Faculty Opinions recommendation of GeneMed: An Informatics Hub for the Coordination of Next-Generation Sequencing Studies that Support Precision Oncology Clinical Trials.

2015 ◽  
Author(s):  
John Jeremy Rice ◽  
Raquel Norel
Keyword(s):  
Clinical Trials ◽  
Next Generation Sequencing ◽  
Precision Oncology ◽  
Next Generation ◽  
Sequencing Studies ◽  
Oncology Clinical Trials ◽  
Generation Sequencing

scholarly journals GeneMed: An Informatics Hub for the Coordination of Next-Generation Sequencing Studies that Support Precision Oncology Clinical Trials

2015 ◽  
Vol 14s2 ◽  
pp. CIN.S17282 ◽  
Author(s):  
Yingdong Zhao ◽  
Eric C. Polley ◽  
Ming-Chung Li ◽  
Chih-Jian Lih ◽  
Alida Palmisano ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Next Generation Sequencing ◽  
Treatment Selection ◽  
Control Treatment ◽  
Precision Oncology ◽  
Next Generation ◽  
Multiple Cancer ◽  
Randomized Design ◽  
Sequencing Studies ◽  
Generation Sequencing

We have developed an informatics system, GeneMed, for the National Cancer Institute (NCI) molecular profiling-based assignment of cancer therapy (MPACT) clinical trial (NCT01827384) being conducted in the National Institutes of Health (NIH) Clinical Center. This trial is one of the first to use a randomized design to examine whether assigning treatment based on genomic tumor screening can improve the rate and duration of response in patients with advanced solid tumors. An analytically validated next-generation sequencing (NGS) assay is applied to DNA from patients’ tumors to identify mutations in a panel of genes that are thought likely to affect the utility of targeted therapies available for use in the clinical trial. The patients are randomized to a treatment selected to target a somatic mutation in the tumor or with a control treatment. The GeneMed system streamlines the workflow of the clinical trial and serves as a communications hub among the sequencing lab, the treatment selection team, and clinical personnel. It automates the annotation of the genomic variants identified by sequencing, predicts the functional impact of mutations, identifies the actionable mutations, and facilitates quality control by the molecular characterization lab in the review of variants. The GeneMed system collects baseline information about the patients from the clinic team to determine eligibility for the panel of drugs available. The system performs randomized treatment assignments under the oversight of a supervising treatment selection team and generates a patient report containing detected genomic alterations. NCI is planning to expand the MPACT trial to multiple cancer centers soon. In summary, the GeneMed system has been proven to be an efficient and successful informatics hub for coordinating the reliable application of NGS to precision medicine studies.

No-cost next generation sequencing of advanced cancer patients within the Strata Precision Oncology Network supports clinical trial enrollment.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 3073-3073
Author(s):  
Marc Ryan Matrana ◽  
Scott A. Tomlins ◽  
Kat Kwiatkowski ◽  
Khalis Mitchell ◽  
Jennifer Marie Suga ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Next Generation Sequencing ◽  
Clinical Samples ◽  
Precision Oncology ◽  
Cancer Type ◽  
Next Generation ◽  
Match Trial ◽  
The Impact ◽  
Generation Sequencing

3073 Background: Widespread integration of systematized next generation sequencing (NGS)-based precision oncology is hindered by numerous barriers. Hence, we developed the Strata trial (NCT03061305), a screening protocol to determine the impact of scaled precision oncology. Methods: We implemented no-cost NGS on formalin fixed paraffin embedded (FFPE) clinical samples for all patients with advanced tumors, a common portfolio of partnered therapeutic clinical trials, and robust infrastructure development across the Strata Precision Oncology Network. Results: Across the network of 17 centers, specimens from 8673/9222 (94%) patients were successfully tested in the Strata CLIA/CAP/NCI-MATCH accredited laboratory using comprehensive amplicon-based DNA and RNA NGS. Patients were tested with one of three StrataNGS test versions; the most recent panel assesses all classes of actionable alterations (mutations, copy number alterations, gene fusions, microsatellite instability, tumor mutation burden and PD-L1 expression). Median surface area of received FFPE tumor samples was 25mm2 (interquartile range 9-95mm2), and the median turnaround time from sample receipt to report was 6 business days. 2577 (27.9%) patients had highly actionable alterations, defined as alterations associated with within-cancer type FDA approved or NCCN guideline recommended therapies (1072 patients), NCI-MATCH trial arms (1467 patients), Strata-partnered therapeutic trials (327 patients), or specific alteration-matched FDA approved therapies in patients with cancers of unknown primary (71 patients). Of the 1467 patients matched to an NCI-MATCH trial arm, 15 enrolled. Of the 327 patients matched to one of nine Strata-partnered clinical trials, 77 (24%) were screen failures, while 250 (76%) have either enrolled or are being actively followed for enrollment upon progression. Conclusions: Through streamlined consent methods, electronic medical record queries, and high throughput laboratory testing at no cost to patients, we demonstrate that scaled precision oncology is feasible across a diverse network of healthcare systems when paired with access to relevant clinical trials. Clinical trial information: NCT03061305.

scholarly journals PhredEM: A Phred-Score-Informed Genotype-Calling Approach for Next-Generation Sequencing Studies

2016 ◽  
Author(s):  
Peizhou Liao ◽  
Glen A. Satten ◽  
Yi-juan Hu
Keyword(s):  
Logistic Regression ◽  
Next Generation Sequencing ◽  
Em Algorithm ◽  
Error Rates ◽  
Next Generation Sequencing Data ◽  
Next Generation ◽  
Sequencing Data ◽  
Genotype Calling ◽  
Sequencing Studies ◽  
Generation Sequencing

ABSTRACTA fundamental challenge in analyzing next-generation sequencing data is to determine an individual’s genotype correctly as the accuracy of the inferred genotype is essential to downstream analyses. Some genotype callers, such as GATK and SAMtools, directly calculate the base-calling error rates from phred scores or recalibrated base quality scores. Others, such as SeqEM, estimate error rates from the read data without using any quality scores. It is also a common quality control procedure to filter out reads with low phred scores. However, choosing an appropriate phred score threshold is problematic as a too-high threshold may lose data while a too-low threshold may introduce errors. We propose a new likelihood-based genotype-calling approach that exploits all reads and estimates the per-base error rates by incorporating phred scores through a logistic regression model. The algorithm, which we call PhredEM, uses the Expectation-Maximization (EM) algorithm to obtain consistent estimates of genotype frequencies and logistic regression parameters. We also develop a simple, computationally efficient screening algorithm to identify loci that are estimated to be monomorphic, so that only loci estimated to be non-monomorphic require application of the EM algorithm. We evaluate the performance of PhredEM using both simulated data and real sequencing data from the UK10K project. The results demonstrate that PhredEM is an improved, robust and widely applicable genotype-calling approach for next-generation sequencing studies. The relevant software is freely available.

scholarly journals PATH-03. CLINICAL UTILITY OF NEXT GENERATION SEQUENCING IN IDH-WILDTYPE GLIOBLASTOMA: THE DANA-FARBER CANCER INSTITUTE EXPERIENCE

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii164-ii164
Author(s):  
Mary Jane Lim-Fat ◽  
Gilbert Youssef ◽  
Mehdi Touat ◽  
Bryan Iorgulescu ◽  
Eleanor Woodward ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Next Generation Sequencing ◽  
Immune Checkpoint Blockade ◽  
Next Generation ◽  
Single Nucleotide Variants ◽  
Dana Farber Cancer Institute ◽  
Clinical Records ◽  
Ngs Data ◽  
Generation Sequencing

Abstract BACKGROUND Comprehensive next generation sequencing (NGS) is available through many academic institutions and commercial entities, and is incorporated in practice guidelines for glioblastoma (GBM). We retrospective evaluated the practice patterns and utility of incorporating NGS data into routine care of GBM patients at a clinical trials-focused academic center. METHODS We identified 1,011 consecutive adult patients with histologically confirmed GBM with OncoPanel testing, a targeted exome NGS platform of 447 cancer-associated genes at Dana Farber Cancer Institute (DFCI), from 2013-2019. We selected and retrospectively reviewed clinical records of all IDH-wildtype GBM patients treated at DFCI. RESULTS We identified 557 GBM IDH-wildtype patients, of which 227 were male (40.7%). OncoPanel testing revealed 833 single nucleotide variants and indels in 44 therapeutically relevant genes (Tier 1 or 2 mutations) including PIK3CA (n=51), BRAF (n=9), FGFR1 (n=8), MSH2 (n=4), MSH6 (n=2) and MLH1 (n=1). Copy number analysis revealed 509 alterations in 18 therapeutically relevant genes including EGFR amplification (n= 186), PDGFRA amplification (N=39) and CDKN2A/2B homozygous loss (N=223). Median overall survival was 17.5 months for the whole cohort. Seventy-four therapeutic clinical trials accrued 144 patients in the upfront setting (25.9%) and 203 patients (36.4%) at recurrence. Altogether, NGS data for 107 patients (19.2%) were utilized for clinical trial enrollment or targeted therapy indications. High mutational burden (>17mutations/Mb) was identified in 11/464 samples (2.4%); of whom 3/11 received immune checkpoint blockade. Four patients received compassionate use therapy targeting EGFRvIII (rindopepimut, n=2), CKD4/6 (abemaciclib, n=1) and BRAFV600E (dabrafenib/trametinib, n=1). CONCLUSION While NGS has greatly improved diagnosis and molecular classification, we highlight that NGS remains underutilized in selecting therapy in GBM, even in a setting where clinical trials and off-label therapies are relatively accessible. Continued efforts to develop better targeted therapies and efficient clinical trial design are required to maximize the potential benefits of genomically-stratified data.

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