Abstract 5073: Advancing precision oncology: Insights from pan-cancer proteogenomic signatures

Abstract

Abstract Somatic mutations in cancer genomes arise from diverse mutational processes, leaving distinct mutational signatures. However, the proteogenomic implications of these signatures and their connections to environmental influences and immune interactions remain largely unexplored. In this study, we comprehensively investigated these signatures from a broader proteogenomic perspective to further discern their genetic, proteomic, phosphoproteomic, and environmental ramifications. The harmonized dataset of 1064 samples across ten cancer types from the Clinical Proteomics Tumor Analysis Consortium (CPTAC) pan-cancer resource was utilized for this investigation. Beyond genetic alterations and mutational signatures, we harnessed high-throughput multi-omics data to detect critical changes and infer expression signatures associated with the transitions to seven tumor-linked phenotypes: aging, Apolipoprotein B mRNA Editing Catalytic Polypeptide (APOBEC), Homologous Recombination Deficiency (HRD), Microsatellite Instability (MSI), Polymerase Epsilon (POLE), smoking, and ultraviolet (UV) light exposure at the pan-cancer level. We prioritized the top differentially expressed markers uniquely found at the protein and phosphoprotein levels and developed a quantitative score predicting each phenotype status, such as MSI. Respectively, we pinpointed potential candidates as actionable therapeutic targets by linking the identified markers to druggable databases. This offers additional cues to optimize therapeutic options for patients. Specifically, directing attention to genes up-regulated in HRD tumors treated with PARP1 inhibitors shows potential promise in overcoming resistance. Moreover, we explored environmental exposure-related tumor proteogenomic signatures and their association with immune infiltration levels and phenotypes. Notably, smoking strongly influenced the tumor microenvironment (TME) and prognosis in lung cancer. In summary, our identification of expression signatures facilitates phenotype prediction and unveils their molecular mechanisms. We anticipate clinical utility in classifying samples using proteogenomics, particularly in cases where a solely genomic assessment is inconclusive. Citation Format: Yize Li, Nadezhda V. Terekhanova, Michael C. Wendl, Feng Chen, Li Ding. Advancing precision oncology: Insights from pan-cancer proteogenomic signatures [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 5073.