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Related Experiment Video

Updated: Apr 30, 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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Using common variants to indicate cancer genes.

Lucy F Stead1, Helene Thygesen, David R Westhead

  • 1Leeds Institute of Cancer and Pathology, University of Leeds, St James's University Hospital, Leeds, United Kingdom.

International Journal of Cancer
|May 7, 2014
PubMed
Summary

This study introduces PRISMAD, a novel method using common DNA variation to identify cancer-driving somatic mutations (SMs), regardless of coding changes. The approach successfully ranked known cancer genes, showing promise for understanding carcinogenesis.

Keywords:
cancer driver genesnext-generation sequencingsomatic mutation

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Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing
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Area of Science:

  • Genomics
  • Cancer Biology
  • Bioinformatics

Background:

  • Next-generation sequencing rapidly expands the catalogue of tumour-specific somatic mutations (SMs).
  • Identifying driver mutations is crucial for understanding carcinogenesis and developing targeted therapies.
  • Current methods primarily focus on nonsynonymous mutations, potentially overlooking noncoding driver mutations.

Purpose of the Study:

  • To test the hypothesis that common human DNA variation rates can serve as a baseline for scoring SM rates.
  • To develop a method applicable to both coding and noncoding mutations.
  • To identify potential driver mutations irrespective of their effect on protein sequence.

Main Methods:

  • Utilized common DNA variation rates as a baseline to score SM rates.
  • Applied the method to a dataset of 159,498 SMs.
  • Ranked genes based on the calculated SM rates.
  • Developed and released the PRISMAD (polymorphism rates indicate somatic mutations as drivers) tool.

Main Results:

  • The approach demonstrated merit by significantly enriching known cancer genes in the rankings.
  • Preliminary testing indicated the method's potential for identifying driver mutations.
  • The method is adaptable for specific cancer subtypes with increasing data availability.

Conclusions:

  • Common DNA variation rates offer a viable baseline for identifying cancer-driving SMs, including noncoding ones.
  • The PRISMAD approach provides a new tool for cancer gene discovery and understanding carcinogenesis.
  • Further refinement and application to diverse cancer datasets are warranted.