Pan-cancer Proteogenomics Expands the Landscape of Therapeutic Targets
We integrate Clinical Proteomic Tumor Analysis Consortium (CPTAC) pan-cancer dataset with other public datasets to provide insights into existing cancer drug targets and to systematically identify candidate new targets for drug repurposing or development. The analyses include overexpressed and hyperactivated protein dependencies, protein dependencies associated with the loss of tumor suppressor genes, and putative neoantigens and tumor-associated antigens. Detailed method description and results can be found in the accompanying Cell publication.
Pan-cancer Proteogenomics Expands the Landscape of Therapeutic Targets
Abstract
Fewer than 200 proteins are targeted by FDA-approved cancer drugs. We integrate CPTAC proteogenomics data from 1,043 patients across 10 cancer types with additional public datasets to identify potential therapeutic targets. Pan-cancer analysis of 2,863 druggable proteins reveals a wide abundance range and identifies biological factors that affect mRNA-protein correlation. Integration of proteomic data from tumors and genetic screen data from cell lines identifies protein overexpression- or hyperactivation-driven druggable dependencies, enabling accurate predictions of effective drug targets. Proteogenomic identification of synthetic lethality provides a strategy to target tumor suppressor gene loss. Combining proteogenomic analysis and MHC binding prediction prioritizes mutant KRAS peptides as promising “public” neoantigens. Computational identification of shared tumor associated antigens followed by experimental confirmation nominates peptides as immunotherapy targets. These analyses form a comprehensive landscape of protein and peptide targets for companion diagnostics, drug repurposing, and therapy development.
Savage et al., Pan-cancer proteogenomics expands the landscape of therapeutic targets, Cell (2024), https://doi.org/10.1016/j.cell.2024.05.039
| Gene | Pharos Family | Target Tier | Drug | Antigen | Score |
|---|