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Emerging landscape of oncogenic signatures across human cancers

Giovanni Ciriello1, Martin L Miller, Bülent Arman Aksoy

  • 1Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, New York, USA.

Nature Genetics
|September 28, 2013
PubMed

Insights

Researchers simplified complex cancer data into selected functional events (SFEs) to classify tumors. This molecular tumor classification reveals patterns for developing personalized cancer therapies and targeted clinical trials.

Area of Science:

  • Oncology
  • Genomics
  • Bioinformatics

Background:

  • Cancer therapy faces challenges due to molecular diversity and varied patient responses.
  • The Cancer Genome Atlas (TCGA) provides detailed molecular tumor data, but interpretation remains difficult.

Purpose of the Study:

  • To simplify complex genomic and epigenetic data from TCGA into a manageable set of selected functional events (SFEs).
  • To develop a hierarchical classification of tumors based on SFEs to identify distinct oncogenic processes and patterns.
  • To guide the development of personalized combination therapies and clinical trial designs.

Main Methods:

  • Distilled thousands of genetic and epigenetic features into approximately 500 selected functional events (SFEs).
  • Applied hierarchical classification to 3,299 TCGA tumors across 12 cancer types using SFEs.
  • Analyzed patterns of SFEs to define oncogenic signature classes across different tumor types.

Main Results:

  • Developed a simplified tumor description using SFEs, enabling hierarchical classification.
  • Identified distinct tumor classes based on mutations (M class) versus copy number changes (C class), particularly evident with genomic instability.
  • Discovered cross-tissue tumor groupings (oncogenic signature classes) based on functional event patterns.
  • Highlighted targetable functional events within tumor classes, suggesting potential for combination therapies.

Conclusions:

  • The SFE-based classification provides a framework for understanding tumor heterogeneity.
  • Identified oncogenic signature classes that can inform the development of personalized, targeted cancer therapies.
  • This approach aids in designing clinical trials to match specific tumor profiles with actionable therapeutic strategies.

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