Abstract 2127: Master regulator analysis of the tumor microenvironment and the distinctive tumor sub-populations in pancreatic ductal adenocarcinoma

Abstract

Abstract By 2030, pancreatic ductal adenocarcinoma (PDAC) is projected to become the second leading cause of cancer-associated mortality in the United States. However, current strategies in targeting key driver mutations such as KRAS, CDKN2A, TP53, and SMAD4, have found limited translational success. As a result, the standard of care for PDAC patients continues to rely on cytotoxic chemotherapies. Master regulator (MR) proteins integrate upstream genomic alterations to generate a common downstream transcriptional signature characteristic of a tumor cell population. Importantly, aberrant MR protein activity is requisite to implementing and maintaining a pro-tumor state. Therefore, MR proteins serve as critical tumor dependencies and represent a promising class of therapeutic targets for cancer treatment. Here, we propose a novel, network-based approach to identify top MR proteins by utilizing a combination of ARACNe (Margolin et al, 2006) and VIPER (Alvarez et al, 2016) algorithms for the analysis of single-nuclei transcriptomic data. Specifically, we used VIPER to investigate the heterogeneity of the tumor microenvironment (TME) and to characterize the tumor sub-populations that co-exist in a patient PDAC specimen. Clustering analysis based on protein activity revealed six populations in the TME. The SingleR classifier subsequently annotated these clusters as myeloid and lymphoid immune, fibroblast, neuroendocrine, epithelial, and putative tumor cells. Next, we validated the putative tumor cells by performing InferCNV which detected widespread chromosomal copy number variations. We identified two distinct tumor subpopulations which were found to be enriched by the MR signatures of the Oncogenic Precursor (OP) and Lineage subtypes, previously demonstrated to represent more and less differentiated tumor cell populations, respectively (Laise et al, 2020). Interestingly, top MR proteins identified for the putative tumor cells using the single-nuclei transcriptomics closely corresponded with those identified by single-cell and previous bulk transcriptomics, providing orthogonal validation for the inferred protein activities. We characterized the diverse cell populations in the PDAC TME, delineated two distinct tumor cell subtypes, and demonstrated high concordance of inferred MR protein activity at the single-cell, single-nuclei, and bulk levels. These results demonstrate the feasibility of applying systems biology-based methods to identify MR proteins in an N = 1 context and represent a novel precision medicine approach in the treatment of PDAC patients. Future lines of inquiry center on identifying treatments against each tumor subpopulation via OncoTreat, an algorithm that leverages high-throughput drug perturbation data to predict effective therapies based on inferred ability to invert MR protein activity (Alvarez, 2018). Citation Format: Jinjie Ling, Lorenzo Tomassoni, Alvaro Curiel, Kenneth Olive, Andrea Califano. Master regulator analysis of the tumor microenvironment and the distinctive tumor sub-populations in pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2127.