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Updated: Jul 21, 2025
11:02
Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing
Published on: October 18, 2013
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HATCHet2: clone- and haplotype-specific copy number inference from bulk tumor sequencing data
Matthew A Myers1, Brian J Arnold2, Vineet Bansal3
1Department of Computer Science, Princeton University, Princeton, USA.
Biorxiv : the Preprint Server for Biology
|July 28, 2023
Summary
HATCHet2 identifies copy-number aberrations (CNAs) across tumor clones and haplotypes from multiple samples. This method reveals novel mirrored-subclonal CNAs impacting cancer genes, advancing cancer genomics.
Area of Science:
- Genomics
- Cancer Biology
- Bioinformatics
Background:
- Identifying somatic aberrations driving cancer requires analyzing multiple tumor samples.
- Existing methods for inferring copy-number aberrations (CNAs) often analyze samples individually, limiting comprehensive analysis.
- Understanding clonal architecture and haplotype-specific CNAs is crucial for cancer development insights.
Approach:
- Introduced HATCHet2, a novel method for simultaneous identification of haplotype- and clone-specific CNAs from multiple bulk samples.
- Developed a new statistic, mirrored haplotype B-allele frequency (mhBAF), to detect mirrored-subclonal CNAs.
- Enhanced HATCHet2 for improved accuracy in identifying focal CNAs and extending previous capabilities.
Key Points:
- HATCHet2 accurately identifies mirrored-subclonal CNAs with varying parental haplotype copy numbers across tumor clones.
- Demonstrated improved accuracy of HATCHet2 through simulations and single-cell sequencing data validation.
- Applied HATCHet2 to prostate cancer samples, uncovering previously unreported mirrored-subclonal CNAs in cancer genes.
Conclusions:
- HATCHet2 provides a powerful tool for dissecting complex CNA profiles in cancer.
- The method advances the ability to identify subclonal CNA events and their impact on cancer genes.
- Findings from prostate cancer highlight the potential of HATCHet2 in discovering novel cancer-driving aberrations.
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