UNCeqR

UNCeqR integrates patient-matched DNA whole exome sequencing (DNA-WES) and RNA sequencing (RNA-seq) to improve somatic mutation detection sensitivity and characterization, particularly in low purity tumors.

Key Features:

• Integration of DNA and RNA Data: Combines patient-matched RNA-seq and DNA-WES to leverage expressed exome coverage, with RNA-seq depth proportional to gene expression.
• Superior Mutation Detection: Simulation studies demonstrate the integrated model outperforms DNA-WES-only approaches, with validation using patient-matched whole genome sequencing.
• Enhanced Sensitivity for Low Purity Tumors: Uses RNA-seq to complement DNA-WES and increase mutation detection sensitivity in low purity tumor samples.
• Increased Signal from Expressed Mutations: Exploits stronger mutation signal in RNA for expressed mutations relative to DNA to enhance detection of somatic alterations.
• Revealing Tumor Genomics Properties: Enables genome-wide mutational analysis that uncovered genomic properties in cancer cohorts including breast and lung cancers (n = 871).
• Higher Mutation Rates in Key Genes: Increases detected mutation rates in driver and therapy-targeted genes such as PIK3CA, ERBB2, and FGFR2 compared to previous studies on the same cohort.

Scientific Applications:

• Cancer genome characterization: Improves somatic mutation detection to support comprehensive cancer genomic analyses.
• Patient treatment prioritization: Provides mutation calls that can guide prioritization of patient treatment based on therapeutically relevant alterations.
• Genomic profiling of low purity tumors: Enhances accuracy of genomic profiling in samples with low tumor purity by integrating RNA-seq evidence.
• Oncological research and clinical applications: Enhances identification of mutations in therapeutically relevant genes for research and clinical use.

Methodology:

Integration of patient-matched DNA-WES and RNA-seq; assessment via simulation studies; validation with patient-matched whole genome sequencing; genome-wide mutational analysis.