Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Oncologist. 2018 Feb;23(2):179-185. doi: 10.1634/theoncologist.2017-0170. Epub 2017 Nov 20.
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.
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.
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.
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.
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.
使用下一代测序（NGS）来指导癌症治疗给分析和向患者和护理人员报告大量基因组数据带来了挑战。具体来说，提供关于新批准的治疗方法和开放临床试验的最新、准确信息需要大量由人类“分子肿瘤委员会”（MTB）执行的人工策管。本研究的目的是确定认知计算（由 Watson for Genomics [WfG] 执行）与人类 MTB 相比的效用。
分析了 2011 年 11 月 7 日至 2015 年 5 月 12 日期间在北卡罗来纳大学（UNC）进行靶向外显子测序并随后通过 UNCseq 信息学管道和 UNC MTB 进行分析的 1018 例患者病例，这些患者病例使用了认知计算技术基因组分析的 Watson for Genomics（WfG）。
使用 WfG 策管的可操作基因列表，我们在 323 名（32%）患者中发现了潜在意义的额外基因组事件（未通过传统 MTB 策管发现）。这些额外的基因组事件大多数被认为是可操作的，因为它们能够使患者有资格参加生物标志物选择的临床试验。事实上，在 WfG 分析前 1 个月内开放了一个相关的临床试验，为近四分之一的 323 名患者确定新的可操作事件提供了依据。这种自动分析每个病例不到 3 分钟。
这些结果表明，体细胞 NGS 结果的解释和可操作性发展得太快，不能仅仅依靠人工策管。由认知计算支持的分子肿瘤委员会可以通过提供快速、全面的数据分析方法和考虑最新临床试验的可用性，潜在地改善患者的护理。
本研究的结果表明，体细胞下一代测序结果的解释和可操作性发展得太快，不能仅仅依靠人工策管。由认知计算提供动力的分子肿瘤委员会可以通过提供快速、经济高效且全面的数据分析方法，为精准医疗的提供显著改善患者的护理。考虑参加临床试验的患者和医生可能会受益于应用于基因组数据的此类工具的支持。
