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Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing
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Genome wide identification of recessive cancer genes by combinatorial mutation analysis

Stefano Volinia1, Nicoletta Mascellani, Jlenia Marchesini

  • 1Data Mining for Analysis of Microarrays, Università degli Studi, Ferrara, Italy.

Plos One
|October 11, 2008
PubMed

Insights

Researchers identified 154 new recessive cancer genes using a novel method that analyzes genetic alterations. This discovery enhances our understanding of cancer genetics and potential therapeutic targets.

Area of Science:

  • Genomics
  • Cancer Biology
  • Molecular Genetics

Background:

  • Recessive cancer genes, unlike dominant ones, require mutations in both gene copies to promote cancer development.
  • Identifying recessive cancer genes is challenging due to the complex interplay of various genetic alterations leading to loss of function.
  • Understanding recessive cancer gene function is crucial for comprehensive cancer gene discovery and targeted therapies.

Purpose of the Study:

  • To develop and apply a novel computational strategy for identifying human cancer genes that act in a recessive manner.
  • To systematically screen a large number of genes for recessive cancer-associated mutations.
  • To validate the identified genes by comparing their functions with known cancer gene pathways.

Main Methods:

  • A novel procedure was developed to identify recessive human cancer genes by integrating different types of genetic alterations (amino-acid substitutions, frame-shifts, gene deletions) contributing to loss of function.
  • Analysis encompassed over 20,000 genes across 3 Gigabases of coding sequences and 700 array comparative genomic hybridizations.
  • Recessive genes were scored based on nucleotide mismatches under positive selective pressure, frame-shifts, and genomic deletions in cancer, using four combined statistical tests to generate a cancer recessive p-value for each gene.

Main Results:

  • 154 candidate recessive cancer genes were identified with a p-value < 1.5 x 10(-7) (False Discovery Rate = 0.39).
  • Prototypical recessive cancer genes TP53, PTEN, and CDKN2A were ranked within the top 0.5% of identified genes, validating the method's efficacy.
  • The identified genes' functions significantly overlapped with known cancer gene functions, notably excluding tyrosine kinases, consistent with a recessive gene set.

Conclusions:

  • The novel procedure effectively identifies candidate recessive cancer genes by integrating diverse genetic alteration data.
  • The findings expand the landscape of known cancer genes, offering new avenues for research into recessive cancer mechanisms.
  • The identified gene set provides a valuable resource for understanding the genetic basis of cancer and developing targeted therapeutic strategies.

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