Cancer Driver Gene Discovery: A Patient-Level Statistical Framework
Bahari, F.; Montazeri, H.
Show abstract
Tumor genomes harbor a mixture of neutral and positively selected mutations, yet distinguishing true cancer drivers remains a major challenge. Several factors can obscure the detection of selection signals, among which patient-specific variation in mutational burden plays a significant role. Current approaches often fail to account for the heterogeneity in mutation burden across different patients; in particular, no existing method explicitly accounts for it when integrating both mutation recurrence and functional impact. Here we present iDriver, a probabilistic graphical model that integrates both mutation recurrence and functional impact at the individual-patient level, enabling an enhanced estimation of positive selection across functional genomic elements. Applying iDriver to 29 cancer types, we identify both known and previously unrecognized drivers spanning coding and noncoding regions, and provide evidence for their clinical and biological relevance. In comprehensive benchmarks against 12 established driver discovery methods, iDriver consistently outperformed all competitors, achieving the highest rankings for known cancer drivers across both coding and noncoding elements.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Identification of Relevant Genetic Alterations in Cancer using Topological Data Analysis 98%
- Integrative ensemble modelling of cetuximab sensitivity in colorectal cancer PDXs 98%
- The DiffInvex evolutionary model for conditional somatic selection identifies chemotherapy resistance genes in 10,000 cancer genomes 98%
Similar papers in this journal
- RNA allelic frequencies of somatic mutations encode substantial functional information in cancers 97%
- SVFX: a machine-learning framework to quantify the pathogenicity of structural variants 97%
- Disruption of metazoan gene regulatory networks in cancer alters the balance of co-expression between genes of unicellular and multicellular origins 97%
Similar papers in this journal
- Interpretable deep learning for chromatin-informed inference of transcriptional programs driven by somatic alterations across cancers 97%
- HYENA detects oncogenes activated by distal enhancers in cancer 97%
- Enhancing Disease Risk Gene Discovery by Integrating Transcription Factor-Linked Trans-located Variants into Transcriptome-Wide Association Analyses 96%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.