HER2 positive breast cancers that overexpress the receptor tyrosine-protein kinase erbB-2 (ERBB2, commonly known as human epidermal growth factor receptor 2 or HER2), and triple negative breast cancers that lack detectable expressions of all three receptors156. More recent efforts classified breast cancers into molecular subtypes based on gene expression patterns157,158 and genomic level changes159 (Table 1.4). Early breast cancers may be treated with primary local therapy (surgery or radiotherapy) with the option of neoadjuvant cytotoxic or endocrine therapy, or primary systemic therapy depending on clinical indications such as hormone receptor status, lymph node involvement and grade156.

Table 1.4. Breast cancer molecular subtypes. Molecular subtype Select features Luminal A High oestrogen signaling; low proliferation Luminal B Less oestrogen signaling and higher proliferation compared to Lum-A; focal genome amplification HER2-enriched Frequent ERBB2 amplification; high genome instability; high proliferation Basal-like Frequent TP53 mutations; high genome instability; high proliferation Adapted from 159. TP53: tumor protein 53.

1.5.2. Triple-negative breast cancer

Triple-negative breast cancers (TNBC) are a group of breast cancers that lack detectable levels of ER, PR, and ERBB2 (HER2) expression160. TNBC account for about 15% of all breast cancers, and are often associated with aggressive clinical course and poor outcome161. Unlike ER+ breast cancers, which were thought to have arisen from breast epithelium, TNBC cells are believed to have originated from basal/myoepithelial cells that are less well differentiated and more stem-like162. Since defining TNBC by their absence of hormone receptors does little to describe their biological dispositions, various efforts were made to further stratify TNBC. Histopathological examination of TNBC

24 divide them into invasive ductal carcinoma, which accounts for 95% of all TNBC, and other rarer subtypes including invasive lobular carcinoma, adenoid cystic carcinoma, secretory carcinoma and metaplasic carcinomas163. Predictive Analysis of Microarray 50 (PAM50) and other gene expression studies showed that 70–80% of TNBC are also basal-like breast cancers, which exhibit gene expression patterns reminiscent of basal or myoepithelial cells164. A claudin-low subtype, which expresses high levels of mesenchymal genes and low levels of claudins, can be further separated from the basal- like group164. Subsequent gene expression studies grouped TNBC into six subtypes, which were further refined to four subtypes—basal-like 1 and 2, mesenchymal-like and luminal androgen receptor (LAR)—that differ in association with histological features and in responses to chemotherapy165,166. Although advancement in molecular characterization of TNBC has yet to fully translate into clinical tools, the discovery of targetable alterations such as androgen receptor positivity and homologous recombination deficiency in TNBC raised new possibilities in treatment options167.

Standard regimens for TNBC use combinations of anthracyclines (daunorubicin, mitoxantrone, epirubicin or doxorubicin) or taxanes (paclitaxel or docetaxel) as neoadjuvant treatments in addition to local therapies156. Although more sensitive to chemotherapies than other breast cancers, the development of drug resistance in TNBC is expected to be more frequent due to high genome instability and heterogeneity160. Novel therapeutic options and drug targets are needed for the management of TNBC, along with a better understanding of the tumour microenvironment and mechanisms of chemotherapy resistance.

1.5.3. Chloroquine and hydroxychloroquine

Hydroxychloroquine (HCQ) and chloroquine (CQ) are structurally related compounds in the family of four-aminoquinolines, commonly used to treat malaria and certain autoimmune diseases168 (Figure 1.9). CQ and HCQ accumulate in lysosomes, raise lysosomal pH, and thereby inhibit lysosomal function and autophagy. In vitro experiments demonstrated anti-tumor effects of CQ169 even before the mechanism of CQ-mediated autophagy inhibition was known170. Although some studies suggested that CQ-mediated anti-cancer effects were independent of autophagy inhibition171,172, more

25 recent findings, especially in Ras-driven cancer models, strengthened the argument to inhibit pro-survival autophagy in cancer cells173,174. Multiple clinical trials have been undertaken in recent years to test the efficacy of combining CQ or HCQ with chemotherapeutic agents175, producing some promising but perplexing results: while CQ/HCQ was well-tolerated in general, the disease response was inconsistent176. While some attribute the discrepancy in the outcome of these clinical trials to the incomplete autophagy inhibition at the concentrations of HCQ used, it’s also true that the cell- extrinsic effects of HCQ are not fully understood. Evidence such as the CQ-mediated stabilization of tumour vasculature177 suggests that the anti-tumour effect of CQ is at least in part dependent on its modulation of the tumour microenvironment, rather than tumour cells themselves.

Figure 1.9. Structures of chloroquine and hydroxychloroquine. Adapted from 168.

1.6. Rationale, hypotheses and specific aims

The aggressive nature of TNBC and shortage of therapy options necessitate a deeper understanding of the disease and identification of novel treatment avenues.

26 Autophagy is a well-conserved process that promotes cell survival under stress. Autophagy inhibition was shown to have anti-cancer effects in multiple cell lines and xenograft models. However, at the start of this thesis project, CQ/HCQ had not yet been tested in the context of TNBC.

The deployment of CQ and HCQ in cancer clinical trials will benefit from a better understanding of the molecular mechanisms and biological effects of lysosomal and autophagy inhibition. Exosomes modulate the tumor microenvironment by mediating cell-cell signaling, and are known to promote tumor aggressiveness. Because lysosomal inhibition will likely affect endocytosis and by extension exosome biogenesis, it is relevant to investigate the consequences of CQ/HCQ treatment on exosome content and function. Although the crosstalk between autophagy and endocytosis is known, the exact molecular interactions between autophagy and exosome biogenesis are not well understood. Whether autophagy machinery contributes to the biogenesis of exosomes, or a distinct subpopulation of EV, remains undetermined.

The overarching aim of this thesis was to investigate the cell-intrinsic and cell extrinsic roles of autophagy in triple-negative breast cancer cells.

Specific aim 1: Determine the role of autophagy in TNBC cell treatment response.

Hypothesis: Autophagy promotes TNBC cell survival.

The first aim of my thesis was to interrogate the cell-intrinsic role of autophagy in TNBC cells. To determine whether epirubicin alters autophagy in TNBC cells, I measured the change in autophagy flux in TNBC cells after epirubicin treatment, and compared the level of autophagy flux between anthracycline-sensitive and resistant cells. To establish the functional role of autophagy in TNBC cell survival, I assessed the effects of autophagy inhibition on TNBC cell viability using genetic and pharmacological inhibition, and explored the efficacy of combining autophagy inhibitors with anthracyclines in TNBC cell lines.

27 Specific aim 2: Investigate the effects of lysosomal inhibition on TNBC cell derived sEV.

Hypothesis: Autophagy machinery contributes to sEV content and function.

The second aim of my thesis explored non-cell-autonomous roles of autophagy, specifically in terms of its effects on sEV composition. I profiled TNBC-derived sEV in the presence and absence of CQ-mediated lysosomal inhibition, and utilized quantitative proteomics methodologies to detect the differences in relative protein abundance between sEV from control and CQ-treated cells. I then validated the presence of autophagy proteins in sEV. Finally, I characterized the impact of CQ on sEV function by measuring the growth of sEV recipient cells.

28 Chapter 2. Materials and Methods

2.1. Key Reagents

Table 2.1. List of key reagents. Tissue culture reagents Name Manufacturer Identifier DMEM Gibco 11995-065 Ham’s F12 Gibco 11765-054 MCDB131 Gibco 10372-019 DMEM/F12 Gibco 11330-032 PBS Gibco 10010-023 HEPES Gibco 15630-080 Insulin Sigma I0516-5mL Non-essential amino acid Gibco 11140-050 Fetal bovine serum Gibco 12483-020 Hydrocortisone Sigma H4001 Epidermal growth factor Gibco PhG0311L Glutamine Gibco 25030081 Growth factor reduced Matrigel Corning #356231 Trypsin-EDTA Gibco 25300-062

Other reagents Name Manufacturer Identifier Caspase-Glo 3/7 Kit Promega G8091 CD63-Dynabead Thermo Fisher 1606D CD9-Dynabead Thermo Fisher 10614D ExoQuick-TC SBI EXOTC50A-1

Antibodies Name Manufacturer Identifier Anti-Mouse IgG, HRP-linked Cell Signaling 7076S Anti-Rabbit IgG, HRP-linked Cell Signaling 7074S CD63 Thermo Fisher Ts63 CD9 Santa Cruz Sc-13118 CD9 Cell Signaling 13174S GABARAP MBL M135-3 GABARAPL1 Abcam ab86497 GABARAPL2 Abcam ab126607 HSP90 Abcam ab13492 IRE1a Cell Signaling 3294S K48 linkage-specific ubiquitinated protein Abcam ab140601 LAMP2 Hybridoma Bank H4B4 MAP1LC3A Cell Signaling 4599S MAP1LC3B (LC3B) Abcam AB48394 MAP1LC3B (LC3B) (IF) Cell Signaling 3868

29 MAP1LC3C Cell Signaling 14736 SQSTM1/p62 Sigma P0067 Name Manufacturer Identifier Syntenin-1 Abcam Ab133267 Total ubiquitinated protein Millipore 04-263 TSG101 Sigma T5701 β-actin Abcam Ab6276

Plasmids Name Source Identifier pSpCas9(BB)-2A-Puro (PX459) Addgene #48139

Oligonucleotide Name Source Sequence/Identifier Medium GC Scramble control siRNA Invitrogen 12935-300 ATG5 siRNA #1 Invitrogen ATG5HSS114105 ATG5 siRNA #2 Invitrogen ATG5HSS114104 ATG7 siRNA #1 Invitrogen NM006395 Stealth 142 ATG7 siRNA #2 Invitrogen NM006395 Stealth 965 ATG4B CRISPR gRNA1 Addgene library TCCTGTCGATGAATGCGTTG #1000000048 ATG4B CRISPR gRNA2 Addgene library TCCTCAACGCATTCATCGAC #1000000048 GABARAP CRISPR gRNA1 Addgene library CCTGGACAAAAAGAAATACC #1000000048 GABARAP CRISPR gRNA2 Addgene library GGATCTTCTCGCCCTCAGAG #1000000048 MAP1LC3B CRISPR gRNA1 Addgene library TTCAAGCAGCGCCGCACCTT #1000000048 MAP1LC3B CRISPR gRNA2 Addgene library GTGAGCTCATCAAGATAATT #1000000048

2.2. Cell lines and tissue culture conditions

MDA-MB-231, Hs578T, BT549, SK-BR-3, and MCF10A cell lines were obtained from American Type Culture Collection (ATCC). SUM159PT was obtained from Asterand (now BioIVT). HMEC-1 was a gift from Dr. Cathie Garnis (BC Cancer Research Centre). Mammalian cell lines were cultured at 37°C and 5% CO2. MDA-MB- 231 and Hs578 cells were cultured in DMEM supplemented with 10mM HEPES, 5µg/ml insulin, 1x non-essential amino acids (NEAA) and 10% heat-inactivated fetal bovine serum (FBS). SUM159PT was cultured in Ham’s F-12 supplemented with 10mM HEPES, 5ug/ml insulin, 1x NEAA, 10% FBS and 1ug/ml hydrocortisone. HMEC-1 cell line was cultured in MCDB131 supplemented with 10ng/ml EGF, 1µg/ml hydrocortisone,

30 2mM glutamine and 10% FBS. MCF10A cells were cultured in DMEM/F12 supplemented with 20ng/ml EGF, 0.5µg/ml hydrocortisone, 100nm/ml cholera toxin, 5% horse serum and 10µg/ml insulin. sEV conditioning media was identical in composition to full culture media except lacking FBS. All cell lines were routinely checked with e-Myco Mycoplasma Detection Kit (iNtRON 25235) to rule out mycoplasma contamination.

Epirubicin-resistant cell lines (R8 and R75) were derived from parental cell lines (MDA-MB-231 and SUM159PT) by culturing cells in gradually increasing concentrations of epirubicin (12.5–100nmol/L for R8; 100nmol/L–1 mmol/L for R75) over the course of one year for R8 or six months for R75.

2.3. Genetic manipulation of cells

2.3.1. siRNA-mediated knockdown

Cell lines were seeded on 6-well plates at the density of 200,000 cells per well. The next day, 75pmol of Invitrogen stealth siRNAs or Medium GC scramble control siRNA were transfected using Lipofectamine RNAiMAX (Thermo Fisher 13778150) in serum-free media and allowed to incubate overnight. Transfection media was removed the next day and replaced with full growth media. Cells were harvested for analyses 72 hours after transfection.

2.3.2. CRISPR-mediated gene knockout

Single guide RNA sequences against human ATG4B, GABARAP and MAP1LC3B gene were obtained from the Addgene Genome-scale CRISPR Knock-Out (V2.0) library and cloned into PX459 using Addgene recommended protocol. The resulting plasmids were sequenced and transfected into MDA-MB-231 cell line using Lipofectamine 3000 (Thermo Fisher). After 48 hours, cells were selected with 1µg/ml puromycin. Serial dilution plating was used to obtain surviving individual clones. Isogenic knockout clones were validated with western blotting.

2.4. sEV isolation

MDA-MB-231, SUM159PT, and Hs578T cells were seeded on 15cm tissue culture plates (Corning 430599) in regular growth media. Upon reaching 50% confluency, the culture media was removed and cells were washed twice with PBS. Serum-free media was added to each plate along with PBS or chloroquine (CQ) to a final concentration of 10µM. After 48 hours, conditioned media and cell pellets were collected. Trypan blue exclusion assay was used to ensure cell viability of greater than 90%.

2.4.1. Pre-clearing of conditioned media

150ml conditioned media was pre-cleared by sequential centrifugation: 10 minutes at 300Xg, 20 minutes at 2,000Xg and twice for 30 minutes at 10,000Xg. High molecular weight centrifugal ultrafiltration (Ultra 15, 100kDa, Amicon) was then carried out at 2,000Xg at 4°C to remove smaller contaminants and to concentrate sEV- containing media.

2.4.2. sEV isolation using ExoQuick

ExoQuick TC (SBI) was used to precipitate sEV from concentrate according to manufacturer’s protocol. Briefly, ExoQuick TC was added to concentrated media at a ratio of 1:5 (v/v), mixed thoroughly, and allowed to incubate overnight at 4°C. The mixture was spun at 10,000Xg for 30 minutes the next day to remove the supernatant, and again at 10,000Xg for 10 minutes to remove residual liquid. sEV were resuspended in 200µl of 0.02µm filtered PBS and frozen in -80°C for future analyses.

32 2.4.3. sEV isolation using immunoaffinity capture

Conditioned media was pre-cleared and concentrated by ultrafiltration as described in 2.4.1. 20µl concentrated media and 180µl IP buffer (0.2um filtered 0.1% BSA in PBS) was added to Dynabeads coupled with CD9 or CD63 (Thermo Fisher) and incubated overnight at 4°C with mixing. Beads were washed twice with cold IP buffer the next day, using a magnetic rack to separate beads from supernatant. Bound sEV were eluted by addition of 30–60µl 1xLDS Gel Loading Buffer (Novex), Bolt Sample Reducing Agent (Invitrogen) and heating at 70–80°C for 10 minutes.

2.5. Characterization of sEV

2.5.1. Nanoparticle tracking analysis (NTA)

A NanoSight LM10 (Malvern) equipped with 405nm laser was used to measure particle size and particle number of isolated EV. Samples were diluted 1/500 to 1/2000 in 0.02µm filtered PBS and injected into the chamber with a constant output syringe pump. The Nanoparticle Tracking Analysis software was used to analyze three recordings captured for each sample.

2.5.2. Transmission electron microscopy

3ul of TBS-diluted sEV preparation was placed on glow-discharged Formvar- coated carbon grids (Ted Pella 01800-F) and dried for 1 minute. Excess liquid was removed with filter paper. 3ul of 2% uranyl acetate was place on the grid as a negative stain and incubated for 1 minute. The excess uranyl acetate was again removed with filter paper. The loaded grids were allowed to dry completely before viewing on a Hitachi H7600 TEM (HV=80kV, camera = XR51, Gamma: 1.00, no sharpening, normal contrast).

33 2.6. Tandem mass spectrometry

The details of all methods associated with mass spectrometry were previously described178,179. Key methods are outlined below. The mass spectrometry proteomics data was deposited to the ProteomeXchange Consortium via the PRIDE partner repository180.

2.6.1. Sample preparation

EV and cell pellets were thawed and lysed in 100μL lysis buffer containing 100 mM HEPES pH 8.5 (Sigma H3375), 2% SDS (Sigma L4509), 10 mM TCEP (Sigma C4706), 40 mM CAA (Sigma C0267), and 1x cOmplete protease inhibitor – EDTA free (Sigma 4693159001). The proteins were then denatured by heating at 95°C for 15 minutes with shaking and incubated at room temperature for 90 minutes in the dark to allow reduction and alkylation of disulfide bonds by TCEP and CAA respectively.

2.6.2. SP3 clean up and digestion

Sample lysates were subjected to magnetic bead-based cleanup using a 1:1 combination of two types of Sera-Mag Speed Beads (GE Life Sceinces, #45152105050350 and #65152105050350) as previously described 178. Briefly, 200μg of bead mix was added to each protein mixture to a working concentration of 20μg/μL, and mixed. Ethanol was added to a final concentration of 50% (v/v) to induce protein binding. Samples were incubated for 10 minutes, then washed with 70% ethanol twice and further washed with 100% ethanol, using magnetic rack to separate beads from supernatant. Beads were then reconstituted in 100μL buffer (50mM HEPES pH 8.0) containing trypsin/LysC (Promega, #V5071), briefly sonicated, and incubated at 37°C for 14 hours. Supernatant was recovered at the end of incubation after brief sonication.

34 2.6.3. TMT labeling

Each 5mg vial of TMT 10-plex labeling kits (Pierce) was reconstituted in 500μL of acetonitrile and frozen at 80C. After thawing, TMT label was added to peptide in two steps 30 minutes apart to achieve a 2:1 label-to-peptide concentration at room temperature. Glycine was added to quench reaction. Labelled samples were concentrated using a SpeedVac, combined, and cleaned up using a SepPak catridge.

2.6.4. High Performance Liquid Chromatography (HPLC) Fractionation

An Agilent 1100 HPLC system equipped with a diode array detector (254, 260, 280nm) was used for high-pH reversed phase analysis, using a Kinetix EVO C18 column (Phenomenex) for fractionation. Fractions were collected every minute for a total of 48 fractions, and concatenated to a set of six. After removing acetonitrile with SpeedVac, the fractions were stage-tipped, dried and reconstituted for MS analysis in dH2O with 1% formic acid and 1% DMSO.

2.6.5. Mass spectrometry analysis

An Orbitrap Fusion Tribrid MS platform (control software version 2.1.1565.20) was used to analyze TMT-labeled peptide fractions as previously described178. Briefly, columns that were packed in house were used to trap a total volume of 10μL. C18 separation columns, also packed in house, were used for gradient elution of peptides. Data was acquired using a data-depended method, which included a survey scan to detect mass range of 350-1500, a MS2 scan for peptide identification, and a MS3 scan for fragment ions for relative quantitation.

35 2.6.6. Mass spectrometry data analysis

Mass spectrometry data were processed using Proteome Discoverer Software (2.1.1.21) as previously described179. Sequest HT was used to search MS2 spectra against UniProt human and E. coli proteome database with a list of common contaminants. Reporter ions from MS3 scans were quantified and output as the signal- to-noise ratio of TMT relative to Orbitrap preamplifier. False discovery was controled by filtering at the peptide spectra match-level using an adjusted p-value cutoff of 0.05 as determined by Percolator.

2.7. Western blot analyses

Cell or sEV pellets were lysed using RIPA (Santa Cruz) using the manufacturer’s protocol. Lysates were spun down at 13,000RPM for 10 minutes at 4°C to remove debris, and then quantitated using a Pierce BSA (Thermo Fisher 23227) protein assay. Gel electrophoresis was conducted using the Bolt (Thermo Fisher) system and transferred using Mini Trans-blot system (BioRad) onto PVDF membranes (Millepore). Gels were run under reducing condition unless CD63 detection was required. Transferred blots were blocked in 2% skim milk and incubated in 1:200–1:1000 dilution of primary antibody overnight at 4C with rocking. Blots were then washed three times in 1X PBS with 0.1% TWEEN 20 and incubated in corresponding secondary antibodies for 2 hours at room temperature with rocking. Signal was detected using a ChemiDox XRS+ (BioRad) and quantitated using Image Lab software (BioRad).

2.8. Cell growth and viability assays

2.8.1. Trypan blue exclusion assay

Adherent cells were trypsinized from culture vessels and pelleted. Cell pellets were resuspended in suitable volume of growth media. 0.4% Trypan blue stain at 1:1 (v/v) was added to cell suspension. 10l of mixture was loaded onto a cell-counting

36 chamber slide (Thermo Fisher C10228 or NanoEnTek EVS-050) and read on an automated cell counter (Countess, Thermo Fisher C10227 or EVE, NanoEnTek E1000).

2.8.2. Incucyte growth and viability assay

SK-BR-3 and MCF10A cells were seeded on 96-well plates with three technical replicate wells for each condition. 24 hours after seeding, the cells were treated with sEV from control and CQ-treated cells at two concentrations (low: equivalent of sEV in 1.5ml conditioned media; high: equivalent of sEV in 3ml conditioned media) and placed in IncuCyte Zoom (Essen Bioscience) for live image capture. Four images were taken per well every 4 hours using a 10x objective. Imaging data were analyzed as percent confluency using Incucyte Zoom software (2015A). Confluency data were normalized to initial confluency at t=0 and fitted to exponential (Mathusian) growth curves using

GraphPad Prism 8.1.0 for Windows (least squares regression, no weighing, Y0=1). Growth rate constants (k) were calculated from biological triplicates and compared by ordinary one-way ANOVA with Tukey multiple comparison correction.

To measure MDA-MB-231, SUM159PT, and Hs578T cell viability in serum-free media, cells were seeded in full growth media on 96-well plates. The next day, the media was removed and replaced with serum-free media with or without 10µM chloroquine. The total number of cells and the number of dead cells were detected using NucLight Rapid Red reagent (Essen BioScience 4717) and Sytox Green (Thermo S7020), respectively. Percentage cell viability was calculated as follows:

(푁푢푐퐿𝑖𝑔ℎ푡 푐표푢푛푡 − 푆푦푡표푥 푐표푢푛푡) × 100% 푁푢푐퐿𝑖𝑔ℎ푡 푐표푢푛푡

2.8.3. Clonogenic assay

TNBC cells were seeded on 6-well plates at the density of 3000 cells per well. Drugs were added the next day and incubated for 4 days, at which point the drug- containing media was replaced with drug-free fresh media and incubated for 5–7 days.

37 Plates were fixed with 4% paraformaldehyde and stained with 0.1% crystal violet to visualize and photograph colonies. Survival index was calculated by solubilizing crystal violet on the plate with 10% acetic acid and measurement of absorbance at 590nm.

2.8.4. Endothelial tube formation assay

50ul of growth factor-reduced Matrigel (Corning) was added to each well of a 96- well plate and allowed to solidify for 30 min at 37°C. HMEC-1 cells were cultured until 80% confluent and trypsinized. 10,000 HMEC-1 cells were seeded in tube formation media (DMEM supplemented with 1% FBS) into each well and photographed after 18 hours. Full HMEC-1 growth media was used as a positive control for tube formation. 2µl of isolated sEV (equivalent of sEV in 1.5ml conditioned media) from control or CQ treated cells were added to each well. Triplicate wells per condition were photographed at 4x magnification with a Canon Rebel T6 camera attached to an Axiovert 25 (Zeiss) inverted microscope. Images were analyzed using Angiogenesis Analyzer (Gilles Carpentier, 2012) for ImageJ (1.51q).

2.9. Immunofluorescence microscopy

Cells were grown on coverslips in 6-well plates and fixed with 4% neutral buffered formalin (NBF) for 10 minutes. Cells on coverslips were washed 3 times in PBS-T (1X PBS + 0.3% Triton-X) and permeabilized for 5 minutes in 0.5% Triton-X. For LC3B IF imaging only, cells were fixed with 20°C methanol on ice for 10 minutes and washed 3 times with PBS-T. Samples were then blocked for 1 hour in PBS-T with 2% bovine serum albumin (BSA), and incubated with primary antibodies at a dilution of 1:200 for 1 hour at room temperature. Samples were washed and incubated for 2 hours in 1:1000 secondary antibodies, 10 min in 5µg/ml DAPI, washed 3 times with PBS-T again and mounted with Mowiol.

Confocal images were acquired on a Nikon TiE microscope with a Nikon A1 plus camera (objective: 60X, numerical aperture: 1.45; refraction index: 1.51). DAPI and Alexa Fluor-conjutated secondary antibodies (488, 568, and 647) were excited with

38 lasers at 405nm, 488nm, 561nm and 640nm respectively. Images were analyzed with NIS-Elements Viewer (4.50).

Conventional fluorescent images were captured using an ApoTome system (Zeiss) with 40x or 63x objective. Images were taken with an AxioCam MRm (Zeiss) camera. Images were analyzed with ZEN 2 application (Zeiss).

2.10. Autophagic flux assays

2.10.1. Flux analysis with western blot

To measure autophagy flux, bafilomycin A1 (Baf) at saturating concentrations was incubated in cell culture media for 5 hours prior to collection. Baf prevents autophagosome-lysosome fusion and allows build-up of autophagosomes, the levels of which may be assessed by lipidated LC3B (LC3B-II) levels used to infer autophagy flux 38. Harvested cells were processed for western blot analyses as described in 2.7. Densitometry measurement of LC3B-II was normalized to a loading control protein (β- Actin, vinculin or β-tubulin) and compared between control and treatment groups.

2.10.2. Flux analysis with fluorescence microscopy

Cell lines stably expressing mRFP-GFP-LC3B (tandem fluorescent LC3B or tfLC3B) plasmid39 were created by transfection with Lipofectamine 2000 and selection with Geneticin (G418, Life Technologies). 20,000 cells were seeded in each wells of a CC2-coated chamber slides (Nalge Nunc) and treated with vehicle or drug for 48 hours. As a control, MDA-MB-231 and SUM159 cells were treated for 5 hours with15 or 25nmol/L bafilomycin A1, respectively, in order to demonstrate the accumulation under lysosomal inhibition. Cells were fixed in 4% PFA and mounted with SlowFade Gold Reagent (Invitrogen). Images were taken with a Zeiss Axioplan 2 microscope using Axio Vision software (Carl Zeiss, version 4.8.2.0). A 568nm or a 488nm laser was used to excite RFP or GFP, respectively. Quantitation of fluorescent puncta was performed manually for 100 cells in each condition.

2.11. Caspase activity assay

Caspase-Glo 3/7 Kit (Promega) was used to measure the activity of caspase-3/7 using manufacturer recommended protocols. Briefly, 8,000 MDA-MB-231 R8 or SUM159PT R75 cells were plated in each well of 96-well plates, and transfected with scramble control siRNA or siRNA targeting ATG5 or ATG7. Staurosporine or cycloheximide were used as a positive control for apoptosis. 90 hours after transfection, Caspase-Glo reagent was added to the plate. Luminescence was measured with a Synergy H4 Hybrid (BioTek) plate reader.

2.12. Flow cytometry analysis of DQ-BSA

MDA-MB-231 cells seeded on 6-well plates were treated with PBS or 10µM CQ in serum-free media for 48 hours. 16 hours prior to harvesting, DQ Red BSA (Thermo) was added to the media to a final concentration of 10µg/ml and allowed to incubate overnight. The next day, cells were trypsinized, washed and resuspended in Opti-MEM containing Sytox Green (Thermo Fisher). DQ Red BSA fluorescence was analyzed on a Fortessa flow cytometer (BD Biosciences) using the FACSDiva software (V6.2). All live cells negative for Sytox Green were used for subsequent data analysis with FlowJo 10.

2.13. Trypsin protection assay

sEV were collected from the conditioned media of ten 15cm plates of MDA-MB- 231 cells that were treated with PBS or 10µM CQ as described in 2.4. 60µl of sEV from each condition were split equally between 3 tubes (20µl each), where 20µl PBS, 20ul 100µg/ml trypsin (Sigma, T1426) in PBS or 20µl 200µg/ml trypsin and 2% Triton-X (Sigma, T8787) was added to each tube as described 181. After incubating on ice for 40 minutes with occasional flickering, the reactions were terminated by addition of 20µl 4X

40 LDS loading buffer (Thermo, B0007) with 2-mercaptoethanol and heating for 10 minutes at 80 degrees. 30µl of each sample was loaded per gel for western blotting analysis.

2.14. Bioinformatics analyses

2.14.1. Analysis of quantitative proteomics data

All bioinformatics data analysis was conducted within the R environment (V3.3.3)182. Abundance values of the identified unique peptides were median-centered across samples and used for downstream analyses. Differential protein abundance analysis between control and CQ-treated samples was performed on the peptide-level using PECA (V1.10.0)183, and p-value was computed from moderated t-statistics with multiple testing correction by Benjamini and Hochberg’s method. Proteins with at least 1.5-fold change and an adjusted p-value less than 0.05 between treated and untreated samples were termed differentially abundant proteins.

2.14.2. Enrichment analyses

GO terms overrepresented in EV relative to WC were identified through the PANTHER web interface184 (accessed August 26th, 2019; GO Ontology database Released 2019-07-03; ReferenceProteome Release 2019_4) using Fisher’s exact test and BH correction. KEGG pathway overrepresentation analysis was performed using ClusterProfiler185 (v3.2.14) with BH correction and pathway annotations from https://www.kegg.jp/ (accessed August 26th, 2019).

2.15. Data repositories

Methods and parameters for sEV isolation were deposited to EV-TRACK (Ref #EV190047). The mass spectrometry proteomics data was deposited to the

41 ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD015614.

2.16. Statistics

Unless otherwise noted, error bars in graphs represent standard deviation (SD) calculated from n≥3 biologically independent experiments. When comparing two groups, Student’s t-tests or paired t-tests were used. When comparing more than two groups, analysis of variance (ANOVA) was used with appropriate post-tests as indicated in the text or in the figure legends. A p-value or adjusted p-value less than 0.05 were considered statistically significant.

42 Chapter 3. Autophagy supports TNBC cell survival and chemotherapy resistance

Portions of this chapter were adapted from Chittaranjan, S. et al. (2014), Autophagy Inhibition Augments the Anticancer Effects of Epirubicin Treatment in Anthracycline-Sensitive and -Resistant Triple-Negative Breast Cancer. Clin. Cancer Res. 20, 3159–3173.

For the original manuscript, I conducted selected in vitro experiments with the help of Nancy Go. Select results from my experiments are shown in Figure 3.1 through Figure 3.8, with the exception of Table 3.1, Figure 3.2, Figure 3.4 and Figure 3.5, which were conducted by Svetlana Bortnik and Amy Leung. The resistant cell lines were developed by Suganthi Chittaranjan. The representative immunofluorescent microscopy images in Figure 3.1 and Figure 3.3 were taken by Lindsay DeVorkin. Animal experiments were conducted by Wieslawa Dragowska and Sherry Weppler in collaboration with Svetlana Bortnik.

3.1. Introduction

Triple-negative breast cancers (TNBC), defined by their lack of hormone receptors (ER, PR, ERBB2) expression, account for 10% to 15% of all breast cancers186. Chemotherapy such as anthracyclines and taxanes remain the standard systemic therapy for TNBC156. Consequently, the development of chemotherapy resistance represents a major challenge in the management of TNBC, underscoring the need for novel treatment options and ways to overcome resistance.

Autophagy has been described as having context-dependent roles in cancer. Autophagy safeguards genome stability and suppresses tumour initiation by serving housekeeping functions in normal cells. In established cancer cells however, autophagy supports tumour progression by enabling cell survival under hypoxia, starvation or chemotherapy treatments 187. Cellular stresses were shown to induce autophagy in cancer cells138,188,189, whereas autophagy inhibition had anti-cancer effects118,190.

43 Therefore, targeting autophagy appears a promising avenue to weaken cancer cells. Clinical trials using FDA-approved lysosomal inhibitor chloroquine (CQ) or hydroxychloroquine (HCQ) to modulate autophagy in various types of cancers have been ongoing176. Yet, the utility of autophagy inhibition in the treatment of TNBC has not been examined.

In this chapter, a series of in vitro experiments were conducted to investigate the cell-intrinsic roles of autophagy in TNBC cell survival and drug resistance. I explored the associations between autophagy flux and chemotherapy treatment, and between flux and chemotherapy resistance in cultured TNBC cells. Next, I examined the effectiveness of combining autophagy inhibition with chemotherapy. I provided in vitro evidence that autophagy plays a pro-survival role in TNBC cells, and that autophagy inhibition has potential therapeutic applications.

3.2. TNBC cells upregulate autophagy in response to epirubicin treatment

Anthracyclines are routinely used in systemic treatment of TNBC186. Epirubicin (EPI), a cytotoxic agent of the anthracycline family, was used to investigate the effects of anthracycline treatment in TNBC cells. To determine whether EPI treatment induced autophagy, MDA-MB-231 and SUM159PT cells stably expressing mRFP-GFP-LC3B (tfLC3B) were used for fluorescent microscopy-based flux assays (see Figure 1.3B). Stable tfLC3B-expressing cells treated with bafilomycin A1 (BAF) showed accumulation of RFP+GFP+ (yellow) autophagosomes (Figure 3.1), indicating intact autophagosome formation and blockage of autophagy flux by BAF as expected. Hence, the number of RFP-only (RFP+) puncta, which corresponded to the number of autolysosomes, was quantified as an indirect measurement of flux. After 48 hours of epirubicin treatment, a significant increase in the number of RFP+ puncta was observed in EPI-treated cells compared with control, suggesting upregulation of autophagy flux (p<0.0001, Figure 3.1).

Figure 3.1. Epirubicin treatment induces autophagy in TNBC cells as indicated by immunofluorescence assay. MDA-MB-231 and SUM159PT cells stably expressing the tfLC3B reporter were treated with epirubicin (EPI) for 48 hours. Red puncta (autolysosomes; indicated by arrows) per cell were manually counted for n=100 cells in control and treated conditions (as indicative of autophagy flux). Treatment with bafilomycin A1 (BAF) resulted in the accumulation of RFP+ GFP+ puncta (shown as yellow; indicated by arrowheads), which indicates intact autophagosome formation and blockage of autophagy flux. Significance was determined by Student’s t-test.

Western blot analyses also showed the induction of autophagy flux in TNBC cells following epirubicin treatment. Saturating concentrations of bafilomycin A1 (BAF) was used to inhibit lysosomal function, which produced autophagosome accumulation. The levels of LC3B bound to autophagosomal membrane (LC3B-II), proportional to the amount of autophagosomes, can be compared to detect changes in autophagy flux. An increase in autophagy flux was observed in MDA-MB-231 cells 6 hours and 6 days after epirubicin treatment, as indicated by increased LC3B-II levels in the presence of BAF (Figure 3.2). In SUM159PT cells, induction of autophagy was seen 24 hours and 5 days after EPI treatment (Figure 3.2).

Figure 3.2. Epirubicin treatment induces autophagy flux in TNBC cells as indicated by western blot flux assay. Epirubicin treatment induces autophagy flux in MDA-MB-231 and SUM159PT cells. MDA-MB-231 cells were treated with 100nmol/L epirubicin (EPI) for 6 hours or 6 days, whereas SUM159PT cells were treated with 400nmol/L epirubicin for 24 hours or 5 days. Bafilomycin A1 (BAF) was added to designated samples to block lysosomal function and allow accumulation of LC3B-II decorated autophagosomes. Representative western blots are shown. Bar graphs represent densitometry quantification of LC3B-II levels (normalizing to actin loading control) expressed as a ratio relative to the LC3B-II levels in untreated cells. Significance was determined by Student’s t- test between the groups indicated (n=3).

46 3.3. Anthracycline-resistant TNBC cells show increased basal autophagy flux

To facilitate the study of chemotherapy resistance in TNBC cells, S. Chittaranjan developed polyclonal epirubicin-resistant cell lines by exposing drug-sensitive TNBC cells to gradually increasing dosage of epirubicin over the course of 1 year (MDA-MB- 231) or 6 months (SUM159PT). The resulting resistant derivative lines R8 and R75, respectively, had drastically higher resistance to epirubicin as indicated by an 8-fold and

147-fold increase in IC50 compared to their respective parental lines (p<0.0001, Student’s t-test) (Table 3.1). Compared to their parental lines, R8 and R75 also showed increased resistance to doxorubicin (DOX) and mitoxantrone (MTX), which are anthracycline family chemotherapeutics (p<0.0001 for all drugs, Student’s t-test). Additionally, 80±3% R8 cells survived 7 days of 100nmol/L epirubicin treatment while less than 15% of MDA-MB-231 cells survived, providing further evidence that R8 had acquired anthracycline resistance.

Table 3.1. Epirubicin-resistant TNBC cell lines have markedly increased anthracycline resistance. Cell Line EPI IC50 (nmol/L) EPI IC50 95% CI (nmol/L) MDA-MB-231 515 389-683 R8 4272 3,155-5,783 SUM159PT 56 35-90 R75 8251 5,954-11,434

Cell Line DOX IC50 (nmol/L) DOX IC50 95% CI (nmol/L) MDA-MB-231 373 241-576 R8 786 357-1728 SUM159PT 69 54-87 R75 1348 471-3859

Cell Line MTX IC50 (nmol/L) MTX IC50 95% CI (nmol/L) MDA-MB-231 329 200-541 R8 1290 446-3733 SUM159PT 218 132-360 R75 1119 439-2856 CI: confidence interval. EPI: epirubicin. DOX: doxorubicin MTX: mitoxantrone

tfLC3B assays were conducted to compare the steady-state (basal) autophagy flux between parental and resistant lines. Epirubicin-resistance lines, R8 and R75, had

47 substantially increased number of RFP+ puncta compared to their parental counterparts, which suggested highly elevated basal autophagy flux (p<0.0001, Figure 3.3A). Similarly, western blot analyses showed higher levels of lipidated LC3B (LC3B-II) in R8 and R75 in the presence of bafilomycin A1 compared to their respective parental lines, confirming that the resistant TNBC cell lines have higher basal autophagy flux (Figure 3.3B).

Figure 3.3. Epirubicin-resistant TNBC cells have higher basal autophagy flux. A) Compared to respective epirubicin-sensitive parental lines, epirubicin-resistant lines R8 and R75 have higher basal autophagy flux as measured by tfLC3B assay. Red puncta per cell were manually counted for 100 cells per condition. B) Representative western blot of R8 and R75 cells showing increased LC3B-II in the presence of bafilomycin A1 (BAF) compared to respective parental cell line, suggest increased basal flux. Densitometry quantification of LC3B-II levels from 3 biological replicates (normalized to actin loading control) are shown as fold increase compared with LC3B-II levels in untreated parental cells. P: parental line. R: resistant line.

48 3.4. Autophagy inhibition improves anthracycline efficacy in TNBC cells

To establish whether autophagy inhibition affected TNBC cell survival, siRNAs targeting core autophagy proteins ATG5 or ATG7 were transfected into TNBC cells. Western blot assessment showed effective knockdown of ATG5 and ATG7 proteins by corresponding siRNA (Figure 3.4).

Figure 3.4. siRNA treatment reduces protein levels of ATG5 and ATG7. Treatment of cells with siRNAs targeting ATG5 or ATG7 reduces the level of corresponding proteins after 48 hours. Scr: scramble control siRNA. EPI: epirubicin. Representative blots from two independent experiments are shown. The levels of ATG5 and ATG7 proteins were quantified and normalized to actin loading control, and expressed as a fraction of the scr-siRNA condition.

The effect of autophagy inhibition on treatment efficacy was then tested by combining the siRNA-mediated knockdown of ATGs with epirubicin. Viability of TNBC

49 cells was measured with Trypan blue exclusion assays after the combined treatment of 25nmol/L epirubicin plus either scramble siRNA or siRNA targeting ATG5 or ATG7. The knockdown of either ATG significantly reduced the viability of MDA-MB-231 and SUM159PT cells compared to epirubicin scramble siRNA control (Figure 3.5), indicating that the knockdown of ATG5 or ATG7 sensitized TNBC cells to epirubicin treatment. Taken together, these results indicate that autophagy inhibition improves the efficacy of anthracycline treatment in TNBC cells.

Figure 3.5. siRNA knockdown of ATG5 or ATG7 further reduced viability in epirubicin-treated cells. MDA-MB-231 and SUM159PT cells were transfected with scramble (Scr) siRNA or siRNA targeting ATG5 or ATG7. In the case of ATG7 knockdown, concurrent transfection of ATG7-1 and ATG7-2 siRNAs were used. 24 hours after transfection, 25nM epirubicin (EPI) was added to appropriate samples and incubated for another 24 hours. Trypan blue exclusion assay was used to measure cell viability. Knocking down ATG5 or ATG7 in addition to epirubicin treatment further reduced TNBC cell viability compared with epirubicin alone. Significance was determined by ordinary one-way ANOVA with Holm-Sidak’s multiple comparisons tests (n=2).

Chloroquine (CQ) and hydroxychloroquine (HCQ) inhibit lysosomal function, and by extension, autophagy. To determine if pharmacological inhibition of autophagy also potentiated epirubicin-mediated cell killing, I examined the effects of combining CQ and epirubicin treatment on TNBC cells. Clonogenic assays were performed to assess cell survival and recovery after drug treatment. MDA-MB-231 cells were treated with drugs for 4 days and then allowed to recover in drug-free media. Consistent with genetic inhibition of autophagy, combination treatment of CQ and epirubicin significantly reduced the number of viable MDA-MB-231 cells compared to epirubicin alone (p<0.01, Figure

50 3.6A). Substituting epirubicin with doxorubicin, another anthracycline, produced similar results (p<0.002, Figure 3.6B), suggesting that CQ sensitizes MDA-MB-231 cells to anthracycline-mediated cell killing.

Figure 3.6. Chloroquine improves efficacy of chemotherapy against MDA-MB-231 cells. 3,000 MDA-MB-231 cells were seeded in each well of a 6-well plate and treated as shown. After 4 days, media containing drugs was removed and replaced with fresh media. After 5 days of recovery, cells were fixed with paraformaldehyde and visualized with crystal violet staining. Combination treatment of CQ with epirubicin (EPI; A) or doxorubicin (DOX; B) significantly reduced the number of viable cells compared with either anthracycline alone. Representative clonogenic assay plate is shown. Viability index was determined by solubilizing retained crystal violet stain and measuring the absorbance of the resulting solution at 590nm. Significance was determined by Student’s t-test from three biologically independent replicates.

51 3.5. Autophagy inhibition re-sensitizes drug-resistant TNBC cells to chemotherapy

To investigate the association between autophagy and cell survival in anthracycline-resistant TNBC cells, siRNA knockdown of ATG5 or ATG7 was used to disrupt autophagy in R8 and R75 cell lines. Reduction in ATG5 or ATG7 hampered autophagy flux in R8 cells, as evidenced by reduced levels of LC3B-II accumulation in the presence of bafilomycin A1 (BAF) compared to the scramble control (Figure 3.7A). Knockdown of ATG5 or ATG7 alone significantly reduced cell viability in R8 and R75 cells as measured by Trypan blue exclusion assays (Figure 3.7B), demonstrating that autophagy inhibition reduced the viability of anthracycline-resistant cells.

To determine whether apoptosis was induced by autophagy inhibition, a Caspase-Glo assay kit was used to measure the activity of executioner caspases, caspases-3 and caspase-7. At 90 hours post-transfection, increased caspase-3/7 activation was observed in R8 and R75 cells transfected with ATG5/7 siRNA compared with scramble siRNA control, indicating the induction of apoptosis (Figure 3.7C). Poly [ADP-ribose] polymerase (PARP) is a target of caspase 3/7. The presence of cleaved PARP detected by western blot in R8 and R75 cells treated with ATG5/7 siRNA also indicated the increase in caspase 3/7 activity (Figure 3.7C). Taken together, these data showed that the suppression of autophagy induced caspase-dependent cell death and reduced the viability of anthracycline-resistant TNBC cells.

Figure 3.7. Autophagy inhibition induces apoptosis in R8 and R75 cells. A) siRNA knockdown of ATG5 or ATG7 reduced autophagy flux in R8 cells. Reduced LC3B-II in the presence of bafilomycin A1 (BAF) in ATG5 or ATG7 knockdown signified reduced autophagosome formation. B) siRNA knockdown of ATG5 or ATG7 reduced cell viability in R8 and R75 cells, as measured by Trypan blue exclusion assay. Bar graphs represent summary statistics from three biologically independent experiments normalized to scramble control. Significance was determined by ordinary one-way ANOVA with Dunnett’s multiple comparisons tests. C) siRNA knockdown of ATG5 or ATG7 induced apoptosis in R8 and R75 cells. Caspase 3/7 activity was measured with Caspase-Glo kit as luminescence value and presented as normalized ratio. Cycloheximide (CHX) and staurosporine (Stauro) are known inducers of apoptosis used as positive controls. Bar graphs represent data from 6 replicates derived from 2 biologically independent experiments. Significance was determined by ordinary one-way ANOVA with Dunnett’s multiple comparisons tests. Cleavage of Poly (ADP-ribose) polymerase (PARP), another indication of apoptosis induction, was detected by western blotting. (*: p<0.05; **: p<0.01; ***: p<0.001; ****: p<0.0001)

Clonogenic assays were used to investigate whether CQ sensitizes R8 cells to epirubicin. R8 cells were able to recover from 50nM and 100nM epirubicin treatment and form colonies as expected (Figure 3.8). Consistent with previous observations where the

53 knockdown of ATG5 or ATG7 reduced R8 viability, treatment with CQ alone reduced the number of viable R8 cells (Figure 3.8). Furthermore, combining epirubicin treatment and CQ resulted in significantly reduced cell viability compared with epirubicin along (Figure 3.8). Taken together, these results suggest that autophagy inhibition can re-sensitize anthracycline-resistant TNBC cells to epirubicin treatment.

Figure 3.8. Chloroquine re-sensitizes chemotherapy-resistant cells to epirubicin. R8 cells were treated with epirubicin (50nM or 100nM) with or without 6µM chloroquine (CQ) for 5 days and then in grown in drug-free media for 5 days to recover. Crystal violet stain was added after paraformaldehyde fixation to visualize colony formation. Viability index was measured by resolubilizing retained crystal violet and measuring the absorbance of the resulting solution at 590nm. R8 cell treated with the combination of CQ and epirubicin showed significant reduction in viability compared with epirubicin alone. Bar graph represents quantitation of re-solubilized crystal violet stains using absorbance measurements, averaged over 3 biologically independent experiments. Significance was determined by ordinary one-way ANOVA with Holm-Sidak’s multiple comparisons tests.

3.6. Discussion

TNBC are a particularly aggressive subtype of breast cancers. Systemic treatments for TNBC have been largely limited to chemotherapies. Although TNBC tend to respond well to chemotherapies initially, the development of drug resistance later on poses major challenges in disease management156. Autophagy inhibition is an emerging anti-cancer treatment showing promising results in various preclinical models. In this chapter, anthracycline-sensitive and resistant cell lines were used to demonstrate that autophagy inhibition can improve the efficacy of anthracyclines in TNBC.

54 Our study implicated autophagy in the survival of TNBC cells, by demonstrating the reduction in cell viability under genetic and pharmacological autophagy inhibition. Remarkably, inhibition of autophagy alone resulted in the induction of apoptosis and loss of cell viability in R8 and R75 cells, suggesting that autophagy supports cell survival in these cells lines. We further demonstrated that autophagy inhibition can improve the efficacy of chemotherapy in TNBC cell lines, especially those that are sensitive to anthracycline treatment. In subsequent mouse studies, combination treatment of CQ and epirubicin significantly reduced the growth of MDA-MB-231 xenograft tumors compared with saline control or epirubicin alone191. Notably, the tumors in the combination treatment group never exceeded 1.5 times their original size, suggesting that autophagy inhibition to be effective against primary tumors. The combination treatment of CQ and epirubicin also significantly reduced the growth of R8 xenograft tumors, but to a lesser extent than observed in the MDA-MB-231 tumors191.

Our study also implicated autophagy in the development and maintenance of anthracycline resistance in TNBC cells. Epirubicin was found to induce autophagy in surviving TNBC cells, in agreement with previous reports where autophagy was induced in epirubicin-treated MCF-7 cells147 and doxorubicin-treated mouse fibroblast cells192. The exact molecular mechanisms involved in anthracycline-induced autophagy upregulation remain unknown. The cytotoxicity of anthracyclines results from multiple mechanisms, which include the production of reactive oxygen species (ROS) and DNA damage193. Hence, the induction of autophagy likely culminated from the signaling inputs of multiple cellular stress pathways. For instance, intracellular ROS is a known regulator of autophagy29. In addition, TNBC cells surviving the epirubicin treatment expressed increased levels of BECN1, LC3B, ATG4A, ATG4B, and ATG4D transcripts191. E2F transcription factor 1 (E2F1) was implicated in the transcriptional control of ULK1, ATG5 and LC3B in response to DNA damage194, making it a possible candidate that mediates anthracycline-induced autophagy. Similarly, transcription factor EB (TFEB) was shown to regulate the levels of ATG4, LC3 and promote cell survival under doxorubicin treatment195,196. Other downstream effects of DNA damage such as ER stress—the stress of unfolded proteins accumulating in the ER, which induces the unfolded protein response (UPR)197— may have also played a role in upregulating autophagy to enable cell survival28.

55 Chemotherapy-resistant R8 and R75 cell lines both had elevated basal autophagy flux compared with their parental lines. Consistent with our findings, other studies also reported increased basal autophagy flux in MCF-7 cells resistant to epirubicin147 or tamoxifen119. However, whether the elevation of basal autophagy flux contributes to chemotherapy resistance or is a result of developing chemotherapy resistance remains to be determined.

Consistent with findings where CQ-mediated autophagy inhibition overcame tamoxifen resistance in breast cancer cells119, or BRAF inhibition resistance in brain cancer cells198, CQ also re-sensitized anthracycline-resistant TNBC cells to epirubicin treatment. However, the sensitization effect of CQ was diminished in vivo. In mouse models, R8 xenograft tumors continued to grow in size despite combined HCQ and epirubicin treatment, in contrast with MDA-MB-231 tumors191. One possible explanation was that the elevated autophagy flux in resistant lines exceeded the inhibitory capacity of HCQ. In addition, autophagy-independent mechanisms were also likely in place that enabled the survival of resistant cell lines. Subsequent studies on R8 cells, for instance, uncovered elevated levels of multidrug-resistance (MDR) proteins compared to MDA- MB-231 cells (unpublished data), which likely confers drug resistance independent of autophagy. Therefore, development of more potent autophagy inhibitors and better understanding of the mechanisms of drug resistance are needed.

56 Chapter 4. Autophagy machinery contributes to small extracellular vesicle composition during lysosomal stress

Portions of Chapter 4 were submitted for publication as a research article titled “Chloroquine-mediated lysosomal inhibition alters composition and function of cancer- derived extracellular vesicles”. I conducted all of the experiments and data analyses with the exception of mass spectrometry, which was done by Shane Colborne, and bioinformatics analyses of mass spectrometry data, which was carried out by Kevin C. Yang.

4.1. Introduction

Hydroxychloroquine (HCQ) and chloroquine (CQ) belong to the family of four- aminoquinolines, historically used as antimalarials. Recent efforts sought to repurpose HCQ and CQ for cancer therapy due to their inhibitory effect on lysosomes and, by extension, autophagy122. Cytoprotective autophagy has been demonstrated in multiple types of cancer, precipitating the development of various autophagy inhibitors199. Multiple clinical trials are examining the safety and efficacy of CQ and HCQ in cancer treatment settings176, which recently stimulated renewed interest amidst promising reports200.

In the previous chapter, I provided evidence that autophagy inhibition with chloroquine (CQ) can reduce TNBC cell viability, which supports the testing of CQ in clinical settings for TNBC. However, the cell-extrinsic effects of CQ on TNBC remain largely unexplored. The endocytic pathway, which terminates in the lysosome, gives rise to small extracellular vesicles (sEV, previously exosomes) with vital signaling roles in cancer53,54. While lysosome dysfunction was found to induce packing and releasing of protein aggregates in sEV in neuronal contexts201,202, whether lysosomal inhibition affects the contents and function of cancer-derived sEV remains unexplored. Therefore, in this chapter, I investigate the effect of CQ on TNBC-derived sEV.

57 In this chapter, I collect and profile sEV from three widely-used TNBC cell lines grown with or without CQ. Utilizing a highly sensitive mass spectrometry approach to examine the proteome of MDA-MB-231 sEV, I discovered CQ induced enrichment of autophagy-related proteins in sEV. I also provide evidence that CQ treatment alters the function of sEV derived from TNBC cell lines. This study demonstrates the flexibility of sEV composition in response to perturbation of intracellular trafficking pathways, and has implications for lysosomal inhibition in therapeutic settings.

4.2. Chloroquine inhibits lysosomal function and autophagy

Since apoptotic bodies comprise a major source of contaminant in sEV isolations, treatment conditions that trigger excess cell death should be avoided 203. Therefore, trypan blue assays were routinely carried out at the time of sEV harvest to ensure donor cells met the viability threshold of 90%. To further rule out the possibility of detached dead cells not being detected by the trypan blue assay, automated time-lapse microscopy (IncuCyte) experiments were conducted over a period of 48 hours under sEV isolating conditions. Cell-permeable and impermeable nuclear dyes were used to mark total and dead cells, respectively. IncuCyte experiments demonstrated >90% viability was achieved in all conditions analyzed in three TNBC cell lines, despite marginal viability loss in MDA-MB-231 and SUM159PT cells (Figure 4.1).

Figure 4.1. 10µM CQ does not reduce TNBC cell viability below 90% at 48 hours. Three TNBC cell lines, MDA-MB-231, SUM159PT, and Hs578T, were seeded on 96-well plates. After 24 hours, the media was removed and replaced with serum-free media with or without 10µM chloroquine (CQ). IncuCyte zoom was used to monitor viability of TNBC cells, using NucLight Rapid Red to mark nuclei and Sytox Green to mark dead cells. The number of live cells was obtained by subtracting the number of Sytox-positive nuclei from the number of total NucLight- positive nuclei. Percent cell viability was calculated by dividing the total number of nuclei with the number of Sytox-negative nuclei. Cell viability remained above 90% throughout 48 hours and there was no significant difference in total cell number in absence or presence of CQ. Solid lines denote the means of three biological replicates where points denote the value of each replicate.

To ensure the concentration of CQ used in this study was sufficient to inhibit lysosomal function, a DQ-BSA assay was used to assess lysosomal degradation capacity. DQ-BSA is heavily labeled with BODIPY dyes such that it remains self- quenched in the absence of proteolysis. DQ-BSA is taken up by endocytosis and degraded in lysosomes, where fluorescent signals are emitted. DQ-Red BSA was incubated in control or CQ-treated cells overnight before flow cytometry analyses of red fluorescence. Compared with control cells, CQ treated MDA-MB-231 cells displayed significantly lower levels of DQ-Red BSA fluorescence, suggesting markedly reduced lysosomal degradative capacity (Figure 4.2A). A similar observation was also seen in Hs578T cells (Figure 4.2B).

Figure 4.2. Chloroquine inhibits lysosomal degradation. MDA-MB-231 and Hs578T cells were seeded on 6-well plates. 24 hours after plating, media was removed and replaced with serum-free media with or without 10µM chloroquine (CQ), and incubated for another 48 hours. 16 hours before flow cytometry analysis, DQ-BSA was added to each well to a final concentration of 10µg/ml. DQ-BSA red fluorescence, which is indicative of lysosomal function, showed significant reduction in CQ-treated A) MDA-MB-231 and B) Hs578T cells. Representative histogram of DQ-BSA fluorescence distribution (normalized to mode) is shown as measured by flow cytometry. Plot represents fluorescence measurement of three biological replicates (10,000 cells in each sample) where significance was determined by paired t- test (**: p<0.01). Error bars represents SD calculated from 3 independent experiments. AU: arbitrary units.

To verify that CQ treatment inhibited autophagy, immunoblot detection of LC3B and cargo adaptor p62 was used. Treatment with 10M CQ for 48 hours led to

60 significant accumulation of lipidated LC3B as well as adaptor protein p62 as expected, suggesting a block in autophagy flux (Figure 4.3).

Figure 4.3. Chloroquine blocks autophagy flux. TNBC cells were seeded on 6-well plates in full growth media. After 24 hours, cells were washed and incubated in serum-free media with or without 10µM CQ. 48 hours of CQ treatment induced accumulation of p62 and lipidated LC3B (lower band) in MDA-MB-231, SUM159PT and Hs578T cells. Plots represent normalized densitometry quantitation of three independent experiments, where significance was determined by paired t-test (*: p<0.05; **: p<0.01.). Error bars represent SD.

61 4.3. Chloroquine induces co-localization of autophagy and endolysosomal markers

Previously, LC3B and GABARAPs were shown to mediate endosome trafficking, indicating that they may also directly contribute to sEV biogenesis204,205. To visualize cell- intrinsic effects of CQ treatment, confocal microscopy was used to determine the localization of autophagy-related proteins. After 48 hours of CQ treatment in full growth media, enlarged vacuoles positive for both endosomal (CD63) and lysosomal (LAMP2) markers accumulated in the cytoplasm. LC3B and GABARAP localized to enlarged endolysosomes. Interestingly, a portion of endolysosomal LC3B appeared in a distinct and punctate pattern (Figure 4.4). The adaptor p62 was also found to co-localize with GABARAP on endolysosomes. Consistent with a previous report of LC3B lipidation on the perturbed endolysosomal membrane51, these results suggest a CQ-induced co- localization of LC3B, GABARAP and p62 to endolysosomes. (Figure 4.5).

63 Figure 4.4. CQ-induced punctate localization of LC3B on endosomes. MDA-MB-231 cells stably expressing a CD63-GFP reporter were either untreated (A, B, and C) or treated with 10µM CQ (D, E, F). CQ induced formation of enlarged endolysosomes and co- localization of LC3B (red) with endosomes labeled with CD63-GFP (D, E, F). LC3B localized to distinct patches of CD63-labeled endosomes, giving a punctate appearance. Nuclei were labeled with DAPI (blue). Images of the same cells were captured with Zeiss Apotome at various Z- positions, from a more basal position (A, D) to increasingly apical positions (B, C, E, and F). Scale bar represents 10µm.

65 Figure 4.5. Chloroquine induces co-localization of autophagy proteins with endolysosomal markers. MDA-MB-231 cells stably expressing CD63-GFP were grown on cover glasses in full media. After 48 hours of 10µM CQ treatment, cells were fixed and immunostained as indicated. Scale bars represent 20µm. Left) LC3B and GABARAP co-localize with endosomes labeled with CD63-GFP. Centre) LAMP2 and LC3B co-localize with endosomes labeled with CD63-GFP. Right) GABARAP and p62 co-localize with endosomes labeled with CD63-GFP. Intensity profiles along the indicated lines of a single z-section from A, B, and C are shown, where overlaps were interpreted as co-localization.

4.4. Lysosomal inhibition with chloroquine does not substantially alter bulk sEV profile

To begin the profiling of sEV, I collected sEV from conditioned media of MDA- MB-231, SUM159PT, and Hs578T cells. Cell culture media was subjected to sequential centrifugation (300Xg, 2,000Xg, and 10,000Xg twice) to remove larger vesicles, followed by high-molecular weight (100kDa) ultrafiltration and finally precipitation by ExoQuick™. Known sEV-associated proteins (CD63, TSG101, and Syntenin-1) were present in the sEV preparations from three TNBC cell lines, while ER marker IRE1a was absent. CD9 was present in the sEV of MDA-MB-231 and SUM159PT but not Hs578T. HSP90 and LAMP2, two proteins commonly associated with sEV, were detected at low levels (Figure 4.6).

Figure 4.6. Immunoblot characterization of TNBC-derived sEV. Protein concentrations of whole cell lysates (WC) and sEV lysates were quantified using a BCA assay. Equal amount of protein was loaded in each lane. Samples from the same experiments were run in parallel blots. Known sEV markers TSG101, Syntenin-1, CD9, and CD63 were present in TNBC-derived sEV. HSP90 and LAMP2 were detectable at low levels. ER marker IRE1a was not detected in sEV samples.

CQ treatment did not substantially alter most aspects of sEV profile. Corrected for the number of donor cells, CQ did not significantly alter the total amount of proteins in TNBC-derived sEV (Figure 4.7A), although the protein yield of MDA-MB-231 and Hs578T sEV showed an increasing trend after CQ treatment. In MDA-MB-231 and Hs578T cells, nanoparticle tracking analysis (NTA) showed no statistically significant differences between the number of sEV released per million control and CQ-treated cells. SUM159PT cells produced fewer sEV after CQ treatment (Figure 4.7A, p<0.01). NTA also detected very similar size distributions between sEV from control and CQ- treated TNBC cell lines (Figure 4.7B), where no significant alterations were observed in mean or modal sEV sizes despite a slightly reduction of modal sEV size (Figure 4.7C,

67 paired t-test). Transmission electron microscopy (TEM) confirmed the presence of sEV in both control and CQ-treatment conditions (Figure 4.7D). While the kinetics of sEV production and uptake under lysosomal inhibition cannot be ascertained, these findings suggest that CQ did not have pronounced effects on the physical profile of TNBC- derived sEV.

Figure 4.7. CQ does not alter bulk sEV profile. A) Total protein content measured by BCA assay and particle count measured by NanoSight. Measurements were normalized to cell number at the time of sEV collection. Significance was determined by paired t-test. (**: p<0.01) Error bars represents SD calculated from three independent experiments. B) Size distribution of sEV measured by nanoparticle tracking analyses (NTA). Solid lines represent average of three independent experiments while dotted lines represent SD. C) CQ treatment did not significantly alter mean or modal size of TNBC-derived sEV. Error bars represent SD. Significance was determined by paired t-test from three independent experiments. D) Transmission electron microscopy (TEM) visualization of sEV

68 morphology. Cup-shaped structures at size range corresponding to sEV measurements in B were shown. Scale bar represents 200nm.

4.5. Chloroquine alters sEV proteomic profile

To determine whether CQ treatment altered the protein cargo of sEV, we conducted a highly sensitive quantitative proteomics analysis of MDA-MB-231-derived sEV. Whole cell (WC) and corresponding sEV fractions from control and CQ-treated MDA-MB-231 cells were analyzed in biological triplicates. Protein isolated from each sample was subjected to SP3 bead cleanup and isobaric tandem-mass tag (TMT) labeling prior to three stage mass spectrometry (MS/MS/MS; MS3) analysis 179. 8512 and 4302 proteins were identified in the WC and sEV samples, respectively, that contained at least one unique peptide in all biological replicates. GO term enrichment analyses found significant enrichment of sEV-related cellular component terms and depletion of mitochondria in the sEV fraction as expected (Figure 4.8).

Figure 4.8. Gene ontology term analyses of MDA-MB-231 sEV. Proteins identified from sEV fractions showed enrichment of proteins associated with exosomes, cytosol and vesicles, while proteins typically associated with mitochondria were de-enriched. All proteins identified from sEV fractions were used in gene ontology (GO) enrichment analysis to identify enriched cellular components terms relative to the total proteome identified from WC fractions. The top 10 enriched and de-enriched GO terms are shown.

Differential abundance analysis of CQ-treated versus untreated samples using probe-level expression change average (PECA)206 revealed that CQ treatment affected a greater proportion of proteins in the sEV fractions compared to the WC fractions (p<0.0001; two-sample Kolmogorov-Smirnov’s test). In addition, a greater number of differentially abundant proteins, defined using a cut-off of 1.5-fold change and a false discovery rate (FDR)-adjusted p value of 0.05, were identified in sEV (189; 4.4% of 4302 identified proteins) than in WC (178; 2.1% of 8512 identified proteins) (p<0.0001; chi- square test, Table B1) (Figure 4.9).

Figure 4.9. CQ treatment affects a greater proportion of EV proteome compared with whole cell proteome. Density plot showing the CQ-induced log2-fold changes (Log2FC) of proteins identified in sEV (brown) or WC (gray) fractions. Dashed lines indicated the mean values of protein Log2FC of the two datasets, which were close to zero. Dotted lines represent absolute log2-fold change cut-off of 0.584 (1.5 fold change).

Consistent with the accumulation of endosomes seen in Figure 4.5, the abundance of endosome-associated sEV markers was significantly increased in the whole cell fraction (Figure 4.10A). In the sEV fraction however, only Syntenin-1 level was significantly increased (Figure 4.10B).

Figure 4.10. CQ treatment differentially affects the levels of sEV-associated proteins in whole cell and sEV fraction. CQ treatment slightly increased the abundance of select sEV markers in A) whole-cell but not in B) sEV fraction. Boxplots illustrate the relative abundance of each unique peptide identified in untreated and CQ-treated samples. Each point corresponds to a unique peptide, and the unique peptides from paired samples are connected by a dotted line. Centre line represents median. Box limits represent upper and lower quartiles.

KEGG pathway enrichment analysis showed CQ-induced enrichment of proteins involved in autophagy-related pathways in both whole cell and sEV fractions (Table B2). While the increase in autophagy-related proteins in the cell was consistent with the effects of CQ-mediated lysosomal and autophagy inhibition207, their enrichment in the sEV fraction following CQ treatment was less well-known and therefore selected for further investigation.

4.6. Chloroquine induces accumulation of mammalian ATG8 homologs in the cytoplasm and sEV

ATG8s are ubiquitin-like proteins that can be conjugated to autophagosomal membranes during the autophagy process. ATG8 adaptor proteins can interact with ATG8s through a LC3-interacting region (LIR) motif, which facilitates incorporation of

72 specific adaptors and their cargos into the autophagosome34. In the proteomic dataset, CQ-induced increase of mammalian ATG8 homologues was apparent in both WC and sEV fractions (Table B1), as were the levels of ATG8 adaptor proteins that facilitate selective autophagy of cytoplasmic contents (NBR1, TAX1BP1, CALCOCO2, and NCOA436,208–210 (Figure 4.11).

Figure 4.11. CQ enriches ATG8 homologs and adaptor proteins in sEV. Mammalian ATG8s and autophagy adaptor proteins are among the top proteins that increased in relative abundance after CQ treatment in both sEV and WC fractions. CQ-induced protein fold changes (log2-scale; Log2FC) in whole cell (WC; X-axis) vs in sEV (Y-axis) are shown as a scatter plot where each dot represents a protein identified in the proteome of both WC and sEV. Threshold for defining significance was set at an adjusted p-value of 0.05 and an absolute log2- fold change of 0.584 (1.5 fold change, dotted rectangle). Proteins meeting the fold-change cutoff in both WC and EV datasets are shown as large opaque dots, whereas proteins meeting the cut- off in only one dataset are shown as semi-transparent dots. Each dot is colored according to its direction of fold change in both WC and sEV. Proteins of interest, including mammalian ATG8s and select autophagy adaptors, are labeled.

The MAP1LC3 family of mammalian ATG8 homologues shares highly similar peptide sequences. The peptide sequences of MAP1LC3B (LC3B) and MAP1LC3B2

73 (LC3B2), in particular, only differ by one amino acid and are hence challenging to differentiate (Figure B2). We identified CQ-induced increases in the abundance of LC3A and LC3B/B2 peptides in the WC dataset. In the sEV MS dataset, we found increased abundance of a peptide that can map to MAP1LC3A (LC3A), LC3B or LC3B2 (Figure 4.12A, Table B1). Subsequent western blot confirmed the presence of both LC3A and LC3B in sEV (Figure 4.12B). Together, these data demonstrated the presence of both the LC3 and the GABARAP family ATG8 proteins and their adaptors in sEV, hinting at the involvement of a process similar to selective autophagy during sEV biogenesis.

Figure 4.12. MAP1LC3 family proteins were present in sEV. A) CQ treatment increased the abundance of peptides mapped to MAP1LC3 family proteins in WC and sEV fractions. Boxplots illustrate the relative abundance (log2-scale) of each peptide from all three biological replicates. Shown are all identified peptides that may be mapped to MAP1LC3 family proteins in WC or sEV. B) MAP1LC3A (LC3A) and MAP1LC3B (LC3B) are both present in the sEV fraction. sEV samples from control and CQ-treated cells were run on parallel blots and probed for LC3A and LC3B. TSG101 was used as internal control.

4.7. Chloroquine enriches ATG8 homologs in sEV lumen

To validate the proteomics findings and determine the topology of ATG8s and related proteins in sEV, a protease protection assay was conducted to rule out the possibility that ATG8s were contaminants co-isolated with sEV instead of sEV cargo. MDA-MB-231-derived sEV were treated either with PBS, 100µg/ml trypsin, or 100µg/ml

74 trypsin plus 1% Triton-X (Figure 4.13), under the assumption that sEV membrane protected luminal cargo proteins from trypsin unless detergent was added. Equal volumes of sEV from control and CQ-treated cells were used in each treatment condition.

Figure 4.13. Schematic representation of a trypsin protection assay. Lipid membranes of sEV protect luminal proteins from trypsin digestion. Membrane disruption with detergent (Triton-X/TX-100) exposes luminal proteins to trypsin digestion.

TSG101 was used as an internal control for the protection assay due to its known luminal localization and stable expression level in sEV despite CQ treatment (Figure 4.10B). LC3B, GABARAP and GABARAPL2 were protected from trypsin digestion in the absence of detergent, which suggested predominant luminal localization (Figure 4.14A). HSP90 however was not protected from trypsin digestion (Figure 4.14A). These ATG8 homologs are also more resistant to trypsin digestion compared with TSG101, likely due to small size or close proximity to the sEV membrane. Furthermore, when normalized to the levels of TSG101 in corresponding samples, the levels of trypsin-resistant Syntenin- 1, LC3B, GABARAP and GABARAPL2 were markedly increased after CQ treatment (Figure 4.14B, p=0.03, p=0.02, p=0.04, p=0.02, respectively), in agreement with mass spectrometry findings. Additionally, while the levels of total p62/SQSTM1 were comparable between sEV from control and CQ treated samples, sEV luminal p62 was also markedly increased after CQ treatment (Figure 4.14B, p=0.03). Therefore we concluded that LC3B, GABARAP, GABARAPL2 were present inside sEV, the levels of which were increased with CQ treatment and coincided with increased luminal p62 levels.

Figure 4.14. Chloroquine enriches sEV luminal ATG8s, Syntenin-1 and p62. A) LC3B, GABARAP and GABARAPL2 are located in the sEV lumen and protected from trypsin digestion. The majority of HSP90 was not protected from trypsin digestion. B) When normalized to trypsin-resistant TSG101 level, the levels of trypsin-resistant Syntenin-1, LC3B, p62, GABARAP and GABARAPL2 were increased after CQ treatment, suggesting enrichment of these proteins within sEV. Significance was determined by paired ratio t-test from three biological replicates (*: p<0.05). Error bars represent SD. Equal volume of sEV samples were loaded in each lane.

4.8. LC3B incorporation in sEV requires lipidation

Previously, LC3B was shown to form puncta and aggregates independent of autophagy211. To determine whether lipidation was required for incorporation of ATG8s in sEV, I generated ATG4B knockout cells using the CRISPR-Cas9 system212. In ATG4B-null MDA-MB-231 cells, LC3B fails to undergo priming and subsequent

76 conjugation to PE and therefore exists solely in its cytosolic form (Figure 4.15A, B). When treated with CQ, LC3B remained diffused in the cytoplasm of ATG4B KO cells, unlike the punctated (autophagosomal) or endosome-associated localization in wildtype cells, indicating defective LC3B membrane association (Figure 4.15C).

Figure 4.15. ATG4B is required for LC3B lipidation. A) Schematic of LC3B processing: pro-LC3B requires priming by ATG4B before subsequent conjugation to phosphatidylethanolamine (PE). B) CRISPR-mediated knockout (KO) of ATGs in MDA-MB-231. Clonal KO lines were established following transient transfection of px459 plasmid carrying single guide RNAs targeting ATGs into MDA-MB-231 lines. ATG4B knockout resulted in failure of LC3B but not GABARAP priming, hence the presence of only pro-LC3B, which runs between LC3B (top band) and LC3B-PE (bottom band). KO lines of LC3B (LKO), GABARAP (GKO) and LC3B, GABARAP double KO (DKO) were also established as shown. C) ATG4B knockout abolishes LC3B lipidation and membrane association. Under CQ treatment, LC3B in wildtype MDA-MB-231 cells display punctated (membrane bound) localization. CQ treatment did not induce LC3B puncta formation in ATG4B KO cells, in accordance with lack of ATG4B- mediated LC3B priming and subsequent failure of PE conjugation.

77 ATG4B knockout did not prevent the release of TSG101 or Syntenin-1 in sEV, suggesting that LC3B lipidation was not required for sEV biogenesis (Figure 4.16). However, LC3B was absent from the sEV of ATG4B knockout cells even under CQ treatment (Figure 4.16), suggesting that membrane association was required for LC3B incorporation into sEV.

Figure 4.16. LC3B lipidation is required for inclusion into sEV. ATG4B or LC3B KO did not prevent sEV-mediated release of Sytenin-1 or TSG101. ATG4B KO cells that lack LC3B lipidation produced sEV lacking LC3B.

4.9. Chloroquine induces accumulation of ATG8 homologs in specific sEV subtype

I next asked whether ATG8-positive vesicles constitute a distinct sEV population as recently reported91. Magnetic beads conjugated with CD63 or CD9 antibodies were used to capture sEV from MDA-MB-231 conditioned media that was pre-cleared by sequential centrifugation and concentrated by ultrafiltration. Both CD63 and CD9 immunoprecipitation (IP) captured sEV that contained CD9, TSG101 and Syntenin-1 but not HSP90 (Figure 4.17A). GABARAP and LC3B were markedly enriched in CD63-high sEV, with a further increase in levels after CQ treatment (Figure 4.17B, p=0.03, p=0.01 respectively). These results suggest that CQ treatment selectively induced LC3B and GABARAP enrichment in CD63-high sEV.

Figure 4.17. CQ treatment selectively enriches LC3B and GABARAP in CD63-high sEV. A) CD63 and CD9 antibody-bound Dynabeads were used in immunoaffinity capture of pre- concentrated sEV. Both CD63 and CD9 IP captured TSG101, Syntenin-1 and CD9. B) Treatment with CQ increased levels of LC3B and GABARAP in CD63-high sEV when normalized to TSG101 levels as determined by ratio paired t-test (*: p<0.05). Error bars represent SD calculated from three independent experiments.

79 4.10. Chloroquine induces accumulation of poly- ubiquitinated proteins in sEV

Since chloroquine treatment impedes lysosomal function and autophagic protein degradation, and enriches selective autophagy apparatus in sEV, I next determined whether proteins can be rerouted instead to sEV for disposal. Selective autophagy is known to utilize ATG8-interacting adaptors to facilitate capture of poly-ubiquitinated cargos for autophagic degradation213. K48 linkage-specific poly-ubiquitination (K48-Ub) was shown as a signal for degradation via the proteasome and selective autophagy214. Trypsin-protection assays indicated the presence of K48 linkage-specific poly- ubiquitinated proteins inside MDA-MB-231 sEV. When normalized to the levels of trypsin-resistant TSG101, CQ treatment increased the relative levels of K48-Ub inside sEV (Figure 4.18, p=0.04). Together, these results point to the enhanced incorporation of degradative cargos into sEV under lysosomal inhibition.

Figure 4.18. CQ induces accumulation of K48 linkage-specific poly-ubiquitination in sEV. Trypsin-resistant K48 linkage-specific poly-ubiquitinated proteins (K48 Ub) were increased in sEV after CQ treatment. The amount of trypsin-resistant K48 was quantified using TSG101 as a control. Significance was determined by paired ratio t-test (*: p<0.05). Error bars represent SD.

80 4.11. CQ alters biological function of TNBC-derived sEV

Because cancer-derived sEV have known signaling functions126, I next asked whether CQ treatment of TNBC cells affects sEV functions. I first determined whether sEV were taken up by cultured cells. sEV were collected from MDA-MB-231 cells stably expressing a CD63-GFP construct and added to cultured HMEC-1 (normal endothelial) cells. After 4 hours of incubation, GFP signal was observed in HMEC-1 cells (Figure 4.19), indicating that sEV were indeed taken up by recipient cells.

Figure 4.19. TNBC-derived sEVs are taken up by HMEC-1 cells. sEV harvested from MDA-MB-231 cells stably expressing CD63-GFP were added to HMEC-1 cell culture media. After 4 hours, GFP fluorescence can be observed in recipient cells, showing that sEV were taken up. Scale bars represent 20µm.

81 To investigate the effects of CQ on sEV function, sEV from control and CQ- treated cells (EV and CQEV, respectively) were incubated with MCF10A, a normal-like epithelial cell line. Growth rate constants (k) of recipient cell lines were calculated by exponential curve fitting of cell confluency measurements over time. The addition of MDA-MB-231 derived sEV decreased growth rate of MCF10A cells in a dose-dependent manner (Figure 4.20). Additionally, CQEV was significantly more growth-suppressive compared with EV at both low and high concentrations (Figure 4.20; p=0.03, p<0.001, respectively).

Similar results were found with sEV derived from SUM159PT and Hs578T as well (Figure 4.21), suggesting that CQ treatment alters growth-promoting potential of sEV.

Figure 4.20. Dose and context dependent effects of MDA-MB-231 derived sEV. MDA-MB-231 derived sEV were added to culture media of MCF10A cells at low or high concentrations (equivalent of sEV in 1.5ml and 3ml of conditioned media, respectively). sEV derived from MDA-MB-231 decreased growth rate of MCF10A cells. High concentrations of sEV were more growth-suppressive. Confluency of recipient cells were measured over time and fitted to exponential growth curves. Growth curve error bars represent SD of normalized confluency value; error bars on growth rate constant plots represent SEM. Significance was determined by ordinary one-way ANOVA with Tukey’s multiple comparison tests in three biological replicates (*: p<0.05; ***: p<0.001). NT: not treated. EV: sEV from control cells. CQEV: sEV from cells treated with 10µM CQ.

Figure 4.21. CQ alters growth effects of sEV from SUM159 and Hs578T cells. sEVs were added to culture media at low or high concentrations (equivalent of sEVs in 1.5ml and 3ml of conditioned media, respectively). Exponential growth curves were generated by fitting to recipient cell confluency data over time. sEV from SUM159 and Hs578T increase growth rate of SK-BR-3 and decreased growth of MCF10A cells. Growth curve error bars represent SD of normalized confluency value; error bars on growth rate constant plots represent SEM. Significance was determined by ordinary one-way ANOVA with Tukey’s multiple comparison tests over 3 biological replicates (**: p<0.01; ***: p<0.001). A) SUM159-derived sEVs reduced MCF10A growth rate. B) Hs578T-derived sEVs reduced MCF10A growth rate. NT: not treated. EV: sEV from control cells. CQEV: sEV from cells treated with 10µM CQ.

Previously CQ was reported to stabilize tumour vasculature177. I investigated whether this effect may be attributed to, at least in part, cancer cell-derived sEV. A tube formation assay was conducted to measure the angiogenic ability of HMEC-1 cells with or without sEV treatment. Addition of sEV from control MDA-MB-231 cells induced tube formation in HMEC-1, as measured by significantly increased total tube length compared to basal media control (p=0.03). However, sEV from CQ-treated MDA-MB-231 cells did not have a significant effect on total tube length compared to negative control (Figure 4.22). From these observations, I conclude that the treatment of TNBC cell lines with CQ alters the angiogenic ability of sEV produced.

Figure 4.22. CQ treatment alters angiogenic ability of MDA-MB-231 sEV. While full media (FM) induced HMEC-1 tube formation, basal media (BM) did not. Addition of control sEV from MDA-MB-231 significantly increased tube formation while addition of sEV from CQ treated cells (cqEV) did not. Significance was determined by RM one-way ANOVA with Bonferroni’s multiple comparison tests. (*: p<0.05; **: p<0.01). Error bars represent SD calculated from four biological replicates.

4.12. Discussion

While it is understood that the signaling effect and molecular cargo of sEV can be context dependent, the regulatory mechanisms involved remain poorly characterized. We conducted an unbiased quantitative proteomic profiling to comprehensively characterize the effect of lysosomal inhibition, and detected increased levels of multiple mammalian ATG8s and their adaptor proteins in sEV. I further validated our mass spectrometry findings and determined the localization of LC3B and GABARAP to be in the sEV lumen. Furthermore, I identified the CQ-mediated enrichment of sEV ATG8 homologs to be largely restricted to the CD63-high sEV subset. Our work is the first to clearly establish that lysosomal inhibition differentially affects heterogeneous sEV subpopulations. We further established the functional consequences of lysosomal inhibition on sEV, highlighting the complexity and context dependency of sEV signaling, which was affected by the state of donor cells, the type of recipient cells as well as the concentration of sEV. Our data are consistent with a model where highly regulated packing governs sEV composition and function. The substantial increase in the level of

84 ATG8s in sEV represents a specific response to lysosomal inhibition. Previous literature in neurodegeneration support the notion that lysosomal status may affect sEV biogenesis215,216, suggesting a conserved response not limited to the context of transformed cells. Together with our functional data and the detection of elevated K48- Ub levels, these observations lead us to propose a model whereby autophagy receptors and their adaptor proteins are repurposed for packaging of degradative cargos into sEV when the autophagy-lysosome route is blocked. The preferential loading of degradative cargos into sEV under these conditions effectively reduces signaling cargos, serving to dampen EV-mediated signaling functions like growth promotion or angiogenesis. The negative correlation between the growth-promoting effect of sEV and concentration of sEV used is also in line with this notion. Previously, endosome microautophagy was demonstrated to play a role in selective degradation and sEV-mediated release of cytosolic cargos71,217. Our study suggests that macroautophagy machinery may also perform a similar function.

This study shows that autophagy-dependent secretion of vesicular content does not constitute a novel sEV subtype. Instead, our findings and existing literature support a model where ATG8s are lipidated at CD63-positive endolysosomal compartments followed by sEV biogenesis105. Under lysosomal inhibition, ATG8s are conditionally incorporated into an sEV subpopulation that is high in CD63 and Syntenin-1, two known markers of bona fide exosomes. Previous observations of LC3, GABARAP and GABARAPL2 conjugation with phosphatidylserine (PS)218, which is enriched in the sEV of multiple cell types219, raises an interesting possibility of a novel regulatory mechanism that governs ATG8 lipidation. LC3B lipidation at endolysosomal membranes was also known to occur in the absence of autophagosome formation51 and on perturbed endosomes220, utilizing a specific ATG16L1 isoform221. Therefore, an endosome- microautophagy-like process involving ATG8s is possible, where an amphisome intermediate is not necessarily required for the release of sEV containing ATG8s. While a previous study with well-fed cells failed to detect LC3B in CD63 or CD9 mediated immunoaffinity sEV capture91, our results indicate that LC3B and GABARAP incorporation into sEV was enhanced significantly upon CQ treatment, and possibly amplified due to upregulation of basal autophagy under serum-free conditions. While the categorization of a new mechanism, worthy of further study, by which CQ may mediate CD63-high and ATG8 positive sEV as an exosome subclass remains open to debate,

85 our study clearly demonstrates that CQ-mediated lysosomal inhibition can have direct consequences on sEV composition and function.

86 Chapter 5. General Discussion

In this thesis I set out to characterize the cell-autonomous and non-cell- autonomous effects of autophagy inhibition in triple-negative breast cancer (TNBC) cells. The overarching purpose of this thesis was to better understand the roles of autophagy in cancer cells, and to determine whether autophagy inhibition can be potentially beneficial in the treatment of TNBC.

5.1. Cell-intrinsic roles of autophagy in TNBC cells

5.1.1. Study summary and significance

In the first part of my thesis, I investigated the relationship between autophagy and TNBC cell survival. I showed that epirubicin treatment upregulated autophagy using autophagy flux assays. TNBC cells that were able to survive epirubicin treatment also developed elevated levels of basal autophagy, suggesting that autophagy functions in a cytoprotective manner in TNBC. Inhibition of autophagy—either by the knockdown of core ATG or inhibition of lysosomal functions—reduced TNBC cell viability and triggered apoptosis in anthracycline-resistant cells. Inhibition of cytoprotective autophagy by lysosomotropic agents (CQ/HCQ) also potentiated the cytotoxicity of anthracycline agents, as evidenced by further reduction of cell viability in combination treatment compared to anthracyclines alone. This effect, however, was less pronounced in epirubicin-resistant lines, indicating the presence of autophagy- or lysosome- independent mechanisms of drug-resistance.

Together, these results clearly demonstrated that autophagy supports the survival of TNBC cells, and provided a rationale to inhibit autophagy in the treatment of TNBC, a conclusion supported by subsequent in vivo studies using cell line xenograft models191. These results also provided rationale to study the less well-known effects of lysosomal inhibition, should CQ be deployed for the treatment of cancers.

87 5.1.2. Limitations and future directions

In examining the cell-autonomous effects of autophagy inhibition, we must recognize any autophagy-independent effects of the treatments tested, and discern the effects of the impairment of a single ATG versus the entire autophagy process. Core ATGs including ATG5 and ATG7 are required for processes other than autophagy, including cell proliferation and unconventional secretion222. Additionally, reports of ATG5/ATG7-independent autophagy that does not rely on LC3B lipidation raises the possibility of undetected autophagy flux in ATG5/ATG7 knockdown cells223. Similarly, the anti-cancer effects of CQ—which ultimately is a lysosomal inhibitor—can be mediated by cell-intrinsic and cell-extrinsic mechanisms independent of autophagy. Lysosomal sequestration is a known mechanism of drug resistance, which prevents weak base chemotherapies from reaching their intended target: the nucleus224. Lysosomal inhibition with CQ could therefore interfere with drug sequestration in lysosomes, resulting in sensitization effects171. CQ was also shown to stabilize tumor vasculature, which reduced tumor hypoxia and improved drug delivery in vivo177. In my study, multiple approaches to autophagy inhibition yielded consistent results, which supported our interpretation that autophagy promotes TNBC cell survival. Our observations indicating that the knockdown of other core ATGs (such as ATG4B and ATG14, unpublished data) had less striking effects on TNBC cell viability hint at more unknowns in autophagy regulation that warrant future investigation.

The use of TNBC cell lines to study autophagy inhibition imparts a set of unique advantages and limitations. TNBC are a molecularly diverse group of cancers that can be further divided into subtypes based on molecular signatures165,166; even within the same tumor, vast clonal heterogeneity exists225,226. For these reasons, data gathered from three cell lines perhaps represent cell line-specific responses, rather than patterns of responses that can be generalized. Additionally, the activating Ras mutations harboured by MDA-MB-231 (KRASG13D) and SUM159PT (HRASG12D) may impose heightened metabolic demands and hence reliance on autophagy114, rendering these cell lines exceptionally sensitive to autophagy inhibition. Clinical relevance of this study may be improved through experiments with patient-derived organoid or xenografts models, and mouse models with intact immune systems.

88 The results presented in this thesis support the investigation of autophagy inhibition as a potential therapeutic option in some types of cancers—an idea that is not unequivocally endorsed. Opponents of autophagy inhibitors cite the potential anti-tumor roles of autophagy and side effects of systemic autophagy inhibition. For instance, in a KRASG12D-driven murine lung cancer model, loss of ATG5 increases tumor initiation, likely due to impaired early immune surveillance227. In addition, loss of p53 abrogates the survival advantages of mice harboring autophagy-deficient tumors in murine lung and pancreatic cancer models, suggesting the pro-cancer roles of autophagy may be context-dependent227,228. Furthermore, autophagy is also involved in the cross- presentation of tumor antigens229, stimulation of anti-cancer immune responses125, and promotion of programmed cell death under certain contexts230. The severe neurotoxicity experienced by ATG7 knock-out mice raises additional concerns regarding potential side effects of systemic autophagy inhibition231, although autophagy inhibitors as a treatment would likely be short-term and with far lower inhibitory efficiency.

Given the current knowledge and renewed interests in using autophagy inhibitors against cancers200, it’s perhaps more relevant to discuss where and how autophagy inhibitors should be deployed. Although the data presented in this thesis could not address the cell-extrinsic, anti-tumor roles of autophagy, an argument can be made for combining autophagy inhibition with other therapies in the subset of cancers that are particularly dependent on autophagy for survival or maintenance of resistance, such as the Ras-driven pancreatic cancers or the brain cancers resistant to BRAF inhibition174,198. Additionally, inhibition of cytoprotective autophagy may be more likely to succeed if cancer cells rely upon it to survive acute stress, as demonstrated by studies combining inhibition of mitogen-activated protein kinase (MAPK)-signaling and autophagy173,232,233. Overall, the costs and benefits of autophagy inhibition need to be carefully evaluated in the context of specific diseases. Therefore, predictive biomarkers for autophagy inhibition therapy would greatly benefit the design of optimal treatment strategies.

5.2. Cell-extrinsic roles of autophagy in TNBC cells

5.2.1. Study summary and significance

To investigate potential interactions between autophagy machinery and sEV biogenesis, I characterized the changes in sEV profile and composition under lysosomal

89 inhibition with CQ. While the physical profiles of bulk sEV showed few disturbances after CQ treatment, high-sensitivity proteomics detected changes in sEV protein composition, which included increased relative abundance of autophagy-related proteins. I proceeded to verify the mass spectrometry findings and identified CQ-induced enrichment of mammalian ATG8 homologs and cargo adaptor proteins in the lumen of sEV. A concurrent increase in K48 poly-ubiquitinated proteins was also detected inside sEV after CQ treatment, raising the possibility of an sEV-mediated excretion of degradative cargo. I further demonstrated via immunoprecipitation that this enrichment of ATG8s was restricted to a subset of sEV that were high in CD63 and Syntenin-1.

While the general consensus in the literature agrees on the crosstalk between autophagy and endocytosis, the links between autophagy and exosomes remain unclear and controversial. For instance, disruption of ATG5 was reported to either induce or inhibit exosome release97,100. Additionally, the route of egress for autophagy-related exosomes remains under debate. While some models suggest an amphisome-mediated exosome release100 (Figure 5.1B), others advocate a model where exosome biogenesis occurs independent of autophagosome formation but utilizes autophagy machinery105, as supported by reports of LC3B lipidation on single-membrane compartments directed by ATG16L150,220,221 (Figure 5.1A).

Moreover, whether the autophagy-related sEV comprise a type of exosome remains unclear. In a recent study, direct immunoprecipitation using endosome- associated tetraspanins (CD9, CD63, CD81) failed to capture LC3B, and fine iodixanol (OptiPrep) gradient density fractionation of sEV showed only partial overlap between LC3B and other exosomal markers91. Based on this evidence the study concluded that a distinct, non-exosome sEV population exists that contains autophagy components, at least under the steady-state conditions that were examined.

Figure 5.1. Potential models of autophagy-exosome crosstalk. Potential modes of interaction between autophagy and exosome biogenesis. A) LC3B lipidation may occur on single-membrane phagosomes and endosomes as dictated by ATG12-ATG5- ATG16L1 complex. B) Alternatively, autophagosomes carrying cytosolic cargo may fuse with MVB to form amphisomes. Through mechanisms yet to be determined, the cargo (in this case, Annexin A2) was released in exosomes.

The results in this thesis support the notion that autophagy machinery contributes to the composition of sEV under lysosomal stress, as mediated by the incorporation of selected ATG8s (LC3A, LC3B/LC3B2, GABARAP, GABARAPL2) and their cargo adaptors into sEV. The ATG-positive sEV did not comprise a novel population as previously suggested91. Rather, these ATG-positive sEV also were enriched for

91 endosomal markers, and therefore likely overlapped with the classically defined exosomes.

Our finding that autophagy proteins constitute a part of, and participate in the loading of sEV raises an exciting possibility: using circulating sEV to monitor autophagy flux, if the origin of sEV can be ascertained. Non-invasive measurement of autophagy flux remains extremely difficult if not impossible in large mammals and humans—a persistent inconvenience in the study of autophagy and autophagy inhibitors in vivo38. By profiling the sEV cargos (protein or RNA) under known autophagy-modulating conditions, it may be possible to detect expression patterns of sEV cargos that can be used to infer the levels of autophagy flux. Detection of autophagy flux in tumors, if possible, can also inform the choice of autophagy inhibitors in combination treatment setups.

5.2.2. Limitations and future directions: sEV biology

The analyses of any EV studies are confounded by EV isolation methods and resulting contaminants. More advanced sEV isolation methods would greatly benefit future studies. For example, the removal of bovine sEV from FBS without altering media composition was difficult, yet the use of serum-free media was not ideal due to the effects of serum-starvation on cellular energetics (see Appendix A). The increase in autophagy flux in response to serum-starvation may promote formation and subsequent degradation of amphisomes96, and thereby reduce the amount of exosomes released and alter the composition of sEV populations. For future studies, full culture media can be used in bioreactors that have physical separation between culture media and sEV collection media. While the isolation methods used in this thesis were able to reduce the presence of non-vesicular nanoparticles (exomeres) compared to ultrafiltration alone (see Appendix A), as evident from the low levels of HSPs, the extracellular matrix proteins detected by MS suggest that the sEV preparation contained non-vesicular entities. Because TMT mass spectrometry utilizes pooling of samples to amplify weak signals, it was not possible to quantify the amount of sEV-associated proteins versus those of contaminants such as ECM and apolipoproteins in each sample. Therefore, the proteomics portion of this study should be interpreted in the context of a comparative

92 analysis between the conditions involved, with the goal being detecting changes in relative protein abundance.

Lack of understanding of sEV subpopulations and specific markers also confounded the quantitative analyses of sEV cargo proteins. Proteomics analysis found the levels of TSG101 in the sEV fraction to be insensitive to CQ treatment (Figure 4.10). Therefore, TSG101 was chosen as the internal control for IP experiments, which assumes its level to be equal across sEV subpopulations. Although the ESCRT machinery (TSG101 is part of the ESCRT-I complex) plays a role in the biogenesis of multiple types of EVs63,234, this assumption remains to be further verified.

A previous proteomics study has identified differences in protein composition of sEV isolated by immunoprecipitation (IP) of surface tetraspanins, where CD9 IP recovered a larger number of proteins compared to CD81 or CD63 IP89. Based on this observation, the authors interpreted that CD63 decorated a more restricted sEV subset, whereas CD9 were present in multiple sEV subsets. This interpretation is consistent with my observation where CD63 IP captured relatively low levels of CD9 protein (Figure 4.17). An alternative explanation could be that CD9 and CD63 decorate separate sEV populations, which do not substantially overlap. Since CD9 was reported to localize predominantly to the plasma membrane235, I attempted immunoblot detection of known PM-derived sEV markers in MDA-MB-231 sEV91,92. Neither ANXA1 nor ARRDC1 could be detected in sEV samples, indicating that PM-derived sEV were not present in high amount in sEV preparations (unpublished data). Future studies are needed to discern sEV population composition. It would also be interesting to determine the changes in the cargos of various sEV subpopulations when the donor cells are exposed to various stressors. A quantitative mass spectrometry study could be conducted on sEV captured by CD63, CD9 and CD81 IP to compare the proteome profile of these sEV subpopulations.

The scope of this thesis did not extend beyond the investigation of sEV protein cargos, as the protocols used for sEV isolation were not optimized to study cargos other than proteins (see Appendix A). Existing literature suggests that the biological functions of sEV are partially mediated by micro-RNAs (miRNAs)130,153,236. Therefore, it is entirely possible that the functional changes observed in sEV under lysosomal inhibition may be due to differential miRNA loading. The same argument can be made with other sEV

93 cargos (other RNA species, DNA, and metabolites) and composition of sEV membrane lipids, all of which could potentially alter sEV uptake or function. Therefore, future studies are needed to clarify the impact of lysosomal inhibition on sEV membrane lipids and non-protein cargos.

Another question not addressed in this thesis was the kinetics of sEV release and uptake. The sEV collected represented the accumulated amount in the media during 48 hours of incubation, minus the amount that was taken up by the cells. Whether and how CQ affects sEV uptake remains unknown. Similarly, the effect of CQ treatment on the rate of sEV release could not be deduced from available data.

5.2.3. Limitations and future directions: autophagy-sEV crosstalk

In this thesis, I was not able to definitively show whether ATG-containing sEV originate from endosomal compartments or amphisomes. Means to reliably differentiate amphisomes from LC3B-positive endosomes would be needed, as both appear as large vesicles in the cytoplasm positive for both endosomal (CD63) and autophagosomal (LC3B) markers by immunofluorescence microscopy. Alternatively, knockout cell lines can be engineered that are deficient in autophagosome formation but not LC3B lipidation or endocytosis. A candidate knockout target is ATG13, the loss of which abolishes autophagosome formation but not LC3B lipidation51. Examination of sEV derived from ATG13 knockout cells for the presence of ATG8 homologs and adaptor proteins could determine whether autophagosome (and subsequently amphisome) formation was required for the incorporation of ATG8s into sEV, or whether incorporation of ATG8s and adaptor proteins is independent of autophagy flux.

In this thesis, I was also not able to determine the functional consequences of removing all ATG8s from sEV. Therefore, I could not conclude whether the presence of ATG8s in sEV was necessary for the loading of ATG8 adaptor proteins. Several attempts were made to remove ATG8 homologues from sEV. Preliminary experiments found GABARAP knockout lines to suffer significant cell viability loss under CQ treatment in serum-free media (Figure B1). Knockout of LC3B alone did not reduce the amount of K48-polyubiquitinated protein or p62 in sEV (Figure B2). Knockout of ATG4B

94 successfully prevented lipidation of LC3A and LC3B, but did not prevent the lipidation of GABARAPs (Figure B3). Experiments are under way to compare the levels of GABARAPs and other adaptor proteins in the sEV of control and ATG4B-null MDA-MB- 231 cells. A very recent report provided another possible approach: the α-isoform of ATG16L1 is sufficient for LC3B/GABARAP lipidation at the autophagosomal membrane, while its β-isoform, possessing an extra C-terminus region, is essential for LC3B lipidation at non-autophagosomal membranes221. Cancer cells deficient in ATG16L1β may be useful for examining the function of ATG8s in sEV biogenesis without affecting macroautophagy.

Going forward, xenografts or genetic mouse models can be used to investigate the contribution of autophagy machinery to the function of cancer-derived sEV in a more biologically meaningful context. Syngeneic mouse models with an intact immune system may be used to fully capture the interactions between tumour sEV and the host immune system. This thesis supports further investigations of lysosomal inhibitors in cancer treatment, which would inhibit autophagy and pro-cancer sEV signaling at the same time. However, reports of clinical trials involving HCQ has so far shown inconsistent results, which possibly stemmed from the inability of HCQ to completely inhibit autophagy flux, even at very high doses237. Therefore, for autophagy inhibition to succeed in clinical settings, it may be important to develop more potent inhibitors.

Finally, disentangling the relationship between the autophagy machinery and the rest of intracellular vesicular trafficking may provide insight into the evolution of membrane dynamics. Autophagy and endocytosis likely both existed in the last eukaryotic common ancestor (LECA). Almost all eukaryotic organisms studied to date contain some variants of endosomal and lysosomal compartments238. Major protein families involved in the endocytic process are speculated to have been present in the LECA239, including as many as 23 Rab GTPases240. Similarly, core autophagy machinery is ubiquitous among eukaryotic organisms241, and highly conserved across species whose genomes have been sequenced to date242. In addition, Rab GTPases that regulate autophagy and a primitive form of the PI3KIII complex are predicted to have existed in the LECA as well243,244. Fast forward to the present time, where autophagy and endocytosis still share many molecular components102,104. Previously, autophagosome biogenesis was thought to occur at ER-mitochondria contact sites245 or at the plasma membrane246. Recent findings of autophagosome assembly taking place on Rab11A-

95 positive endosomal compartments247 suggest that autophagy could have diverged from endocytosis-like processes in the LECA. The presence of ATG8s on the endolysosomal membrane therefore represents another link between autophagy and endocytosis that can shed light on the relationship between these processes.

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115 Appendix A. Considerations in sEV collection

The selection of sEV isolation protocols is not straightforward. While many studies have compared various isolation methods, the applicability of such comparisons is usually restricted to the system and specific application. More often than not, the controversy over sEV isolation method is simultaneously comical and frustrating in a way mimicking the preference of the terms “EV” or “exosome”248,249. Furthermore, the evolution of EV methodology often brings aspects of previous protocols into question. For example, ultracentrifugation of sEV-conditioned media was found to produce EV and protein aggregation250. For these reasons and others, it is vital to characterize and optimize sEV methods and explicitly acknowledge their limitations.

The ideal sEV collection method separates sEV using both size and density criteria, in order to minimize contaminants that are of the same size (exomeres or high- density lipoproteins) and the same density as sEV (larger EVs). The ideal method also balances the cost of time and reagents with the purity of sEV required for downstream applications. For instance, while multi-step ultracentrifugation with high-resolution density gradient separation appears to be most effective at density-based separation between sEV populations and between EV and non-vesicle particles, the resulting “pure” sEV population may not be appropriate or necessary for all types of downstream applications. In this section, I will discuss each of the sEV enrichment and isolation protocols that have been tested in this study to provide context for the method chosen.

Ultracentrifugation

Ultracentrifugation (UC) relies on the sedimentation of sEV by high g force (usually 100,000Xg), and has been the most popular method of sEV isolation to date. Larger vesicles are usually depleted prior to UC through sequential centrifugation at lower g forces, in a process known as pre-clearing. Due to the co-sedimentation of protein aggregates by UC, later protocols often incorporate either a sucrose cushion at the bottom of the tube or subsequent density gradient UC to separate sEV from contaminants. sEV pelleted from UC may be used in a top-down or bottom-up density

116 gradient to separate the desired population by sedimentation rate or floatation density, respectively.

While widely used, UC is time-consuming and requires consistent, lengthy access to an ultracentrifuge, which was difficult at the beginning of my project. Additionally, comparative studies conducted at the beginning of my project showed large between-batch variation in sEV protein content and particle count, and inconsistent yield (Figure A1). Part of this discrepancy was later understood to have arisen from the differences in k factors of the rotors used251. For these reasons, I explored additional sEV enrichment methods and compared them with UC in the initial phase of this study.

Figure A.1. Evaluation of ultracentrifugation-based sEV isolation with 30% sucrose cusion in D2O A) Size distribution of exosomes derived from two sEV samples were measured by nanoparticle tracking analysis (NTA) showed a peak at 141nm and 163nm. B) Bar chart showing the particle number per ml for both sEV isolates. C) Protein Concentration of sEVs derived from two sEV isolates. Data collected and analyzed by Dr. Elham Hosseini-Beheshti.

117 Ultrafiltration

Ultrafiltration (UF) can be used to enrich for sEV based on molecular weight. Centrifugal filters are commonly used due to ease of operation and relative low cost. Entities above the molecular weight cut-off will be retained while the others will flow through, irrespective of whether they are vesicular in nature. UF with high molecular weight cut-off is considered to be of intermediate specificity and intermediate yield among sEV isolation methods93. Initial characterization under the parameters of this study, however, showed enrichment of HSPs in UF samples compared to other methods (Figure A2), which were later understood to represent exomeres252. Additionally, filtering a large volume of albumin-containing media inevitably led to the retention of albumin and formation of a viscous liquid. Therefore, UF was not used as the sole method of sEV isolation but rather as one step in sEV the workflow to eliminate smaller or soluble contaminants and concentrate serum-free conditioned media.

Figure A2. Ultrafiltration enriches for heat-shock proteins Equal volume of sEV sample obtained by various enrichment methods were compared by immunoblotting for known sEV markers. Ultrafiltration (UF) produced high abundance of HSP90 and HSPA8 (HSP7C) and low amount of sEV marker TSG101 and membrane protein LC3B-II compared with precipitation-based methods applied after UF. EQ: ExoQuick. TE: Total Exosome reagent (Thermo Fisher).

118 ExoQuick

ExoQuick (EQ) is a trademarked polymer from Systems Biosciences that precipitates sEV and allows sedimentation at lower g forces. Although the manufacturer’s protocol indicates that EQ may be used directly on conditioned media with reasonable yield, initial characterization in this study found otherwise. First of all, EQ cannot be used with media containing serum albumin, as an enormous amount of albumin was co-precipitated. Secondly, using EQ on unconcentrated media was neither efficient nor cost-effective.

With the considerations above in mind, I developed a protocol where UF was first used to concentrate pre-cleared media, followed by sEV precipitation with EQ (Figure A3). Using this method, cytosolic contaminants (IRE1, GRP78) were undetectable in the sEV obtained, while the levels of HSPs (HSP90, HSP7C) were very low compared to whole cell lysate or UF alone. Total protein yield for UC and EQ averaged at 120ug and 77ug, respectively (n=2 for UC and n=3 for EQ) for 10X 15cm plates of untreated MDA- MB-231 cells, suggesting that EQ did not introduce large amounts of contaminants compared with UC. However, as proteomics results would indicate, extracellular contaminants such as ECM components and apolipoproteins were not completely eliminated. Finally, this sEV isolation method was not optimal for downstream analyses involving sEV-associated RNA, as it lacks a density-dependent isolation step that can separate vesicles from extracellular RNA-protein complexes.

Figure A3. Finalized sEV isolation workflow. sEV conditioned media was pre-cleared by sequential centrifugation at the g force shown. Pre- cleared media was then filtered and concentrated using a 100kDa cutoff centrifugal filter. ExoQuick was added to the concentrated media to precipitate sEVs.

119 FBS in conditioning media

Another consideration for sEV collection was whether FBS should be included in the conditioning media. FBS is known to contain bioactive sEV of bovine origin that remain in media despite overnight ultracentrifugation253. Ultrafiltration was reported as a possible way to deplete bovine EV from FBS-containing media254, yet ultrafiltration- based clearance of FBS-containing media resulted also in the removal of albumin1, thus altering media formulation. Serum starvation can alter metabolism and cellular response to drugs. In the case of TNBC cell lines, removal of serum from cell culture media increased the level of basal autophagy flux in MDA-MB-231 at 24, 48 and 72 hours (Figure A4), and in SUM159PT cells after 24 but not 48 hours (Figure A5). Induction of autophagy may promote formation and subsequent degradation of amphisomes96, thereby reducing the amount of exosomes released, and potentially altering composition of the sEV population.

Figure A4. Serum starvation induced autophagy in MDA-MB-231 Full growth media of MDA-MB-231 cells grown on 6-well plates was replaced with either full or serum-free media. At each time point, bafilomycin A1 (Baf) was added to corresponding wells to facilitate flux measurement. In the presence of Baf, serum-starved cells at 24, 48 and 72 hours all had higher LC3B-II level and thus higher autophagy flux. Representative blot shown from 3 biological replicates.

1 Manifested as viscous, amber-coloured goop unable to pass through the filter.

120

Figure A5. 48 hours of serum starvation did not increase autophagy flux in SUM159PT Full growth media of SUM159PT cells grown on 6-well plates was replaced with either full or serum-free media. At each time point, bafilomycin A1 (Baf) was added to corresponding wells to facilitate flux measurement. In the presence of Baf, serum-starved cells at 24 but not 48 hours showed increased LC3B-II level, indicating that autophagy flux was induced 24 hours after media change, but not at 48 hours. Representative blot shown from 2 biological replicates.

121 Appendix B. Supplementary Figures and Tables

Figure B1. GABARAP knockout reduces cell survival under CQ treatment in serum-free media (preliminary result) Wildtype (WT) MDA-MB-231, GABARAP knockout (GKO), and LC3B/GABARAP double knockout (DKO) cells were seeded on 96 well plates. After 24 hours, media was replaced with serum-free media with 10µM CQ. Cell viability was measured over time with Incucyte. At 48 hours, the viability of GABARAP KO and double KO cells was reduced by more than 20% (n=1).

Figure B2. Knockout of LC3B does not reduce p62 or K48 Ub in sEV sEV were collected from wildtype (WT) and LC3B knockout (LC3BKO) MDA-MB-231 cells treated with 10µM CQ as described. Trypsin protection assays were used to determine sEV protein localization. The levels of luminal (trypsin-resistant) proteins were normalized to TSG101. LC3B knockout did not significantly reduced sEV p62 or K48 Ub (n=3).

122

Figure B3. Effect of ATG4B knockout on ATG8 homologs in MDA-MB-231 CRISPR-mediated knockout of ATG4B prevented priming and subsequent lipidation of LC3A and LC3B. The lipidation of GABARAP or GABARAPL2 were not affected. Aliquots of the same samples were run on parallel blots. ND: not detected.

123

Figure B2: Protein sequence alignment of the human ATG8 homologues Protein sequences alignment of the four human MAP1LC3 family (LC3A, LC3B, LC3B2, and LC3C) and three GABARAP family proteins (GABARAP, GABARAPL1, and GABARAPL2) by CLUSTAL Omega (1.2.4) 255. LC3A, LC3B and LC3B2 sequences are highly similar. “*” denotes fully conserved residue. “:” (colon) and “.” (period) denote conservation between groups of strongly or weakly similar residues, respectively.

Table B1: CQ-induced significant changes in protein abundance of MDA-MB-231 derived sEV Protein log2FC t.stat score raw p-value p.FDR SLC39A1 1.79 7.44 1.4E-04 1.4E-04 2.4E-03 NDFIP1 1.57 7.41 1.4E-04 1.4E-04 2.4E-03 TMEM156 1.49 5.35 1.0E-03 1.0E-03 1.3E-02 TMEM165 1.44 7.53 1.2E-04 2.4E-06 7.3E-05 ATP2C1 1.40 4.80 1.9E-03 1.4E-04 2.4E-03 SPRY2 1.25 6.30 3.8E-04 3.8E-04 5.9E-03 SPOCD1 1.23 4.72 2.1E-03 2.1E-03 2.4E-02 LRP10 1.17 5.51 8.6E-04 8.6E-04 1.1E-02 NCOA4 1.15 5.28 1.1E-03 1.1E-03 1.4E-02 TGFBR2 1.13 5.25 1.1E-03 2.4E-07 9.2E-06 LAPTM5 1.07 4.19 4.0E-03 4.0E-03 3.8E-02 SLC35F2 1.02 4.19 4.0E-03 4.0E-03 3.8E-02

124 Protein log2FC t.stat score raw p-value p.FDR TM9SF2 1.01 3.99 5.1E-03 5.1E-03 4.5E-02 SLC38A2 1.01 5.22 1.2E-03 1.6E-08 7.7E-07 TM7SF3 1.00 5.52 8.5E-04 8.5E-04 1.1E-02 PLEKHB2 1.00 4.03 4.9E-03 4.9E-03 4.4E-02 LAMTOR1 1.00 5.85 6.0E-04 6.0E-04 8.6E-03 GABARAPL2 0.98 5.70 7.0E-04 7.0E-04 9.7E-03 MFSD1 0.98 5.68 7.1E-04 3.2E-05 7.0E-04 SLC20A1 0.97 4.15 4.1E-03 7.1E-07 2.5E-05 NBR1 0.94 5.14 1.3E-03 1.3E-03 1.6E-02 GABARAP 0.93 4.78 1.9E-03 1.9E-03 2.2E-02 SPTLC1 0.93 3.72 7.3E-03 1.6E-04 2.7E-03 TAX1BP1 0.89 3.96 5.3E-03 7.3E-11 6.2E-09 HRH1 0.88 4.53 2.6E-03 2.3E-04 3.7E-03 STAT2 0.87 3.22 1.4E-02 2.9E-03 3.0E-02 PTTG1IP 0.87 3.35 1.2E-02 2.2E-03 2.5E-02 ITM2B 0.86 4.90 1.7E-03 1.2E-04 2.1E-03 TGFBR1 0.86 4.77 2.0E-03 2.0E-03 2.2E-02 RNF149 0.81 3.59 8.7E-03 3.8E-05 8.1E-04 OASL 0.81 4.36 3.2E-03 3.2E-07 1.2E-05 SERINC1 0.80 4.15 4.2E-03 6.1E-06 1.6E-04 F2RL1 0.77 3.50 9.8E-03 1.6E-03 1.9E-02 MERTK 0.76 4.02 4.9E-03 4.9E-03 4.4E-02 GINM1 0.75 4.16 4.1E-03 4.4E-04 6.7E-03 MAPKAP1 0.75 3.93 5.5E-03 5.5E-03 4.8E-02 TNFRSF10A 0.75 4.34 3.3E-03 3.2E-04 5.1E-03 TSPAN3 0.75 4.10 4.5E-03 5.0E-04 7.5E-03 SLC2A1 0.71 3.43 1.1E-02 2.4E-06 7.3E-05 ALDH6A1 0.71 4.07 4.6E-03 4.6E-03 4.3E-02 BMPR1A 0.71 3.99 5.1E-03 5.1E-03 4.5E-02 CMIP 0.71 2.88 2.3E-02 1.2E-04 2.2E-03 MYO9B 0.70 3.35 1.2E-02 3.1E-08 1.4E-06 RPS24 0.69 3.42 1.1E-02 1.9E-03 2.2E-02 JAK1 0.68 3.97 5.2E-03 2.6E-08 1.2E-06 DAGLB 0.67 3.03 1.9E-02 4.4E-03 4.1E-02 COPZ1 0.66 2.91 2.2E-02 4.0E-04 6.1E-03 GPR116 0.65 3.25 1.4E-02 1.2E-04 2.1E-03 RAB1A 0.64 3.18 1.5E-02 6.8E-04 9.5E-03 RPS23 0.64 3.17 1.5E-02 3.6E-05 7.8E-04 SLC2A14 0.62 3.06 1.8E-02 4.1E-03 3.9E-02 RPL15 0.62 3.03 1.9E-02 1.7E-05 4.0E-04

125 Protein log2FC t.stat score raw p-value p.FDR MRTO4 0.61 2.22 6.1E-02 9.4E-04 1.2E-02 RPS8 0.61 3.09 1.7E-02 3.0E-06 8.7E-05 ELP3 0.59 3.14 1.6E-02 3.5E-03 3.4E-02 TVP23B 0.59 3.38 1.2E-02 2.1E-03 2.4E-02 GNB2L1 0.59 2.79 2.6E-02 4.0E-10 2.8E-08 SDCBP 0.59 2.43 4.5E-02 2.0E-05 4.5E-04 LTBP3 -0.59 -1.98 8.8E-02 4.1E-06 1.2E-04 LGMN -0.59 -3.40 1.1E-02 3.7E-04 5.7E-03 MAN2A1 -0.59 -2.45 4.4E-02 1.0E-08 5.1E-07 CTSF -0.60 -3.05 1.8E-02 2.3E-07 9.2E-06 DCBLD1 -0.60 -2.56 3.7E-02 1.7E-04 2.9E-03 HMCN1 -0.60 -2.71 3.0E-02 1.0E-08 5.1E-07 ADAMTS15 -0.61 -3.13 1.6E-02 5.1E-10 3.4E-08 VWF -0.61 -2.58 3.6E-02 4.4E-04 6.6E-03 TRIP11 -0.61 -2.51 4.0E-02 4.7E-03 4.3E-02 SEMA5A -0.61 -2.80 2.6E-02 5.0E-05 1.0E-03 C2 -0.61 -3.35 1.2E-02 2.2E-03 2.5E-02 UXS1 -0.61 -2.77 2.7E-02 1.8E-05 4.2E-04 FTH1 -0.62 -3.11 1.7E-02 4.7E-05 9.8E-04 DMBT1 -0.62 -3.07 1.8E-02 9.2E-04 1.2E-02 COL12A1 -0.62 -3.17 1.5E-02 8.8E-71 3.8E-67 CST3 -0.62 -3.43 1.1E-02 1.9E-03 2.2E-02 PTPRS -0.63 -3.12 1.7E-02 4.9E-13 5.1E-11 FGFRL1 -0.63 -2.99 2.0E-02 5.1E-06 1.4E-04 LAMB2 -0.63 -2.73 2.9E-02 3.5E-26 1.4E-23 CPE -0.63 -3.47 1.0E-02 6.8E-08 3.0E-06 ADAMTSL1 -0.63 -3.50 9.8E-03 1.1E-08 5.6E-07 DNAJB9 -0.63 -2.48 4.2E-02 5.1E-03 4.5E-02 C1R -0.64 -3.61 8.4E-03 8.7E-13 8.7E-11 PRCP -0.64 -3.39 1.1E-02 5.8E-07 2.1E-05 LTBP4 -0.64 -3.29 1.3E-02 2.2E-09 1.3E-07 NDNF -0.65 -3.18 1.5E-02 8.0E-06 2.1E-04 RCN1 -0.65 -2.67 3.2E-02 9.4E-04 1.2E-02 GALNT18 -0.65 -3.72 7.2E-03 1.6E-04 2.7E-03 CHID1 -0.65 -3.36 1.2E-02 3.2E-06 9.4E-05 PSMA1 -0.65 -2.83 2.5E-02 3.0E-08 1.4E-06 FAT4 -0.66 -2.77 2.8E-02 2.0E-14 2.3E-12 COL6A3 -0.66 -3.27 1.3E-02 9.5E-36 5.1E-33 ARPC5 -0.66 -2.62 3.4E-02 1.5E-05 3.6E-04 FSTL1 -0.66 -3.49 1.0E-02 1.7E-03 2.0E-02

126 Protein log2FC t.stat score raw p-value p.FDR SERPINF1 -0.67 -2.25 5.9E-02 4.3E-03 4.0E-02 APOM -0.67 -3.50 9.8E-03 1.6E-03 1.9E-02 CNTN1 -0.67 -3.26 1.4E-02 5.5E-04 8.0E-03 CLU -0.67 -2.15 6.8E-02 7.9E-06 2.1E-04 FAT1 -0.67 -3.01 1.9E-02 1.3E-49 1.4E-46 LAMC1 -0.68 -3.04 1.8E-02 4.9E-31 2.1E-28 SERPINA7 -0.68 -2.75 2.8E-02 2.3E-03 2.5E-02 C1S -0.68 -3.65 8.0E-03 8.3E-07 2.8E-05 COL18A1 -0.68 -3.46 1.0E-02 5.7E-15 7.4E-13 LIPG -0.69 -4.01 5.0E-03 1.6E-07 6.7E-06 TMEM132A -0.69 -2.66 3.2E-02 1.3E-06 4.2E-05 PLOD1 -0.69 -3.71 7.4E-03 4.5E-24 1.4E-21 TGFBI -0.69 -3.47 1.0E-02 6.2E-20 1.2E-17 EXT1 -0.69 -3.35 1.2E-02 1.2E-13 1.2E-11 CNTNAP3 -0.70 -3.38 1.1E-02 7.6E-05 1.5E-03 FRAS1 -0.70 -2.84 2.5E-02 3.5E-20 7.6E-18 ESPL1 -0.70 -4.04 4.8E-03 4.8E-03 4.3E-02 UCHL1 -0.71 -3.24 1.4E-02 2.8E-03 3.0E-02 B3GNT5 -0.72 -3.87 5.9E-03 1.1E-04 1.9E-03 SEMA3B -0.72 -3.14 1.6E-02 5.5E-07 2.0E-05 PTPRF -0.72 -3.12 1.7E-02 2.9E-17 4.7E-15 OAF -0.72 -3.48 1.0E-02 5.4E-05 1.1E-03 NXPE3 -0.73 -4.08 4.5E-03 7.5E-06 2.0E-04 LUM -0.73 -3.39 1.1E-02 7.5E-05 1.5E-03 PHB2 -0.74 -2.21 6.3E-02 5.0E-03 4.5E-02 CHPF -0.74 -2.97 2.0E-02 4.9E-03 4.4E-02 SEMA6B -0.74 -4.38 3.1E-03 3.0E-05 6.5E-04 CRTAP -0.74 -4.25 3.7E-03 7.2E-10 4.7E-08 NDST1 -0.75 -3.38 1.2E-02 6.1E-07 2.2E-05 MARCKSL1 -0.75 -2.04 8.0E-02 4.6E-03 4.2E-02 CHSY1 -0.76 -3.01 1.9E-02 2.8E-04 4.5E-03 COL6A2 -0.76 -3.84 6.2E-03 3.8E-12 3.6E-10 QSOX1 -0.77 -3.14 1.6E-02 8.4E-17 1.3E-14 VWA1 -0.77 -2.53 3.9E-02 7.1E-05 1.4E-03 FOXRED2 -0.77 -4.55 2.5E-03 2.5E-03 2.7E-02 SLC34A3 -0.78 -4.00 5.0E-03 5.0E-03 4.5E-02 FGB -0.78 -3.07 1.8E-02 4.0E-03 3.8E-02 LAMB1 -0.78 -3.54 9.2E-03 3.4E-37 2.1E-34 RYR3 -0.78 -4.04 4.8E-03 4.8E-03 4.4E-02 COL1A1 -0.78 -2.19 6.4E-02 2.4E-05 5.3E-04

127 Protein log2FC t.stat score raw p-value p.FDR ECM1 -0.79 -3.64 8.1E-03 7.3E-10 4.7E-08 KLHL29 -0.80 -4.44 2.9E-03 2.9E-03 3.0E-02 MARCKS -0.81 -2.14 6.9E-02 3.6E-05 7.8E-04 ST3GAL1 -0.81 -3.46 1.0E-02 1.8E-03 2.1E-02 CCDC80 -0.82 -3.62 8.3E-03 5.7E-06 1.6E-04 LTBP1 -0.82 -3.10 1.7E-02 1.1E-20 2.5E-18 SRPX2 -0.83 -3.92 5.6E-03 9.3E-05 1.7E-03 COL6A1 -0.83 -4.04 4.8E-03 2.4E-23 6.5E-21 PLOD3 -0.84 -4.29 3.5E-03 5.7E-26 2.0E-23 ADAM15 -0.86 -4.16 4.1E-03 4.1E-03 3.9E-02 NES -0.88 -3.81 6.5E-03 8.9E-04 1.2E-02 APLP2 -0.88 -4.81 1.9E-03 4.2E-24 1.4E-21 ARL6IP1 -0.88 -3.78 6.7E-03 9.3E-04 1.2E-02 MBD3 -0.89 -4.06 4.7E-03 4.7E-03 4.3E-02 GCNT3 -0.89 -4.36 3.2E-03 3.2E-03 3.3E-02 COL5A1 -0.90 -3.28 1.3E-02 1.3E-14 1.7E-12 PRDM15 -0.91 -4.11 4.4E-03 4.4E-03 4.1E-02 VEGFC -0.92 -4.11 4.4E-03 7.0E-06 1.9E-04 OLFML2A -0.92 -4.81 1.9E-03 1.7E-15 2.3E-13 STC1 -0.92 -4.37 3.2E-03 3.1E-04 4.9E-03 NEO1 -0.93 -4.23 3.8E-03 6.0E-08 2.7E-06 ADAMTSL5 -0.93 -4.71 2.1E-03 2.1E-03 2.4E-02 TNC -0.94 -4.63 2.3E-03 7.3E-54 1.6E-50 CTNNA3 -0.95 -5.45 9.1E-04 9.1E-04 1.2E-02 IGFBP7 -0.97 -3.24 1.4E-02 6.7E-08 2.9E-06 NR2C2 -0.97 -4.18 4.0E-03 4.0E-03 3.8E-02 TGFB2 -1.02 -4.81 1.9E-03 2.8E-12 2.7E-10 MMP19 -1.04 -3.47 1.0E-02 1.1E-05 2.7E-04 COL4A2 -1.06 -3.86 6.0E-03 1.5E-10 1.1E-08 HIST1H1E -1.06 -3.51 9.6E-03 1.6E-03 1.9E-02 CHPF2 -1.09 -6.07 4.8E-04 6.9E-07 2.4E-05 ARHGAP22 -1.10 -4.83 1.8E-03 1.8E-03 2.1E-02 PRTG -1.10 -4.67 2.2E-03 2.2E-03 2.4E-02 TGFB1 -1.10 -4.05 4.7E-03 2.3E-09 1.3E-07 TFPI -1.12 -2.94 2.2E-02 3.6E-04 5.6E-03 PUSL1 -1.14 -5.76 6.6E-04 6.6E-04 9.3E-03 LAMC2 -1.15 -4.01 5.0E-03 6.1E-24 1.7E-21 CLSTN1 -1.15 -4.13 4.3E-03 3.6E-22 8.6E-20 GNPTG -1.17 -4.98 1.5E-03 1.0E-04 1.9E-03 COL7A1 -1.17 -5.26 1.1E-03 1.8E-52 2.6E-49

128 Protein log2FC t.stat score raw p-value p.FDR DNAJC22 -1.21 -6.74 2.5E-04 2.5E-04 4.1E-03 TGFB3 -1.21 -3.98 5.2E-03 5.2E-03 4.5E-02 LAMB3 -1.32 -5.97 5.3E-04 6.5E-34 3.1E-31 HIST1H1C -1.33 -3.64 8.1E-03 1.2E-03 1.5E-02 SOD3 -1.33 -4.78 1.9E-03 5.8E-09 3.2E-07 PAPLN -1.33 -5.49 8.7E-04 8.7E-04 1.2E-02 GGH -1.38 -4.74 2.0E-03 7.1E-09 3.8E-07 CLN5 -1.38 -4.17 4.1E-03 4.1E-03 3.9E-02 ANGPTL4 -1.39 -6.93 2.1E-04 1.3E-07 5.7E-06 ALPK3 -1.39 -5.70 7.0E-04 7.0E-04 9.7E-03 IGFBP3 -1.42 -7.52 1.3E-04 2.4E-06 7.3E-05 DUSP18 -1.45 -5.27 1.1E-03 1.1E-03 1.4E-02 SNED1 -1.47 -6.87 2.2E-04 8.8E-14 9.8E-12 ANGPT1 -1.51 -6.32 3.8E-04 1.2E-05 3.1E-04 BNC2 -1.57 -5.81 6.3E-04 6.3E-04 8.9E-03 HS3ST1 -1.85 -6.11 4.6E-04 9.7E-10 6.0E-08 PECA analyses of sEV proteome for differentially abundant proteins. Differential protein abundance (defined by absolute fold-chang>1.5 and adjusted p-value< 0.05) was determined by paired PECA analyses after median normalization. Log2FC: log2 fold change. T.stat: t-statistics. Score: median p value. p.FDR: false discovery rated- adjusted p-value.

Table B2. KEGG pathway enrichment analysis on differentially abundant proteins in whole cell and sEV datasets FDR- Data KEGG KEGG Gene Backgrou adjusted set Dir ID Description Ratio nd Ratio p-value Proteins TGFBR2/TNFRSF21 Cytokine- /BMPR2/TNFRSF10 cytokine B/IFNGR1/OSMR/IL hsa04 receptor 13RA1/GDF15/TNF WC + 060 interaction 10/80 43/3818 1.2E-06 RSF10A/LTBR TAX1BP1/CALCOC O2/NBR1/SQSTM1/ GABARAPL2/GABA hsa04 Mitophagy - RAP/BNIP3/GABAR WC + 137 animal 9/80 51/3818 4.5E-05 APL1/UBB GABARAPL2/SLC38 hsa04 GABAergic A2/SLC38A1/GABA WC + 727 synapse 5/80 35/3818 2.8E-02 RAP/GABARAPL1 Viral protein interaction with cytokine and hsa04 cytokine TNFRSF10B/TNFRS WC + 061 receptor 3/80 10/3818 2.9E-02 F10A/LTBR

129 FDR- Data KEGG KEGG Gene Backgrou adjusted set Dir ID Description Ratio nd Ratio p-value Proteins hsa05 Staphylococcus WC - 150 aureus infection 3/9 22/3818 5.5E-04 KRT10/KRT9/KRT20 Estrogen hsa04 signaling WC - 915 pathway 3/9 77/3818 1.2E-02 KRT10/KRT9/KRT20 TAX1BP1/GABARA hsa04 Mitophagy - PL2/NBR1/GABARA sEV + 137 animal 4/29 27/2281 2.0E-02 P Cytokine- cytokine hsa04 receptor TGFBR2/TNFRSF10 sEV + 060 interaction 4/29 33/2281 2.2E-02 A/TGFBR1/BMPR1A hsa04 Osteoclast JAK1/TGFBR2/TGF sEV + 380 differentiation 4/29 43/2281 3.5E-02 BR1/STAT2 hsa04 GABAergic SLC38A2/GABARA sEV + 727 synapse 3/29 21/2281 3.5E-02 PL2/GABARAP TGFBR2/GABARAP hsa04 FoxO signaling L2/GABARAP/TGFB sEV + 068 pathway 4/29 48/2281 3.7E-02 R1 TNC/LAMB1/COL6A 3/LAMB3/LAMC1/LA MB2/LAMC2/COL6A 1/FRAS1/COL6A2/C hsa04 ECM-receptor OL4A2/COL1A1/VW sEV - 512 interaction 13/75 51/2281 2.6E-07 F COL12A1/COL7A1/ COL6A3/COL6A1/C Protein OL18A1/COL5A1/C hsa04 digestion and OL6A2/COL4A2/PR sEV - 974 absorption 10/75 29/2281 4.0E-07 CP/COL1A1 LAMB1/LAMB3/LAM C1/LAMB2/LAMC2/T hsa05 GFB2/COL4A2/TGF sEV - 146 Amoebiasis 10/75 48/2281 5.3E-05 B1/COL1A1/TGFB3 TNC/LAMB1/COL6A 3/LAMB3/LAMC1/LA MB2/LAMC2/COL6A PI3K-Akt 1/COL6A2/COL4A2/ hsa04 signaling VEGFC/ANGPT1/C sEV - 151 pathway 14/75 122/2281 5.3E-04 OL1A1/VWF TNC/LAMB1/COL6A 3/LAMB3/LAMC1/LA MB2/LAMC2/COL6A 1/COL6A2/COL4A2/ hsa04 VEGFC/COL1A1/V sEV - 510 Focal adhesion 13/75 114/2281 9.2E-04 WF LAMB1/LAMB3/LAM C1/LAMB2/LAMC2/T hsa05 GFB2/TGFB1/TGFB sEV - 145 Toxoplasmosis 8/75 54/2281 3.9E-03 3

130 FDR- Data KEGG KEGG Gene Backgrou adjusted set Dir ID Description Ratio nd Ratio p-value Proteins Complement hsa04 and coagulation C1R/C1S/CLU/TFPI/ sEV - 610 cascades 7/75 44/2281 5.3E-03 VWF/C2/FGB TNC/LAMB1/COL6A 3/LAMB3/LAMC1/LA Human MB2/LAMC2/COL6A hsa05 papillomavirus 1/COL6A2/COL4A2/ sEV - 165 infection 12/75 143/2281 1.9E-02 COL1A1/VWF LAMB1/LAMB3/LAM hsa05 Small cell lung C1/LAMB2/LAMC2/ sEV - 222 cancer 6/75 42/2281 1.9E-02 COL4A2 Glycosaminogly can biosynthesis hsa00 - heparan EXT1/HS3ST1/NDS sEV - 534 sulfate / heparin 3/75 10/2281 2.8E-02 T1 AGE-RAGE signaling pathway in TGFB2/COL4A2/TG hsa04 diabetic FB1/VEGFC/COL1A sEV - 933 complications 6/75 47/2281 2.8E-02 1/TGFB3 hsa05 Rheumatoid TGFB2/TGFB1/ANG sEV - 323 arthritis 4/75 22/2281 3.4E-02 PT1/TGFB3 hsa05 TGFB2/TGFB1/MAR sEV - 140 Leishmaniasis 4/75 23/2281 3.5E-02 CKSL1/TGFB3 Mucin type O- hsa00 glycan GALNT18/ST3GAL1 sEV - 512 biosynthesis 3/75 12/2281 3.5E-02 /GCNT3 TGF-beta hsa04 signaling LTBP1/TGFB2/TGF sEV - 350 pathway 5/75 37/2281 3.5E-02 B1/NEO1/TGFB3 Overrepresentation analysis of KEGG pathways was done using differentially abundant proteins (defined by absolute fold change > 1.5 and adjusted p-value < 0.05) between CQ-treated and untreated samples in WC or sEV fractions. UniProt accession numbers of the differentially abundant proteins were converted to Entrez gene IDs using org.Hs.eg.db (v3.4.0) for KEGG pathway annotations. Shown are all pathways past significance threshold (adjusted p- value < 0.05) with at least 5% of the proteins associated with the particular pathway. The analysis was independently performed for proteins increased or decreased by CQ treatment, indicated as the direction of change (Dir). Gene Ratio: the fraction of differentially abundant proteins involved in a given pathway. Background Ratio: the fraction of proteins within the entire WC or sEV dataset that can be mapped to the same pathway.

131