RESUMEN
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.
Asunto(s)
Protocolos de Quimioterapia Combinada Antineoplásica/uso terapéutico , Neoplasias/tratamiento farmacológico , Biomarcadores de Tumor , Estudios de Casos y Controles , Terapia Combinada , Estudios de Seguimiento , Regulación Neoplásica de la Expresión Génica , Secuenciación de Nucleótidos de Alto Rendimiento , Humanos , Metástasis Linfática , Invasividad Neoplásica , Recurrencia Local de Neoplasia/tratamiento farmacológico , Recurrencia Local de Neoplasia/patología , Neoplasias/patología , Pronóstico , Estudios Retrospectivos , Tasa de SupervivenciaRESUMEN
We present a systematic analysis of the effects of synchronizing a large-scale, deeply characterized, multi-omic dataset to the current human reference genome, using updated software, pipelines, and annotations. For each of 5 molecular data platforms in The Cancer Genome Atlas (TCGA)-mRNA and miRNA expression, single nucleotide variants, DNA methylation and copy number alterations-comprehensive sample, gene, and probe-level studies were performed, towards quantifying the degree of similarity between the 'legacy' GRCh37 (hg19) TCGA data and its GRCh38 (hg38) version as 'harmonized' by the Genomic Data Commons. We offer gene lists to elucidate differences that remained after controlling for confounders, and strategies to mitigate their impact on biological interpretation. Our results demonstrate that the hg19 and hg38 TCGA datasets are very highly concordant, promote informed use of either legacy or harmonized omics data, and provide a rubric that encourages similar comparisons as new data emerge and reference data evolve.