Enhancing Next-Generation Sequencing-Guided Cancer Care Through Cognitive Computing

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.

Keywords: Artificial intelligence; Genomics; High‐throughput nucleotide sequencing; Precision medicine.

Figure 1.

Study outline. Sequencing data from 1,018 cases were run through the UNCseq informatics pipeline to generate lists of variants and copy number alterations. Genomic profiles for each patient were reviewed at the UNCseq MTB and, based on the genomic alteration and its presence on the list of actionable genes previously determined by the UNCseq Clinical Committee for Genomic Research, were deemed actionable. For each patient, WfG was provided information on the type of cancer and the full list of variants and copy number alterations that had been detected by the UNCseq informatics pipeline to derive its list of actionable genomic events. Abbreviations: MTB, molecular tumor board; UNC, University of North Carolina; WfG, Watson for Genomics.

Figure 2.

Mutational and actionable mutation burden by tumor type. (A): Median (dark line), interquartile range (box), and range (whiskers) of protein‐altering mutations by cancer type. (B): Number of actionable mutations (blue bar) as determined by the University of North Carolina Molecular Tumor Board by cancer type. Abbreviation: GI, gastrointestinal.

Figure 3.

Sankey diagram of the flow of the UNCseq molecular tumor board (MTB) and WfG comparison. Of the 1,018 patients previously analyzed by the University of North Carolina (UNC) MTB, 703 were determined to have alterations in genes that met the UNC MTB definition of actionability (A) and 315 did not (B). The WfG analysis suggested that an additional eight genes not previously defined as actionable should be added to the actionable gene list. (C): Mutations in these eight genes were found in 231 and 96 patients out of the 703 and 315 patients with actionable mutations and no actionable mutations, respectively. (D): Of the eight newly identified WfG genes, seven passed the criteria for actionability as determined by the UNC CCGR. Mutations in at least one of these seven genes were found in 323 patients. (E): Re‐examination of these 323 patients revealed that while 47 had potential to change therapy, the majority of patients did not have the potential to change therapy for several reasons (no evidence of disease, n = 145; lost to follow‐up, n = 29; withdrew from study, n = 4; and deceased, n = 98). Abbreviations: CCGR, Clinical Committee for Genomic Research; pts, patients; WfG, Watson for Genomics.

Figure 4.

Additional actionable mutations identified by WfG and not by the University of North Carolina Molecular Tumor Board (MTB), categorized by tumor type. Tumor types are plotted on the y‐axis and number of mutations are plotted on the x‐axis. The solid circles represent the mean number of mutations identified by WfG and not by the MTB for each subtype, and the whiskers show the minimum and maximum. Abbreviations: GI, gastrointestinal; WfG, Watson for Genomics.