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A comparative study of curated contents by knowledge-based curation system in cancer clinical sequencing

Kazuko Sakai1, Masayuki Takeda2, Shigeki Shimizu3

  • 1Department of Genome Biology, Kindai University Faculty of Medicine, Osaka-Sayama, Osaka, 589-8511, Japan.

Scientific Reports
|August 7, 2019
PubMed

Insights

This study compared three knowledge-based curation systems for personalized cancer medicine. While all systems aided treatment decisions, Watson for Genomics offered more clinical trial information, highlighting the need for standardized data and databases.

Area of Science:

  • Oncology
  • Genomics
  • Bioinformatics

Background:

  • Personalizing cancer medicine requires integrating scientific evidence, drug approvals, and treatment guidelines.
  • Next-generation sequencing (NGS) generates complex data that necessitates sophisticated interpretation tools.
  • Knowledge-based curation systems aim to assist medical oncologists in making evidence-based treatment decisions.

Purpose of the Study:

  • To evaluate and compare the evidence levels and clinical trial information provided by three knowledge-based curation systems: Watson for Genomics (WfG), QIAGEN Clinical Insight Interpret (QCII), and Oncomine knowledge-based reporter (OKR).
  • To assess the concordance of variant curation and evidence levels across these systems for solid cancer patients.

Main Methods:

  • Sequencing of 31 formalin-fixed paraffin-embedded solid tumor samples using the Oncomine Comprehensive Assay v3.
  • Identification of variants including nonsynonymous mutations, gene fusions, and copy number alterations using Ion reporter software.
  • Independent curation of identified variants using WfG, QCII, and OKR systems, followed by analysis of concordance and evidence levels.

Main Results:

  • A total of 58 nonsynonymous mutations, 2 gene fusions, and 12 copy number variants were detected and curated across the three systems.
  • The number of curated variants varied, with 51 out of 72 variants receiving curation in at least one system.
  • Evidence level concordance was moderate, ranging from 56.9% to 66.7% between system pairs. WfG provided a higher number of clinical trial matches.
  • Significant differences were observed in clinical trial matching, potentially due to variations in filtering processes and database content.

Conclusions:

  • Knowledge-based curation systems (WfG, OKR, QCII) can be valuable tools for supporting solid cancer treatment decisions.
  • Discrepancies in evidence levels, particularly concerning clinical trial information, were noted between the systems.
  • Standardizing filtering procedures and enhancing Japanese clinical trial databases are recommended for improved accuracy and utility.

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