Biology subjects

Begg, C.

Publications and source records attributed to Begg, C..

2 recordsLinked to original sources

bioRxiv2025

A Bayesian Approach for Identifying Driver Mutations within Oncogenic Pathways through Mutual Exclusivity

Distinguishing driver mutations from the large background of passenger mutations remains a major challenge in cancer genomics. Evidence-based approaches to nominate driver mutations are often limited by the availability of experimental or clinical validation for specific variants. As clinical sequencing becomes integrated into patient care, computational methods provide powerful opportunities to analyze expanding genomic datasets and identify functional candidates beyond the current knowledge base. Among various analytical frameworks, mutual exclusivity, the observation that mutations in two or more genes tend not to co-occur within the same tumor, has been particularly attractive. Building on this principle, we propose BayesMAGPIE, a refined version of a statistical method, MAGPIE, developed previously for identifying driver genes within oncogenic pathways. The new method introduces two key innovations. First, it incorporates information on mutation type using a Bayesian hierarchical modeling framework, enabling the distinction between potential differences in functional effects among variants within the same gene, thereby improving the accuracy of driver identification. Second, it models gene-specific driver frequencies with a Dirichlet prior which effectively controls the sparsity of the inferred driver set and aligns with the biological expectation that most tumor types are driven by a small number of genes. We evaluate BayesMAGPIE through extensive simulation studies to assess its estimation bias and accuracy in driver identification, and benchmark its performance against MAGPIE using TCGA data from eight cancer types.

bioinformatics↗

bioRxiv2023

Adaptation of a Mutual Exclusivity Framework to Identify Driver Mutations within Biological Pathways

Distinguishing genomic alterations in cancer genes that have functional impact on tumor growth and disease progression from the ones that are passengers and confer no fitness advantage has important clinical implications. Evidence-based methods for nominating drivers are limited by existing knowledge on the oncogenic effects and therapeutic benefits of specific variants from clinical trials or experimental settings. As clinical sequencing becomes a mainstay of patient care, applying computational methods to mine the rapidly growing clinical genomic data holds promise in uncovering novel functional candidates beyond the existing knowledge-base and expanding the patient population that could potentially benefit from genetically targeted therapies. We propose a statistical and computational method (MAGPIE) that builds on a likelihood approach leveraging the mutual exclusivity pattern within an oncogenic pathway for identifying probabilistically both the specific genes within a pathway and the individual mutations within such genes that are truly the drivers. Alterations in a cancer gene are assumed to be a mixture of driver and passenger mutations with the passenger rates modeled in relationship to tumor mutational burden. A limited memory BFGS algorithm is used to facilitate large scale optimization. We use simulations to study the operating characteristics of the method and assess false positive and false negative rates in driver nomination. When applied to a large study of primary melanomas the method accurately identified the known driver genes within the RTK-RAS pathway and nominated a number of rare variants with previously unknown biological and clinical relevance as prime candidates for functional validation.

cancer biology↗