Identification of targetable BRAF ΔN486_P490 variant by whole-genome sequencing leading to dabrafenib-induced remission of a BRAF-mutant pancreatic adenocarcinoma.

The tumor genome of a patient with advanced pancreatic cancer was sequenced to identify potential therapeutic targetable mutations after standard of care failed to produce any significant overall response. Matched tumor-normal whole-genome sequencing revealed somatic mutations in BRAF, TP53, CDKN2A, and a focal deletion of SMAD4 The BRAF variant was an in-frame deletion mutation (ΔN486_P490), which had been previously demonstrated to be a kinase-activating alteration in the BRAF kinase domain. Working with the Novartis patient assistance program allowed us to treat the patient with the BRAF inhibitor, dabrafenib. The patient's overall clinical condition improved dramatically with dabrafenib. Levels of serum tumor marker dropped immediately after treatment, and a subsequent CT scan revealed a significant decrease in the size of both primary and metastatic lesions. The dabrafenib-induced remission lasted for 6 mo. Preclinical studies published concurrently with the patient's treatment showed that the BRAF in-frame mutation (ΔNVTAP) induces oncogenic activation by a mechanism distinct from that induced by V600E, and that this difference dictates the responsiveness to different BRAF inhibitors. This study describes a dramatic instance of how high-level genomic technology and analysis was necessary and sufficient to identify a clinically logical treatment option that was then utilized and shown to be of clinical value for this individual.

Correction to: Sequencing and curation strategies for identifying candidate glioblastoma treatments.

Following publication of the original article [1], it was reported that the given name of the fourteenth author was incorrectly published. The incorrect and the correct names are given below.

Sequencing and curation strategies for identifying candidate glioblastoma treatments.

BACKGROUND: Prompted by the revolution in high-throughput sequencing and its potential impact for treating cancer patients, we initiated a clinical research study to compare the ability of different sequencing assays and analysis methods to analyze glioblastoma tumors and generate real-time potential treatment options for physicians. METHODS: A consortium of seven institutions in New York City enrolled 30 patients with glioblastoma and performed tumor whole genome sequencing (WGS) and RNA sequencing (RNA-seq; collectively WGS/RNA-seq); 20 of these patients were also analyzed with independent targeted panel sequencing. We also compared results of expert manual annotations with those from an automated annotation system, Watson Genomic Analysis (WGA), to assess the reliability and time required to identify potentially relevant pharmacologic interventions. RESULTS: WGS/RNAseq identified more potentially actionable clinical results than targeted panels in 90% of cases, with an average of 16-fold more unique potentially actionable variants identified per individual; 84 clinically actionable calls were made using WGS/RNA-seq that were not identified by panels. Expert annotation and WGA had good agreement on identifying variants [mean sensitivity = 0.71, SD = 0.18 and positive predictive value (PPV) = 0.80, SD = 0.20] and drug targets when the same variants were called (mean sensitivity = 0.74, SD = 0.34 and PPV = 0.79, SD = 0.23) across patients. Clinicians used the information to modify their treatment plan 10% of the time. CONCLUSION: These results present the first comprehensive comparison of technical and machine augmented analysis of targeted panel and WGS/RNA-seq to identify potential cancer treatments.

Evaluating Clinical Genome Sequence Analysis by Watson for Genomics.

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.

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

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.

Comparing sequencing assays and human-machine analyses in actionable genomics for glioblastoma.

OBJECTIVE: To analyze a glioblastoma tumor specimen with 3 different platforms and compare potentially actionable calls from each. METHODS: Tumor DNA was analyzed by a commercial targeted panel. In addition, tumor-normal DNA was analyzed by whole-genome sequencing (WGS) and tumor RNA was analyzed by RNA sequencing (RNA-seq). The WGS and RNA-seq data were analyzed by a team of bioinformaticians and cancer oncologists, and separately by IBM Watson Genomic Analytics (WGA), an automated system for prioritizing somatic variants and identifying drugs. RESULTS: More variants were identified by WGS/RNA analysis than by targeted panels. WGA completed a comparable analysis in a fraction of the time required by the human analysts. CONCLUSIONS: The development of an effective human-machine interface in the analysis of deep cancer genomic datasets may provide potentially clinically actionable calls for individual patients in a more timely and efficient manner than currently possible. CLINICALTRIALSGOV IDENTIFIER: NCT02725684.