Settings

Sample metadata

  • Sample name  :   SAMPLE-001 

Report configuration

The report is generated with cpsr version 0.6.2, ( pcgr version 0.9.2), using the following key settings:

  • Genome assembly: grch37
  • Report settings
    • R Markdown theme (Bootstrap): default
    • Datatable display: light
    • Non-floating TOC: FALSE
  • Control population gnomAD: Non-Finnish European non-cancer subset
  • Upper MAF threshold (gnomAD) for unclassified variants included in report: 0.9
  • Ignore non-proteincoding variants in report: FALSE
  • Generate CPSR variant classifications also for existing ClinVar variants: TRUE
  • Ignore ClinVar variants in target genes if they are reported only for non-cancer phenotypes: FALSE
  • Include secondary findings in report: TRUE
  • Include GWAS hits in report: TRUE
    • Minimum p-value for association: 0.000005



Summary of findings

Variant statistics

Variant numbers in the selected cancer predisposition genes (n = 105)

  • Number of SNVs: 25
  • Number of InDels: 13
  • Number of protein-coding variants: 21



Variant classification

Class 5 - Pathogenic variants


  • A total of n = 5 variants are registered with a Pathogenic clinical significance in ClinVar.
  • A total of n = 4 non-ClinVar variants (i.e. not registered in ClinVar) are classified with a Pathogenic significance by CPSR (ACMG criteria - based on population frequency and variant effect).


ClinVar







Non-ClinVar






Class 4 - Likely Pathogenic variants


  • A total of n = 1 variants are recorded with a Likely Pathogenic clinical significance in ClinVar.
  • A total of n = 0 non-ClinVar variants (i.e. not registered in ClinVar) are classified with a Likely Pathogenic significance by CPSR (ACMG criteria - based on population frequency and variant effect).



ClinVar







Non-ClinVar

Class 3 - Variants of Uncertain Significance (VUS)


  • A total of n = 4 variants are recorded with a VUS clinical significance in ClinVar.
  • A total of n = 7 non-ClinVar variants (i.e. not registered in ClinVar) are classified with a VUS significance by CPSR (ACMG criteria - based on population frequency and variant effect).



ClinVar







Non-ClinVar


  • IMPORTANT NOTE: Non-ClinVar variants with a CPSR pathogenicity score of 2.0 (maximum score for VUS in CPSR) represent the most challenging collection of variants when it comes to classification. Based on calibration of classification score thresholds with high-confident ClinVar variants, we discovered a heterogeneous mix of Likely Pathogenic, Pathogenic, and VUS variants that were all attributed with this score. Henceforth, we recommend that these variants should be prioritized for careful examination, and any such cases are conveniently higlighted in the table below

:







Class 2 - Likely Benign variants


  • A total of n = 3 variants are recorded with a Likely Benign clinical significance in ClinVar.
  • A total of n = 14 Other variants (i.e. not registered in ClinVar) are classified with a Likely Benign significance by CPSR (ACMG criteria - based on population frequency and variant effect).



ClinVar







Non-ClinVar







Class 1 - Benign variants


  • A total of n = 0 variants are recorded with a Benign clinical significance in ClinVar.
  • A total of n = 0 Other variants (i.e. not registered in ClinVar) are classified with a Benign significance by CPSR (ACMG criteria - based on population frequency and variant effect).



ClinVar

Non-ClinVar



Biomarkers

  • Variants (class 4/5) in the query sample that overlap with reported clinical biomarkers from the database for clinical interpretations of variants in cancer, CIViC are considered. Note that several variants in the query can overlap the same existing biomarker, given that biomarkers are reported at different resolutions (variant/gene level). Total number of clinical evidence items that coincide with query variants:
    • Predisposing: 1 evidence items
    • Predictive: 2 evidence items
    • Prognostic: 0 evidence items
    • Diagnostic: 0 evidence items



NOTE: Reported biomarkers in CIViC are mapped at different resolutions (i.e. filter Biomarker mapping). The accuracy of a match between variants in the tumor sample and the reported biomarkers will vary accordingly (highlighted by gene symbols with different color backgrounds):

  • Biomarker match at the exact variant/codon level

  • Biomarker match at the exon/gene level


Predisposing


The table below lists all variant-evidence item associations:




Predictive


The table below lists all variant-evidence item associations:




Prognostic


No variant-evidence item associations found.




Diagnostic


No variant-evidence item associations found.






Secondary findings (ACMG-based - v3.0)


GWAS hits


No GWAS variants with a p-value < 5e-06 were found.

Documentation

Introduction

This report is intended for interpretation of inherited DNA variants implicated with cancer susceptibility and inherited cancer syndromes. Variants in Class 1-5 are limited to a selected set of known cancer predisposition genes, for which the report lists ONLY those variants that are

  1. Previously classified in ClinVar (five-level significance scheme: pathogenic/likely pathogenic/VUS/likely benign/benign), or
  2. Coding variants not recorded in ClinVar with germline population frequency below the user-defined threshold, i.e. 
    • Minor allele frequency (MAF) < 0.9) in the user-defined population set in the gnomAD database

Annotation resources

The analysis performed in the cancer genome report is based on the following underlying tools and knowledge resources:

  • Software
    • VEP - Variant Effect Predictor (v104)
    • LOFTEE - Loss-Of-Function Transcript Effect Estimator (VEP plugin) (v1.0.3)
    • vcfanno - Rapid annotation of VCF with other VCFs/BEDs/tabixed files (v0.3.2)
    • vcf-validator - Validation suite for Variant Call Format (VCF) files, implemented using C++11 (v0.9.3)
    • vt - A tool set for short variant discovery in genetic sequence data (v0.57721)

  • Databases/datasets
    • GENCODE - high quality reference gene annotation and experimental validation (release 38/19)
    • dbNSFP - Database of non-synonymous functional predictions (20210406 (v4.2))
    • dbMTS - Database of alterations in microRNA target sites (v1.0)
    • ncER - Non-coding essential regulation score (genome-wide percentile rank) (v2)
    • GERP - Genomic Evolutionary Rate Profiling (GERP) - rejected substitutions" (RS) score (v1)
    • Pfam - Collection of protein families/domains (2021_03 (v34.0))
    • UniProtKB - Comprehensive resource of protein sequence and functional information (2021_03)
    • gnomAD - Germline variant frequencies exome-wide (r2.1 (October 2018))
    • dbSNP - Database of short genetic variants (154)
    • DoCM - Database of curated mutations (release 3.2)
    • CancerHotspots - A resource for statistically significant mutations in cancer (2017)
    • ClinVar - Database of genomic variants of clinical significance (20210529)
    • CancerMine - Literature-mined database of tumor suppressor genes/proto-oncogenes (20210611 (v36))
    • OncoTree - Open-source ontology developed at MSK-CC for standardization of cancer type diagnosis (20201104)
    • DiseaseOntology - Standardized ontology for human disease (20210609)
    • EFO - Experimental Factor Ontology (v3.30.0)
    • GWAS_Catalog - The NHGRI-EBI Catalog of published genome-wide association studies (20210608)
    • CIViC - Clinical interpretations of variants in cancer (20210615)
    • CGI - Cancer Genome Interpreter Cancer Biomarkers Database (20180117)

Variant classification


All coding, non-ClinVar variants in the set of cancer predisposition genes have been classified according to a five-level pathogenicity scheme (coined CPSR_CLASSIFICATION in the tables above). The scheme has the same five levels as those employed by ClinVar, e.g. pathogenic/likely pathogenic/VUS/likely benign/benign. The classification performed by CPSR is rule-based, implementing refined ACMG criteria, many of which were outlined in SherLoc (Nykamp et al., Genetics in Medicine, 2017). Important attributes of cancer predisposition genes, such as mode of inheritance and mechanism of disease (loss-of-function), have been harvested from Genomics England PanelApp, Maxwell et al., Am J Hum Genet, 2016, and Huang et al., Cell, 2018

The ACMG criteria listed in the table below form the basis for the CPSR_CLASSIFICATION implemented in CPSR. Specifically, the score column indicates how much each evidence item contribute to either of the two pathogenicity poles (positive values indicate pathogenic support, negative values indicate benign support). Evidence score along each pole (‘B’ and ‘P’) are aggregated, and if there is conflicting or little evidence it will be classified as a VUS. The contribution of ACMG evidence items pr. variant can be seen in the CPSR_CLASSIFICATION_CODE and CPSR_CLASSIFICATION_DOC variables.

Calibration of classification thresholds

How do we derive the variant classification (P, LP, VUS, LB, B) from the aggregated variant pathogenicity score (CPSR_PATHOGENICITY_SCORE)?

We calibrated the thresholds for conversion of pathogenicity scores to categorical variant classification using high-quality ClinVar-classified variants in cancer predisposition genes (see details in BioRxiv preprint). The following thresholds are currently used to assign classifications based on pathogenicity scores:


CPSR_CLASSIFICATION CPSR_PATHOGENICITY_SCORE
Pathogenic [5, ]
Likely Pathogenic [2.5, 4.5]
VUS [-1.0, 2.0]
Likely Benign [-4.5, -1.5]
Benign [, -5]



In the table below, a detailed description of all evidence criteria that are currently used for variant classification in CPSR (green elements indicate criteria that contribute with a benign effect, red elements contribute with a pathogenic effect):



References

Griffith, Malachi, Nicholas C Spies, Kilannin Krysiak, Joshua F McMichael, Adam C Coffman, Arpad M Danos, Benjamin J Ainscough, et al. 2017. “CIViC Is a Community Knowledgebase for Expert Crowdsourcing the Clinical Interpretation of Variants in Cancer.” Nat. Genet. 49 (2): 170–74. http://dx.doi.org/10.1038/ng.3774.
Huang, Kuan-Lin, R Jay Mashl, Yige Wu, Deborah I Ritter, Jiayin Wang, Clara Oh, Marta Paczkowska, et al. 2018. “Pathogenic Germline Variants in 10,389 Adult Cancers.” Cell 173 (2): 355–370.e14. https://doi.org/10.1016/j.cell.2018.03.039.
Martin, Antonio Rueda, Eleanor Williams, Rebecca E Foulger, Sarah Leigh, Louise C Daugherty, Olivia Niblock, Ivone U S Leong, et al. 2019. “PanelApp Crowdsources Expert Knowledge to Establish Consensus Diagnostic Gene Panels.” Nat. Genet. 51 (November): 1560–65. http://dx.doi.org/10.1038/s41588-019-0528-2.
Maxwell, Kara N, Steven N Hart, Joseph Vijai, Kasmintan A Schrader, Thomas P Slavin, Tinu Thomas, Bradley Wubbenhorst, et al. 2016. “Evaluation of ACMG-Guideline-Based Variant Classification of Cancer Susceptibility and Non-Cancer-Associated Genes in Families Affected by Breast Cancer.” Am. J. Hum. Genet. 98 (5): 801–17. http://dx.doi.org/10.1016/j.ajhg.2016.02.024.
Nykamp, Keith, Michael Anderson, Martin Powers, John Garcia, Blanca Herrera, Yuan-Yuan Ho, Yuya Kobayashi, et al. 2017. “Sherloc: A Comprehensive Refinement of the ACMG–AMP Variant Classification Criteria.” Genet. Med. 19 (May): 1105. https://doi.org/10.1038/gim.2017.37.
Richards, Sue, Nazneen Aziz, Sherri Bale, David Bick, Soma Das, Julie Gastier-Foster, Wayne W Grody, et al. 2015. “Standards and Guidelines for the Interpretation of Sequence Variants: A Joint Consensus Recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology.” Genet. Med. 17 (5): 405–24. https://doi.org/10.1038/gim.2015.30.



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