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Genomic medicine holds great promise for improving health care, but integrating searchable and actionable genetic data into electronic health records (EHRs) remains a challenge. Here we describe Neptune, a system for managing the interaction between a clinical laboratory and an EHR system during the clinical reporting process. We developed Neptune and applied it to two clinical sequencing projects that required report customization, variant reanalysis, and EHR integration. Neptune has been applied for the generation and delivery of over 15,000 clinical genomic reports. This work spans two clinical tests based on targeted gene panels that contain 68 and 153 genes respectively. These projects demanded customizable clinical reports that contained a variety of genetic data types including single-nucleotide variants (SNVs), copy-number variants (CNVs), pharmacogenomics, and polygenic risk scores. Two variant reanalysis activities were also supported, highlighting this important workflow. Methods are needed for delivering structured genetic data to EHRs. This need extends beyond developing data formats to providing infrastructure that manages the reporting process itself. Neptune was successfully applied on two high-throughput clinical sequencing projects to build and deliver clinical reports to EHR systems. The software is open source and available at https://gitlab.com/bcm-hgsc/neptune .

The advancement of precision medicine requires new methods to coordinate and deliver genetic data from heterogeneous sources to physicians and patients. The eMERGE III Network enrolled >25,000 participants from biobank and prospective cohorts of predominantly healthy individuals for clinical genetic testing to determine clinically actionable findings. The network developed protocols linking together the 11 participant collection sites and 2 clinical genetic testing laboratories. DNA capture panels targeting 109 genes were used for testing of DNA and sample collection, data generation, interpretation, reporting, delivery, and storage were each harmonized. A compliant and secure network enabled ongoing review and reconciliation of clinical interpretations, while maintaining communication and data sharing between clinicians and investigators. A total of 202 individuals had positive diagnostic findings relevant to the indication for testing and 1,294 had additional/secondary findings of medical significance deemed to be returnable, establishing data return rates for other testing endeavors. This study accomplished integration of structured genomic results into multiple electronic health record (EHR) systems, setting the stage for clinical decision support to enable genomic medicine. Further, the established processes enable different sequencing sites to harmonize technical and interpretive aspects of sequencing tests, a critical achievement toward global standardization of genomic testing. The eMERGE protocols and tools are available for widespread dissemination.

Comparison of sequencing data from a tumor sample with data from a matched germline control is a key step for accurate detection of somatic mutations. Detection sensitivity for somatic variants is greatly reduced when the matched normal sample is contaminated with tumor cells. To overcome this limitation, we developed deTiN, a method that estimates the tumor-in-normal (TiN) contamination level and, in cases affected by contamination, improves sensitivity by reclassifying initially discarded variants as somatic.

With advances in high-throughput sequencing technologies and analytical tools, genomic analysis of tumors has led to the identification of various important somatic mutations that shed light on diagnosis, prevention, and treatment for cancer. However, detecting somatic variants is not a trivial task in terms of the technical aspects (e.g. filtering germline events and removing a variety of noises in tumor samples) and computational resources to handle large-scale cohort analysis. There is also a demand for maintaining stable software versions and the workflows for studies over extended periods of time, that need consistency and traceability, such as clinical trials. We introduce here, the Translational Analysis Group (TAG), a team which deploys, validates, and conducts scalable analytical workflows in a secure, cloud-based environment. We maintain 29 well-tested workflows with best practice methods and ample resources for both somatic and germline analysis. Our cloud platform, FireCloud, enables us to run workflows at any scale. Since May 2017, our team has performed nearly 10,000 analyses for mutation detection (SNV, InDel, CNV, and SV) and cohort analysis on tumor samples and cell-free DNA samples. TAG offers a range of options for somatic and germline variant detection, from legacy pipelines through recently validated contemporary pipelines to allow for continuity across long running projects. Citation Format: Junko Tsuji, Andrew Hollinger, Alyssa MacBeth, Brian R. Grander, Micah Rickles-Young, Tera Bowers, Carrie Cibulskis, Niall Lennon. Somatic analysis services with best practice workflows in a cloud-based platform [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2485.

Whole genome sequencing (WGS) offers the greatest potential to comprehensively and accurately identify all forms of human genetic variation. The accessibility of WGS data for diagnostic use has increased due to cost reductions in recent years, driving the need for a high quality, clinically validated offering. Our platform has completed an extensive benchmarking study of the Illumina PCR-free whole genome pipeline to establish clinical validity for the end-to-end laboratory, analytical, and computational processes. Our benchmarking study is comprehensive in nature, including measuring the precision, robustness, limits of detection for variant classes, and contamination estimates. A cohort of well-characterized reference controls and clinical samples with previously identified pathogenic variants were used to establish the performance characteristics of our clinical WGS test, which at 30X coverage has >99% analytical sensitivity for SNVs and >98% analytical sensitivity for small insertions and deletions. Our platform is capable of operating at a scale that supports applications from individual clinics (cancer and medical genetics related applications) as well as large scale ambitious collaborative projects such as the All Of Us program which aims to generate clinical grade whole genome variant calls for 1 million healthy research participants. Citation Format: Alyssa MacBeth, Tera Bowers, Betty Woolf, Maegan Harden, Niall Lennon, Stacey Gabriel. Clinical whole genome sequencing at scale [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3543.

The recent development of a semiconductor-based, non-optical DNA sequencing technology promises scalable, low-cost and rapid sequence data production. The technology has previously been applied mainly to genomic sequencing and targeted re-sequencing. Here we demonstrate the utility of Ion Torrent semiconductor-based sequencing for sensitive, efficient and rapid chromatin immunoprecipitation followed by sequencing (ChIP-seq) through the application of sample preparation methods that are optimized for ChIP-seq on the Ion Torrent platform. We leverage this method for epigenetic profiling of tumour tissues., National Human Genome Research Institute (U.S.) (1P5)HG006193-01), National Institutes of Health (U.S.) (Pioneer Award DP1OD003958-01), Klarman Cell Observatory

The presence of specific somatic copy number alterations (SCNAs) in tumor genomes can be used to predict sensitivity to specific treatments and to predict outcomes. CapSeg Revised (ReCapSeg) detects SCNAs from tumor sequencing data. The Broad Institute is integrating ReCapSeg into the Clinical Research Sequencing Platform (CRSP), a CLIA-certified platform, to enable physicians to use detected SCNAs in treatment decisions. By using sequencing data CRSP can produce SCNA reports using the same data being generated for other reports (e.g. somatic single nucleotide variants). ReCapSeg produces copy ratio estimates for regions of the genome and includes a caller that labels regions as amplified, deleted, or neutral. ReCapSeg leverages a panel of normals (PoN), which obviates the need for the matched normal for a given tumor sample. Validation for ReCapSeg, in CRSP, is repeatable and standardized with a process for creating a PoN and gathering performance metrics from case samples. Additional QC metrics and criteria on input sequencing data were identified and included, so that reports can indicate when reduced performance can be expected. Citation Format: Lee Lichtenstein, Betty Woolf, Alyssa MacBeth, Ozge Birsoy, Niall Lennon. ReCapSeg: Validation of somatic copy number alterations for CLIA whole exome sequencing. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3641.