Abstract 5552: Uncovering clinically significant tumor microenvironment interaction programs across diverse cancers

Abstract Background: Cell-cell interactions (CCI) within the tumor microenvironment (TME) play pivotal roles in various tumor behaviors, including cancer growth, metastasis, immune evasion, and resistance to therapy. Compared to front-line treatments, targeting specific CCIs marks a paradigm shift in cancer therapy, aiming for enhanced response rates with reduced side effects. Although current approaches targeting immune regulation pathways exhibit effective anti-tumor responses, substantial variability in response rates across patients and cancer types persists. Consequently, a critical imperative emerges to discover novel interaction programs (IPs) across diverse TMEs, demonstrating clear clinical impact across a broad spectrum of patients. Methods: Characterization of pan cancer IPs. We compiled a pan cancer single-cell RNA-seq (scRNA-seq) atlas, including 4 million cells from 890 tumors across 14 cancer types. In each sample, CCIs were inferred using a ligand-receptor (LR) analysis framework, generating ranked CCI scores based on consensus from various LR inference methods. To find IPs enriched across TMEs, we applied an unsupervised factorization approach. The outcome represents CCIs as factors, where sample loading indicates factors’ strength, and feature loading highlights the cells and LR pairs constituting each IP. Evaluating IPs in clinical RNA-seq cohorts. Given the more immediate translational potential of bulk cancer sequencing for patient stratification, we devised an approach to predict IPs' strengths in bulk RNA-seq. Random Forest regression models using pseudo-bulk expression of cancer genes were trained to predict the loading (strength) of factors (IPs) in each sample in the scRNA-seq pan-cancer atlas. These models were subsequently applied to cancer samples in The Cancer Genome Atlas (TCGA) cohort to assess the clinical impact of various IPs on patient mortality. Results: We assessed the impact of IPs on 5-year overall survival using Cox proportional hazard models. We identified multiple factors significantly associated with survival in at least 4 cancer types. Notably, a robust prognostic factor linked to the inclusion of anti-tumor natural killer cell interactions and exclusion of pro-tumor macrophage interactions was found in MESO, LGG, ACC, and UVM (HR 1. 6-6. 6, p 0. 05). The validation of IPs using spatial transcriptomics and evaluation of IP prediction's utility in targeted treatment cohorts are currently underway. Conclusion: We highlight a potent method for detecting clinically significant IPs involving cells in TME communicating through LR pairs. The ability to assess these scRNA-seq-derived IPs in clinical bulk RNA-seq cohorts signifies a valuable advancement. Our approach holds promise for expanding the repertoire of cell therapies, identifying new treatment targets, and improving our predictive capabilities for responses to existing therapies. Citation Format: Ido Nofech-Mozes, Vivian Wang, Philip Awadalla, Sagi Abelson. Uncovering clinically significant tumor microenvironment interaction programs across diverse cancers abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts) ; 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84 (6Suppl): Abstract nr 5552.