An evolution-based machine learning to identify cancer type-specific driver mutations. Issue 1 (27th December 2022)

• Record Type: Journal Article
• Authors: Kim, Donghyo; Ha, Doyeon; Lee, Kwanghwan; Lee, Heetak; Kim, Inhae; Kim, Sanguk
• Is Part Of: Briefings in bioinformatics. Volume 24:Issue 1(2023)
• Publication Date: 2022-12-27
• Subjects: protein–protein interactions -- cancer type-specificity -- driver mutations -- machine learning -- sequence co-evolution; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbac593
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: Identifying cancer type-specific driver mutations is crucial for illuminating distinct pathologic mechanisms across various tumors and providing opportunities of patient-specific treatment. However, although many computational methods were developed to predict driver mutations in a type-specific manner, the methods still have room to improve. Here, we devise a novel feature based on sequence co-evolution analysis to identify cancer type-specific driver mutations and construct a machine learning (ML) model with state-of-the-art performance. Specifically, relying on 28 000 tumor samples across 66 cancer types, our ML framework outperformed current leading methods of detecting cancer driver mutations. Interestingly, the cancer mutations identified by sequence co-evolution feature are frequently observed in interfaces mediating tissue-specific protein–protein interactions that are known to associate with shaping tissue-specific oncogenesis. Moreover, we provide pre-calculated potential oncogenicity on available human proteins with prediction scores of all possible residue alterations through user-friendly website (http://sbi.postech.ac.kr/w/cancerCE ). This work will facilitate the identification of cancer type-specific driver mutations in newly sequenced tumor samples.