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Combined Analysis of SNP Array Data Identifies Novel CNV Candidates and Pathways in Ependymoma and Mesothelioma

Gabriel Wajnberg1, Benilton S Carvalho2, Carlos G Ferreira3

  • 1Bioinformatics Unit, Clinical Research Coordination, National Cancer Institute of Brazil (INCA), 20231-050 Rio de Janeiro, RJ, Brazil ; Graduate Program in Systems and Computational Biology, Oswaldo Cruz Institute, Oswaldo Cruz Foundation (Fiocruz), 21040-360 Rio de Janeiro, RJ, Brazil ; Laboratory of Functional Genomics and Bioinformatics, Oswaldo Cruz Institute, Oswaldo Cruz Foundation (Fiocruz), 21040-360 Rio de Janeiro, RJ, Brazil.

Biomed Research International
|July 18, 2015
PubMed

Insights

This study introduces an innovative open-source strategy to detect copy number variations and cancer-associated pathways. The method identified novel variations linked to chemotherapy resistance and specific disease pathways.

Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • Copy number variation (CNV) is a key genomic alteration in cancer.
  • Existing methods often rely on proprietary software, limiting accessibility.
  • Identifying CNVs and associated pathways is crucial for understanding cancer.

Purpose of the Study:

  • To develop and validate an innovative strategy for identifying novel CNVs and cancer-associated pathways.
  • To utilize open-source software for improved accessibility and reproducibility.
  • To assess the tool's efficacy using mesothelioma and ependymoma datasets.

Main Methods:

  • Combined two open-source bioinformatics packages for CNV detection.
  • Applied the strategy to published mesothelioma and ependymoma genomic datasets.
  • Analyzed identified CNVs for associations with cancer chemotherapy resistance and altered pathways.

Main Results:

  • Successfully detected known and novel CNVs associated with cancer chemotherapy resistance.
  • Identified specific altered pathways, including cell adhesion in mesothelioma and glutamatergic synapse regulation in ependymoma.
  • Demonstrated the utility of the open-source strategy in analyzing complex cancer genomics data.

Conclusions:

  • Presents a novel, accessible strategy for CNV and pathway analysis in cancer using open-source tools.
  • Highlights the potential for discovering novel cancer-related genomic alterations and pathways.
  • Provides a valuable tool for cancer genomics research and drug discovery.

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