Inferring causal genomic alterations in breast cancer using gene expression data

Area of Science:

• Genomics
• Cancer Research
• Bioinformatics

Background:

• Identifying causal genomic alterations like somatic copy number variation (CNV) is crucial in cancer research.
• Many studies lack genomic data for CNV detection, limiting comprehensive analysis.
• Inferring CNVs from gene expression data can maximize the utility of existing studies.

Purpose of the Study:

• To develop a framework for inferring CNVs from gene expression data.
• To identify recurrent CNV regions and distinguish cancer driver genes.
• To leverage gene expression data for cancer driver gene discovery.

Main Methods:

• Developed a framework combining wavelet analysis of copy number alteration based on expression.
• Integrated gene regulatory network analysis to prioritize cancer driver genes.
• Validated novel cancer susceptibility genes using siRNA experiments.

Main Results:

• Identified 109 recurrent amplified/deleted CNV regions across multiple datasets.
• Discovered numerous genes within these regions involved in tumorigenesis and cancer progression.
• Uncovered known oncogenes and novel cancer susceptibility genes, validated experimentally.

Conclusions:

• This study presents the first systematic effort to identify and validate drivers for expression-based CNV regions in breast cancer.
• The integrative framework provides a blueprint for leveraging genomic data to identify key regulatory components and gene targets.
• The approach is applicable to large-scale gene expression studies and novel cancer data types like RNA-Seq and CNV data.