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Related Concept Videos

Comparing Copy Number Variations and SNPs02:26

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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
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Related Experiment Video

Updated: Feb 28, 2026

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies
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Consensus Copy-Number Alteration Signatures from Clinical Panels Enable Pan-Cancer Risk Stratification and Therapy

Adar Yaacov1,2

  • 1Helmsley Cancer Center, Shaare Zedek Medical Center, Jerusalem 9103102, Israel.

International Journal of Molecular Sciences
|February 27, 2026
PubMed
Summary

This study introduces a new framework to analyze copy-number alterations (CNAs) from cancer gene panels. The framework extracts CNA signatures, offering valuable prognostic and therapeutic insights for precision oncology.

Keywords:
biomarkerscancer genomicscopy-number alteration signaturesdeconvolution algorithmsprecision oncology

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Area of Science:

  • Oncology
  • Genomics
  • Bioinformatics

Background:

  • Somatic copy-number alterations (CNAs) are common in cancer but difficult to analyze from targeted sequencing panels.
  • Existing methods provide limited data for comprehensive CNA signature analysis.

Purpose of the Study:

  • To develop a consensus framework integrating multiple algorithms for extracting CNA signatures from targeted panel data.
  • To identify and characterize reproducible CNA signatures across a large cancer cohort.

Main Methods:

  • Developed a consensus framework combining four deconvolution algorithms.
  • Applied the framework to analyze CNA data from 24,870 tumors sequenced with MSK-IMPACT.
  • Validated the identified signatures through internal cross-validation and external cohorts (sarcoma, hepatocellular carcinoma).

Main Results:

  • Identified five reproducible CNA signatures (CON1-CON5), distinguishing near-diploid from aneuploid patterns.
  • Signatures showed significant associations with overall survival across tumor types (FDR < 0.01).
  • Signatures provided prognostic information beyond Fraction of Genome Altered and correlated with driver mutations and treatment resistance.

Conclusions:

  • The framework effectively extracts biologically interpretable CNA signatures from routine panel data.
  • These signatures enhance prognostic capabilities and can guide therapeutic decisions in precision oncology.
  • Enables risk stratification and personalized treatment strategies by converting sparse panel data into actionable features.