Shirts, Brian H.

Abstract Objectives The study aimed to understand potential barriers to the adoption of health information technology projects that are released as free and open source software (FOSS). Methods We conducted a survey of research consortia participants engaged in genomic medicine implementation to assess perceived institutional barriers to the adoption of three systems: ClinGen electronic health record (EHR) Toolkit, DocUBuild, and MyResults.org. The survey included eight barriers from the Consolidated Framework for Implementation Research (CFIR), with additional barriers identified from a qualitative analysis of open-ended responses. Results We analyzed responses from 24 research consortia participants from 18 institutions. In total, 14 categories of perceived barriers were evaluated, which were consistent with other observed barriers to FOSS adoption. The most frequent perceived barriers included lack of adaptability of the system, lack of institutional priority to implement, lack of trialability, lack of advantage of alternative systems, and complexity. Conclusion In addition to understanding potential barriers, we recommend some strategies to address them (where possible), including considerations for genomic medicine. Overall, FOSS developers need to ensure systems are easy to trial and implement and need to clearly articulate benefits of their systems, especially when alternatives exist. Institutional champions will remain a critical component to prioritizing genomic medicine projects.

Abstract Background: Established laboratory methods, including next-generation sequencing (NGS), can accurately and inexpensively detect certain classes of clinically important mutations in readily accessible protein coding regions of a patient's DNA. These events include single nucleotide variants (SNVs), small insertions/deletions (indels), and sometimes medium sized copy-number deletions or duplications (del/dups). However, other mutation types with clinical relevance are known, and indeed data on over 80,000 patients show that pathogenic, medically important variants of other, more technically challenging types are prevalent in genes related to hereditary breast and ovarian cancer (see associated abstract by Lincoln et al.). We sought to develop and evaluate a reference standard by which laboratories may be able to assess and improve their performance on representative examples of complex, technically “hard” mutations in medically important genes associated with breast, ovarian, and other cancers. Methods: We selected a diverse set of 23 challenging alterations, uncovered by the aforementioned clinical study, all of which are considered pathogenic and potentially actionable under ACMG and NCCN guidelines when uncovered in the germline DNA of a patient. These mutations are in the BRCA1, BRCA2, MLH1, MSH2, MSH6, and PMS2 genes. We generated a single synthetic DNA specimen with all 23 mutations introduced into a known genomic background. Following presentation of this general strategy at a 2016 meeting, 7 laboratories volunteered to collaboratively evaluate this approach. This DNA was created, validated, blinded and provided to these laboratories who sequenced it using a total of 9 different NGS based workflows, including 5 validated clinical tests with customized methods and two sequencing technology vendor (Illumina, Ion Torrent) default pipelines. Multiple target capture biochemical methods were used, as was whole genome sequencing. Results: Twelve of the 23 variants were detected by all 9 laboratory workflows, but just 2 workflows detected all 23. Many, but not all, of these test limitations were previously known. Importantly, evidence of each variant was present in the raw data, suggesting that this strategy is compatible with the diverse biochemical methods used in NGS laboratories today. Raw data for the synthetic variants mimicked that of the endogenous ones (including presenting similar technical artifacts which make these variants “hard”) demonstrating that controls such as these may be useful in the development of methods with improved sensitivity. The vendor-supplied bioinformatics pipelines fared the worst, reinforcing the importance of carefully selecting bioinformatics algorithms and parameters in any laboratory developed test. Discussion: Medically important but technically challenging mutations are prevalent in genes involved in hereditary breast, ovarian, and other cancers. Although patient specimens are also critical, synthetic controls may help efficiently assess the analytic range of any clinical test, highlighting certain strengths and limitations, and can help laboratories develop new methods to improve sensitivity for these challenging variants. Citation Format: Lincoln S, Zook J, Truty R, Chowdhury S, Fellowes A, Mahamdallie S, Ferber M, Cleveland M, Huang C, Tomson F, Klee E, DeSilva W, Seal S, Aradhya S, Nussbaum R, Garlick R, Kingsmore S, Rahman N, Salit M, Shirts B. An interlaboratory study of complex mutation detection in genes associated with hereditary breast and ovarian cancer highlights both successes and current challenges [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P4-06-08.

Abstract Objective Clinicians’ ability to use and interpret genetic information depends upon how those data are displayed in electronic health records (EHRs). There is a critical need to develop systems to effectively display genetic information in EHRs and augment clinical decision support (CDS). Materials and Methods The National Institutes of Health (NIH)-sponsored Clinical Sequencing Exploratory Research and Electronic Medical Records & Genomics EHR Working Groups conducted a multiphase, iterative process involving working group discussions and 2 surveys in order to determine how genetic and genomic information are currently displayed in EHRs, envision optimal uses for different types of genetic or genomic information, and prioritize areas for EHR improvement. Results There is substantial heterogeneity in how genetic information enters and is documented in EHR systems. Most institutions indicated that genetic information was displayed in multiple locations in their EHRs. Among surveyed institutions, genetic information enters the EHR through multiple laboratory sources and through clinician notes. For laboratory-based data, the source laboratory was the main determinant of the location of genetic information in the EHR. The highest priority recommendation was to address the need to implement CDS mechanisms and content for decision support for medically actionable genetic information. Conclusion Heterogeneity of genetic information flow and importance of source laboratory, rather than clinical content, as a determinant of information representation are major barriers to using genetic information optimally in patient care. Greater effort to develop interoperable systems to receive and consistently display genetic and/or genomic information and alert clinicians to genomic-dependent improvements to clinical care is recommended.