BoostDM

BoostDM scores all possible single base substitutions (point mutations) within cancer genes using tumor somatic mutation datasets and in silico saturation mutagenesis to identify candidate driver mutations across 568 cancer genes and 66 tumor types.

Key Features:

• Comprehensive mutation scoring: Scores point mutations by analyzing observed somatic mutations in sequenced tumors and annotating each site with relevant features based on a systematic analysis of tens of thousands of tumor samples.
• In silico saturation mutagenesis: Evaluates all possible single base substitutions within specified cancer genes to achieve exhaustive mutation coverage.
• Integration of public databases: Incorporates additional features from public databases to enrich mutation annotations and scoring.
• Driver mutation identification: Identifies candidate driver mutations and evaluates cancer driver genes estimated at approximately 500–600 in number.
• Mutation probability and selection analysis: Considers tissue-specific mutation probabilities driven by active mutational processes and assesses selection differences between tumor suppressor genes and oncogenes, including scenarios where observed and unobserved driver mutations occur with similar likelihood.

Scientific Applications:

• Precision cancer medicine: Prioritizes candidate driver mutations to inform research in precision oncology across hundreds of cancer genes and tumor types.
• Research on mutation probability versus selection: Supports studies of the interplay between mutation probability and selection during tumor development.
• Predicting detectability of driver mutations: Helps predict which driver mutations are likely to be observed in newly sequenced tumors based on the number of potential driver mutations in a gene.

Methodology:

Performs in silico saturation mutagenesis by systematically evaluating all possible single base substitutions within specified cancer genes and analyzes mutation probabilities and selection pressures across tumor types.