The Role of the Tumor Microenvironment in Promoting Tumor Growth

Timothy Ratliff, Ph.D. Distinguished Professor Robert Wallace Miller Director Tumor Microenvironment (TME)

• The TME is an integrated environment of cells and extracellular matrix proteins that support tumor cell growth • The TME is a diverse environment that is considered pro-tumorigenic supporting not only growth but migration and the spread of cancer cells • The TME is composed of activated fibroblasts (cancer associated fibroblasts (CAF)), endothelial cells (part of blood and lymphatic vessels), extracellular matrix proteins and a diverse set of tumor infiltrating immune cells • Approximately 50% of the cells in a tumor mass are non-cancer cells Tumor Microenvironment (TME)

• The TME is an interactive environment where communication among cell types occurs that structures their phenotype • We do not understand the communication networks nor their interactive nature • By better understanding the mechanisms of communication, better therapies can be developed Frances R. Balkwill et al. J Cell Sci 2012;125:5591-5596 Fibroblasts 3T3 Fibroblasts Actin Filaments Fibroblasts in Interstitial Areas

• Fibroblasts maintain structural integrity of connective tissues by continuously secreting extracellular matrix proteins (e.g., collagen) • Fibroblasts express vimentin and actin filaments • Fibroblasts secrete growth factors that drive proliferation of epithelial cells and blood and lymphatic vessels • Important cells in wound healing, which if not controlled can lead to fibrosis • Fibroblasts are present in tumors but have different characteristics and are called cancer associated fibroblasts (CAF) Cancer Associated Fibroblasts (CAF)

• CAF are distributed throughout the cancer tissue but are in abundance at the periphery of the tumor • CAF function to maintain the structural integrity of the tumor by producing extracellular proteins, growth factors and tissue remodeling enzymes (enzymes that break down extracellular matrix proteins and allow escape of cancer cells to form metastases in distant organs) • CAF appear to be “stuck” in the tissue rebuilding phenotype • CAF inhibit antitumor immunity

With the multiplicity of functions – are there subsets? CAF Subsets

• Initiated studies to determine if distinct CAF subsets are present in cancers • Used single cell RNA-seq to analyze the transcriptome of CAF • Used unsupervised segregation processes to identify populations with distinct transcriptomes • Initial studies on primary cell lines (isolated from tumors and cultured short term in vitro • The objective is to better define interactive pathways that support tumor growth and spread in order to identifiy new therapeutic targets Unsupervised Clustering CAF

A 0 1 2 3 4 5 B Tumor Fibroblast Subclasses Fibroblast Communication

Vickman, RE et al. Prostate 2020; 80:173 Immune Cells in TME

• Suppress immunity via IL-10

Frances R. Balkwill et al. J Cell Sci 2012;125:5591-5596 Immune Cells in TME

• We will discuss two types of immune cells: myeloid cells and T cells • Myeloid cells include macrophages and an immune regulatory cell population called myeloid derived suppressor cells (MDSC). • T cells include CD4+ helper T cells, CD8+ cytotoxic T cells, γδ T cells, regulatory T cells (Treg) and NKT cells. We will focus on CD8+ cytotoxic T cells because they are the primary killer cells that eliminate tumor cells Myeloid Cells

• Macrophages are an important phagocytic cell in the immune system. They internalize bacteria in infections, other particles and tumor cells • Macrophages are divided into phenotypes based on secretory products: M1 (Type I) or M2 (Type II). • M1 macrophages are killer cells. This is the desired phenotype for eliminating infectious agents and tumor cells • M2 macrophages support tissue growth and repair. They are not killer cells but rather help tissue cells grow including cancer cells. • M2 macrophages are the dominant phenotype in the TME • M2 macrophages also inhibit the immune response • Macrophages in the TME are called tumor associated macrophages (TAM) Macrophage Phenotype

Murry, P Ann Rev Physiol 2017; 79:541 Macrophage Phagocytosis Myeloid Derived Suppressor Cells

• Myeloid derived suppressor cells are immature myeloid cells that inhibit the immune system • They are generated by inflammation and the presence of cancer cells • They are important cells for controlling inflammation but are abducted by cancer cells to enable the cancer to escape being killed by immune cells

Kumar, V et al. Trends Immunol 2016;37:208 Myeloid Derived Suppressor Cells

L-Arginine Binds PD1 and L-Arg depletion Inhibits T cells

PD-L1

iNOS NO +

- Arginase 1 O2

H2O

MDSC

Modified from Condamine, T and Gabriolvich, DI Trends Immunol 2011; 32:19 CD8+ Cytotoxic T Cells TAM and MDSC

L-Arginine Binds PD1 and L-Arg depletion Inhibits T cells

PD-L1

iNOS NO +

- Arginase 1 O2

H2O

MDSC

Modified from Condamine, T and Gabriolvich, DI Trends Immunol 2011; 32:19 TAM and MDSC express FRβ

• Gene array comparing tumor derived MDSC to spleen MDSC: folate receptor beta (FRβ) was significantly upregulated in MDSC

M-MDSC G-MDSC FRβ Targeting of Myeloid Cells

OTL38 Immune Suppression Antitumor Activity

TLR7a FRβ Targeting Myeloid Cells

• FRβ was identified to be expressed only on myeloid cells within a tumor • Targeting FRβ+ myeloid cells changes myeloid phenotype and induces antitumor activity Immune cell Communication

• What subsets of immune cells are present in inflammatory processes? • How do they communicate? • To address these questions single cell RNA-seq of all inflammatory cells in benign prostate hyperplasia were analyzed Single Cell RNA-seq

• Benign Prostatic Hyperplasia (BPH) is the most common prostatic disease among older men • Inflammation is associated with BPH but the relationship between BPH and inflammation is unclear • The Northshore/Purdue P20 focuses on characterizing inflammation in BPH through 10x Genomics single cell RNA-seq analyses Prostate Inflammation Large vs Small Prostate

Small Prostate

CD45+ immune cells

80 *

60

40 Large Prostate 20 %of Viable cells %of Viable

0

S m all Large Single Cell RNA-seq

• Isolate all leukocytes by flow cytometry • Evaluate using 10x Genomics Drop-seq single cell RNA- seq • Generate unsupervised populations of cells • Analyze cell-to-cell signaling • Data are integrated from 10 small prostates, 4 large and 3 normal Unsupervised Segregation

Large Small Normal Large vs Small Prostate

Integrated Segregated ID Cell Types

Plasma cells

MDSC

Myeloid/Macrophage B cells Macrophages

NK cells

NK cells Mast cells CTL CTL Helper T cells Ligand-Receptor Interaction

Compare Calculate interaction Interaction scores Interaction Scores Kumar MP et al, Cell ReportsS2018cores Between Sample Kumar MP et al, Cell Reports 2018 Types

• Large BPH, 49,651 interaction scores >0 • Small BPH, 41,069 interaction scores >0 • Normal prostate, 6,949 interaction scores >0 Large vs Small Prostate

CTL Mast Cells CTL CTL

Helper T Cells NK Cell 0: helper t cells 1: cytotoxic t-cells 2: cytotoxic t-cells 3: macrophages Myeloid 4: macrophage 5: mast cells 6: cytotoxic t cells MAC 7: NK cells Plasma Cells 8: B cells MAC 9: plasma cells 10: myeloid 11: unknown (mono/mac?) B Cells 12: unknown (lymphoid?) Collaborators

Ratliff Lab Computational Genomics Low Lab Scott Crist Nadia Lanman Phil Low Ben Elzey Sagar Utturkar Bing Bing Wang Jiang Yang Rami Akram Tawfiq Alfar Cansu Cimen Hayward Lab Greg Cresswell Simon Hayward Endocyte, a Paula Cooper Rene Vickman Novartis Company Meaghan Broman Omar Franco Brian Helfand ON TARGET, Inc. Strand Lab Yuan Ji Sumith A. Kularatne Doug Strand Gervaise Henry