Novel Driver Strength Index highlights important cancer genes in TCGA PanCanAtlas patients

• Published in: PeerJ (San Francisco, CA) Vol. 10; p. e13860
• Main Authors: Belikov, Aleksey V, Vyatkin, Alexey D, Leonov, Sergey V
• Format: Journal Article
• Language: English
• Published: United States PeerJ. Ltd 11.08.2022; PeerJ, Inc; PeerJ Inc
• Subjects: AKT1 protein; Algorithms; Analysis; Aneuploidy; Bioinformatics; Cancer; Cancer patients; Computer applications; Development and progression; Driver; Fibroblast growth factor receptor 2; Fibroblast growth factor receptors; Gene expression; Gene mutations; Genes; Genetic aspects; Genetic research; Genomics; Growth factor receptors; Humans; Mathematical Biology; Medical Genetics; Mutation; Mutation - genetics; Mutation Rate; Mutations; Neoplasms - genetics; Oncogenes - genetics; Oncology; Pathways; Patients; Point mutation; Protein families; Proteins; Proteins - genetics; Therapeutic targets; TOR protein; Tumors; VHL protein; Mutations; Driver; Pathways; Genes; Cancer
• ISSN: 2167-8359; 2167-8359
• DOI: 10.7717/peerj.13860

Cancer driver genes are usually ranked by mutation frequency, which does not necessarily reflect their driver strength. We hypothesize that driver strength is higher for genes preferentially mutated in patients with few driver mutations overall, because these few mutations should be strong enough to initiate cancer. We propose formulas for the Driver Strength Index (DSI) and the Normalized Driver Strength Index (NDSI), the latter independent of gene mutation frequency. We validate them using TCGA PanCanAtlas datasets, established driver prediction algorithms and custom computational pipelines integrating SNA, CNA and aneuploidy driver contributions at the patient-level resolution. DSI and especially NDSI provide substantially different gene rankings compared to the frequency approach. ., NDSI prioritized members of specific protein families, including G proteins , and , isocitrate dehydrogenases and , and fibroblast growth factor receptors and . KEGG analysis shows that top NDSI-ranked genes comprise pathway, pathway, and pathway. Our indices are able to select for driver gene attributes not selected by frequency sorting, potentially for driver strength. Genes and pathways prioritized are likely the strongest contributors to cancer initiation and progression and should become future therapeutic targets.