muTarget

muTarget links somatic mutation status to gene expression using paired genomic and transcriptomic data from The Cancer Genome Atlas (TCGA) across 7876 solid tumors from 18 tumor types to facilitate discovery of cancer biomarkers and therapeutic targets.

Key Features:

• Data integration and analysis: Integrates RNA-sequencing and somatic mutation data from TCGA to identify gene expression changes associated with specific gene mutations and to detect mutations that alter expression levels of selected genes.
• Dataset scope: Operates on paired genomic and transcriptomic TCGA data comprising 7876 solid tumors across 18 tumor types.
• Computational environment: Performs data processing and analysis within the R statistical environment.
• RNA-seq processing: Uses DESeq2 for RNA-seq normalization and AnnotationDbi for transcript variant annotation.
• Somatic mutation processing: Calls somatic mutations with MuTect2 and summarizes mutation data with MAFtools (Bioconductor).
• Differential expression testing: Applies the Mann-Whitney U test to assess significant gene expression changes associated with mutation status.
• Validation and use cases: Demonstrated reproducibility with a significant overlap between training and test datasets (chi-square = 16,719.7; P < .00001) and includes breast cancer analyses focusing on TP53 and CDH1 mutations and effects on PGR expression.

Scientific Applications:

• Biomarker and therapeutic target discovery: Correlates somatic mutations with gene expression to identify candidate biomarkers and therapeutic targets across solid tumors.
• Cross-tumor mutation-expression analysis: Enables systematic detection of mutation-associated expression changes across multiple tumor types in TCGA.
• Breast cancer mutation impact studies: Supports investigation of the transcriptional consequences of TP53 and CDH1 mutations and mutations affecting PGR expression in breast cancer.
• Prioritization of mutational targets: Facilitates prioritization of mutations that significantly alter gene expression for downstream experimental or therapeutic follow-up.

Methodology:

Integrates RNA-sequencing and somatic mutation data from TCGA; processes data in R with DESeq2 for RNA-seq normalization and AnnotationDbi for transcript annotation; calls somatic mutations with MuTect2 and summarizes them with MAFtools; and assesses differential expression using the Mann-Whitney U test.