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A molecular taxonomy of tumors independent of tissue-of-origin

Peter T Nguyen1, Simon G Coetzee1, Daniel L Lakeland2

  • 1The Center for Bioinformatics and Functional Genomics, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA.

Iscience
|October 6, 2021
PubMed

Insights

This study introduces a pathway-centric approach to cancer analysis, revealing distinct molecular phenotypes and improving prognosis prediction by examining somatic mutations across tissues. It highlights convergent mechanisms driving cancer progression.

Area of Science:

  • Oncology
  • Genomics
  • Systems Biology

Background:

  • Cancer is a complex organism-level disease affecting metabolism, microenvironment, and immunity.
  • Distinguishing general mutational processes from tumor-specific origins remains challenging for personalized medicine.

Purpose of the Study:

  • To develop a pathway-centric method for analyzing somatic mutational profiles across tissues.
  • To identify overarching mutational processes independent of tumor cell of origin, burden, or stage.

Main Methods:

  • Extraction of somatic mutational profiles within and between diverse tissues.
  • Clustering analysis to group tumors based on pathway-centric mutational signatures.
  • Correlation analysis with predisposition variants, molecular subtypes, prognosis, and metastatic patterns.

Main Results:

  • Identified distinct tumor clusters based on pathway-centric mutational profiles, largely independent of cell of origin or mutational burden.
  • Demonstrated that prognosis and risk of death vary by cancer type and cluster.
  • Revealed cluster-specific differences in metastatic tumors, suggesting convergent evolution of distinct molecular phenotypes.

Conclusions:

  • A pathway-centric approach offers a novel perspective on cancer, orthogonal to traditional classifications.
  • Convergent mechanisms involving known drivers and rare lesions contribute to distinct cancer phenotypes.
  • Understanding these organism-level and tissue-specific interactions is crucial for advancing individualized cancer medicine.
Keywords:
BioinformaticsCancerGenomics

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