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Beyond Synthetic Lethality: Charting the Landscape of Pairwise Gene Expression States Associated with Survival in

Assaf Magen1, Avinash Das Sahu2, Joo Sang Lee3

  • 1Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, USA; Cancer Data Science Laboratory, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA; Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.

Cell Reports
|July 25, 2019
PubMed

Insights

This study introduces survival-associated pairwise gene expression states (SPAGEs), a broader concept than synthetic lethality, to understand gene interactions in cancer. SPAGE-finder identified thousands of SPAGEs, revealing new insights into cancer drivers, drug responses, and tumor subtypes.

Area of Science:

  • Genomics
  • Systems Biology
  • Cancer Research

Background:

  • Phenotypic effects arise from complex gene networks, not isolated genes.
  • Current cancer research often focuses narrowly on synthetic lethality (SL) gene interactions.
  • A broader understanding of gene pair interactions is needed for cancer insights.

Purpose of the Study:

  • To define and identify "survival-associated pairwise gene expression states" (SPAGEs) as a general concept.
  • To develop and apply a data-driven method (SPAGE-finder) for detecting SPAGEs.
  • To explore the implications of SPAGEs in cancer driver gene specificity, drug response, and tumor stratification.

Main Methods:

  • Defined SPAGEs as gene pairs with joint expression levels linked to patient survival.
  • Developed the SPAGE-finder algorithm for data-driven identification of SPAGEs.
  • Applied SPAGE-finder to The Cancer Genome Atlas (TCGA) data.

Main Results:

  • Identified 71,946 SPAGEs across 12 types from TCGA data.
  • Found that most SPAGEs are not synthetic lethal pairs.
  • Demonstrated that SPAGEs explain tissue-specific cancer drivers and drug response variations.
  • Successfully stratified breast cancer tumors into refined subtypes using SPAGEs.

Conclusions:

  • SPAGEs represent a more comprehensive framework for understanding gene interactions in cancer.
  • The SPAGE-finder approach effectively identifies novel survival-associated gene pairs.
  • SPAGEs have significant potential for explaining cancer biology and guiding translational applications.
Keywords:
cancer stratificationdata miningdrug responsegene interactionspatient survival analysissynthetic lethality

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