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.
Highlights
Recent progress in precision medicine applied to cancer therapy, i.e., targeted treatment based on the specific molecular features of a patient’s tumor has changed cancer treatment strategies [1, 2]
Next-generation sequencing is a powerful tool for building a tumor-specific profile of genomic alterations, such as single nucleotide variants, copy number variants, and gene translocations
This is generally done by teams of human experts who reference databases of genomic alterations, published literature, regulatory approval data for pharmaceutical agents, and clinical trial protocols to identify patients who may benefit from treatment by molecularly targeted therapies [4, 5]
Summary
Recent progress in precision medicine applied to cancer therapy, i.e., targeted treatment based on the specific molecular features of a patient’s tumor has changed cancer treatment strategies [1, 2]. The challenge lies in identifying which mutations are pathogenic and actionable This is generally done by teams of human experts who reference databases of genomic alterations, published literature, regulatory approval data for pharmaceutical agents, and clinical trial protocols to identify patients who may benefit from treatment by molecularly targeted therapies [4, 5]. This translation of genome sequencing results into potential treatment options is time-consuming, laborious, and requires a high degree of specialization, but the growing list of targeted therapies is making it more and more essential for patient care. This study assesses the validity and utility of the artificial intelligence Watson for Genomics (WfG) for analyzing clinical sequencing results
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