Enhancing Next‐Generation Sequencing‐Guided Cancer Care Through Cognitive Computing. (20th November 2017)

• Record Type: Journal Article
• Authors: Patel, Nirali M.; Michelini, Vanessa V.; Snell, Jeff M.; Balu, Saianand; Hoyle, Alan P.; Parker, Joel S.; Hayward, Michele C.; Eberhard, David A.; Salazar, Ashley H.; McNeillie, Patrick; Xu, Jia; Huettner, Claudia S.; Koyama, Takahiko; Utro, Filippo; Rhrissorrakrai, Kahn; Norel, Raquel; Bilal, Erhan; Royyuru, Ajay; Parida, Laxmi; Earp, H. Shelton; Grilley‐Olson, Juneko E.; Hayes, D. Neil; Harvey, Stephen J.; Sharpless, Norman E.; Kim, William Y.
• Is Part Of: Oncologist. Volume 23:Number 2(2018)
• Page Start: 179
• Page End: 185
• Publication Date: 2017-11-20
• Subjects: Genomics -- High‐throughput nucleotide sequencing -- Artificial intelligence -- Precision medicine; Oncology -- Periodicals; Tumors -- Periodicals; Cancérologie -- Périodiques; Tumeurs -- Périodiques; Oncology; Tumors; Neoplasms; Electronic journals; Periodicals; Periodicals; 616.994
• DOI: 10.1634/theoncologist.2017-0170
• Languages: English
• ISSNs: 1083-7159

Abstract

Abstract : Background: Using next‐generation sequencing (NGS) to guide cancer therapy has created challenges in analyzing and reporting large volumes of genomic data to patients and caregivers. Specifically, providing current, accurate information on newly approved therapies and open clinical trials requires considerable manual curation performed mainly by human "molecular tumor boards" (MTBs). The purpose of this study was to determine the utility of cognitive computing as performed by Watson for Genomics (WfG) compared with a human MTB. Materials and Methods: One thousand eighteen patient cases that previously underwent targeted exon sequencing at the University of North Carolina (UNC) and subsequent analysis by the UNCseq informatics pipeline and the UNC MTB between November 7, 2011, and May 12, 2015, were analyzed with WfG, a cognitive computing technology for genomic analysis. Results: Using a WfG‐curated actionable gene list, we identified additional genomic events of potential significance (not discovered by traditional MTB curation) in 323 (32%) patients. The majority of these additional genomic events were considered actionable based upon their ability to qualify patients for biomarker‐selected clinical trials. Indeed, the opening of a relevant clinical trial within 1 month prior to WfG analysis provided the rationale for identification of a new actionable event in nearly a quarter of the 323 patients. This automated analysis took <3 minutes per case. Conclusion: These results demonstrate that the interpretation and actionability of somatic NGS results are evolving too rapidly to rely solely on human curation. Molecular tumor boards empowered by cognitive computing could potentially improve patient care by providing a rapid, comprehensive approach for data analysis and consideration of up‐to‐date availability of clinical trials. Implications for Practice: The results of this study demonstrate that the interpretation and actionability of somatic next‐generation sequencing results are evolving too rapidly to rely solely on human curation. Molecular tumor boards empowered by cognitive computing can significantly improve patient care by providing a fast, cost‐effective, and comprehensive approach for data analysis in the delivery of precision medicine. Patients and physicians who are considering enrollment in clinical trials may benefit from the support of such tools applied to genomic data. Abstract : Next‐generation sequencing (NGS) has emerged as an affordable and reproducible means to query tumors for somatic genetic anomalies. To help interpret somatic NGS data, many institutions have created a molecular tumor board to analyze the results of NGS and make recommendations. This article evaluates the utility of cognitive computing systems to analyze data for clinical decision‐making.