Computational structure prediction methods enable the systematic identification of oncogenic mutations

Abstract

Oncogenic mutations are associated with the activation of key pathways necessary for the initiation, progression and treatment-evasion of tumors. While large genomic studies provide the opportunity of identifying these mutations, the vast majority of variants have unclear functional roles presenting a challenge for the use of genomic studies in the clinical/therapeutic setting. Recent developments in predicting protein structures enable the systematic large-scale characterization of structures providing a link from genomic data to functional impact. Here, we observed that most oncogenic mutations tend to occur in protein regions that undergo conformation changes in the presence of the activating mutation or when interacting with a protein partner. By combining evolutionary information and protein structure prediction, we introduce the Evolutionary and Structure (ES) score, a computational approach that enables the systematic identification of hotspot somatic mutations in cancer. The predicted sites tend to occur in Short Linear Motifs and protein-protein interfaces. We test the use of ES-scores in genomic studies in pediatric leukemias that easily recapitulates the main mechanisms of resistance to targeted and chemotherapy drugs. To experimentally test the functional role of the predictions, we performed saturated mutagenesis in NT5C2, a protein commonly mutated in relapsed pediatric lymphocytic leukemias. The approach was able to capture both commonly mutated sites and identify previously uncharacterized functionally relevant regions that are not frequently mutated in these cancers. This work shows that the characterization of protein structures provides a link between large genomic studies, with mostly variants of unknown significance, to functional systematic characterization, prioritizing variants of interest in the therapeutic setting and informing on their possible mechanisms of action.