Format

Send to

Choose Destination
Front Med (Lausanne). 2018 Nov 9;5:305. doi: 10.3389/fmed.2018.00305. eCollection 2018.

Evaluating Clinical Genome Sequence Analysis by Watson for Genomics.

Author information

1
Department of Experimental Therapeutics, National Cancer Center Hospital, Tokyo, Japan.
2
Department of Hepatobiliary and Pancreatic Oncology, National Cancer Center Hospital, Tokyo, Japan.
3
Division of Translational Genomics, National Cancer Center-Exploratory Oncology Research and Clinical Trial Center, Tokyo, Japan.
4
Department of Bioinformatics, National Cancer Center Research Institute, Tokyo, Japan.
5
Department of Clinical Genomics, National Cancer Center Research Institute, Tokyo, Japan.
6
IBM T. J. Watson Research Center, Yorktown Heights, NY, United States.
7
Tokyo Software & Systems Development Laboratory, IBM Japan, Tokyo, Japan.
8
IBM Watson Health, Cambridge, MA, United States.
9
Division of Genome Biology, National Cancer Center Research Institute, Tokyo, Japan.

Abstract

Background: Oncologists increasingly rely on clinical genome sequencing to pursue effective, molecularly targeted therapies. This study assesses the validity and utility of the artificial intelligence Watson for Genomics (WfG) for analyzing clinical sequencing results. Methods: This study identified patients with solid tumors who participated in in-house genome sequencing projects at a single cancer specialty hospital between April 2013 and October 2016. Targeted genome sequencing results of these patients' tumors, previously analyzed by multidisciplinary specialists at the hospital, were reanalyzed by WfG. This study measures the concordance between the two evaluations. Results: In 198 patients, in-house genome sequencing detected 785 gene mutations, 40 amplifications, and 22 fusions after eliminating single nucleotide polymorphisms. Breast cancer (n = 40) was the most frequent diagnosis in this analysis, followed by gastric cancer (n = 31), and lung cancer (n = 30). Frequently detected single nucleotide variants were found in TP53 (n = 107), BRCA2 (n = 24), and NOTCH2 (n = 23). MYC (n = 10) was the most frequently detected gene amplification, followed by ERBB2 (n = 9) and CCND1 (n = 6). Concordant pathogenic classifications (i.e., pathogenic, benign, or variant of unknown significance) between in-house specialists and WfG included 705 mutations (89.8%; 95% CI, 87.5%-91.8%), 39 amplifications (97.5%; 95% CI, 86.8-99.9%), and 17 fusions (77.3%; 95% CI, 54.6-92.2%). After about 12 months, reanalysis using a more recent version of WfG demonstrated a better concordance rate of 94.5% (95% CI, 92.7-96.0%) for gene mutations. Across the 249 gene alterations determined to be pathogenic by both methods, including mutations, amplifications, and fusions, WfG covered 84.6% (88 of 104) of all targeted therapies that experts proposed and offered an additional 225 therapeutic options. Conclusions: WfG was able to scour large volumes of data from scientific studies and databases to analyze in-house clinical genome sequencing results and demonstrated the potential for application to clinical practice; however, we must train WfG in clinical trial settings.

KEYWORDS:

artificial intelligence; clinical genome sequencing; genome sequencing interpretation; precision medicine; watson for genomics

Supplemental Content

Full text links

Icon for Frontiers Media SA Icon for PubMed Central
Loading ...
Support Center