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Passenger mutations accurately classify human tumors.

Marina Salvadores1, David Mas-Ponte1, Fran Supek1,2

  • 1Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Baldiri Reixac, Barcelona, Spain.

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|April 16, 2019
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Summary
This summary is machine-generated.

Passenger mutations, not driver mutations, accurately identify cancer type and origin. Regional mutation density (RMD) and mutation spectra significantly improve tumor classification, aiding diagnosis from liquid biopsies.

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Area of Science:

  • Genomics
  • Computational Biology
  • Oncology

Background:

  • Accurate cancer typing and subtyping are crucial for clinical management.
  • Identifying the primary tumor site is challenging for metastatic cancers.
  • Genomic classifiers offer a promising approach for tumor origin determination.

Purpose of the Study:

  • To evaluate the efficacy of causal (driver) somatic mutations versus global patterns of passenger mutations in classifying cancer type and origin.
  • To assess the contribution of regional mutation density (RMD) and mutation spectra to tumor classification.
  • To explore the utility of genomic features for discriminating molecular subtypes and anatomical sites.

Main Methods:

  • Comparative analysis of classification accuracy using driver mutations versus passenger mutations.
  • Feature extraction based on regional mutation density (RMD) and trinucleotide mutation spectra.
  • Development of a combined classification model incorporating RMD features.
  • Evaluation of classifier performance under varying conditions, including false negative mutation calls and exome sequencing data.

Main Results:

  • Passenger mutations achieved 92% accuracy in distinguishing 18 cancer types, significantly outperforming driver mutations (36% accuracy).
  • RMD and mutation spectra were key contributors to passenger mutation-based classification.
  • Incorporating RMD features into a combined model improved diagnosed patient fraction by 50 percentage points.
  • RMD demonstrated ability to discriminate molecular subtypes and anatomical sites for major cancers.

Conclusions:

  • Global patterns of passenger mutations, particularly RMD and spectra, are highly effective for cancer site and type classification.
  • Whole genome sequencing is valuable for capturing comprehensive mutational patterns informative of tumor biology.
  • Genomic classification using passenger mutations holds significant potential for improving diagnostic accuracy, especially with liquid biopsies.