Identification of cis-regulatory mutations generating de novo edges in personalized cancer gene regulatory networks

Area of Science:

• Genomics
• Computational Biology
• Cancer Research

Background:

• Functional non-coding mutations are challenging to identify.
• Understanding their impact on gene regulation is critical for disease insights.

Purpose of the Study:

• Introduce μ-cisTarget, a novel tool for filtering, annotating, and prioritizing cis-regulatory mutations.
• Assess the effect of mutations on personal gene regulatory networks.

Main Methods:

• Utilized μ-cisTarget to analyze known mutations in T-ALL and melanoma.
• Performed whole-genome sequencing on ten cancer cell lines.
• Integrated transcriptome data and motif discovery to identify master regulators and de novo binding sites.

Main Results:

• Successfully validated μ-cisTarget on established cancer mutation datasets.
• Identified master regulators with de novo binding sites driving oncogene upregulation in cancer cell lines.

Conclusions:

• μ-cisTarget provides a robust method for prioritizing functional cis-regulatory mutations.
• The tool aids in uncovering novel cancer driver mechanisms by linking mutations to gene expression changes.