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PertInInt: An Integrative, Analytical Approach to Rapidly Uncover Cancer Driver Genes with Perturbed Interactions and

Shilpa Nadimpalli Kobren1, Bernard Chazelle2, Mona Singh3

  • 1Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA; Department of Computer Science, Princeton University, Princeton, NJ, USA; Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA.

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Identifying cancer genes is challenging. This study introduces a new computational framework, PertInInt, to find genes driving cancer by analyzing mutation patterns in functional sites, revealing disrupted interactions as a key event.

Keywords:
bioinformaticscancergenome informaticssoftware

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Area of Science:

  • Genomics
  • Computational Biology
  • Cancer Research

Background:

  • Identifying cancer-driving genes and their mechanisms is a critical challenge in cancer genomics.
  • Somatic mutations in cancer genomes offer clues to gene function, but pinpointing functionally relevant mutations requires sophisticated analysis.

Purpose of the Study:

  • To develop and validate an integrative computational framework for identifying cancer-relevant genes.
  • To uncover the functional sites and interaction mechanisms disrupted by somatic mutations in cancer.

Main Methods:

  • Developed an integrative framework and accompanying software (PertInInt) to identify cancer-relevant genes.
  • Utilized analytical calculations to efficiently assess the significance of mutation enrichment in functional sites, avoiding computationally intensive permutation tests.
  • Integrated data on protein-DNA, RNA, peptide, ion, and small molecule interactions with domain, evolutionary conservation, and gene-level mutation data.

Main Results:

  • Applied PertInInt to 10,037 tumor samples, successfully identifying known and predicting novel cancer genes.
  • Revealed specific types of interactions and functionalities disrupted by somatic mutations.
  • Demonstrated that somatic mutations are frequently enriched in interaction sites and domains, highlighting interaction perturbation as a significant cancer-driving mechanism.

Conclusions:

  • The PertInInt framework provides a computationally efficient and effective method for identifying cancer genes and their disrupted functional mechanisms.
  • Interaction perturbation is a pervasive event in cancer development, driven by somatic mutations at functional sites.