Abstract 7687: Pan-cancer characterization of protein isoforms uncovers driving alternative splicing events at protein level

Abstract Over 90% of human genes undergo alternative splicing, generating numerous transcripts or isoforms with distinct functions for each gene. This highly regulated process is often disrupted in cancer, leading to the production of harmful protein isoforms that contribute to tumor growth, survival, metastasis, and immune evasion. Accurately identifying these aberrant isoforms is essential for understanding cancer biology and developing targeted therapies. Although RNA sequencing has advanced our understanding of alternative splicing in cancer, the study of protein isoforms at the proteomic level is crucial as mRNA expression and protein abundance are only moderately correlated. Advancements in mass spectrometry (MS)-based shotgun proteomics have enabled unbiased identification and quantification of more than 10,000 protein coding genes from biological sample, as demonstrated in the proteomic datasets from the Clinical Proteomic Tumor Analysis Consortium (CPTAC). However, typical shotgun proteomics analysis is done at the gene level and is unable to quantify protein isoforms. To better use protein isoform information from CPTAC global proteomics data set, we published a tripartite graph modeling approach that groups peptides based on their mapping relationships to protein isoforms. In this study, we utilized our recent algorithm to analyze global proteomics data from ten tumor cohorts from CPTAC to generate protein isoform or protein isoform group level quantification matrices. These matrices were integrated with other omics, including somatic mutation, copy-number variation, methylation, gene expression, and gene level protein abundance, and clinical data from the harmonized dataset of the CPTAC pan-cancer project. After constructing this dataset, at first, protein isoforms were compared between tumor and adjacent normal tissues, linked to phenotype, and correlated with other omics; Then, we investigated the effects of transcription factors and mutated splicing factors on protein isoforms; Moreover, we performed eQTL analysis using protein isoform abundance and isoform level mRNA expression. Through this approach, we identified numerous alternative splicing events that drive human tumors. Overall, our study provides a comprehensive characterization of protein isoforms in human cancers, offering deep insights into alternative splicing and its potential for advancing cancer biology and targeted therapy development. Citation Format: Yongchao Dou, Lindsey Olsen, Bing Zhang. Pan-cancer characterization of protein isoforms uncovers driving alternative splicing events at protein level [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 7687.