Genome Interpretation Research Articles

A novel online genomic counseling and variant interpretation certificate: Learning design, learning analytics, and evaluation.

In this paper we describe the analysis, planning, design, development, implementation and evaluation of a new online Graduate Certificate in Genomic Counselling and Variant Interpretation (GCGCVI) at The University of British Columbia (UBC). Genetic counselling is now a prerequisite for diagnostic genomic testing in many countries, demanding that genetic counselling practitioners have up-to-the-moment genomic counselling skills and knowledge. Current practitioners reported a desire for more training in this rapidly developing field: our international survey revealed substantial interest in online continuing education addressing themes such as testing and clinical bioinformatics, applied variant interpretation, evidence-based genomic counselling, and other emerging genomic topics. However, our market analysis found no post-graduate program globally that offered such training. To fill this gap, our oversight team of genetic counsellors and geneticists therefore guided development of curriculum and materials, and online learning specialistsdeveloped rigorous interactive asynchronous online graduate courses through collaboration with subject matter experts, following best practices in online learning design. Since launch in September 2020, we have gathered learner feedback using surveys and focus groups, and we have used learning analytics to understand how learners engaged with each other and with course materials. Together, these have helped us understand learner behaviour and guide the continuous process of design improvement to support the learning goals of this audience of professional learners. Our courses have been reviewed and approved by the UBC Faculty of Medicine, UBC Senate, and the Province of British Columbia Ministries of Advanced Education and Health, and assessed by the National Society of Genetic Counselors (NSGC, USA) and the Canadian Association of Genetic Counsellors (CAGC) to enable learners to receive North American continuing education credits. To date, 151 individuals from 18 countries have succeeded in one or more course and 43 have completed the entire certificate.

BG01 Confirming small nuclear ribonucleoprotein polypeptide E as a cause of nonsyndromic hypotrichosis

Abstract Hereditary nonsyndromic hypotrichosis comprises at least 15 genetically distinct but phenotypically overlapping disorders. Affected individuals generally have hair at birth, which sheds during infancy or childhood. Severity ranges from partial alopecia to complete loss of scalp and body hair, with phenotypic variability between and within families. Genetic testing is essential for accurate diagnosis and counselling. We describe a case where several factors contributed to a delay in genetic diagnosis. A female child of nonconsanguineous White parents was born at term with normal hair that started to shed at 2 weeks of age. By 6 weeks of age, there was a complete absence of scalp, eyelash and eyebrow hair. There was no relevant family history apart from atopy. On examination at 3.3 years of age, she had total alopecia apart from some hairs in the external auditory canals. Nails, teeth and sweating were normal. She appeared developmentally normal and healthy apart from mild eczema with persistent erythema on both cheeks. The differential diagnosis included autoimmune alopecia and genetic nonsyndromic hypotrichosis. She was recruited to the 100 000 Genomes Project, but primary analysis did not identify any causative variants. Reanalysis using a limited gene-agnostic approach identified heterozygosity for the c.1A>G; p.(Met1?) variant in small nuclear ribonucleoprotein polypeptide E (SNRPE) that was not present in parents. This variant is not present in the gnomAD population database and is predicted to alter the initiation codon of SNRPE. It has been reported de novo in a British girl with sparse scalp hair and eyelashes and absent eyebrows, and in eight affected members of a Spanish family whose phenotypes ranged from sparse eyebrows and eyelashes to complete alopecia (Pasternack SM, Refke M, Paknia E et al. Mutations in SNRPE, which encodes a core protein of the spliceosome, cause autosomal dominant hypertrichosis simplex. Am J Hum Genet 2013; 92:81–7). This condition has been designated hypotrichosis type 11. SNRPE encodes a core protein of U small nuclear ribonucleoproteins, which are essential to the removal of introns from pre-mRNA and thus critical for the post-transcriptional modification of RNA. In our case, review of the primary analysis showed that the incorrect gene panel was applied (‘familial cicatricial alopecia’ instead of ‘nonsyndromic hypotrichosis’) and that SNRPE was listed as ‘amber’ for hypotrichosis, meaning ‘moderate evidence for this gene–disease association and should not yet be used for genome interpretation’. Genomics England has now incorporated nonsyndromic hypotrichosis into the ‘ectodermal dysplasia’ panel, with SNRPE now listed as ‘green’ for hypotrichosis 11, meaning a ‘high level of evidence for this gene–disease association’. This case emphasizes the importance of revisiting negative results as knowledge evolves.

PhD-SNPg: updating a webserver and lightweight tool for scoring nucleotide variants.

One of the primary challenges in human genetics is determining the functional impact of single nucleotide variants (SNVs) and insertion and deletions (InDels), whether coding or noncoding. In the past, methods have been created to detect disease-related single amino acid changes, but only some can assess the influence of noncoding variations. CADD is the most commonly used and advanced algorithm for predicting the diverse effects of genome variations. It employs a combination of sequence conservation and functional features derived from the ENCODE project data. To use CADD, a large set of pre-calculated information must be downloaded during the installation process. To streamline the variant annotation process, we developed PhD-SNPg, a machine-learning tool that is easy to install and lightweight, relying solely on sequence-based features. Here we present an updated version, trained on a larger dataset, that can also predict the impact of the InDel variations. Despite its simplicity, PhD-SNPg performs similarly to CADD, making it ideal for rapid genome interpretation and as a benchmark for tool development.

Resources and tools for rare disease variant interpretation.

Collectively, rare genetic disorders affect a substantial portion of the world's population. In most cases, those affected face difficulties in receiving a clinical diagnosis and genetic characterization. The understanding of the molecular mechanisms of these diseases and the development of therapeutic treatments for patients are also challenging. However, the application of recent advancements in genome sequencing/analysis technologies and computer-aided tools for predicting phenotype-genotype associations can bring significant benefits to this field. In this review, we highlight the most relevant online resources and computational tools for genome interpretation that can enhance the diagnosis, clinical management, and development of treatments for rare disorders. Our focus is on resources for interpreting single nucleotide variants. Additionally, we present use cases for interpreting genetic variants in clinical settings and review the limitations of these results and prediction tools. Finally, we have compiled a curated set of core resources and tools for analyzing rare disease genomes. Such resources and tools can be utilized to develop standardized protocols that will enhance the accuracy and effectiveness of rare disease diagnosis.

Functional Profiling and Evolutionary Analysis of a Marine Microalgal Virus Pangenome.

Phycodnaviridae are large double-stranded DNA viruses, which facilitate studies of host-virus interactions and co-evolution due to their prominence in algal infection and their role in the life cycle of algal blooms. However, the genomic interpretation of these viruses is hampered by a lack of functional information, stemming from the surprising number of hypothetical genes of unknown function. It is also unclear how many of these genes are widely shared within the clade. Using one of the most extensively characterized genera, Coccolithovirus, as a case study, we combined pangenome analysis, multiple functional annotation tools, AlphaFold structural modeling, and literature analysis to compare the core and accessory pangenome and assess support for novel functional predictions. We determined that the Coccolithovirus pangenome shares 30% of its genes with all 14 strains, making up the core. Notably, 34% of its genes were found in at most three strains. Core genes were enriched in early expression based on a transcriptomic dataset of Coccolithovirus EhV-201 algal infection, were more likely to be similar to host proteins than the non-core set, and were more likely to be involved in vital functions such as replication, recombination, and repair. In addition, we generated and collated annotations for the EhV representative EhV-86 from 12 different annotation sources, building up information for 142 previously hypothetical and putative membrane proteins. AlphaFold was further able to predict structures for 204 EhV-86 proteins with a modelling accuracy of good-high. These functional clues, combined with generated AlphaFold structures, provide a foundational framework for the future characterization of this model genus (and other giant viruses) and a further look into the evolution of the Coccolithovirus proteome.

Engineering CpG island DNA methylation in pluripotent cells through synthetic CpG-free ssDNA insertion

Cellular differentiation requires global changes to DNA methylation (DNAme), where it functions to regulate transcription factor, chromatin remodeling activity, and genome interpretation. Here, we describe a simple DNAme engineering approach in pluripotent stem cells (PSCs) that stably extends DNAme across target CpG islands (CGIs). Integration of synthetic CpG-free single-stranded DNA (ssDNA) induces a target CpG island methylation response (CIMR) in multiple PSC lines, Nt2d1 embryonal carcinoma cells, and mouse PSCs but not in highly methylated CpG island hypermethylator phenotype (CIMP)+ cancer lines. MLH1 CIMR DNAme spanned the CGI, was precisely maintained through cellular differentiation, suppressed MLH1 expression, and sensitized derived cardiomyocytes and thymic epithelial cells to cisplatin. Guidelines for CIMR editing are provided, and initial CIMR DNAme is characterized at TP53 and ONECUT1 CGIs. Collectively, this resource facilitates CpG island DNAme engineering in pluripotency and the genesis of novel epigenetic models of development and disease.

NeuronMotif: Deciphering cis-regulatory codes by layer-wise demixing of deep neural networks

Discovering DNA regulatory sequence motifs and their relative positions is vital to understanding the mechanisms of gene expression regulation. Although deep convolutional neural networks (CNNs) have achieved great success in predicting cis-regulatory elements, the discovery of motifs and their combinatorial patterns from these CNN models has remained difficult. We show that the main difficulty is due to the problem of multifaceted neurons which respond to multiple types of sequence patterns. Since existing interpretation methods were mainly designed to visualize the class of sequences that can activate the neuron, the resulting visualization will correspond to a mixture of patterns. Such a mixture is usually difficult to interpret without resolving the mixed patterns. We propose the NeuronMotif algorithm to interpret such neurons. Given any convolutional neuron (CN) in the network, NeuronMotif first generates a large sample of sequences capable of activating the CN, which typically consists of a mixture of patterns. Then, the sequences are "demixed" in a layer-wise manner by backward clustering of the feature maps of the involved convolutional layers. NeuronMotif can output the sequence motifs, and the syntax rules governing their combinations are depicted by position weight matrices organized in tree structures. Compared to existing methods, the motifs found by NeuronMotif have more matches to known motifs in the JASPAR database. The higher-order patterns uncovered for deep CNs are supported by the literature and ATAC-seq footprinting. Overall, NeuronMotif enables the deciphering of cis-regulatory codes from deep CNs and enhances the utility of CNN in genome interpretation.

Implementation and evaluation of personal genetic testing as part of genomics analysis courses in German universities

PurposeDue to the increasing application of genome analysis and interpretation in medical disciplines, professionals require adequate education. Here, we present the implementation of personal genotyping as an educational tool in two genomics courses targeting Digital Health students at the Hasso Plattner Institute (HPI) and medical students at the Technical University of Munich (TUM).MethodsWe compared and evaluated the courses and the students’ perceptions on the course setup using questionnaires.ResultsDuring the course, students changed their attitudes towards genotyping (HPI: 79% [15 of 19], TUM: 47% [25 of 53]). Predominantly, students became more critical of personal genotyping (HPI: 73% [11 of 15], TUM: 72% [18 of 25]) and most students stated that genetic analyses should not be allowed without genetic counseling (HPI: 79% [15 of 19], TUM: 70% [37 of 53]). Students found the personal genotyping component useful (HPI: 89% [17 of 19], TUM: 92% [49 of 53]) and recommended its inclusion in future courses (HPI: 95% [18 of 19], TUM: 98% [52 of 53]).ConclusionStudents perceived the personal genotyping component as valuable in the described genomics courses. The implementation described here can serve as an example for future courses in Europe.

338 The Alabama Genomic Health Initiative: Integrating Genomic Medicine into Primary Care

OBJECTIVES/GOALS: Supported by the State of Alabama, the Alabama Genomic Health Initiative (AGHI) is aimed at preventing and treating common conditions with a genetic basis. This joint UAB Medicine-HudsonAlpha Institute for Biotechnology effort provides genomic testing, interpretation, and counseling free of charge to residents in each of Alabama’s 67 counties. METHODS/STUDY POPULATION: Launched in 2017, as a state-wide population cohort, AGHI (1.0) enrolled 6,331 Alabamians and returned individual risk of disease(s) related to the ACMG SF v2.0 medically actionable genes. In 2021, the cohort was expanded to include a primary care cohort. AGHI (2.0) has enrolled 750 primary care patients, returning individual risk of disease(s) related to the ACMG SF v3.1 gene list and pre-emptive pharmacogenetics (PGx) to guide medication therapy. Genotyping is done on the Illumina Global Diversity Array with Sanger sequencing to confirm likely pathogenic / pathogenic variants in medically actionable genes and CYP2D6 copy number variants using Taqman assays, resulting in a CLIA-grade report. Disease risk results are returned by genetic counselors and Pharmacogenetics results are returned by Pharmacists. RESULTS/ANTICIPATED RESULTS: We have engaged a statewide community (>7000 participants), returning 94 disease risk genetic reports and 500 PGx reports. Disease risk reports include increased predisposition to cancers (n=38), cardiac diseases (n=33), metabolic (n=12), other (n=11). 100% of participants harbor an actionable PGx variant, 70% are on medication with PGx guidance, 48% harbor PGx variants and are taking medications affected. In 10% of participants, pharmacists sent an active alert to the provider to consider/ recommend alternative medication. Most commonly impacted medications included antidepressants, NSAIDS, proton-pump inhibitors and tramadol. To enable the EMR integration of genomic information, we have developed an automated transfer of reports into the EMR with Genetics Reports and PGx reports viewable in Cerner. DISCUSSION/SIGNIFICANCE: We share our experience on pre-emptive implementation of genetic risk and pharmacogenetic actionability at a population and clinic level. Both patients and providers are actively engaged, providing feedback to refine the return of results. Real time alerts with guidance at the time of prescription are needed to ensure future actionability and value.

Evaluation of an automated genome interpretation model for rare disease routinely used in a clinical genetic laboratory

PurposeThe analysis of exome and genome sequencing data for the diagnosis of rare diseases is challenging and time-consuming. In this study, we evaluated an artificial intelligence model, based on machine learning for automating variant prioritization for diagnosing rare genetic diseases in the Baylor Genetics clinical laboratory. MethodsThe automated analysis model was developed using a supervised learning approach based on thousands of manually curated variants. The model was evaluated on 2 cohorts. The model accuracy was determined using a retrospective cohort comprising 180 randomly selected exome cases (57 singletons, 123 trios); all of which were previously diagnosed and solved through manual interpretation. Diagnostic yield with the modified workflow was estimated using a prospective “production” cohort of 334 consecutive clinical cases. ResultsThe model accurately pinpointed all manually reported variants as candidates. The reported variants were ranked in top 10 candidate variants in 98.4% (121/123) of trio cases, in 93.0% (53/57) of single proband cases, and 96.7% (174/180) of all cases. The accuracy of the model was reduced in some cases because of incomplete variant calling (eg, copy number variants) or incomplete phenotypic description. ConclusionThe automated model for case analysis assists clinical genetic laboratories in prioritizing candidate variants effectively. The use of such technology may facilitate the interpretation of genomic data for a large number of patients in the era of precision medicine.

Abstract P1-08-01: Virtual molecular and precision medicine (vMAP) clinic to improve access to clinical trials for patients with metastatic breast cancer: an academic-community collaboration

Abstract Background: Despite improved outcomes for patients with metastatic breast cancer (MBC), attributed in part to advancements in precision therapeutics, clinical trial enrollment and genomic testing are relatively underutilized in the community oncology setting. To address this need, we launched a virtual molecular and precision medicine (vMAP) clinic to improve access for patients in our community network. Here we describe the initial results from the virtual clinic initiative. Methods: In 2020, the vMAP clinic was launched to provide real-time access to precision medicine expertise through virtual, email-based consultation directed to a multidisciplinary team. As part of the service, providers contacted vMAP regarding patients with MBC at the time of initial presentation or disease progression. Upon receipt, the vMAP team (including breast medical oncologists, a breast oncology research nurse, a research assistant, and ad hoc consultation from cancer genetics and molecular pathology representatives) engaged in discussion to identify potential clinical trials with slot availability or standard of care (SOC) options and provided recommendations to the referring provider within 72 hours. Relevant patient data and vMAP referral outcomes were recorded. Providers across the community network were surveyed at project onset and after seventeen months. Results: In its first 14-month period (June 2020-July 2021), 47 cases from 16 providers across seven community oncology sites and one academic site were referred to vMAP. A majority (88.6%) of patients had undergone somatic genomic testing (tissue-based, plasma-based, or both) to inform treatment guidance. All cases were screened for available clinical trials as well as SOC options at the time of referral. The average response time to the referring provider with a finalized recommendation was 1.77 days (range 0-5). Forty cases (85.1%) had clinical trial options identified on vMAP pre-screen. Subsequently, 22 patients (46.8%) screened for vMAP recommended clinical trials with 18 (38.3%) initiating enrollment. Of the remaining patients that did not pursue clinical trial screening, 17 (36.2%) started vMAP-recommended SOC treatment, five (10.6%) pursued a different treatment, one (2.1%) was not a candidate for further treatment, and two (4.3%) opted to receive care at a non-affiliated institution, and subsequent treatment information was not available. Reasons that patients did not enroll on trials included trial ineligibility, formal clinical trial screening failure, and patient/provider preference. At project onset, network provider survey results confirmed a previously suggested demand for improvements in processes surrounding genomic testing interpretation and clinical trial enrollment. Post-referral survey results indicated vMAP recommendations and pre-screenings improved the processes surrounding interpretation of genomic testing and clinical trial screening and enrollment, with high rates of referring provider satisfaction. Conclusion: A precision medicine virtual clinic demonstrated an efficient and flexible means to offer real-time interpretation of genomic and molecular test results and identification of appropriate treatment options, including clinical trials when applicable, for patients with MBC. The service highlights an example of an academic-community collaboration model to expand precision medicine and clinical trial access for patients with metastatic breast cancer. Citation Format: Lindsey Mortensen, Jennifer C. Keenan, Elizabeth Abraham, Sarah Padden, Annie Ma, Elyssa Denault, Lianne Ryan, Arielle J. Medford, Leif Ellisen, Joel H. Schwartz, Aditya Bardia, Laura M. Spring. Virtual molecular and precision medicine (vMAP) clinic to improve access to clinical trials for patients with metastatic breast cancer: an academic-community collaboration [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P1-08-01.

Visual and rapid identification of Chlamydia trachomatis and Neisseria gonorrhoeae using multiplex loop-mediated isothermal amplification and a gold nanoparticle-based lateral flow biosensor.

Sexually transmitted chlamydia and gonorrhea infections caused by the bacteria Chlamydia trachomatis and Neisseria gonorrhoeae remain a major public health concern worldwide, particularly in less developed nations. It is crucial to use a point of care (POC) diagnostic method that is quick, specific, sensitive, and user-friendly to treat and control these infections effectively. Here, a novel molecular diagnostic assay, combining multiplex loop-mediated isothermal amplification (mLAMP) with a visual gold nanoparticles-based lateral flow biosensor (AuNPs-LFB) was devised and used for highly specific, sensitive, rapid, visual, and easy identification of C. trachomatis and N. gonorrhoeae. Two unique independent primer pairs were successful designed against the ompA and orf1 genes of C. trachomatis and N. gonorrhoeae, respectively. The optimal mLAMP-AuNPs-LFB reaction conditions were determined to be 67°C for 35min. The detection procedure, involving crude genomic DNA extraction (~5 min), LAMP amplification (35min), and visual results interpretation (<2min), can be completed within 45min. Our assay has a detection limit of 50 copies per test, and we did not observe any cross-reactivity with any other bacteria in our testing. Hence, our mLAMP-AuNPs-LFB assay can potentially be used for POC testing to detect C. trachomatis and N. gonorrhoeae in clinical settings, particularly in underdeveloped regions.

Topological identification and interpretation for single-cell gene regulation elucidation across multiple platforms using scMGCA

Single-cell RNA sequencing provides high-throughput gene expression information to explore cellular heterogeneity at the individual cell level. A major challenge in characterizing high-throughput gene expression data arises from challenges related to dimensionality, and the prevalence of dropout events. To address these concerns, we develop a deep graph learning method, scMGCA, for single-cell data analysis. scMGCA is based on a graph-embedding autoencoder that simultaneously learns cell-cell topology representation and cluster assignments. We show that scMGCA is accurate and effective for cell segregation and batch effect correction, outperforming other state-of-the-art models across multiple platforms. In addition, we perform genomic interpretation on the key compressed transcriptomic space of the graph-embedding autoencoder to demonstrate the underlying gene regulation mechanism. We demonstrate that in a pancreatic ductal adenocarcinoma dataset, scMGCA successfully provides annotations on the specific cell types and reveals differential gene expression levels across multiple tumor-associated and cell signalling pathways.

High-throughput genomic and proteomic interpretation of gene duplication in Vibrio cholera genomes: An in silico study

BackgroundVibrio cholerae leads to severe acute watery diarrhea with a high mortality rate; owing to several virulence factors, especially the cholera toxin. Generally, gene duplication is a major evolutionary process that affects the environmental adaptation of bacteria. In this study, we aimed to investigate the repetitive virulence factors in V. cholera genomes. MethodTotally, 1530 genomes were collected and the possible duplication of 25 known virulence factors was assessed. Next, the genomes carrying duplicated cholera toxins (e.g. ctxA and ctxB) were further analyzed for the identification of novel repetitive virulence factors. All duplicated potential virulence proteins were classified based on clusters of orthologous groups (COGs) and their functions in different pathways were assessed. ResultsGenome-wide analysis showed eight repetitive known virulence factors including rstA, ctxA, ctxB, zot, ace, espA, VC_RS09110, and mshA in V. cholerae genomes. It seems that the majority of the duplicated virulence factors are encoded on the CTXφ such as cholera toxin. Interestingly, our analysis revealed that fourteen genomes had simultaneously two copy numbers of ctxA and ctxB. The proteome of these strains harbored some repetitive proteins that resembled virulence factors of other bacteria. These duplicated proteins were mainly involved in replication, transcription, metabolism, and pathogenesis including Elongation factor Tu, Transketolase, Methyl-accepting chemotaxis protein, GNAT family N-acetyltransferases, cGAMP-activated phospholipase of Vibrio (CapV), HigA, and Helix-turn-helix transcriptional regulator. ConclusionSurprisingly, a considerable number of duplicated genes have essential roles in the virulence and pathogenesis of V. cholerae. Moreover, it seems that novel duplicated genes had different roles in cellular processes or metabolic pathways.

Whole-genome sequencing reveals genotypic resistance in phenotypically susceptible Mycobacterium tuberculosis clinical isolates.

Whole-genome sequencing (WGS) data of Mycobacterium tuberculosis (MTB) complex strains have revealed insights about genetic variants associated with drug resistance (DR). Rapid genome-based diagnostics are being sought for specific and sensitive identification of DR; however, correct prediction of resistance genotypes requires both informatics tools and understanding of available evidence. We analyzed WGS datasets from phenotypically susceptible MTB strains using MTB resistance identification software. WGS data for 1526 MTB isolates classified as phenotypically drug susceptible were downloaded from the ReSeqTB database. The TB-Profiler software was used to call Single Nucleotide Variants (SNV) associated with resistance to rifampicin (RIF), isoniazid (INH), ethambutol (EMB), pyrazinamide, fluoroquinolone (FLQ), streptomycin (STR), and aminoglycosides. The SNV were further matched against the 2021 World Health Organization (WHO) catalogue of resistance mutations. Genome analysis of 1526 MTB strains susceptible to first-line drugs revealed 39 SNV associated with DR to be present in across 14 genes in 5.9% (n = 90) isolates. Further interpretation of SNV based on the WHO catalog of mutations revealed resistance that 21 (1.4%) MTB isolates were resistant to first-line (4 to RIF, 14 to INH, 3 to EMB) drugs. While, 36 (2.6%) isolates were resistant to second-line (19 to STR, 14 to FLQ, and three to capreomycin) agents. The most frequent predictive SNV were; rpoB Ser450 Leu for RIF; katG Ser315Thr, inhA Ser94Ala, fabG1-15C >T (for INH); gyrA Asp94Gly for FLQ; embB Met306 Leu for EMB; rpsL Lys43Arg for STR; and tlyA Asn236 Lys for Capreomycin. Our study highlights the value of WGS-based sequence data for identifying resistance in MTB. It also shows how MTB strains may be misclassified simply on phenotypic drug susceptibility testing, and that correct genome interpretation is key for correct interpretation of resistance genotypes that can be used to guide clinical treatment.

Automated HL7v2 LRI informatics framework for streamlining genomics-EHR data integration

While VCF formatted files are the lingua franca of next-generation sequencing, most EHRs do not provide native VCF support. As a result, labs often must send non-structured PDF reports to the EHR. On the other hand, while FHIR adoption is growing, most EHRs support HL7 interoperability standards, particularly those based on the HL7 Version 2 (HL7v2) standard. The HL7 Version 2 genomics component of the HL7 Laboratory Results Interface (HL7v2 LRI) standard specifies a formalism for the structured communication of genomic data from lab to EHR. We previously described an open-source tool (vcf2fhir) that converts VCF files into HL7 FHIR format. In this report, we describe how the utility has been extended to output HL7v2 LRI data that contains both variants and variant annotations (e.g., predicted phenotypes and therapeutic implications). Using this HL7v2 converter, we implemented an automated pipeline for moving structured genomic data from the clinical laboratory to EHR. We developed an open source hl7v2GenomicsExtractor that converts genomic interpretation report files into a series of HL7v2 observations conformant to HL7v2 LRI. We further enhanced the converter to produce output conformant to Epic's genomic import specification and to support alternative input formats. An automated pipeline for pushing standards-based structured genomic data directly into the EHR was successfully implemented, where genetic variant data and the clinical annotations are now both available to be viewed in the EHR through Epic's genomics module. Issues encountered in the development and deployment of the HL7v2 converter primarily revolved around data variability issues, primarily lack of a standardized representation of data elements within various genomic interpretation report files. The technical implementation of a HL7v2 message transformation to feed genomic variant and clinical annotation data into an EHR has been successful. In addition to genetic variant data, the implementation described here releases the valuable asset of clinically relevant genomic annotations provided by labs from static PDFs to calculable, structured data in EHR systems.

A cervical cancer biorepository for pharmacogenomics research in Zimbabwe

BackgroundResearch infrastructures such as biorepositories are essential to facilitate genomics and its growing applications in health research and translational medicine in Africa. Using a cervical cancer cohort, this study describes the establishment of a biorepository consisting of biospecimens and matched phenotype data for use in genomic association analysis and pharmacogenomics research.MethodWomen aged > 18 years with a recent histologically confirmed cervical cancer diagnosis were recruited. A workflow pipeline was developed to collect, store, and analyse biospecimens comprising donor recruitment and informed consent, followed by data and biospecimen collection, nucleic acid extraction, storage of genomic DNA, genetic characterization, data integration, data analysis and data interpretation. The biospecimen and data storage infrastructure included shared -20 °C to -80 °C freezers, lockable cupboards, secured access-controlled laptop, password protected online data storage on OneDrive software. The biospecimen or data storage, transfer and sharing were compliant with the local and international biospecimen and data protection laws and policies, to ensure donor privacy, trust, and benefits for the wider community.ResultsThis initial establishment of the biorepository recruited 410 women with cervical cancer. The mean (± SD) age of the donors was 52 (± 12) years, comprising stage I (15%), stage II (44%), stage III (47%) and stage IV (6%) disease. The biorepository includes whole blood and corresponding genomic DNA from 311 (75.9%) donors, and tumour biospecimens and corresponding tumour DNA from 258 (62.9%) donors. Datasets included information on sociodemographic characteristics, lifestyle, family history, clinical information, and HPV genotype. Treatment response was followed up for 12 months, namely, treatment-induced toxicities, survival vs. mortality, and disease status, that is disease-free survival, progression or relapse, 12 months after therapy commencement.ConclusionThe current work highlights a framework for developing a cancer genomics cohort-based biorepository on a limited budget. Such a resource plays a central role in advancing genomics research towards the implementation of personalised management of cancer.

KMT2A-D pathogenicity, prevalence, and variation according to a population database.

The KMT2 family of genes is essential epigenetic regulators promoting gene expression. The gene family contains three subgroups, each with two paralogues: KMT2A and KMT2B; KMT2C and KMT2D; KMT2F and KMT2G. KMT2A-D are among the most frequent somatically altered genes in several different cancer types. Somatic KMT2A rearrangements are well-characterized in infant leukemia (IL), and growing evidence supports the role of additional family members (KMT2B, KMT2C, and KMT2D) in leukemogenesis. Enrichment of rare heterozygous frameshift variants in KMT2A and C has been reported in acute myeloid leukemia (AML), IL, and solid tumors. Currently, the non-synonymous variation, prevalence, and penetrance of these four genes are unknown. This study determined the prevalence of pathogenic/likely pathogenic (P/LP) germline KMT2A-D variants in a cancer-free adult population from the Genome Aggregation Database (gnomAD). Two methods of variant interpretation were utilized: a manual genomic variant interpretation and an automated ACMG pipeline. The ACMG pipeline identified considerably fewer P/LP variants (n=89) compared to the manual method (n=660) in all 4 genes. Consequently, the total P/LP prevalence and allele frequency (AF) were higher in the manual method (1:112, AF=4.46E-03) than in ACMG (1:832, AF=6.01E-04). Multiple ancestry-exclusive P/LP variants were identified along with an increased frequency in males compared to females. Many of these variants identified in this population database are also associated with severe juvenile conditions. These data demonstrate that putatively functional germline variation in these developmentally important genes is more common than previously appreciated and identification in cancer-free adults may indicate incomplete penetrance for many of these variants. Future research should examine a genetic predisposing role in IL and other pediatric cancers.

Comparison of actionable events detected in cancer genomes by whole-genome sequencing, in silico whole-exome and mutation panels

BackgroundNext-generation sequencing is used in cancer research to identify somatic and germline mutations, which can predict sensitivity or resistance to therapies, and may be a useful tool to reveal drug repurposing opportunities between tumour types. Multigene panels are used in clinical practice for detecting targetable mutations. However, the value of clinical whole-exome sequencing (WES) and whole-genome sequencing (WGS) for cancer care is less defined, specifically as the majority of variants found using these technologies are of uncertain significance.Patients and methodsWe used the Cancer Genome Interpreter and WGS in 726 tumours spanning 10 cancer types to identify drug repurposing opportunities. We compare the ability of WGS to detect actionable variants, tumour mutation burden (TMB) and microsatellite instability (MSI) by using in silico down-sampled data to mimic WES, a comprehensive sequencing panel and a hotspot mutation panel.ResultsWe reveal drug repurposing opportunities as numerous biomarkers are shared across many solid tumour types. Comprehensive panels identify the majority of approved actionable mutations, with WGS detecting more candidate actionable mutations for biomarkers currently in clinical trials. Moreover, estimated values for TMB and MSI vary when calculated from WGS, WES and panel data, and are dependent on whether all mutations or only non-synonymous mutations were used. Our results suggest that TMB and MSI thresholds should not only be tumour-dependent, but also be sequencing platform-dependent.ConclusionsThere is a large opportunity to repurpose cancer drugs, and these data suggest that comprehensive sequencing is an invaluable source of information to guide clinical decisions by facilitating precision medicine and may provide a wealth of information for future studies. Furthermore, the sequencing and analysis approach used to estimate TMB may have clinical implications if a hard threshold is used to indicate which patients may respond to immunotherapy.

Exome/Genome-Wide Testing in Newborn Screening: A Proportionate Path Forward.

Population-based newborn screening (NBS) is among the most effective public health programs ever launched, improving health outcomes for newborns who screen positive worldwide through early detection and clinical intervention for genetic disorders discovered in the earliest hours of life. Key to the success of newborn screening programs has been near universal accessibility and participation. Interest has been building to expand newborn screening programs to also include many rare genetic diseases that can now be identified by exome or genome sequencing (ES/GS). Significant declines in sequencing costs as well as improvements to sequencing technologies have enabled researchers to elucidate novel gene-disease associations that motivate possible expansion of newborn screening programs. In this paper we consider recommendations from professional genetic societies in Europe and North America in light of scientific advances in ES/GS and our current understanding of the limitations of ES/GS approaches in the NBS context. We invoke the principle of proportionality—that benefits clearly outweigh associated risks—and the human right to benefit from science to argue that rigorous evidence is still needed for ES/GS that demonstrates clinical utility, accurate genomic variant interpretation, cost effectiveness and universal accessibility of testing and necessary follow-up care and treatment. Confirmatory or second-tier testing using ES/GS may be appropriate as an adjunct to conventional newborn screening in some circumstances. Such cases could serve as important testbeds from which to gather data on relevant programmatic barriers and facilitators to wider ES/GS implementation.