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VarSeq VSClinical AMP Tutorial

Welcome to the VSClinical AMP Tutorial!

This tutorial covers a basic VSClinical AMP workflow with an emphasis on understanding and exploring VSClinical AMP classification tools.


To complete this tutorial you will need to download and unzip the following file, which includes a starter project.


The majority of the workflow described in this tutorial requires VarSeq with the VSClinical AMP algorithm. You can go to Discover VarSeq and request a viewer or evaluation license.



Files included in the above ZIP file: AMP 2.3.0 Tutorial Example – Starter project containing 1 VCF sample file for the AMP Guidelines example.


The most recent version of VarSeq can be downloaded from here: VarSeq Download.

varseq download

Select your operating system and download. Additional information for platform specific installation can be found in the Installing and Initializing section of the manual.

The Setup Wizard will then guide you through the setup process.

setup wizard start

On the final page of the Setup Wizard, select Finish with the Launch VarSeq option checked.

setup wizard finish

This will bring up the introductory VarSeq page where new users can register their information. This will lead to a confirmation email being sent to confirm the email address.


Once the email has been confirmed, users can select the Login tab and enter their login email and password.


At this point, the VarSeq Viewer mode is accessed and can be used. If the user already has a license key, this can be activated by selecting Help on the title bar and then selecting Activate a VarSeq License Key.

activate license

This will bring up a dialog where the license key can be entered. Enter you license key, select and select Verify.

activate license key

Once the license key is verified, select the I accept the license agreement after reading the agreement, and select Verify.

Congratulations! At this point, the product license is activated and you are ready to start an example project or a tutorial!


During the initial installation process, the user will be asked where to store the AppData folder. Although this location can be changed after installation, it is recommended that multiple-user organizations select a shared drive location to increase ease of project sharing and to decrease redundancy.


VarSeq VSClinical AMP

The VSClinical workflow is used to first pass selected variants through a user-defined filter chain, analyze the filtered variants according to industry standard guidelines in a streamlined fashion to reach an interpretation and then generate a clinical report. The user-defined filter chain can be included to eliminate low quality, common, or known benign variants for example and then the filtered variants can be pulled into VSClincial for the processing of somatic and germline variants according to the AMP and ACMG guidelines, respectively. 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 rapidly be generated. Please view the VSClinical ACMG Guidelines tutorial for workflow generation and details specific to the ACMG Guidelines and germline variant analysis.

The VSClinical AMP guidelines differ from the ACMG Guidelines by analyzing the oncogenicity of somatic biomarkers. These biomarkers include single nucleotide variants, insertions or deletions, copy number variants, gene fusions, and considerations for wild type genes. Furthermore, the goal is to not only account for the various biomarkers but to store the final classifications and interpretations and supply treatment options for the patient in a clinical setting. This means the drug sensitivity and resistance information, and prognostic and diagnostic information will be used to determine the biomarker classification and oncogenicity score.

oncogenic scale

Oncogenicity and AMP Tiers

VSClinical serves as the ACMG and AMP guideline interpretation hub and allows for creating more intuitive workflows and higher consistency in results. In the case of the ACMG Guidelines, VSClinical provides a workflow that integrates the exact criteria and classification rules to follow the guidelines like shown below.

Figure 2-1: Pathogenic classification recommendations.

The Golden Helix Oncogenicity Score was developed to provide a criteria-based scoring system similar to the ACMG guidelines but with the numeric pathogenicity scale introduced by Invitae’s Sherloc scoring system. Many of the scoring criteria are similar if not identical to those used by the ACMG guidelines, while others are specialized to match somatic annotations and clinical evidence. For example, missense variants are checked against the cancer hotspot annotation as well as active binding sites as these are often present for activating missense mutations. The criteria and strengths were developed in consultation with the GA4GH Variant Interpretation in Cancer Consortium (VICC).

Similar to the Pathogenicity Score produced by the ACMG Guidelines, the Oncogenicity Score allows the user to get a quick snapshot of how much evidence is pushing the variant in a direction that warrants more careful review or whether it can be safely ignored as Benign. In fact, the Benign and Likely Benign classifications utilize the exact same criteria, rules and thresholds as the ACMG scoring system. Because it is based on numeric criteria, the summation of all scored criteria place the variant on a scale from Benign to Oncogenic.

Figure 2-2: Oncogenicity scoring recommendations.

After a variant is suspected to be Oncogenic, the next level of evaluation is to consider the variant as a Biomarker for the current patient’s tumor type. This requires a review of many sources of clinical evidence that may contain links between this biomarker and targeted therapies as well as potential diagnostic and prognostic implications.

The AMP Guidelines provide a rubric to grade the summation of this clinical evidence and categorize these into “tiers”. To reach Tier 1, a variant of strong clinical significance would require Level A and B evidence i.e. known FDA approved therapies or well powered studies with expert consensus respectively. While Tier II variants of potential significance require level C and D evidence which is FDA approved treatments for different tumor types, investigational therapies, multiple published studies with consensus, or preclinical trials with case reports. Tier III variants of unknown clinical significance will have little or no presence in general frequency, nor cancer specific databases and no publications with cancer association. Lastly, Tier IV variants are benign or likely benign due to high allele frequency in population databases and have no published evidence for association to cancer.

Figure 2-3: FDA Standards and Guidelines for the Interpretation and Reporting of Sequence Variants in Cancer (2017).

The capture of all this relevant evidence to determine the tier is a large undertaking, but VSClinical creates the ability to not only automate the classification process but also automate the presentation of all relevant content in a final report.

CancerKB and Annotations Databases

The AMP Guidelines paper provides several tables of public and proprietary databases that contain information about variant frequencies, known somatic mutations, functional predictions, and treatment/clinical trial information. These categorized lists are meant as a non-exhaustive survey of resources that clinical labs may reference when following the AMP guidelines. VarSeq and VSClinical includes all of these sources that are publicly available and many of the proprietary sources (some are not available through commercial licensing).

The first table from the AMP paper enumerates population databases that can be used to determine common variants versus rare variants.

Figure 3-1: The population database used by AMP Guidelines analyses.

Many cancer variants recur in specific types of cancer, and somatic catalogs allow annotating how many samples and in which tumor types a variant has previously been observed. Clinical databases such as CIViC and PMKB catalog evidence statements and clinical interpretations about mutations and specific tumor types. Finally, other cancer-specific sources such as mutation hot-spots and clinical trials for relevant cancer drugs are referenced.

Figure 3-2: Cancer specific databases used.

Sequence repositories are used for sequence alignments and the definition of genes and transcripts on the reference sequence.

Figure 3-3: The list of sequence repository annotation sources.

We also support Clinical, Drug and Prediction annotations such as DrugBank, ClinVar, the Clinical Genomics Database and the Genetics Home Reference. DrugBank in particular provides critical information about FDA approved drugs with indications for a given gene or biomarker.

Figure 3-4: Clinical, drug, and prediction sources.

The next group of annotations is used to highlight splice site regions and incorporate functional prediction scores for possible sequence disruption.

Figure 3-5: Splice site and functional prediction sources.

An additional feature of the VSClinical AMP Guidelines is the GoldenHelix CancerKB catalog which is accessible for any GoldenHelix user with purchase of the AMP Guidelines. This catalog is manually curated dataset containing assessments of biomarkers and genes in the context of specific cancers, including information on the Gene, Biomarker and available treatments, and at the level of the cancer type. This catalog is built by an expert panel of curators and professionals in the clinical context that aggregate and write up interpretations the most commonly seen biomarkers and genes.

What is curated in CancerKB

Targeted Therapies – FDA labels and NCCN guidelines list genetic biomarkers indications for specific tumor types

Off-label use – Drugs may be effective in other tumor types, and are often tested in ongoing trials

Investigational Therapies – New-generation drugs list specific genomic biomarkers as enrollment criteria for ongoing trials

Biomarker Driven Oncology – Specific mutations may also provide prognostic, diagnostic, and known drug resistance clinical evidence

These interpretations will be based on FDA approval and professional guidelines, mainly NCCN (Tier1 Level A), or consensus of well-powered studies from experts in the field (Tier1 Level B), FDA approved off-label or investigational therapies or published consensus studies  (Tier 2 Level C), or pre-clinical trial case reports (Tier2 Level D).

In general, these types of drugs will NOT be listed as treatment options in CancerKB, unless they appear with a targeted therapy:

·        Chemotherapy drugs

·        Experimental drugs without enough evidence

For example, CancerKB will contain interpretations for the clinically relevant BRAF V600E mutation in melanoma. Interpretations provided by CancerKB are a great starting point, and as you save your own lab interpretations, your internal knowledgebase will grow to cover more and more of the biomarkers seen in each new sample. Additionally, users of the AMP feature can choose to share their interpretations back to the GHI curators anonymously. CancerKB will be updated on a monthly basis to serve as an ever-growing cancer resource.

Figure 3-6: The ACMG Sample Classifier algorithm selection.

The CancerKB catalog can be used as a starting point for a lab to finalize an interpretation and streamline the progress to final report, which we will see in the following examples.

Project Workflow

This tutorial was accompanied by a VarSeq project contained in a ZIP file. Before starting, download the ZIP file, and extract the contents to a convenient location. Then open up the project, AMP Tutorial Example, by double clicking on the file.

This is a pre-made project where variants have been imported, a filter chain composed, and biomarkers selected. Upon first opening the project, you can see a filter chain on the left and larger variant table on the right. Looking to the upper right-hand corner will show the summation of this project which contains data from 4 samples and a total of 126,966 variants.

Figure 4-1: A new project introduction in VarSeq

The VarSeq workflow is a modified version of our Comprehensive Cancer Template, which is based the strategy laid out by the ComPerMed working group, a panel of Belgian experts in cancer diagnostics that set out to establish a uniform biological classification system and streamline clinical interpretation of somatic variants detected by NGS. The filtering strategy focuses in on whether a variant falls in a tumor suppressor gene or an oncogene. Several ComPerMed recommendations are addressed in the Comprehensive Cancer Template discussed below. We also address other factors within VSClinical evaluation, such as our clinical reporting formats and Oncogenicity scoring recommendations which cover presence in Somatic Catalogs, in silico predictions, and previous reports of oncogenicity or pathogenicity in Civic or ClinVar. The filter template will do a lot of the heavy lifting of prioritizing variants before import into VSClinical for final oncogenic classification and Biomarker, Drugs, and Clinical Trials evaluation according to the AMP guidelines. Closer examination of the filter chain shows the following filters were selected:

  • Variant Quality Filters
    • Filter field = PASS
    • Variant Allele Frequency >0.01
    • Read Depth >= 300
  • Variant Function and Frequency
    • Splice Variants, Loss of Function and Missense Variants
    • Population Frequency – <1% or missing from GnomAD Exomes, 1KG Phase 3 and dbSNP Common
  • Remove Benigns
    • Exclude variants that are Benign or Likely Benign in ClinVar with 3 or 4 star status
    • Exclude variants that are classified Benign or Likely Benign by ACMG autoclassifier
  • Variant Filtering Workflows
    • Tumor Type Specific filters using ComPerMed based gene panels for solid and myeloid tumors and MSK based Cancer Hotspots
    • TP53 variants with clear or moderate dominant negative effect
    • LoF mutations in a TS gene
    • LoF mutations in a Oncogene
    • Missense or other mutations in well known (in COSMIC, CIViC or ClinVar) and disease causing oncogenes and TS genes
  • Cancer type PhoRank gene ranking >0.95

Overall, this filter chain is used to first reduce the large amount of variants down to higher quality variants, then variants that look to be causing problems like LoF or splice disrupting. And finally, variants are filtered based on tumor specific workflows. For the sample displayed on opening, this reduces the pool of variants from over 100,000 to 8 candidates.


These filters where chosen to provide interesting examples in our final filter list and not as an example production filtering workflow. For example, a user may want to adjust the quality filter thresholds, choose not to remove benign variants or use a more specific gene list for their tumor type.

Figure 4-2: The VarSeq project with filter chain and variant table.

Once we have determined a smaller selection of variants to explore further, we will open up the AMP Guidelines. This is done by selecting the plus sign next to the Variants tab and selecting VSClinical.

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Figure 4-3: The VSClinical Dependencies Download dialog.

VSClinical Workflow

From the drop-down menu on this new VSClinical tab, select AMP Guidelines.

The first dialog that appears on the new tab is the VSClinical AMP Options dialog. This dialog prompts the user to choose the assessment catalogs for saving Drug Interpretations, Cancer Interpretations, Somatic Variants, and Germline ACMG Variants. If assessment catalogs for any category already exist, they can be found or navigated to from the corresponding drop-down menu, but we want to create new assessment catalogs for this tutorial by selecting Create first for the Internal Database of Cancer Interpretations section. As a shortcut, you may select Create Missing catalogs. This will create by default SQLite catalogs for all missing options.

Figure 4-4: Creating an assessment catalog

The create catalog will first ask you to determine the database type. The options are SQLite, PostgreSQL, MySQL, or if you have the added feature connected, VSWarehouse.

If not using VSWarehouse, a common choice is using the SQLite type and saving the catalog locally. In either case, select a name or location for the new assessment catalog and select, OK.

Repeat this step for the Internal Database of Classified Somatic Variants and Internal Database of Classified Germline ACMG Variants. The example below shows 4 created SQLite assessment catalogs called Drug Interpretations AC, Cancer AC, Somatic AC. and Germline ACMG.

Figure 4-5: Filling assessment catalog selections

The next dialog is the Annotations Versions and Download dialog. Download all of the Required sources.

It should also be noted that users may have previous versions of dependencies already saved. If so, the blue Update All icon will show up, and and orange update arrow will indicate which source(s) have updates available. The user can choose to update, by selecting the Update All icon or continue with the older version of the annotation source. to download the current version. Make your download selection and select the Download button. Download progress will be displayed and once complete, select Close .

Figure 4-6: Annotations Versions and Downloads dialog

The next dialog prompts the user to set up record sets to not only track variants in the project but also to assign reporting sections for variants. These are optional and do not have to be created. For this tutorial leave the default No Record Sets and select Apply.

Figure 4-7: Creating variant set and reporting section dialog

Another optional, but useful *new feature* for the VSClinical AMP user is to choose the Interpretation Match Behavior. Click on the General tab to view the options interpretation match behavior. There are three options:

  • Match Best: This is the default option which autopopulates the best matching previous interpretation for the tissue type (from CanerKB or from your own knowledgebase)
  • Match All: Autopopulates all the previous interpretations that match the biomarker scope (e.g. activating mutation) for the tissue type.
  • Match All Including other Tumor Types: Autopopulates all previous interpretations that match the biomarker scope for the selected tissue type plus all tier 1 drug interpretations that match the biomarker scope for other tissue types.

For this tutorial we will choose Match All including other tissue types.

Figure 4-8: Choosing interpretation match behavior.

As one last step before we explore the AMP Guidelines, we will stash the filter chain by hovering the mouse over the upper left-hand corner of the filter chain tab and click on the arrow icon that appears.

Figure 4-9: Stash filter chain with arrow

AMP Guidelines

Selecting a tumor type

Start by selecting a tumor type. The tumor type can be selected by first narrowing down the tissue type on the left side of the selection menu, by searching for the tumor type directly in the Search Tumor Type search bar, or by using the tumor type acronym (for example (NSCLC) for Non-Small Cell Lung Cancer). It is important to select the tumor type at this stage as future analysis will use this to collect relevant data. This tutorial is examining several cancer types, starting with non-small cell lung cancer (NSCLC). After selecting this tumor type, click on Start New Evaluation underneath the tumor type section.

Figure 5-1: Selecting a tumor type and starting a new evaluation

The top of the AMP Guidelines screen has a title bar that shows the tab navigation between the EvaluationGenes, VariantsClinical Evidence and Report tabs. The three lines on the right side of the title bar will show a summary of the evaluation, close evaluation option, and the option to view the annotation sources used for the evaluation. The AMP Guidelines initially opens on the Evaluation tab where the sample and collection data can be entered, and patient information can be added (this has already been done for this tutorial). This information will be used to automatically populate the report information.

Figure 5-2: Evaluation summary and VSClinical navigation.

On the left side of the screen below Sample, the Cancer type may be edited before adding variants to an evaluation. Our new Evaluation Script feature is discussed below.

Importing Variants and Genomic Signatures

Below the Sample and Patient Section, we have options to bring in several types of variants to evaluate. These include:

  • Small variants
  • CNVs
  • Structural Variants (Fusions)
  • Genomic Signatures
  • Negative Findings
Negative Findings and Tumor Type Level Interpretations

Navigate to the negative findings tab to explore the genes related to NSCLC that have significant therapies or related interpretations when a patient is wild type for these genes, which in this example are EGFR, ALK and ROS. In the Knowledge Base Interpretations section, we automatically populate the available drugs for the negative findings as well as the level of the tumor type without any specific biomarker as shown below.

Figure 5-3: Exploring negative findings in NSCLC.
Adding SNPs/Indels, CNVs and Structural variants

To add variants to the evaluation, start by choosing Add Variants from Project. Eight variants should auto-populate from the project. Then select the BRAF p.V600E variant at the top and choose Select. Once the variant is added on the right, select Add 1 Variants.

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Figure 5-4: Adding BRAF p.V600E variant from project

We also have the option to manually enter variants. Select Manually Enter Variants and enter RAF1 S257L. Under Mutation Origin, select Germline Suspected instead of Somatic. Then choose Select and Add 1 Variants.

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Figure 5-5: Manually add germline suspected RAF1 S257L variant

Next, select CNVs (0). Select Manually Add CNVs and enter ERBB2 and make sure Duplication is selected. Then choose Select and Add 1 CNVs.

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Figure 5-5: Manually enter ERBB2 CNV Duplication

Lastly, select SVs (0) and choose Manually Add SVs. Enter BCR-ABL1, choose Select, Add 1 Fusions .

Figure 5-6: Manually enter BCR-ABL1 fusion
Add Variants, CGP data and More Using Evaluation Scripts

A *new feature* in VSClinical AMP is our evaluation scripts which are for importing the outputs of comprehensive genomic profiling (CGP) kits such as TSO500 kit and otherwise streamlining and automating portions of the VSClinical AMP evaluation.

The scripts are:

  • Add all associated clinical trials
  • Import Archer Fusions and Deletions
  • Import Ion Torrent Signatures
  • Import Project Variants, CNVs, SVs
  • Import TSO500 Combined TSV
  • Import TSO500 All Fusions
  • Import TSO500 ALL Splice Variants
  • Sync report status with variant sets
Figure 5-7 VSClinical AMP evaluation scripts

Users may also create custom scripts to further streamline their own workflow and can use any one of our evaluation scripts as a starting point.

Navigate to Evaluation Scripts and choose Import TSO 500 Combined TSV. Once the tab for the script shows up, click on Run to upload the TSV file included with this project. After uploading the file, click on Run Import TSO 500 Combined TSV. This will import any available CNVs, fusions, splice variants and genomic signatures.

Figure 5-8: Adding TSO500 output from combined TSV file.

After all of the variants are added, the evaluation should look similar to Figure 5-9. Please note that CancerKB is updated on a monthly basis, so the numbers of interpretations may differ from what is shown below.

Figure 5-9: VSClinical variants and Knowledge Base interpretations.

Below this will be the Drugs and Trials summary. To populate this section with clinical trials for the biomarkers in the project, select and run the Add all associated clinical trials script. The tab should look like this once the script is complete. Please note that due to the potential large volume, clinical trials at the tumor type level are not added by this script. These can be selected manually.

Figure 5-10: Evaluation script added all clinical trials associated with a specific biomarker.
NGS Sequencing summary

The final section in the Evaluation tab is the NGS Sequencing Summary showing the NGS statistics like the variant allele frequencies and the types of variants being analyzed.

Figure 5-11: The AMP Guidelines Evaluation tab NGS Sequencing Summary section.

Before taking a closer look at the BRAF and RAF1 small variants in the Variants tab, the Genes tab can be used to analyze the coverage for this sample. Select Genes from the title bar.

Genes Tab

The Genes tab not only includes the NGS Coverage Summary, but also includes a section on reporting coverage regions, and a deeper analysis of calculated coverage and hotspot analysis.

Figure 6-1: Gene tab including NGS Coverage of Summary, Reported Genes, and Reported Failed Targets and Hotspots

The coverage region plot allows user to adjust the read depth and mapping quality thresholds, and also allows the switching between showing coverage on a per-gene or per-target region.

As an example, hover over the BRAF gene in the plot and see the coverage statistics on a gene level. If you click on the gene, the plot changes to show each target from the coverage panel.

Figure 6-2: BRAF coverage on a gene level and manipulating the plot

After ensuring that the coverage of the sample is of high quality for the variants and biomarkers that will be analyzed, we are now ready to take a closer look at the BRAF and RAF1 Gene interpretations for Clinical Evidence and Frequency and Outcomes, as well as our Genomic Signature interpretations.

The Genes tab also lists each gene in the evaluation from the list of small variants, CNV, SVs and negative findings, as well as genomic signatures. In this tab you will find interpretations on the Clinical Significance of each gene and gene signature, as well as detailed information about the Frequency and Outcomes with regards to all cancer and specific cancer types.

Figure 6-3: Biomarkers and gene interpretations.

For this example we will explore the BRAF Gene Summary section, where we collect data about the BRAF gene, especially using the different catalog tools on the right. These descriptions and interpretations can be expanded and then copied using the plus signs at the end of each entry and pasted into the Interpretation on the left. Note that interpretations will fall into several categories: generally applicable to “all cancers”, specific to solid tumors, broadly to a cancer type (lung cancer) or be tumor type specific interpretation as shown here for Non-Small Cell Lung Cancer and would be reusable for all biomarkers in this gene.

Figure 6-4: The VSClinical AMP Clinical Significance summary for the BRAF gene.

Of note, the default interpretation will be tumor type specific if one is available, but that can be changed from the “Interpretation Saved For:” drop down menu, which allows users to switch to a more broad tumor type if the selected one is too specific for the interpretation being written. If a user creates or edits and interpretation, it is important to select the Review & Save Now… button to compare and save any changes.

To show this, click within the interpretation text box on the left, hit Enter on your keyboard to reach a new line, and paste. Since the entry was adjusted, the option to Review & Save Now… becomes available so select it. If there was already an interpretation for this variant saved, the dialog that appears would show the differences and allow the user to select which interpretation to save.

The other important feature in this dialog is the blue checkbox in the lower left-hand corner. If this is checked, the interpretation will be shared with the Golden Helix Curation Team to improve the CancerKB database. For this example, though, close out of this dialog.

Figure 6-5: Reviewing Interpretation changes and submitting to CancerKB

The next section is BRAF Alteration Frequency and Outcomes and goal of this section is to analyze the gene in terms of the related tissue and tumor types and describe the frequency of mutation and prognostic outcomes related to biomarkers in this gene.

Figure 6-6: The AMP Guidelines Biomarkers tab Alteration Frequency and Outcomes section for BRAF

Once this is complete we will then take a deeper look at the BRAF and RAF1 specific mutations as biomarkers in the Variants tab.

Variants Tab

The Variants tab is arranged with a Variant Summary at the top, the Gene Summary (repeated from the previous tab), the Variant Interpretation, scoring and classification sections and biomarker interpretation in the body of the tab, with a variant detail sidebar on the right side. The sidebar contains a numerical tab for easy navigation through each variant and genomic signature, the selected variant position and identifiers on the top, the origin, mutation impact and report information. Some of this information is also displayed in the Variant interpretation tab as well as the current oncogenicity or pathogenicity classification. Within the Variant Classification and Details window on the right, clicking on Edit Variant Scoring will expand the details for Somatic Variant classification and the different criteria used to reach the classification.

Figure 7-1: The AMP Guidelines Variants tab overview.

Example 1 SNV RAF1 S257L

For this tutorial, we will first look at the RAF1 germline variant which was the thirteenth variant in the list. This variant can be selected by clicking on the number 13 card at the top of the sidebar on the right.

Since the RAF1 variant was added as a germline variant, the variant page is formatted to utilize the ACMG Guidelines which were discussed in a previous ACMG tutorial found here. But to review the classification that was reached here, click on Edit Variant Scoring and review the criteria that were selected to reach the classification of Pathogenic.

Figure 7-2: Variant selection from the Variants tab sidebar.

The first criteria selected, PM2 was added since the variant is novel in gnomAD and 1000 Genomes population catalogs. Next, PM1 was selected since nearby variants have shown to be pathogenic as well. The third criteria, PP2 was selected since this RAF1 variant is a missense variant with a high Z-Score and therefore has a higher likelihood to be pathogenic by its very nature. The fourth criteria, PP3 relates that both GERP++ and PhyloP predicted damaging and conserved across different species. The fifth criteria PS1 was selected because the S257L variant results in an amino acid change which is shared by a previously classified pathogenic variant. Finally, last criteria, PM5 confirms pathogenic by showing that this variant has been classified as pathogenic in ClinVar. This was a straight-forward pathogenic variant but note that the scored criteria and the pathogenic classification are reflected in the variant sidebar on the right.

Figure 7-3: The ACMG Criteria Recommendations for Example 1

After confirming the classification of this variant, scroll down in the main page to the next section, Variant Interpretation for SampleNA12877-50-Horizon_S1, to create the interpretation that will be used in the final report.

This section shows the variant classification and allows the user to specify the related disorder and inheritance model. Make sure the the Report As dropdown has Secondary Germline selected and In the For disorder dropdown select Noonan syndrome 5. The interpretation was created by adding explanations from the different scoring criteria in the Scored Criteria section of the variant. To include this interpretation in the report, click the blue Add to Interpretation button, then click Review & Save Now…, then Save and Close.

Figure 7-4: Report Secondary Germline interpretation section

As mentioned previously, the user can scroll down from this section to re-analyze any of the scored criteria in the ACMG analysis of this germline variant. Next, we will look at the other Small Variant, the BRAF variant by closing the Variant Scoring window and selecting the 4 card at the top of the sidebar on this page.

Example 2 SNV BRAF V600E

This BRAF V600E variant represents the first somatic marker to analyze in this project. Details such as the somatic origin of the variant and the choice to report as a Biomarker are selected by default. These can be edited by selecting from the options in the drop down menu. On the right, the tabs on the Variant Classification and Details card present various sources of information about the current biomarker to assist in writing a high level summary of the biomarker (save clinical evidence for the remaining type-specific sections). If there are entries for this variant or other related variants in CancerKB or your own interpretation catalog, the will show up in the Related Interpretations and Related Interpretations in Other Tumor Type cards. Similarly to the previous variant analysis, Report BRAF V600E Biomarker Summary section shows the information and the interpretation that will be used in the final report, but this somatic variant has a related tumor type which in this example is Non-Small Cell Lung Cancer.

Figure 7-5: Biomarker Summary for BRAFV600E.

Clicking on Edit Interpretation in the Report BRAF will bring up an editable biomarker summary interface. It is important to note that the biomarker interpretation is still specific to a tumor type as well as a biomarker scope. The scope should be changed at this point if the following clinical interpretation is not specific to the exact variant but more of a class of variants in a region or for the entire gene. For example, a loss-of-function variant in a tumor suppressor gene should be interpreted at the broadest scope of “Gene LoF” so future variants in this gene that are loss-of-function share the same interpretation. In this case, BRAF V600E has specific clinical evidence and we will keep the scope at the level of this mutation. If there was already an interpretation for this variant saved, clicking the “Version History” will open up a dialog to show the differences and interpretation history and allow the user to select which interpretation to save.

Figure 7-6: Biomarker Summary Interpretation and changing Biomarker Scope.

After editing the Biomarker summary, close the window and explore the Variant Classification and Details window. The variant tab is arranged very similarly to the other small variant, but due to the somatic nature, the ACMG Criteria section is replaced with an Oncogenicity Scoring Recommendations section when you click on Edit Variant Scoring. Also, the pathogenicity color scale in the slide bar has been replaced with an Oncogenicity Scale color bar.

Figure 7-7: Editing variant classification and exploring GHI oncogenicity scoring.

Browsing the Oncogenicity Scoring Recommendations section shows that this variant has been scored with an oncogenicity score of +10 by all of the criteria listed on the left, but we will go through each of these separately.

Scrolling down to the next section gets us to the start of our somatic variant classification.

The Somatic Catalogs section asks if this variant appears in COSMIC, and if it does, how often. Since this variant appears in almost 30,000 samples, the highest value, SC+3 is selected. The card on the right shows the tumor type frequency for different catalogs like COSMIC and ICGC.

Figure 7-8: The AMP Guidelines Variant tab Somatic Catalogs section

The next section looks at the frequency of the variant in different Germline Population Catalogs. This variant shows up rarely in gnomAD (only in the South Asian group) and not at all in 1000 Genomes, so PF+0 is selected for this category and the oncogenicity score is not adjusted

Figure 7-9: The AMP Guidelines Variant tab Population Catalogs section.

The next section, Relevant Clinical Assessments results in the CE+3 criteria being selected since this variant has been previously classified as pathogenic in ClinVar as well as previously classified as oncogenic in CiVIC.

Figure 7-10: The AMP Guidelines Variant tab Relevant Clinical Assessments section

The following sections analyze the impact of the variant on the gene level and since there are multiple pathogenic variants nearby, the NP +1 criteria can be selected. ClinVar gets updated quite frequently, so the numbers reflected here may be different than what is current.

Figure 7-11: The AMP Guidelines variant tab assessing nearby pathogenic variants

It also turns out that his BRAF variant occur in a cancer hotspot, so we can also select the HR+1 criterion in the Hotspots & Active Binding Sites section. This section also shows us that the variant occurs in an active binding site, so the oncogenicity score is increased when adding the AR+1 criterion.

Figure 7-12: The AMP Guidelines Variant tab Relevant Hotspots and Active Binding Sites section.

The final section on this page is Computational Evidence and shows the functional predictions and splice site results.

Figure 7-13: The AMP Guidelines Variant tab Computational Evidence section

This section lets us see that all 4 functional prediction scores support that this variant will likely cause a deleterious effect on the gene, so an IP+1 is selected, but there does not appear to be a splice site disruption, so SP+0 was selected.

Figure 7-14: The AMP Guidelines Variant tab In Silico Predictions section

All of these scores combined give us an oncogenicity score of +10 and confirm that this is indeed an oncogenic variant.

Clinical Evidence Tab

The Clinical Evidence for BRAF V600E includes information covering Drug SensitivityDrug Resistance, Drug Descriptions, Prognostic data, and Diagnostic data. This section also include clinical trail information but we will use another example to demonstrate this.

Figure 8-1: The AMP Guidelines Clinical Evidence section for BRAF V600E.

This information is used to start filling in the interpretation for the final report, including important drugs, and specifying the clinical evidence tier value. The table displays all Tier1 and Tier2 interpretations, and clicking on an individual interpretation will reflect the Drug Sensitivity details below on the left, with the Clinical Evidence about each drug and Matching Clinical trials on the right. The details of each assertion is on the right when selecting each row, and often includes citations. In this example, there are multiple FDA approved drug therapies for BRAF V600E, so this definitely shows a Tier I biomarker entry.

To explore the therapeutic options at all Tier levels, click on “Search All Drug Evidence”. The dialog that pops up will group all the Drug Sensitivity (or Drug Resistance on the next tab) together by Biomarker. Navigating through the tabs on the right you can see the treatments listed by Tier level, and the relevant clinical sources used for the interpretations, specifically CancerKB and CIViC or your internal knowledgebase.

Next, we will look at the ERBB2 CNV biomarker by navigating to the Genes tab and selecting ERBB2 from the Biomarker Gene Interpretations list.

Example 3 CNV ERBB2 Amplification

The next biomarker is a CNV amplification in the ERBB2 gene. Looking at this entry, the ERBB2 information has been switched to apply to the tumor type scope of “All Cancers”. The ERBB2 Function and Descriptions: section on the right shows multiple entries in the COSMIC database. These COSMIC entries have also been organized into categories like Function Summary, Role in Cancer, and Change of Cellular Energetics.

Figure 9-1: The AMP Guidelines Biomarkers tab Summary section for ERBB2

It is also important to notice the multiple PubMed IDs shown in the Interpretation text box of the ERBB2 Gene Summary section on the left. Since these PubMed IDs have been written in the format (PMID: 12345678), they have been automatically recognized and listed in the Inline References: array below the Interpretation text box. These PubMed IDs and titles will also be automatically included and populated in the final report.

The CNV evaluation in VSClinical AMP now includes a section in the variants tab that displays the CNV Summary. This details the number of genes affected by the CNV, brings in the Gene Summary from the previous tab, and displays a CNV Evidence graphic depicting the chromosomal location and other genomic information and quality metrics about the CNV:

Figure 9-2: VSClinical AMP CNV Summary for ERBB2.

Similarly to the ACMG guidelines workflow we also now display Genomic Region evidence. This will include information on the overlap with this CNV and any CNVs in the Cosmic Cancer Gene Census track, several population frequency databases and any previous interpretations. Please note that if the size of the CNV exceeds a certain number of genes this information will not be displayed.

Figure 9-3: VSClinical AMP CNV genomic region section.

Below this, we also display the overlapping genes and the Biomarker Summary. The overlapping genes will list the most significant affected gene at the top. We only show ERBB2 in this case because the Filter to Cancer Genes option is selected. A user always has the option to investigate what other genes may be affected and chose to set a different gene as the biomarker gene in the Selected Gene tab on the right. Here in this Overlapping Genes section we can also see the options for the origin and impact and choose to Report the CNV as a biomarker. We can also Edit the Interpretation from CancerKB, which is specific for Non-Small Cell Lung Cancer in this example. On the right, Related Interpretations can be explored.

Figure 9-4: CNV biomarker summary and overlapping genes.

Select the Clinical Evidence section and choose Search all Drug Evidence.

Figure 9-5: Clinical evidence and theraputic options for ERBB2.

Select ERBB2 Amplification to look at clinical evidence for this gene within all tumor types. Selecting the Drugs tab lists over 100 clinically relevant drug trial entries for this specific mutation in the ERBB2 gene covering all cancer types.

Figure 9-6: Searching all drug evidence.

Data from this table can be used to populate the clinically relevant interpretation section for drug interactions which will be carried to the final report. Since we had added a clinical trial for ERBB2 by running the Add All Associated Clinical Trials script , the related Tier2 drug sensitivity interpretation for Trastuzumab for ERBB2 needs to be saved. Close the search window and locate this record below in the list and Edit Interpretation.

Figure 9-7 Editing interpretation for ERBB2 Tier2 drug sensitivity record.

Add the relevant text for the FDA approved tumor type and Review and Save.

Figure 9-8: Editing, reviewing and saving an interpretation.

Next, we will examine the BCR-ABL1 biomarker by clicking on the seven (7) card at the top of the sidebar.

Example 4 Fusion BCR-ABL1

VSClinical AMP 2.3.0 now has a Variant summary section for fusions that displays the SV Summary and SV Evidence card that shows:

  • the fusion type and effect
  • breakend locations within each chromosome
  • number of COSMIC records for each gene in the breakend pair.

There are interpretations for other tumor types, but no CancerKB interpretations related to NSCLC at this time, as BCR-ABL fusion is most prevalent in hematological cancers. However, cases where a CancerKB interpretation is not curated provide an opportunity for the user to add a custom interpretation to their internal knowledgebase of interpretations.

Figure 10-1: VSClinical AMP summary for the SV BRC-ABL1.

A user may choose “New Biomarker Summary” and build an interpretation for a biomarker in their sample that may not have a CancerKB interpretation for their specified cancer type.

Figure 10-2: Create your own summary using CancerKB interpretations as a starting point.

To explore any drug sensitivity for the ABL1BCR gene fusion, navigate to the Clinical Evidence tab identify the fusion in the list of interpretations that were automatically loaded, or perform the Search all Drug Evidence option to find drugs at all Tier levels that may have not been included in the list.

Figure 10-3: Search all drug evidence for BCR-ABL1.

From here, we can further populate the Drug Sensitivity by selecting any additional therapy for BCR-ABL.

Figure 10-4: Explore additional lower tier options that were not automatically added to the evaluation.

Close the search window and if any changes or interpretations were added for BCR-ABL1 locate this record, edit and select the Review & Save… button.

At this point, we have collected information on the markers of interest and completed our pathogenicity and oncogenicity scoring so we are ready to move on to the final report by selecting Report in the title bar.

Generating a Clinical Report

One of the main value points of VSClinical’s ACMG/AMP reports is in the auto-population of data from your workflow into your clinical reports. In doing so, the results are faster reporting and allowing users to have control over the standardization of the reporting which reduces errors throughout the reporting process.

The report page shows the automatically populated fields and is organized first with Patient and Sample information, Reported Interpretations, Variants and Secondary Germline Findings, Drugs and Trials and we analyzed, then the Secondary Germline FindingsCoverage Statistics, and finally any inline references collected from the entirety of the project with hyperlinks to the source data.

This is a good place to review the data, and any incomplete sections will provide warnings to the user under the Interpretations section in the sidebar. Since we do not have any incomplete or unsaved interpretations, the report is ready to be finalized, so select the green Sign Out & Finalize button.

Figure 11-1: Review and sign out clinical report

This will finalize the project and will not allow any changes to be made without first revoking the report freeze. This concept is reiterated with a follow-up dialog that makes sure the user want to finalize, so select Confirm Sign Out.

Next, click on the plus sign next to the Report Exports section and select New Word Export so we can render a Word format report document. Now we need to generate a new report template. To do so, click on the blue +New Report Template button. From the Copy System Template dropdown, choose Cancer Report Template V3.docx, name the new template AMP Tutorial, and click Create.

Figure 11-2: Creating a new report template and rendering a report.

Now that the report template is defined click the Render icon to generate the final report.

Below is an example of a finalized report. This report is easy to modify as the template is based on Microsoft word, which allows you to orient the template to fit your labs specific branding needs. The report can also integrate sample and patient level information if that data is present in a manifest text file or it can be added manually. Most importantly, the Reports can include interpretations for all variants and genes, somatic and germline, that were added to the VSClinical interface. Lastly, coverage statistics with low quality regions can be incorporated and in-line references are also cited.

CancerKB boosts clinical reports as a starting point, minimizing errors in the process, reducing paste and copy mistakes, improves speed with autopopulated fields, and ensures standardization and consistency across an entire team.

Figure 11-3: New VSClinical Cancer Report v3.

More Examples

Example 5 PIK3CA H1047R

VSClinical AMP also provides a simple and efficient means to search for relevant Clinical Trials for sample biomarkers. We’ll explore this feature using additional example biomarkers starting with a PIK3CA H1047R missense variant for invasive breast carcinoma. The first step will be to browse to Sample S4.

Figure 12-1: Changing current sample view to sample S4

A message will inform you that the sample has changed and confirm the switch. Then open the existing evaluation by selecting Open and Continue.

Figure 12-2: Switching samples and creating a new evaluation

After the sample change, you will notice an existing evaluation has already been created for this sample. Choose Open and Continue on the evaluation card to open this evaluation. You may then be prompted to update the sources used in the evaluation, at the bottom of the Annotations Versions and Download window, choose Update All, then Rescore Evaluation.

You will also notice in the Evaluation Tab that the Tumor Type is set for Breast – Invasive Breast Carcinoma and the PIK3CA biomarker has already been added.

Figure 12-3: Sample S4 Evaluation tab

At this point you can explore the interpretation summaries in the Evaluation tab and variant oncogenicity score in the Variant Tab, but the primary focus will next be in the Clinical evidence Tab. Golden Helix supplies the CancerKB catalog which contains expert reviewed drug interpretations which are linked to available clinical trials.

In this tab, you can see the assessment of available treatment options which are not only also auto-filled from CancerKB, but are also specific to the tissue type and matching H1047R mutation. The list of these drugs are in hierarchical order for matches in DrugBank, PMKB, and CIViC. We also have a drug sensitivity record for Bevacizumab at the tumor type level.

Figure 12-4: Relevant treatment options for PIK3CA biomarker in breast cancer.

From the Clinical Evidence sub window, we can explore matching clinical trials. Our PI3KCA variant has multiple drugs listed such as Alpelisib and Fulvestrant which will be the focus for the clinical trial search. So, select “Matching trials” for these drugs and set the location search to Trials in North America, within 1000 miles of zipcode 59718 (MT zipcode). By default we are looking at the recruiting trials and all phases, but these can be edited.

Figure 12-5 : Matching Clinical Trials for Alpelsib and Fulvestrant for PIK3CA variant in breast cancer.

These results are evolving over time, so what is seen by a user may differ from the figure in this tutorial.

Clicking on the NCT053230810 trial, then the Sites tab, will show that the nearest site selected to the 59718 zipcode location is in Aurora, Colorado, which we will select.

Figure 12-6 : Site selection for clinical trials

Closing that window then scrolling to the bottom of the Clinical Evidence Tab users will be presented with not only summary details for the final report, but also the selection of inclusion and exclusion criteria relevant for the patient, which can be selected for addition to the report.

Figure 12-7: Summary of selected trials and selectable inclusion criteria.

Let’s explore another clinical trial search for a different sample.

Example 6 NRAS Q61K

Switch to sample S3 and open the existing evaluation, update, and rescore the evaluation if necessary. In this sample evaluation the tumor type for this sample is Skin-Melanoma and the biomarker under investigation is Q61K missense variant in NRAS.

Figure 13-1: Evaluation Tab for sample NA12877-10-Horizon_S

Moving to the Clinical Evidence Tab, the interpretations for NRAS and the Q61K variant are prefilled from CancerKB for Melanoma. Focusing on the listed treatment options, the cancer type has a Tier 1 Level A match for an FDA approved combination of therapies while this specific biomarker has Tier2D level matches, a combination of therapies. Exploring the Drug Sensitivity interpretation will reveal that these are mostly investigational drugs with preclinical studies related to NRAS in melanoma.

Figure 13-2: NRAS Q61K Melanoma treatment options listed from CancerKB

With the preferred treatments selected, this patient also wishes to find clinical trials near their new home in Belgium. In the small Clinical Evidence tab, set Trials in to Europe and Filter by distance to Belgium. There is now an updated list of trials in Germany with a match for the treatments Trametinib and Dabrafenib.

Figure 13-3: Tramentinib clinical trial in Belgium

After clicking on Matching Trials, the Summary details list Trametinib as one drug being studied for any biomarkers in NRAS among other genes for melanoma.

Figure 13-4: Summary details for Trametinib clinical trial

Selecting the Sites tab, the patient could potentially enroll in the trial at Universitair Ziekenhuis Brussel.

Figure 13-5: Enrollment sites for the Trametinib clinical trial

Now that we’ve covered how to change trial search locations, let us switch to our last sample and variant to show these new features in a final report.

Example 7 EGFR T790M

Switch to the last sample S2 and open the existing evaluation. The sample in this evaluation with the tissue type selection made for Lung-Non-Small Cell Lung Cancer and the search for treatments will be specific for the oncogenic T790M variant in EGFR.

Figure 14-1: Evaluation Tab for sample S2

Switch to the negative findings tab and you will see that EGFR is one of the genes listed as a negative finding. Select Remove Conflicting Negative Findings.

Figure 14-2: Removing conflicting EGFR negative finding.

Notice again the pre-filled CancerKB interpretations for EGFR and T790M in the Knowledge Base Interpretation summary, as these interpretations will be automatically pulled into the final report. Unlike our earlier NSCLC example, there are no drugs for our negative findings since this example does not have wild type EGFR. Reviewing treatment options in Clinical Evidence, we see FDA-Approved treatments for T790M.

Figure 14-3: List of any FDA-Approved therapies for EGFR T790M

Unfortunately, some of the drugs used for EGFR T790M also have known submissions for drug resistance. This concern is relevant to the clinical trial search as some trials may take ineffective treatments into consideration as an inclusion criteria.

Figure 14-4:  List of drugs with known resistance for EGFR T790M.

Select the available matching trial for Dacomitinib. This trial serves as a great example for specific inclusion criteria for troublesome biomarkers like T790M that infer resistance to treatments.

Figure 14-5: Clinical trial inclusion criteria for Dacomitinib

Reviewing the details of the clinical trial at the bottom of the page, select the inclusion criteria as seen in the figure 11-20. Reading through these criteria, you’ll notice the requirements that the patient must have had an activating mutation in EGFR based on a tissue biopsy and not liquid biopsy.

Figure 14-6: Adding inclusion criteria to the report.

Now that the trial is selected, click on the Report Tab and confirm the sign out clicking on the green icon. Then select New Word Export.

Figure 14-7: Sign out for clinical report

Now, click on the blue +New Report Template icon. Make sure that the Cancer Drugs and Trials Template V3.docx is selected from the Copy System Template dropdown, and give the report template a new name, click Create and then Render.

Figure 14-8: Create a drugs and trials report.

Below is an example of a rendered Drugs and Trails report.

Not only can users open the report in Microsoft word, but also convert the report to a pdf and open directly in VarSeq. Render the word report then test a New PDF export.

Figure 14-9: Example Drugs and Trials Clinical Report.


That concludes the VarSeq VSClinical AMP Guidelines workflow tutorial. More information about the VSClinical AMP Guidelines can be found in the following Golden Helix webcasts. The first is an introduction of the AMP Guidelines and the second is a deep dive into CancerKB 2.0.

The above webcasts go through a VSClinical AMP Guidelines workflow similar to the process shown in this tutorial.

A more in depth look into the Oncogenicity Scoring can be found in the following webcast.

And finally for more details of using CancerKB and building up your lab specific interpretation knowledgebase.

Information can be found from our eBook Library. The eBook that focuses on the AMP Guidelines is Clinical Variant Analysis for Cancer.

If you are interested in getting a demo license to try out additional features that require an active license, such as creating a project, adding annotation sources, and saving project, please request a demo from Golden Helix.

This tutorial was designed to give a taste of all the features and capabilities of VarSeq and a brief orientation to key features. For any additional assistance, please contact our support team (support@goldenhelix.com).

If you are interested in getting a demo license to try out additional features that require an active license, such as creating a project, adding annotation sources, and saving project, please request a demo from: Discover VarSeq

If you have an active license, we encourage you to try out the intermediate tutorial on Cancer Gene Panels: Cancer Gene Panel Tutorial.

Additional features and capabilities are being added all the time, so if you do not see a feature you need for your workflows please do not hesitate to let us know!

Updated on April 21, 2023

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