ArticlePloS one2018
Cancer driver mutation prediction through Bayesian integration of multi-omic data.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.
- Uncovering Oncogenic Mechanisms of Tumor Suppressor Genes in Breast Cancer Multi-Omics Data.International journal of molecular sciences · 2022Pooled it
- Cancer gene identification through integrating causal prompting large language model with omics data-driven causal inference.Briefings in bioinformatics · 2025Article
- Integrative Computational Framework,bioRxiv : the preprint server for biology · 2024Article
- Article
- Gsw-fi: a GLM model incorporating shrinkage and double-weighted strategies for identifying cancer driver genes with functional impact.BMC bioinformatics · 2024Article
- Integrating Clinical Cancer and PTM Proteomics Data Identifies a Mechanism of ACK1 Kinase Activation.Molecular cancer research : MCR · 2024Article
- Pan-cancer illumination of TRIM gene family reveals immunology regulation and potential therapeutic implications.Human genomics · 2022Article
- Interpretable deep learning for chromatin-informed inference of transcriptional programs driven by somatic alterations across cancers.Nucleic acids research · 2022Article
- Patient subgrouping with distinct survival rates via integration of multiomics data on a Grassmann manifold.BMC medical informatics and decision making · 2022Article
- The structure-based cancer-related single amino acid variation prediction.Scientific reports · 2021Article
- Clonal evolution driven by superdriver mutations.BMC evolutionary biology · 2020Article
- FI-Net: Identification of Cancer Driver Genes by Using Functional Impact Prediction Neural Network.Frontiers in genetics · 2020Article
- Systematic discovery of the functional impact of somatic genome alterations in individual tumors through tumor-specific causal inference.PLoS computational biology · 2019Article
- DriverML: a machine learning algorithm for identifying driver genes in cancer sequencing studies.Nucleic acids research · 2019Article
- CEPICS: A Comparison and Evaluation Platform for Integration Methods in Cancer Subtyping.Frontiers in genetics · 2019Article
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
Abstract
Identification of cancer driver mutations is critical for advancing cancer research and personalized medicine. Due to inter-tumor genetic heterogeneity, many driver mutations occur at low frequencies, which make it challenging to distinguish them from passenger mutations. Here, we show that a novel Bayesian hierarchical modeling approach, named rDriver can achieve enhanced prediction accuracy by identifying mutations that not only have high functional impact scores but also are associated with systemic variation in gene expression levels. In examining 3,080 tumor samples from 8 cancer types in The Cancer Genome Atlas, rDriver predicted 1,389 driver mutations. Compared with existing tools, rDriver identified more low frequency mutations associated with lineage specific functional properties, timing of occurrence and patient survival. Evaluation of rDriver predictions using engineered cell-line models resulted in a positive predictive value of 0.94 in PIK3CA genes. Our study highlights the importance of integrating multi-omic data in predicting cancer driver mutations and provides a statistically rigorous solution for cancer target discovery and development.
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