FunMap

Large-scale omics profiling and genetic screen studies have greatly expanded the landscape of somatic mutations and cancer-associated proteins, posing significant challenges for their functional interpretation. We present FunMap, a systems biology approach that utilizes a pan-cancer functional map comprising over 10,000 protein coding genes. Leveraging supervised machine learning on recently released massive proteomics and RNASeq data from 1,194 patients across 11 cancer types, FunMap connects functionally associated genes with unprecedented precision, surpassing protein-protein interaction maps. Network analysis uncovers known and novel protein modules, revealing a hierarchical modular organization linked to cancer hallmarks and clinical phenotypes. Our results demonstrate the utility of FunMap in predicting functions of understudied cancer proteins, providing insights into established cancer drivers, and identifying new somatic drivers. This study highlights FunMap as a powerful and unbiased framework for interpreting somatic mutations and understudied proteins, with broad implications for advancing cancer biology and informing therapeutic strategies.