Spatial functional genomics to identify regulators of the tumor microenvironment and cancer immunity

Abstract public domain (NIH RePORTER)

PROJECT SUMMARY Tumor growth and response to therapy, particularly immunotherapy, are all highly dependent on the tumor microenvironment (TME): the collection of cells and extracellular factors (cytokines, chemokines, collagens, etc.) that form around cancer cells. This is evident from the major impact drugs targeting TME components can have on cancers, including immune checkpoint blockade (ICB). Though there is a relatively good understanding of key genes regulating cancer cell intrinsic processes, such as cell cycle, there is less known about genes controlling the extrinsic environment that protects cancer cells from immunity and aids growth. The objective of this project is to determine the genes controlling tumor composition and facilitating tumor growth and resistance to immunity & immunotherapy, with the goal of identifying vulnerability factors that can be targeted to enhance tumor immunity and improve cancer treatment. The overarching hypothesis, which forms the rationale for this U01, is that malignant cells turn on or off genes, including intrinsically operating genes, through mutations and selective gene expression, that act extrinsically to recruit, position, & polarize immune & stroma cells into a state that subverts immunity & facilitates tumor growth1, 2. To reach our objective, we will employ a first-of-its-kind spatial functional genomics platform, called Perturb-map, which permits extensive phenotypic analysis of dozens of single or multiple gene perturbations in a tumor at single cell resolution and with spatial architecture preserved. With Perturb-map, CRISPR knockout (KO) or cDNA overexpression (OE) screens are resolved by multiplex imaging & spatial transcriptomics (ST), and this allows study of entire classes of genes (e.g. secreted factors) and phenotypes (e.g. TME composition) not feasible with existing screens. We will use Perturb-map to determine the role of 100s of genes in controlling many critical tumor processes, including tumor: (i) growth, (ii) morphology, (iii) metastasis, (iv) cell-cell interactions, (v) subclonal interactions, (vi) immune/stroma recruitment & polarization, (vii) resistance to immunotherapy & other treatments. The breadth & depth of analysis of each gene will be achieved at a scale and efficiency not previously feasible. We will focus on 3 broad categories of genes, identified through analysis of TCGA, ICB-treated cohorts, single cell-omics, and other patient data, including: commonly mutated cancer genes in solid tumors (Aim 1), genes correlating with resistance or response to ICB immunotherapy (Aim 2), and cancer cell-derived ligands and secreted molecules (Aim 3). Studies will be carried out in immunocompetent, orthotopic models of non-small cell lung carcinoma, high grade serous ovarian carcinoma, pancreatic adenocarcinoma, and oral squamous cell carcinoma. The study outcome will determine the roles of 100s of genes in many processes critical to unimpeded cancer growth, including identifying genes shielding cancers from immunity. In doing so, they will generate insights into mechanisms of aggressive tumor behavior and treatment resistance that will help to improve and personalize treatment selection and drive the immediate next steps towards the development of novel therapeutic strategies.

Public health relevance public domain (NIH RePORTER)

PROJECT NARRATIVE The objective of this project is to determine the genes regulating tumor composition and facilitating tumor growth and resistance to immunity & immunotherapy, with the goal of identifying factors that can be targeted to enhance tumor immunity and improve cancer treatment. We will focus on four highly prevalent and deadly cancers - non small cell lung cancer (NSCLC), pancreatic ductal adenocarcinoma (PDAC), high grade serous ovarian cancer (HGSOC), and oral squamous cell carcinomas (OSCC), and utilize large-scale genomic and transcriptomic data from patient tumors, spatial functional genomics, and computational analysis, to reach our goal.