Abstract
Objective: To analyze a glioblastoma tumor specimen with 3 different platforms and compare potentially actionable calls from each.
Methods: Tumor DNA was analyzed by a commercial targeted panel. In addition, tumor-normal DNA was analyzed by whole-genome sequencing (WGS) and tumor RNA was analyzed by RNA sequencing (RNA-seq). The WGS and RNA-seq data were analyzed by a team of bioinformaticians and cancer oncologists, and separately by IBM Watson Genomic Analytics (WGA), an automated system for prioritizing somatic variants and identifying drugs.
Results: More variants were identified by WGS/RNA analysis than by targeted panels. WGA completed a comparable analysis in a fraction of the time required by the human analysts.
Conclusions: The development of an effective human-machine interface in the analysis of deep cancer genomic datasets may provide potentially clinically actionable calls for individual patients in a more timely and efficient manner than currently possible.
ClinicalTrials.gov identifier: NCT02725684.
GLOSSARY
- CNV=
- copy number variant;
- EGFR=
- epidermal growth factor receptor;
- GATK=
- Genome Analysis Toolkit;
- GBM=
- glioblastoma;
- IRB=
- institutional review board;
- NLP=
- Natural Language Processing;
- NYGC=
- New York Genome Center;
- RNA-seq=
- RNA sequencing;
- SNV=
- single nucleotide variant;
- SV=
- structural variant;
- TCGA=
- The Cancer Genome Atlas;
- TPM=
- transcripts per million;
- VCF=
- variant call file;
- VUS=
- variants of uncertain significance;
- WGA=
- Watson Genomic Analytics;
- WGS=
- whole-genome sequencing
Footnotes
↵* These authors contributed equally to the manuscript.
Funding information and disclosures are provided at the end of the article. Go to Neurology.org/ng for full disclosure forms. The Article Processing Charge was funded by the authors.
Supplemental data at Neurology.org/ng
- Received December 12, 2016.
- Accepted in final form April 19, 2017.
- Copyright © 2017 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology.
This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND), which permits downloading and sharing the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.
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