Immunomodulation in wound healing and oncogenesis Dissertation zur Erlangung des Grades „Doktor der Naturwissenschaften“ im Promotionsfach Biologie am Fachbereich Biologie der Johannes Gutenberg-Universität Mainz Aus der Hautklinik und Poliklinik der Universitätsmedizin der Johannes Gutenberg-Universität Mainz Emily Renee Trzeciak geboren in Toledo (USA) Mainz, 2024 ii Dekan: Prof. Dr. Eckhard Thines Erster Berichterstatter: Prof. Dr. med. Andrea Tüttenberg Zweiter Berichterstatter: Dr. Joachim Urban Tag der mündlichen Prüfung: 26.11.2024 iii iv DECLARATION OF AUTHORSHIP I hereby confirm that this work was performed from January 2021 to July 2024 under the supervision of Prof. Dr. med. Andrea Tüttenberg at the Dermatology Department of the University Medical Center Mainz. I ensure that I have authored this dissertation independently without the assistance of paid third parties. All sources and aids used were specified. July 11th, 2024, Mainz v vi For the patients and their families vii viii PUBLICATION LIST The contents of this cumulative dissertation are based on the following works achieved during the doctoral studies of Emily Trzeciak (* denotes shared first authorship). From here on, these works will be referred to as Paper 1, Paper 2, and Paper 3: Paper 1: Trzeciak*, E. R., Zimmer*, N., Kämmerer, P. W., Thiem, D., Al-Nawas, B., Tuettenberg, A., & Blatt, S. (2022). GARP Regulates the Immune Capacity of a Human Autologous Platelet Concentrate. Biomedicines, 10(12), 3136. https://doi.org/10.3390/biomedicines10123136 Paper 2: Zimmer*, N., Trzeciak*, E. R., Müller, A., Licht, P., Sprang, B., Leukel, P., Mailänder, V., Sommer, C., Ringel, F., Tuettenberg, J., Kim, E., & Tuettenberg, A. (2023). Nuclear Glycoprotein A Repetitions Predominant (GARP) Is a Common Trait of Glioblastoma Stem-like Cells and Correlates with Poor Survival in Glioblastoma Patients. Cancers, 15(24), 5711. https://doi.org/10.3390/cancers15245711 Paper 3: Trzeciak, E. R., Zimmer, N., Gehringer, I., Stein, L., Graefen, B., Schupp, J., Stephan, A., Rietz, S., Prantner, M., & Tuettenberg, A. (2022). Oxidative Stress Differentially Influences the Survival and Metabolism of Cells in the Melanoma Microenvironment. Cells, 11(6), 930. https://doi.org/10.3390/cells11060930 The following coauthorships achieved during the doctoral studies of Emily Trzeciak will not be discussed in this cumulative dissertation: Paper 4: Stiller, H.L., Perumal, N., Manicam, C. Trzeciak, E.R., Todt, J., Jurk, K. Schiegnitz, E., Blatt, S. (2024). First- vs. second generation autologous platelet concentrates: insights for the infight. Manuscript submitted for publication in the International Journal of Implant Dentistry. Paper 5: Damara, A., Wegner, J., Trzeciak, E. R., Kolb, A., Nastaranpour, M., Khatri, R., Tuettenberg, A., Kramer, D., Grabbe, S., & Shahneh, F. (2024). LL37/self-DNA complexes mediate monocyte reprogramming. Clinical immunology, 265, 110287. https://doi.org/10.1016/j.clim.2024.110287 Paper 6: Zimmer, N., Trzeciak, E. R., Graefen, B., Satoh, K., & Tuettenberg, A. (2022). GARP as a Therapeutic Target for the Modulation of Regulatory T Cells in Cancer and Autoimmunity. Frontiers in immunology, 13, 928450. https://doi.org/10.3389/fimmu.2022.928450 Paper 7: Krebs, F. K., Trzeciak, E. R., Zimmer, S., Özistanbullu, D., Mitzel-Rink, H., Meissner, M., Grabbe, S., Loquai, C., & Tuettenberg, A. (2021). Immune signature as predictive marker for response to checkpoint inhibitor ix immunotherapy and overall survival in melanoma. Cancer medicine, 10(5), 1562–1575. https://doi.org/10.1002/cam4.3710 The following publications were achieved prior to the start of Emily Trzeciak’s doctoral studies and will not be discussed in this cumulative dissertation: Paper 8: Trzeciak*, E., Zimmer*, N., Kim, E., Schupp, J., Sprang, B., Leukel, P., Khafaji, F., Ringel, F., Sommer, C., Tuettenberg, J., Tuettenberg, A. (2019). Dual Expression of GARP in Immune and Glioma Cells: Yet Another Mechanism of Cancer Immune Escape. Journal of cellular immunology, 1(2):45-49. https://doi.org/10.33696/immunology.1.009 Paper 9: Keogh, R. A., Zapf, R. L., Trzeciak, E., Null, G. G., Wiemels, R. E., & Carroll, R. K. (2019). Novel Regulation of Alpha-Toxin and the Phenol-Soluble Modulins by Peptidyl-Prolyl cis/trans Isomerase Enzymes in Staphylococcus aureus. Toxins, 11(6), 343. https://doi.org/10.3390/toxins11060343 Paper 10: Zapf, R. L., Wiemels, R. E., Keogh, R. A., Holzschu, D. L., Howell, K. M., Trzeciak, E., Caillet, A. R., King, K. A., Selhorst, S. A., Naldrett, M. J., Bose, J. L., & Carroll, R. K. (2019). The Small RNA Teg41 Regulates Expression of the Alpha Phenol- Soluble Modulins and Is Required for Virulence in Staphylococcus aureus. mBio, 10(1), e02484-18. https://doi.org/10.1128/mBio.02484-18 Paper 11: Schupp, J., Krebs, F. K., Zimmer, N., Trzeciak, E., Schuppan, D., & Tuettenberg, A. (2019). Targeting myeloid cells in the tumor sustaining microenvironment. Cellular immunology, 343, 103713. https://doi.org/10.1016/j.cellimm.2017.10.013 x ABSTRACT Immunotherapy has emerged as a promising approach to treat a wide range of clinical conditions. Herein, immunomodulatory agents are applied to induce favorable immune responses. Depending on the condition, immune responses may be amplified (e.g., cancer) or attenuated (e.g., wound healing). Cancer and deficiencies in wound healing represent significant global healthcare burdens. Cancer is the second leading cause of death worldwide, and more than 40 million people are suffering from chronic wounds. Although the incidence of both conditions is increasing, many patients still lack effective treatment options. Therefore, the development of novel immunomodulatory approaches to treat these conditions is an upmost priority. Before these approaches can be developed into therapies, they must first be thoroughly characterized to ensure their safety and efficacy. Therefore, this cumulative dissertation characterized two novel immunomodulatory approaches, namely the protein, glycoprotein A repetitions predominant (GARP) as a new target, and cold atmospheric plasma (CAP) as a device, in the immunological contexts of cancer and wound healing. In the past, GARP was described as a potent mechanism of immune tolerance through its regulation of the suppressive cytokine, transforming growth factor beta (TGF-β). The present work provides some of the first insights into the role of GARP in wound healing and in cancer stem cell biology. In “GARP Regulates the Immune Capacity of a Human Autologous Platelet Concentrate”, the immunological influence of GARP in injectable platelet rich fibrin (iPRF) was investigated. It was determined that GARP mediates T cell immunity by inducing regulatory T cells, a key cell type that facilitates the wound healing process. Conversely, GARP was found to inhibit the production of proinflammatory cytokines, which can prevent effective wound healing by sustaining chronic inflammation. Application of an anti-GARP antibody reversed these effects. The present work analyzed another aspect of GARP biology, namely its role in glioblastoma stem-like cells (GSCs). GSCs are a small subset of tumor cells, which drive tumor initiation, therapy resistance, and recurrence in glioblastoma. Prior to this study, no markers had been found that were universally expressed by all GSCs due to their innately high heterogeneity. In “Nuclear Glycoprotein A Repetitions Predominant (GARP) Is a Common Trait of Glioblastoma Stem-like Cells and Correlates with Poor Survival in Glioblastoma Patients”, GARP was evaluated and confirmed as a possible marker for GSCs. Functionally, GARP was found to be implicated in the self-renewal of GSCs. Due to the high expression of GARP on GSCs, the protein was also investigated as a prognostic biomarker for glioblastoma. Hereby, the abnormal nuclear localization of the protein was linked to reduced overall survival of patients with glioblastoma for the first time. The immunomodulatory effects of CAP were examined in the context of cancer. CAP is a partially ionized gas, which is also compromised of neutral atoms, excited electrons, and electromagnetic radiation. In contrast to thermal plasma, CAP can be applied within xi physiological temperatures. CAP has been reported to selectively target cancer cells by increasing exogenous reactive oxygen and nitrogen species (RONS). Prior to this work, little was known regarding how CAP influences immune cells, key determiners of immunotherapy response in the melanoma microenvironment. In “Oxidative Stress Differentially Influences the Survival and Metabolism of Cells in the Melanoma Microenvironment”, the effects of CAP on T cells, macrophages, and tumor cells were investigated. All cell types exhibited signs of oxidative stress following CAP treatment, but T cells were the most sensitive. These effects could be partially reversed with the application of antioxidants. Surprisingly, CAP was found to influence the polarization of macrophages to an anti-inflammatory “M0/M2” phenotype. Collectively, this work sheds light on the immunomodulatory properties and functional relevance of GARP and CAP in distinct immunological settings. In the future, these immunomodulatory mechanisms might be used as possible therapeutic approaches to elicit favorable immune responses in the treatment of cancer and the optimization of wound healing. xii KURZZUSAMMENFASSUNG Die Immuntherapie hat sich als vielversprechender Ansatz zur Behandlung eines breiten Spektrums klinischer Erkrankungen erwiesen. Dabei werden immunmodulierende Wirkstoffe eingesetzt, um gezielt Immunreaktionen hervorzurufen. Je nach Erkrankung und erwünschter Wirkung kann die Immunantwort verstärkt (z.B. Krebs) oder abgeschwächt (z.B. Wundheilung) werden. Krebs und Wundheilungsstörungen stellen weltweit eine erhebliche Belastung für die Gesundheitsversorgung dar. Krebs ist weltweit die zweithäufigste Todesursache und mehr als 40 Millionen Menschen leiden an chronischen Wunden. Obwohl die Häufigkeit beider Erkrankungen zunimmt, gibt es für viele Patienten immer noch keine wirksamen Behandlungsmöglichkeiten. Die Entwicklung neuartiger immunmodulatorischer Therapien zur Behandlung dieser Erkrankungen hat daher höchste Priorität. Bevor diese Wirkstoffe zu Therapien weiterentwickelt werden können, müssen sie zunächst gründlich charakterisiert werden, um ihre Sicherheit und Wirksamkeit nachzuweisen. In dieser kumulativen Dissertation wurden daher zwei neuartige immunmodulatorische Ansätze, das Protein „Glycoprotein A repetitions predominant“ (GARP) als potenzielle Zielstruktur und kaltes atmosphärisches Plasma (CAP) als Behandlungsmethode, in den immunologischen Zusammenhängen von Krebs und Wundheilung charakterisiert. GARP wurde bereits in der Vergangenheit als wirksamer Mechanismus der Immuntoleranz durch seine Regulierung des suppressiven Zytokins „transforming growth factor beta“ (TGF-β) beschrieben. Diese Arbeit liefert erste Einblicke in die Rolle von GARP sowohl bei der Wundheilung als auch in der Biologie von Krebsstammzellen. In „GARP Regulates the Immune Capacity of a Human Autologous Platelet Concentrate“ wurde der immunologische Einfluss von GARP in injizierbarem plättchenreichem Fibrin (iPRF) untersucht. Es wurde festgestellt, dass GARP T-Zell-Immunität vermittelt, indem es regulatorische T-Zellen induziert, einen wichtigen Zelltyp, der die Wundheilung fördert. Umgekehrt konnte gezeigt werden, dass GARP die Produktion von proinflammatorischen Zytokinen hemmt, die eine wirksame Wundheilung durch Aufrechterhaltung einer chronischen Entzündung verhindern können. Bemerkenswert ist, dass diese Effekte durch einen Anti-GARP-Antikörper verhindert werden können. In der vorgelegten Arbeit wurde ein weiterer Aspekt der GARP-Biologie untersucht, nämlich die Rolle des Proteins in Glioblastom-Stammzellen (GSCs). GSCs sind eine kleine Untergruppe von Tumorzellen, die für die Tumorentstehung, Therapieresistenz und das Wiederauftreten von Glioblastomen verantwortlich sind. Bis zu dieser Studie gab es keine Marker, die universell von allen GSCs exprimiert werden, da diese von Natur aus sehr heterogen sind. In der Studie „Nuclear Glycoprotein A Repetitions Predominant (GARP) Is a Common Trait of Glioblastoma Stem-like Cells and Correlates with Poor Survival in Glioblastoma Patients“ wurde GARP untersucht und als möglicher Marker für GSCs bestätigt. Funktionell konnte gezeigt werden, dass GARP an der Selbsterneuerung von GSCs beteiligt ist. Aufgrund der hohen Expression von GARP auf GSCs wurde das Protein auch als prognostischer Biomarker für das Glioblastom untersucht. Erstmals konnte eine abnorme nukleare Lokalisation dieses Proteins mit einer xiii verkürzten Gesamtüberlebenszeit von Glioblastom-Patienten in Verbindung gebracht werden. Im Zusammenhang mit Krebs wurden die immunmodulatorischen Wirkungen von CAP untersucht. CAP ist ein teilweise ionisiertes Gas, das zudem neutrale Atome, angeregte Elektronen und elektromagnetische Strahlung enthält. Im Gegensatz zu thermischem Plasma kann es bei physiologischen Temperaturen angewendet werden. Es wurde berichtet, dass CAP selektiv auf Krebszellen wirkt, indem es die exogenen reaktiven Sauerstoff- und Stickstoffspezies (RONS) erhöht. Vor dieser Arbeit war wenig darüber bekannt, wie CAP Immunzellen beeinflusst, die für das Ansprechen auf eine Immuntherapie in der Mikroumgebung des Melanoms entscheidend sind. In „Oxidative Stress Differentially Influences the Survival and Metabolism of Cells in the Melanoma Microenvironment“ wurden die Auswirkungen von CAP auf T-Zellen, Makrophagen und Tumorzellen untersucht. Alle Zelltypen wiesen nach der CAP-Behandlung Anzeichen von oxidativem Stress auf, wobei die T-Zellen am stärksten betroffen waren. Diese Auswirkungen konnten durch die Anwendung von Antioxidantien teilweise rückgängig gemacht werden. Überraschenderweise wurde festgestellt, dass CAP die Polarisierung von Makrophagen in Richtung eines immunsuppressiven „M0/M2“-Phänotyps fördert. Insgesamt erklärt diese Arbeit die immunmodulatorischen Eigenschaften und die funktionelle Bedeutung von GARP und CAP in verschiedenen immunologischen Umgebungen. In Zukunft könnten diese immunmodulatorischen Mechanismen als mögliche therapeutische Ansätze genutzt werden, um günstige Immunreaktionen in der Krebstherapie und zur Optimierung der Wundheilung zu induzieren. xiv ACKNOWLEDGEMENTS My sincerest thanks go to my supervisor, Prof. Dr. med. Andrea Tüttenberg, for her steadfast support throughout my scientific career, fostering both my professional and personal development. She has provided me with excellent supervision, insightful feedback, and a welcoming work environment. I am deeply grateful for the opportunity to do my PhD in her lab and her trust in me to independently develop my research projects. I would not be the scientist I am today without her. A very warm thanks go to my co-supervisor Dr. Anne Régnier-Vigouroux. I appreciate your enlightening feedback, kindness, and enthusiastic support of my research. I express gratitude to all our funders, who made this work possible and who provided financial support of my PhD, including the Deutscher Akademischer Austauschdienst (DAAD), BOWA- Electronic GmbH & Co. KG, the Hiege Foundation, the Wilhelm Sander Foundation, and the Collaborative Research Center 1066 (SFB 1066). I thank the Mainz Research School of Translational Biomedicine (TransMed) for enabling me to attend training courses. I am grateful for the ADA Lovelace Career Orientation Mentoring Program for their personal development workshops. I especially thank my professional mentor, Dr. Christoph Hüls, for his eye-opening coaching sessions. I am honored to have been a part of the Gutenberg Academy Fellows Program (GAFP). I thank them for the lively interdisciplinary discourse and their financial support, which allowed me to attend conferences and covered the costs of printing my dissertation. Additionally, I would like to thank the International Studies and Language College (ISSK) for the opportunity to learn German during my PhD. I especially thank Dr. Dorota Piestrak- Demirezen for her constant encouragement. I deeply grateful to all the patients and blood donors. Without them, this work would not be possible. This work was achieved with the help of our great collaboration partners: I thank Dr. med. Dr. med. dent. Sebastian Blatt for conceptualizing Paper 1 and Christina Babel for preparing the injectable platelet rich fibrin needed for the study. I am grateful for PD Dr. rer. nat. Ella Kim and Andreas Müller’s expertise in functionally evaluating and culturing glioblastoma stem-like cells. I thank Bettina Sprang and Petra Leukel for performing the immunofluorescence and immunohistochemical stainings in Paper 2. I appreciate Priv.-Doz. Dr. med. Jochen Tüttenberg’s help in obtaining the patient samples (shown in Paper 2) and Univ.-Prof. Dr. med. Clemens Sommer for pathologically evaluating them. Additionally, I would like to thank Philipp Licht for performing the bioinformatics analyses in Paper 2. I thank Dr. med. Stephan Rietz, Michael Prantner, and Dr. Achim Stephan for helping us obtain the cold atmospheric plasma device used in Paper 3. I am very grateful for the excellent xv preliminary work performed by Lara Stein and Dr. Isabelle Gehringer that served as the foundation for Paper 3. Furthermore, I profited from the feedback from Dr. rer. nat. Jonathan Schupp and Dr. rer. nat. Niklas Zimmer, who originally conceptualized Paper 3. I am grateful for the biochemical expertise of Dr. rer. nat. Daniela Kramer and Antonia Kolb. My research benefited greatly from their technical support. I appreciate PD Dr. rer. net et med. habil. Matthias Bros’ insightful ideas and suggestions, especially regarding GARP as a moonlighting protein. I thank Univ.-Prof. Dr. Miguel Andrade for performing the bioinformatics analyses on nuclear GARP. It has been a great pleasure to be a part of the Tüttenberg lab. I thank my past and present colleagues, Prof. Dr. med. Andrea Tüttenberg, Dr. rer. nat. Niklas Zimmer, Dr. rer. nat. Jonathan Schupp, Dr. rer. nat. Franziska Krebs, Barbara Gräfen, Lara Stein, Maximilian Sturm, and Jana-Marie Schmidt for their eagerness to problem solve, passion for science, humor, and overall good nature. Additionally, I grateful for my past and present colleagues in the Dermatology Department, especially Dr. rer. nat. Fatemeh Shahneh, Dr. Rahul Khatri, Anna Tobien, Elisa Mazza, Robert Ose, Pia Neuhaus, and the ActiTrexx team, Dr. rer. nat. Janine Schlöder, Philipp Hölter, Jenny Fuchs, Dr. Christoph Rücker, and HD Dr. rer. nat. Helmut Jonuleit. Thank you for brightening my days and for your constructive feedback on my research. My heartfelt thanks go to my family and friends who have been a source of endless encouragement throughout my PhD. Thank you for always cheering me on and believing in me! I am very thankful for Dr. rer. nat. Nina Hoinkis for her constant support and for giving me excellent formatting advice. Much love goes to my dear friends Dr. rer. nat. Niklas Zimmer and Lara Stein for their helpful feedback on my dissertation and their willingness to always meet for a “Weinschorle” after a hard day. I am especially grateful for the support of my family: Deborah Trzeciak, Dennis Trzeciak, Eric Trzeciak, Andy Trzeciak, Brittany Trzeciak, Kerstin Leukel, Peter Leukel, Steffen Leukel, Abby Leukel, and Alina Weber. Above all, I am forever indebted to my husband, Sebastian Leukel, who fed me, brought me on walks, tutored me in chemistry, and committed himself to making every day a bit easier for me. You inspire me to become a better scientist. Words cannot express my love for you. xvi TABLE OF CONTENTS Declaration of Authorship ............................................................................................................................ v Publication List ................................................................................................................................................ ix Abstract .............................................................................................................................................................. xi Kurzzusammenfassung ............................................................................................................................. xiii Acknowledgements ...................................................................................................................................... xv Table of Contents ........................................................................................................................................ xvii List of Figures .................................................................................................................................................xix List of Abbreviations ...................................................................................................................................xxi 1. Introduction .................................................................................................................................................. 1 1.1 The immune system ............................................................................................................................ 1 1.1.1 Innate and adaptive immunity ............................................................................................... 1 1.1.2 T cell activation ............................................................................................................................. 2 1.1.3 CD4+ and CD8+ T cells ................................................................................................................. 3 1.1.4 T cell subsets .................................................................................................................................. 3 1.1.5 Th1 ..................................................................................................................................................... 4 1.1.6 Regulatory T cells (Tregs) ........................................................................................................ 4 1.1.7 Suppressive Mechanisms of Tregs ........................................................................................ 5 1.1.8 Glycoprotein A repetitions predominant (GARP) ........................................................... 7 1.2 Wound healing ................................................................................................................................... 11 1.3 Cancer .................................................................................................................................................... 14 1.3.1 Cancer initiation ........................................................................................................................ 14 1.3.2 Immune surveillance ............................................................................................................... 15 1.3.3 GARP in cancer ........................................................................................................................... 17 1.4 Glioblastoma ....................................................................................................................................... 18 1.4.1 Epidemiology .............................................................................................................................. 18 1.4.2 Risk factors .................................................................................................................................. 19 1.4.3 Diagnosis ...................................................................................................................................... 19 1.4.4 Pathology ..................................................................................................................................... 21 1.4.5 Treatment .................................................................................................................................... 22 1.4.6 Glioblastoma stem-like cells (GSCs) .................................................................................. 24 1.5 Melanoma ............................................................................................................................................ 26 1.5.1 Epidemiology .............................................................................................................................. 26 xvii 1.5.2 Risk factors .................................................................................................................................. 27 1.5.3 Diagnosis ...................................................................................................................................... 28 1.5.4 Pathology ..................................................................................................................................... 29 1.5.5 Treatment .................................................................................................................................... 29 2. Aims .............................................................................................................................................................. 33 3. Results .......................................................................................................................................................... 35 3.1 Paper 1 .................................................................................................................................................. 35 3.1.1 Summary ...................................................................................................................................... 35 3.1.2 Zusammenfassung .................................................................................................................... 36 3.1.3 Author Contributions .............................................................................................................. 37 3.1.4 Publication ................................................................................................................................... 39 3.2 Paper 2 .................................................................................................................................................. 55 3.2.1 Summary ...................................................................................................................................... 55 3.2.2 Zusammenfassung .................................................................................................................... 56 3.2.3 Author Contributions .............................................................................................................. 57 3.2.4 Publication ................................................................................................................................... 59 3.3 Paper 3 .................................................................................................................................................. 81 3.3.1 Summary ...................................................................................................................................... 81 3.3.2 Zusammenfassung .................................................................................................................... 82 3.3.3 Author Contributions .............................................................................................................. 84 3.3.4 Publication ................................................................................................................................... 85 4. Discussion .................................................................................................................................................117 5. Conclusions ..............................................................................................................................................124 References ........................................................................................................................................................... I Appendix ....................................................................................................................................................... XLV Supporting Information ...................................................................................................................... XLV Paper 1 .................................................................................................................................................. XLV Paper 2 ............................................................................................................................................... XLVII Paper 3 .................................................................................................................................................... LXI Discussion ........................................................................................................................................... LXIX Curriculum Vitae ................................................................................................................................. LXXV Conference List ................................................................................................................................ LXXVII xviii LIST OF FIGURES Figure 1: Activation & differentiation of T cells ................................................................................. 2 Figure 2: Suppressive mechanisms of regulatory T cells (Tregs) ............................................... 7 Figure 3: Localization of GARP .................................................................................................................. 8 Figure 4: Functions of GARP ...................................................................................................................... 9 Figure 5: Stages of wound healing ........................................................................................................ 11 Figure 6: The immune surveillance hypothesis .............................................................................. 16 Figure 7: Immune checkpoint inhibitors (ICIs) ............................................................................... 23 Figure 8: Traits of glioblastoma stem-like cells (GSCs) ................................................................ 25 Figure 9: Graphical abstract (Paper 1) ................................................................................................ 35 Figure 10: Graphical abstract (Paper 2) ............................................................................................. 55 Figure 11: Graphical abstract (Paper 3) ............................................................................................. 81 Supplemental figures Previous naming of the supplemental figures originally referenced in-text in Paper 1, Paper 2, and Paper 3 are indicated in the parentheses. Figure S1 (Paper 1): Flow cytometric gating strategy ............................................................... XLV Figure S2 (Paper 2, prev. S1): Characterization of patient derived GSC cell lines ....... XLVII Figure S3 (Paper 2, prev. S2): Anti-GARP antibody validation for flow cytometry ....... XLIX Figure S4 (Paper 2, prev. S3): Specificity demonstration of anti-GARP antibodies ............. L Figure S5 (Paper 2, prev. S4): Flow cytometric gating strategy for GSCs............................... LI Figure S6 (Paper 2, prev. S5): Validation of anti-GARP antibodies for confocal microscopy ...................................................................................................................................................... LII Figure S7 (Paper 2, prev. S6): Flow cytometric gating strategy for GARP sorted GSCs . LIII Figure S8 (Paper 2, prev. S7): Representative immunohistochemical stainings of xenograft tumors derived from GSC cell lines, #1051 and #1043 .......................................... LIV Figure S9 (Paper 2, prev. S8): Representative immunofluorescence stainings of xenograft tumors derived from GSC cell lines, #1051 and #1043 ............................................ LV Figure S10 (Paper 2, prev. S9): Study design and models used for the assessment of GARP ............................................................................................................................................................... LVII Figure S11 (Paper 2, prev. S10): Frequency of GFAP+ GARPhigh and GARPlow GSCs ......... LIX Figure S12 (Paper 3, prev. S1): Flow cytometric gating strategies ........................................ LXI Figure S13 (Paper 3, prev. S2): MiniJet-R and intracellular ROS quantification ............LXIII Figure S14 (Paper 3, prev. S3): UKRV-Mel-15a cell cycle and cell death results ........... LXIV Figure S15 (Paper 3, prev. S4): Non-significant macrophage polarization markers ..... LXV Figure S16 (Paper 3, prev. S5): Example Seahorse OCR and ECAR results ...................... LXVI Figure S17 (Paper 3, prev. S6): UKRV-Mel-15a antioxidant treatment results ............ LXVII Figure S18: In silico predictions of GARP protein interactors utilizing the Human Integrated Protein-Protein Interaction rEference (HIPPIE) database ................................ LXIX Figure S19: In silico predictions of protein interactors with GARP utilizing the Physical Protein-Protein Interactions with Reality Scores (HitPredict) database ............................ LXX xix Figure S20: In silico predictions of protein interactors with GARP utilizing the BioGRID4.4 database ................................................................................................................................. LXXI Figure S21: Biochemical confirmation of nuclear localization of GARP and its interaction with STAT3................................................................................................................................................ LXXII Figure S22: In silico predictions of a nuclear localization sequence in the GARP protein ...................................................................................................................................................................... LXXIII xx LIST OF ABBREVIATIONS Meaning Abbreviation or symbol Glycoprotein A repetitions predominant GARP Cold atmospheric plasma CAP Transforming growth factor beta TGF-β Injectable platelet rich fibrin iPRF Glioblastoma stem-like cells GSCs Reactive oxygen and nitrogen species RONS Deutscher Akademischer Austauschdienst DAAD Collaborative Research Center 1066 SFB1066 Mainz Research School of Translational Biomedicine TransMed Gutenberg Academy Fellows Program GAFP International Studies and Language College ISSK Regulatory T cells Tregs Human Integrated Protein-Protein Interaction HIPPIE database rEference database Physical Protein-Protein Interactions With HitPredict database Reality Scores database Dendritic cell DC Pattern recognition receptors PRRs Pathogen-associated molecular patterns PAMPs T lymphocyte T cell B lymphocyte B cell T cell receptor TCR B cell receptor BCR Antigen presenting cell APC Major histocompatibility complex MHC CD40 ligand CD40L Interleukin 2 IL-2 Effector T cells Teff Cytotoxic T lymphocytes CTL T follicular helper cells Tfh Helper T cell Th Interferon-gamma IFN-γ Interleukin 12 IL-12 T-box expressed in T cells Tbet Classically activated macrophages M1 macrophages Transforming growth factor beta TGF-β Interleukin 6 IL-6 Forkhead box P3 Foxp3 Interleukin 10 IL-10 xxi IL-2 receptor α chain CD25 IL-7 receptor α chain CD127 Immune dysregulation Polyendocrinopathy, IPEX Enteropathy, X-linked Natural killer cell NK cell Cytotoxic T-lymphocyte-associated Protein 4 CTLA-4 Lymphocyte activation gene LAG3 Cyclic adenosine monophosphate cAMP Alternatively activated macrophages M2 macrophages Leucine rich repeats LRRs Cysteine Cys Latent TGF-β LTGF-β Soluble GARP sGARP Nuclear GARP GARPNU Latency associated peptide LAP Experimental autoimmune encephalomyelitis EAE TGF-β type II receptor TGF-βRII Graft-versus-host disease GvHD Tumor necrosis factor alpha TNF-α Vascular endothelial growth factor VEGF Extracellular matrix ECM Tissue inhibitor of metalloproteinases TIMPs Matrix metalloproteinases MMPs Tumor microenvironment TME Ultra-violet UV Programmed death ligand 1 PD-L1 Myeloid-derived suppressor cells MDSC Epithelial-mesenchymal transition EMT World Health Organization WHO Computed tomography CT Magnetic resonance imaging MRI Isocitrate dehydrogenase IDH Not otherwise specified NOS Not elsewhere classified NEC 2-hydroxyglutarate 2-HG O6-methylguanine-DNA methyltransferase MGMT Temozolomide TMZ Epidermal growth factor receptor EGFR Mitogen-activated protein kinase MAPK Phosphatidylinositol 3 PI3K Blood brain barrier BBB Tumor mutational burden TMB xxii Glioma associated macrophages GAMs Gross total resection GTR Subtotal resection STR Bevacizumab Avastin® Vascular endothelial growth factor A VEGF-A Vascular endothelial growth factor receptor 1 VEGFR1 Vascular endothelial growth factor receptor 2 VEGFR2 Erlotinib Tarceva® Gefitinib Iressa® Depatuxizumab-Mafodotin Depatux-M Cetuximab Erbitux® Immune checkpoint inhibitors ICIs Programmed death-1 PD-1 Programmed death ligand-2 PD-L2 Nivolumab Opdivo® Pembrolizumab Keytruda® Prominin-1 CD133 Reactive oxygen species ROS Asymmetry, Border, Color, Diameter, and Elevation ABCDE Tumor, Node, Metastasis TMN Tumor infiltrating lymphocytes TILs Vemurafenib Zelboraf® Dabrafenib Tafinlar® Extracellular signal-regulated kinases ERK Trametinib Mekinist® Ipilimumab Yervoy® Antibody-dependent cellular cytotoxicity ADCC Immune related adverse events irAE Atezolizumab Tecentriq® Cobimetinib Cotellic® Cyclin-dependent kinase inhibitor 1 p21 Hematopoietic stem cells HSCs Signal transducer and activator of transcription 3 STAT3 Decapping MRNA 2 DCP2 Protein phosphatase 1 regulatory subunit 42 PPP1R42 Nuclear localization sequence NLS Arginine R Lysine K Immunogenic cell death ICD Glial fibrillary acidic protein GFAP Platelet-derived growth factor receptor alpha PDGFR-α Aldehyde dehydrogenase 1 family member A3 ALDH1A3 xxiii Mean fluorescence intensity MFI Antibody Ab Wildtype WT GARP overexpression GARP+ Empty vector EV Newly diagnosed glioblastoma ndGB Recurrent glioblastoma recGB Glioblastoma GB Lower left LL Lower right LR Upper right UR Upper left UL Relative fluorescence units RFU Propidium iodide PI Oxygen consumption rate OCR Extracellular acidification rate ECAR Transmembrane TM University Medical Center Mainz UMC-Mainz Johannes Gutenberg University Mainz JGU Arbeitsgemeinschaft Dermatologische Forschung ADF Arbeitsgemeinschaft Dermatologische Onkologie ADO xxiv 1. INTRODUCTION Modulation of immune responses has emerged as a promising way to treat a wide range of clinical conditions1–7. This cumulative dissertation characterized two novel immunomodulatory approaches, namely the suppressive protein, glycoprotein A repetitions predominant (GARP) and cold atmospheric plasma (CAP). This work provides valuable insights into the functional role of GARP in two distinct immunological settings: wound healing (Paper 1) and cancer (Paper 2)8,9. Furthermore, the influence of CAP on immune cells found in the melanoma microenvironment was investigated (Paper 3)10. 1.1 THE IMMUNE SYSTEM The immune system is an intricate biological network, consisting of physical and chemical barriers, organs, cells, and proteins11. The principal function of the immune system is to protect the cells of the body, either from foreign material, like viruses and bacteria, or from abnormal cells, such as cancer, all the while maintaining immune homeostasis. The immune system can be subdivided two categories, namely innate and adaptive immunity. 1.1.1 INNATE AND ADAPTIVE IMMUNITY Innate immunity is made-up of the skin and mucosal membranes, phagocytic cells, anti- microbial proteins, and the complement system11,12. Acting as the first line of defense, the skin and mucosal membranes form an external barrier, preventing the entry of foreign material into the body. Epithelial cells, that form these barriers, secrete antimicrobial peptides and enzymes, which can disrupt the membranes and cell walls of invading microbes, leading to pore formation11. If these protective barriers become compromised due to injury, plasma proteins in the blood that form the complement system can bind to and coat the surface of the intruding microbe in a process called opsonization11,12. This not only can neutralize the microbe but also increase the recognition efficiency by phagocytic cells, like macrophages, dendritic cells (DCs), and neutrophils. Phagocytic cells can also detect microbes with pattern recognition receptors (PRRs), which bind to evolutionary conserved structures on microbes, called pathogen-associated molecular patterns (PAMPs)11,12. Upon recognition, phagocytic cells engulf the invading cells and destroy them11. Besides opsonization, proteins from the complement system can also trigger inflammation and lyse microbes independently of phagocytic cells. Destruction of microbes by the innate immune system occurs rapidly within the scope of hours11,12. If innate immunity alone fails to clear an infection, an adaptive immune response will take place. One-way innate immunity differs from adaptive immunity is that it is unspecific, meaning that upon repeated exposure to the same foreign material, neither recognition of nor immune response against it is improved. The cellular and humoral components of adaptive immunity consist of T and B lymphocytes (T cells, B cells) as well as antibodies, produced by B cells11. In contrast to innate immunity, adaptive immune responses are specific, meaning that immune responses are orchestrated against a particular foreign antigen11–13. In more detail, lymphocytes can specifically bind to foreign antigens with T or B cell receptors (TCR, BCR) and 1 thereby initiate an adaptive immune response11,13. Upon repeated exposure to the same foreign antigen, both the speed and strength of the adaptive immune response improves with time. 1.1.2 T CELL ACTIVATION Unlike innate immunity, adaptive immune responses can take days to occur. This is due to the time needed for T and B cells, expressing TCRs and BCRs specific to the foreign antigen, to be activated, undergo proliferation, and subsequent differentiation into specialized effector or memory cells11,13. This process is collectively referred to as priming11. Adaptive immune responses begin with the recognition and phagocytosis of foreign material by antigen presenting cells (APCs), frequently DCs11. Once internalized, APCs degrade the material, breaking down proteins into smaller units known as peptides. These peptides undergo additional processing to be loaded onto major histocompatibility (MHC) receptors, which present the antigen on the surface of APCs11,14. Recognition of foreign material by DCs not only results in antigen presentation but also triggers DC maturation and migration to neighboring lymph nodes. Once in the lymph nodes, DCs encounter circulating naïve T cells. If the TCR of the naïve T cell can bind to the presented antigen on the DC, the first stage of T cell activation begins (Fig. 1)11,13. Mature DCs express costimulatory molecules, like CD40 as well as the B7 molecules CD80/86, which bind to CD40 ligand (CD40L) and CD28, expressed on the surface of T cells; this forms the second step of T cell activation (Fig. 1)11,13,14. If this step fails to occur, the T cell will become functionally inactivated or anergic11,13. This serves as a mechanism to protect against the generation of mature autoreactive T cells, which recognize self-antigens and thus could enact destructive immune responses against the healthy tissues of the body. Figure 1: Activation & differentiation of T cells Fig. 1: Schematic representation of T cell activation and differentiation. All signals must be present for successful activation to occur11. T cell differentiation is dependent on the 2 cytokines present during priming. Adapted from “T Cell Co-stimulation”, by BioRender.com (2024)15. Upon successful costimulatory binding, activated T cells undergo rapid proliferation, in a process known as clonal expansion11. Clonal expansion is dependent on the cytokine, interleukin 2 (IL-2), which is secreted by DCs and other activated T cells (Fig. 1)11,13. Clonal expansion occurs over a period of several days and results in the formation of differentiated effector T cells (Teff)11. Once differentiated, Teff leave the lymph nodes and reenter circulation, where they can bind to target cells using their TCR without need of further costimmulation, and thereby enact adaptive immune responses. Therefore, T cell activation represents a key first step in adaptive immune responses. 1.1.3 CD4+ AND CD8+ T CELLS Teff can be broadly classified into two categories based on the surface expression of the MHC co-receptors, CD4 and CD8, as well as the class of MHC receptors that their respective TCRs bind to11,13. Differentiated CD8+ T cells, also known as cytotoxic T lymphocytes (CTL), mainly function to directly kill cells that present foreign antigens in MHC class I receptors. In more detail, CTLs play a vital role in defending the body from intracellular pathogens, including viruses and bacteria, as well as from cancer cells11,16. Of note, all healthy nucleated cells in the human body express MHC class I receptors, allowing CTLs to scan them for signs of intracellular infection11,17. Once CTLs identify a target cell via TCR binding, CTLs release cytotoxic granules, containing perforin, granzymes, and granulysins11,16. Perforin forms pores in the membrane of the target cell, enabling the entry of the apoptosis, or programmed cell death, inducing factors, granzyme and granulysin. Collectively, this results in the rapid death of the target cell. CD4+ T cells differ from CD8+ T cells in several important ways11,13. CD4+ T cells bind antigens presented in MHC class II receptors and can differentiate into various T cell subsets depending on the cytokines present in their environment during priming (Fig. 1). There are five main subsets of CD4+ T cells including helper Th1, Th2, Th17, and T follicular (Tfh) cells as well as regulatory T cells (Tregs)11. Some subsets of CD4+ T cells are referred to as “helper” T cells (Th cells) as they can enhance the function and/or the recruitment of other immune cells, like aiding in the clearance of pathogens or cancer cells from the body. For example, Tfh cells aid in the activation of B cells and thus enable them to differentiate into mature antibody producing plasma cells and memory B cells. This differs from CD8+ T cells, which exert their function directly on target cells. CD4+ T cell subsets are distinguished from each other based off the cytokines they produce as well as the surface markers and transcription factors they express upon differentiation11,13. 1.1.4 T CELL SUBSETS This work primarily focused on the examination of CD4+ Th1 cells and Tregs, which will be discussed now in further detail. 3 1.1.5 TH1 Th1 cells result from the abundance of the cytokines, interferon-gamma (IFN-γ), and interleukin 12 (IL-12), during the T cell activation process11,13. This induces the expression of the transcription factor, T-box expressed in T cells (Tbet), the master transcription factor in Th1 differentiation11,13,18. Expression of Tbet leads to the production of IFN-γ, the principal cytokine produced by Th1 cells, which can act on both cells from the innate and adaptive immune system. The main role of Th1 cells in the body is to aid in the clearance of intracellular infections, especially in the case of macrophages11,18. Upon recognition of a presented microbial antigen on the surface of an infected macrophage, Th1 cells release proinflammatory IFN-γ11. This results in the classical activation of macrophages, also known as proinflammatory M1 macrophages. These M1 macrophages display increased antigen presentation ability and production of proinflammatory cytokines, like IL-12, as well as reactive oxygen and nitrogen species (RONS) that aid in the killing of intracellular pathogens11,19. Altogether, IFN-γ produced by Th1 cells enhances the abilities of infected macrophages to both engage with other immune cells and to clear intracellular infections. IFN-γ produced by Th1 cells not only activates macrophages but also CTLs. The cytolytic capability of CD8+ T cells is dependent on exposure to IFN-γ11,19. Upon exposure, CD8+ T cells upregulate expression of the IL-2 receptor and granzyme. Furthermore, IFN-γ influences the proliferation of CD8+ T cells upon antigen exposure19–21. Responding to IL-2 is critical in the activation and differentiation of naïve CD8+ T cells to CTLs as previously described in “1.1.2 T cell activation”11,13. Additionally, as mentioned in “1.1.3 CD4+ and CD8+ T cells”, granzyme can induce apoptosis in CTL target cells11,16. Of note, Th1 cells have an important function in anti- cancer immunity by producing IFN-γ and enhancing the cytolytic function and proliferation of tumor killing CTLs11,19–22. Although Th1 type 1 immune responses mediated by IFN-γ help clear intracellular infections, dysregulated production of IFN-γ has been linked to several autoimmune diseases, like rheumatoid arthritis and systemic lupus erythematosus11,18,22. Therefore, it is important to tightly regulate Th1 immune responses to maintain immune homeostasis. This is where Tregs come into play. 1.1.6 REGULATORY T CELLS (TREGS) In contrast to Th cells, Tregs mediate immune tolerance11,23,24. This means that they suppress immune responses against antigens that their TCRs recognize11. Therefore, Tregs are essential in preventing autoimmunity by maintaining immunological tolerance to the body’s own tissues11,23,24. Additionally, they regulate homeostasis of the immune system by suppressing immune responses. This includes but is not limited to regulating inflammation in wound healing as well as maintaining peripheral tolerance to commensal bacteria and consumed food11,25–27. 4 Tregs are differentiated by the cytokines, transforming growth factor beta (TGF-β) and IL-2, in the absence of pro-inflammatory cytokines, like interleukin 6 (IL-6), during T cell activation11. In the gastrointestinal tract, all-trans-retinoic acid and TGF-β are essential for the development of intestinal Tregs. However, it is important to clarify that this work only focused on immune cells isolated from peripheral blood. Tregs express the master Treg transcription factor, forkhead box P3 (Foxp3)11,23,24,28. This transcription factor regulates the expression of TGF-β and interleukin 10 (IL-10), two key anti- inflammatory cytokines produced by Tregs. In addition to the expression of Foxp3, Tregs can be identified by surface expression of CD4, CD25 (high), and CD127 (low) (CD4+CD25highCD127low/-Foxp3+ Tregs)28. CD25, or IL-2 receptor α chain, is upregulated upon T cell activation and forms part of the high affinity IL-2 receptor with IL-2 receptor β and common γ chains11. Because Tregs cannot produce their own IL-2, CD25 is constitutively expressed to obtain IL-2 from the local environment with high affinity. CD127, or IL-7 receptor α chain, is normally expressed on the surface of naïve T cells, but it is downregulated upon T cell activation. Activated Tregs can be identified by the upregulation of the transmembrane protein GARP onto the cell surface28–30. Foxp3 plays an essential role in both the differentiation and suppressive capacity of Tregs31,32. Missense mutations in the FOXP3 gene have been found to lead to the development of x- linked recessive autoimmune syndrome IPEX (Immune dysregulation, Polyendocrinopathy, Enteropathy, X-linked) in humans31,33–35. Patients with the disease lack functional Tregs and as a result, experience severe unchecked autoimmunity. Symptoms include but are not limited to severe allergic inflammation and autoimmune polyendocrinopathy (autoimmune responses against multiple endocrine organs), often resulting in premature death. In mice, a frameshift mutation of the FOXP3 gene, commonly referred to as the “scurfy” mutation, renders Scurfin, the murine equivalent of Foxp3, incapable of binding DNA, and thus unable to exhibit its function as a transcription factor36. Mice with scurfy mutations have lower numbers of Tregs, resulting in the development of fatal autoimmune disease. 1.1.7 SUPPRESSIVE MECHANISMS OF TREGS Tregs suppress a wide variety of immune cells, including both adaptive (T cells and B cells) and innate (DCs, natural killer (NK) cells, macrophages, and monocytes) immune cells23,28,37,38. The suppressive functions of Tregs are mediated through multiple mechanisms — highlighting the protective role Tregs play in preventing autoimmunity and maintaining peripheral tolerance. These mechanisms of immune suppression can be broadly classified as either cell contact dependent or independent23,24,28,37,38. Tregs exhibit several contact dependent mechanisms of immune suppression. Firstly, Tregs express the inhibitory protein, cytotoxic T-lymphocyte-associated Protein 4 (CTLA-4), on their surfaces (Fig. 2A)24. CTLA-4 has a higher binding affinity for B7 molecules on APCs than CD28 expressed by naïve T cells. This prevents effective costimmulation of naïve T cells from occurring, and thus inhibits T cell activation and subsequent differentiation into Teff. It is also known that Tregs express lymphocyte activation gene (LAG3), which can bind MHC class II 5 receptors expressed on the surface of immature DCs (Fig. 2A)24,39. As a result, this prevents the maturation of DCs and thereby their ability to costimmulate T cells. Additionally, it was discovered that Tregs can transfer cyclic adenosine monophosphate (cAMP) to Teff via gap junctions, which in turn inhibits the proliferation and IL-2 synthesis of Teff (Fig. 2B)37. Tregs employ multiple contact independent mechanisms of immune suppression. It has been proposed that Tregs can “starve” Teff by depleting IL-2 in the environment, needed for Teff cell survival (Fig. 2C)24,40,41. This is a result of Tregs outcompeting Teff for IL-2 via elevated expression of the high affinity IL-2 receptor, CD25. Tregs have been found to produce cytolytic granzymes and perforins, similarly to CTLs (Fig. 2D)24,42,43. This enables Tregs to directly kill B cells, NK cells, and CTLs, consequently suppressing their functions. However, production of TGF-β and IL-10 is predominantly responsible for the contact independent suppression mechanisms of Tregs (Fig. 2E). TGF-β and IL-10 inhibit the development of Th cells as well as promote the differentiation of Tregs from naïve T cells44. Notably, Tregs are also able to induce Tregs from differentiated Teff through the secretion of TGF-β and IL-10 as well as the transfer of cAMP in a process known as infectious tolerance44,45. It is known that TGF-β inhibits the development and thereby the proliferation and function of Th1 and Th2 cells11. IL-10, produced by Tregs, can both downregulate the expression of costimulatory molecules and the production of proinflammatory cytokines by APCs, further preventing T cell activation and differentiation. TGF-β and IL-10, produced by Tregs, can also induce tolerogenic phenotypes in DCs and macrophages38,46. In more detail, TGF-β and IL-10 drive monocyte differentiation into alternatively activated or anti-inflammatory M2 macrophages. Another mechanism of suppression utilized by activated Tregs is the overexpression of the TGF-β activating protein, GARP. 6 Figure 2: Suppressive mechanisms of regulatory T cells (Tregs) Fig. 2: Suppressive mechanisms employed by regulatory T cells (Tregs). (A-B) indicate contact dependent mechanisms, whereas (C-E) display contact independent mechanisms. Adapted from “T Cell Co-stimulation”, by BioRender.com (2024)15. 1.1.8 GLYCOPROTEIN A REPETITIONS PREDOMINANT (GARP) GARP is a type I transmembrane protein, weighing around 78 kDa, and consists of 662 amino acids47. In the past, GARP has been referred to by several names, including glycoprotein A repetitions predominant, garpin, leucine-rich repeat-containing protein 32, CPPRDD, and D11S833E28. However, for the rest of this work, the protein will be referred to as GARP. GARP is encoded by the LRRC32 gene (gene ID: 2615)48. LRRC32 was first described by Ollendorff et al., 1992 as a gene amplification in breast cancer found in chromosome 11 (11q13.5-11q14)49. LRRC32 contains two exons that encode three domains: a 15 amino acid cytoplasmic tail, a hydrophobic transmembrane domain, and an extracellular domain, 7 consisting of 20 leucine rich repeats (LRR) that comprise about 70% of the protein28,47. Within the 7th and 12th LRR, two cysteines (Cys-192 and Cys-331) are encoded. These amino acids enable two disulfide bonds to form between GARP and its binding partner latent TGF-β (LTGF- β)28,50. A signal peptide is encoded in the N-terminus of the protein. This peptide must undergo cleavage before GARP is expressed on the surface of the cell28,51. The prominent LRRs and the post-translational glycosylation of the protein are responsible for its namesake. The GARP transcript, also known as GARP mRNA, is diffusely expressed in healthy tissues found throughout the body, including but not limited to the placenta, heart, lung, liver, pancreas, kidney, skeletal muscle, and lymphoid tissues28,48,52. In contrast, the expression of the GARP protein is restricted to a narrow range of cell types, namely immune cells (platelets, activated Tregs, macrophages, activated B cells), mesenchymal cells (mesenchymal stem cells, fibroblasts, hepatic stellate cells), and endothelial cells28–30,53–60. In the past, it was well established that GARP is expressed on the cell surface and is secreted as a soluble factor, known as soluble GARP (sGARP) (Fig. 3)28,61–64. However, in recent years, Zimmer et al., 2019 reported for the first time that GARP is also enriched in the nuclei (GARPNU) of activated Tregs, tumor cells, and glioblastoma stem-like cells (GSCs) (Fig. 3)65. Figure 3: Localization of GARP Fig. 3: GARP is differentially localized inside and outside the cell. Created with BioRender.com15. GARP functions by binding LTGF-β, an inactive precursor form of TGF-β bound to latency associated peptide (LAP) (Fig. 4A)28,29,66,67. In more detail, GARP binds every isoform of TGF-β (TGF-β1-3) and aids in its activation. TGF-β is a pleiotropic cytokine that is expressed throughout the body. It mediates several fundamental biological processes, such as development, tolerance, wound healing, and cancer28,68. As a result, expression of TGF-β is tightly regulated in a multi-step process28. GARP plays an essential role in the final steps of TGF-β activation by presenting LTGF-β on the cell surface (Fig. 4A)28,69. This enables LTGF-β to bind to αVβ6 and αVβ8 integrins, which releases mature and biologically active TGF-β into the 8 surrounding environment. It is important to point out that activating LTGF-β has only been attributed to the surface and soluble forms of GARP (sGARP), whereas the functional relevance of GARPNU remains unclear28,65,70. This means that GARP is required for both the surface expression and activation of LTGF-β, and thereby regulates its bioavailability50. As GARP controls the expression of TGF-β, it is no surprise that the protein has been identified as an important factor in TGF-β mediated processes. Figure 4: Functions of GARP Fig. 4: Functions of GARP. GARP functions as a LTGF-β activating protein (A) and induces immunosuppressive cell types (B). Created with BioRender.com15. GARP has been found to play an essential role in development. Wu et al., 2017 reported that knockout of GARP in mice was lethal within 24 h after birth67. GARP knockout mice displayed developmental defects in palatogenesis (palate formation) possibly impairing their ability to feed. It was speculated that cleft palate formation resulted from the reduced production of TGF-β3. Disruption of LRRC32 has also been linked to developmental defects in humans as shown in a case study by Harel et al., 201971. They described three patients (2-3 years old) who had a homozygous stop gain variant of LRRC32 (c.1630C>T, p.(Arg544Ter), also known as a premature stop codon. This resulted in a truncation of both the intracellular and transmembrane domains of GARP and thus a loss of the protein’s functionality. The patients exhibited a range of developmental defects, including but not limited to cleft palate formation, proliferative retinopathy (blood vessel and scar formation over the retinas), hypotonia (decreased muscle tone), and ventriculomegaly (enlargement of the cranial ventricles). Notably, Roubin et al., 1996 showed in mice that the GARP mRNA is expressed in 9 late organogenesis, notably in the nasal cavity, eyes, muscle, and choroid plexus, offering a possible explanation for why these developmental defects may have occurred72. Dysregulation of GARP has been associated with numerous inflammatory conditions. Downregulation of the GARP transcript has been observed in the blood of patients with inflammatory bowel disease in comparison to healthy controls73. LRRC32 is considered a risk locus for many inflammatory diseases (e.g., atopic dermatitis, eczema, colitis, primary immunodeficiency) and allergic conditions (e.g., asthma, eosinophilic esophagitis)28,74–79. In addition, decreased numbers of CD25+Foxp3+GARP+ Tregs have also been inversely associated with the immune inflammatory response system, a form of chronic inflammation, and neurotoxicity in patients with major depressive disorder80. Similarly to mutations in FOXP3 as described in “1.1.6 Regulatory T cells (Tregs)”, disruption of LRRC32 has been connected to severe autoimmunity. It has been shown that loss of GARP expression on Tregs leads to the development of fatal autoimmunity in mice28,81,82. In more detail, Zhang et al., 2015 demonstrated that knocking out the obligate chaperone of GARP, heat shock protein GP96, in Tregs resulted in the generation of dysfunctional Tregs81. These Tregs were characterized by unstable expression of Foxp3 and reduced suppressive function. Decreased suppressive capacity was attributed to loss of surface GARP expression and consequently reduced production of mature TGF-β. This led to uncontrolled inflammatory T cell activation and the development of fatal multi-organ inflammatory disease. Similar findings were also observed in patients with primary immunodeficiency, a disorder characterized by immune dysregulation, who had mutations in LRRC3282. Lehmkuhl et al., 2021 also demonstrated that GARP deficient mice were more susceptible to developing autoimmune disorders, including severe arthritis, experimental autoimmune encephalomyelitis (EAE), and colitis82. Of note, LRRC32 appears to be evolutionary conserved, with humans and mice having a 80% similarity in nucleotide sequence72. Collectively, these studies indicate that GARP protects against autoimmunity by activating TGF-β – a cytokine needed for the development and suppressive function of Tregs. GARP, itself, has also been shown to display strong anti-inflammatory properties via the modulation of T cell responses. Hahn et al., 2016 reported that sGARP induced Foxp3+ Tregs and “M2-like” macrophages (Fig. 4B)61. They could also show that sGARP strongly suppresses Teff cell function. sGARP inhibited the proliferation and production of effector molecules in both Th1 cells (IL-2, IFN-γ) and CTLs (IFN-γ, granzyme B) (Fig. 4B). Similar GARP-mediated effects were shown by Zimmer et al., 2020 using GARP expressing platelets56. Interestingly, blocking experiments using antibodies against TGF-β1-3 and the TGF-β type II receptor (TGF- βRII) revealed that the suppressive effects of GARP are only partially mediated by TGF-β56,83. Therapeutically, sGARP has been successfully applied to protect animals from developing lethal xenogeneic graft-versus-host disease (GvHD) by preventing destructive T cell mediated inflammation83. Additionally, in a humanized mouse model of airway inflammation, sGARP treatment was found to reduce airway inflammation and hyperresponsiveness in a TGF-β dependent manner84. 10 Altogether, these findings demonstrate the essential role of GARP in maintaining peripheral tolerance, which is mediated in part through its function in activating LTGF-β. The immunomodulatory effects of GARP are implicated in a broad range of immunological contexts, including wound healing and cancer. GARP was further characterized in these distinct immunological settings in Paper 1 and Paper 2 of this work8,9. 1.2 WOUND HEALING Upon tissue injury, a complex multi-step process, known as wound healing, begins in order to repair the damage and to prevent infection11,85,86. Tissue resident cells, immune cells, platelets, the vascular system, and soluble mediators, like cytokines and chemokines, work together to regenerate the tissue. Wound healing consists of four overlapping steps: (1) hemostasis, (2) inflammation, (3) proliferation, and (4) remodeling (Fig. 5). Figure 5: Stages of wound healing Fig. 5: Schematic representation of the four phases of wound healing in the skin. These phases can be divided into inflammatory (hemostasis, inflammation) or anti-inflammatory (proliferation, remodeling) immune responses. Adapted from “Wound Healing”, by BioRender.com (2024)15. Hemostasis occurs rapidly within minutes following damage to blood vessels86,87. To stop bleeding, blood vessels near the wound site constrict, and platelets aggregate to form clots (Fig. 5)86. 11 Inflammation begins following hemostasis (Fig. 5). This step is important in preventing infection and typically lasts a few days87. Inflammation stems from the Latin word inflammatio, meaning to set ablaze. Fittingly, the main symptoms of inflammation are redness (rubor), heat (calor), swelling (tumor), and pain (dolor), which were correctly described by Aulus Cornelius Celsus 2000 years ago88,89. During inflammation, Th1 cells activate tissue resident macrophages to a proinflammatory M1 phenotype, which in turn secrete proinflammatory cytokines, like tumor necrosis factor alpha (TNF-α)90–93. These soluble mediators trigger vasodilation, the recruitment of immune cells, and the activation of endothelial cells that line blood vessels near the site of injury11. Vasodilation increases blood flow to the site of injury, responsible for the redness and heat associated with inflammation. Increased blood flow brings an influx of immune cells, like neutrophils and monocytes, into the damaged tissue. Neutrophils primarily function to eliminate pathogens via phagocytosis and the secretion of cytotoxic granules91,94. Recruited monocytes differentiate into M1 macrophages, which phagocytose pathogens and cellular debris, as well as secrete proinflammatory cytokines11,93. Activated endothelial cells upregulate cell adhesion molecules that enable immune cells to attach to blood vessels and to enter the damaged tissue in a process known as extravasation11. Blood vessels near the site of injury increase vascular permeability allowing fluid from the blood and plasma proteins, including those from the complement system, to accumulate in the damaged tissue. This acts as another layer of defense against infection and is responsible for the swelling and pain associated with inflammation. Lastly, during this stage, additional clotting in microvessels at the site of the damaged tissue occurs to prevent the spread of infection into the blood stream. The proliferative phase represents the regrowth of tissue at the site of injury and a shift in the immune response from inflammatory to anti-inflammatory (Fig. 5)87. This is mediated in part by macrophages that transition from a proinflammatory M1 phenotype to an anti- inflammatory M2 phenotype, triggered by the phagocytosis of dead neutrophils at the wound site91,93. These M2 macrophages support wound healing through several mechanisms87,91,95. Firstly, they secrete growth factors, like vascular endothelial growth factor (VEGF) and TGF- β1, which encourage new blood vessel formation, in a process called angiogenesis, as well as promote the proliferation of fibroblasts and epithelial cells. Fibroblasts, in turn, secrete extracellular matrix (ECM), depositing collagen and fibronectin into the wound site. M2 macrophages support the accumulation of ECM by preventing its degradation through the production of tissue inhibitor of metalloproteinases (TIMPs)95. The newly formed vascularized ECM is known as granulation tissue, which functions to fill the wound site and serves as an attachment site for epithelial cells. Epithelial cells migrate to the wound site and cover the newly formed tissue, in a process known as reepithelization. Additionally, M2 macrophages suppress inflammatory immune responses through the production of IL-10 and TGF-β93,95. The end of the proliferative phase is marked by wound contraction and closure by myofibroblasts87. 12 The remodeling stage is the longest stage of the wound healing process, lasting weeks to up to a year depending on the initial injury (Fig. 5)87. In this phase, the mechanical strength of the injured tissue is restored. This is accomplished by replacing collagen fibers in the wound site from type III to more resistant type I as well as restructuring the fibers so that they align along tension lines. M2 macrophages facilitate this remodeling process by secreting matrix metalloproteinases (MMPs)87,95. As the remodeling stage progresses, macrophages, myofibroblasts, and vascular cells undergo apoptosis. The resulting collagen-rich scar tissue marks the end of the wound healing process. Disruption to any of these wound healing stages can result in infection, the development of chronic wounds, and the formation of excessive scar tissue26. Chronic wounds, in particular, represent a significant and growing global healthcare burden — costing the US alone over $25 billion each year96. Chronic wounds often occur in the growing elderly, diabetic, and obese populations. As a result, the incidence of chronic wounds is rapidly increasing. Chronic wounds occur when wound healing fails to proceed to the anti-inflammatory proliferation stage (Fig. 5)95,97. Instead, the wound remains stuck in the inflammation stage, preventing it from properly healing and effectively responding to available therapies. Therefore, the wound healing process must be tightly regulated, especially as the immune response transitions from inflammatory (inflammation phase) to anti-inflammatory (proliferation phase) (Fig. 5)87,97. Herein, Tregs have been shown to play an important role. It is becoming increasingly recognized that Tregs orchestrate the wound healing process by controlling inflammation25–27. Nosbaum et al., 2017 showed in vivo that the number of Tregs in the wound site increased by 20-fold seven days after the initial injury, during the transition from the inflammation to the proliferation phase25. Of note, the majority of these Treg were found to be activated. Depletion of said Tregs decreased reepithelization and wound closure, essential components of the proliferation phase. Furthermore, depletion of Tregs during the inflammation phase resulted in increased numbers of inflammatory IFN-γ producing Teff and thereby an increased number of M1 macrophages, which persisted throughout the duration of wound closure (14 days). Meanwhile, control animals had nearly no M1 macrophages present after 14 days, offering a possible explanation for the observed delay in wound closure in mice depleted of Tregs. Collectively, this indicates that Tregs play an important role in facilitating the transition between the inflammation and proliferation stages of wound healing. This is achieved by the regulation of type I immunity, specifically limiting the number of IFN-γ producing Teff and M1 macrophages. In the case of chronic wounds, which remain stuck in the inflammation phase, the induction of Tregs represents a promising therapeutic approach. As discussed in “1.1.8 Glycoprotein A repetitions predominant (GARP)”, GARP is a potent inducer of Tregs and “M2-like” macrophages, which are required for the transition to the anti-inflammatory proliferation phase (Fig. 4B)25,56,83,87,95. GARP also inhibits IFN-γ producing Teff, which help sustain the inflammation phase. This makes modulating GARP levels a promising therapeutic approach 13 for the treatment of chronic wounds. However, up until now, the possible involvement of GARP in wound healing remains unclear. It can be speculated that GARP may influence wound healing as it regulates TGF-β, a cytokine that has a well-established connection to wound healing28,29,50,66–68,98. TGF-β has been found to be involved in all stages of the wound healing phase, including inflammation initiation, granulation tissue formation, angiogenesis, re-epithelization, wound contraction, and collagen organization98. Despite this, the role of GARP in early stages of the wound healing phase has yet to be explored. Therefore, this study set out to examine how GARP may influence T cells and macrophages, key cellular mediators of wound healing , especially in the inflammation and proliferation phases (Paper 1)8. In more detail, GARP was evaluated as a possible underlying immunological mechanism responsible for the wound healing properties of the autologous platelet concentrate, injectable platelet rich fibrin (iPRF)8. On the flipside, in other immunological settings, like cancer, GARP has been linked to driving disease progression and to poor patient prognosis28,56,60,63,99–104. 1.3 CANCER Cancer refers to a group of over 100 diseases, which can originate in nearly every tissue of the body105. These diseases are chiefly characterized by the uncontrolled proliferation of abnormal autologous cells. Globally, cancer is one of the leading causes of death with nearly 10 million deaths attributed to the disease in 2020105,106. Depending on where the cancer arises, it may manifest in the form of a solid tumor, either restrained to the primary tissue of origin (benign), or it may be capable of invading neighboring tissues and even metastasizing to distant parts of the body (malignant). Widespread metastases can interfere with the functioning of essential organs; therefore, they are attributed as the main cause of death from cancer. Tumors do not consist solely of cancer cells. Instead, they are made-up of a dynamic and heterogeneous environment, including infiltrating immune cells, resident stromal cells, blood vessels, ECM, and secreted factors107,108. Collectively, this is known as the tumor microenvironment (TME). Of note, the composition of the TME can vary greatly depending on the cancer type. 1.3.1 CANCER INITIATION Cancer cells are derived from healthy autologous cells, which have undergone a complex malignant transformation105,108. The development of cancer usually occurs slowly, most often resulting from the gradual accumulation of mutations in tumor suppressor and proto- oncogenes overtime109. Tumor suppressor genes refers to a category of genes that negatively regulate the cell cycle, aid in the recognition and repair of damaged DNA, and can induce programmed cell death as a mechanism to prevent abnormal cells from further proliferating110. Conversely, proto-oncogenes promote cell division and survival and therefore, are tightly regulated in healthy cells. When proto-oncogenes are mutated, in which they become constitutively activated or overexpressed, they are referred to as oncogenes109. 14 The gradual accumulation of mutations in both tumor suppressor genes and proto-oncogenes can result in uncontrolled cell division and thereby malignant transformation108,109. DNA repair genes are an important class of tumor suppressor genes, and as their name suggests, they function to ensure the genetic integrity of the cell111. These genes aid in both the detection and the repair of damaged DNA, frequently resulting from errors in replication during the S phase of cell cycle or from exposure to carcinogens. Individuals can be genetically predisposed to develop certain cancers by inheriting mutated DNA repair genes, such as BRCA1, BRCA2, or TP53112–114. These mutations result in a reduced efficacy in recognizing and repairing damaged DNA. This allows cells with damaged DNA to proceed through the cell cycle, when they would have normally undergone programmed cell death, and to pass down altered DNA to daughter cells. Over time, this enables a faster acquisition and accumulation of mutations in the genome, including in tumor suppressor genes and proto-oncogenes. This slow but steady alteration of the genome is known as genomic instability and is often considered a driving force behind developing cancer115. This is because it results in an increased frequency of mutations, which can be either advantageous or harmful for cancer cells. Advantageous mutations that aid in the survival of cancer cells are selected for and passed down to daughter cells, increasing cellular fitness. Mutations do not only result from ineffective DNA repair genes but also from the exposure of cells to external physical, chemical, and biological carinogens105,116. Carcinogens increase the risk of developing cancer, and they can be considered as environmental and lifestyle risk factors for developing the disease. Carcinogens can directly damage the structural integrity of DNA or bind to DNA, preventing effective DNA replication. When DNA repair mechanisms fail to recognize and correct these damages, a mutation will result, which can be inherited by daughter cells. Carcinogens can also act indirectly by resulting in conditions that support the development of the disease, such as triggering chronic inflammation (e.g., Helicobacter pylori) and immunosuppression (e.g., human immunodeficiency virus)117,118. Although risk factors vary depending on the cancer type, common environmental and lifestyle risk factors, include but are not limited to exposure to ultra-violet (UV) radiation, x-rays, air pollution, tobacco use, alcohol consumption, obesity, poor diet, and low physical activity105,116,119,120. Aging is considered the most important risk factor for developing cancer105,121,122. This is due to several factors, namely the accumulation of mutations over one's lifetime, the reduction in the efficiency of DNA repair, and the impaired functioning of the immune system. 1.3.2 IMMUNE SURVEILLANCE In addition to keeping the body free from foreign pathogens, the immune system detects and destroys cancer cells123,124. This concept, also known as the immune surveillance hypothesis, was originally formulated by Frank Macfarlane Burnet and Lewis Thomas in the 1950’s123,125– 128. Since then, the hypothesis has been expanded to include four distinct phases: (1) cancer 15 initiation (described in “1.3.1 Cancer Initiation”), (2) elimination, (3) equilibrium, and (4) escape (Fig. 6)124,129. Figure 6: The immune surveillance hypothesis Fig. 6: Schematic representation of immune surveillance. Adapted from “Cancer Immunoediting”, by BioRender.com (2024)15. Elimination is the second phase of immune surveillance (Fig. 6). During this stage, immune cells effectively recognize and destroy cancer cells124,130,131. The immune system can distinguish cancer cells from healthy cells by several key differences. Due to acquired genomic instability, malignant cells accumulate mutations in genes131,132. When these genes are translated, this can lead to the production of altered proteins that are not normally produced by the host organism. CTLs can recognize parts of these proteins as foreign, also known as neoantigens, when they are presented by APCs or by the tumor cell itself. Upon recognition, CTLs destroy target cancer cells through the release of cytotoxic granules described in “1.1.3 CD4+ and CD8+ T cells”131,133. NK cells aid in the recognition of tumor cells, namely through the detection of abnormal ligands on the surface of tumor cells130,133. For example, tumor cells can express NK cell activating ligands, like NKG2D. When bond to the respective NK cell receptor, NK cells become activated and target the malignant cell for destruction. NK cells can also recognize malignant cells as “non-self” through their downregulation of MHC class I receptors131,133. In both cases, NK cells directly kill the identified cancer cell through the delivery of cytotoxic granules133. If the immune system fails to completely eliminate tumor cells, the equilibrium phase begins (Fig. 6)124. During this stage, tumor cells and immune cells coexist in a balance where tumor cell growth is restrained by the immune system, also known as tumor dormancy134. Tumor cells continue to proliferate and accumulate mutations that allow them to better adapt to their environment. Meanwhile, the immune system exerts a strong selective pressure on the cancer cells, in a process referred to as immunoediting. Herein, IFN-γ and IL-12, a stimulator of IFN-γ production, play an important role in enhancing the proliferation and cytotoxicity of Th1, CTLs, and NK cells, thereby aiding in the destruction of tumors cells134–136. In response, cancer cells evolve mechanisms of immune evasion to minimize their detection by the 16 immune system131. Cancer cells may downregulate the expression of immunogenic neoantigens to avoid detection by T cells. They may also express coinhibitory checkpoint molecules, like programmed death ligand 1 (PD-L1), to repress T cell mediated immune responses. Additionally, tumor cells can reduce their antigen presenting capabilities. For example, tumor cells can acquire mutations in MHC molecules or downregulate their expression131,137. This results in the reduced presentation of neoantigens to CTLs as well as reduced detection by NK cells. Tumor cells employ mechanisms of immune tolerance to repress anti-tumor immune responses131,138. Cancer cells secrete suppressive factors, like TGF-β and IL-10, into their local environment. These factors recruit and promote the development of suppressive immune cell types, such as Tregs, tumor-associated macrophages, and myeloid-derived suppressor cells (MDSC) as well as inhibit the maturation of DCs, necessary for T cell activation138. These suppressive cells, coupled with factors produced by tumor cells themselves, work together to create an immunosuppressive microenvironment and to inhibit anti-tumor immune responses. In this way, tumor cells actively transform their microenvironment to evade detection by the immune system. By the end of the equilibrium phase, cancer cells will have acquired several mechanisms to evade detection by the immune system131. The escape phase begins once tumor cells can proliferate unrestrained by the immune system (Fig. 6). It is in this phase that cancer becomes clinically detectable. 1.3.3 GARP IN CANCER As described in detailed in section “1.1.8 Glycoprotein A repetitions predominant (GARP)”, GARP is a potent mechanism of immunological tolerance through its regulation of the suppressive cytokine, TGF-β28–30,64,66,67,81–83. Cancer cells frequently overexpress GARP to evade detection by the immune system. Elevated expression of the GARP mRNA transcript and protein has been reported across a broad range of cancer types, including but not limited to melanoma, glioblastoma, lung cancer, breast cancer, bone sarcoma, gastric, and papillary thyroid carcinoma28,49,61,65,99,100,102,104,139,140. Secretion of sGARP into the TME by cancer cells (e.g., melanoma), activated Tregs, and platelets has also been reported28,56,61,63. GARP exhibits several suppressive effects in the TME. GARP induces tolerogenic immune cells, namely Tregs and M2 macrophages (Fig. 4B)28,55,56,61,63,83. It is well known that these cells promote tumor survival by downregulating anti-tumor immune responses and sustaining the suppressive TME131,138,141. Additionally, GARP prevents T cell mediated immune responses against the tumor from occurring. In more detail, GARP inhibits the proliferation and production of effector molecules in both Th1 cells (IL-2, IFN-γ) and CTLs (IFN-γ, granzyme B)61,65,83. These cells play an essential role in detecting and destroying tumor cells131. In this way, GARP production by tumor cells results in both the accumulation of tumor promoting immune cells in the TME as well as the downregulation of anti-tumor immune responses. 17 Besides suppression in the TME, GARP has been linked to oncogenesis. Carrillo-Gálvez et al., 2020 showed in bone sarcoma that GARP enhances the proliferation of tumor cells and their resistance to irradiation and chemotherapy99. Metelli et al., 2016 found that GARP promotes the malignant transformation of murine mammary cells in vitro via the upregulation of epithelial-mesenchymal transition (EMT) markers and by increasing the migratory ability of the cells100. Furthermore, they demonstrated that GARP expression increased tumor growth and metastasis in an in vivo murine breast cancer model. Both Carrillo-Gálvez et al., 2020 and Metelli et al., 2016 found that the observed tumor promoting effects of GARP are mediated in a TGF-β dependent manner99,100. Collectively, these studies demonstrate that tumor cells upregulate GARP to not only avoid detection by the immune system but also to promote tumor growth. This suggests that the protein is indispensable for oncogenesis and may have multiple tumor-promoting functions in the TME. Furthermore, high expression of GARP on both tumor cells and tumor supporting immune cells (activated Tregs, platelets) makes GARP a promising therapeutic target and biomarker candidate for cancer. Therefore, this work focused on further characterizing the role of GARP in cancer, specifically in glioblastoma biology (Paper 2)9. 1.4 GLIOBLASTOMA Glioblastoma, previously known as glioblastoma multiforme, refers to a highly heterogeneous and malignant type of brain tumor. The WHO classifies glioblastoma as the most aggressive form of astrocytoma, grade IV142. Although it is possible for glioblastoma to occur in all parts of the brain, tumors are most often found in the supratentorial compartment, specifically the frontal and temporal lobes143–145. It remains unclear from which cell type(s) glioblastoma arises. It is thought that malignant cells may originate from neural stem cells and glial precursor cells, e.g., oligodendrocyte and astrocytic precursor cells146–148. 1.4.1 EPIDEMIOLOGY Glioblastoma is the most common primary brain tumor in adults. Nearly 50% of all malignant central nervous system tumors are attributed to glioblastoma, and the disease has an incidence rate of 3.19 cases per 100,000 persons (age adjusted, United States, 2006- 2010)119,142,147,149,150. It is well known that men have a higher incidence of the disease and a reduced survival rate in comparison to women151,152. Reasons for these sex disparities remain unclear, but studies have shown that hormonal differences may play a role151,153–155. The incidence of glioblastoma has been associated with ethnicity152,156. Overall, Non-Hispanic Whites have the highest incidence of the disease. In comparison, American Indians and Alaska Natives have a 40% lower incidence. Additionally, the incidence in Whites is 1.95 times greater than in Blacks as well as 2.97 times higher than in Asian or Pacific Islanders156. Increased incidence of the disease has been linked to higher socioeconomic status; however, the underlying reasons why remain poorly understood157. 18 From 1978 to 1992, the incidence of glioblastoma in the US increased by 2.9%. This can be explained in part by improvements in diagnostic technologies158. For example, the introduction of computed tomography (CT) and magnetic resonance imaging (MRI) into the clinics enabled the improved detection of brain tumors158–160. However, from 1992 to 2018, incidence slowed down, increasing only by 0.2%158. Initial projections suggest that the annual incidence of glioblastoma cases will increase by 50% in the next 30 years, explained in part by the simultaneously aging and growing global population161. 1.4.2 RISK FACTORS There are almost no known risk factors for developing glioblastoma. Prior exposure to mutagenic ionizing radiation is the only known environmental risk factor of the disease119. Lifestyle factors, including alcohol consumption and mobile phone use, are not associated with risk of developing glioblastoma119,162–164. Age is the best associated risk factor143,165–167. Most cases occur in adults over the age of 40, and incidence peaks between 75 to 84 years of age. Individuals with allergic conditions, such as asthma, atopic dermatitis, and eczema, have been associated with a reduced risk of developing the disease119,168. Although it is not fully understood why, it is speculated that the hyperactivity of the immune system in these conditions may have a protective anti-tumor effect. Most glioblastoma cases are thought to arise spontaneously without a family history of disease169,170. However, a fraction of these cases (~5%) is linked to hereditary cancer syndromes, in which inheritable mutations predispose an individual to developing cancer. The following genetic conditions with mutations in tumor suppressor genes have been linked to increased risk of developing glioblastoma: Li-Fraumeni syndrome (TP53), Neurofibromatosis type 1 (NF1), and Turcot syndrome type II (APC)169. Mutations in mismatched repair genes, responsible for repairing improper DNA insertions or deletions, as seen in Turcot syndrome type I and Lynch syndrome are also associated with increased risk of developing glioblastoma. Collectively, these conditions result in either increased cell growth or genomic instability, driving oncogenesis. 1.4.3 DIAGNOSIS There are no preventive screening methods for glioblastoma. Individuals tend to first exhibit abnormal neurological symptoms, such as persistent headaches or seizures, before being recommended by their physician for neurological imaging171. Some of the most common methods are by MRI and CT158. These techniques provide essential diagnostic information, regarding the size, location, and presence of necrosis in the tumor, amongst other things171. Although imaging can reveal the presence of a brain tumor, diagnosis of the cancer type is confirmed following surgical resection where the excised tumor undergoes histological und molecular evaluation171,172. Four different types of glioblastomas can be distinguished from one and other based off their molecular characteristics173: 19  Glioblastoma, isocitrate dehydrogenase (IDH) wildtype: the majority of cases, often primary tumors that develop around 60 years of age  Glioblastoma, IDH mutant: most commonly secondary glioblastomas that develop in younger individuals with preexisting lower grade astrocytomas (WHO grades I-III)  Glioblastoma not otherwise specified (NOS): unclear IDH mutational status resulting from insufficient material for histological and molecular testing  Not elsewhere classified (NEC) glioblastoma: glioblastomas that do not fit the categories mentioned above, resulting from inconsistent traits, not yet classified by the WHO Patients with IDH wildtype glioblastomas have worse prognoses and exhibit a more aggressive form of the disease in comparison to those with IDH mutant glioblastomas174–176. Normally, the IDH genes (IDH1, IDH2) encode isocitrate dehydrogenase (IDH), which catalyzes the oxidative decarboxylation of isocitrate, a reaction that is part of the Krebs cycle in the mitochondria or can alternatively take place in the cytoplasm174. This reaction yields the metabolite alpha-ketoglutarate and in the process reduces the electron carrier, NADP, to NADPH. In contrast, mutated IDH1/IDH2 produces the oncometabolite, 2-hydroxyglutarate (2- HG) that inhibits the demethylation of histones and DNA174,177. The resulting widespread epigenetic remodeling induces alterations in gene expression and is considered to be a starting point in gliomagenesis174,175. The improved treatment prognosis of IDH mutant glioblastomas results in part from their reduced metabolic fitness and thereby slower proliferation rate175. In addition to IDH status, glioblastomas are classified as either primary or secondary, depending on how the disease originates. Primary is the most common form of glioblastoma (~95%), arising de novo in older patients170,178. Secondary glioblastoma is most often found in younger patients, developing from pre-existing low grade astrocytomas. New research studies show that glioblastomas can further be subtyped off their transcriptomic profiles, but this classification has yet to reach to the clinic179–181. Promotor methylation of O6-methylguanine-DNA methyltransferase gene (MGMT) is a prognostic and predictive biomarker in glioblastoma182. Methylation of the promotor prevents transcription from occurring. This in turn prevents production of the functional protein, MGMT. This protein catalyzes the transfer of methyl groups from O(6)-alkylguanine to itself. This results in the restoration of the nucleic acid base guanine, thereby repairing damaged DNA. Patients with MGMT promoter methylation respond better to treatment with the chemotherapy, temozolomide (TMZ)182–184. This is because their cancer cells are less able to repair TMZ induced O(6)-alkylguanine DNA lesions, increasing therapeutic efficacy183. Patients with MGMT promoter methylation exhibit improved median survival in comparison to patients with unmethylated MGMT promoters (2 years, 48.9% vs. 14.8%)174,184. Epidermal growth factor receptor (EGFR) is another prognostic biomarker in glioblastoma and similarly to IDH, it can be used to molecularly classify glioblastoma tumors142,185,186. EGFR is a receptor tyrosine kinase, which becomes activated through the binding of its respective 20 ligands. Activation of EGFR represents the start of signal transduction for both the mitogen- activated protein kinase (MAPK) and phosphatidylinositol 3 (PI3K) growth pathways. In around 60% of primary glioblastoma cases, the EGFR gene is amplified187–189. Often, amplified EGFR genes contain a deletion of exons 2-7, which encode a truncated receptor known as EGFRvIII188–190. This mutated receptor is unable to bind its normal ligands, and instead remains constitutively active. Amplification of EGFR and EGFRvIII have both been found to increase the proliferation, invasiveness, resistance, and the angiogenesis of glioma cells. This explains why patients with abnormal EGFR expression have a reduced prognosis185,189. 1.4.4 PATHOLOGY Glioblastoma presents as a highly aggressive and heterogenous disease, characterized by tumor cells diffusely invading the surrounding healthy brain tissue191. Primary IDH wildtype tumors are characterized by rapid growth175. Patients typically present with large hypoxic tumors containing necrotic cores142,192–194. Unlike other cancer types, glioblastomas exhibit a very low frequency of metastasis (~0.5%), and it remains unclear why195. It is speculated that glioma cells may struggle to leave the central nervous system due to physical barriers, like the blood brain barrier (BBB)196. Another possible explanation is that patients may die from primary tumors before secondary metastases are detected. Glioblastoma is classified as a immunologically “cold” tumor, protected by powerful mechanisms of immune suppression197,198. Glioblastoma has a low tumor mutational burden (TMB) and is known for having a TME depleted of neoantigens, impeding its detection by Teff199. The TMB indicates the number of somatic mutations per mega base pair found in the genome of cancer cells and positively correlates with the number of neoantigens expressed. Detection of glioblastoma by Teff is further complicated by poor infiltration of immune cells into the glioblastoma microenvironment due to the BBB, which protects the immune privileged brain11. However, it is worth noting that intracranial lymphatic vessels have been recently discovered, challenging the established paradigm that immune cells struggle to migrate to the brain197,200. Approximately 50% of cells that make-up the glioblastoma microenvironment are immune cells; the majority are either microglia or glioma associated macrophages (GAMs)197,201,202. Microglia, GAMs, and glioma cells secrete suppressive cytokines, e.g., IL-10 and TGF-β, that inhibit the activity of NK cells and T cells – required for effective anti-tumor immunity198,203. These cytokines induce suppressive immune cell types, like M2-like GAMs that further contribute to immunosuppression in the TME. In addition, IL- 10 and TGF-β downregulate MHC expression on microglia cells, preventing them from presenting antigens to CTLs198,204. Elevated levels of Tregs expressing coinhibitory molecules, like CTLA-4, are also found in the TME198,205,206. Of note, glioblastoma cells and GSCs innately downregulate the expression of MHC to reduce their recognition by T cells198,207. Collectively, these layered mechanisms of immune suppression prevent effective anti-tumor immune responses in the glioblastoma microenvironment from occurring. 21 1.4.5 TREATMENT Glioblastoma is in incurable disease with limited treatment options. Typically, patients undergo surgical resection, followed by radiotherapy and concomitant and/or adjuvant chemotherapy with the alkylating agent, TMZ208. With this treatment regimen, patients exhibit a mean survival of 14.6 months, whereas untreated patients have a median survival of only 3 months209. In comparison to other cancers, patients with glioblastoma exhibit some of the worst prognoses with a five-year overall survival rate of only 5% post-diagnosis210. In recent years, the recommendations for surgical excision of glioblastoma tumors have improved208,211,212. Surgery can either remove all (gross total resection, GTR) or part of the tumor (subtotal resection, STR). GTR has been shown to significantly improve the survival of glioblastoma patients in comparison to STR211,212. However, complete removal of the tumor is not always possible. Challenges in effective removal include but are not limited to the location, size, and number of tumors present. Additionally, glioblastoma is highly infiltrative, and it is difficult to distinguish between healthy and cancerous tissue. Although healthy tissue can be removed around the tumor, this must be performed in careful consideration of the patient’s wellbeing208. In both cases of GTR and STR, recurrence is inevitable. One recommended chemotherapeutic approach following surgery is the use of TMZ208. Nevertheless, over 50% of patients with glioblastoma who are treated with TMZ fail to respond to therapy213. Despite multimodal treatment and maximal surgical resection, patients exhibit high rates of recurrence (70-80%), most commonly in the local area of the primary tumor193,209,214–216. Patients with recurrent disease can undergo additional surgical resection to alleviate symptoms, but it remains unclear if reresection improves overall survival193. In some cases, additional treatment with radiation is possible, but this comes with the increased risk of radiation induced necrosis of healthy brain tissue. Altogether, patients with glioblastoma face high rates of therapy resistance and recurrence as well as limited treatment options. Characterization and development of novel therapeutic agents is urgently needed for the disease. Many attempts have been made to test targeted therapies and immunotherapies for glioblastoma; however, nearly all attempts have been unsuccessful190,217–228. One exception is the targeted therapy, Bevacizumab (Avastin®), which was approved for the treatment of recurrent glioblastoma in 2009229,230. The monoclonal antibody, Bevacizumab, targets vascular endothelial growth factor A (VEGF-A), thereby preventing the binding of VEGF-A with its corresponding receptors, VEGFR1 and VEGFR2229–231. As mentioned in “1.4.4 Pathology”, glioblastoma tumors are highly hypoxic194. Glioma cells upregulate the expression of VEGF-A and VEGFRs to promote angiogenesis232. Newly formed blood vessels in turn supply glioma cells with oxygenated blood. Bevacizumab inhibits angiogenesis, and thereby starves glioma cells of oxygen229,230. Notably, Bevacizumab treatment improved progression-free survival of patients with glioblastoma, but it failed to improve overall survival231. Various EGFR inhibitors have been tested in clinical trials for treatment of glioblastoma, including Erlotinib (Tarceva®), Gefitinib (Iressa®), GC1118, Depatuxizumab-Mafodotin 22 (Depatux-M), and Cetuximab (Erbitux®)190,217–226. Overall, these inhibitors have shown limited efficacy or troubling side effects. Until now, no targeted therapy against EGFR has been approved. Significant challenges facing EGFR based targeted therapy approaches include delivering sufficient doses across the BBB, limiting side-effects, and combatting intrinsic resistance mechanisms190. Notably, almost all glioblastomas (88-100%) have been found to express PD-L1 in addition to suppressive microglia and GAMs in the TME198,233. This would make glioblastoma a seemingly an ideal candidate to respond to immunotherapy. Immunotherapy, as its name suggests, refers to a class of therapies that act on the immune system. In cancer, immunotherapies are used to stimulate cells involved in generating an anti-tumor immune response, most notably T cells101,234,235. Immune checkpoint inhibitors (ICIs) are a form of immunotherapy that help sustain the activation of T cells. The process of T cell activation is tightly regulated to maintain immune homeostasis11. Upon activation, T cells express inhibitory molecules, known as immune checkpoints, on their surfaces11,235. The best characterized checkpoints are CTLA-4 and programmed death-1 (PD- 1). CTLA-4 binds to CD80 and CD86 ligands expressed on the surface on APCs with a higher affinity than CD28, preventing costimmulation and thus activation of T cells (Fig. 7). Like, CTLA- 4, expression of PD-1 is also increased on the surface of activated T cells, but PD-1 binds to different B7 ligands, namely PD-L1 and programmed death ligand-2 (PD-L2) (Fig. 7). PD-L1 is widely expressed by different cell types in the body to prevent autoimmunity, whereas PD-L2 mainly limited to APCs235. Binding of PD-1 to its respective ligands, reduces TCR signaling and in turn inhibit T cell activation11,235. Figure 7: Immune checkpoint inhibitors (ICIs) Fig. 7: Schematic representation of T cell deactivation and immune checkpoint blockade. Common immune checkpoint inhibitors (ICIs) used in the clinic and their mechanism of 23 action is portrayed. Adapted from “T-cell Deactivation vs. Activation”, by BioRender.com (2024)15. There are three main classes of ICIs used in cancer treatment, namely anti-CTLA-4, anti-PD-1, and anti-PD-L1 inhibitors (Fig. 7)234,236. These inhibitors refer to humanized monoclonal antibodies that block the interaction of immune checkpoint molecules with their respective ligands (Fig. 7). This “releases the brakes” on T cell activation, boosting activation, proliferation, and infiltration of Teff into the TME, where they can recognize and destroy cancer cells. Due to the high expression of PD-L1 on glioblastoma cells as well as on suppressive microglia and GAMs, it could be expected that patients with glioblastoma may respond well to anti-PD- 1 inhibitors, like Nivolumab (Opdivo®) and Pembrolizumab (Keytruda®)198,233,234. In a phase III clinical study, Nivolumab was tested on patients with recurrent glioblastoma, but the treatment yielded no improvement in overall survival227. However, it was observed that a fraction of the patients who did respond to Nivolumab exhibited a more durable response. In a different phase III clinical study, Nivolumab was also tested in combination with TMZ and radiation in newly diagnosed methylated MGMT promotor positive patients, but it failed to improve survival198,228. This failure to respond to ICIs highlights the profoundly suppressive glioblastoma microenvironment as detailed in “1.4.4 Pathology”. 1.4.6 GLIOBLASTOMA STEM-LIKE CELLS (GSCS) In addition to overcoming suppression in the TME, targeting GSCs is a highly promising therapeutic approach. GSCs form only a small subset of tumor cells and as their name suggests, share properties of healthy neural stem cells237,238. GSCs were only recently discovered in the early 2000’s237–240. Therefore, it is still not completely understood on how they may originate; it is speculated from either malignant neural stem cells or from de-differentiated glioblastoma cells. However, it is agreed upon that GSCs share several common characteristics (Fig. 8). Firstly, GSCs are described to exist in a slow growing quiescent state, and they can undergo unlimited self- renewal (Fig. 8)238,241. Therefore, they are attributed to sustaining the tumor mass by slowly giving rise to more differentiated, rapidly proliferating, daughter cells. These daughter cells constitute a much larger portion of the tumor and are responsible for rapid tumor growth. The ability of GSCs to differentiate and thereby produce different lineages of daughter cells is responsible in part for the remarkable heterogeneity seen in glioblastoma tumors. Of note, differentiation of GSCs is not linear – rather they can interconvert between differentiated non- GSCs and undifferentiated GSCs (Fig. 8)242. This high plasticity is due to epigenetic reprogramming and is triggered by various environmental stimuli, such as hypoxia, nutrient deprivation, and radiation242–244. One-way GSCs are distinguished from differentiated glioblastoma cells is their ability to initiate tumors (Fig. 8)239,245. In xenograft experiments, involving the implantation of human glioblastoma cells into the brains of immunodeficient mice, only GSCs were found to be capable of forming heterogeneous tumors239,245,246. These collective characteristics distinguish GSCs from their differentiated glioblastoma cell 24 counterparts and explain in part the profound heterogeneity observed in the glioblastoma TME. Figure 8: Traits of glioblastoma stem-like cells (GSCs) Fig. 8: Key differences between glioblastoma stem-like cells (GSCs) and glioblastoma cells. Created with BioRender.com (2024)15. GSCs pose a major challenge in treating glioblastoma. Due to their quiescent state, GSCs are innately more resistant to cytotoxic radiotherapy and chemotherapy, which target rapidly dividing cells238,247–249. High intratumoral heterogeneity and plasticity, resulting from GSCs, further complicate treatment efficacy241,245. GSCs can both initiate and propagate tumors following cytotoxic treatment, thereby driving recurrence239,245,246,248. It has also been reported that GSCs promote invasion and angiogenesis in the TME245,250–252. The highly invasive nature of glioblastoma limits the efficacy of surgical resection, highlighting yet another way GSCs can reduce therapeutic efficacy and thereby promote recurrence253,254. Altogether, GSCs limit the efficacy of current therapies and drive recurrence – two major factors responsible for poor prognosis245. As a result, they are considered one of most compelling cellular targets in glioblastoma. Despite this, there are currently no approved therapies against GSCs. This is because the field of GSC research faces several significant challenges. Firstly, GSCs are a rare cell type and are distributed unequally throughout the TME; this impairs the assessment of GSCs in tumor samples255,256. In more detail, enrichment of GSCs is found in areas of the TME where GSCs have direct contact with capillaries and arterioles (perivascular niche), the invasive edges of 25 the tumor (invasive niche), and the hypoxic necrotic core (perinecrotic niche)257. Low sample availability coupled with the innately slow growth rate and complicated culture conditions of GSCs further lag research. Unlike many other cell types, there are no universal markers to identify GSCs. Instead, they only can be identified by functional traits, namely unlimited self- renewal as well as the abilities to differentiate and initiate heterogeneous tumors in immunodeficient mice245. In the past, many markers have been associated with GSCs, including but not limited to Prominin1 (CD133), SOX2, nestin, L1CAM, CD15, and CD44239,246,258–262. However, due to the high plasticity and heterogeneity of GSCs, these markers all fail to universally identify them. For example, the best characterized GSC associated marker, CD133, is limited to a subset of GSCs, and expression of CD133 was found to be dependent on the cell cycle249,263–266. Therefore, there is an urgent need to discover novel GSCs markers, which are universally expressed regardless of GSC subset or cell state. Zimmer et al., 2019 reported for the first time that GARP is highly expressed by GSCs65. In more detail, they found that GARP was highly expressed across several patient derived GSC cell lines. This work expanded on their study by evaluating GARP as a potential novel marker for GSCs and investigated its possible functional relevance in GSC biology for the first time (Paper 2)9. 1.5 MELANOMA Melanoma, also known as cutaneous melanoma, is a form of skin cancer that arises from melanocytes, which reside in the basal layer of epidermis267,268. Melanocytes produce the pigments eumelanin and phenomelanin. The intermix of these pigments is responsible for the coloration of skin, eyes, and hair. Melanin serves as a physical barrier against UV radiation from the sun. In more detail, melanin can both scatter and absorb UV radiation to prevent it from penetrating the epidermis, where it can induce DNA damage269,270. Melanin can also scavenge, or neutralize, harmful reactive oxygen species (ROS) that can damage vital cellular structures, like DNA269,271. As discussed in “1.3.1 Initiation of cancer”, accumulation of DNA damage and mutations overtime can lead to the malignant transformation of healthy cells108,109. 1.5.1 EPIDEMIOLOGY Around 20% of all skin cancer diagnoses each year are attributed to melanoma272. In 2022, 331,722 cases of melanoma were diagnosed and 58,667 deaths were reported worldwide106,273. The global incidence of melanoma varies greatly by geographical region and by economic status. Europe and Northern America (United States and Canada) alone reported 44.1% and 34.0% of all melanoma cases in 2022 (both sexes), whereas Africa only reported 2.3% of all cases (both sexes). High income nations (World Bank Classification) reported the majority of melanoma diagnoses, accounting for 261,662 cases in 2022 (both sexes). Collectively, this indicates that the highest incidence of melanoma cases is reported in predominantly White developed nations. 26 In the last decades, developed nations have implemented robust preventative skin cancer screening programs, enabling the earlier detection of melanoma272,274,275. As a result, a greater number of cases were identified, thereby inflating the incidence of the disease. The incidence of melanoma is also known to vary by sex, but the reason why remains unclear. It is well documented that older men exhibit a higher incidence of the disease than women and correspondingly exhibit worse clinical outcomes, in terms of progression, recurrence and mortality174,276–278. However, this effect reverses with age with younger women, especially between the ages of 20-24, having higher incidence rates than men276,277. These differences between sexes are not believed to solely originate from differing UV exposure but also in part from behavioral practices, such as tanning bed use, application of sun protectant, and self- screening for skin cancer277,279–281. The incidence of melanoma has rapidly increased from 1975 to 2018 in developed countries, rising over 320% in the US alone273,282. The global incidence of melanoma (both sexes) is predicted to continue to increase by 2050106,283. In addition to a rise in skin cancer screenings, this projected increase can be attributed to several factors106,272,274,275,283. Firstly, the global population is predicted to continue to grow from 7.6 billion to 9.8 billion in 2050106,283. Collectively, this is predicted to increase the absolute number of reported melanoma cases. The global population is also aging, a known risk factor for developing melanoma, with the percentage of individuals aged 65 and above increasing from 10% in 2022 to 16% in 2050284,285. Climate change is also predicted to increase the incidence of skin cancers, due to an accumulation of several factors286,287. This includes increased exposure to air pollution, depletion of the ozone layer that provides protection from UV radiation, and behavioral changes, resulting in increased sun exposure due to increased temperatures. Despite the increasing incidence of melanoma, the overall mortality of the disease decreased by 17.9% in White Americans from 2013 to 2016282,288. This dramatic improvement resulted from the approval of several targeted therapies and immunotherapies for the treatment of metastatic and inoperable melanoma288. 1.5.2 RISK FACTORS Exposure to UV radiation, most often in the form of sunlight, is the biggest risk factor for developing melanoma. It is well known that high energy UV radiation is genotoxic, causing DNA damage by directly inducing structural damage and indirectly through the production of ROS120. This leads in turn to the accumulation of somatic mutations. Excessive UV exposure that damages the skin results in sunburns, which are also a risk factor for developing melanoma289. Behaviors connected to UV exposure can correspondingly exacerbate (e.g., tanning bed use) or reduce (e.g., sun protectant use, avoiding sun exposure between 10 am and 4 pm) the risk of developing melanoma282,289. As mentioned earlier, advanced age is also a risk factor285. This is due in part to the long-term exposure of the skin to mutagenic UV radiation and the gradual worsening of immune function over time. 27 The coloration of skin and the risk of developing melanoma are tightly connected. Individuals with darker skin tones have a reduced risk of developing melanoma290,291. This is because they have a higher concentration of eumelanin in their skin. In contrast, individuals with lighter skin tones have more phenomelanin. Eumelanin is more effective at scattering UV radiation before it is absorbed into the skin, providing superior protection against DNA damage282. Although individuals with darker skin tones have a lower risk of developing melanoma, melanoma can be harder to identify, which can result in later diagnosis of disease291. The presence and number of nevi, also known as moles, on the skin are risk factors for developing melanoma282,292. Nevi are benign outgrowths of melanocytes. Although rare, nevi can undergo malignant transformation and serve as initiation sites for melanoma. Genetic predispositions to developing melanoma are only partially understood. A family history of the disease is reported in about 10% of melanoma patients282. Some of the most commonly mutated genes in familial melanoma include MC1R (70-90%), CDKN2A (20-40%), MITF (1-5%), and CDK4 (<1%)293. MC1R and MITF are involved in the production of melanin and the development of melanocytes. CDKN2A is the most commonly associated mutation in risk of developing familial melanoma293,294. This gene encodes two tumor suppressor proteins, p16 and p14, which arrest cell cycle and promote apoptosis. On the flipside, mutated CDK4 is an oncogene that supports cell cycle progression293. Other genes found to be frequently mutated are involved in the maintenance of telomeres, like POT1, ACD, TERF2IP, and TERT, enabling enhanced proliferation of cancer cells293,295. However, not all cases of familial melanoma can be explained by these mutations293. 1.5.3 DIAGNOSIS Individuals with increased risk of developing melanoma are advised to perform monthly self- examinations and to undergo regular skin cancer screenings at their dermatologist to recognize early on new or abnormal nevi296. Nevi are photographed to track how they change over time. Diagnosis of melanoma typically begins with the examination of an abnormal nevi by a dermatologist using a dermatoscope297. The visual guidelines for distinguishing between benign and potentially cancerous nevi follow the ABCDE rule: Asymmetry, Border, Color, Diameter, and Elevation of the nevi297,298. These findings are validated by biopsy, where frequently the entire abnormal nevi and surrounding skin are removed. Biopsies undergo histological evaluation to identify the subtype of melanoma present as well as the molecular biomarkers present. The most common subtypes are superficial spreading melanoma (~70%), nodular melanoma (~20%), lentigo maligna melanoma (~5-10%), and acral lentiginous melanoma (1-2% in western countries)299–304. Diagnostic staging is performed in accordance with the Eighth Edition of the American Joint Committee on Cancer Melanoma Staging297,305. This system uses the TMN (Tumor, Node, and Metastasis) classification, which categorizes tumors based off their size and degree of spreading. In more detail, T indicates the tumor thickness and presence of ulceration297. N details whether local or regional metastases are present, whereas M indicates the presence 28 of distant metastases. The TMN classification, combined with information obtained from biopsies, form the foundation for the overall prognostic staging of the disease. Stages with lower numbers (0-II) represent less advanced forms of melanoma and have better prognoses than later stages of the disease (III and IV)297,298,305,306. In stages 0-II, there are no signs of spreading. These stages differ from one and other mainly by the thickness of the primary tumor and whether ulceration is present. Stage III is distinguished by the presence of cancer cells in nearby tissues, including lymph nodes, lymphatic vessels, and surrounding skin. Stage IV denotes that the cancer has spread to distant parts of the body, most commonly the skin and subcutaneous tissue, followed by the lungs, liver, bones and brain307. 1.5.4 PATHOLOGY Melanoma presents as a highly heterogeneous disease. This heterogeneity can be attributed in part to its high genetic instability and exceptionally high TMB308. It is estimated that melanoma has up to 15 mutations per mega base, making it one of the cancers with the highest TMBs. This is connected to the classification of melanoma as an immunogenic cancer type, meaning that the immune system can induce immune responses against it309,310. The elevated TMB leads to the increased prevalence of neoantigens that can be recognized as foreign by the immune system. Melanoma is often called a “hot” tumor, which describes the immunological status of the TME. The melanoma microenvironment is characterized by a high degree of tumor infiltrating lymphocytes (TILs) and expression of PD-L1 by immune cells. Melanoma stands out from other cancer types due to its highly invasive nature, which can be explained in part by its developmental origins311. Melanocytes originate from the neural crest, a transient, highly mobile, and multipotent group of cells, arising from the ectoderm312. Neural crest cells migrate through the developing embryo to different tissues, where they differentiate into melanocytes, glial cells, peripheral neurons, smooth muscle cells, amongst other things. Melanoma cells express many of the same signaling molecules and factors of neural crest cells due to their shared evolutionary origin311. These traits help account for their innate plasticity and high motility, enabling them to invade neighboring tissues and to metastasize to distant organs. 1.5.5 TREATMENT The gold standard for the treatment of melanoma is resection of the affected area(s)313. This is very effective in the early stages of the disease when the tumor remains limited to the site of origin, with five-year survival rates of nearly 100% (Stages 0-II)306. However, when the disease becomes malignant, spreading into surrounding (Stage III) or distant (Stage IV) tissues, the efficacy of surgical removal decreases (five-year survival rates: stage III 60.3%; stage IV 16.2%). There are several treatment options for malignant melanoma in addition to surgical resection. In some cases, radiation and chemotherapy are applied, but these therapies do not work as well in melanoma in comparison to other cancer types314. Instead, advanced stage patients are treated with targeted therapies and ICIs. 29 Genetic classification of a patient’s melanoma, through biopsies, is essential for deciding on a fitting targeted therapy approach. Cutaneous melanoma is categorized into four genetic subtypes based off the mutational profile, namely BRAF mutant (45-50%), RAS mutant (30%), NF1 mutant (10-15%), and triple wildtype (5-10%)315. Triple wildtype represents melanomas that contain no mutations in BRAF, RAS, and NF1316. Vemurafenib (Zelboraf®) was the first targeted therapy approved for the treatment of metastatic and inoperable melanoma in 2011, followed quickly by Dabrafenib (Tafinlar®) in 2013317,318. Both therapies target a molecular defect in melanoma, a mutation in a kinase called BRAF, inhibiting its function, hence their colloquial name “BRAF inhibitors”. Specifically, Vemurafenib and Dabrafenib target a point mutation in codon 600 of the protein, called the BRAFV600E mutation, which results in the substitution of valine for glutamic acid. Mutations in BRAF are very common in melanoma and are considered a driver of the disease319,320. The most common mutation in BRAF is BRAFV600E, prevalent in 70-90% of BRAF mutations. Mutated BRAF is an oncogene that increases cell proliferation via the constitutive activation of the MAPK/ERK (extracellular signal-regulated kinases) (Ras-Raf-MEK-ERK) pathway. Treatment with BRAF inhibitors has been shown to effectively induce apoptosis in tumor cells, increase expression of melanoma antigens, and the number of infiltrating CTLs, favorable for anti-tumor immune responses321. The approval of Vemurafenib was highly significant as it represented one of the first improved treatment options for metastatic melanoma in decades. In comparison to the standard of care at the time, Dacarbazine, a chemotherapy approved in 1975, Vemurafenib demonstrated superior 6-month overall survival (84% vs. 64%) and response rates (48% vs. 5%)322. About half of BRAF mutant melanomas respond to BRAF inhibitors; however, most patients rapidly develop resistance against the therapy after 6 months, reducing its efficacy323. Currently, work on targeted therapies in melanoma is centered on testing novel combination therapy approaches as well as other drug targets, in hopes of prolonging therapeutic efficacy. To overcome resistance, BRAF inhibitors are combined with MEK inhibitors to further disable the MAPK/ERK pathway324. Combination treatment of Dabrafenib and Trametinib (Mekinist®), a MEK inhibitor, improved the overall survival of patients with metastatic melanoma in comparison to patients treated with Vemurafenib alone (72% combination vs. 65% Vemurafenib alone)325. RAS and its negative regulator NF1 also form parts of the MAPK/ERK pathway, but there are no approved targeted therapies against them. Instead, advanced staged patients lacking mutations in BRAF undergo immunotherapy. In 1998, high-dose systemic IL-2 treatment became the first immunotherapy to be approved to treat advanced stage melanoma patients326–329. As discussed in “1.1.2 T cell activation”, IL- 2 is required for clonal expansion of T cells, including Th1 cells and CTL, which are hallmarks of effective anti-tumor immune responses11,13,19,22. Although a small fraction of patients responded to IL-2 therapy (objective response rate of 15.9%), about 1 in 3 responders exhibited durable remission, marking an important improvement in the treatment of advanced stage melanoma. A major drawback from this treatment was high toxicity, most 30 commonly capillary leak syndrome, which could involve multiple organ systems327. Patients required extensive observation while undergoing treatment, frequently inside an intensive care unit. Due to the low response rate and high toxicity of IL-2, other forms of immunotherapy, most notably ICIs, were later developed. There are three main classes of ICIs approved for the treatment of melanoma: anti-CTLA-4, anti-PD-1, and anti-PD-L1 antibodies (Fig. 7)234. The first ICI, Ipilimumab (Yervoy®), a CTLA-4 inhibitor, was approved in 2011 for late-stage melanoma330. In the groundbreaking phase III clinical study, Ipilimumab treated patients showed a drastic improvement in overall survival, with 20% of patients demonstrating an extended duration response3. In addition to boosting the activity of T cells, anti-CTLA-4 inhibitors deplete suppressive Tregs, which express a high level of CTLA-4 on their surfaces, through antibody-dependent cellular cytotoxicity (ADCC)11,235. A major drawback of Ipilimumab is that it is frequently accompanied by autoimmune side effects, also known as immune related adverse events (irAE). Depending on the severity, this can lead to the secession of ICI treatment. This is thought to arise from CTLA’s broad scale effects on activating the T cell compartment, including autoreactive T cells. Approval of the PD-1 inhibitors, Nivolumab and Pembrolizumab, for the treatment of advanced melanoma quickly followed in 2014 (Fig. 7)4,5. The overall response rate for both inhibitors was threefold higher than Ipilimumab (~30% in comparison to ~10%), and the inhibitors exhibited an improved safety profile7,331,332. PD-1 blockade primarily results in the functional restoration and expansion of tumor infiltrating CTLs that can directly kill tumor cells333,334. In 2020, Atezolizumab (Tecentriq®), a PD-L1 inhibitor, in combination with the BRAF inhibitors, Cobimetinib (Cotellic®) and Vemurafenib, was approved for the treatment of BRAFV600E positive metastatic or inoperable melanoma, prolonging median progression free survival (10.6 months Cobimetinib and Vemurafenib only; 15.1 months with Atezolizumab) (Fig. 7)335. In 2016, Ipilimumab – Nivolumab combination therapy was approved for the treatment of unresectable and metastatic melanoma, regardless of BRAF status6. Combination therapy showed the highest median overall survival (+60 months, median not reached) in comparison to the individual treatments alone (Ipilimumab, 19.9 months; Nivolumab, 36.9 months)7. As a result, Ipilimumab – Nivolumab combination therapy are considered as the standard of care for inoperable stage III and IV melanoma patients in Europe. Like targeted therapy, patients can also develop resistance to ICIs during treatment. Loss of functional T cells and poor infiltration of T cells into the TME are amongst the factors responsible336. Approval of CTLA-4 and PD-1 inhibitors revolutionized the treatment of melanoma. The five- year overall survival rate of advanced melanoma patients has risen dramatically from 5% to ~50% when patients are treated with Ipilimumab – Nivolumab combination therapy7,314,337– 339. So great was the improvement in clinical outcomes that Dr. James P. Allison and Dr. Tasuku Honjo were awarded the Nobel Prize in Physiology or Medicine in 2018 for their pivotal work in discovering the immune checkpoints, CTLA-4 and PD-1340. 31 Despite the monumental advancements made in the treatment of melanoma in the last decade, the field still faces several significant challenges. Only half of patients effectively respond to BRAF inhibitors and ICIs7,314,322,337–339. Acquired resistance to these treatments reduces their efficacy323,336. Currently, researchers are exploring how ICIs and targeted therapies can be combined to prolong patient survival and to avoid the development of resistance. Researchers are looking for biomarkers to optimize individualized patient treatment regimens. This is in the hopes of identifying responders and individuals at high-risk for developing severe side-effects as well as reducing therapy costs. Furthermore, novel targets and therapeutic strategies are still needed for advanced patients that fail to respond to conventional targeted therapies and ICIs. To address this unmet need, this work sought out to characterize cold atmospheric plasma (CAP) as a promising, adjuvant, anticancer approach that could be applied in combination with existing conventional therapies (Paper 3)10. 32 2. AIMS Optimization of immune responses has become increasingly recognized as a promising strategy to treat a wide range of clinical conditions1–7. Therefore, it is important to investigate novel forms of immunomodulation, which could be applied to generate favorable immune responses. This dissertation characterized two immunomodulatory approaches, namely GARP and CAP. This work aimed to characterize the functional role of GARP in two distinct immunological settings: wound healing (Paper 1) and cancer (Paper 2)8,9. In addition, the influence of CAP on cells in the melanoma microenvironment was also investigated (Paper 3)10. Paper 1 GARP is a potent mechanism of immunological tolerance28,61,83. Through its function in regulating and activating the pleiotropic cytokine, TGF-β, GARP is key to preventing the development of autoimmunity28,29,50,66,67,69,70,81,82. As GARP controls the bioavailability of TGF- β, it is speculated that GARP plays a vital role in TGF-β mediated pathways. This has already been supported in the immunological settings of development, tolerance, and cancer61,67,71,83,99,100. However, up until now, no studies have examined the possible involvement of GARP in early stages of the wound healing phase. This study investigated how GARP may influence the wound healing process by utilizing an endogenous source of GARP, injectable platelet rich fibrin (iPRF), a type of autologous platelet concentrate that is used to treat chronic wounds8. In more detail, this study aimed to analyze the influence of GARP, contained in iPRF, on immune cells (T cells and monocyte derived macrophages) that play key roles in early stages of the wound healing phase. Paper 2 GSCs are a major cause of tumor initiation, therapy resistance, and recurrence in glioblastoma239,245,247–249. Therefore, they are one of the top cellular targets to therapeutically address. However, there are no universal markers for GSCs due to their innately high heterogeneity and plasticity9. Recently, Zimmer et al., 2019 discovered that GSCs express high levels of GARP, especially in the nucleus65. Widespread expression of GARP was present on all GSC cell lines tested, suggesting a possible utility of the protein in identifying GSCs. This study aimed to analyze the expression of GARP across different GSC cell lines that replicated key features of the disease (e.g., intratumoral heterogeneity, cellular hierarchy, and longitudinal GSC evolution)9. This study also investigated the potential functional relevance of GARP in GSC biology and the application of GARP as a biomarker for glioblastoma. Paper 3 Despite recent therapeutic advancements, patients with advanced stage melanoma still face limited treatment options3–5,7,314,317,318,322,323,336–339. CAP, non-thermal plasma that can be applied within physiological temperatures (<40°C), has emerged as a promising selective anti- cancer strategy341–343. It is well known that CAP is toxic to cancer cells by raising exogenous RONS levels341. However, until now, little is known regarding how immune cells react to CAP. Immune cell composition in the melanoma microenvironment is key to ICI therapy response 33 and can predict patient prognosis101. Therefore, this study aimed to examine the effects of CAP treatment on cancer cells and immune cells (T cells and monocyte derived macrophages) commonly found in the melanoma microenvironment10. In more detail, this study analyzed CAP treated cells for signs of oxidative stress and sought out to identify optimal CAP treatment strategies. 34 3. RESULTS 3.1 PAPER 1 GARP Regulates the Immune Capacity of a Human Autologous Platelet Concentrate Figure 9: Graphical abstract (Paper 1) Fig. 9: Graphical abstract of Paper 1. Created with BioRender.com (2024)15. 3.1.1 SUMMARY Autologous platelet concentrates represent a class of blood preparations produced from a patient’s own blood8,344. Autologous platelet concentrates are produced chairside via a specific centrifugation process, resulting in the separation of plasma from whole blood. This blood plasma can be applied to a patient either as a solid clot or as a liquid solution, known as injectable platelet rich fibrin (iPRF), to accelerate the healing of chronic wounds. iPRF represents an optimized form of autologous platelet concentrates, which contains an enrichment of platelets, growth factors, and white blood cells8,345. Although widely used in regenerative medicine, the underlying immunological mechanisms behind the wound healing properties of iPRF remain poorly understood8,344,346,347. Platelets are the main cellular component of iPRF and highly express GARP on their surfaces8,56. GARP exhibits potent immunomodulatory properties through its function in aiding in the activation of the cytokine, LTGF-β28,29,50,56,61,66,67,69,83. However, it remains unclear if GARP plays a role in the immune capacity of iPRF. Therefore, this study aimed to analyze the effects of iPRF on key cells involved in the wound healing phase, specifically human monocyte derived macrophages and CD4+ T cells, and the influence of GARP in these interactions8. 35 It was found that GARP is expressed on the surface of platelets isolated from iPRF, and the protein was detected as a soluble factor in iPRF8. Both iPRF and platelets isolated from iPRF were found to induce Foxp3+GARP+ Tregs and to inhibit the production of Th1 effector cytokines, IL-2 and IFN-γ. Treatment with an anti-GARP antibody reversed these effects. iPRF was also found to induce an anti-inflammatory “M0/M2-like” phenotype in macrophages in a GARP independent manner. Collectively, this study identified a novel immunological mechanism, namely GARP dependent induction of Tregs, which mediates the immune capacity of iPRF. 3.1.2 ZUSAMMENFASSUNG Autologe Thrombozytenkonzentrate sind eine Klasse von Blutpräparaten, die aus dem Eigenblut des Patienten hergestellt werden8,344. Autologe Thrombozytenkonzentrate werden vor Ort (am Behandlungsstuhl) durch ein spezielles Zentrifugationsverfahren hergestellt, bei dem das Blutplasma vom Vollblut getrennt wird. Dieses Blutplasma kann dem Patienten entweder als festes Zellaggregat oder als flüssige Lösung, sogenanntes injizierbares plättchenreiches Fibrin (iPRF), verabreicht werden, um die Heilung chronischer Wunden zu beschleunigen. iPRF stellt eine optimierte Form des autologen Thrombozytenkonzentrates dar, welches mit Thrombozyten, Wachstumsfaktoren und Leukozyten angereichert ist8,345. Obwohl iPRF in der regenerativen Medizin weit verbreitet ist, sind die immunologischen Mechanismen, die der wundheilenden Wirkung von iPRF zugrunde liegen, noch wenig verstanden8,344,346,347. Thrombozyten sind die zelluläre Hauptkomponente von iPRF und exprimieren auf ihrer Oberfläche in hohem Maße GARP8,56. GARP hat starke immunmodulatorische Eigenschaften, da es zur Aktivierung des Zytokins LTGF-β beiträgt28,29,50,56,61,66,67,69,83. Es ist jedoch unklar, ob GARP eine Rolle bei der Immunkapazität von iPRF spielt. Ziel dieser Studie war es daher, die Wirkung von iPRF auf Zellen in den frühen Stadien der Wundheilung zu untersuchen, insbesondere auf aus humanen Monozyten gebildete Makrophagen und CD4+ T-Zellen. Außerdem wurde der Einfluss von GARP auf diese Wechselwirkung untersucht8. Es zeigte sich, dass GARP auf der Oberfläche von Thrombozyten exprimiert wird aber auch als löslicher Faktor in iPRF vorhanden ist8. Sowohl iPRF als auch aus iPRF isolierte Thrombozyten induzieren Foxp3+GARP+ Tregs und hemmen die Produktion von Th1-Effektorzytokinen, IL-2 und IFN-γ. Diese Effekte konnten durch die Behandlung mit einem Anti-GARP-Antikörper umgekehrt werden. Unabhängig von GARP induziert iPRF auch einen anti-inflammatorischen „M0/M2-ähnlichen“ Phänotyp in Makrophagen. Somit konnte in dieser Studie ein neuer immunologischer Mechanismus identifiziert werden: die GARP-abhängige Induktion von Tregs, welche die Immunkapazität von iPRF vermittelt. 36 3.1.3 AUTHOR CONTRIBUTIONS Disclaimer: First authorship was shared between E.R. Trzeciak and N. Zimmer. Both authors contributed equally to the manuscript. N. Zimmer produced the data and generated Figure 1 (A-B) and Figures 2-3. E.R. Trzeciak produced the data and generated Figure 1 (C), Figure 4, and Figure S1. E.R.T predominantly wrote the original draft of the manuscript as well as handled the reviewing and editing process. Conceptualization P.W. Kämmerer, N. Zimmer, S. Blatt, A. Tüttenberg Methodology N. Zimmer, E.R. Trzeciak, S. Blatt Validation D. Thiem, N. Zimmer, E.R. Trzeciak, S. Blatt Formal analysis D. Thiem, N. Zimmer, E.R. Trzeciak Investigation N. Zimmer, E.R. Trzeciak Resources P.W. Kämmerer, B. Al-Nawas, A. Tüttenberg, S. Blatt Data Curation D. Thiem, N. Zimmer, E.R. Trzeciak, S. Blatt Writing (Original draft preparation) E.R. Trzeciak, N. Zimmer, S. Blatt, P.W. Kämmerer, A. Tüttenberg Writing (Review and editing) E.R. Trzeciak, N. Zimmer, S. Blatt, D. Thiem, B. Al-Nawas, A. Tüttenberg Visualization N. Zimmer, E.R. Trzeciak, S. Blatt Supervision P.W. Kämmerer, N. Zimmer, S. Blatt, A. Tüttenberg Project administration P.W. Kämmerer, B. Al-Nawas, N. Zimmer, S. Blatt Funding acquisition B. Al-Nawas, A. Tüttenberg, S. Blatt 37 38 3.1.4 PUBLICATION Disclosure: This paper was published open access and permission of re-use in this dissertation was granted by the publisher (MDPI)8,348. The authors maintain all copyrights. 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 3.2 PAPER 2 Nuclear Glycoprotein A Repetitions Predominant (GARP) Is a Common Trait of Glioblastoma Stem-like Cells and Correlates with Poor Survival in Glioblastoma Patients Figure 10: Graphical abstract (Paper 2) Fig. 10: Graphical abstract of Paper 2. Created with BioRender.com (2024)15. 3.2.1 SUMMARY Glioblastoma, stage IV astrocytoma, is the most common malignant central nervous system tumor and is an incurable disease119,142,147,149,150. Patients with glioblastoma have a five-year overall survival rate of only 5% post-diagnosis210. This poor prognosis can be attributed in part to GSCs. Although GSCs represent only a small subset of tumor cells, they are a driving force behind tumor initiation, therapy resistance, and recurrence in glioblastoma239,245,247–249. Therefore, targeting GSCs is considered a promising therapeutic strategy to improve clinical outcomes. However, due to the innately high plasticity and heterogeneity of GSCs, there are no universal markers to identify them9. Recently, the tolerogenic protein GARP was reported to be highly expressed by GSCs65. This study investigated whether GARP could be used to detect GSCs, its functional relevance in GSC biology, and evaluated the potential of GARP as a biomarker for glioblastoma9. To accomplish this, several patient derived GSC cell lines were employed, and GARP expression was analyzed9. These cell lines recapitulated key features of the disease, including 55 intratumoral heterogeneity, cellular hierarchy, and recurrence. GARP was found to be expressed on GSCs, regardless of cell state and disease stage. It was determined that high levels of GARP on GSCs reduced their self-renewal capacity and increased the expression of differentiation inducing factor, p21. Interestingly, higher levels of GARP decreased the expression of differentiation commitment markers, GFAP and PDGFR-α. This suggests that GARP may play a role earlier in the differentiation process, namely regulating the self-renewal of GSCs. GARP was found to be abnormally expressed in the nucleus of GSCs, and the frequency of GARPNU+ cells correlated inversely with patient survival. Altogether, this study demonstrated both the promise of GARP as a marker to identify GSCs and as a biomarker to predict patient prognosis. 3.2.2 ZUSAMMENFASSUNG Das Glioblastom, ein Astrozytom im Stadium IV, ist der häufigste bösartige Tumor des zentralen Nervensystems und eine unheilbare Krankheit119,142,147,149,150. Patienten mit Glioblastom haben nach der Diagnose eine Fünfjahres-Gesamtüberlebensrate von nur 5 %210. Diese schlechte Prognose kann zum Teil den GSCs zugeschrieben werden. Obwohl GSCs nur eine kleine Untergruppe der Tumorzellen darstellen, sind sie eine treibende Kraft hinter der Tumorentstehung, der Therapieresistenz und dem Wiederauftreten des Glioblastoms213,245,247–249. Daher sind sie ein ideales therapeutisches Ziel, um die klinischen Behandlungsergebnisse zu verbessern. Aufgrund der hohen Plastizität und Heterogenität der GSCs gibt es jedoch keine universellen Marker, um diese zu identifizieren9. Kürzlich wurde berichtet, dass das tolerogene Protein GARP in hohem Maße von GSCs exprimiert wird65. In dieser Studie wurde untersucht, ob GARP zur Erkennung von GSCs verwendet werden kann, welche funktionelle Bedeutung es für die Biologie von GSCs hat und ob GARP als Biomarker für Glioblastome geeignet ist9. Zu diesem Zweck wurden mehrere von Patienten stammende GSC-Zelllinien verwendet und deren die GARP-Expression analysiert9. Diese Zelllinien zeigen wesentliche Merkmale der Erkrankung, wie intratumorale Heterogenität, zelluläre Hierarchie und Rezidiv. Es konnte gezeigt werden, dass GARP auf GSCs unabhängig von Zellstatus und Krankheitsstadium exprimiert wird. Zudem wurde festgestellt, dass hohe GARP-Konzentrationen auf GSCs deren Selbsterneuerungskapazität reduzieren und die Expression des differenzierungsfördernden Faktors p21 erhöhen. Interessanterweise verringerte sich bei höheren GARP-Konzentrationen die Expression der Marker für die Differenzierungsbereitschaft, GFAP und PDGFR-α. Dies deutet darauf hin, dass GARP bereits früh im Differenzierungsprozess eine Rolle bei der Regulation der Selbsterneuerung von GSCs spielen könnte. Es konnte gezeigt werden, dass GARP im Zellkern von GSCs abnormal exprimiert wird und dass die Häufigkeit von GARPNU+ Zellen invers mit dem Überleben der Patienten korreliert. Insgesamt zeigte diese Studie, dass GARP sowohl ein vielversprechender Marker zur Identifizierung von GSCs als auch ein Biomarker zur Vorhersage der Patientenprognose ist. 56 3.2.3 AUTHOR CONTRIBUTIONS Disclaimer: First authorship was shared between E.R. Trzeciak and N. Zimmer. Both authors contributed equally to the manuscript. This publication is also part of N. Zimmer’s cumulative dissertation. N. Zimmer produced the data and generated Figures 1-3, 6-7, S4-5, S8-9. E.R. Trzeciak produced the data and generated Figures 3-4, S10, S2-3, S6, S10. Both authors wrote the original draft of the manuscript, and E.R.T primarily handled the review and editing process. Conceptualization N. Zimmer, A. Tüttenberg, E. Kim, J. Tüttenberg, F. Ringel, V. Mailänder, C. Sommer Methodology N. Zimmer, E.R. Trzeciak, A. Müller, P. Licht, P. Leukel, B. Sprang Validation N. Zimmer, E.R. Trzeciak Formal analysis N. Zimmer, E.R. Trzeciak, A. Müller, P. Licht Resources A. Tüttenberg, E. Kim Writing (Original draft preparation) N. Zimmer, E.R. Trzeciak, A. Tüttenberg, E. Kim Writing (Review and editing) N. Zimmer, E.R. Trzeciak, A. Müller, P. Licht, B. Sprang, P. Leukel, V. Mailänder, C. Sommer, F. Ringel, J. Tüttenberg, E. Kim, A. Tüttenberg Visualization N. Zimmer Supervision A. Tüttenberg, E. Kim Project administration N. Zimmer Funding acquisition A. Tüttenberg, E. Kim 57 58 3.2.4 PUBLICATION Disclosure: This paper was published open access and permission of re-use in this dissertation was granted by the publisher (MDPI)9,348. The authors maintain all copyrights. 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 3.3 PAPER 3 Oxidative Stress Differentially Influences the Survival and Metabolism of Cells in the Melanoma Microenvironment Figure 11: Graphical abstract (Paper 3) Fig. 11: Graphical abstract of Paper 3. Created with BioRender.com (2024)15. 3.3.1 SUMMARY Striking progress have been made in the treatment of melanoma in the last decade. The introduction of targeted therapies and ICIs into the clinics has greatly improved the overall survival of advanced stage patients3–5,7,317,318,322. Despite these improvements, a significant portion of patients fail to respond to these therapies or rapidly develop resistance against them, leaving them with limited treatment options7,314,323,336–339. CAP represents a promising anticancer approach by targeting the redox balance in cancer cells349. CAP is a partially ionized gas that also consists of neutral atoms, excited electrons, and 81 electromagnetic radiation, which can be applied within physiological temperatures. The interaction of CAP with biological material results in the production of RONS. Cancer cells are innately more vulnerable to increases in oxidative stress, and many studies have shown that CAP exhibits selective anticancer properties. However, little is known regarding how CAP influences immune cells, key determiners of immunotherapy response in the melanoma microenvironment10,101. This study aimed to determine the influence of CAP on cells found in the melanoma microenvironment in vitro, namely human melanoma cells, T cells, and monocyte derived macrophages10. CAP treatment was found to increase intracellular ROS levels10. CAP reduced the proliferation and viability of T cells and tumors cells (in 2D and 3D culture) in a dose dependent manner. This resulted from CAP induced G2/M cell cycle arrest, increased mitochondrial stress, and apoptosis. The anti-proliferative and cytotoxic effects of CAP could be partially reversed through the application of antioxidants. Cells displayed varying sensitivity to CAP treatment with T cells being the most affected. M1 macrophages exhibited higher levels of mitochondrial stress following CAP treatment. It was also found that CAP treatment polarizes macrophages to an anti-inflammatory “M0/M2” phenotype. Collectively, this study showed that CAP strongly induces oxidative stress in cells found in the melanoma microenvironment, with T cells displaying the highest sensitivity. 3.3.2 ZUSAMMENFASSUNG Im letzten Jahrzehnt wurden bei der Behandlung des Melanoms beachtliche Fortschritte erzielt. Die Einführung von zielgerichteten Therapien und ICIs in die klinische Praxis hat das Gesamtüberleben von Patienten im fortgeschrittenen Stadium erheblich verbessert3– 5,7,317,318,322. Trotz dieser Verbesserungen spricht jedoch ein erheblicher Anteil der Patienten jedoch nicht auf diese Therapien an oder entwickelt rasch Resistenzen, was die verfügbaren Behandlungsoptionen einschränkt7,314,323,336–339. CAP ist ein vielversprechender Ansatz zur Krebsbekämpfung, da es auf das Redox- Gleichgewicht in Krebszellen abzielt349. CAP ist ein teilweise ionisiertes Gas, das zudem neutrale Atome, angeregte Elektronen und elektromagnetische Strahlung enthält und welches bei physiologischen Temperaturen angewendet werden kann. Die Wechselwirkung von CAP mit biologischem Material führt zur Produktion von RONS. Krebszellen sind von Natur aus anfälliger für erhöhten oxidativen Stresses, und viele Studien haben gezeigt, dass CAP selektiv krebshemmende Eigenschaften besitzt. Es ist jedoch nur wenig darüber bekannt, wie CAP Immunzellen beeinflusst, die für das Ansprechen auf eine Immuntherapie in der Mikroumgebung des Melanoms entscheidend sind10,101. Ziel dieser Studie war es, den Einfluss von CAP auf Zellen zu bestimmen, die in der Mikroumgebung des Melanoms in vitro vorkommen, nämlich menschliche Melanomzellen, T-Zellen und von Monozyten abstammende Makrophagen10. Es wurde festgestellt, dass die Behandlung mit CAP die intrazellulären ROS-Werte erhöht10. CAP verringerte dosisabhängig die Proliferation und Lebensfähigkeit von T-Zellen und Tumorzellen (in 2D- und 3D-Kultur). Dies resultierte aus einem CAP-induzierten G2/M- 82 Zellzyklus-Stillstand, erhöhtem mitochondrialen Stress und Apoptose. Die antiproliferativen und zytotoxischen Effekte von CAP konnten durch die Applikation von Antioxidantien teilweise rückgängig gemacht werden. Die Zellen reagierten unterschiedlich empfindlich auf die CAP- Behandlung, wobei die T-Zellen am stärksten betroffen waren. M1-Makrophagen wiesen nach der CAP-Behandlung einen erhöhten mitochondrialen Stress auf. Es wurde auch festgestellt, dass die CAP-Behandlung Makrophagen zu einem anti-inflammatorischen „M0/M2“- Phänotyp hin polarisiert. Insgesamt zeigte diese Studie, dass CAP starken oxidativen Stress in den Zellen des Melanom-Mikromilieus induziert, wobei T-Zellen die höchste Empfindlichkeit aufweisen. 83 3.3.3 AUTHOR CONTRIBUTIONS Conceptualization E.R. Trzeciak, N. Zimmer, J. Schupp, S. Rietz, A. Tüttenberg Methodology E.R. Trzeciak, N. Zimmer, I. Gehringer, L. Stein, B. Gräfen Validation E.R. Trzeciak, N. Zimmer Formal analysis E.R. Trzeciak, N. Zimmer, I. Gehringer, L. Stein Investigation E.R. Trzeciak, I. Gehringer, L. Stein, N. Zimmer, J. Schupp, B. Gräfen Resources S. Rietz, A. Stephan, M. Prantner Writing (Original draft preparation) E.R. Trzeciak, N. Zimmer, A. Tüttenberg Writing (Review and editing) E.R. Trzeciak, N. Zimmer, A. Tüttenberg, L. Stein, I. Gehringer, J. Schupp, S. Rietz, M. Prantner, A. Stephan Visualization E.R. Trzeciak, N. Zimmer Supervision A. Tüttenberg Project administration A. Tüttenberg Funding acquisition A. Tüttenberg 84 3.3.4 PUBLICATION Disclosure: This paper was published open access and permission of re-use in this dissertation was granted by the publisher (MDPI)10,348. The authors maintain all copyrights. 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 4. DISCUSSION Disclaimer: In-depth discussions of the results in Paper 1, Paper 2, and Paper 3 can be found in their respective publications in the “3. Results” section8–10. The following “4. Discussion” places the collective findings of this work into a greater context and explores outstanding questions facing the respective research fields. Immunotherapy has emerged as an increasingly promising method to treat a diverse range of clinical conditions, ranging from cancer to autoimmunity1–7. Herein, immune responses are optimized using immunomodulatory agents. Depending on the condition, immune responses may be amplified (e.g., cancer) or attenuated (e.g., wound healing). Cancer and deficiencies in wound healing represent significant global healthcare burdens106,350. Globally, there are more than 40 million patients suffering from chronic wounds351. Additionally, 20 million cases of cancer were diagnosed, and 9.7 million deaths were attributed to the disease in 2022106. These numbers are expected to increase as the global population continues to age106,350. Despite this, many patients lack effective treatment options. To address this unmet need, this work characterized two novel immunomodulatory approaches, namely GARP and CAP8–10. In the future, these mechanisms could be used as possible therapeutic approaches for the treatment of cancer and the optimization of wound healing. GARP is a tolerogenic protein, expressed by activated Tregs and platelets, that is essential for the maintenance of peripheral tolerance28,30,61–64,81,82. Cancer cells upregulate GARP to evade detection by the immune system and to drive tumor progression55,61,65,99,100. Therefore, depletion of GARP in the TME has emerged as a highly promising anti-cancer approach28. Currently, there are ten registered clinical studies, ranging from phase I to 2/3, testing GARP as a therapeutic target352–361. Nine studies are examining anti-GARP antibodies as possible treatment options for advanced or metastatic solid tumors as well as lymphomas352,353,355–361. In more detail, companies are testing monoclonal antibodies that either target GARP alone (ClinicalTrials.gov IDs: NCT05606380, NCT05483530, NCT04419532, NCT05821595), GARP bound to LTGF-β1 (ClinicalTrials.gov IDs: NCT03821935, NCT06109272, NCT05822752, NCT06236438, NCT06310746), or are bispecific against GARP and PD-L1 (ClinicalTrials.gov IDs: NCT05869240)352–361. Although all antibodies address GARP in some form, they vary in their method of action. Many prevent the release of active TGF-β into the TME, whereas others deplete GARP+ cells, like activated Tregs and tumor cells, via ADCC28,352–366. Most studies are examining the efficacy of anti-GARP antibodies in combination with anti-PD-1 antibodies, which has previously shown compelling synergy in preclinical in vivo experiments353,355– 358,362,367–369. These preclinical studies showed that this combination approach (anti-GARP/anti-PD-1 antibodies) enhanced the function of Teff, increased the infiltration of CTLs into the TME, depleted GARP+ Tregs in the TME, and resensitized PD-1 resistant tumors to therapy367,369. This combined treatment approach would be particularly interesting to test in glioblastoma as the disease is characterized by low immune cell infiltration, high resistance to PD-1/PD-L1 ICIs, and the fact that glioblastoma cells use GARP as a mechanism of immune 117 evasion65,198,227,228,370. Paper 2 showed that GARP is universally expressed by GSCs, which are known to drive tumor initiation, therapy resistance, and recurrence9. Targeting of GARP+ glioblastoma cells, GSCs, and Tregs offers a compelling approach to overcome suppression in the glioblastoma microenvironment, but this must be evaluated in future in vivo studies. It is important to mention that there is a pressing concern that anti-GARP antibody therapy may deplete platelets and thereby increase the risk of bleeding related complications. Initial in vivo experiments, utilizing the anti-GARP monoclonal antibody, PIIO-1, which specifically recognizes GARP unbound to LTGF-β, have demonstrated that this approach can be safely tolerated in mice369. It is important to note that platelets only express GARP-LTGF-β complexes, which prevented PIIO-1 from depleting GARP+ platelets. Future studies are needed to determine if anti-GARP antibodies with differing modes of action, especially those that target GARP-LTGF-β complexes or act by depleting GARP+ cells with ADCC, pose a risk for humans by excessively depleting platelets28,352–359,362. So far, the first results from clinical studies have been promising. AbbVie reported the results of their phase I clinical trial (ClinicalTrials.gov ID: NCT03821935) of an anti-GARP/LTGF-β1 antibody (ABBV-151) alone and in combination with an anti-PD-1 monoclonal antibody (Budigalimab)355,371. They observed a manageable safety profile and, in some patients, even durable responses following combination therapy. In the upcoming years, results from the other clinical studies will be made available. Only then, can the safety and efficacy of anti- GARP monoclonal antibodies for the treatment of cancer be determined. Modulation of GARP levels may also serve as a promising therapeutic strategy to enhance wound healing. Paper 1 reported for the first time that GARP may play an important role in early stages of wound healing through the induction of Tregs8. It is known that Tregs promote tissue repair and regeneration by regulating inflammation25–27. For example, Tregs suppress the production of proinflammatory cytokines by neutrophils as well as by CD4+ and CD8+ Teff, which sustain the inflammation phase. Similarly, Paper 1 demonstrated that GARP, derived from iPRF and iPRF derived platelets, inhibits the production of the proinflammatory Th1 cytokines, IFN-γ and IL-28. Paper 1 also showed that iPRF and iPRF derived platelets induce suppressive GARP+Foxp3+ Tregs in a GARP dependent manner8. This opens-up the possibility of modulating GARP levels to enhance wound healing, e.g., the addition of sGARP to autologous platelet concentrates. It is known that sGARP displays strong biological potency in inducing anti-inflammatory immune responses, making this approach promising for wound healing56,70,83,84. This approach is particularly attractive for the treatment of chronic wounds, which fail to transition from the inflammation stage to the anti-inflammatory proliferation stage95,97. However, the efficacy of this approach must be evaluated in future studies. It should be noted that GARP has been previously reported to be dysregulated in wound healing, specifically in the later remodeling stage of wound healing372–374. GARP has been implicated in the development of fibrosis (the formation of excessive scar tissue)372–376. Fibrosis occurs when there is an excessive accumulation of ECM. Over time, this increases tissue rigidity and can alter the functionality of the affected organ. GARP has been found to 118 enhance fibrosis through its function in activating the profibrotic cytokine, LTGF-β374. TGF-β acts as a driving force behind the development of fibrosis by simultaneously activating cells, like hepatic stellate cells and myofibroblasts, to produce excessive ECM and by inhibiting the degradation of ECM374–376. Zhang et al., 2023 demonstrated that selective deletion of GARP on hepatic stellate cells in an in vivo model of liver fibrosis decreased collagen deposition374. Additionally, they found that GARP mRNA levels are upregulated in patients with fibrosis in comparison to healthy donor controls. Similarly, activated GARP+ Tregs were also reported in patients with chronic hepatitis C, a disease characterized by liver fibrosis, and correlated with fibrosis staging372,373. Therefore, in the case of fibrosis, it would not be recommended to increase GARP levels to promote wound healing — but rather to deplete them. This approach, specifically using anti-GARP antibodies, has already shown promise as a novel treatment approach for fibrosis354,364,377. Shanghai Henlius Biotech, Inc. has developed HLX6018, an anti-GARP/TGF-β1 antibody with the aim of treating idiopathic pulmonary fibrosis354,364. The company recently gained approval for a phase I clinical trial, examining the safety and immunogenicity of HLX6018 in healthy participants354. Collectively, these studies support the promise of modulating GARP levels to optimize immune responses in cancer (depletion) as well as in the early (enhancement) and late (depletion) stages of wound healing. Upregulation of GARP in the tumor microenvironment, coupled with its dual role in immune evasion and tumor progression, makes GARP a promising biomarker candidate for cancer28,49,55,61,65,99,100,102,104,139,140. Previous studies have analyzed different forms of GARP (sGARP, GARP+ cells, and GARP mRNA levels) as predictive, prognostic, and diagnostic biomarkers for different tumor entities28,56,60,63,99–104. Paper 2 stands out from them as it was the first study to analyze GARPNU as a biomarker9. In more detail, a high frequency of GARPNU+ cells in the glioblastoma microenvironment correlated inversely with overall survival of patients. Similarly, studies have reported that other abnormally localized nuclear proteins, like CD55 and PD-L1, exhibit promise as prognostic biomarkers for cancer378–381. However, future studies are needed to expand sample sizes and to optimize the cut-off before it can be determined if GARPNU can be used as a prognostic biomarker for glioblastoma. The strong potential of GARP as a therapeutic target and biomarker makes it important to better understand the biology of the protein. Paper 2 reported for the first time a possible regulatory relationship between GARP and cyclin-dependent kinase inhibitor 1 (p21)9. In more detail, it was found that GARPhigh GSCs express a higher level of p21 than GARPlow GSCs. It is well known that p21 promotes cell cycle arrest by inhibiting CDK4,6/cyclin-D and CDK2/cyclin- E382. Therefore, higher levels of p21 are associated with decreased progression through the cell cycle. It has been reported that expression of p21 can be induced by TGF-β382,383. The results of Paper 2 suggest a possible upstream regulation of p21 expression by GARP, in a seemingly TGF-β dependent manner9. It is important to point out that a significant difference in the expression of the proliferation marker, phosphorylated histone H3, between GARPhigh 119 and GARPlow GSCs was not observed. This could be attributed to the innate slow growing nature of GSCs, making differences in the proliferation marker marginal. Future experiments, including the inhibition of TGF-β and extended functional proliferation assays of GSCs, are required to verify this potential regulatory relationship. It has been found that p21 is essential for maintaining the quiescence of hematopoietic stem cells (HSCs)384. By inhibiting the cell cycle, p21 limits cell division of HSCs, which puts them at risk for developing DNA damage385,386. Over time, the accumulation of DNA damage reduces the long-term ability of HSCs to self-renew and subsequently leads to their functional exhaustion. Similarly, p21 has been found to maintain quiescence in cancer stem cells386,387. p21 inhibits the hyperproliferation of cancer stem cells, and thus safeguards the long-term self-renewal capacity of cancer stem cells. The findings of Paper 2 suggest that GARP may play an important role in maintaining quiescence in GSCs by regulating p219. This is further supported by the discovery that GARPhigh GSCs exhibit significantly less self-renewal than GARPlow GSCs. This makes sense as the process of self-renewal requires cell division to generate daughter cells, whereas cells in a quiescent state do not replicate. Analysis of additional quiescence and self-renewal markers on GARPhigh/low GSCs are needed to test this hypothesis. Paper 2 reported the abnormal nuclear localization of GARP in GSCs and the glioblastoma microenvironment9. Previously, it was widely accepted that GARP is located on surface of cancer cells and can be released as a soluble factor into the TME (sGARP)28,56,61,65. Zimmer et al., 2019 reported for the first time that GARP is highly expressed in the nuclei of glioblastoma cells and GSCs65. Paper 2 demonstrated for the first time that a high frequency of GARPNU+ cells in glioblastoma tumor tissue is linked to poor prognosis9. These factors coupled with knowledge that GARP’s only known binding partner, LTGF-β, has not been reported to be localized to the nucleus, compelling support the potential of GARP acting as a moonlighting protein388. Moonlighting proteins refers to a subset of proteins, in which a single protein exhibits more than one function389–391. The function of a moonlighting protein can vary depending on where it is localized in the cell or by what cell type expresses it. Graeme Wistow and Joram Piatigorsky first described the concept in 1987, where they reported that crystallins exhibit dual structural and enzymatic functions392. For example, in Anas platyrhynchos (Mallard ducks), ε-crystallin acts as both a structural component of the lens as well as the enzyme, lactate dehydrogenase393,394. Since the initial discovery of moonlighting proteins, more than 500 proteins have been identified390,393,395. Many of these proteins are highly conserved enzymes and exhibit a diverse range of functions (e.g., acting as a transcription factor, DNA binding protein, cytokine, receptor, chaperone, proteasomal subunit, etc.)390. GARP has structural characteristics of a moonlighting protein as it is highly conserved, is rich in LRRs that are known to facilitate protein-protein interactions, and is differentially localized in and outside of the cell (Fig. 3)28,47,49,72,396,397. 120 Currently, the search for an alternative function of GARP in the nuclei of cancer cells is underway. Initial in silico predictions have identified two possible interaction partners, namely the proteins signal transducer and activator of transcription 3 (STAT3) and decapping MRNA 2 (DCP2) (Fig. S18-20). Across several protein interaction prediction platforms, STAT3 and DCP2 were found have a high likelihood of interacting with GARP; they are also known to be localized to the nucleus (Fig. S18-20)398–408. STAT3 is a transcription factor that translocates to the nucleus upon phosphorylation. DCP2 removes the 7-methyl guanine cap from mRNA molecules and is required for mRNA degradation. Notably, STAT3 and DCP2 are upregulated in the glioblastoma microenvironment, where they promote tumor growth and immune evasion, similar to the known functions of GARP65,99,100,409,410. Preliminary work has biochemically confirmed for the first time the presence of GARP in the nucleus and its enrichment in the chromatin of tumor cells (Fig. S21A). Furthermore, co- immunoprecipitation of GARP and STAT3 has confirmed the direct interaction of the proteins (Fig. S21B). Two independently performed high-throughput affinity capture mass spectrometry experiments also reported purification of GARP and STAT3 from tumor cells, providing further evidence for the binding interaction411,412. These same studies reported purification of GARP and DCP2, but this binding interaction must still be biochemically confirmed on a smaller scale. These results indicate the possibility of GARP acting as moonlighting protein, most promisingly by binding STAT3, but there is much to be understood. Future studies must determine how the proteins interact with each other. It remains unclear what domains are essential for the binding interaction. GARP has 20 LRRs that contain conserved domains, including a protein phosphatase 1 regulatory subunit 42 (PPP1R42) domain and an another domain implicated in transcription28,47,49,72,397,397,413. The transcription factor, STAT3 contains a SH2 domain that can be phosphorylated by tyrosine kinases, leading to its translocation to the nucleus399,400. Of note, LRRs are reported to interact with SH2 domains414. Therefore, it can be speculated that GARP and STAT3 may interact with each other via binding of their respective LRR and SH2 domains; however, this must be evaluated in vitro by mutating suspected amino acids involved in the binding interaction. The localization of the GARP-STAT3 binding interaction must also be determined to evaluate whether this can be attributed as a function of GARPNU. This could be accomplished by performing subcellular fractionation, followed by co-immunoprecipitation. A possible regulatory relationship between GARP and STAT3 as well as the functional implications of the interaction must also be clarified. Kuhn et al., 2017 reported that IL-6 treated murine naïve CD4+ T cells inhibited the expression of the GARP mRNA in a STAT3 dependent manner415. Furthermore, Walton et al., 2020 showed that human Tregs treated with a pSTAT3 inhibitor had significantly higher levels of GARP+ Tregs416. It remains unclear whether a similar regulatory relationship of GARP by STAT3 exists in human malignant cells. Future studies will investigate both the regulatory and functional relationships of the proteins, specifically using GARP overexpression and knockdown cell lines. RNA sequencing 121 experiments are also planned to examine if GARP may influence transcription, especially in the case of STAT3 regulated genes. Paper 2 reported the abnormal localization of GARP in the nuclei of GSCs and cells in the glioblastoma microenvironment9. It remains unclear how GARP translocates into the nucleus. There are no annotated nuclear localization sequences (NLS) for the GARP protein. NLS are often conserved and consist of basic amino acids, like arginine (R) and lysine (K)417,418. Preliminary in silico predictions identified a possible NLS candidate for the GARP protein (Fig. S22A). The identified bipartite NLS candidate (KR 517-518, RR 564-565) was found to be evolutionary conserved (Fig. S22A). The identified candidate is located outside the LTGF-β binding cleft on the surface of the protein, enabling it to be theoretically accessed by nuclear importin proteins (Fig. S22B-C). Selective mutation of the amino acids in the identified NLS candidate are planned to determine whether nuclear translocation of the protein can be prevented. Lastly, it remains unclear from where GARPNU originates. Traditionally, nuclear bond proteins are translated in the cytoplasm before being trafficked into the nucleus419. Bharti et al., 2024 recently challenged this paradigm by demonstrating that the moonlighting protein CD55 can be trafficked from the cell surface into the nucleus381. Future experiments are planned to see if this is also the case for GARP. In more detail, tumor cells will be treated with the GARP cleaving protease thrombin to remove GARP from the cell surface63. Following cleavage, cells will be assessed for their expression of cytoplasmic and GARPNU over time to determine if they are dependent on surface GARP expression. Besides GARP, this work analyzed the effects of CAP on cells found in the melanoma microenvironment (Paper 3)10. It is not fully understood how CAP interacts with the cell. It is known that CAP treatment induces the production of diverse and highly reactive RONS349. These include but not limited to hydrogen peroxide (H2O2), hydroxyl radical (OH•), superoxide (O •-2 ), ozone (O3), singlet oxygen (1O2), nitric oxide (NO), nitrogen dioxide (NO2), nitrous oxide (N2O), nitrogen trioxide (NO3), and dinitrogen tetroxide (N O )349,420,4212 4 . RONS indiscriminately interact with fundamental components of the cell (e.g., DNA, protein, lipids), which leads to a plethora of biological effects422. The findings of Paper 3 indicate several distinct biological effects of CAP10. Firstly, it was observed that CAP treatment triggered G2/M cell cycle arrest, inhibited cell proliferation, and induced apoptosis in melanoma cells — characteristic signs of excessive DNA damage10,423. Although DNA damage was not evaluated in Paper 3, it has already been shown to result from CAP treatment in the literature349,424–428. Paper 3 found that CAP reduced the proliferation and viability of cancer cells and T cells; this could be reversed in part with the application of antioxidants10. This indicates that excessive RONS are responsible in part for these observed biological effects. Additionally, CAP treated cells showed signs of mitochondrial stress. This may be explained by possible damage to mitochondrial membranes, leading to metabolic dysfunction. This is supported by recent studies that have demonstrated that CAP disrupts mitochondrial membrane potential as well as induces mitochondrial swelling and the intrinsic 122 apoptosis pathway429,430,430. Excessive lipid peroxidation induced by CAP in cancer cells has also been linked to ferroptosis, indicating yet another biological effect of CAP431. Although the biological effects of CAP are still far from being understood, the clinical promise of CAP has already been supported. Currently, CAP is predominantly used in clinical dermatology to treat chronic wounds, such as ulcers, to accelerate wound healing432,433. In 2010, a phase II clinical trial reported that CAP treated wounds exhibited a significant reduction in bacterial loads, regardless of the bacteria species present at the wound site432. They also reported no associated side effects of CAP treatment. These anti-bacterial effects have been attributed to innate characteristics of CAP, such as the emission of UV radiation as well as the generation of RONS and electrical current433. Besides disinfection, CAP promotes wound healing by promoting angiogenesis as well as increasing the proliferation and migration of keratinocytes433–438. The first clinical trial investigating the safety of CAP for cancer treatment was conducted from 2020 to 2021 (ClinicalTrials.gov ID: NCT04267575)439. The phase I clinical trial examined CAP as a post-operative treatment for the surgical margins of advanced (IV) and recurrent solid tumors. In more detail, this study aimed to target residual microscopic tumor cells at the surgical margin to prevent local recurrence of the disease. It was found that CAP exhibited a selectivity for cancer cells in comparison to healthy cells. Strikingly, the study reported no adverse effects related to CAP treatment. Although the authors did not analyze alterations in immune cell composition in the TME, like Paper 3, assurance that CAP exhibits a strong safety profile is highly encouraging10. Recent studies have shown that CAP is not limited to the treatment of superficial tumors. Rather, it shows strong promise as a post-operative adjuvant treatment440,441. Yoon et al., 2018 demonstrated that in an in vivo model of vestibular schwannoma, post-operative CAP treatment following primary tumor resection significantly reduced recurrent tumor growth440. Chen et al., 2021 showed similar results in a in vivo model of recurrent breast cancer441. These results are in alignment with Paper 3, which showed CAP exerts cytotoxic and anti- proliferative effects on melanoma cells in vitro10. Of note, the duration of CAP as a post- operative treatment varied widely by cancer type (vestibular schwannoma, 10 min; breast cancer, 1-4 min)440,441. Paper 3, in contrast, utilized a repeated treatment approach (1 min CAP/24h, 1 min CAP/48 h), which is more suitable for easily accessible superficial tumors10. This implies that CAP treatment should be optimized to the respective clinical application (post-operative treatment or direct treatment of tumors) as well as tumor type. CAP can also induce anti-tumor immune responses when applied as an adjuvant post- operative treatment strategy441,442. Chen et al., 2021 found that CAP treatment induced anti- tumor T cell mediated immunity in a model of recurrent breast cancer in vivo441. In more detail, CAP treatment resulted in the upregulation of the immunogenic cell death (ICD) marker, surface calreticulin, on residual tumor tissue. It is known that when cancer cells undergo ICD, they release tumor associated antigens; this leads to the activation and maturation of DCs443. The findings from Chen et al., 2021 supported this as they found a higher 123 frequency of mature DCs in the tumor draining lymph nodes of CAP treated animals441. Mature DCs aid in the activation of T cells443. They also found a significant increase in the infiltration of CD3+CD4+ and proliferating CD3+CD8+ T cells in CAP treated recurrent tumors441. Similarly, Paper 3 demonstrated increased proliferation of CD8+ T cells in vitro when in co-culture with irradiated CAP treated melanoma cells, suggestive of ICD10. Chen et al., 2021 also showed an upregulation of pro-inflammatory cytokines, like IFN-γ, TNF-α, IL-2, and IL-12, in the serum of mice increased in a dose-dependent manner following CAP treatment441. Anti-tumor immunity can be potentially further enhanced through the combination of CAP and anti-PD- L1 antibody therapy442. Han et al., 2020 showed using a murine melanoma model (B16F10) in vivo that the combination treatment synergistically inhibited the tumor growth rate as well as increased the infiltration of CD4+ and CD8+ T cells into tumor tissue442. Although Paper 3 reported that CD3+, CD4+, and CD8+ T cells were highly sensitive to CAP treatment in vitro, the described works above indicate that a robust infiltration of functional CD4+ and CD8+ T cells into the TME in vivo is indeed possible10,441,442. This does not rule out the possibility of initial T cell death in the TME following CAP treatment as the findings of Paper 3 suggest10. Rather, it suggests that ICD induced infiltration of T cells into the TME seemingly plays a larger role in influencing T cell numbers in the TME in vivo. Collectively, these studies demonstrate that CAP exhibits a dual role in both inhibiting tumor growth as well as promoting anti-tumor T cell mediated immunity in the TME. Altogether, these findings show that CAP exhibits diverse immunomodulatory effects that are desirable in distinct immunological settings. CAP has been clinically proven to optimize wound healing and exhibits strong promise as a post-operative adjuvant anti-cancer therapy432,440,441. Furthermore, CAP has been reported to have an excellent safety profile in both immunological settings, encouraging therapeutic development and application of CAP to other clinical conditions432,439. 5. CONCLUSIONS This work contributed to the understanding of two promising immunomodulatory approaches, namely GARP and CAP. In more detail, the functional relevance of GARP in wound healing (Paper 1) and GSC biology (Paper 2) was described for the first time8,9. Furthermore, the biological effects of CAP on cells found in the melanoma microenvironment were elucidated (Paper 3)10. 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Science, 362(6417), 952–956. https://doi.org/10.1126/science.aau2909 XLIV APPENDIX SUPPORTING INFORMATION PAPER 1 Disclosure: This paper was published open access and permission of re-use in this dissertation was granted by the publisher (MDPI)8,348. The authors maintain all copyrights. The figure was minimally modified to fit the text. Figure S1 (Paper 1): Flow cytometric gating strategy XLV Fig. S1 (Paper 1): Representative flow cytometric gating strategies used for iPRF derived platelets (A), CD4+ T cells (B), and monocyte derived macrophages (C). Debris, doublets, and dead cells were excluded from analysis. XLVI PAPER 2 Disclosure: This paper was published open access and permission of re-use in this dissertation was granted by the publisher (MDPI)9,348. The authors maintain all copyrights. Figures were minimally modified to fit the text. Figure S2 (Paper 2, prev. S1): Characterization of patient derived GSC cell lines XLVII Fig. S2 (Paper 2, prev. S1): Heterologous GSC cell lines differing in their self-renewal capacity. Previous characterization of the heterologous patient derived GSC cell lines used in this work (A, B)266,444–446. Considerable variation in the expression of several GSC markers, including glial fibrillary acidic protein (GFAP), CD133, platelet-derived growth factor receptor alpha (PDGFR-α), and aldehyde dehydrogenase 1 family member A3 (ALDH1A3), was observed in both heterologous and isogenic GSC cell lines. (B) Example analysis of several GSC markers, including CD133, PDGFR-α, and ALDH1A3, in the isogenic GSC cell lines, IT-726- 1 and IT-726-2 (GSC cell lines indicated by the red arrows, featured in A), via western blot using the following antibodies: anti-CD133/1 (clone: W6B3C1), anti-PDGFR-α (D13C6) (Cell Signaling, #5341), anti-ALDH1A3 (Thermo Fisher Scientific, MA5-25528), anti-p53 (DO-1) (Cell Signaling, #18032), anti-actin (C4) (Santa Cruz Biotechnology, sc-47778), goat anti-mouse IgG horseradish peroxidase (Santa Cruz Biotechnology, sc-2055), goat anti-rabbit horseradish peroxidase (Santa Cruz Biotechnology, sc-2054). Cell lysates were loaded in increasing volumes, and actin was used as a loading control. XLVIII Figure S3 (Paper 2, prev. S2): Anti-GARP antibody validation for flow cytometry Fig. S3 (Paper 2, prev. S2): Anti-GARP antibody (Ab) validation for flow cytometry. Comparative flow cytometric analysis of surface GARP levels on a control human melanoma cell line, Mewo, using three different human anti-GARP antibodies. The following antibodies were analyzed: Miltenyi (130-103-890), Biolegend (352502), Origene (AP17415PU-N). Doublets, debris, and dead cells were excluded from the analysis. Graph shows the mean fluorescence intensity (MFI) normalized to the MFI of the respective isotype control, whereas histograms display one representative result (n=3, ± SD, *** p < 0.001, and **** p < 0.0001 determined by two-way ANOVA). XLIX Figure S4 (Paper 2, prev. S3): Specificity demonstration of anti-GARP antibodies Fig. S4 (Paper 2, prev. S3): Specificity demonstration and validation of anti-GARP antibodies (Ab). Comparative flow cytometric analysis of surface GARP levels on wildtype (WT) and transfected (GARP overexpression (GARP+), empty vector control (EV)) Mewo cells using three different human anti-GARP Abs. The following antibodies were analyzed: Miltenyi (130-103-890) (A), Biolegend (352502) (B), Origene (TA337028) (C). Doublets, debris, and dead cells were excluded from the analysis. Graph shows the mean fluorescence intensity (MFI) normalized to the MFI of the respective isotype control, whereas histograms display one representative result (n=3, ± SD, * p < 0.05, ** p < 0.01, and **** p < 0.0001 determined by two-way ANOVA). L Figure S5 (Paper 2, prev. S4): Flow cytometric gating strategy for GSCs Fig. S5 (Paper 2, prev. S4): Flow cytometric gating strategy for GSCs. Representative flow cytometric gating strategy used for GSCs. Debris, doublets, and dead cells were excluded from analysis. LI Figure S6 (Paper 2, prev. S5): Validation of anti-GARP antibodies for confocal microscopy Fig. S6 (Paper 2, prev. S5): Anti-GARP antibody validation for confocal microscopy. Confocal images of the human GARP expressing cell lines, Ma-Mel-19 and T98G. Cells were stained for GARP using two different antibodies (Origene, AP17415PU-N; Origene, TA337028) as seen in orange. Cells were counterstained for their nuclei with Hoechst (blue). Note the intranuclear localization of GARP (GARPNU+) detectable with both antibodies. Scale bar corresponds to 20 µm. LII Figure S7 (Paper 2, prev. S6): Flow cytometric gating strategy for GARP sorted GSCs Fig. S7 (Paper 2, prev. S6): (A) Representative flow cytometric gating strategy used for sorting GARPhigh and GARPlow GSCs. Sorted cells were re-measured via flow cytometry to confirm sorting efficacy. (B) Example GARP staining of GSCs (mean fluorescence intensity shown) compared to its respective isotype and unstained controls. Debris, doublets, and dead cells were excluded from analysis. LIII Figure S8 (Paper 2, prev. S7): Representative immunohistochemical stainings of xenograft tumors derived from GSC cell lines, #1051 and #1043 Fig. S8 (Paper 2, prev. S7): Invasive xenograft tumors arisen from GSC cell lines, #1051 and #1043. Representative images of xenograft tumors grown from human GSCs in an orthotopic mouse model for brain tumors. Immunohistochemistry stainings for human nestin (anti- human nestin antibody PA5-82905, 1:100, Life Technologies). LIV Figure S9 (Paper 2, prev. S8): Representative immunofluorescence stainings of xenograft tumors derived from GSC cell lines, #1051 and #1043 Fig. S9 (Paper 2, prev. S8): GARP is expressed in xenograft tumors arisen from GSC cell lines, #1051 and #1043. Immunofluorescence of GARP and nestin of (A) #1051 and (B) #1043 xenograft tumors. GARP seems to be exclusively expressed on GSC cells. Confocal images of GARP and nestin expressing GSCs stained for GARP and nestin. Cells were stained for their LV nuclei. Nuclear counterstaining with Hoechst (blue), GARP (red), and nestin (green). Scale bar corresponds to 100 µm. LVI Figure S10 (Paper 2, prev. S9): Study design and models used for the assessment of GARP Fig. S10 (Paper 2, prev. S9): Study design and models used for the assessment of GARP. Cohort 1: For the analysis of GARP and CD133 expression in GB, the online tool OncoLnc was used. Based on 152 complete data sets, including complete survival data, patients were divided 50/50 into “low” or “high” groups based off their mRNA expression of GARP and CD133 and were analyzed for their survival. The results shown are in whole or part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga and were analyzed using OncoLnc447. Cohort 2: „GARP in situ“ corresponds to GARP assessments in tumor specimens from newly diagnosed or recurrent GBs. Investigation track (1) corresponds to in vitro assessments in GSCs either isogenic or heterogenic originating from ndGBs. Track (2) corresponds to in vivo assessments of GARP in tumor xenografts grown LVII from orthotopically implanted GSCs. Track (3) corresponds to GARP assessments in GSCs explanted from tumor xenografts. Track (4) corresponds to tumor-matched GSCs isolated from the same patient at the ndGB or recGB stage. Furthermore, retrospective analysis of transcriptome data of 155 GB samples from 28 patients of Kim et al., 2020. ndGBs, first and second recurrent tumors were analyzed for their GARP and CD133 expression levels across tumor stages445. Cohort 3: A cohort of 35 patients with (WHO grade IV) glioblastoma (Zimmer et al., 2019) were analyzed for their GARP expression by immunohistochemistry and analyzed for their survival65. LVIII Figure S11 (Paper 2, prev. S10): Frequency of GFAP+ GARPhigh and GARPlow GSCs Fig. S11 (Paper 2, prev. S10): Frequency of GFAP+ GARPhigh and GARPlow GSCs. The GSC cell line, #1095, was sorted into GARPhigh and GARPlow populations. Cells were cultured in self- renewal promoting conditions (NB+bFGF/+EGF) and assessed for their frequency of glial fibrillary acidic protein (GFAP), an astrocyte differentiation associated marker, via immunofluorescence. (A) The percentage of GFAP+ cells were quantified from the total cells counted. (B) Representative images of GFAP (green) stained GARPhigh and GARPlow GSCs with paired DAPI (blue) controls. The white scale bar corresponds to 50 μm. LIX LX PAPER 3 Disclosure: This paper was published open access and permission of re-use in this dissertation was granted by the publisher (MDPI)10,348. The authors maintain all copyrights. Figures were minimally modified to fit the text. Figure S12 (Paper 3, prev. S1): Flow cytometric gating strategies LXI Fig. S12 (Paper 3, prev. S1): Model gating strategies used for melanoma (A), macrophages (B), and CD3+CD4+ and CD3+CD8+ T cells (C). For cell death analysis using annexin V and propidium iodide (A), the gating contents of each quadrant are as follows: lower left (LL) live cells, lower right (LR) early apoptotic cells, upper right (UR) late apoptotic cells, and upper left (UL) necrotic cells. In addition, the macrophage gating strategy (B) shows one gated polarization marker, CD163, as an example. This same gating strategy was applied for other measured macrophage polarization markers; however, the channel of interest changed depending on the measured marker. Debris, doublets, and dead cells were excluded from analysis. LXII Figure S13 (Paper 3, prev. S2): MiniJet-R and intracellular ROS quantification Fig. S13 (Paper 3, prev. S2): (A) The MiniJet-R, from Heuermann HF Technik, used to produce CAP in this study, is comprised of a microwave generator and handpiece. In the feed line, the process gas argon and electromagnetic waves with a frequency of 2.45 GHz are transported to the handpiece. Gas flow can be monitored with the gas flow meter, and the starter box is necessary to ignite the plasma448. (B) Example image of the MiniJet-R in use. A beam of CAP applied to the tissue of a human hand448. (C) Ma-Mel-19 cells were treated with varying amounts of CAP (5s, 30s, 60s) and their intracellular ROS levels were measured 1 h after treatment. The positive control indicates cells treated with a control ROS Inducer, while the negative control represents untreated cells. Bar diagram shows the average intracellular ROS levels in relative fluorescence units (RFU) normalized to the untreated control (n=6/control, n=3/CAP) ± SD; Statistical significance was calculated by performing ordinary one-way ANOVAs corrected for multiple comparisons with Tukey tests and is indicated by the asterisks as follows: *, p < 0.05; ***, p < 0.001; ****, p < 0.0001. LXIII Figure S14 (Paper 3, prev. S3): UKRV-Mel-15a cell cycle and cell death results Fig. S14 (Paper 3, prev. S3): (A-B) UKRV-Mel-15a cells were treated with varying amounts of CAP (60s, 120s, 180s). After 3 days, cells were analyzed via flow cytometry. (A) Cells were stained with Hoechst. Bar diagram shows the percentage of cells in each gate (n=3) ± SD. (B) Cells were stained with annexin V and propidium iodide (PI). Bar diagram shows the percentage of cells in each quadrant gate (n=4) ± SD. The contents of each gate are as follows: lower left (LL) live cells, lower right (LR) early apoptotic cells, upper right (UR) late apoptotic cells, and upper left (UL) necrotic cells. Histograms and dot plots paired to bar diagrams show one representative result. Statistical significance was calculated by performing two-way ANOVAs corrected for multiple comparisons with Tukey tests and is indicated by asterisks as follows: *, p < 0.05; ***, p < 0.001; ****, p < 0.0001. LXIV Figure S15 (Paper 3, prev. S4): Non-significant macrophage polarization markers Fig. S15 (Paper 3, prev. S4): M0 macrophages were treated with 120s of CAP. M1 and M2 polarized macrophages served as controls. After 2 days, cells were analyzed via flow cytometry. Cells were stained with different macrophage polarization markers. Bar diagram shows the average mean fluorescent intensity (MFI) of each measured marker normalized to untreated M0 macrophages and represents the pooled results of twelve independent experiments (n=12 donors). Histograms paired to bar diagrams show one representative result. Statistical significance was calculated by performing two-way ANOVAs corrected for multiple comparisons with Tukey tests. LXV Figure S16 (Paper 3, prev. S5): Example Seahorse OCR and ECAR results Fig. S16 (Paper 3, prev. S5): Representative (A) oxygen consumption rate (OCR) and (B) extracellular acidification rate (ECAR) graphs produced from a Seahorse Cell Mito Stress test are shown. Line graphs show means ± SD (n=3) of untreated and 120s CAP treated Ma-Mel-19 cells. Statistical significance was calculated by performing two-way ANOVAs corrected for multiple comparisons with Tukey tests and is indicated by the asterisks as follows: *, p < 0.05; **, p < 0.01; ***, p < 0.001; ****, p < 0.0001. LXVI Figure S17 (Paper 3, prev. S6): UKRV-Mel-15a antioxidant treatment results Fig. S17 (Paper 3, prev. S6): CFSE prelabeled UKRV-Mel-15a cells were plated in the presence of various antioxidants and differentially treated with CAP (0s, 120s, 180s). After 3 days, cells were analyzed via flow cytometry. (A) UKRV-Mel-15a cells were stained for viability. Bar diagram shows the average percentage of live cells measured normalized to the untreated control (0s) ± SD (n=12/untreated, n=4/antioxidant). (B) Bar diagram shows the average CFSE MFI measured normalized to the untreated control (n=12/untreated, n=4/antioxidant) ± SD. Histograms paired to bar diagrams show one representative result. Statistical significance was calculated by performing two-way ANOVAs corrected for multiple comparisons with Dunnett tests and is indicated by the asterisks as follows: *, p < 0.05; **, p < 0.01; ***, p < 0.001; ****, p < 0.0001. LXVII LXVIII DISCUSSION Figure S18: In silico predictions of GARP protein interactors utilizing the Human Integrated Protein-Protein Interaction rEference (HIPPIE) database Interactor UniProt ID Interactor ID Interactor Gene Symbol Confidence Score TGFB1_HUMAN 7040 TGFB1 0.87 DCP2_HUMAN 167227 DCP2 0.82 BIP_HUMAN 3309 HSPA5 0.82 MOT10_HUMAN 117247 SLC16A10 0.82 NSMA_HUMAN 6610 SMPD2 0.82 S11IP_HUMAN 114790 STK11IP 0.82 STAT3_HUMAN 6774 STAT3 0.82 ZDHC6_HUMAN 64429 ZDHHC6 0.82 MAN1_HUMAN 23592 LEMD3 0.72 TMM11_HUMAN 8834 TMEM11 0.72 1C01_HUMAN 3107 HLA-C 0.63 CANT1_HUMAN 124583 CANT1 0.63 CNPY3_HUMAN 10695 CNPY3 0.63 DAD1_HUMAN 1603 DAD1 0.63 ECE1_HUMAN 1889 ECE1 0.63 FMOD_HUMAN 2331 FMOD 0.63 ITB8_HUMAN 3696 ITGB8 0.63 L9056_HUMAN 100288413, 100290734, 100293101 ERVMER34-1 0.63 MAGT1_HUMAN 84061 MAGT1 0.63 NAGPA_HUMAN 51172 NAGPA 0.63 PEBB_HUMAN 865 CBFB 0.63 PPM1B_HUMAN 5495 PPM1B 0.63 RPN1_HUMAN 6184 RPN1 0.63 RTN4R_HUMAN 65078 RTN4R 0.63 S39AA_HUMAN 57181 SLC39A10 0.63 TR10D_HUMAN 8793 TNFRSF10D 0.63 Fig. S18: In silico predictions of possible protein interaction partners of human glycoprotein A repetitions predominant (GARP). Results were obtained by querying the Human Integrated Protein-Protein Interaction rEference (HIPPIE) database for “LRC32_HUMAN“405. A total of 26 hits were found. The confidence score, ranging from 0 (very low) to 1 (very high), indicates the likelihood of interaction. Notably, TGF-B1, the best-known interaction partner of GARP has the highest confidence score of 0.87. Confidence scores were calculated as the weighted sum of several parameters: the number of works reporting the interaction, the number and quality of experimental methods used to detect the interaction, and the number of times the interaction was shown in non-human organisms. LXIX Figure S19: In silico predictions of protein interactors with GARP utilizing the Physical Protein-Protein Interactions with Reality Scores (HitPredict) database Interactor UniProt ID Interaction ID Interactor Gene Symbol Interaction Score TGFB1_HUMAN 331548 TGFB1 0.847 STAT3_HUMAN 709279 STAT3 0.789 1C01_HUMAN 456987 HLA-C 0.618 FMOD_HUMAN 906368 FMOD 0.618 RTN4R_HUMAN 967209 RTN4R 0.618 BIP_HUMAN 469173 HSPA5 0.611 MOT10_HUMAN 967206 SLC16A10 0.611 NSMA_HUMAN 231315 SMPD2 0.585 DCP2_HUMAN 967204 DCP2 0.585 S11IP_HUMAN 967205 STK11IP 0.558 TMM11_HUMAN 525113 TMEM11 0.513 MAN1_HUMAN 967216 LEMD3 0.489 ITB8_HUMAN 592026 ITGB8 0.479 ECE1_HUMAN 723848 ECE1 0.479 TR10D_HUMAN 967213 TNFRSF10D 0.479 S39AA_HUMAN 967215 SLC39A10 0.479 PEBB_HUMAN 961478 CBFB 0.458 CANT1_HUMAN 967207 CANT1 0.458 MAGT1_HUMAN 967210 MAGT1 0.458 L9056_HUMAN 967212 ERVMER34-1 0.458 RPN1_HUMAN 367476 RPN1 0.437 CNPY3_HUMAN 967214 CNPY3 0.437 NAGPA_HUMAN 967214 NAGPA 0.437 ZDHC6_HUMAN 967211 ZDHHC6 0.318 PPM1B_HUMAN 257969 PPM1B 0.249 DAD1_HUMAN 825574 DAD1 0.249 Fig. S19: In silico predictions of possible protein interaction partners of human glycoprotein A repetitions predominant (GARP). Results were obtained by querying the Physical Protein- Protein Interactions With Reality Scores (HitPredict) database for “H. sapiens” > “LRRC32” (Protein ID: Q14392)406–408. A total of 26 hits were found. The interaction score, ranging from 0 (very low) to 1 (very high), indicates the reliability of the interaction. Notably, TGF-B1, the best-known interaction partner of GARP has the highest confidence score of 0.847. Interaction scores were calculated by taking the geometric mean of the annotation-based and methods-based scores. Annotation-based scores are based on the properties of the interacting proteins, e.g., structure of interaction domains, gene ontology, and homologous interactions. The methods-based score is calculated from experimental evidence of the interaction, e.g., number of publications or experiments showing support of the interaction, experimental methods used to detect the interaction, and the type of the interaction. High confidence interaction scores are over 0.28. LXX Figure S20: In silico predictions of protein interactors with GARP utilizing the BioGRID4.4 database Interactor UniProt ID Interactor Gene Symbol Evidence BIP_HUMAN HSPA5 3 DCP2_HUMAN DCP2 2 MOT10_HUMAN SLC16A10 2 NSMA_HUMAN SMPD2 2 STAT3_HUMAN STAT3 2 S11IP_HUMAN STK11IP 2 TGFB1_HUMAN TGFB1 2 ZDHC6_HUMAN ZDHHC6 2 B4GT5_HUMAN B4GALT5 1 CANT1_HUMAN CANT1 1 PEBB_HUMAN CBFB 1 CNPY3_HUMAN CNPY3 1 DAD1_HUMAN DAD1 1 ECE1_HUMAN ECE1 1 L9056_HUMAN ERVMER34-1 1 FMOD_HUMAN FMOD 1 1C01_HUMAN HLA-C 1 ITB8_HUMAN ITGB8 1 MAN1_HUMAN LEMD3 1 MAGT1_HUMAN MAGT1 1 NAGPA_HUMAN NAGPA 1 PPM1B_HUMAN PPM1B 1 RPN1_HUMAN RPN1 1 RTN4R_HUMAN RTN4R 1 S39AA_HUMAN SLC39A10 1 STT3A_HUMAN STT3A 1 TMM11_HUMAN TMEM11 1 TR10D_HUMAN TNFRSF10D 1 Fig. S20: In silico predictions of possible protein interaction partners of human glycoprotein A repetitions predominant (GARP). Results were obtained by querying the BioGRID4.4 database for “Homo sapiens” > “LRRC32”403,404. A total of 28 hits were found. The interaction score, ranging from 1 (low) to 3 (high), indicating the evidence of the interaction. Notably, TGF-B1, the best-known interaction partner of GARP has an evidence score of 2. Evidence scores were calculated from experimental evidence codes designed by the developers. Theses codes describe the experimental evidence of the interaction, e.g., affinity capture western blot. Experimental results were derived from peer-reviewed publications. LXXI Figure S21: Biochemical confirmation of nuclear localization of GARP and its interaction with STAT3 Fig. S21: (A) Chromatin fractionation of the human melanoma cell line, Mewo, was performed as described in Méndez and Stillman, 2000449. Subcellular fractions were analyzed via western blot as described in Müller et al., 2018450. GAPDH was used as a positive control for the cytoplasmic and nuclear fractions and as a negative control for the chromatin fraction. Histone H3 was used as a positive control for nuclear and chromatin fractions and a negative control for the cytoplasmic fraction. Chromatin fractionation and western blotting was performed by Antonia Kolb. (B) Immortalized human embryonic kidney cells (HEK293T) were cotransfected with five μg of GARP and STAT3 overexpression plasmids as well as an empty vector (EV) control plasmid using CaCl2 and HBS buffer. After 48 h, cells were lysed, and co-immunoprecipitation was performed utilizing an anti-STAT3 antibody (MA1-13042, Thermo Fisher Scientific) as described in Müller et al., 2020451. Purified and control whole cell lysates were detected via western blot as described above using an anti-GARP antibody (#83565, Cell Signaling). Co-immunoprecipitation and western blot were performed together with Antonia Kolb. LXXII Figure S22: In silico predictions of a nuclear localization sequence in the GARP protein LXXIII Fig. S22: (A) Multiple sequence alignment of GARP orthologs. GARP was found to be conserved in the vertebrate clade, Gnathostomata, which includes humans and bony fish (Teleostomi) as well as rays and sharks (Chondrichthyes). Conserved amino acids across species are highlighted. A possible bipartite nuclear localization sequence (NLS) was identified at K517, R518 and R564, R565 (arrows). (B) Location of the identified NLS candidate (red) in the tertiary structure of the GARP protein was predicted in silico using the AlphaFold model (AF-Q14392-F1)452,453. Leucine rich repeats (LRR) and the transmembrane (TM) domain are indicated as a reference. (C) Location of the identified NLS candidate (red) in relationship to the latent TGF-β1 complex (purple, yellow, and brown) in the tertiary structure of the GARP protein (blue) using the PDB6GFF model454. Multiple sequence alignment analysis and prediction of the location of the identified NLS in the tertiary structure of the GARP protein was performed by Dr. Miguel Andrade. LXXIV CURRICULUM VITAE LXXV LXXVI CONFERENCE LIST 09/2023 33rd German Skin Cancer Conference (ADO) Hamburg, Germany Presentation: “Nuclear Glycoprotein A repetitions predominant (GARP) as a novel potential prognostic biomarker for melanoma and glioblastoma” 03/2023 Gutenberg Academy Fellows Program Networking Weekend Osthofen, Germany Presentation: “Uncovering the function of nuclear Glycoprotein A repetitions predominant (GARP)” 02/2023 49th Annual Meeting of the Arbeitsgemeinschaft Dermatologische Forschung (ADF) Innsbruck, Austria Poster presentation: "Uncovering the moonlighting function of nuclear GARP in melanoma" 02/2022 48th Annual Meeting of the Arbeitsgemeinschaft Dermatologische Forschung (ADF) Virtual Poster presentation: “GARP, a novel driver of proliferation and growth in 2D and 3D cultured melanoma cells” 09/2021 31st German Skin Cancer Conference (ADO) Virtual Short talk: “Journey to another dimension: the development and application of a 3D melanoma spheroid model” 03/2021 47th Annual Meeting of the Arbeitsgemeinschaft Dermatologische Forschung (ADF) Virtual Poster presentation: "Evaluation of miR-142-3p and GARP as novel diagnostic biomarkers in melanoma patients" 01/2020-02/2020 Arbeitsgemeinschaft Dermatologische Forschung (ADF) Winter School Zugspitze, Germany Poster presentation: “Post-Transcriptional Regulation of GARP Expression in Melanoma and Glioblastoma by miR-142-3p” 04/2019 Ohio University Student Research and Creative Activity Expo Athens, USA Poster presentation: “Targeting Staphylococcus aureus infections from the inside-out” 06/2018 62nd Annual Wind River Conference on Prokaryotic Biology Estes Park, USA Poster presentation: “Investigating the contribution of the small RNA Teg41 to Staphylococcus aureus infection-related phenotypes” 03/2017 Ohio University Student Research and Creative Activity Expo Athens, USA Poster presentation: “ATPase, Ectonucleoside Triphosphate Diphosphohydrolase 2, as a Potential Novel Anticancer Therapeutic” LXXVII LXXVIII