Abstract
Advances in mass-spectrometry have generated increasingly large-scale proteomics datasets containing tens of thousands of phosphorylation sites (phosphosites) that require prioritization. We develop a bioinformatics tool called HotPho and systematically discover 3D co-clustering of phosphosites and cancer mutations on protein structures. HotPho identifies 474 such hybrid clusters containing 1255 co-clustering phosphosites, including RET p.S904/Y928, the conserved HRAS/KRAS p.Y96, and IDH1 p.Y139/IDH2 p.Y179 that are adjacent to recurrent mutations on protein structures not found by linear proximity approaches. Hybrid clusters, enriched in histone and kinase domains, frequently include expression-associated mutations experimentally shown as activating and conferring genetic dependency. Approximately 300 co-clustering phosphosites are verified in patient samples of 5 cancer types or previously implicated in cancer, including CTNNB1 p.S29/Y30, EGFR p.S720, MAPK1 p.S142, and PTPN12 p.S275. In summary, systematic 3D clustering analysis highlights nearly 3,000 likely functional mutations and over 1000 cancer phosphosites for downstream investigation and evaluation of potential clinical relevance.
Highlights
Phosphosites co-clustering with activating mutations are likely of functional relevance. We examined these clusters on protein structures (Fig. 3b, Supplementary Fig. 5)
We evaluated whether HotPho can effectively prioritize functional mutations in hybrid clusters by comparing with functional scores predicted by VEST21, Mutation Assessor[22], PolyPhen[223], SIFT24, and a composite Eigen score composed of all these scores[25] (Fig. 4a)
To further demonstrate that hybrid clusters enrich for functional mutations, we examined whether clustered mutations are associated with the protein or phosphoprotein changes, as previously found for functional and pathogenic mutations[26,27]
Summary
PTM sites from PTMcosmos were retrieved from UniProt Knowledge Base (UniProtKB) version 2018.01, PhosphoSitePlus (snapshot on the date 2018–02–14), and CPTAC2 MS phosphoproteome data. A PTM site was included if it satisfied either of the following: (1) included in UniProtKB and was reported in at least one publication or by sequence similarity. To match phosphosites between multiple databases, we used transvar[40] to map amino acid residues on different protein isoforms to their unique genomic positions. We used somatic mutations from the TCGA cohort as provided by the publicly-available MC311 mutation annotation file (MAF) (syn7824274). These mutations were further filtered based on flagged artifacts, hypermutators, and pathology to a driver discovery dataset of 9062 samples with
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