MutationExplorer

MutationExplorer analyzes somatic mutations from RNA sequencing to characterize the mutational landscape of primary breast cancer.

Key Features:

• Optimized detection pipeline: Uses a refined RNA-seq–based pipeline optimized to detect single nucleotide variants and small insertions and deletions.
• Large SCAN-B cohort profiling: Profiles 3,217 breast tumors from the Sweden Cancerome Analysis Network–Breast (SCAN-B) cohort.
• Gene and pathway profiling: Identifies mutations in genes including PIK3CA, TP53, and ERBB2 and assesses molecular pathways and tumor mutational burden.
• Druggable gene identification: Identifies potentially druggable genes in 86.8% of tumors.
• Clinical biomarker integration: Incorporates hardcoded biomarkers for ER (estrogen receptor) and PgR (progesterone receptor) clinical status.
• Rapid biomarker interrogation: Enables interrogation of gene expression–based and mutation-based biomarkers within one week of tumor sampling.

Scientific Applications:

• Mutational landscape analysis: Supports analysis of somatic mutation patterns across breast cancer tumors and molecular subtypes.
• Biomarker and subtype association studies: Facilitates studies linking mutations and gene expression–based biomarkers to molecular subtypes and ER/PgR status.
• Therapeutic target identification: Enables identification of potentially druggable genes to inform targeted therapy research.
• Prognostic outcome studies: Supports analyses of how genomic alterations relate to therapy response and overall survival.

Methodology:

A refined RNA-seq–based detection pipeline calls single nucleotide variants and small indels, profiles mutations in genes and pathways and tumor mutational burden across a 3,217-tumor SCAN-B cohort, identifies potentially druggable genes, integrates ER and PgR clinical status, and enables interrogation of gene expression– and mutation-based biomarkers within one week of sampling.