![Hotspot Region (HR) or Active Region (AR)
Exon 15 of BRAF shows regions designated as somatic missense mutation hotpsots
as well as key activating sites and binding site annotations
Possible Scores: +2
+1
Region Tracks:
+1 Cancer Hotspots
- Single residue and in-frame indel mutation
hotspots identified in 24,592 tumor samples by
the algorithm described in [Chang et al. 2017]
and [Chang et al. 2016]
+1 Binding Sites / Active Regions
- Curated through InterPro
- Residue annotations from CDD
- More specific than large domain annotations](https://image.slidesharecdn.com/webcastslides-9-230920191204-ef31f79b/75/identifying-oncogenic-variants-in-varseq-27-2048.jpg?cb=1695237565)
Identifying Oncogenic Variants in VarSeq
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The interpretation of somatic variants can be a challenging process. While AMP Guidelines provide detailed rules for accessing the clinical evidence associated with a specific variation, they do not specify criteria for determining if a variant is likely to be a driver mutation, which generates functional changes that enhance tumor cell proliferation. In this webcast, we will discuss a new VarSeq algorithm for estimating the oncogenicity of a variant. This will include a deep dive into our oncogenicity scoring system and a discussion of the various criteria used to distinguish driver mutations from benign variations and variants of uncertain significance. What you will learn in this webcast: -How to use the scoring algorithm to identify variants with evidence of oncogenicity -Which criteria are used to assess a variant's oncogenicity -How to evaluate the oncogenicity of a variant in VSClinical
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Identifying Oncogenic Variants in VarSeq
- 1. Identifying Oncogenic Variants in VarSeq September 20, 2023 Presented by Nathan Fortier, Director of Research
- 2. 2
- 3. Identifying Oncogenic Variants in VarSeq September 20, 2023 Presented by Nathan Fortier, Director of Research
- 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 • 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. Who Are We? 5 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
- 6. Cited in 1,000s of Peer-Reviewed Publications 6
- 7. Over 400 Customers Globally 7
- 8. The Golden Helix Difference 8 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
- 9. Today’s Presenters 9 Rana Smalling, PhD Field Application Scientist Nathan Fortier Director of Research Best Practices for Validating a Next-Gen Sequencing Workflow
- 10. 10 Confidential | NGS Clinical Workflow Golden Helix provides comprehensive data analytics software that scales across gene panels, whole exomes, and whole genomes DNA Extraction in Wet Lab and Sequence Generation Interpretation and Result Reporting Primary Read Processing and Quality Filtering Alignment and Variant Calling Secondary *Golden Helix provides Secondary Analysis through a reseller agreement Tertiary Golden Helix’s software and primary focus Comprehensive secondary and tertiary analysis solutions for primary data aggregated by all commercially available sequencers Type Size Gene Panel Small (100MB) Whole Exome Medium (1GB) Whole Genome Large (100GB) Cancer use case Hereditary use case Process Analysis … and scales across multiple data set sizes for cancer and hereditary use cases Filtering and Annotation Data Warehousing Workflow Automation Golden Helix works with all major sequencers… Topic of Validation
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- 12. NGS Testing in Cancer 12 • Personalized medicine in cancer requires genetic testing • Genetic tests can recommend drugs that target cancer cells based on the analysis of mutations • ”Targeted” drugs interact with specific proteins to block cancer growth, progression, and metastasis • Less toxic than chemotherapy Vemurfenib BRAF inhibitor Erlotinib Nilotinib EGFR inhibitor Targets BCR-ABL fusion gene
- 13. More than one type of genetic mutation can drive oncogenesis • Mutations that activate oncogenes: o Missense o In-frame insertions/deletions o Fusions o Copy number amplifications • Functions that inhibit tumor suppressor genes: o Gene deletions o Loss of function nonsense, frameshift indels o Disabling fusions, structural variants o Genomic Signatures that describe overall state of somatic genome Comprehensive Genomic Profiling Tests 13 The next step for precision medicine Copy Number Rearrangements Base Substitutions Deletions Insertions Genomic Signatures
- 14. Identify Clinically Relevant Variants 14 Patient data, variant files, and alignment files Variant QC and Annotation Analyze copy number, structural and single nucleotide variants Report clinical variants with tiered drug sensitivity, resistance, prognostic and diagnostic findings Cancer Genome TruSight Oncology 500 Content Validated Filter Report Filter
- 15. AMP Guidelines 15 AMP Classification Tier Classification (Therapeutic, Prognostic, Diagnostic) Evidence Tier Evidence I Variants of Strong Clinical Significance 1A 1B FDA-approved therapy; included in professional guidelines Well-powered studies with consensus from field experts II Variants of Potential Clinical Significance 2C 2D FDA-approved therapies in another tumor type or investigational therapies; multiple small studies w/consensus Preclinical trials or a few case reports without consensus III Variants of Unknown Clinical Significance 3 Not observed at a significant allele frequency in the general or specific subpopulation databases, or pan- cancer or tumor-specific variant databases IV Benign or Likely Benign Variants 4 Observed at significant allele frequency in the general or specific subpopulation databases No existing published evidence of cancer association Li et al. “Standards and Guidelines for the Interpretation and Reporting of Somatic Variants in Cancer”, 2017
- 16. Cancer Classifier: Oncogenicity Scale Oncogenic Likely Oncogenic Likely Benign Benign -5 0 +3 +5 -4 +2 -2 +1 +4 -1 -3 Variant of Unknown Significance • Germline Population Catalogs • In-Silico Functional/Splicing • Previous / Clinical Evaluations • Somatic Catalogs • Domain / Hotspot Analysis • Gene Affinity to Variant Type
- 17. Oncogenicity Scoring Applies To Criteria -5B -3B -2B -1B +1O +2O +3O All Population Frequency -5 -3 -1 Homozygous in Controls -2 -1 In Somatic Catalogs +1 +2 +3 Relevant Variant Assessments -1 +2 +3 Null Damaging LoF +1 +2 LoF are Oncogenic Mutations in Gene +1 Missense Nearby Pathogenic Missense Variants +2 In-Frame not in Repeat Region +1 Somatic Hotspot & Active Binding Sites +1 +2 Non-Null Computational Evidence -1 +1 All Splice Site Prediction +1 +2 Non-Coding Silent, Intronic, UTR, Intergenic Variants w/ No Splice Effect -3
- 18. Population Frequency (PF) • The maximum sub-population frequency is used. • We use gnomAD and 1000 Genomes (choosing the maximum frequency between both catalogs) • Our thresholds are equivalent to those used in the ACMG Guideline automation for BA1/BS1 but there is no PM2 (+2) for being novel (not in germline catalogs) • Recessive genes allow for higher frequency (two-hit) Possible Scores: Recessive Dominant -5B 1.00% 0.50% -3B 0.15% 0.05% -1B 16 individuals (all) 16 individuals (all) -5 -1 -3
- 19. Homozygous in Populations (HP) • Controls include 1000 genomes and gnomAD “Controls” subset. • Score counts of being homozygous in recessive gene • Score counts of being heterozygous / hemizygous in a dominant / x-linked gene respectively Possible Scores: Number of Individuals -2B Multiple individual -1B Exactly one individual -2 -1
- 20. Somatic Catalogs (SC) • Look at total sample count in COSMIC 96 (tumor type agnostic) • Thresholds chosen to match power law of mutation occurrence in somatic catalogs • +2D/+3D only apply if variant < 16 AC in germline catalogs Possible Scores: +3 +2 +1 # Samples (At Least) Variants in COSMIC +1D 1 3,296,000 (100%) +2D 5 43,000 (1.4%) +3D 35 1,000 (0.03%)
- 21. Clinical Evidence (CE) Possible Scores: Classified variants - Internal Knowledge-Base of classified variants - ClinVar 1+ star Likely Pathogenic / Pathogenic - CIViC 1+ star variants - Other “Consortium” sources Score - +3 if Pathogenic Same Change - +2 if Pathogenic Missense Same Codon - -1 if Benign Scored +3 +2 -1
- 22. Variant Type Specific Criteria Groups of Variant Types: • Null variant: frameshift, stop gain, start loss • Previously classified mutation? • Does mutation result in null / truncated gene product? • Are Null variants shown to be drivers in cancer for this gene? • Missense variants: amino acid substations and length polymorphisms • Previously classified amino acid (same codon)? • In local region of previously classified variants? • In active binding site or mutation hot-spot? • In-silico evidence: functional prediction and splicing? • Non-coding variants: silent mutations, intronic, utr • Predicted to disrupt canonical splice site? Sequencing Ontology on Current Transcript (Selectable)
- 23. Null Downstream (ND) The p.K1358Dfs variant occurs in the last exon of MSH6. There are no other pathogenic loss of function variants downstream of the variant p.K1358Dfs. Possible Scores: Truncating / Null Variant Evidence: +1 Previously classified variant downstream - Any LoF variant downstream of this variant’s position - Sources of previously classified variants: - Internal KnowledgeBase of classified / interpreted variant - ClinVar 1+ star Likely Pathogenic / Pathogenic - CIViC variants with certain evidence threshold / star-rating - Other “consortium” sources +1
- 24. Null-Oncogenic Gene (NG) Possible Scores: +1 Affinity with Gene: Classified variants - 1 or more LoF Pathogenic / Likely Pathogenic Proportion of COSMIC mutations: - 5% of variants are LoF LoF CIViC Evidence - Overlapping oncogenic LoF regions in CIViC - 1 Star+ rating
- 25. Nearby Pathogenic Missense (NP) Possible Scores: Using Previous Classified: There are no benign missense variations within three amino acids of the variant There are at least two pathogenic missense variants within six amino acids of the variant The number of pathogenic missense variants within six amino acids exceeds the number of benign missense variants +1
- 26. In-Frame Not in Repeat Region (IF) For In-Frame Insertions / Deletions: • +1 If the inserted sequence is not repeated two or more times • Considering a version of “Nearby Pathogenic Inframe Variants” for another +1 to boost variants in inframe indel hotspots (i.e. EGFR exon 19) The p.A3571_V3572del variant is a in-frame deletion of an amino acid sequence that is repeated 2 times in the surrounding region. Possible Scores: +1
- 27. Hotspot Region (HR) or Active Region (AR) Exon 15 of BRAF shows regions designated as somatic missense mutation hotpsots as well as key activating sites and binding site annotations Possible Scores: +2 +1 Region Tracks: +1 Cancer Hotspots - Single residue and in-frame indel mutation hotspots identified in 24,592 tumor samples by the algorithm described in [Chang et al. 2017] and [Chang et al. 2016] +1 Binding Sites / Active Regions - Curated through InterPro - Residue annotations from CDD - More specific than large domain annotations
- 28. In-Silico Predictions (IP) & Splice Predictions (SP) In-Silico Evidence (for Non-LoF Variants) • +2: 3 or 4 out of 4 splice site predictions of damaging • +1: In-silico predictions in agreement variant is damaging & conserved • -1: If variant amino acid present in mammalian species • -1: In-silico predictions in agreement that variant is tolerated & not conserved Synonymous / UTR / Intronic Variants • -3: Not predicted to disrupt a canonical splice site and no Pathogenic clinical assessment Possible Scores: +3 -1
- 29. Example: BRAF V600E General Scoring • +0: novel in gnomAD • +3: Somatic catalog of 29,735 samples in COSMIC 96 • +3: In ClinVar as Pathogenic, in CIViC 1+ star Missense/Computational Evidence • +1: Nearby pathogenic variants • +2: In Cancer Hotspot and Active Binding Site • +1: Functional & Conservation all agree Final Score: +10
- 30. Example: SLX4 A1461Pfs*2 General Scoring • +0: 0.0009% (1 of 109874 European) in gnomAD • +1: Somatic catalog of 1 sample in COSMIC • +0: Not in ClinVar or CIViC Loss of Function • +2: Not at end of gene, downstream pathogenic LoF • There are 2 downstream pathogenic loss of function variants, with the furthest variant being 283 residues downstream of the variant p.A1461Pfs*2. • +1: LoF are Driver Mutation in Gene • The p.A1461Pfs*2 variant is a loss of function variant in the gene SLX4, which is intolerant of Loss of Function variants, as indicated by the presence of existing pathogenic loss of function variant NP_115820.2:p.Leu20Argfs*24 and 5 others Final Score: +4 (Likely Oncogenic)
- 31. 31 Product Demo
- 32. NIH Grant Funding Acknowledgments 32 • 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 • 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.
- 33. 33
- 34. 25 Licenses for 25 Months 34 Celebrating 25 Years in Business • Limited quantity • Licenses are 25-month license periods • Available to new customers only • Orders must be received by Oct. 15, 2023 • Visit goldenhelix.com/forms/25-for-25 or scan the QR code below
- 35. Conferences 35 BioJapan 2023, Booth D-55 • October 11 - 13 • Yokohama, Japan ASHG 2023, Booth 506 • November 2 - 4 • Washington D.C. • Friday, November 3rd, 12:20 – Colab Session, CoLab Theater 2 Achieving Economic Success as an NGS Lab: Strategy and Implementation AMP 2023, Booth 1500 • November 16 - 18 • Salt Lake City, Utah • Friday, November 17, 12:40 – Innovation Spotlight, Stage 2 Achieving Economic Success as an NGS Lab: Strategy and Implementation
- 36. 36
Hinweis der Redaktion
- 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 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.
- Let's start with a bird's-eye view of an NGS clinical workflow, and explore how VarSeq fits in. When validating a workflow, it is important to plan with the beginning and end in mind, starting from sample collection and primary analysis to get your samples sequenced then run through the secondary stage handling alignment and variant calling then lasttly through the tertiary stage paired with data Warehousing. VarSeq mainly encompasses the tertiary analysis steps of filtering, annotation, interpretation and result reporting. However, its modular and flexible design makes it compatible with a variety of inputs coming from many secondary pipelines. Golden Helix software functions with all major sequencers, and our partnership with Sentieon allows users to establish industry-leading secondary analysis. Moreover, VarSeq tackles the issue of scalability quite well, allowing users to automate workflows for increasing sizes of datasets from small gene panels to the increasingly affordable whole genome sequencing workflows.
- VarSeq facilitates handling of all your variant types for both somatic and germline analysis. The utility of the software can be broken into stages. The first being the import of your SNVs/indels, CNVs and fusions, then passed through a user defined variant filter coupled with many annotations and algorithms to isolate the clinically relevant variants. These filters and project structure are saved as templates to facilitate automation with our VSPipeline command line tool. Once the clinically relevant variants are isolated, they are then imported into VSClinical which serves as the interpretation hub to collect all relevant evidence for germline or somatic variants via the ACMG and AMP guidelines. Once the variants have been evaluated, it is saved locally in a user database and VSClinical is used to generate a final clinical report. So now that you have a high-level understanding of the tool’s purpose, lets move into discussing today’s topic.
- Personalized medicine is the application of the right drug and treatment strategy to the right patient and there is nothing more personalized than getting down to the genetics of the patient. In the context of cancer this is the genetics of that patient’s tumor. Because cancer is a disease of the genome, we know that even within a given class of cancers, such as lung cancer, the difference in their response to drugs will often be explained by the difference in those somatic mutations and which proteins are being expressed in the cancer’s cells. For instance, a patient’s response to a broad-spectrum chemotherapy may be impacted by the specific mutations present in the tumor. As drug development has continued, researchers have developed targeted drugs that take into account specific proteins and block those to inhibit cancer growth and metastasis. One example of such a drug is vemurafenib, which is a BRAF inhibitor. About half of melanomas have a mutated BRAF gene, and vemurafenib, as a BRAF inhibitor, helps prevent further growth and can potentially shrink tumors in BRAF-mutated melanomas. We also have Erlotinib, which targets a protein called the epidermal growth factor receptor or EGFR. This protein is involved in cell signaling and is mutated in up to 25% of lung cancers, especially in those developing in light smokers or non-smokers. Another example is Nilotinib, which is a tyrosine kinase inhibitor that targets this recurrent BCR-ABL1 fusion gene which is critical for the growth of certain leukemias. There are many other treatments in addition to these that list in their indications for use specific genes and mutations. These drugs provide personalized treatments that directly target the specific cancer cells of a given patient in a way that is potentially less toxic than alternative treatments such as broad-spectrum chemotherapy. So that is the primary motivation to perform genetic testing in the context of cancer.
- Traditionally genetic tests in cancer have focused on small gene panels that include just the most common 50 or so genes, where the test is only looking at small gene mutations such as BRAF V600E. However, there are many other classes of variants that we can’t capture with these small gene panels. This shortcoming is being addressed by a new generation of genomic tests in cancer called Comprehensive Genomic Profiling Tests. These tests are looking at more than one type of mutation that can drive oncogenesis. In some cases we are looking for specific mutations that activate oncogenes. These can be missense mutations and in-frame insertions or deletions but can also include gene fusions and copy number amplifications. These tests also look for mutations that inhibit tumor suppressor genes, such as full gene deletions, nonsense or frameshift mutations, and disabling fusions. Finally, there is the question of what is going on at the genome scale for this tumor. This information is quantified using metrics called genomic signatures, which provide useful supporting information about how the cancer can be treated. This includes metrics like tumor mutational burden and microsatellite instability.
- So let’s discuss the overall strategy. Whether we’re using TSO 500 or another comprehensive panel, we know we’re not getting everything from the cancer genome. Rather, we’re getting a selective set of content and that content defines the scope of the test. Ultimately we’re calling everything we can see at the NGS level. Of course, much of what is called are low quality variants with metrics that fall below the thresholds necessary for us to be confident in the call and will be filtered out purely at a quality level. Additionally, most variants that are high-quality will not be clinically relevant and will be excluded from the final analysis. Finally, many clinically relevant variants will simply not have any available clinical evidence that can be reported.
- So what is our strategy for assessing the available clinical evidence? Well, this is where the AMP Guidelines come in. The AMP Guidelines suggest a clustering of available clinical evidence in four tiers. Tier 1 variants are those that have strong clinical significance in the form of known FDA approved therapies or well powered clinical studies from experts in the field. Tier 2 variants are those with FDA approved therapies for a different tumor type, investigational therapies, or preclinical trials. Tier 3 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, which is why having a tool like VSClinical is vital to performing these evaluations in a streamlined way. VSClinical presents the relevant clinical evidence in a streamlined user-friendly interface and guides the user through an assessment of each variant’s oncogenicity through a series of easy-to-understand questions which will be automatically answered whenever possible. Additionally, VSClinical provides bioinformatic support in the form of graphs, visualizations, and tables to support you during the interpretation process. The first pass of this interpretation can be easily performed by a laboratory technician or resident and can be approved by the laboratory director. We record your notes and scores along with the final interpretation. Once saved to your knowledgebase, each previous interpretation can be automatically reused, so that next time you see that variant no additional work will need to be done. This is perhaps the most labor-intensive step in the analysis process. However, for a previously interpretated variant little work is required and for a novel variant many of the questions are easy to answer. Where you spend the most time will likely be performing literature reviews to look up a novel variant that doesn’t have an existing clear cut clinical interpretation. However, for well studied genes this will often be unnecessary as you will likely be focusing on well classified variants.
- Now before you can begin interpreting variants using the AMP guidelines, you must first filter out likely benign variants and identify variants that may be driver mutations, so that they can be prioritized in the analysis process. This is where our new Cancer Classifier algorithm comes in. This is a new algorithm that can be added to your VarSeq workflow and is designed to help you easily identify driver mutations and filter out variants that are likely benign. During the development of our cancer scoring system we worked with a number of different stakeholders to identify a set of criteria that can be used to quantify whether a variant is likely to be oncogenic and whether it should be prioritized for interpretation. This classifier is based on an additive scoring system in which a variant is categorized into one of four classifications based on a set of thresholds. Specifically, scores exceeding a threshold of 3 are classified as likely oncogenic or oncogenic, while scores falling below a threshold of -3 are classified as benign or likely benign.
- Here you can see the kinds of evidence that we utilize to determine the oncogenicity of a given variant. When determining if a variant is benign, we rely on things like population frequency, whether the variant is homozygous in controls, and whether the variant is a silent or intronic variant that is known to have no effect on splicing. Evidence that we use to determine if a variant is oncogenic includes things like the variant’s occurrence in somatic catalogs, the presence of the variant in databases like CIViC or ClinVar. We also take into account the effect of the variant. For example, if we are looking at a LoF variant, the software will check to see whether loss of function variants tend to be oncogenic in the gene by looking at various cancer databases. Now that you have a high-level overview of the types of evidence used by the classifier, let’s look at some specific criteria.
- Color code our criteria here
- Color code our criteria here
- 571,000 (19%) for 2 or more
- ~1,400 1+ civic variants, all evidence level’s add something worth investigating in the interpretation Pathogenic / Likely and Benign / Likely
- alanine valine repeated twice more
- alanine valine repeated twice more
- alanine valine repeated twice more
- Pathogenic / Likely Pathogenic
- alanine valine repeated twice more
- alanine valine repeated twice more
- alanine valine repeated twice more
- alanine valine repeated twice more
- alanine valine repeated twice more
- 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.
- Title: Project resources Category: Data, Images Tags: table, project, resources, data, cost, picture, image, vial, bottle
- Again, I want to mention how grateful we are we are thankful of grants such as this which support the advancement and development of our software to create the high quality software you'll see today. So with that covered, lets take a few minutes to talk a little bit about our company Golden Helix.