Journal Article

Systematic analysis of somatic mutations in phosphorylation signaling predicts novel cancer drivers

Bader, Gary D, Reimand, Jüri

2013

Overview

Large‐scale cancer genome sequencing has uncovered thousands of gene mutations, but distinguishing tumor driver genes from functionally neutral passenger mutations is a major challenge. We analyzed 800 cancer genomes of eight types to find single‐nucleotide variants (SNVs) that precisely target phosphorylation machinery, important in cancer development and drug targeting. Assuming that cancer‐related biological systems involve unexpectedly frequent mutations, we used novel algorithms to identify genes with significant phosphorylation‐associated SNVs (pSNVs), phospho‐mutated pathways, kinase networks, drug targets, and clinically correlated signaling modules. We highlight increased survival of patients with TP53 pSNVs, hierarchically organized cancer kinase modules, a novel pSNV in EGFR , and an immune‐related network of pSNVs that correlates with prolonged survival in ovarian cancer. Our findings include multiple actionable cancer gene candidates ( FLNB , GRM1 , POU2F1 ), protein complexes (HCF1, ASF1), and kinases (PRKCZ). This study demonstrates new ways of interpreting cancer genomes and presents new leads for cancer research. Phosphorylation sites of human proteins are frequently mutated in cancer. Statistical analysis of phosphorylation‐associated single nucleotide variants (pSNVs) predicts novel cancer drivers and phospho‐mutation mechanisms in known cancer genes. Synopsis Phosphorylation sites of human proteins are frequently mutated in cancer. Statistical analysis of phosphorylation‐associated single nucleotide variants (pSNVs) predicts novel cancer drivers and phospho‐mutation mechanisms in known cancer genes. We designed the ActiveDriver method to identify significantly mutated signaling regions in proteins. ActiveDriver is complementary to standard frequency‐based methods of mutation significance and helps interpret rare, but site‐specific mutations. Analysis of somatic mutations in 800 cancer genomes reveals dozens of known and novel cancer genes, including potential drivers that are apparent only when integrating multiple cancer types. Pathway and network analysis identifies systems with significantly enriched pSNVs, including kinase modules and protein complexes. Clinical data analysis identifies phospho‐mutations of TP53 that correlate with prolonged patient survival in ovarian and brain cancer. Kinase network analysis highlights multiple survival‐associated signaling modules with pSNVs.

• Publisher: Nature Publishing Group UK,John Wiley & Sons, Ltd,EMBO Press,Nature Publishing Group,Springer Nature
• Subject: Algorithms / Cancer / cancer drivers / Contractile Proteins - genetics / Contractile Proteins - metabolism / EMBO24 / EMBO37 / Epidermal growth factor receptors / ErbB Receptors - genetics / ErbB Receptors - metabolism / Female / Filamins / Gene expression / Gene sequencing / Genes / Genes, p53 / Genomes / Glioblastoma - genetics / Humans / Kinases / Leukemia / Medical prognosis / Medical research / Microfilament Proteins - genetics / Microfilament Proteins - metabolism / Models, Statistical / Modules / Mutation / Neoplasms - genetics / Neoplasms - mortality / Nucleotides / Octamer Transcription Factor-1 - genetics / Octamer Transcription Factor-1 - metabolism / Ovarian cancer / Ovarian Neoplasms - genetics / Ovarian Neoplasms - mortality / p53 Protein / Phosphorylation / Polymorphism, Single Nucleotide / Predictive Value of Tests / Protein Kinase C - genetics / Protein Kinase C - metabolism / Protein Kinases - genetics / Protein Kinases - metabolism / Proteins / Proteins - genetics / Proteins - metabolism / Signal Transduction - genetics / Signaling / somatic mutations / Survival / Writers