Jeffrey W. Pennington

Clinical phenotype-based gene prioritization: an initial study using semantic similarity and the human phenotype ontology

BackgroundExome sequencing is a promising method for diagnosing patients with a complex phenotype. However, variant interpretation relative to patient phenotype can be challenging in some scenarios, particularly clinical assessment of rare complex phenotypes. Each patient’s sequence reveals many possibly damaging variants that must be individually assessed to establish clear association with patient phenotype. To assist interpretation, we implemented an algorithm that ranks a given set of genes relative to patient phenotype. The algorithm orders genes by the semantic similarity computed between phenotypic descriptors associated with each gene and those describing the patient. Phenotypic descriptor terms are taken from the Human Phenotype Ontology (HPO) and semantic similarity is derived from each term’s information content.ResultsModel validation was performed via simulation and with clinical data. We simulated 33 Mendelian diseases with 100 patients per disease. We modeled clinical conditions by adding noise and imprecision, i.e. phenotypic terms unrelated to the disease and terms less specific than the actual disease terms. We ranked the causative gene against all 2488 HPO annotated genes. The median causative gene rank was 1 for the optimal and noise cases, 12 for the imprecision case, and 60 for the imprecision with noise case. Additionally, we examined a clinical cohort of subjects with hearing impairment. The disease gene median rank was 22. However, when also considering the patient’s exome data and filtering non-exomic and common variants, the median rank improved to 3.ConclusionsSemantic similarity can rank a causative gene highly within a gene list relative to patient phenotype characteristics, provided that imprecision is mitigated. The clinical case results suggest that phenotype rank combined with variant analysis provides significant improvement over the individual approaches. We expect that this combined prioritization approach may increase accuracy and decrease effort for clinical genetic diagnosis.

Harvest: an open platform for developing web-based biomedical data discovery and reporting applications

Biomedical researchers share a common challenge of making complex data understandable and accessible as they seek inherent relationships between attributes in disparate data types. Data discovery in this context is limited by a lack of query systems that efficiently show relationships between individual variables, but without the need to navigate underlying data models. We have addressed this need by developing Harvest, an open-source framework of modular components, and using it for the rapid development and deployment of custom data discovery software applications. Harvest incorporates visualizations of highly dimensional data in a web-based interface that promotes rapid exploration and export of any type of biomedical information, without exposing researchers to underlying data models. We evaluated Harvest with two cases: clinical data from pediatric cardiology and demonstration data from the OpenMRS project. Harvests architecture and public open-source code offer a set of rapid application development tools to build data discovery applications for domain-specific biomedical data repositories. All resources, including the OpenMRS demonstration, can be found at http://harvest.research.chop.edu

CORRIGENDUM: Novel findings with reassessment of exome data: implications for validation testing and interpretation of genomic data

PurposeThe objective of this study was to assess the ability of our laboratory’s exome-sequencing test to detect known and novel sequence variants and identify the critical factors influencing the interpretation of a clinical exome test.MethodsWe developed a two-tiered validation strategy: (i) a method-based approach that assessed the ability of our exome test to detect known variants using a reference HapMap sample, and (ii) an interpretation-based approach that assessed our relative ability to identify and interpret disease-causing variants, by analyzing and comparing the results of 19 randomly selected patients previously tested by external laboratories.ResultsWe demonstrate that this approach is reproducible with >99% analytical sensitivity and specificity for single-nucleotide variants and indels <10 bp. Our findings were concordant with the reference laboratories in 84% of cases. A new molecular diagnosis was applied to three cases, including discovery of two novel candidate genes.ConclusionWe provide an assessment of critical areas that influence interpretation of an exome test, including comprehensive phenotype capture, assessment of clinical overlap, availability of parental data, and the addressing of limitations in database updates. These results can be used to inform improvements in phenotype-driven interpretation of medical exomes in clinical and research settings.

Anticipated responses of early adopter genetic specialists and nongenetic specialists to unsolicited genomic secondary findings

PurposeSecondary findings from genomic sequencing are becoming more common. We compared how health-care providers with and without specialized genetics training anticipated responding to different types of secondary findings.MethodsProviders with genomic sequencing experience reviewed five secondary-findings reports and reported attitudes and potential clinical follow-up. Analyses compared genetic specialists and physicians without specialized genetics training, and examined how responses varied by secondary finding.ResultsGenetic specialists scored higher than other providers on four-point scales assessing understandings of reports (3.89 vs. 3.42, p = 0.0002), and lower on scales assessing reporting obligations (2.60 vs. 3.51, p < 0.0001) and burdens of responding (1.73 vs. 2.70, p < 0.0001). Nearly all attitudes differed between findings, although genetic specialists were more likely to assert that laboratories had no obligations when findings had less-established actionability (p < 0.0001 in interaction tests). The importance of reviewing personal and family histories, documenting findings, learning more about the variant, and recommending familial discussions also varied according to finding (all p < 0.0001).ConclusionGenetic specialists felt better prepared to respond to secondary findings than providers without specialized genetics training, but perceived fewer obligations for laboratories to report them, and the two groups anticipated similar clinical responses. Findings may inform development of targeted education and support.

Using electronic medical record data to report laboratory adverse events

Despite the importance of adverse event (AE) reporting, AEs are under‐reported on clinical trials. We hypothesized that electronic medical record (EMR) data can ascertain laboratory‐based AEs more accurately than those ascertained manually. EMR data on 12 AEs for patients enrolled on two Childrens Oncology Group (COG) trials at one institution were extracted, processed and graded. When compared to gold standard chart data, COG AE report sensitivity and positive predictive values (PPV) were 0–21·1% and 20–100%, respectively. EMR sensitivity and PPV were >98·2% for all AEs. These results demonstrate that EMR‐based AE ascertainment and grading substantially improves laboratory AE reporting accuracy.

Genomic decision support needs in pediatric primary care

Clinical genome and exome sequencing can diagnose pediatric patients with complex conditions that often require follow-up care with multiple specialties. The American Academy of Pediatrics emphasizes the role of the medical home and the primary care pediatrician in coordinating care for patients who need multidisciplinary support. In addition, the electronic health record (EHR) with embedded clinical decision support is recognized as an important component in providing care in this setting. We interviewed 6 clinicians to assess their experience caring for patients with complex and rare genetic findings and hear their opinions about how the EHR currently supports this role. Using these results, we designed a candidate EHR clinical decision support application mock-up and conducted formative exploratory user testing with 26 pediatric primary care providers to capture opinions on its utility in practice with respect to a specific clinical scenario. Our results indicate agreement that the functionality represented by the mock-up would effectively assist with care and warrants further development.

The biorepository portal toolkit: an honest brokered, modular service oriented software tool set for biospecimen-driven translational research

BackgroundHigh throughput molecular sequencing and increased biospecimen variety have introduced significant informatics challenges for research biorepository infrastructures. We applied a modular system integration approach to develop an operational biorepository management system. This method enables aggregation of the clinical, specimen and genomic data collected for biorepository resources.MethodsWe introduce an electronic Honest Broker (eHB) and Biorepository Portal (BRP) open source project that, in tandem, allow for data integration while protecting patient privacy. This modular approach allows data and specimens to be associated with a biorepository subject at any time point asynchronously. This lowers the bar to develop new research projects based on scientific merit without institutional review for a proposal.ResultsBy facilitating the automated de-identification of specimen and associated clinical and genomic data we create a future proofed specimen set that can withstand new workflows and be connected to new associated information over time. Thus facilitating collaborative advanced genomic and tissue research.ConclusionsAs of Janurary of 2016 there are 23 unique protocols/patient cohorts being managed in the Biorepository Portal (BRP). There are over 4000 unique subject records in the electronic honest broker (eHB), over 30,000 specimens accessioned and 8 institutions participating in various biobanking activities using this tool kit. We specifically set out to build rich annotation of biospecimens with longitudinal clinical data; BRP/REDCap integration for multi-institutional repositories; EMR integration; further annotated specimens with genomic data specific to a domain; build application hooks for experiments at the specimen level integrated with analytic software; while protecting privacy per the Office of Civil Rights (OCR) and HIPAA.

Utility and limitations of exome sequencing as a genetic diagnostic tool for children with hearing loss

PurposeHearing loss (HL) is the most common sensory disorder in children. Prompt molecular diagnosis may guide screening and management, especially in syndromic cases when HL is the single presenting feature. Exome sequencing (ES) is an appealing diagnostic tool for HL as the genetic causes are highly heterogeneous.MethodsES was performed on a prospective cohort of 43 probands with HL. Sequence data were analyzed for primary and secondary findings. Capture and coverage analysis was performed for genes and variants associated with HL.ResultsThe diagnostic rate using ES was 37.2%, compared with 15.8% for the clinical HL panel. Secondary findings were discovered in three patients. For 247 genes associated with HL, 94.7% of the exons were targeted for capture and 81.7% of these exons were covered at 20× or greater. Further analysis of 454 randomly selected HL-associated variants showed that 89% were targeted for capture and 75% were covered at a read depth of at least 20×.ConclusionES has an improved yield compared with clinical testing and may capture diagnoses not initially considered due to subtle clinical phenotypes. Technical challenges were identified, including inadequate capture and coverage of HL genes. Additional considerations of ES include secondary findings, cost, and turnaround time.

Abstract 2593: Accelerating pediatric brain tumor research through team science solutions

Introduction: The Children’s Brain Tumor Tissue Consortium (CBTTC), an international repository of genomic and phenotypic data, has partnered with Blackfynn, Inc., to create a cloud-based data management platform to facilitate team-science across disciplines. Background: The CBTTC through the CHOP Department of Biomedical and Health Informatics (DBHi) has developed a network of informatics and data applications for researchers across the globe to work together and perform real-time analyses on existing clinical, phenotypic, and genomic data. Historically, rare disease datasets are siloed, locked in proprietary formats, segregated by data types, and hidden from the view of experts in the field. This has been a significant barrier to finding effective therapeutics for children with pediatric brain tumors. Blackfynn was founded by a group of multidisciplinary experts in neuroscience, neurology, medicine, software development, engineering, computer science and business with the goal to empower researchers to cure neurologic disease and provide solutions to these challenges. Description of Methods: The CBTTC and Blackfynn teamed up to provide a cloud-based, team-focused data management and analytics platform. The platform provides a commercial grade, scalable approach to upload, view, and integrate digital pathology images with relevant subject data such as MRIs, pathology reports and genomic information. Stakeholders can search integrated data without requiring users to change their current workflow or conform to imposed data standards. This platform is a simple, intuitive, end-to-end software platform for teams of scientists and pathologists to review, annotate and discuss cases, enabling rapid diagnostic consensus, quality control, and empowered discovery. Summary of Unpublished Results: The CBTTC/Blackfynn data platform enabled CBTTC members to engage in a cross-institutional collaboration to reach consensus on digital pathology data in ways that were previously not possible. We demonstrated that this solution removes existing barriers to collaborative efforts and provides a rich analytic and discovery platform bridging imaging with genomics and other data formats. The platform provides a new model for the scientific community to facilitate translation towards improved treatments for children diagnosed with brain tumors. Discussion and Future Direction: This pilot project will be scaled to other CBTTC sites for centralized review of pathology images to enable the research community to collaborative on specific projects. The next phase of platform development will include further integration CBTTC platforms fully integrating genomics data, and side-by-side viewing and analyses of MRI, pathology and clincal data to facilitate specific project work around large and complex research data types in a cloud environment. Citation Format: Amanda Christini, Angela J. Waanders, Joost B. Wagenaar, Alex S. Felmeister, Mariarita Santi, Nitin R. Wadhwani, Jennifer L. Mason, Mateusz P. Koptyra, Jena V. Lilly, Jeffrey W. Pennington, Rishi R. Lulla, Adam C. Resnick. Accelerating pediatric brain tumor research through team science solutions [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2593. doi:10.1158/1538-7445.AM2017-2593