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Updated: Aug 22, 2026

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing
11:02

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

Published on: October 19, 2013

Systematic analysis of somatic mutations in phosphorylation signaling predicts novel cancer drivers

Jüri Reimand1, Gary D Bader

  • 1The Donnelly Centre, University of Toronto, Toronto, Canada. Juri.Reimand@utoronto.ca

Molecular Systems Biology
|January 24, 2013
PubMed

Insights

Analyzing 800 cancer genomes, this study identifies phosphorylation-associated single-nucleotide variants (pSNVs) crucial for cancer development. These findings reveal novel therapeutic targets and biomarkers for improved patient survival.

Area of Science:

  • Genomics
  • Cancer Biology
  • Molecular Oncology

Background:

  • Distinguishing cancer driver mutations from passenger mutations is critical for targeted therapy.
  • Phosphorylation machinery plays a key role in cancer development and represents a potential drug target.

Purpose of the Study:

  • To identify single-nucleotide variants (SNVs) targeting cancer phosphorylation machinery.
  • To discover novel cancer driver genes, pathways, and therapeutic targets through analysis of phosphorylation-associated SNVs (pSNVs).

Main Methods:

  • Analysis of 800 cancer genomes across eight cancer types.
  • Application of novel algorithms to detect significant pSNVs, phospho-mutated pathways, and kinase networks.
  • Correlation of identified mutations with clinical data and patient survival.

Main Results:

  • Identification of TP53 pSNVs associated with increased patient survival.
  • Discovery of a novel pSNV in EGFR and an immune-related pSNV network linked to prolonged ovarian cancer survival.
  • Pinpointing actionable cancer gene candidates, protein complexes, and kinases, including FLNB, GRM1, POU2F1, HCF1, ASF1, and PRKCZ.

Conclusions:

  • Novel methods for interpreting cancer genomes by focusing on phosphorylation machinery.
  • Identification of new leads for cancer research, including potential therapeutic targets and biomarkers.
  • Demonstration of the clinical relevance of pSNVs in predicting patient outcomes.

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