From somatic variants towards precision oncology: Evidence-driven reporting of treatment options in molecular tumor

Júlia Perera-Bel1, Barbara Hutter2, Christoph Heining3

  • 1Department of Medical Statistics, University Medical Center Göttingen, 37073, Göttingen, Germany.

Genome Medicine
|March 17, 2018
PubMed
Abstract

Insights

A new method automatically matches patient genomic alterations to cancer treatments using public data. This tool aids clinicians in interpreting genomic variants for precision medicine, supporting tumor board discussions and treatment decisions.

Area of Science:

  • Genomic Medicine
  • Bioinformatics
  • Computational Biology

Background:

  • Next-generation sequencing (NGS) advances cancer understanding but genomic data interpretation remains complex.
  • Identifying clinical significance of genomic variants is challenging due to intricate gene-drug interactions.

Purpose of the Study:

  • To develop an automated method for matching patient-specific genomic alterations to potential cancer treatment options.
  • To support clinicians in assigning clinical significance to genomic variants for improved patient care.

Main Methods:

  • The method utilizes public knowledge of somatic variants and their predictive evidence on drug response.
  • Applied to The Cancer Genome Atlas (TCGA) and Genomics Evidence Neoplasia Information Exchange (GENIE) cohorts.
  • Validated on 11 patients from the NCT MASTER trial with genomic profile information.

Main Results:

  • The reporting strategy identified a significant number of actionable variants in TCGA and GENIE samples.
  • The method successfully reproduced expert treatment suggestions in a retrospective analysis of NCT MASTER trial patients.
  • Established proof of concept for evidence-based reports to aid tumor board discussions.

Conclusions:

  • A standardized method for reporting actionable somatic variants can facilitate NGS integration into clinical practice.
  • Tools summarizing precision medicine evidence are crucial for clinicians.
  • The presented method and code aim to streamline genomic data interpretation for targeted therapies.

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