Extratumoral Effects of Highly Aggressive Prostate Cancer

Kerstin Strömvall

Department of Medical Biosciences Umeå 2017

Responsible publisher under swedish law: the Dean of the Medical Faculty This work is protected by the Swedish Copyright Legislation (Act 1960:729) ISBN: 978-91-7601-770-8 ISSN: 0346-6612, New series No.: 1918 Electronic version is available at http://umu.diva-portal.org/ Printed by: Umu Printing Service, Umeå University Umeå, Sweden 2017

“The more you know, the more you know you don't know.”

― Aristotle

Table of Contents

Table of Contents i Abstract iii Populärvetenskaplig sammanfattning iv Abbreviations vii Gene/Protein symbols and names viii List of Papers xi Paper I xi Paper II xi Paper III xi Introduction 1 Prostate Cancer 1 The Hallmarks of Cancer 2 Tumor Immunity 3 Lymph Node Anatomy and Function 6 Tumor-Induced Systemic Effects 9 The Lymph Node Pre-Metastatic Niche 10 TINT 12 Aims of the Thesis 13 Overall aim 13 Specific aims 13 Materials and Methods 15 Patient Cohort 15 Dunning Model 15 Experimental Design 16 Whole Genome Gene Expression Microarray 18 qRT-PCR 19 IHC 21 Results and Discussion 23 The Pro-Metastatic Tumor Micro-and Macroenvironment 23 Tumor Immunity and Establishment of a Lymph Node Pre-Metastatic Niche 25 Clinical Relevance 28 General Discussion 30 Conclusions 35 Acknowledgements 36 References 40

i ii Abstract

Prostate cancer (PC) is the most common cancer in Sweden. Most patients have slow growing tumors that will not cause them any harm within their lifetime, but some have aggressive tumors and will die from their disease. The ability of current clinical practice to predict tumor behavior and disease outcome is limited leading to both over- and undertreatment of PC patients. The men who die from their disease are those that develop metastases. It is therefore of great value to find better and more sensitive prognostic techniques, so that metastatic spread can be detected (or predicted) at an early time point, and so that appropriate treatment can be offered to each subgroup of patients. The aim of this thesis was to investigate if, and by what means, highly aggressive prostate tumors influence extratumoral tissues such as the non- malignant parts of the prostate and regional lymph nodes (LN), and also if any of our findings could be of prognostic importance. Gene- and protein expression analysis were the main methods used to address these questions. Our research group has previously introduced the expression Tumor Instructed (Indicating) Normal Tissue (TINT), and we use the term TINT- changes when referring to alterations in non-malignant tissue due to the growth of a tumor nearby or elsewhere in the body. In the Dunning rat PC-model we found that MatLyLu (MLL)-tumors, having a high metastatic ability, caused pre-metastatic TINT-changes that differ from those caused by AT1-tumors who have low metastatic ability. Prostate-TINT surrounding MLL-tumors had elevated immune cell infiltration, and gene ontology enrichment analysis suggested that biological functions promoting tumor growth and metastasis were activated in MLL- while inhibited in AT1-prostate-TINT. In the regional LNs we found signs of impaired antigen presentation, and decreased quantity of T cells in the MLL- model. One of the downregulated genes in the MLL-LNs was Siglec1 (also known as Cd169), expressed by LN resident macrophages that are important for antigen presentation. When examining metastasis-free LN tissue from PC patients we found CD169 expression to be a prognostic factor for PC-specific survival, and reduced expression was linked to an increased risk of PC- specific death. Some of our findings in prostate- and LN-TINT could be seen already when the tumors were very small suggesting that differences in TINT- changes between tumors with different metastatic capability can be detected early in tumor progression. However, before coming of use in the clinic more research is needed to better define a suitable panel of prognostic TINT- factors as well as the right time window of when to use them.

iii Populärvetenskaplig sammanfattning

Prostatacancer är den i särklass vanligaste cancerformen hos män i Sverige. De flesta patienter har en mycket långsamt växande tumör som inte orsakar dem några större besvär under deras livstid, men enbart i Sverige dör ca 2500 patienter/år av sjukdomen. Det är först vid uppkomst av metastaser som sjukdomen blir dödlig. Befintliga diagnos- och prognosmetoder är otillräckliga när det gäller att uppskatta och förutse tumörens aggressivitet och risk för att bilda metastaser. Detta gör att vissa patienter inte får tillräcklig behandling eller behandlas försent medan andra behandlas i onödan. Behovet av förbättrad diagnostik är därför stort. Om vi kan hitta markörer för potentiellt metastaserande sjukdom, och i bästa fall också behandla innan metastaser uppstår, skulle det förbättra chansen för överlevnad markant.

För att kunna växa och spridas behöver en tumör inte bara förbereda närliggande vävnader utan förmodligen hela kroppen. Vår hypotes är att potentiell dödliga tumörer sannolikt är bättre på detta än mer ofarliga. Man vet från studier av andra cancerformer att farliga tumörer orsakar förändringar i det organ dit cancern senare sprids. Dessa förändringar sker för att de tumörceller som senare anländer ska kunna överleva, och processen har fått namnet pre-metastatisk nisch. Bl.a. har man sett att immunsystemet hämmas och nybildning av kärl ökar. Det är vanligt att metastaser uppstår i närliggande lymfkörtlar innan uppkomst av metastaser i andra organ. Dock är väldigt lite känt om pre-metastatiska förändringar i lymfkörtlar eftersom den forskning som hittills är gjord främst har tittat på andra organ. Inom prostatacancer finns det förvånande få studier av pre- metastatiska nischer överhuvudtaget, och man vet därför inte om de alls förekommer eller vilka förändringar som i så fall sker. Vår grupp har tidigare myntat uttrycket TINT som står för Tumor Instructed (Indicating) Normal Tissue (TINT är ett engelskt verb som betyder färga) och syftar på förändringar i normal vävnad som inducerats av tumören, dvs. att tumörer färgar av sig på omgivningen. Det kan vara förändringar i normal vävnad nära tumören, som i det här fallet resten av prostatan, eller i vävnad långt ifrån tumören som till exempel regionala lymfkörtlar, lungor och benmärg.

Syftet med det här avhandlingsarbetet var att undersöka TINT-förändringar inducerade av aggressiv cancer och se om dessa skiljer sig från TINT- förändringar inducerade av mindre farliga tumörer, samt att utvärdera om

iv någon TINT-förändring skulle kunna användas för att prognostisera vilka patienter som har hög risk att få metastaser.

Vi har använt oss av en prostatacancer-modell i råtta där vi analyserat gen- och proteinuttryck i pre-metastatiska regionala lymfkörtlar, tumörer och prostata-TINT (dvs. prostatavävnad utanför tumören). TINT-förändringar inducerade av MatLyLu (MLL), en tumör med hög metastaserande förmåga, jämfördes mot TINT-förändringar inducerade av AT1, en snabbväxande tumör men med låg förmåga att bilda metastaser.

Vi kunde vi se flera skillnader mellan modellerna. Genuttrycket i MLL- prostata-TINT indikerade en aktivering av cellulära funktioner som visat sig stimulera tumörväxt och spridning såsom celldelning, viabilitet, migration, invasion, och angiogenes (nybildning av kärl). I AT1-prostata-TINT var genuttrycket kopplat till samma funktioner men verkade istället inhibera dessa. Genom att titta på vävnaderna i mikroskop kunde vi se att MLL- tumörer rekryterade färre T-celler (som har en viktig funktion i immunsvaret mot tumören), men istället fler makrofager och granulocyter till både tumören och prostata-TINT (dessa typer av immunceller har visats kunna hjälpa tumörer att växa och sprida sig). MLL-tumörer hade också fler blodkärl och lymfkärl strax utanför tumören.

I de regionala lymfkörtlarna från djur med MLL-tumörer visade genuttrycket tecken på försämrad antigenpresentation, samt immunhämning och/eller induktion av immuntolerans. Immuntolerans innebär att immuncellen inte längre reagerar mot det specifika antigen den blivit tolerant emot. Detta är vanligt förekommande hos individer med cancer och är ett sätt för tumören att undkomma immunförsvaret. I vävnadsprover av lymfkörtlarna kunde vi se färre antigenpresenterande celler, och liksom i tumörerna fanns det färre T-celler i MLL-modellen, något vi kunde se redan när tumörerna var väldigt små.

CD169 är ett protein som bl.a. uttrycks av sinus-makrofager i lymfkörtlar. Dessa makrofager har en central funktion i att aktivera ett tumör-specifikt immunsvar. I råttmodellen kunde vi se att regionala lymfkörtlar från djur med MLL-tumörer hade lägre nivåer av CD169 än regionala lymfkörtlar från djur med AT1-tumörer, och då antalet sinus-makrofager visat sig ha prognostiskt värde i t.ex. tjocktarmscancer, ville vi se om det kunde vara så även i prostatacancer. Därför kvantifierade vi uttrycket av CD169 i metastasfria regionala lymfkörtlar från prostatacancerpatienter och såg att låga nivåer av CD169 medförde en ökad risk för att dö i prostatacancer.

v Sammantaget tyder resultaten på att MLL-tumören jämfört med AT1- tumören bättre lyckas förbereda omgivande vävnad för att gynna tumörväxt och spridning, både lokalt i prostatan men också längre bort från tumören i de regionala lymfkörtlarna. Våra fynd stämmer väl överens med aktuell tumörbiologisk forskning om hur tumörer påverkar sin omgivning. Något som inte visats tidigare är att miljön utanför tumören verkar skilja sig drastiskt beroende på tumörens metastaserande förmåga, samt att dessa skillnader går att se relativt tidigt under sjukdomsförloppet och förmodligen även långt bort från tumören. Vi har också visat att särskilt aggressiv prostatacancer verkar inducera en pre-metastatisk nisch i tumördränerande lymfkörtlar likt det som beskrivits i andra modellsystem och i andra cancertyper, men hittills inte i prostatacancer. Fler studier behövs för att bättre karaktärisera de förändringar som en potentiellt dödlig prostatacancer orsakar i andra vävnader, och för att ta reda på hur denna kunskap kan användas för att förbättra diagnostik och behandling.

vi Abbreviations

APC Antigen Presenting Cell CAF Cancer Associated Fibroblast DC Dendritic Cell DEG Differentially Expressed Gene DNA DeoxyriboNucleic Acid cDNA complementary DNA EMT Epithelial to Mesenchymal Transition FC Fold Change FRC Fibroblastic Reticular Cell HEV High Endothelial Venule IHC ImmunoHistoChemistry IPA Ingenuity Pathway Analysis IPC InterPlate Calibrator LN Lymph Node MDSC Myeloid Derived Suppressor Cell MLL MatLyLu PC Prostate Cancer PMN PolyMorphoNuclear qRT-PCR quantitative Reverse Transcription Polymerase Chain Reaction RNA RiboNucleic Acid mRNA messenger RNA SCS SubCapsular Sinus TAM Tumor Associated Macrophage Th1/2 T helper cell type 1/2 TINT Tumor Instructed (Indicating) Normal Tissue Treg regulatory T cell

vii Gene/Protein symbols and names

ANG-II Angiotensin II ANXA2 Annexin A2 BMP2 Bone morphogenetic protein 2 CCL21 C-C motif chemokine ligand 21 CD(no.) Cluster of differentiation (same for all CD-molecules, CD3, CD4, CD8, etc.) CLEC1B C-type lectin domain family 1 member B CSF1 Colony stimulating factor 1 CSF3 Colony stimulating factor 3 CSF1R Colony stimulating factor 1 receptor CTGF Connective tissue growth factor CTLA4 Cytotoxic T-lymphocyte associated protein 4 CYR61 Cysteine rich angiogenic inducer 61 DSC3 Desmocollin 3 EDN1 Endothelin 1 EMR1 EGF-like module containing, mucin-like, hormone receptor-like 1 FABP4 Fatty acid binding protein 4 FASLG Fas ligand FOXP3 Forkhead box P3 GATA3 GATA binding protein 3 GDF15 Growth differentiation factor 15 GLI1 GLI family zinc finger 1 IDO1 Indoleamine 2,3-dioxygenase 1 IFNG Interferon gamma IGF2 Insulin like growth factor 2 IL(no.) Interleukin (same for all interleukins, IL2, IL4, IL6 etc.) IL1R2 Interleukin 1 receptor type 2 IL2RA Interleukin 2 receptor alpha [CD25] ITGAM Integrin subunit alpha M [CD11B] LAG3 Lymphocyte activating 3 LOC100363492 Lymphocyte antigen 6 complex, locus K-like [LY6K – Lymphocyte antigen 6 family member K] LOX Lysyl oxidase LYVE1 Lymphatic vessel endothelial hyaluronan receptor 1 Mageb1 MAGE family member B1 MARCO Macrophage receptor with collagenous structure MCP1 Monocyte chemotactic protein 1 MGC114427 Similar to melanoma antigen family A, 5 [Magea9 – MAGE family member A9]

viii OPN Osteopontin OSM Oncostatin M PGF Placental growth factor PI15 Peptidase inhibitor 15 PI4KB Phosphatidylinositol 4-kinase beta PLA2G7 Phospholipase A2 group VII PLAU Plasminogen activator, urokinase PSA Prostate specific antigen pSTAT3 phospho-STAT3 RAB14 RAB14, member RAS oncogene family RGD1311578 Similar to PRO1853 homolog S100A9 S100 calcium binding protein A 9 S100A11 S100 calcium binding protein A 11 SDF1 Stromal cell-derived factor 1 SIGLEC1 Sialic acid binding Ig like lectin 1 [CD169] STAT3 Signal transducer and activator of transcription 3 SULF1 Sulfatase 1 TBX21 T-box 21 TGFB1 Transforming growth factor beta 1 TNF Tumor necrosis factor TNFSF11 TNF superfamily member 11 VCAN Versican VEGFA Vascular endothelial growth factor A VEGFR1 Vascular endothelial growth factor receptor 1 VTCN1 V-set domain containing T-cell activation inhibitor 1 * Alternative symbols used in this thesis are shown within brackets.

ix x List of Papers

Paper I

Aggressive rat prostate tumors reprogram the benign parts of the prostate and regional lymph nodes prior to metastasis. Strömvall K, Thysell E, Halin Bergström S, Bergh A. PLoS ONE. (2017) 12(5): e0176679. https://doi.org/10.1371/journal.pone.0176679

Paper II

Highly aggressive rat prostate tumors rapidly precondition regional lymph nodes for subsequent metastatic growth. Strömvall K, Lundholm M, Thysell E, Bergh A, Halin Bergström S. (2017) Manuscript under revision.

Paper III

Reduced number of CD169+ macrophages in pre-metastatic regional lymph nodes is associated with subsequent metastatic disease in an animal model and with poor outcome in prostate cancer patients. Strömvall K, Sundkvist K, Ljungberg B, Halin Bergström S, Bergh A. Prostate. (2017) 1–10. https://doi.org/10.1002/pros.23407

The original articles were reprinted with permission from the publishers.

xii

Introduction

Prostate Cancer

Prostate cancer (PC) is the most common cancer in Sweden representing ~30 % of all male cancers (and ~17 % of all cancers). ~10000 new cases are diagnosed each year, and ~50 % of the PC patients are older than 70 years old1. Generally, the disease is multifocal, i.e. each patient has several different prostate tumors. The relative 5-year survival is ~90 % but still ~2500 PC-specific deaths occur each year1. PC becomes lethal first when it metastasizes and most often the metastases are found in bone, sometimes preceded by detection of lymph node metastases.

PC is diagnosed by measuring the level of prostate specific antigen (PSA) in blood samples, followed by rectal exams and biopsies. Clinical pathological parameters, such as Gleason score and pathological stage, are then combined to assess the aggressiveness of the disease and the choice of treatment. Due to the slow growth of PC many patients with localized disease are not treated but instead monitored by so called active surveillance, however ~50% are treated with prostatectomy or radiation therapy, and ~25 % of those will later have PSA-relapse1. Castration- and/or chemotherapy are the most common therapies for metastatic PC but new drugs targeting the androgen signaling pathway or immune therapies are also being used.

Unfortunately, current clinical practices have limitations in their ability of both diagnosing the disease, and predicting disease aggressiveness. Increased PSA is not always caused by cancer as it could be due to for example prostatitis, and benign prostate hyperplasia. When taking needle biopsies, only ~1% of the prostate is sampled, thus the probability of finding the tumors is small resulting in false negative biopsies. Moreover, due to the heterogeneity of PC and the fact that there are often multiple primary tumors, positive biopsies might not sample the most aggressive tumor. The difficulties of accurate diagnosis and prognostication lead to both under- and overtreatment of patients, and many patients are treated too late. Therefore there is a great need for better diagnostic and prognostic tools, and if it would be possible to predict metastatic disease that would of course be of great value.

The Hallmarks of Cancer

Uncontrolled proliferation is not the only trait required for a tumor to become malignant. Hanahan and Wienberg have described “the hallmarks of cancer” as well as some “enabling characteristics”, pointing out important steps of tumor development, such as, Sustaining proliferative signaling, Evading growth suppressors, Resisting cell death, Inducing angiogenesis, Activating invasion and metastasis, Avoiding immune destruction, and Tumor promoting inflammation2.

Proliferation is controlled by numerous mechanisms within cells. There are signaling molecules that are promoting or inhibiting proliferation via cellular signaling pathways. These signaling pathways interplay and regulate each other seeing to that the cells only proliferate when they should and otherwise stay quiescent. Sometimes this regulation fails and the cells start to proliferate extensively. When this happens in normal cells an “alarm signal” is activated within the cells and the cells either “commit suicide” by a mechanism called apoptosis or become senescent2. There are also other cellular events leading to induction of apoptosis or senescence such as irreparable DNA-damage, and when cells loose contact to each other and/or to the basement membrane2. In tumor cells, this “alarm system” is put out of function and the cells keep dividing. Moreover, the tumor cells often find ways of accelerating the proliferation rate by increasing growth-promoting signaling and escaping growth inhibiting or death inducing pathways2.

The success of a tumor is not solely dependent on characteristics intrinsic to the tumor cells, but tumor cells also need to interact with surrounding cells in order to grow and spread2,3. A tumor not only contains tumor cells but also for example fibroblasts, endothelial cells, and various types of immune cells2,3. These non-tumor cells contribute more or less to that tumors acquire each of the hallmarks of cancer2. Cancer associated fibroblasts (CAF) have a phenotype resembling that of myofibroblasts that are activated during wound healing and constitute one of the major tumor-promoting cell populations within the tumor microenvironment4. Tumor associated macrophages (TAM) is another population of cells commonly ascribed to promote tumor growth and metastasis3.

When the tumor mass grows larger the demand for nutrients and oxygen increases. One mechanism by which tumors can meet this increased demand is to induce the formation of new blood vessels, a process called angiogenesis2. Tumor-induced angiogenesis results in the formation of

abnormal leaky blood vessels, which is believed to facilitate tumor cell intravasation into the blood stream and metastatic spread2,5,6.

One prerequisite of tumor cell invasion seem to be remodeling of the extra cellular matrix which require secretion and activation of specific proteins called proteinases2,3. These proteins can be synthesized and activated either by the tumor cells themselves or by stromal- and immune cells in the tumor microenvironment2,3,6,7. The metastatic process involves a series of steps including local invasion and dissemination, survival in the vasculature, extravasation and survival at the metastatic site, and finally the ability to grow in this new environment2,3,6. One mechanism by which tumor cells can become invasive is the epithelial to mesenchymal transition (EMT) where tumor cells of epithelial origin transform into a mesenchymal-like phenotype, and thereby shifts from an immobile cell type to a mobile one, enabling the tumor cells to spread2,3. Once the tumor cells reach the metastatic site and no longer profit from being mobile but instead need to reside, this process is probably reversed2,3. Tumor cells can also take advantage of already mobile cell types, such as immune cells, and “hitchhike” with them to distant organs3, or they could be spread in a more passive way6. It is not known if metastatic spread is an early or late event during tumor progression, but circulating tumor cells and micrometastases are commonly found early while outgrowth of macrometastases seem in most cases to take substantial amount of time2,3,6.

One important feature of tumor development is the ability of the tumor to escape the immune system2. Since most of the research presented in this thesis is focused on the interaction between tumors and the immune system, the subject of tumor immunity is introduced in more detail in a chapter of its own.

Tumor Immunity

The immune system has an important function in protecting us from all kinds of disease, including cancer. It is a complex, well-coordinated and regulated system consisting of different types of immune cells all having specific functions, such as phagocytic cells, antigen presenting cells (APC), and cytotoxic cells. The type of immune response being elicited depends on the type of immunogenic insult, the types of immune cells being involved, how well they communicate, and the context of the microenvironment where they interact8-11. There are three major types of immune responses8, but recent research implies the existence of additional types as well, illustrating

the complexity of this system9,11. For simplicity, only the type 1 and type 2 responses will be described below since these have been most studied, although a role also for type 3 immune cells in cancer progression have recently become evident7,12. The type 1 response is sometimes referred to as the “classically activated” while the type 2 (or type 3) is referred to as “alternatively activated”7,12. A type 1 response leads to activation of cytotoxic T cells and natural killer cells9,12. The type 1 response mediates effective protection against microorganisms, and sometimes tumors, but also causes tissue damage9. Thus, in order to heal the injured tissue, a type 1 response is typically followed by a type 2 response9. The type 2 response results in tissue remodeling, activation of myofibroblasts, collagen deposition, neoangiogenesis, and other processes typical of tissue repair9,10,12. Cell types and cytokines characteristic for each response are illustrated in figure 1.

Still many questions remain unanswered regarding tumor immunity, but there is growing evidence that an efficient tumor immunosurveillence require the presence of a type 1 immune response, whereas a type 2 immune response is considered to be pro-tumorigenic7,9,10,12. In reality, this is an oversimplification, it is seldom either or but instead the response is often skewed towards a type 1 or type 2 response12. It is also a dynamic process that shifts over time, even single immune cells can change from one phenotype to another depending on context7,9,12.

Prolonged activation or hyperactivity of either type of immunity will lead to disease so the immune system must have regulatory mechanisms9,10. One such mechanism is that the different types of immune responses counterbalance each other, i.e. the type 1 response suppresses the type 2 response and vice versa9,10. However, the most important mechanism is probably the existence of regulatory immune cells that suppress the actions of the effector cells and thereby dampen the immune response9,13,14. In a healthy situation these suppressor cells protect us from pathologic inflammation and autoimmune disease, and reset the immune system to homeostasis once the immunogenic insult is cleared9,13,14.

Chronic inflammation has for long been recognized to be pro-tumorigenic as patients with different inflammatory diseases or chronic infections have increased risk of developing tumors2,3,15. One outcome of chronic inflammation is that it induces an immunosuppressive environment, mainly due to the actions of myeloid derived suppressor cells (MDSC)15. This is a feature of chronic inflammation in general and does not require the presence

of a tumor15. In the case of a growing tumor, the immune system (most often) fails to resolve the tumor, leading to a state of chronic inflammation and thus an immunosuppressive environment. The type 2-driven inflammation that is commonly seen in tumors also directly promotes tumor growth and metastatic spread via secretion of growth factors, angiogenic factors, metalloproteinases, EMT-inducing factors, and chemokines2,16. Most of these tumor promoting functions are ascribed to the actions of TAMs and other innate cells, like polymorphonuclear (PMN) leukocytes, and MDSCs3,16- 18 (Fig. 1).

Figure 1. Tumor immunity. Simplified illustration of anti-tumoral type 1 (colored white) and pro-tumoral type 2 response (colored dark grey), including presence of MDSCs and Tregs (colored light grey). Markers used to identify specific cell types in paper II are depicted in italic. Th: T helper cell, NK: natural killer cell, M1: type 1 macrophage, M2: type 2 macrophage, DC: dendritic cell, CTL: cytotoxic T lymphocyte, PMN: polymorphonuclear cell, Treg: regulatory T cell, MDSC: myeloid derived suppressor cell, EMT: epithelial to mesenchymal transition.

One of the latest concepts within the field of tumor immunity is that of tumor immunoediting12,19,20 (Fig. 1). According to this theory there is first an “elimination phase”, followed by an “equilibrium phase”, and finally an “escape phase”12,19. In the process of tumor immunoediting, the immune system does not only protect the host from the tumor but also becomes involved in tumor progression. The immune response shapes the tumor and the tumor shapes the immune response. Sometimes the first and/or second phase does not take place in which case the tumor-immunity interaction enters the escape phase sooner19. In the elimination phase an initial antitumorigenic immune response is seen, which in some cases of successful eradication of the tumor also will be the end stage12,19. Otherwise, the tumor immunoediting process will enter the equilibrium phase where the tumor growth is kept under control by the immune system, but is not successfully eradicated12,19,20. This can also represent the end stage of the immunoediting process for some individuals if the equilibrium phase extends throughout their life time19. The escape phase is when the tumor growth progresses and metastatic colonization becomes evident12,19,20.

There are several different mechanisms proposed by which a tumor can escape from the immune system. The tumor cells can become invisible to the immune cells by downregulating their expression of tumor antigens or genes involved in antigen presentation19,20. They can protect themselves from cytotoxic cells by upregulation of anti-apoptotic proteins or they can counterattack the immune cells by the expression of the apoptosis-inducing FASLG19. The tumors (including stromal- and immune cells like CAFs and TAMs) can also suppress the actions of the immune cells by secretion of immunosuppressive factors like VEGFA, TGFB1, IL10, and IDO1, and by recruiting immune suppressor cells, like regulatory T cells (Treg) and MDSCs, to the site of the tumor2,4,7,17,19-22. Immune effector cell apoptosis, impaired cytotoxicity, impaired antigen presentation, and induction of tolerance to tumor antigens are some of the effects following the actions of these suppressor cells12,17,21,22.

Lymph Node Anatomy and Function

Lymph nodes (LN) are secondary lymphoid organs that are populated by immune cells whose function is to filter the lymph for foreign antigens and elicit an adaptive immune response when appropriate. The LN has a complex structure made up of different types of immune cells and stromal cells such as fibroblasts, blood endothelial cells, and lymphatic endothelial cells, each

cell type populating different areas of the node23,24 (Fig. 2). There are LN resident cell types but also migrating immune cells that are constantly migrating from peripheral tissues into the LNs, and vice versa23-25. Proper architecture is essential for LN function and the stromal cells play a very important role, providing both physical support and chemical cues that are required for correct localization, and thus function, of the immune cells23,25,26. LN stromal cells also have immune regulatory functions that are important to maintain T cell homeostasis and tissue integrity27,28.

Figure 2. LN anatomy. A) The main areas of a LN are illustrated in the diagram (left) and in a section of a rat iliac LN stained with CD169 monoclonal antibody (right). I – subcapsular sinus, II – B cell follicle, III – paracortex (T cell area), and IV – medulla. Afferent lymph reaches the LN at multiple sites and drain into the subcapsular sinus (small dark grey arrows), while efferent lymph exits from the medulla via a single collecting lymph vessel23 (big light grey arrow). B) Diagram of the main cell types populating each of the areas shown in A. Cell surface markers of LN resident macrophages are also annotated. SCS: subcapsular sinus, DC: dendritic cell, HEV: high endothelial venule, FRC: fibroblastic reticular cell.

B cells are localized in B cell follicles in the LN cortex23,24. When stimulated they proliferate and form germinal centers which finally results in development of antibody-producing plasma cells23. The plasma cells migrate to the medulla where they secrete antibodies into the efferent lymph23. Within the B cell follicles other cell types are also present such as dendritic cells (DC), Th cells, and macrophages, whose interaction with the B cells is important for the induction of an antibody response23,24,29.

T cells are situated in the paracortex which is the area beneath and between the B cell follicles23,24. Apart from T cells the paracortex is also populated by DCs, macrophages, a specialized type of fibroblasts called fibroblastic reticular cells (FRC)23, and a specialized type of blood endothelial cells that forms high endothelial venules (HEV)23. Circulating immune cells enter the LN via HEVs, and then migrate by the aid of FRCs to their specific areas23,25,26. Homing to the LNs requires expression of specific adhesion molecules on both the HEVs and the incoming cells as well as secretion of chemoattractants by FRCs and HEVs24,25. Once the immune cells have entered the LN they are dependent on FRCs, and the FRC-produced reticular network, for migration and correct localization, which in turn mediates the correct cell-cell interactions needed for the activation of an adaptive immune response23,25,26.

There are three major subsets of LN resident macrophages, subcapsular sinus (SCS) macrophages, medullary sinus macrophages, and medullary cord macrophages29. They are classified by their localization as well as their expression of specific cell surface markers, each subset having specific functions29. It is not much known about how these macrophage subsets are maintained but CSF1, and its receptor CSF1R, seem to be important especially for the existence of SCS macrophages29,30. Medullary sinus macrophages are highly phagocytic and are important for clearance of lymph borne pathogens, dead cells and particulates29. Medullary cord macrophages are also highly phagocytic, their main function is to clear up apoptotic plasma cells as well as provide trophic support to those that are alive29. Both SCS- and medullary sinus macrophages have an important function as APCs presenting lymph borne antigens to T cells, B cells or DCs29. SCS macrophages have long been known for their importance in eliciting anti- viral responses29,30 but have in recent years also been recognized as important players in the activation of tumor-specific immune responses30-32.

Tumor-Induced Systemic Effects

Apart from affecting neighboring cells in the local tissue, tumor secreted factors also induce systemic effects that supports tumor growth and spread33. The main systemic responses observed so far include the recruitment of hematopoietic progenitor cells, endothelial progenitor cells, and different populations of myeloid cells from the bone marrow and spleen to the tumor microenvironment, where they promote tumor growth and survival, angiogenesis, migration, invasion, metastasis, and drug resistance33. These cells also contribute to the immunosuppressive environment and the process of immune escape described above3,33. Some of these myeloid cells can also differentiate, or promote differentiation of local fibroblasts, into CAFs4,16.

Only a few of the tumor-derived factors causing a systemic response have yet been identified, and for many of them the exact cellular source has not been determined. Examples of tumor-derived factors of unknown cellular origin are CSF3, VEGFA, PGF, and ANG-II, while IGF2 and OPN were shown to be produced by tumor cells, and SDF1 and GDF15 were produced by CAFs33.

Apart from being recruited to the tumor microenvironment, bone marrow derived cells are also recruited to sites of future metastasis where they modulate the environment to facilitate tumor cell extravasation, survival and growth16,33,34. Modulations that occur prior to arrival of tumor cells are referred to as formation of a pre-metastatic niche34. The environment of the pre-metastatic niche, although promoting tumor cell arrest and survival, is often not favorable for tumor growth but need further modification. Thus, maturation of the metastatic niche must take place before outgrowth of metastases can occur35,36.

The tumor-derived factors responsible for the induction of the pre- metastatic niche are not known, but suggestions include CSF3, VEGFA, PGF, TGFB1, TNF, LOX, MCP1, and VCAN33-35,37. One early and supposedly key event in the formation (as well as maintenance) of the pre-metastatic niche is the recruitment of bone marrow derived immature myeloid cells34. Other frequently reported traits for pre-metastatic niches are presence of immunosuppressive cells and factors, high supply of growth- and survival factors, and increased tissue remodeling34.

Except secretion of the above mentioned soluble factors, tumor cells (as all cells) also release exosomes that can transport lipids, proteins, and RNAs to distant sites33,38. Tumor-derived exosomes have been implicated in systemic effects like mobilization of bone marrow cells and induction of pre-

metastatic niches, as well as contributing to the tumor promoting events taking place in and around primary tumors33,34,37,38.

Also platelets have been recognized to help in the metastatic process by promoting angiogenesis and tissue remodeling, providing physical protection to disseminated tumor cells in the blood stream, and facilitate tumor cell arrest at the metastatic site33,39,40. Some evidence has also emerged that platelets might aid in the systemic distribution of tumor- derived factors33,39,41.

The Lymph Node Pre-Metastatic Niche LN metastases are commonly seen prior to solid tissue metastases, and the presence of LN metastases is often used as a prognostic tool in the clinic. It is not known whether LN metastases is an intermediate stage where the metastatic cells subsequently disseminate from the LNs and form metastases in other organs, or if LN metastases merely represent aggressive cancer having the ability to metastasize (it could be both)42,43. Despite the high prevalence of LN metastases, not much is known about pre-metastatic LNs, instead most research on pre-metastatic niches have considered other organs such as lung, liver and bone-marrow34,36. Besides being the site for pre-metastatic niche formation the tumor-draining LN is also the organ in which the tumor-specific immune response is elicited. Therefore it is not surprising that most of what have been described so far concerning pre-metastatic LNs is related to tumor immune escape mechanisms. These will of course not only facilitate growth and invasion at the primary site but also enable outgrowth of metastatic cells once arrived in the LNs.

MDSCs and immature DCs, either from the bone marrow or tumor-derived (or both), have been shown to be recruited to pre-metastatic LNs causing immunosuppression34,35,44-46. Peripheral tolerance to tumor antigens is a key mechanism for tumor immune escape. MDSCs and immature DCs can directly induce tolerance in tumor-specific T cells by presenting tumor antigens in the presence/absence of inhibitory/stimulatory molecules, but also indirectly by inducing Tregs17,22,35. Due to secretion of immunosuppressive factors like IDO1, TGFB1, and IL10, and other mechanisms, MDSCs can also impair the antigen presenting function of LN resident APCs or directly suppress T cell function17,22. Increased expression of IL10, IFNG, IDO1, and FOXP3 have been observed in metastasis-free regional LNs primarily from melanoma and breast cancer patients

suggesting an immunosuppressive environment in these LNs47,48. It has also been reported that metastasis-free regional LNs contain reduced amounts of APCs like DCs and macrophages, and/or reduced amounts of T cells32,35,47,48. The reason behind this reduction is not known but could be due to for example apoptosis, loss of trophic support, or impaired trafficking of these cells into the LNs17,22,49-51. In melanoma models it has been shown that tumor growth negatively affects FRCs in pre-metastatic tumor draining LNs. Downregulation of IL7 and CCL21 (important for T cell survival and homing), but also modulation of the reticular network was observed resulting in subsequent abnormal localization and trafficking of lymphocytes and DCs, as well as increased recruitment of CD11B+ immature myeloid cells49-51. The presence of angiogenesis in the LN pre-metastatic niche seems to be context dependent and differ between studies, increased angiogenesis has been reported while others have seen no such effect35. However, remodeling of HEVs have consistently been observed regarding pre-metastatic modulation of blood vessels35,47,52. Since HEVs are required for homing of lymphocytes to the LNs, remodeling of these vessels probably leads to impaired function of the immune system47,49. Some studies also imply that the observed flattening and dilation (and sometimes increase in vessel density) of the vessels have yet another metastasis-promoting effect in that it leads to increased blood supply supporting metastatic outgrowth as well as subsequent hematogenous dissemination to other metastatic sites52.

Tumor cells are spread to LNs via the lymphatic vasculature and enter the LNs at the SCS35. Increased lymphangiogenesis or modulation of the lymphatic vasculature resulting in vessel dilation and/or increased vessel density has been reported to precede metastasis in many cancers42,52. In a study of patients with cervical cancer this increased vessel density was primarily seen in the capsular and subcapsular area of the pre-metastatic LNs, and in metastatic LNs the tumor foci were located within these new vessels53.

The first cells that encounter the lymph borne metastatic tumor cells are SCS macrophages. As already mentioned, these macrophages are important APCs who have a key role in the activation of tumor-specific immune responses30- 32. One marker that is used for identification of SCS macrophages is CD16929, and high amount of CD169+ macrophages in pre-metastatic LNs has been associated with favorable prognosis in melanoma, colorectal, and endometrial cancer54-56. High density of CD169+ macrophages also correlated

to high numbers of cytotoxic CD8+ T cells in the tumors. SCS macrophages might also constitute a physical barrier that protects the LN from lymph borne pathogens and particles57. This was shown to have a protective effect in melanoma where SCS macrophages hindered the interaction between tumor-derived vesicles and B cells and thereby suppressed tumor growth57.

TINT As mentioned, tumors need to interact with surrounding cells in order to grow and spread. Most research on this topic has been done examining interactions in the tumor microenvironment (within the tumor mass). In our research group we have instead focused on how the tumor affects the extended micro- and macroenvironment, i.e. tissues outside of the tumor, and the term Tumor Instructed (and thus tumor Indicating) Normal Tissue, TINT, was introduced. TINT refers to all normal (non-malignant) tissue that has changed in some aspect, presumably related to tumor aggressiveness, due to the growth of a tumor58. It could be non-malignant tissue surrounding the tumor but also more remote tissues such as bone marrow, lungs, and LNs.

Non-malignant tissue surrounding tumors have for long been considered to be normal, and as such it has been used as control tissue when searching for cancer biomarkers. We believe that the use of this “normal” tissue as control is inappropriate since it is far from normal, and a better control would be tissue from healthy individuals. The non-malignant tissue surrounding tumors could instead be used to search for cancer biomarkers58, and when defined, these biomarkers could improve the diagnostic and/or prognostic procedures as well as choice of therapy. In previous studies we have for example observed increased vascular density59, increased expression of hyaluronan60 and LOX61 (components of the extracellular matrix), and recruitment of macrophages62-64 and mast cells65, in the non-malignant prostate tissue surrounding tumors. These alterations were related to tumor size and/or aggressiveness, and some have aslo been shown to have prognostic value in PC patients59-61,65.

Some other researchers that have detected changes in the non-malignant tissue surrounding tumors have suggested that they were caused early by the cancerogenic agent, a so called cancer field effect66,67. Accordingly, the cancer field effect is not an adaptive response to a growing tumor and is therefore different to the concept of TINT.

Aims of the Thesis

Overall aim

Explore if, and by what means, highly aggressive prostate tumors influence extratumoral tissues such as the non-malignant prostate tissue and regional lymph nodes.

Specific aims Characterize tumor-induced alterations in tumor adjacent non-malignant tissue and regional lymph nodes = TINT-changes. (Paper I and II)

Explore if identified TINT-changes depend on tumor aggressiveness. (Paper I and II)

Investigate at what time point during tumor progression TINT-changes are detectable, and explore their stability over time. (Paper II) Evaluate if some of the detected TINT-changes could be of prognostic importance for prostate cancer patients. (Paper III)

Materials and Methods

Patient Cohort

In paper III we analysed the expression of CD169 in metastasis-free LNs from 109 PC patients. The tissue samples were retrieved from Pathology archives at Umeå University Hospital. The patients underwent radical prostatectomy between January 1996 and April 2002, and the regional LNs (right and left obturatorius area) were also sampled. The tumor specimens were Gleason graded, and pathological stage and surgical margin status was determined. Gleason scores ranged from 6 (3+3) to 9 (4+5) and pathological stage pT2 (n = 45) to pT3 (n = 64), all cases were free from regional LN metastases (pN0).

From the National Prostate Cancer Registry68 and patient records we obtained information about pre-operative PSA, post-operative PSA, PSA nadir value, dates of PSA-relapse, detected bone metastases, initiation of additional therapy, and cancer-specific death.

Time from surgery to PSA-relapse and time from surgery to death caused by PC was calculated. PSA-relapse was defined as an increase above nadir, or above 0.2 ng/ml for patients with undetectable PSA after surgery (below 0.1 ng/ml). Of the 109 patients included in this study, 43 were categorized to the relapse group, and 9 of these had confirmed bone metastases. At the end of 2016, 11 patients had died from PC and 20 from other causes. The local ethical review board at Umeå Universiy approved the study (dnr 2010/366-31M).

Dunning Model The Dunning model is a rat model of PC. It first originated from a spontaneous prostate tumor in a Copenhagen rat69. By in vivo passaging of this original tumor several tumor subtypes with different characteristics developed69. The model system was further expanded by the establishment of stable in vitro cell lines and now consists of many different lines representing tumors of different malignancy69. The cell lines grow out as tumors when implanted, either orthotopically or subcutaneously, in immunecompetent Copenhagen rats. Thus this model system is useful for both in vitro and in vivo studies of PC at different stages of the disease. Two of the Dunning cell lines, MatLyLu (MLL) and AT1, have been used in my thesis. The MLL cell line is derived from the AT1 cell line69. Both cell lines

represent androgen insensitive, anaplastic, and fast growing tumors but AT1 has a somewhat slower growth rate69. Their major difference is that MLL have high metastatic potential and metastasize to LNs and lungs while AT1 have low metastatic potential69. These cell lines were therefore convenient to use when studying pre-metastatic signs of metastatic disease.

All animal work was approved by the local ethical committee for animal research at Umeå University (permit number A42-15).

Experimental Design

In the animal experiments 1 x 103 MLL cells (ECACC), 2 x 104 AT1 cells (ECACC), or vehicle (10 µl RPMI, i.e. cell culture medium (Gibco)) were injected into the ventral prostate of syngeneic immunocompetent Copenhagen rats. In order to perform the injection, the rats were anesthetized and an incision was made in the lower abdomen to access the prostate. Rats were sacrificed at 3, 7, 10, 14, and 28 days post-injection, and the prostate tissue and regional LNs were dissected out, weighed, frozen in liquid nitrogen, and stored in -80 ˚C until being analysed for either gene- or protein expression. When calculating relative expression levels we used two types of controls, results from treatment-naïve animals were used to determine the baseline expression level, and results from sham-operated animals were used to control for changes induced by the surgery and prostate injection. Results from MLL and AT1 were either evaluated individually using one or both of the controls, or compared to each other. The procedure of the animal experiments is summarized in figure 3.

The tissue samples analysed in paper I was collected at 10 days post- injection of tumor cells into the prostate. At this time point both MLL- and AT1-tumors had a size corresponding to ~30 % of the injected prostate lobe, permitting enough tissue material to study both tumors and prostate-TINT (i.e. the entire non-malignant part of the prostate lobe). In the MLL-model, microscopic LN metastases could be seen at 14 days post-injection. Thus, the 10 day time point was suitable for studies of pre-metastatic events as well as parallel examination of tumors, prostate-TINT and regional LNs. In paper II we wanted to investigate tumor-induced changes over time during tumor growth. Since we were interested in pre-metastatic changes we chose the time point of visible LN metastases as endpoint, which as already mentioned was 14 days post-injection in the MLL-model. However, due to different growth rates between the tumors we also included the time point of 28 days post-injection for the AT1-model. At this time point, the AT1-tumors were of

similar size as the 14 day MLL-tumors, and we also observed LN-metastases in one animal (out of eight in total).

In paper III, we again analysed samples from the 10 day time point since this was the time point where we first detected the difference in CD169 expression. However we also wanted to see if this difference could be seen earlier so we also included the 3 day time point. The main purpose of this study was to see if a downregulation of CD169 could be seen also in humans, and if so, if it could be related to disease outcome. Metastasis-free LNs from 109 PC patients were therefore analysed, and the prognostic value of CD169 expression was evaluated in relation to traditional prognostic markers.

Figure 3. Experimental design. Schematic view of the animal experiments, and subsequent gene or protein analysis performed in this thesis project. MLL: MatLyLu, VP: ventral prostate, Tu: tumor, TINT: tumor indicating normal tissue, LN: lymph node, IHC: immunohistochemistry, qRT-PCR: quantitative reverse transcription polymerase chain reaction.

Whole Genome Gene Expression Microarray

In paper I, whole genome gene expression microarrays were used to examine the gene expression pattern in tumors, prostate-TINT, and regional LNs 10 days post-injection. This method allows for a high throughput screening, evaluating the expression of thousands of genes simultaneously. mRNA is reverse transcribed into cDNA, which is fragmented and labelled with a fluorescent dye, and finally hybridized to probes on the microarray. The amount of fluorescent color emitted from each spot on the array (corresponding to a specific probe/gene) is measured and quantified giving an expression signal intensity value.

The Affymetrix Rat Gene 1.1 ST array (Affymetrix) was chosen for our analysis. It covers ~27000 protein coding transcripts and ~24000 genes identified and listed in the Entrez gene database70. It is designed so that it has multiple probes for each transcript distributed over every exon providing the possibility, unlike traditional 3’ arrays, to detect also splice variants, truncated transcripts, and other transcript isoforms that lack the 3’ exon70.

Low signal intensity values correspond to genes with low expression. However, very low values can be hard to distinguish from background noise, and therefore it is common to set a threshold where everything below the threshold is considered to be noise and everything above is considered to be a true signal71. This will minimize the risk of false positive results but at the same time increases the risk of omitting small but important findings. After background filtering the data was first analyzed by multivariate statistical analysis using SIMCA software (Umetrics). Principal component analysis was used to create an overview of the data and to see how the different samples clustered in relation to each other from their whole genome expression profile. The next step was to identify differentially expressed genes (DEG). Average signal intensity of each group was computed as well as fold change (FC) between groups of interest. Student’s T-test was used to test significance of difference between groups. Most of the genes in both LNs and prostate-TINT had low FC values. Since these tissues consist of several populations of cells, each population’s gene expression being diluted in the signals from the others, we decided that also small differences could be of importance, so a FC of 1.25 (absolute value) and a p-value of 0.05 was used to define DEGs.

Gene ontology enrichment analysis is a method to use when trying to find biological meaning to transcriptomics data (or other “omics” data). For this

purpose we used the web-based software Ingenuity Pathway Analysis (IPA) (Qiagen) and performed “downstream effects analysis”, and “upstream regulator analysis”, of our gene expression data72. In the downstream effects analysis, the IPA-application returns biological functions or pathways that are over-represented in the gene expression data72. The upstream regulator analysis returns potential upstream regulators that could be responsible for the observed gene expression72.

The algorithms in IPA utilize a causal network that is based on the Ingenuity Knowledge Base which is a collection of observations either manually curated from the literature or integrated from third-party databases72. The nodes of the network represent genes, gene products, chemical compounds, microRNAs and biological functions, and are connected by edges representing experimentally observed cause–effect relationships72.

The pathways, functions, and upstream regulators are given together with a p-value (Fisher’s exact test), and when available, an activation z-score. The activation z-score predicts if the particular function or pathway is increased or decreased, or if the upstream regulator is activated or inhibited72. The prediction is based on the gene expression pattern and how that correlates to what is reported in the published literature in the manually curated content of the Ingenuity Knowledge Base72. A z-score > 2 is considered to be significant72. It should be noted though, that although results with a z-score >2 might be more reliable, potentially important information useful for hypothesis generation could be lost using a too stringent cutoff. Functions and pathways with a small z-score could still be highly relevant. qRT-PCR

Quantitative reverse transcription polymerase chain reaction (qRT-PCR) have become a standard method for the analysis of gene expression. It can be done using a one- or two-step protocol and the detection method can be either SYBR Green or probe-based73. In my project the two-step protocol was used, where mRNA is reversed transcribed into cDNA (step one), which is then amplified and measured (step two). The advantage of this approach is that the cDNA-samples can be stored and used for subsequent analysis of a large number of genes. Probe-based assays are more sensitive than SYBR Green73 but also more expensive. Due to the large amount of reactions to run in this project we therefore chose to use the SYBR Green detection method for the experiments in paper II. Another advantage of this method is the

possibility to perform melt curve analysis and thereby control for reaction specificity73.

The panel of genes analysed in paper II were Itgam (also known as Cd11b), Emr1, Marco, Cd209b, Lyve1, Gata3, Tbx21, Cd3e, Cd4, Cd8a, Cd69, Il2ra (also known as Cd25), Ctla4, Foxp3, Ido1, Ifng, Il10, Il4, Il6, and Tgfb1. These genes are markers for Th1, Th2, cytotoxic T cells, APCs, and markers for immunosuppression8,10-12,14,15,17,22,29,74 (see Fig. 1 and 2). The APC-markers were primarily chosen to identify LN resident macrophages29, but most of them can also be expressed by other populations of myeloid cells10,15,17,18, and Lyve1 is also expressed by lymphatic endothelial cells43. CD69 (not included in Fig. 2) is a costimulatory molecule for T cells and is often used as a marker for T cell activation, but can also be expressed by other cell types74. Rab14, Pi4kb, and RGD1311578 were used as endogenous controls for normalization, previously selected using Normfinder75 on the data from the gene expression microarray (and again evaluated using Normfinder also on the qRT-PCR data). Due to the high number of samples and genes included in this project it was not possible to run all reactions in one plate, therefore we used the TATAA Interplate Calibrator for SYBR (TATAA Biocenter) for interplate calibration. All primer pairs were evaluated by the use of standard curves to ensure that the amount of sample analyzed in our experiment was within the dynamic range of the assays73. Each qPCR reaction was run in duplicates, and the interplate calibrator (IPC) was run in 4 replicates/plate. Two types of non-template controls were used, total RNA to check for contamination of genomic DNA, and water to check for unspecific signals from the reagents. Melt curves and amplification plots were manually checked with SDS software (Applied Biosystems) to evaluate product specificity and quality of the Ct-measurements. The threshold was individually set for each target gene so that the threshold was set the same for all compared samples. The data was imported into Genex software (MultiD Analyses AB) for further pre-processing, followed by computation of relative expression values and statistical analysis.

Variation between replicates was first assessed, and samples having a difference in Ct-value > 0.5 cycles were excluded from the analysis. Interplate calibration was done using the IPC mean value for each plate. Average Ct-value for each sample was computed and the data was normalized to the expression of Rab14, Pi4kb, and RGD1311578. Gene expression in LN-tissue from left and right side was measured separately, but since no obvious difference between sides could be seen, these measures

were averaged. The ∆∆Ct-method73 was used to calculate relative expression, and results from treatment-naïve animals were used as reference (baseline expression level). To get an estimate of the magnitude of difference between groups FC was calculated from the group mean, however both Student’s T- test and Mann Whitney U test was used to test for statistical differences since the criteria for parametric tests were not fulfilled. A p-value < 0.05 was considered to be significant.

IHC

Immunohistochemistry (IHC) is a method used for detection of proteins in a tissue section. The advantage of IHC over other protein detection methods, like western blot, is that it does not only enable quantification of protein (at least semiquantitative scoring) but also show where in the tissue the protein is expressed. The tissue section is first incubated with a primary antibody that binds to the protein of interest followed by incubation with a secondary antibody (that binds to the primary antibody). The secondary antibody is conjugated to an enzyme, that when given the right substrate will catalyze a chemical reaction resulting in a colored (brown or red, depending on what substrate is being used) precipitate. The precipitate can be visualized in a light microscope, and, since it corresponds to the amount of target protein present in the tissue sample, it can be quantified and relative expression between samples can be calculated. In all three papers IHC was used to investigate morphological differences in tumors, prostate-TINT and/or regional LNs. In paper I and II, we also wanted to see if some of the found differences in gene expression could be seen at protein level as well, and if some of the predictions made in IPA could be visualized. Stainings were manually quantified by an experienced pathologist by the use of a light microscope. All stainings of rat tissue were quantified using point counting morphometry76 where a square lattice was mounted in the eyepiece of the microscope and volume density was calculated, i.e. percentage of tissue volume occupied by the stained tissue. Results were presented as mean +/- standard deviation, and Student’s T-test was used to compare groups in paper I and II, while Mann-Whiney U was used in paper III.

The human samples in paper III were quantified as either high or low. LNs from both left and right side were included and at least 2 LNs/patient were scored. In most cases, all tissue from one patient showed similar staining pattern. For the few patients whose staining was heterogeneous, they were

classified as low if > 50% of the LNs had low or absent staining. Tissue from all patients were sectioned, stained, and scored twice, and the results were compared to evaluate the robustness of the method. The relation between CD169 expression and traditional prognostic markers was assessed using Fisher’s exact test, Kaplan-Meier survival analysis and Log Rank test, and Cox regression analysis.

Results and Discussion

The Pro-Metastatic Tumor Micro-and Macroenvironment

In paper I, the gene expression profiles and the results from the IPA- analysis of MLL- and AT1-tumors showed great similarity. However, some of the DEGs comparing MLL- to AT1-tumors, for example Pi15, Fabp4, and Dsc3, have also been seen in human PC and been linked to aggressive disease77-80, showing that our model system could capture differences between tumors with different metastatic ability, and also to some extent resemble human PC. The major difference between the tumors according to the IPA-downstream effects analysis was the immune response, which will be further discussed below.

When examining the tumor surrounding non-malignant tissue, i.e. prostate- TINT, results from paper I showed that both MLL- and AT1-tumors induced profound alterations. One remarkable finding was the different responses comparing MLL to AT1. MLL-prostate-TINT was characterized by inflammation, and the IPA-downstream effects analysis indicated an activation of pro-tumorigenic and pro-metastatic biological processes such as cell proliferation, angiogenesis, and cell invasion. In AT1-prostate-TINT, these processes were instead markedly inhibited. As in the tumors, several of the DEGs comparing MLL- to AT1-prostate-TINT are previously known from human PC and have been associated to tumor growth and metastasis, such as Plau, Ctgf, Gli1, Anxa2, Bmp2, Edn1, Vtcn1, S100A11, and Cyr6181-90. It should be noted that AT1-TINT had more DEGs compared to control prostate than MLL-TINT and that most of these DEGS were downregulated. Considering the similarity of the cell lines, as well as the in vivo tumors, it was unexpected to see such a different response in prostate-TINT. MLL is originally derived from AT1 and it seems like while evolving from a cell line with low metastatic potential into one with high metastatic potential it lost the trait of inducing these inhibitory mechanisms in the tumor environment, and also gained the ability of inducing a potentially tumor-promoting inflammation.

Neovascularization is an important feature of the pro-metastatic tumor microenvironment. The IPA-downstream effects analysis in paper I predicted increased angiogenesis in MLL-TINT but not in MLL-tumors compared to AT1. However, the morphological examination showed that both tumors and prostate-TINT had a higher blood vessel density in the MLL-model. This implies that for the metastatic process the formation of

new blood vessels outside of the tumor might be more important than that going on inside the tumor. Since tumor cells at the invasive front probably are the cells that are most likely to disseminate, the presence of leaky blood vessels at this site would probably be beneficial. In paper I we could see that this might be true, at least for metastatic spread to LNs, as MLL-TINT had increased lymph vessel density compared to AT1-TINT, with some vessels containing tumor cells at the MLL-tumor border. The association between increased lymph vessel density at the tumor border and LN metastases has been shown also in other animal models of PC91,92, as well as in PC patients93.

The crude tumor extracts used for sample preparation before gene expression analysis mainly contain tumor cells, and only a small fraction of stromal and immune cells. On the contrary, the TINT-samples contain a mix of epithelial-, stromal- and immune cells of varying proportions. It is possible that the differences we observed in prostate-TINT also exist within the tumors, but that gene expression signals from minor cell populations are diluted in the signals from the tumor cells and therefore these finding could be masked. It is previously known that the tumor microenvironment at the tumor border differs from that in the bulk of the tumor, and that most of the pro-metastatic events are taking place in proximity to the tumor border2,3. This is probably true also in our model system. We did not include the tumor border for the gene expression analysis, but from the morphological analysis it was apparent that recruitment of immune cells were more enriched at the tumor border.

Regardless of what is going on within the tumor microenvironment, we have shown that very important pro- or antimetastatic processes also takes place outside of the tumor, a fact that have been somewhat neglected in the research field. It is still unknown whether or not the tumor-induced effect in prostate-TINT is occurring along a gradient. Different alterations could of course behave differently, some forming a gradient reaching only a limited distance outside the tumor, and some affecting the entire organ equally. It would be interesting to characterize both tumors and prostate-TINT in more detail, examining clean populations of cells, to try to find the signals sent from the tumors, and to identify the cell types that are involved in the induced biological processes and what signals they use. Perhaps the AT1- model could be used to find important metastasis inhibitory mechanisms that could be utilized in the development of new therapies, which then could be tested in the MLL-model. The gene expression data contains loads of unexploited information that can be used for generating hypotheses about pro- and antimetastatic mechanisms, new therapeutic targets, and cancer

biomarkers. From the gene expression analysis we could see, for both MLL and AT1 that some of the DEGs in the tumors and prostate-TINT were shared (compared to control prostate) but the magnitude of change was lower in prostate-TINT. Some of these genes could perhaps be used as cancer biomarkers that would be useful also in negative biopsies.

Tumor Immunity and Establishment of a Lymph Node Pre- Metastatic Niche

Within the tumors, our results from the IPA-downstream effects analysis in paper I suggested a more effective T cell response in the AT1-tumors compared to MLL. One reason to this could be the difference in expression of tumor antigens since for example LOC100363492 (Ly6k), MGC114427 (Magea9), and Mageb1 were highly expressed in AT1 but not in MLL. Histological examination of the tumors and prostate-TINT in paper I and II showed differences in immune cell infiltration at all time points studied. In the AT1-model there were more CD3+ and CD8+ lymphocytes within the tumors, especially at the tumor border, while MLL had a large amount of PMN cells both in the tumors and in prostate-TINT. The number of macrophages was also higher in MLL-tumors and in MLL-TINT. We did not characterize these macrophages further but have previously shown that MLL-tumors and MLL-prostate-TINT have increased amounts of CD163+ macrophages64 (CD163 is one marker for M2 macrophages7). Increased amount of M2 macrophages have also been observed in PC patients62, and in one study this was related to a less activated T cell response, and also related to poor prognosis94. The identity and phenotype of the PMN cells is not known from our studies, but they could be MDSCs, neutrophils, or eosinophils, which all have been recognized as players in the alternatively activated immune response, and the following immunosuppressive microenvironment often seen in aggressive tumors3,10,15,16,18,22.

Some of the DEGs in paper I that had higher expression in MLL-vs. AT1- prostate-TINT were Lox and Cd11b. Expression of LOX in the tumor environment has been shown to be both pro-and antitumorigenic depending on context95,96. Not much is known about LOX in the tumor macroenvironment, but we have previously shown that increased expression in prostate-TINT is linked to poor prognosis61. In pre-metastatic niches increased expression of LOX is associated to recruitment of MDSCs33,34, whether or not this is true also for prostate-TINT is unknown. The observed increased expression of Cd11b, at least indicates influx of myeloid cells. Also,

some of the predicted upstream regulators from the IPA-analysis, for example TGFB1 and IL1B, indicate an immunosuppressive environment in MLL-prostate-TINT. On the other hand, the pro-inflammatory IFNG and TNF were suggested to be activated as well. When taking the result of the downstream effects analysis and the immune related biological functions into consideration, the majority of the IPA-predictions indicate a pro- inflammatory environment in MLL-prostate-TINT, including functions belonging to the adaptive immune response. These results from the IPA- analysis highlight the complexity of the immune system and its possible contribution to tumor growth. Perhaps, we managed to capture the equilibrium phase in the process of immunoediting, where an initial type 1 response is counterbalanced by an alternatively activated response. Subsequently, when the tumor reaches further out into what was previously TINT, then this equilibrium has already evolved into the immune escape phase. From my knowledge the concept of tumor immunoediting is based on observations from the tumor microenvironment, and if and how this process also goes on in the tumor macroenvironment is not known. It seems probable that it will, and also that there will be a lag, so that the immunoediting process is a step behind in the yet non-malignant tissue.

The results from both paper I and II indicate a better activation of the adaptive immune response in the AT1-model. According to the IPA- downstream effects analysis in paper I, the MLL-LNs showed signs of decreased antigen presentation, decreased amount of T cells, as well as increased immunosuppression when compared to AT1. Some of the key DEGs behind these predictions were Csf1, Csf1r, Tnfsf11, Osm, Clec1b, Ctla4, and Lag3. In paper II we could see a decrease of the T cell marker CD3 at both gene-and protein level, in the MLL-LNs already at day 3 post-injection, and the expression remained low throughout the time of the study (but was significantly lower compared to AT1 only at 3 and 10 days post-injection). Also Cd8a, a marker for cytotoxic T cells, was lower in MLL-LNs compared to AT-LNs at the 3 and 10 day time points. At 3 days post-injection there was also an increase in Cd11b in the MLL-LNs while in the AT1-LNs there was instead an increase in Emr1, suggesting different populations of myeloid cells. It could reflect an influx of MDSCs or immature DCs into the MLL- LNs, but of course that cannot be stated from the results in paper II. However, it seems possible considering the higher levels of immunosuppressive markers compared to AT1 at later time points (found in both paper I and II) such as, Foxp3, Ido1, Il1r2, Ctla4, Pla2g7, and Lag3. Also, the coinciding higher levels of Cd25, Cd4, and Foxp3 in the MLL-LNs

compared to AT1 at 7 days post-injection suggest a higher amount of Tregs in the MLL-LNs at this time point. Further supporting the idea of an impaired immune activation in the MLL-LNs are the observed differences in expression of the APC-markers Marco, Cd209b, and CD169, which were lower in MLL-LNs compared to AT-LNs at the 10 day time point. One of the most significant differences between MLL- and AT1-LNs in the IPA- downstream effects analysis in paper I was decreased differentiation of cells. This prediction does not specify the affected cell type, but since LNs mainly contain immune cells it is probable that this prediction reflects the presence of immature immune cells, either recruited from the bone marrow or the tumor, or due to impaired maturation of LN-resident cells.

In paper II we also looked at the LN-expression of typical type 1/type 2 cytokines as well as cytokines and growth factors related to immunosuppression (Fig. 1). The expression of Tgfb1, Ifng, and Il6, were increased in all experimental groups at the 3 day time point, with Tgfb1 being significantly lower in the MLL-LNs compared to AT-LNs or sham- operated controls. The expression of Il4 and Il10 was increased only in the MLL-LNs at this time point. At 7 days post-injection, expression of all five genes was higher in the MLL-LNs vs. AT1-LNs. MDSCs secrete high levels of TGFB1, IL6, and IL1022, thus the expression profile of the MLL-LNs again indicate presence of these cells in these LNs. Although the type 1 cytokine IFNG was highly expressed also in the MLL-LNs, the combined results from paper I and II (different immune cell infiltrates in tumors and prostate- TINT, lower expression of Th1- and APC markers, higher expression of Th2- and immunosuppressive markers in the MLL- vs. AT1-LNs) indicate some sort of alternatively activated immune response in the MLL-model while mainly a type 1 response seem to be present in the AT1-model. Moreover, it has become more and more evident that the response to specific cytokines is highly context dependent. For example it has been shown that IFNG activation of DCs in the presence of IL10 induces an immature phenotype with impaired APC function47. So perhaps in the context of our model, IFNG might aid in the maintenance of the immunosuppressive chronic inflammation typically observed in pre-metastatic niches16,34,35. The mixed cytokine profile could also reflect the process of immunoediting (as already mentioned in the discussion above concerning TINT), where an initial type 1 response is later suppressed by a type 2 response. This idea is supported also by the expression of Gata3, a type 2 response transcription factor (but has also been implicated in induction of Tregs)97,98, that had a 3-fold higher expression in the MLL- vs. AT1-LNs at 7 days post-injection.

The observation in paper I of tumor cells within the lymph vessels at the MLL-tumor border is consistent with the common notion that metastatic spread to LNs occurs via the lymphatic system. In paper II we observed higher Lyve1 expression in the MLL- vs. AT1-LNs at the 7 day time point, which might indicate lymphangiogenesis. However, in paper I we could not see any clear morphological differences reagarding lymph vessels later on at the 10 day time point. The increased lymph vessel density preceding metastasis reported by Balsat et al.53 occurred just outside of the capsule, if this is the case also in our model we probably missed this since our analysis did not include extracapsular tissue. The IPA-downstream effects analysis suggested decreased angiogenesis in the MLL-LNs compared to AT1-LNs, and a slight decrease could also be seen from the IHC. When considering some of the affected genes behind this prediction, for example Clec1b and Osm, we believe that the prediction might reflect a negative effect on the HEVs, since both these proteins are important for HEV-maintenance99,100. In summary, the results presented in this thesis indicate that the AT1-tumor with low metastatic ability elicits more of a type 1 anti-tumor immune response than the MLL-tumor with high metastatic ability. This is consistent to what have been seen in PC patients where a type 1 response seems favorable over a type 2 response94,101,102. It seems like the immune response elicited by MLL-tumors follow the pattern of immunoediting that is described in the literature. We could see a reduction of T cells (and perhaps also an influx of immature myeloid cells) as early as 3 days post-injection suggesting that the tumor immunoediting already was initiated. It would be interesting to investigate earlier time points to see if the initial elimination phase exists, and if so, also see if it is possible to identify when and how the immunologic switch occurs.

Clinical Relevance

In this project, CD169 was selected for further evaluation as a potential new prognostic marker in PC. This selection was based on the results from paper I showing decreased antigen presentation in the MLL-LNs. A literature search gave that CD169+ SCS macrophages had an important function as APCs specifically for tumor antigens31, and in colorectal cancer, the expression of CD169 was correlated to the number of CD8+ T cells in the tumors and to disease outcome54. Going back to the array data we found Cd169 to be downregulated in the MLL-LNs compared to controls (also compared to AT1-LNs but not significant (FC -1.5, p=0.08), a

downregulation that also could be confirmed at protein level (paper I). So, in paper III we further examined the expression of CD169 in PC associated pre-metastatic LNs, and also assessed if CD169 expression could have prognostic value. This was done by IHC analysis of pre-metastatic LNs from our rat model and from PC patients. Consistent with our findings from paper I, CD169 was downregulated in MLL-LNs compared to AT1 (and treatment-naïve controls) 10 days post-injection suggesting a possible relation between CD169 expression and metastatic disease. However, CD169 expression in metastasis-free LNs from PC patients was not associated to metastasis (defined as PSA-relapse), but low levels of CD169 was linked to increased risk of PC death. Importantly, in the subgroup of patients with PSA-relapse, CD169 was the only marker of the ones examined that was related to PC death.

This study included only 109 patients, whereof 11 died from PC, and 9 had detected bone metastases. A larger sample size is required to make conclusions about the prognostic value of CD169 expression, however, this pilot study indicate a link between CD169 expression and poor outcome, and also that this marker could be specifically useful in the subgroup of patients with PSA-relapse.

Since the initiation of our study presented in paper III, the importance of CD169+ LN-macrophages and the link to a tumor-specific CD8+ T cell response have been shown also in melanoma, endometrial, and breast cancer55,56,103. In melanoma and endometrial cancer, the amount of CD169+ macrophages was also related to disease outcome55,56. In our study we did not investigate tumor infiltrates of immune cells. The main objective of this thesis was to find extratumoral alterations, and in paper III to assess the possible clinical use of this specific extratumoral change. Still, it would be interesting to examine the type and amount of tumor infiltrating immune cells in the future. There could also be a second mechanism explaining a possible relationship between metastatic disease and reduced amount of CD169+ macrophages. Since SCS macrophages not only present antigens but perhaps also have a barrier function57, reduced amounts of SCS macrophages could result in an “open door” for metastatic tumor cells and thus simplify their arrest in the LN.

Based on the literature of pre-metastatic niches, including LNs, early recruitment of immature myeloid cells seems to be important for the establishment of the pre-metastatic niche (as well as its maturation)34,35. The results from our rat model presented in this thesis, and findings from PC

patients indicate that this could be true also in PC. Presence of VEGFR1+ myeloid cells in metastasis-free regional LNs was linked to shorter time to PSA-relapse45,46. The maintenance of the LN pre-metastatic niche by MDSCs was also shown to be STAT3-dependent44, and increased p-STAT3 expression was observed in CD68+ myeloid cells recruited to metastasis-free regional LNs in PC patients44. Neither the expression of VEGFR1 or STAT3 was assessed in this project but it would be interesting to do, and also to see if their expression correlates to that of CD169.

General Discussion All model systems have weaknesses, and they all differ to some extent from the clinical situation. Our model system has been a very good tool in the investigation of metastatic PC. Orthotopic implantation of tumors with different aggressiveness into fully immunocompetent hosts has enabled us to study the development of PC, and PC metastases in a setting resembling that in patients. The major weakness is the invasiveness of the method used to implant the tumors. Since the operation in itself has an effect on the immune response and thus the appearance of the regional LNs, some of the tumor- induced alterations might be masked by the response to the surgery. In an attempt to address this problem we have used both tissue from sham- operated rats and tissue from treatment-naïve rats as control tissue when evaluating the two tumor models. Another problem could be the different growth rates of the tumors. In this project we decided to aim for similar sized tumors and in doing so we had to inject different amounts of tumor cells. It would not be possible to inject 2 x 104 MLL cells and wait 10 days, but it would be interesting to see the early tumor-induced response. If we went the other way and injected only 1 x 103 AT1 cells, there would probably be no tumors. This could in itself be interesting to investigate, if it is the immune response that eradicates the tumors or if the tumor cells die from other causes. Publications of pre-metastatic LNs in melanoma have shown downregulation of T cell homing signals, and negative effects on FRCs with subsequent modulation of the reticular network49-51. We could not see these changes in our model system. Some of this discrepancy is of course due to differences in methods used and differences of our model systems. It could also be the case that these specific events do not occur in our model. Still, I think that some of them might but perhaps need more time to become clearly established. Our model is very quick, which is of advantage for many reasons, but in this case many of the weak signs we found (including statistically non-significant alterations that were not reported in the papers)

would probably have been more pronounced if the tumors had grown slower. Therefore it would be interesting to see if we could establish MLL-tumors using even fewer cells, enabling us to follow them a bit longer, and see if some of the minor differences now observed would become more evident. Another approach could be to use clean populations of cells which then might enhance the perceptibility of signals coming from minor cell populations.

Once tumor cells have arrested at the metastatic site, it is common that they stay dormant during the maturation of the niche until there are proper conditions for tumor growth. In our model, visible LN metastases were seen in about 50 % of the MLL-animals at 14 days post-injection (and in 100 % at 18 days post-injection). At 10 days post-injection we could see tumor cells within lymph vessels at the tumor border so it cannot be stated for sure that there are no tumor cells in the LNs also at this time point, but at least they were not visible in the light microscope. So, in our model the maturation of the metastatic niche probably happens sometime between 10 and 18 days. The results from the MLL-LNs at the 14 day time point are difficult to interpret due to the lack of a proper comparison. Compared to both AT1-LNs and sham-operated controls at 14 day post-injection, the MLL-LNs had higher expression of Cd3e, Cd4, Foxp3, Gata3, Tbx21, Emr1, CD11b, Il10, Il4, and Tgfb1, and lower expression of Cd69 and IL10. At this time point the MLL-tumors are much larger than the AT1-tumors, and some of the MLL- LNs already have LN metastases. When instead comparing the 14 day MLL- LNs to the 28 day AT-LNs there were not many differences. Another issue is that the detected gene expression in the 14 day MLL-LNs could be influenced by the presence of tumor cells since half of the LNs used for sample preparation contained metastases. To better understand the later time points of the metastatic event it would be required to analyze more samples allowing for separation of metastatic and non-metastatic LNs but still having big enough sample size. One huge challenge of this project was to try to simplify the complexity of the tumor immune response in order to find possible biological explanations to our results. Not only do different cell surface markers and cytokines have different meaning depending on context, their expression also fluctuate over time. This became very apparent from the results presented in paper II. Moreover, many of the studied genes also showed great variability between individual animals. One explanation could of course be that some animals are less affected than others by their tumor, but one idea could also be that they are not all within the exact same time window, some are leading and

some are lagging. For example, the two groups of MLL-LNs apparent in paper I probably represent one group of animals that will have LN metastases sooner than the other group. Since there is no real evidence for this hypothesis in the current data, it would be interesting to follow the animals more closely, for example every day. Or even better would be if it was possible to follow every individual over time throughout the time of the study, by the use of techniques allowing for live monitoring.

From the gene expression data and the IPA-upstream regulator analysis in paper I, we tried to identify possible tumor-derived signals that could be inducing the observed changes in the LNs and prostate-TINT. It was not obvious how to proceed in doing this and the approach we chose was just one of many possible. Most of the assumptions and conclusions made in paper I (and II), and also the choice of upstream regulators, was made from the presumption that the gene expression corresponds to the amount of gene product. This is often the case but far from always, meaning that some of our interpretations might be wrong and we also might have missed something important. Regarding the upstream regulators we also decided that the predicted activation status of the upstream regulator should match the regulation of the tumor-expressed gene, i.e. activated/inhibited upstream regulator needed to be upregulated/downregulated in the tumor. The upstream regulators presented in paper I are probably correct in that they are in part responsible for the detected gene expression and the related biological processes, but if these are really the tumor-derived factors behind the identified TINT-changes remains unknown. Another thing that we could not take into consideration was the time. It is likely that some of the TINT- changes seen at 10 days post-injection are induced by signals coming from the tumor days earlier. So in order to get a more complete picture of the interplay between the tumor and the extratumoral tissues it would be necessary to examine all tissue compartments at several time points. It is also possible, and very likely, that tumor-derived exosomes play a key role in inducing TINT-changes. Injection of MLL-exosomes into the prostate before implantation of indolent G-tumor cells (another Dunning cell line that is slow growing, androgen sensitive and with low metastatic potential) makes the tumor more aggressive104. The impact of exosomes on the LN pre- metastatic niche in our model has not been investigated. S100A9 is a well- known factor for establishment of pre-metastatic niches in lungs, where increased expression of S100A9 is induced by tumor secreted factors, and S100A9 then increases the recruitment of myeloid cells to the pre-metastatic niche34. We did not see increased expression of S100A9 in the MLL-LNs but

since it was ~10-fold upregulated in the MLL-vs. AT1-tumors it could perhaps influence the pre-metastatic LNs via tumor-secreted exosomes.

Considering the amount of data from the gene expression analysis in paper I, this project could have taken any direction but it happened to become a project about tumor immunity. In the long term it would be desirable if some of the data generated in this project also could come of use in the clinic. Several immunological markers as well as immune therapies for PC have been investigated, mainly in the subgroup of metastatic castration resistent cases105. Vaccines have shown promising results with increased Th1 response and improved overall survival105-107. Inhibitors agianst PD1/PDL1 and CTLA4 have also looked promising in some trials105,108. Still, further studies are required to identify appropriate prognostic markers and suitable therapies, but also investigate the use of immunological markers and immune therapies in earlier stages of the disease.

There are many different types of cancer, and many cancers are heterogeneous making it hard to generalize both in prognostication and treatment, with PC being a good example. However, the concept of immunoediting seems to be consistent, and have been observed in many different types of cancer, but probably there is also in this a personal twist. The complexity of the immune system with different combinations of cell types and factors leading to different outcomes depending on context makes it hard to find robust independent immunological markers, as well as new therapeutic targets.

The aim of this thesis was to elucidate how highly aggressive prostate tumors influence extratumoral tissues such as the non-malignant prostate tissue and regional LNs. This was in part accomplished but many new questions also arose. When better defined I am confident that immune response markers can come of use in the clinic, using the right panel of genes, at the right time point, in the right patient.

Conclusions

Tumor growth induces alterations in adjacent non-malignant tissue as well as in more remote organs like regional lymph nodes.

Tumor aggressiveness affects the magnitude and type of alterations being induced.

Some extratumoral effects can be seen very early during tumor progression.

In our model, potentially metastatic prostate cancer does not elicit an adaptive (T cell) tumor immune response but instead recruits macrophages and polymorphonuclear cells to the tumor and to the prostate-TINT.

Quantification of CD169+ subcapsular sinus macrophages in regional lymph nodes can possibly be used as a prognostic tool to predict prostate cancer- specific survival.

Acknowledgements

Det här var det svåraste att skriva.

Borde egentligen bara skriva TACK! Alla.

Tänker att ni vet vilka ni är, ni som stöttat mig i jobbet eller i livet utanför. Ni har alla bidragit till den här boken. Utan er hade det inte gått. Tack!

Eftersom jag helt säkert kommer att glömma någon, vill jag egentligen inte nämna några namn, men känns samtidigt konstigt att inte göra det så det blir en kompromiss.

Börjar med att säga förlåt till dig som känner dig glömd. Om det är någon tröst är det ingen som kommer att läsa den här boken efter idag. Och kom ihåg, att om du finns i mitt liv och ger mig positiv energi så är jag tacksam för att du finns  oavsett vad som står i den här boken… så, TACK!

Dalfolket. Ni är många som bidragit till att jag alls flyttade till Umeå, tack för det! Och tack alla vänner som finns kvar. Tur att det finns Facebook! (Sa hon som fixade fb-konto först 2014…)

Hästfolket. Alla vet ju hur det är, man kommer till stallet, en kvart senare tittar man på klockan och det har gått fyra timmar... Bättre fritid kan man inte ha! Och ännu bättre blir det när jag får dela nöjet med alla er härliga människor. Ni är så otroligt många som på olika sätt är, eller har varit, involverade i mitt hästliv och därför också hjälpt mig att må bra. Tack!

Molbifolket. Tack för en rolig studietid! Klart över förväntan  Det var här det började… Doktorandfolket. Till alla tidigare och nuvarande doktorander jag haft nöjet att träffa och umgås med, ni har bidragit med inspiration, stöd, och en härlig gemenskap, tack! (Flera av er tillhör även kategorin jobbfolket så ta åt er av det med.)

Jobbfolket. Vilka härliga människor som jobbar på den här institutionen! Den mest positiva arbetsmiljö jag hittills har upplevt och det har varit värt mycket. Vet att jag inte varit översocial och hängt så väldigt ofta i fikahörnan eller lunchrummet, men jag tycker om er ändå  Glada, varma, och omtänksamma människor som gör det roligt att gå till jobbet. Tack!

Prostatafolket hör visserligen till jobbfolket men förtjänar ändå att få ett eget tack. Det har varit roligt och lärorikt att jobba i prostatagruppen. Tack också för alla glada upptåg (kommer sakna dansen )!

Mamma och pappa, mina största supportrar. Tack känns inte stort nog. Ni är fantastiska. Johan och Sandra. Anders och Mia. Vi hörs och ses inte så ofta, men jag vet ju att ni finns där, långt bort (verkar dock vara längre för er till mig än tvärtom ). I alla fall är jag glad och tacksam för att ni finns. Och Casper, vilket härligt avbrott i vardagen, för vem vill tänka på cancerforskning när man istället kan köra bil i en evighet på sandlådekanten, sopa grusgången i timmar, springa runt med ett paketsnöre i tre dagar, och sjunga ”Imse Vimse”… Gladare unge får man leta efter. Isak, ny i familjen, men du verkar minst lika glad som din bror. Ser fram emot att lära känna dig!

Daniel. Sweetie, darling. Vad vore livet utan dig? Vi hörs typ aldrig och ses ännu mer sällan. Det fantastiska är att jag bara behöver tänka på dig så börjar jag skratta…  … och det har verkligen varit välbehövt i perioder av hopplöshet och utmattning. Så tack för att du hjälpte mig göra klart det här projektet. Och tack för att du är du, och att du vill vara min vän. Det blir du och jag på hemmet darling!

Katta. Du vet ju själv. Tack för allt. Gudars skymning. Helena. Blond och blondare. Vi kanske inte alltid har tagit de klokaste besluten, men roligt har vi haft! Tur att vi blev klokare med tiden  Tack för fin vänskap som jag hoppas ska fortsätta lång tid framöver.

Helen. Vi ses inte så ofta och oftast inte så långa stunder men det är alltid roligt att ses. Tack för att du låter mig få äran att motionera fina gamla Winston varje gång jag hälsar på. Lyxåka får man inte göra varje dag  kan leva länge på det…

Christina och Erik som supportat med däckbyten, middagar, akuten- skjuts/sällskap (visserligen var det er hästs fel, men ändå), och mycket mycket mer. Stort tack!

Marlene och Johanna. Min tid i Braxselestallet blev tyvärr inte alls vad någon av oss hade tänkt sig, men tack för att jag och H fick komma! Ni anar nog inte hur mycket ni faktiskt bidragit… Tack för all hjälp alla de kvällar jag ”fastnade” på jobbet. Och tack för värme, omtanke och fin vänskap.

Christine. Tack för alla luncher, alla intressanta samtal (oftast hästrelaterat såklart…), och all hjälp med H. Du har varit ett fantastiskt stöd på många sätt! Maria. Alltid glad och omtänksam, snällare människa finns nog inte. Din positivitet smittar av sig, tack för det. Och tack för att jag fått hjälpa dig med dina fina hästar! Linda och Jessica. Mina nyaste stallkompisar. Tack Linda för att jag fick komma till ditt stall och tack till er båda för att ni hjälpt och stöttat under slutspurten! Ingrid. Min ständiga hjälpreda tidiga lördagmorgnar på ridskolan. Guld värt, jättetack! Och som även kommit med kloka ord angående doktorerandet, tack för det med. Cecilia. Galet positiv människa. Känner ingen som tycker att alla lektioner är roliga, och att alla hästar är bäst, men det gör du  Du har alltid något positivt att säga och du verkligen öser ur dig beröm, otroligt upplyftande, tack!

Rickard. Mycket har vi hunnit med under dessa tio år… luncher, spelkvällar, pubkvällar, roadtrip till P & L, jag lyckades till och med få upp dig på en häst  Om ytterligare tio år kanske Nils har fått en ponny…

Maria. Från Molbisar till doktorander... Också vi har tio roliga år bakom oss (även om vi inte haft så mycket kontakt på slutet, vilket jag hoppas kunna ändra på framöver!). Du har en härligt glad personlighet. Och du har varit ett stort stöd i stunder när vi inte hade så roligt, tack!

Erik, Hani, och Helena. Doktorandkollegor och kontorskompisar. Det har varit roligt att lära känna er och att jobba med er. Mycket värt att kunna bolla idéer och funderingar med andra som är i ”samma” situation, eller bara prata strunt för den delen  Karin, Terry, Åsa, Clas, Carina, och Clara. Det är ni som får det här bygget att gå runt. Tack för all er hjälp, ni gör (eller vissa av er har gjort  ) ett fantastiskt jobb! Och tack Terry för all omtanke! Anna. Egentligen har vi inte haft så mycket alls med varandra att göra. Ännu härligare blir det då att du är så himla omtänksam. Ett jättetack för att du brytt dig om i stunder när det varit kämpigt. Det har betytt mer än du kanske tror.

Sofie. Tack för alla råd kring PCR-analyserna. Du har hjälpt mig med flertalet steg på vägen från RNA-prepp till beräkning av relativt uttryck, tack!

Stina. Tack för all hjälp med cellodlingsexperimenten. Visserligen blev inget av det publicerat men det var inte ditt fel  Mycket värt att få bolla idéer och tack för goda råd!

Marie. Vet inte om det var Anders eller Sofia som kallade dig för immunolog… Oavsett har du varit till stor hjälp med både upplägg och genomförande av ”immuncellsprofileringen”, tack!

Elin. Du har varit ett stort stöd på flera sätt, men främst hjälpt mig med analys av den monsterstora datafilen, tack!

Pernilla, Susanne, Birgitta och Siggan. Vilken osannolik arbetsinsats ni gjort! Den här boken är lika mycket er som min. Utan er hade det tagit minst tio år till. Pernilla – som du slitit med evighetsgörat att snitta vävnad till RNA-prepp. Susanne – galet (och oväntat) hur svårt det kunde vara att färga lymfkörtlar, och när det väl fungerade så blev även det ett evighetsgöra. Birgitta – jag vet att också du fick din beskärda del av dessa lymfkörtlar... Och Siggan – du fick ju det roligaste jobbet    att hitta de små liven inbäddade i allt fett. Som ni har kämpat, tappra, alltid glada, och ni tar er alltid tid trots att ni redan har mycket att göra. Grymt stort tack! Ni är bäst!

Sofia. Det har nog inte undgått någon av oss att vi inte riktigt tänker lika du och jag. Men det gick ju bra ändå! Tack för support och pepp, du gjorde ett bra jobb 

Anders. Tack för att du gav mig ett superintressant projekt. Och för att du inte gav upp det när det gick lite knackigt. ”Det får ta den tid det tar…” Lugnet själv. Har aldrig varit någon stress, eller någon tvekan, ”Det får ta den tid det tar”… Och det fick det verkligen, ta tid, så stort tack för all tid jag fick. Och för friheten och förtroendet att få göra själv, och att få tänka själv. Tack också för bra support de gånger jag inte tänkte så bra på egen hand 

References

1. Socialstyrelsen. Cancer i siffror 2013. ISBN: 978-91-89446–64-9. Accessed September 24, 2017. http://www.socialstyrelsen.se/publikationer2013/2013-6-5. 2. Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011;144(5):646-674. 3. Quail DF, Joyce JA. Microenvironmental regulation of tumor progression and metastasis. Nat Med. 2013;19(11):1423-1437. 4. Augsten M. Cancer-associated fibroblasts as another polarized cell type of the tumor microenvironment. Front Oncol. 2014;4:62. 5. Jain RK. Normalization of tumor vasculature: an emerging concept in antiangiogenic therapy. Science. 2005;307(5706):58-62. 6. Bockhorn M, Jain RK, Munn LL. Active versus passive mechanisms in metastasis: do cancer cells crawl into vessels, or are they pushed? Lancet Oncol. 2007;8(5):444-448. 7. Mantovani A, Sozzani S, Locati M, Allavena P, Sica A. Macrophage polarization: tumor-associated macrophages as a paradigm for polarized M2 mononuclear phagocytes. Trends Immunol. 2002;23(11):549-555. 8. Annunziato F, Romagnani C, Romagnani S. The 3 major types of innate and adaptive cell-mediated effector immunity. J Allergy Clin Immunol. 2015;135(3):626-635. 9. Eberl G. Immunity by equilibrium. Nature reviews Immunology. 2016;16(8):524-532. 10. Wynn TA. Type 2 cytokines: mechanisms and therapeutic strategies. Nature reviews Immunology. 2015;15(5):271-282. 11. Raphael I, Nalawade S, Eagar TN, Forsthuber TG. T cell subsets and their signature cytokines in autoimmune and inflammatory diseases. Cytokine. 2015;74(1):5-17. 12. Lakshmi Narendra B, Eshvendar Reddy K, Shantikumar S, Ramakrishna S. Immune system: a double-edged sword in cancer. Inflamm Res. 2013;62(9):823-834. 13. Mauri C, Bosma A. Immune regulatory function of B cells. Annu Rev Immunol. 2012;30:221-241. 14. Nagler-Anderson C, Bhan AK, Podolsky DK, Terhorst C. Control freaks: immune regulatory cells. Nat Immunol. 2004;5(2):119-122.

15. Baniyash M, Sade-Feldman M, Kanterman J. Chronic inflammation and cancer: suppressing the suppressors. Cancer immunology, immunotherapy : CII. 2014;63(1):11-20. 16. Smith HA, Kang Y. The metastasis-promoting roles of tumor- associated immune cells. J Mol Med (Berl). 2013;91(4):411-429. 17. Motallebnezhad M, Jadidi-Niaragh F, Qamsari ES, Bagheri S, Gharibi T, Yousefi M. The immunobiology of myeloid-derived suppressor cells in cancer. Tumour biology : the journal of the International Society for Oncodevelopmental Biology and Medicine. 2016;37(2):1387-1406. 18. Zilio S, Serafini P. Neutrophils and Granulocytic MDSC: The Janus God of Cancer Immunotherapy. Vaccines (Basel). 2016;4(3). 19. Schreiber RD, Old LJ, Smyth MJ. Cancer immunoediting: integrating immunity's roles in cancer suppression and promotion. Science. 2011;331(6024):1565-1570. 20. Vinay DS, Ryan EP, Pawelec G, et al. Immune evasion in cancer: Mechanistic basis and therapeutic strategies. Seminars in cancer biology. 2015;35 Suppl:S185-S198. 21. Takeuchi Y, Nishikawa H. Roles of regulatory T cells in cancer immunity. International immunology. 2016;28(8):401-409. 22. Chen J, Ye Y, Liu P, et al. Suppression of T cells by myeloid-derived suppressor cells in cancer. Human immunology. 2017;78(2):113-119. 23. Willard-Mack CL. Normal structure, function, and histology of lymph nodes. Toxicol Pathol. 2006;34(5):409-424. 24. von Andrian UH, Mempel TR. Homing and cellular traffic in lymph nodes. Nature reviews Immunology. 2003;3(11):867-878. 25. Bajenoff M, Egen JG, Qi H, Huang AY, Castellino F, Germain RN. Highways, byways and breadcrumbs: directing lymphocyte traffic in the lymph node. Trends Immunol. 2007;28(8):346-352. 26. Bajenoff M, Egen JG, Koo LY, et al. Stromal cell networks regulate lymphocyte entry, migration, and territoriality in lymph nodes. Immunity. 2006;25(6):989-1001. 27. Siegert S, Luther SA. Positive and negative regulation of T cell responses by fibroblastic reticular cells within paracortical regions of lymph nodes. Front Immunol. 2012;3:285. 28. Tewalt EF, Cohen JN, Rouhani SJ, Engelhard VH. Lymphatic endothelial cells - key players in regulation of tolerance and immunity. Front Immunol. 2012;3:305.

29. Gray EE, Cyster JG. Lymph node macrophages. J Innate Immun. 2012;4(5-6):424-436. 30. O'Neill AS, van den Berg TK, Mullen GE. Sialoadhesin - a macrophage-restricted marker of immunoregulation and inflammation. Immunology. 2013;138(3):198-207. 31. Asano K, Nabeyama A, Miyake Y, et al. CD169-positive macrophages dominate antitumor immunity by crosspresenting dead cell- associated antigens. Immunity. 2011;34(1):85-95. 32. Komohara Y, Ohnishi K, Takeya M. Possible functions of CD169- positive sinus macrophages in lymph nodes in anti-tumor immune responses. Cancer Sci. 2017;108(3):290-295. 33. McAllister SS, Weinberg RA. The tumour-induced systemic environment as a critical regulator of cancer progression and metastasis. Nat Cell Biol. 2014;16(8):717-727. 34. Peinado H, Zhang H, Matei IR, et al. Pre-metastatic niches: organ- specific homes for metastases. Nature reviews Cancer. 2017;17(5):302-317. 35. Sleeman JP. The lymph node pre-metastatic niche. J Mol Med (Berl). 2015;93(11):1173-1184. 36. Sleeman JP. The metastatic niche and stromal progression. Cancer Metastasis Rev. 2012;31(3-4):429-440. 37. Peinado H, Lavotshkin S, Lyden D. The secreted factors responsible for pre-metastatic niche formation: old sayings and new thoughts. Seminars in cancer biology. 2011;21(2):139-146. 38. Lobb RJ, Lima LG, Moller A. Exosomes: Key mediators of metastasis and pre-metastatic niche formation. Semin Cell Dev Biol. 2017;67:3- 10. 39. Kuznetsov HS, Marsh T, Markens BA, et al. Identification of luminal breast cancers that establish a tumor-supportive macroenvironment defined by proangiogenic platelets and bone marrow-derived cells. Cancer Discov. 2012;2(12):1150-1165. 40. Gay LJ, Felding-Habermann B. Contribution of platelets to tumour metastasis. Nature reviews Cancer. 2011;11(2):123-134. 41. Nilsson RJ, Balaj L, Hulleman E, et al. Blood platelets contain tumor-derived RNA biomarkers. Blood. 2011;118(13):3680-3683. 42. Hirakawa S. From tumor lymphangiogenesis to lymphvascular niche. Cancer Sci. 2009;100(6):983-989. 43. Alitalo A, Detmar M. Interaction of tumor cells and lymphatic vessels in cancer progression. Oncogene. 2012;31(42):4499-4508.

44. Deng J, Liu Y, Lee H, et al. S1PR1-STAT3 signaling is crucial for myeloid cell colonization at future metastatic sites. Cancer Cell. 2012;21(5):642-654. 45. Fujita K, Nakayama M, Nakai Y, et al. Vascular endothelial growth factor receptor 1 expression in pelvic lymph nodes predicts the risk of cancer progression after radical prostatectomy. Cancer Sci. 2009;100(6):1047-1050. 46. Pal SK, Vuong W, Zhang W, et al. Clinical and Translational Assessment of VEGFR1 as a Mediator of the Premetastatic Niche in High-Risk Localized Prostate Cancer. Mol Cancer Ther. 2015;14(12):2896-2900. 47. Cochran AJ, Huang RR, Su A, Itakura E, Wen DR. Is sentinel node susceptibility to metastases related to nodal immune modulation? Cancer J. 2015;21(1):39-46. 48. Kim R, Emi M, Tanabe K, Arihiro K. Immunobiology of the sentinel lymph node and its potential role for antitumour immunity. Lancet Oncol. 2006;7(12):1006-1016. 49. Carriere V, Colisson R, Jiguet-Jiglaire C, et al. Cancer cells regulate lymphocyte recruitment and leukocyte-endothelium interactions in the tumor-draining lymph node. Cancer Res. 2005;65(24):11639- 11648. 50. Riedel A, Shorthouse D, Haas L, Hall BA, Shields J. Tumor-induced stromal reprogramming drives lymph node transformation. Nat Immunol. 2016;17(9):1118-1127. 51. Soudja SM, Henri S, Mello M, et al. Disrupted lymph node and splenic stroma in mice with induced inflammatory melanomas is associated with impaired recruitment of T and dendritic cells. PLoS One. 2011;6(7):e22639. 52. Qian CN, Berghuis B, Tsarfaty G, et al. Preparing the "soil": the primary tumor induces vasculature reorganization in the sentinel lymph node before the arrival of metastatic cancer cells. Cancer Res. 2006;66(21):10365-10376. 53. Balsat C, Blacher S, Herfs M, et al. A specific immune and lymphatic profile characterizes the pre-metastatic state of the sentinel lymph node in patients with early cervical cancer. Oncoimmunology. 2017;6(2):e1265718. 54. Ohnishi K, Komohara Y, Saito Y, et al. CD169-positive macrophages in regional lymph nodes are associated with a favorable prognosis in patients with colorectal carcinoma. Cancer Sci. 2013;104(9):1237- 1244.

55. Ohnishi K, Yamaguchi M, Erdenebaatar C, et al. Prognostic significance of CD169-positive lymph node sinus macrophages in patients with endometrial carcinoma. Cancer Sci. 2016;107(6):846- 852. 56. Saito Y, Ohnishi K, Miyashita A, et al. Prognostic Significance of CD169+ Lymph Node Sinus Macrophages in Patients with Malignant Melanoma. Cancer Immunol Res. 2015;3(12):1356-1363. 57. Pucci F, Garris C, Lai CP, et al. SCS macrophages suppress melanoma by restricting tumor-derived vesicle-B cell interactions. Science. 2016;352(6282):242-246. 58. Halin S, Hammarsten P, Adamo H, Wikstrom P, Bergh A. Tumor indicating normal tissue could be a new source of diagnostic and prognostic markers for prostate cancer. Expert Opin Med Diagn. 2011;5(1):37-47. 59. Josefsson A, Wikstrom P, Egevad L, et al. Low endoglin vascular density and Ki67 index in Gleason score 6 tumours may identify prostate cancer patients suitable for surveillance. Scand J Urol Nephrol. 2012;46(4):247-257. 60. Josefsson A, Adamo H, Hammarsten P, et al. Prostate cancer increases hyaluronan in surrounding nonmalignant stroma, and this response is associated with tumor growth and an unfavorable outcome. Am J Pathol. 2011;179(4):1961-1968. 61. Nilsson M, Hagglof C, Hammarsten P, et al. High Lysyl Oxidase (LOX) in the Non-Malignant Prostate Epithelium Predicts a Poor Outcome in Prostate Cancer Patient Managed by Watchful Waiting. PLoS One. 2015;10(10):e0140985. 62. Halin Bergstrom S, Nilsson M, Adamo H, et al. Extratumoral Heme Oxygenase-1 (HO-1) Expressing Macrophages Likely Promote Primary and Metastatic Prostate Tumor Growth. PLoS One. 2016;11(6):e0157280. 63. Halin S, Rudolfsson SH, Van Rooijen N, Bergh A. Extratumoral macrophages promote tumor and vascular growth in an orthotopic rat prostate tumor model. Neoplasia. 2009;11(2):177-186. 64. Adamo HH, Stromvall K, Nilsson M, Halin Bergstrom S, Bergh A. Adaptive (TINT) Changes in the Tumor Bearing Organ Are Related to Prostate Tumor Size and Aggressiveness. PLoS One. 2015;10(11):e0141601. 65. Johansson A, Rudolfsson S, Hammarsten P, et al. Mast cells are novel independent prognostic markers in prostate cancer and represent a target for therapy. Am J Pathol. 2010;177(2):1031-1041.

66. Nonn L, Ananthanarayanan V, Gann PH. Evidence for field cancerization of the prostate. Prostate. 2009;69(13):1470-1479. 67. Trujillo KA, Jones AC, Griffith JK, Bisoffi M. Markers of field cancerization: proposed clinical applications in prostate biopsies. Prostate Cancer. 2012;2012:302894. 68. Tomic K, Sandin F, Wigertz A, Robinson D, Lambe M, Stattin P. Evaluation of data quality in the National Prostate Cancer Register of Sweden. Eur J Cancer. 2015;51(1):101-111. 69. Isaacs JT, Isaacs WB, Feitz WF, Scheres J. Establishment and characterization of seven Dunning rat prostatic cancer cell lines and their use in developing methods for predicting metastatic abilities of prostatic cancers. Prostate. 1986;9(3):261-281. 70. Thermo Fisher Scientific. Product overview obtained from www.thermofisher.com. Accessed September 24, 2017. https://www.thermofisher.com/order/catalog/product/901421?SID =srch-srp-901421. 71. Miller LD, Long PM, Wong L, Mukherjee S, McShane LM, Liu ET. Optimal gene expression analysis by microarrays. Cancer Cell. 2002;2(5):353-361. 72. Kramer A, Green J, Pollard J, Jr., Tugendreich S. Causal analysis approaches in Ingenuity Pathway Analysis. Bioinformatics. 2014;30(4):523-530. 73. Applied Biosystems. Real-time PCR 3rd Edition (2014), obtained from www.thermofisher.com. Accessed September 24, 2017. https://www.thermofisher.com/se/en/home/life-science/pcr/real- time-pcr/qpcr-education/real-time-pcr-handbook.html. 74. Ziegler SF, Ramsdell F, Alderson MR. The activation antigen CD69. Stem Cells. 1994;12(5):456-465. 75. Andersen CL, Jensen JL, Orntoft TF. Normalization of real-time quantitative reverse transcription-PCR data: a model-based variance estimation approach to identify genes suited for normalization, applied to bladder and colon cancer data sets. Cancer Res. 2004;64(15):5245-5250. 76. Weibel ER. Stereological methods. London ; New York: Academic Press; 1979. 77. Vainio P, Wolf M, Edgren H, et al. Integrative genomic, transcriptomic, and RNAi analysis indicates a potential oncogenic role for FAM110B in castration-resistant prostate cancer. Prostate. 2012;72(7):789-802.

78. Zhao S, Geybels MS, Leonardson A, et al. Epigenome-Wide Tumor DNA Methylation Profiling Identifies Novel Prognostic Biomarkers of Metastatic-Lethal Progression in Men Diagnosed with Clinically Localized Prostate Cancer. Clin Cancer Res. 2016. 79. Herroon MK, Rajagurubandara E, Hardaway AL, et al. Bone marrow adipocytes promote tumor growth in bone via FABP4-dependent mechanisms. Oncotarget. 2013;4(11):2108-2123. 80. Pan J, Chen Y, Mo C, et al. Association of DSC3 mRNA downregulation in prostate cancer with promoter hypermethylation and poor prognosis. PLoS One. 2014;9(3):e92815. 81. McMahon BJ, Kwaan HC. Components of the Plasminogen-Plasmin System as Biologic Markers for Cancer. Adv Exp Med Biol. 2015;867:145-156. 82. Yang F, Tuxhorn JA, Ressler SJ, McAlhany SJ, Dang TD, Rowley DR. Stromal expression of connective tissue growth factor promotes angiogenesis and prostate cancer tumorigenesis. Cancer Res. 2005;65(19):8887-8895. 83. Shaw A, Bushman W. Hedgehog signaling in the prostate. J Urol. 2007;177(3):832-838. 84. Valverde Lde F, Pereira Tde A, Dias RB, et al. Macrophages and endothelial cells orchestrate tumor-associated angiogenesis in oral cancer via hedgehog pathway activation. Tumour biology : the journal of the International Society for Oncodevelopmental Biology and Medicine. 2016;37(7):9233-9241. 85. Dunne JC, Lamb DS, Delahunt B, et al. Proteins from formalin-fixed paraffin-embedded prostate cancer sections that predict the risk of metastatic disease. Clin Proteomics. 2015;12(1):24. 86. Lai TH, Fong YC, Fu WM, Yang RS, Tang CH. Osteoblasts-derived BMP-2 enhances the motility of prostate cancer cells via activation of integrins. Prostate. 2008;68(12):1341-1353. 87. Bhalla A, Haque S, Taylor I, Winslet M, Loizidou M. Endothelin receptor antagonism and cancer. Eur J Clin Invest. 2009;39 Suppl 2:74-77. 88. Zang X, Thompson RH, Al-Ahmadie HA, et al. B7-H3 and B7x are highly expressed in human prostate cancer and associated with disease spread and poor outcome. Proc Natl Acad Sci U S A. 2007;104(49):19458-19463. 89. Rehman I, Azzouzi AR, Cross SS, et al. Dysregulated expression of S100A11 (calgizzarin) in prostate cancer and precursor lesions. Hum Pathol. 2004;35(11):1385-1391.

90. D'Antonio KB, Toubaji A, Albadine R, et al. Extracellular matrix associated protein CYR61 is linked to prostate cancer development. J Urol. 2010;183(4):1604-1610. 91. Wong SY, Haack H, Crowley D, Barry M, Bronson RT, Hynes RO. Tumor-secreted vascular endothelial growth factor-C is necessary for prostate cancer lymphangiogenesis, but lymphangiogenesis is unnecessary for lymph node metastasis. Cancer Research. 2005;65(21):9789-9798. 92. Karpanen T, Egeblad M, Karkkainen MJ, et al. Vascular endothelial growth factor C promotes tumor lymphangiogenesis and intralymphatic tumor growth. Cancer Research. 2001;61(5):1786- 1790. 93. Zeng YP, Opeskin K, Horvath LG, Sutherland RL, Williams ED. Lymphatic vessel density and lymph node metastasis in prostate cancer. Prostate. 2005;65(3):222-230. 94. Lundholm M, Hagglof C, Wikberg ML, et al. Secreted Factors from Colorectal and Prostate Cancer Cells Skew the Immune Response in Opposite Directions. Sci Rep. 2015;5:15651. 95. Nilsson M, Adamo H, Bergh A, Halin Bergstrom S. Inhibition of Lysyl Oxidase and Lysyl Oxidase-Like Enzymes Has Tumour- Promoting and Tumour-Suppressing Roles in Experimental Prostate Cancer. Sci Rep. 2016;6:19608. 96. Barker HE, Cox TR, Erler JT. The rationale for targeting the LOX family in cancer. Nature reviews Cancer. 2012;12(8):540-552. 97. Tindemans I, Serafini N, Di Santo JP, Hendriks RW. GATA-3 function in innate and adaptive immunity. Immunity. 2014;41(2):191-206. 98. Wang Y, Su MA, Wan YY. An essential role of the transcription factor GATA-3 for the function of regulatory T cells. Immunity. 2011;35(3):337-348. 99. Louis I, Dulude G, Corneau S, et al. Changes in the lymph node microenvironment induced by oncostatin M. Blood. 2003;102(4):1397-1404. 100. Herzog BH, Fu J, Wilson SJ, et al. Podoplanin maintains high endothelial venule integrity by interacting with platelet CLEC-2. Nature. 2013;502(7469):105-109. 101. Quatan N, Meyer B, Bailey M, Pandha H. Persistently high levels of immunosuppressive cytokines in patients after radical prostatectomy. Prostate Cancer Prostatic Dis. 2006;9(4):420-425.

102. Bhavsar NA, Bream JH, Meeker AK, et al. A peripheral circulating TH1 cytokine profile is inversely associated with prostate cancer risk in CLUE II. Cancer Epidemiol Biomarkers Prev. 2014;23(11):2561- 2567. 103. Shiota T, Miyasato Y, Ohnishi K, et al. The Clinical Significance of CD169-Positive Lymph Node Macrophage in Patients with Breast Cancer. PLoS One. 2016;11(11):e0166680. 104. Halin Bergstrom S, Hagglof C, Thysell E, Bergh A, Wikstrom P, Lundholm M. Extracellular Vesicles from Metastatic Rat Prostate Tumors Prime the Normal Prostate Tissue to Facilitate Tumor Growth. Sci Rep. 2016;6:31805. 105. Gaudreau PO, Stagg J, Soulieres D, Saad F. The Present and Future of Biomarkers in Prostate Cancer: Proteomics, Genomics, and Immunology Advancements. Biomark Cancer. 2016;8(Suppl 2):15- 33. 106. Abate-Shen C, Shen MM, Gelmann E. Integrating differentiation and cancer: the Nkx3.1 homeobox gene in prostate organogenesis and carcinogenesis. Differentiation. 2008;76(6):717-727. 107. Maia MC, Hansen AR. A comprehensive review of immunotherapies in prostate cancer. Crit Rev Oncol Hematol. 2017;113:292-303. 108. Ren R, Koti M, Hamilton T, et al. A primer on tumour immunology and prostate cancer immunotherapy. Can Urol Assoc J. 2016;10(1- 2):60-65.