WeiFu: A novel pan-cancer driver gene identification method using incidence-weighted mutation scores - Ren, Yanjie; Azlan, Mohd Zain; Zhang, Yan; Rozita, Abdul Jalil; Mahadi, Bahari; Norfadzlan, Yusup; Mazlina, Abdul Majid; Azurah, A. Samah; Prasetya, Didik Dwi; Nurhafizah Moziyana, Mohd Yusop (IEEE, 2024)
Genetic and genomic variations are primary drivers of tumor development. Identifying driver genes from numerous passenger genes across pan-cancer poses a significant challenge due to varying mutation loads. While independent studies have elucidated cancer-associated mutation patterns within specific cancer types, a systematic approach to integrating these mutation data for assessing the impact of gene mutations has been lacking. This study addresses this gap by integrating pan-cancer genomic somatic mutation data and introducing a novel mutation weight fusion (WeiFu) score calculation method. WeiFu computes frequency and weighted fusion scores by cancer type, facilitating the identification of potential driver genes. Evaluation results on an integrated pan-cancer dataset comprising 29 different cancer types demonstrate that WeiFu significantly outperforms current well-known approaches in prediction accuracy, sensitivity, and specificity. Notably, WeiFu recovers 277 known cancer genes among the top 500 ranked candidates and successfully identifies potential driver genes supported by strong evidence. Consequently, WeiFu shows considerable promise for identifying driver genes within the rapidly expanding corpus of cancer genomic data.