STAC: A method for testing the significance of DNA copy number aberrations across multiple array-CGH experiments

Area of Science:

• Genomics
• Cancer Biology
• Bioinformatics

Background:

• Copy number aberrations (CNAs) are crucial in cancer development and progression.
• High-resolution detection of CNAs is possible with microarray techniques.
• Robust statistical methods are essential for analyzing CNA data across multiple samples.

Purpose of the Study:

• To develop a robust statistical method for identifying nonrandom genomic gains and losses in cancer.
• To provide a tool for unbiased prioritization of genomic regions for further investigation.

Main Methods:

• Development of Significance Testing for Aberrant Copy number (STAC) method.
• STAC combines two complementary statistics with a novel search strategy.
• Utilizes a permutation approach with multiple testing correction to assign P-values.

Main Results:

• STAC successfully identifies clinically and biologically significant genomic alterations.
• The method provides statistical support for 85% of previously reported regions in validation datasets.
• Identifies numerous novel regions of significant gain/loss warranting further investigation.

Conclusions:

• STAC is a powerful tool for identifying nonrandom genomic amplifications and deletions across multiple experiments.
• The P-values generated by STAC enable unbiased prioritization of regions for follow-up studies.
• A Java version of STAC is publicly available for download.