Introducing Drugs & Trials for Cancer Diagnostics
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When interpreting a variant using the AMP/ASCO guidelines for somatic variant interpretation, clinicians must determine whether the variant can be considered a biomarker that affects clinical care by predicting sensitivity, resistance, or toxicity to a specific therapy. Such a determination requires the investigation of multiple evidence sources, including clinical trials, FDA approved therapies and peer-reviewed studies. Unfortunately, strong evidence linking specific genetic biomarkers to FDA-approved therapies only exists for a small number of cancers. Thus, most variants require an exploration of clinical practice guidelines, peer-reviewed literature, and large-scale cancer mutation databases to effectively assess the clinical significance of a given mutation. This webcast explores this new incorporation of Drugs & Trials Annotations in VSClinical's AMP Workflow covering: Identification of relevant clinical evidence for drug sensitivity and resistance based on patient biomarkers and tumor type Review of clinical trial information including inclusion criteria, trial status, and contact information Management of citations associated with relevant, targeted therapies Evaluation of a biomarkers clinical evidence tier based on available evidence for drug sensitivity and resistance
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Introducing Drugs & Trials for Cancer Diagnostics
- 1. Introducing Drugs & Trials for Cancer Diagnostics Nathan Fortier, Ph.D, Director of Research 20 Most Promising Biotech Technology Providers Top 10 Analytics Solution Providers
- 2. Questions & Answers *All questions will be anonymous*
- 3. NIH Grant Funding Acknowledgments • Research reported in this publication was supported by the National Institute Of General Medical Sciences of the National Institutes of Health under: • Award Number R43GM128485-01 • Award Number R43GM128485-02 • Award Number 2R44 GM125432-01 • Award Number 2R44 GM125432-02 • Montana SMIR/STTR Matching Funds Program Grant Agreement Number 19-51-RCSBIR-005 • PI is Dr. Andreas Scherer, CEO 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.
- 4. Filtering and Annotation ACMG & AMP Guidelines Clinical Reports CNVAnalysis Pipeline: Run Workflows Variant Warehouse CentralizedAnnotations Hosted Reports Sharing and Integration CNVAnalysis GWAS |Genomic Prediction Large-NPopulation Studies RNA-Seq Large-NCNV-Analysis Who Are We? Golden Helix is a global bioinformatics company founded in 1998
- 5. Cited in 1,000s of Peer-Reviewed Publications
- 6. Over 400 Customers Globally
- 7. SIMPLE, SUBSCRIPTION- BASED BUSINESSMODEL o Yearlyfee o Unlimitedtraining&support SOFTWARE ISVETTED o 20,000+ usersat 400+ organizations o Quality&feedback DEEPLY ENGRAINED IN SCIENTIFIC COMMUNITY o Give backto thecommunity o Contributecontentandsupport INNOVATIVESOFTWARE SOLUTIONS o Cited in1,000s ofpublications When you choose Golden Helix, you receive more than just the software
- 8. PDF REPORT WORD REPORT EXCEL TABLE B A M Calling of CNVs V C F Annotating, filtering & prioritizing of clinically relevant SNPs and CNVs ‐ Clinical interpretation of SNPs & CNVs ‐ ACMG & AMP guidelines assessing germline and somatic variations ‐ Clinical reporting
- 9. VSClinical - AMP Guidelines: Analyzing Biomarkers Haroche J. et al. Dramatic efficacy of vemurafenib in both multisystemic and refractory Erdheim-Chester disease and Langerhans cell histiocytosis harboring the BRAF V600E mutation. Blood 2013 121 • Biomarker Definition - Biological states with indications for treatments, prognostic, or diagnostic outcomes - Presence or absence of proteins, antigens, and specific genomic attributes of the tumor • Common Cancer Biomarkers - HER2+: High levels of HER2 receptor protein - MSI-H: Microsatellite instability-high - BRAFV600E: Activating mutation V600E - ERBB2Amp: Amplification of ERBB2 - BCR-ABL1: Activation of ABL1 with BCR fusion - TP53WT: No significant alterations of critical TSG
- 10. Tier I: Variants of Strong Clinical SignificanceTherapeutic, prognostic & diagnostic Level A Evidence FDA-approved therapy Included in professional guidelines Level B Evidence Well-powered studies with consensus from experts in the field Tier II: Variants of Potential Clinical Significance Therapeutic, prognostic & diagnostic Level C Evidence FDA-approved therapy for different tumor types or investigational therapies Multiple small published studies with some consensus Level D Evidence Preclinical trials or a few case reports without consensus Tier III: Variants of Unknown Clinical Significance Not observed at a significant allele frequency in the general or specific subpopulation database, or pan- cancer or tumor- specific variant database No convincing published evidence of cancer association Tier IV: Benign or Likely Benign Variants Observed at a significant allele frequency in the general or specific subpopulation databases No existing published evidence of cancer association (2017) Standards and Guidelines for the Interpretation and Reporting of Sequence Variants in Cancer: VSClinical - AMP Guidelines: Reporting Biomarkers • Clinically Actionable Biomarkers - AMP guidelines best practice with clinical significance “Tiers” for drug response + prognostic/diagnostic implications - Reports should include actionable results and include quality of clinical evidence in context to patient’s tumor
- 11. VSClinical – AMP Guidelines: Four Phased Workflow 1. Filter and select variants for evaluation • Follows existing VarSeq filter workflow 2. Assess all evidence for variant • Presented in VSClinical interpretation hub 3. Develop final classification • Somatic - Oncogenicity scoring, Drug sensitivity, Drug Resistance, Prognostic and Diagnostic • Secondary Germline - ACMG based classification criteria 4. Include interpretations in final clinical report Benign Oncogenic Uncertain Significance
- 12. VSClinical – AMP and ACMG Guidelines: One Suite • Increased lab throughput • Consistent results • Shorten learning curve • Staying abreast of new developments Germlin e Somatic
- 13. AMP Guidelines – Annotations Population Database to exclude common variants Cancer Specific Databases Sequence Repositories Clinical, Drug, and Prediction annotations Splice Site and Functional Prediction Algorithms
- 14. Golden Helix - CancerKB • Cancer Interpretation Catalog - Included with AMP Guidelines • Primary Values - Curated by professionals in the clinical context - Jump starts interpretation - Accelerates time to reporting
- 15. Clinical Trials The Importance of Clinical Trials - Clinical trials are vital for the development of new and improved cancer treatments - Only around 5% of patients participate in a clinical trial - One reason for this is that patients cannot find relevant trials NCI provides a comprehensive database of clinical trials for cancer VSClinical can search NCI-supported trials based on: - Cancer Type - Biomarker - Location
- 16. VSClinical – Clinical Report • Customizable Clinical Reports including • Patient information • Biomarkers • Drugs and trials • Failed hotspots and targets • Coverage and sequence summary • Clinical methods and citations • Rendering of clinical reports within seconds • Supported output formats - Word - PDF
- 18. NIH Grant Funding Acknowledgments • Research reported in this publication was supported by the National Institute Of General Medical Sciences of the National Institutes of Health under: • Award Number R43GM128485-01 • Award Number R43GM128485-02 • Award Number 2R44 GM125432-01 • Award Number 2R44 GM125432-02 • Montana SMIR/STTR Matching Funds Program Grant Agreement Number 19-51-RCSBIR-005 • PI is Dr. Andreas Scherer, CEO 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.
- 20. Questions & Answers *All questions will be anonymous*
- 21. Golden Helix Resources Visit our site for more webcasts, eBooks, and tutorials!
- 22. Upcoming Conferences ACMG 2020 San Antonio, TX | Mar 17-21, 2020 BIO Asia Tokyo, Japan | Mar 10-11, 2020
- 23. Questions & Answers *All questions will be anonymous*
Editor's Notes
- First foremost, we recently received grant funding from NIH which we are incredibly grateful for. 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. Additionally we are also grateful for receiving 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. Again, we are thankful of grants such as this which provides huge momentum in developing the quality software we provide. Now let’s learn more Golden Helix as a company.
- GoldenHelix 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 of pharmacogenomics work performed at GalxoSmithKline who was and still is a primary investor in our company. We currently have two flagship products Varseq and SNP and Variation Suite (SVS) for short. SVS is our research application platform that enables researchers to perform complex analysis and visualizations on genomic and phenotypic data. SVS has a broad range of tools to easily perform GWAS, Genomic Prediction, Differential expression analysis on RNA-Seq Data and has the ability to process CNV analysis, which we will demonstrate today. VarSeq, on the other hand, is our clinical application platform that is used for filtering and annotating variants of interest. We can also evaluate variants according to the ACMG guidelines with VSCLincal and have the option to automatically create clinical reports from the results of various workflows. Using the same software, we can also perform CNV analysis on targeted gene panels, whole exome, and whole genome sequencing data, and We also have an add-on feature called VSPipeline – which takes workflows created in VarSeq and automates the process of variant annotation and filtering. Now all of the information produced from VarSeq can be stored in our Warehouse solution, which is designed to be installed on a server location and serve as a repository for your variants evaluations, annotations, and hosted reports. Lastly, VSWarehouse can also be used for sharing and integration between license holders.
- 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. pharmaceutical companies, Bayer and Lilly top-tier institutions, Stanford and yale government organizations, NCI clinics, Sick kids genetic testing labs, prevention genetics and lineage With now well over 20,000 installs of our products and with 1,000’s of unique users. So why is this relevant to you?
- This means that over the course of 20 years our products have received a lot user feedback, which we are always very receptive to when developing and releasing newer versions of our products. This user feedback allows our software to stay relevant and well vetted in it’s capabilities and qualities which builds our products reputation, trust, and client experience. We also stay on the forefront of the needs of the industry and community by regularly attending conferences and providing useful product information via eBooks, tutorials, and blog posts. Your access to the software is a simple subscription based model where we don’t charge per sample nor per version. You also maintain full access to our support and training staff to get you up to speed quickly with your analysis
- The Golden Helix clinical stack supports the entire workflow for NGS genetic testing of cancer. (review each step in summary)
- We utilize AMP guidelines not only create a full understanding of a biomarker’s impact, but also to investigate and report on a variety of biomarker types. This could include single nucleotide variants, insertions or deletions, copy number variants, gene fusions, and considerations for wild type genes. The fundamental goal here is to leverage known clinical interpretations, drug sensitivity and resistance, as well as prognostic and diagnostic information from cancer databases which will inform treatment- an example can be seen in the image on the right, demonstrating the efficacy of vemurafenib in a patient harboring the BRAF V600E mutation. VSClinical’s framework follows the AMP Guideline suggested tier system to analyze available clinical evidence and clinical significance of any given variant.
- The clinical evidence available for a given biomarker is always building and evolving, and the Amp Guidelines suggests a clustering of available clinical evidence in four tier levels that most of you are likely familiar with. Tier 1 variants are those that have strong clinical significance i.e. known FDA approved therapies or well powered clinical studies from experts in the field. Tier II variants are those with FDA approved therapies for a different tumor type, investigational therapies, and preclinical trials. Tier III variants are those without convincing clinical evidence and Tier IV variants are those that are classified as benign or likely benign due to high allele frequency in population databases. As you can imagine, capturing all of the relevant evidence to determine the tier level of a given biomarker is a large undertaking. Thus, we have made an effort to automate as much of the AMP guidelines process as possible by leveraging information from the most up-to-date annotation sources.
- From a workflow perspective, variants selected for interpretation in VSClinical will initially pass though a user-defined filter chain. This could include eliminating low quality, common, or known benign variants. The filtered variants can then be pulled into VSClinical for the interpretation of somatic and germline variants according to the AMP and ACMG guidelines, respectively. Essentially users will be guided through all the available evidence for the variant or biomarker in a streamlined fashion that locks in a consistent interpretation process. After reaching the final classification and interpretation, a clinical report can be rapidly generated. Let's discuss in more detail the value of VSClinical.
- The major hurdle VSClinical overcomes is the inherent limitation with manually accounting for all evolving knowledge for any given variant. This is especially true regarding cancer databases which evolve and grow exponentially, and it is unrealistic for a single person to promptly keep track of all the information. The need for automation is critical and VSClinical is the solution. Not only is consistency upheld through handling all available evidence across multiple databases, but it also removes any issue with underlying workflow fatigue. Moreover, the improved efficiency provided by VSClinical allows less experienced users to process more data more quickly while still maintaining interpretation consistency. A more subtle value but just as critical is the educational benefit VSClinical provides in familiarizing new users with the AMP and ACMG guidelines. Lastly, users benefit from the fact that we integrate advances in these guidelines directly into the software so users spend more time performing variant analysis and less time having to modify or update their bioinformatic pipeline. Because VSClinical serves as the ACMG and AMP guideline interpretation hub it is important to discuss the cancer-based annotations that much of the interpretation is based on.
- Here we see a list of the various evolving cancer databases that are automatically incorporated into the AMP guidelines workflow. These databases contain information about variant frequencies, known somatic mutations, functional predictions, and treatment information. This categorized list is meant to allow for a quick breakdown of all the databases relevant for their application. Some popular cancer databases, such as COSMIC and CIVIC, contain vital data about each variant, tumor location, and histology, along with links to original publications and case reports. We also support Clinical, Drug and Prediction annotations such as DrugBank, PMKB and Clinical trial information, that can provide insight into targeted therapies for a given gene or biomarker. Not seen on this list but is incredibly useful to users is our Golden Helix supported Cancer KB catalog.
- The Cancer KB catalog is accessible for any GoldenHelix user with purchase of the AMP Guidelines. This catalog is a carefully reviewed dataset containing assessments of biomarkers and genes in the context of specific cancers, including information on the Gene, Biomarker and available treatments. This catalog is built by an expert panel of curators and professionals in the clinical context that aggregate and write up interpretations for user submitted biomarkers and genes, such as this interpretation for the clinically relevant BRAF V600E mutation in melanoma. Additionally, users of the AMP guidelines can choose to anonymously integrate their interpretations into this database, which will be reviewed by GHI curators and updated on a regular basis to serve as an ever-growing cancer resource. These interpretations can be used as a starting point for a lab to create an interpretation and streamline the progress to a final report.
- Clinical trials are vital for the development of new and improved cancer treatments, making these therapies available to patients in clinics and hospitals around the world. Unfortunately, too few people with cancer enroll in clinical trials. In fact, only around 5% of patients have participated in a clinical trial. While there are many reasons for this, such as patients being too sick, or distrusting the idea or intent of a clinical trial, a leading reason for lack of enrollment is that patients and clinicians are unable to find trials for which they are eligible. We strive to help remedy this situation by providing a streamlined, user-friendly interface for searching NCI-supported cancer clinical trials. Though VSClinical clinicians can easily identify nearby trials relevant to their patient’s biomarkers and cancer type, greatly reducing the difficulty of searching for relevant clinical trials.
- Following variant interpretation and classification is the ability to easily and quickly create standardized clinical reports including information on … All relevant annotations and evidence for a variant are automatically added to the report, with the report customizations made possible through a Microsoft word template. As mentioned in the previous slide, the time to reporting is greatly reduced even more when leveraging CancerKB’s predefined clinical assessments.
- First foremost, we recently received grant funding from NIH which we are incredibly grateful for. 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. Additionally we are also grateful for receiving 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. Again, we are thankful of grants such as this which provides huge momentum in developing the quality software we provide. Now let’s learn more Golden Helix as a company.