Abstract 4944: Divergent proteogenomic gene expression is driven by microenvironment across tumor types
Authors: Joseph LaMorte, Nicholas Bateman, Thomas Conrads, Robert F. Browning, Craig D. Shriver, Robert L. Kortum, Matthew D. Wilkerson
Journal: Cancer Research (2024)

Abstract Solid tumors develop through a complex series of genome and downstream expression alterations that interact with the local tissue microenvironment, leading to a transformed cell and malignant phenotype. With massive multi-omic data, recent proteogenomics studies have jointly analyzed RNA and protein expression to uncover new gene regulatory relationships; however, many of these studies focused on single tumor types and lack a generalizable view of joint RNA and protein expression. Here, we present the first pan-cancer analysis of sample-wise RNA to protein correlation (SRPC). We re-analyzed over 1,000 tumors across 10 tumor types from published proteogenomic data from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) and the Applied Proteogenomics OrganizationaL Learning and Outcomes (APOLLO) Research Network. We identified a wide range of SRPC across all tumors (ρ range: -0.08 to 0.70, median: 0.45). We then analyzed tumor pathologic and molecular characteristics versus the range of SRPC. High SRPC tumors had high tumor purity by pathology review, by somatic DNA whole exome sequencing, and by RNA and protein purity scores. In contrast, low SRPC tumors had high immune cell and stromal cell expression scores as inferred by either protein or RNA based signatures. We observed marked differences in cell type populations estimated by expression in different SRPC tumor tranches (low SRPC: macrophages, endothelial cells, cancer-associated fibroblasts; high SRPC: CD4+ Th2 cells). Tumors expressed signaling pathways depending on their SRPC (low SRPC: hypoxia, inflammatory response, and KRAS signaling; high SRPC: DNA repair and E2F targets). SRPC also stratifies tumors with distinct somatic DNA alterations (low SRPC: HRAS and NF2; high SRPC: TP53, MEN1, high Tumor Mutational Burden and high DNA chromosomal instability). Finally, we found that cancer driver genes displayed divergent gene-wise RNA to protein correlations (GRPC) among tumor types with a median absolute deviation interquartile range of 0.1 - 0.2, suggesting tumor-type-specific regulation. In summary, divergent RNA and protein expression is driven, in part, by tumor microenvironment composition across tumor types. Tumors with a diverse cellular microenvironment display a summation of RNA and protein expression resulting from this cell type diversity leading to a low SRPC, while tumors predominated by tumor cells display coordinated RNA and protein expression levels resulting from a pure clonal or cell type leading to a high SRPC. This first deep analysis into sample-wise RNA to protein correlation represents a large proteogenomic community resource for informing biomarker analysis by modality and by tranches of tumor microenvironment. The views expressed in this abstract are solely of the authors and do not reflect the official policy of the Departments of Army/Navy/Air Force, Department of Defense, USUHS, HJF, or U.S. Government. Citation Format: Joseph LaMorte, Nicholas Bateman, Thomas Conrads, Robert F. Browning, Craig D. Shriver, Robert L. Kortum, Matthew D. Wilkerson. Divergent proteogenomic gene expression is driven by microenvironment across tumor types [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(6_Suppl):Abstract nr 4944.