Cancer Genomics and Diagnostics

The paper assesses confidence and stability in decomposing mutational signatures in breast cancer genomes and identifies stable, unstable, and previously missed signatures.

SiFit infers tumor phylogenies from single-cell sequencing data using a finite-sites model, improving accuracy over infinite-sites methods in colorectal cancer datasets.

The paper introduces a new algorithm for de novo discovery of mutated driver pathways in cancer, showing improved efficiency and scalability over existing methods.

The paper analyzes amino acid mutation patterns across 29 cancers, identifying six mutation signatures, five of which are validated using COSMIC data.

This paper introduces SurvLRT, a statistical method to identify epistatic gene pairs and triplets from cancer genomic and survival data, demonstrating its utility in finding biomarkers for synthetic lethal therapy.

This paper reviews mathematical models and computational techniques for discovering mutational signatures in cancer genomes.

This study identifies technical artifacts in TCGA germline variant calls, showing how batch effects from DNA amplification confound germline-tumor type associations and recommends removing amplified DNA samples.

This study characterizes genomic, transcriptomic, and epigenomic artifacts in FFPE tissues and provides best practices to mitigate their impact on molecular analysis.

This study identifies an extended context-specific mutational signature in melanoma promoter mutations, showing UV-induced vulnerability and subclonal mutation development in affected positions.

This paper describes a method to identify drug resistance mutations in cancer using genome-wide chemical mutagenesis and next-generation sequencing, enabling the discovery of both known and novel resistance mechanisms.

The paper introduces Galaxy tools for analyzing cancer genomics data, demonstrating their use in discovering new lymphoma-related genes from a cohort of 96 diffuse large B-cell lymphomas.