Introducing VSPGx: Pharmacogenomics Testing in VarSeq
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Inter-individual variability in drug response poses a significant challenge for clinicians, with much of this variability resulting from inherited genetic differences. While the field of pharmacogenomics (PGx) can provide powerful insights into how genomic factors affect drug response, the implementation of PGx testing in the clinic is hampered by the difficulty of translating genetic test results into actionable recommendations. In this webcast, we will discuss VarSeq’s new PGx testing capabilities, including the ability to identify actionable pharmacogenomic diplotypes and generate clinical reports. In this webcast you will learn: -How to identify pharmacogenomic diplotypes and drug recommendations from NGS data. -How to incorporate externally called CNVs and SVs into your PGx annotations. -How to generate customizable PGx reports from these annotations.
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Introducing VSPGx: Pharmacogenomics Testing in VarSeq
- 1. Introducing VSPGx: Pharmacogenomics Testing in VarSeq March 6, 2024 Presented by Nathan Fortier, Director of Research & Julia Love, Associate Director of Product Quality
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- 3. Introducing VSPGx: Pharmacogenomics Testing in VarSeq March 6, 2024 Presented by Nathan Fortier, Director of Research & Julia Love, Associate Director of Product Quality
- 4. NIH Grant Funding Acknowledgments 4 • Research reported in this publication was supported by the National Institute Of General Medical Sciences of the National Institutes of Health under: o Award Number R43GM128485-01 o Award Number R43GM128485-02 o Award Number 2R44 GM125432-01 o Award Number 2R44 GM125432-02 o Montana SMIR/STTR Matching Funds Program Grant Agreement Number 19-51-RCSBIR-005 o NIH SBIR Grant 1R43HG013456-01 • PI is Dr. Andreas Scherer, CEO of Golden Helix. • The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
- 5. ISO Certification 13485:2016 5 • ISO 13485:2016 from TÜV SÜD • ISO 13485:2016 is an international standard that specifies requirements for a quality management system (QMS) for organizations involved in the design, development, production, and servicing of medical devices. o maintain a quality management system o demonstrate sufficient risk management o show consistent tracking of customer satisfaction and safety in the market o demonstrate continued improvement efforts on the product and system level. • ISO 13485:2016 is designed to objectively document that we are holding ourselves to the highest quality standards as we are providing innovative solutions to hospitals, testing labs, and research institutions globally.
- 6. Who Are We? 6 Golden Helix is a global bioinformatics company founded in 1998 Filtering and Annotation ACMG & AMP Guidelines Clinical Reports CNV Analysis CNV Analysis GWAS | Genomic Prediction Large-N Population Studies RNA-Seq Large-N CNV-Analysis Variant Warehouse Centralized Annotations Hosted Reports Sharing and Integration Pipeline: Run Workflows
- 7. Cited in 1,000s of Peer-Reviewed Publications 7
- 8. Over 400 Customers Globally 8
- 9. The Golden Helix Difference 9 FLEXIBLE DEPLOYMENT On premise or in a private cloud BUSINESS MODEL Annual fee for software, training and support CLIENT CENTRIC Unlimited support from the very beginning SINGLE SOLUTION Comprehensive cancer and germline diagnostics SCALABILITY Gene panels to whole exomes or genomes THROUGHPUT Automated pipeline capabilities QUALITY Clinical reports correct the first time
- 10. Today’s Presenters 10 Rana Smalling, PhD Field Application Scientist Nathan Fortier Director of Research Introducing VSPGx: Pharmacogenomics Testing in VarSeq Julia Love Associate Director of Product Quality
- 11. Pharmacogenomics and PGx Testing 11 • Pharmacogenomics combines pharmacology with genetics and can help elucidate how an individual’s genetic makeup influences their response to drugs. • Genetic variability in certain gene families affects how drugs are metabolized, absorbed, distributed, and excreted. • PGx Testing can be used to • predict how an individual will respond to a drug • determine what an appropriate drug dose should be • if an individual is at risk of toxicity if prescribed a drug • Test results have three main components • genetic results for a gene in the form of a diplotype defined by star (*) alleles • An individual’s metabolism phenotype • A drug treatment strategy recommendation
- 12. PGx alleles and diplotype determination 12 • Star/named alleles are functional haplotypes for a gene • NGS a great strategy for detecting alleles • Can perform PGx analysis with existing exomes, genome data • Most PGx alleles are defined by specific combinations of SNPs or indels but also by structural variations. • A diplotype is a specific combination of two haplotypes or PGx alleles • Example: CYP2D6 CYP2D6 Diplotype Metabolizer Phenotype CYP2D6 *1/*1 Normal metabolizer CYP2D6 *2/*122 Intermediate metabolizer CYP2D6 *3/*3 Poor Metabolizer CYP2D6 *1/*1x2 Ultrarapid metabolizer CYP2D6 *5/*5 Poor metabolizer Required Variants for CYP2D6 *2/*122 • CYP2D6 *2: 2851C>T (rs16947), 4181G>C (rs1135840) • CYP2D6*122 3280G>A (rs61745683)
- 13. Clinical Pharmacogenetics Implementation Consortium (CPIC) 13 • CPIC has developed best practice guidelines for pharmacogenomic tests • Includes standardized grading of evidence linking genotypes to phenotypes • Assigning phenotypes to genotypes and diplotypes • Prescribing recommendations based on diplotype and phenotypes
- 14. PGx Variant Detection and Recommendations Algorithm 14 • Algorithm Function: • Diplotype caller • Phenotype and drug recommendation annotation • Algorithm Requirements: • Call variants at all required positions for PGx genes • Required annotations • Optional Customization • Customize annotation sources used by the algorithm • Recommendations can also be provided for structural variant data
- 15. Pharmacogenomic Report 15 • What’s in the PGx Report from VarSeq? • Current Patient Medications and Recommendations • Gene-Drug Interactions • Prescribing Recommendations • Phenotype Associations • Tested Alleles • Word-based reports are fully customizable • Reports can be rendered for a single sample or a batch of samples
- 16. 16 Product Demo
- 17. NIH Grant Funding Acknowledgments 17 • Research reported in this publication was supported by the National Institute Of General Medical Sciences of the National Institutes of Health under: o Award Number R43GM128485-01 o Award Number R43GM128485-02 o Award Number 2R44 GM125432-01 o Award Number 2R44 GM125432-02 o Montana SMIR/STTR Matching Funds Program Grant Agreement Number 19-51-RCSBIR-005 o NIH SBIR Grant 1R43HG013456-01 • PI is Dr. Andreas Scherer, CEO of Golden Helix. • The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
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- 20. Golden Helix User Meeting 20 Join us for an enlightening and engaging Golden Helix User Meeting at ACMG 2024, where we dive deep into the latest advancements and updates from Golden Helix. This is your chance to connect with fellow professionals, explore cutting-edge genetic analysis tools, and get firsthand insights into how our solutions are evolving to meet the future of genomic analysis. This is an opportunity to ask questions on the fly and engage directly with the Golden Helix team. Please note that there is limited availability for this event. Date & Time: March 12th, 8:00 am - 11:30 pm Location: Toronto Marriott City Centre Hotel, One Blue Jays Way, Toronto, ON M5V 1J4, Canada Agenda: • Presenting VSPGx • Panels to Whole Genome Analysis with Long-Read Data • NGS Enterprise Capabilities • Golden Helix CancerKB Database Updates Presented by: Dr. Andreas Scherer, President & CEO Darby Kammeraad, Director of Field Application Services Nathan Fortier, Director of Research
- 21. 21 Find us in Booth #1313 Come check out our exciting product demos and meet with our team to discuss your needs in Booth #1313. Plus, don't miss the chance to score brand-new t-shirts designed exclusively for ACMG demo attendees! See you there! UNLOCKING GENETIC MYSTERIES: MASTERING EXOME ANALYSIS WITH VSCLINICAL & VS-CNV Friday, March 15th, 11:20 am, Theater 2 Presented by: Nathan Fortier, PhD, Golden Helix Director of Research
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Editor's Notes
- Thanks Casey! We can’t wait to dive in to this subject
- Thanks Casey! We can’t wait to dive in to this subject
- Before we start diving into the subject, I wanted mention our appreciation for our grant funding from NIH. The research reported in this publication was supported by the National institute of general medical sciences of the national institutes of health under the listed awards. We are also grateful to have received local grant funding from the state of Montana. Our PI is Dr. Andreas Scherer who is also the CEO at Golden Helix and the content described today is the responsibility of the authors and does not officially represent the views of the NIH. So with that covered, lets take just a few minutes to talk a little bit about our company Golden Helix.
- Golden Helix is proud to announce that we have received the certification for ISO 13485:2016 from TUV SUD.
- Golden Helix is a global bioinformatics software and analytics company that enables research and clinical practices to analyze large genomic datasets. We were originally founded in 1998 based off pharmacogenomics work performed at GlaxoSmithKline, who is still a primary investor in our company. VarSeq, our flagship product, serves as a clinical tertiary analysis tool. At its core, it serves as a variant annotation and filtration engine. Additionally, users have access to automated AMP or ACMG variant guidelines. VarSeq also has the capability to detect copy number variations scaling from single exome to large aneuploidy events. Lastly, the finalization of variant interpretation and classification is further optimized with VarSeq’s clinical reporting capability. Users can integrate all of these features into a standardized workflow. Paired with VarSeq are VSWarehouse and VSPipeline. VSWarehouse serves as a repository for the large amount of useful genomic data wrangled by our customers. Warehouse not only solves the issue of data storage for ever-increasing genomic content, but also is fully queryable and auditable and allows for the definability of user access for project managers or collaborators. In tandem with this, VSPipeline, allows for the automated execution of routine workflows, further optimizing users' abilities to handle large amounts of data. Lastly, our research platform, SVS, enables researchers to perform complex analysis and visualizations on genomic and phenotypic data. SVS has a range of tools to perform GWAS, genomic prediction, and RNA-Seq analysis, among other common research applications.
- Our software has been very well received by the industry. We have been cited in thousands of peer-reviewed publications, and that’s a testament to our customer base.
- We work with over 400 organizations all over the globe. This includes top-tier institutions, like Stanford and yale, government organizations like the NCI and NIH, clinics such as Sick Kids, and many other genetic testing labs. We now have well over 20,000 installs of our products and with 1,000’s of unique users.
- At Golden Helix, we focus on the seven pillars of customer success. Golden Helix offers a single software solution that encompasses germline, somatic, and CNV analysis. Our software is also highly scalable, supporting gene panel to whole genome sequencing workflows. With our automation capabilities, we now offer a complete FASTQ or VCF to report pipeline. Our software can be locally deployed, or installed in the cloud, and our business model of annual subscription per user means you are able to increase your workload without increasing analysis fees. And it goes without saying, that our FAS team is here to support you on every step of your analysis journey.
- Thank you to everyone who is in attendance today. My name is Nathan Fortier, I’m the director of research here at Golden Helix and I work with the development team that has helped develop the capabilities we are highlighting today.
- The concept of pharmacogenomics has been around for a long time, but was not used in practice until in the 1950s/1960s. Advancements in drug development and an improved understanding of the genetic component of phenotypic variation has helped the field of pharmacogenomics take off. Pharmacogenomics is a discipline at the intersection of genetics and pharmacology, that combines these disciplines to predict drug efficacy and safety, tailoring medication regimens to individual genetic profiles. Genetic variability in certain pharmacogenes affects how drugs are metabolized, absorbed, distributed, and excreted. Over the years, significant progress has been made in understanding genetic variation and pharmacogenes' role in drug response, with around 50% of prescriptions in the US being influenced by actionable germline pharmacogenes. As individuals age and become susceptible to more diseases requiring drug therapy, nearly 90% of patients older than 70 will be exposed to at least one drug with pharmacogenomic guidance. Pharmacogenomic testing can be used to predict how an individual will respond to a given drug, determine appropriate dosing, and assess whether an individual is at risk of toxicity if prescribed a drug. Pharmacogenomic test results have three main components. First, are genetic results for each tested pharmacogene. In autosomal chromosomes, these take in the form of a diplotype defined by a combination of two specific named alleles. Second is an individual’s metabolism phenotype based on the specific alleles present for a given gene. Finally, a drug treatment strategy recommendation is provided based on the combined phenotypes across the tested pharmacogenes.
- NGS is an excellent tool for pharmacogenomic testing because it can perform this analysis using existing exome and genome sequencing data. The first challenge in the utilization of NGS data for pharmacogenomic testing is the determination of which specific named alleles are present in each pharmacogene. These alleles are typically described using star allele notation, which identifies pharmacogenomic markers by means of a designated number for a given gene. For example, the star allele notation for the deletion of a single amino acid at coding position 775 in CYP2D6 is *3. Many star alleles represent a combination of several variants when compared to the human reference sequence and some are defined by structural variations, such as gene fusions and full gene deletions. For autosomal genes, allelic determination algorithms assign a diplotype for each gene, specifying a specific combination of two haplotypes or named PGx alleles. As an example let’s take a look at the gene CYP2D6. CYP2D6 is a great example to discuss as it is highly polymorphic and involved in the metabolism of many commonly prescribed drugs. With over 170 star alleles defined there are thousands of possible diplotype combinations. However, only a few hundred diplotypes are associated with a non-normal metabolizer phenotype.
- Now that we have discussed star allele notation in general, let’s take a look at what is perhaps the most comprehensive source of allele definitions and recommendation. The Clinical Pharmacogenomic Implementation Consortium or CPIC is an international consortium dedicated to facilitating the use of pharmacogenetic tests for patient care. Their goal is to create clinical best-practice guidelines for pharmacogenomic testing so that clinicians, health care providers and vendors can leverage genetic laboratory results into actionable prescribing recommendations. CPIC provides a publicly available database that links phenotypes to specific allele combinations and provides prescribing recommendations based on this phenotypic information, with each recommendation being ranked based on the level of supporting evidence. This database has been indispensable in the development of our own pharmacogenomic solution.
- Golden Helix is excided to announce the upcoming release of VS-PGx, a new product offering a set of tools within VarSeq to support pharmacogenomic workflows by simplifying the calling of named alleles, the annotations of relevant recommendations, and the reporting of pharmacogenomic findings. The two primary components of this new offering are the PGx Variant Detection and Recommendation algorithm and the PGx Report Generation system. The PGx Variant Detection and Recommendation algorithm identifies pharmacogenomic diplotypes and annotates them against drug recommendations. By default, diplotypes are annotated against recommendations provided by CPIC, but custom annotations can be used provided that the required fields are present. This algorithm begins by identifying the named alleles present in the sample. For autosomal chromosomes, this process consists of identifying the best matched diplotypes, which consist of a pair of named alleles for each gene. Once diplotypes have been assigned for each gene, the algorithm matches these diplotypes to phenotypes and recommendations.
- After running the PGx Variant Detection and Recommendations algorithm, a clinical report can be generated using VarSeq’s customizable reporting system. Clinical reports are generated using an easy-to-modify Microsoft Word report template and VarSeq comes shipped with an initial PGx report template that serves as an excellent starting point for creating custom reports. Information included in this report includes Implications for Current Patient Medications, Gene-Drug Interactions, Prescribing Recommendations, Phenotype Associations, and a description of all tested alleles. The calling of diplotypes, annotation of phenotypes, and reporting of recommendations is performed by VS-PGx in just a few simple steps with minimal user involvement. While the annotations and report templates can be customized, the annotation tracks and report templates provided by VarSeq have everything you need to start annotating and reporting all alleles defined in the CPIC database.
- Before wrapping up, we'd like to again state our appreciation for the grants included here. And with that, I'll hand things back to Casey to talk about some exciting marketing updates and take us through a Q&A session.
- Questions: Are we limited to TSO500 data for somatic analysis or can we build our own panel and include any number of customer genomic signatures? Answer: No limitations at all. User can build any somatic panel or workflow overall and include any specific sample level data either in the variant tables or from the sample manifest. TSO data is just one example among any physical or virtual panel your lab would run 2. Can the CNV calling in the whole exome example be automated with pipeline too? A: Yes! When you build the original workflow template, you can include CNV calling, or importing externally called CNVs and SVs. These will also go through the annotation process. 3. How to save time and effort in variant interpretation if you or a teammate need to sequence the whole genome for a sample already analyzed in VarSeq as a gene panel?A: You can save already assessed variant interpretations to assessment catalogs and reuse to avoid rework.