Overview

Row

Overview

SNVs/InDels

Total variants

1926

SNVs

1910

InDels

16

TIER 1 variants

0

TIER 2 variants

1

Row

Variants per tier


Allelic support plot

Documentation

The prioritization of SNV and InDels found in the tumor sample is done according to a four-tiered structure, adopting the joint consensus recommendation by AMP/ACMG Li et al., 2017.

Row

Global variant datatable - filters

The global variant datatable (right) can be filtered according to various criteria:

  • Filtering on sequencing depth/variant allelic fraction depends on input provided by user
  • Filtering performed here will only apply to the datatable and not any other visualizations presented in this page


Global variant datatable

Tier 1

Row

SNVs and InDels

TIER 1

Biomarker genes

0

Biomarker variants

0

Diagnostic evidence items

0

Prognostic evidence items

0

Predictive evidence items

0

Row

Tier 1 variant evidence items - filters


  • No variants of strong clinical significance found.

Tier 1 - variant evidence items


  • No variants of strong clinical significance found.

Tier 2

Row

SNVs and InDels

TIER 2

Biomarker genes

1

Biomarker variants

1

Diagnostic evidence items

0

Prognostic evidence items

0

Predictive evidence items

1

Row

Tier 2 variant evidence items - filters



Evidence items associated with variants in TIER 2 (right panel) can be interactively explored according to various criteria :


NOTE: Reported biomarkers in CIViC/CGI 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


Tier 2 - variant evidence items



Tier 3

Row

SNVs and InDels

TIER 3

Total variants

51

Variants in proto-oncogenes

18

Variants in tumor suppressor genes

28

Variants in genes with dual roles

5

Row

Tier 3 - variant filters


Variants in TIER 3 (right panel) can be interactively explored according to various criteria :

Tier 3 - variant datatable


Tier 4

Row

SNVs and InDels

TIER 4

Total variants

906

SNVs

899

InDels

7

Block substitutions

0

Row

Tier 4 - variant filters



Variants in TIER 4 (right panel) can be interactively explored according to various criteria :

Tier 4 - variant datatable


Noncoding

Row

SNVs and InDels

Noncoding

Total variants

968

SNVs

959

InDels

9

Block substitutions

0

Row

Noncoding - variant filters



Noncoding variants (right panel) can be interactively explored according to various criteria :

Noncoding - variant datatable

Complete biomarker set

Row

SNVs and InDels

Biomarkers

Biomarker genes

1

Biomarker variants

1

Diagnostic evidence items

0

Prognostic evidence items

0

Predictive evidence items

1

Row

All biomarker evidence items - filters


NOTE: Reported biomarkers in CIViC/CGI 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


All biomarker evidence items


Overview

Row

Overview

sCNA

Copy number gains

53

Copy number losses

59

TIER 1 biomarkers

0

TIER 2 biomarkers

4

Row

Copy number segments - filters

The following user-defined thresholds determine copy number aberrations shown here:

  •   Copy number amplifications   : Log(2) ratio >= 0.4

  •   Homozygous deletions   : Log(2) ratio <= -0.4

A total of 112 unfiltered aberration segments satisfied the above criteria.

Copy number segments

Row

Documentation

Somatic copy number aberrations identified in the tumor sample are classified into two main tiers:

Included in the report is also a complete list of all oncogenes subject to amplifications, tumor suppressor genes subject to homozygous deletions, and other drug targets subject to amplifications

  • Status as oncogenes and/or tumor suppressors genes are done according to the following scheme in PCGR:
    • Five or more publications in the biomedical literature that suggests an oncogenic/tumor suppressor role for a given gene (as collected from the CancerMine text-mining resource), OR
    • At least two publications from CancerMine that suggests an oncogenic/tumor suppressor role for a given gene AND an existing record for the same gene as a tumor suppressor/oncogene in the Network of Cancer Genes (NCG)
    • Status as oncogene is ignored if a given gene also has literature evidence support for a role as a tumor suppressor gene which is three times as large (and vice versa)
    • Oncogenes/tumor suppressor candidates from CancerMine/NCG that are found in the curated list of false positive cancer drivers compiled by Bailey et al. (Cell, 2018) have been excluded

Tier 1

Row

sCNA

TIER 1

Biomarker genes

0

Biomarker aberrations

0

Diagnostic evidence items

0

Prognostic evidence items

0

Predictive evidence items

0

Row

Tier 1 variant evidence items - filters



  • No somatic copy-number aberrations of strong clinical significance found.

Tier 1 - variant evidence items



  • No somatic copy-number aberrations of strong clinical significance found.

Tier 2

Row

sCNA

TIER 2

Biomarker genes

4

Biomarker aberrations

4

Diagnostic evidence items

0

Prognostic evidence items

4

Predictive evidence items

2

Row

Tier 2 variant evidence items - filters



Evidence items associated with variants in tier 1 (right panel) can be filtered according to various criteria:

Tier 2 - variant evidence items



TMB & MSI

Row

Tumor mutational burden & MSI classification

TMB/MSI status

Coding target size

34 Mb

Coding mutations

958

TMB estimate

37.71 mut/Mb

MSI prediction

MSS

Row

TCGA TMB distribution

MSI evidence I


The plot below illustrates the mutational burden of indels in TCGA-IR-A3LA-01A (black dashed line) along with the distribution in TCGA samples for samples with known MSI status ( MSI.H = high microsatellite instability, MSS = microsatellite stable):


Row

MSI evidence II


The plot below illustrates the fraction of indels among all calls in TCGA-IR-A3LA-01A(black dashed line) along with the distribution in TCGA for samples with known MSI status ( MSI.H = high microsatellite instability, MSS = microsatellite stable):


Somatic mutations in MMR genes


No variants found.



Mutational signatures

Row

Mutational Signatures (SBS)

SIGNATURES

SNVs eligible for analysis

1910

Most dominant aetiology

AID/APOBEC

Accuracy of signature fitting (%)

98.4

High confident kataegis events

0

Row

Mutational signatures - aetiology contributions

Mutational signatures - aetiologies

Row

Mutational context frequency

Genomic distribution - rainfall

Kataegis events

Trials

Row

Molecularly targeted trials

Clinical trials

Not yet recruiting

11

Recruiting

40

Enrolling by invitation

0

Active, not recruiting

19

Unknown status

8

Row

Molecularly targeted trials - filters

  • Ongoing or planned clinical trials in the relevant tumor type have been retrieved from clinicaltrials.gov, focusing on the subset with molecularly targeted therapies
  • Key information entities (interventions/drugs, conditions) in trial records have been mapped to established thesauri (ChEMBL, NCI Thesaurus, UMLS/MedGen)
  • Results from a text-mining procedure on unstructured trial text (e.g. inclusion/exclusion criteria) attempts to highlight the presence of established molecular biomarkers in cancer and relevant therapeutic contexts.


Molecularly targeted trials


Settings & Docs

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Settings - sample and report

The report is generated with PCGR version 0.10.19.

Key report configuration settings:

  • Genome assembly: grch37
  • Minimum sequencing depth tumor (DP, SNV/InDel): 0
  • Minimum allelic fraction tumor (VAF, SNV/InDel): 0
  • Minimum sequencing depth control (DP, SNV/InDel): 0
  • Maximum allelic fraction control (VAF, SNV/InDel): 1
  • Tier system (VCF): pcgr_acmg
  • Show noncoding variants: TRUE
  • MSI prediction: ON
  • Mutational burden estimation: ON
    • TMB algorithm: all_coding
  • Mutational signatures estimation: ON
    • Minimum number of mutations required: 200
    • All reference signatures: FALSE
    • Inclusion of artefact signatures: FALSE
    • Minimum tumor-type prevalence (percent) of reference signatures used for refitting: 5
  • Clinical trial inclusion: ON
  • Variant Effect Predictor (VEP) settings:
    • Transcript set: GENCODE - basic set (v19)
    • Transcript pick order: canonical,appris,biotype,ccds,rank,tsl,length,mane
    • Regulatory regions annotation: TRUE


Sample information:

  • Tumor primary site: Cervix

Documentation - sequencing assay

  • Assay sequencing type (VCF): WES
  • Assay coding target size (VCF): 34 Mb
  • Assay sequencing mode (VCF): Tumor-Control


Estimated properties on DNA cellularity and ploidy:

  • Tumor purity: NA
  • Tumor ploidy: NA


Documentaton - report data sources

The report generated with PCGR is based on the following underlying tools and knowledge resources:

  • PCGR databundle version

    • 20220203

  • Software

    • LOFTEE - Loss-Of-Function Transcript Effect Estimator (VEP plugin) (v1.0.3)
    • vcfanno - Rapid annotation of VCF with other VCFs/BEDs/tabixed files (v0.3.3)
    • MutationalPatterns - Comprehensive genome-wide analysis of mutational processes (v3.4.0)
    • vcf2maf - VCF to MAF conversion (v1.6.21)


  • Databases/datasets
    • GENCODE - high quality reference gene annotation and experimental validation (release 39/19)
    • 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_11 (v35.0))
    • TCGA - The Cancer Genome Atlas - somatic mutations (20211029 (v31))
    • ICGC-PCAWG - ICGC-Pancancer Analysis of Whole Genomes - somatic mutations (2020_01)
    • TCGA-PCDM - Putative Cancer Driver Mutations based on multiple discovery approaches (2019)
    • UniProtKB - Comprehensive resource of protein sequence and functional information (2021_04)
    • gnomAD - Germline variant frequencies exome-wide (r2.1 (October 2018))
    • COSMIC - Catalogue of somatic mutations in cancer (92)
    • dbSNP - Database of short genetic variants (154)
    • 1000Genomes - Germline variant frequencies genome-wide (20130502 (phase 3))
    • 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 (20220103)
    • CancerMine - Literature-mined database of tumor suppressor genes/proto-oncogenes (20211106 (v42))
    • OncoTree - Open-source ontology developed at MSK-CC for standardization of cancer type diagnosis (2021-11-02)
    • DiseaseOntology - Standardized ontology for human disease (20220131)
    • EFO - Experimental Factor Ontology (v3.38.0)
    • OpenTargetsPlatform - Comprehensive and robust data integration for access to potential drug targets associated with disease (2021.11)
    • ChEMBL - Manually curated database of bioactive molecules (20210701 (v29))
    • KEGG - Knowledge base on the molecular interaction, reaction and relation networks (20211223)
    • CIViC - Clinical interpretations of variants in cancer (20220201)
    • CGI - Cancer Genome Interpreter Cancer Biomarkers Database (20180117)

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Documentation - report content

SNVs/InDels

The prioritization of SNV and InDels found in the tumor sample is done according to a four-tiered structure, adopting the joint consensus recommendation by AMP/ACMG Li et al., 2017.

A complete list of reported biomarkers that associate with variants in the tumor sample (not necessarily qualifying for assignment to TIER 1/TIER 2) is also shown in a separate section.

Somatic copy number aberrations

Somatic copy number aberrations identified in the tumor sample are classified into two main tiers:

Included in the report is also a complete list of all oncogenes subject to amplifications, tumor suppressor genes subject to homozygous deletions, and other drug targets subject to amplifications

Mutational signatures

The set of somatic mutations observed in a tumor reflects the varied mutational processes that have been active during its life history, providing insights into the routes taken to carcinogenesis. Exogenous mutagens, such as tobacco smoke and ultraviolet light, and endogenous processes, such as APOBEC enzymatic family functional activity or DNA mismatch repair deficiency, result in characteristic patterns of mutation. Mutational signatures can have significant clinical impact in certain tumor types (Póti et al., 2019, Ma et al., 2018)

The MutationalPatterns package (Blokzijl et al., 2018) is used to estimate the relative contribution of known mutational signatures in a single tumor sample. MutationalPatterns makes an optimal reconstruction of the mutations observed in a given sample with a COSMIC’s (V3.2) reference collection of n = 78 mutational signatures (SBS, including sequencing artefacts). By default, we restrict the signatures in the reference collection to those already observed in the tumor type in question (i.e. from large-scale de novo signature extraction on ICGC-PCAWG tumor samples).

Specifically, for tumors of type Cervix , mutational signature reconstruction is limited to the following reference collection:
  • SBS1 - Aging
  • SBS2 - AID/APOBEC
  • SBS5 - Unknown
  • SBS13 - AID/APOBEC


The accuracy of signature fitting (highlighted in value box) reflects how well the mutational profile can be reconstructed with signatures from the reference collection. Reconstructions with fitting accuracy below 90% should be interpreted with caution.

Tumor mutational burden (TMB)

Tumor mutational load or mutational burden is a measure of the number of mutations within a tumor genome, defined as the total number of mutations per coding area of a tumour genome. TMB may serve as a proxy for determining the number of neoantigens per tumor, which in turn may have implications for response to immunotherapy. For estimation of TMB, PCGR employs two different algorithms (one to be chosen by the user):

  1. all_coding: the same approach as was outlined in a recently published large-scale study of TMB (Chalmers et al., 2017), i.e. counting all somatic base substitutions and indels in the protein-coding regions of the sequencing assay, including synonymous alterations.
  2. nonsyn: non-synonymous variants only, i.e. as employed by Fernandez et al., 2019

Numbers obtained with 1) or 2) is next divided by the coding target size of the sequencing assay.

MSI classification

Microsatellite instability (MSI) is the result of impaired DNA mismatch repair and constitutes a cellular phenotype of clinical significance in many cancer types, most prominently colorectal cancers, stomach cancers, endometrial cancers, and ovarian cancers (Cortes-Ciriano et al., 2017). We have built a statistical MSI classifier from somatic mutation profiles that separates MSI.H (MSI-high) from MSS (MS stable) tumors. The MSI classifier was trained using 999 exome-sequenced TCGA tumor samples with known MSI status (i.e. assayed from mononucleotide markers), and obtained a positive predictive value of 100% and a negative predictive value of 99.4% on an independent test set of 427 samples. Details of the MSI classification approach can be found here.

Note that the MSI classifier is applied only for WGS/WES tumor-control sequencing assays.

Kataegis

Kataegis describes a pattern of localized hypermutations identified in some cancer genomes, in which a large number of highly-patterned basepair mutations occur in a small region of DNA (ref Wikipedia). Kataegis is prevalently seen among breast cancer patients, and it also exists in lung cancers, cervical, head and neck, and bladder cancers, as shown in the results from tracing APOBEC mutation signatures (ref Wikipedia). PCGR implements the kataegis detection algorithm outlined in the KataegisPortal R package, applied in the study by Yin et al. (2020).

Explanation of key columns in the resulting table of potential kataegis events:

  • weight.C>X: proportion of C>X mutations
  • confidence: confidence degree of potential kataegis events (range: 0 to 3)
    • 0 - a hypermutation with weight.C>X < 0.8;
    • 1 - one hypermutation with weight.C>X >= 0.8 in a chromosome;
    • 2 - two hypermutations with weight.C>X >= 0.8 in a chromosome;
    • 3 - high confidence with three or more hypermutations with weight.C>X >= 0.8 in a chromosome)

Germline findings

For PCGR reports that are fueled with CPSR report contents (JSON), we here list the main findings from the CPSR report, i.e. the collection of Pathogenic/Likely Pathogenic/VUS variants (ClinVar and novel CPSR-classified variants). We also show whether any of the query variants are associated with established biomarker evidence items with respect to cancer predisposition, prognosis, therapeutic regimens etc.

Clinical trials

Each report is provided with a list of trials for the tumor type in question, where we limit the trials listed to ongoing or forthcoming trials with a “molecular focus” (presence of molecular biomarkers in inclusion criteria, targeted drugs as interventions etc.). Recognition of biomarkers in trials is conducted through an in-house text mining procedure.

Note that the trials have currently not been subject to any matching with respect to the molecular profile of the tumor, trials are thus basically unprioritized, and have to be explored interactively by the user in order to discover relevant trials.

References

Alexandrov, Ludmil B, Jaegil Kim, Nicholas J Haradhvala, Mi Ni Huang, Alvin Wei Tian Ng, Yang Wu, Arnoud Boot, et al. 2020. “The Repertoire of Mutational Signatures in Human Cancer.” Nature 578 (7793): 94–101. http://dx.doi.org/10.1038/s41586-020-1943-3.
Alexandrov, Ludmil B, Serena Nik-Zainal, David C Wedge, Samuel A J R Aparicio, Sam Behjati, Andrew V Biankin, Graham R Bignell, et al. 2013. “Signatures of Mutational Processes in Human Cancer.” Nature 500 (7463): 415–21. https://doi.org/10.1038/nature12477.
Bailey, Matthew H, Collin Tokheim, Eduard Porta-Pardo, Sohini Sengupta, Denis Bertrand, Amila Weerasinghe, Antonio Colaprico, et al. 2018. “Comprehensive Characterization of Cancer Driver Genes and Mutations.” Cell 173 (2): 371–385.e18. http://dx.doi.org/10.1016/j.cell.2018.02.060.
Blokzijl, Francis, Roel Janssen, Ruben van Boxtel, and Edwin Cuppen. 2018. “MutationalPatterns: Comprehensive Genome-Wide Analysis of Mutational Processes.” Genome Med. 10 (1): 33. http://dx.doi.org/10.1186/s13073-018-0539-0.
Chalmers, Zachary R, Caitlin F Connelly, David Fabrizio, Laurie Gay, Siraj M Ali, Riley Ennis, Alexa Schrock, et al. 2017. “Analysis of 100,000 Human Cancer Genomes Reveals the Landscape of Tumor Mutational Burden.” Genome Med. 9 (1): 34. https://doi.org/10.1186/s13073-017-0424-2.
Cortes-Ciriano, Isidro, Sejoon Lee, Woong-Yang Park, Tae-Min Kim, and Peter J Park. 2017. “A Molecular Portrait of Microsatellite Instability Across Multiple Cancers.” Nat. Commun. 8: 15180. https://doi.org/10.1038/ncomms15180.
Fernandez, Evan M, Kenneth Eng, Shaham Beg, Himisha Beltran, Bishoy M Faltas, Juan Miguel Mosquera, David M Nanus, et al. 2019. “Cancer-Specific Thresholds Adjust for Whole Exome Sequencing–Based Tumor Mutational Burden Distribution.” JCO Precision Oncology, no. 3 (December): 1–12. https://doi.org/10.1200/PO.18.00400.
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.
Lever, Jake, Eric Y Zhao, Jasleen Grewal, Martin R Jones, and Steven J M Jones. 2019. “CancerMine: A Literature-Mined Resource for Drivers, Oncogenes and Tumor Suppressors in Cancer.” Nat. Methods 16 (6): 505–7. http://dx.doi.org/10.1038/s41592-019-0422-y.
Li, Marilyn M, Michael Datto, Eric J Duncavage, Shashikant Kulkarni, Neal I Lindeman, Somak Roy, Apostolia M Tsimberidou, et al. 2017. “Standards and Guidelines for the Interpretation and Reporting of Sequence Variants in Cancer: A Joint Consensus Recommendation of the Association for Molecular Pathology, American Society of Clinical Oncology, and College of American Pathologists.” J. Mol. Diagn. 19 (1): 4–23. https://doi.org/10.1016/j.jmoldx.2016.10.002.
Ma, Jennifer, Jeremy Setton, Nancy Y Lee, Nadeem Riaz, and Simon N Powell. 2018. “The Therapeutic Significance of Mutational Signatures from DNA Repair Deficiency in Cancer.” Nat. Commun. 9 (1): 3292. http://dx.doi.org/10.1038/s41467-018-05228-y.
Póti, Ádám, Hella Gyergyák, Eszter Németh, Orsolya Rusz, Szilárd Tóth, Csenger Kovácsházi, Dan Chen, et al. 2019. “Correlation of Homologous Recombination Deficiency Induced Mutational Signatures with Sensitivity to PARP Inhibitors and Cytotoxic Agents.” Genome Biol. 20 (1): 240. http://dx.doi.org/10.1186/s13059-019-1867-0.
Repana, Dimitra, Joel Nulsen, Lisa Dressler, Michele Bortolomeazzi, Santhilata Kuppili Venkata, Aikaterini Tourna, Anna Yakovleva, Tommaso Palmieri, and Francesca D Ciccarelli. 2019. “The Network of Cancer Genes (NCG): A Comprehensive Catalogue of Known and Candidate Cancer Genes from Cancer Sequencing Screens.” Genome Biol. 20 (1): 1. http://dx.doi.org/10.1186/s13059-018-1612-0.
Tamborero, David, Carlota Rubio-Perez, Jordi Deu-Pons, Michael P Schroeder, Ana Vivancos, Ana Rovira, Ignasi Tusquets, et al. 2018. “Cancer Genome Interpreter Annotates the Biological and Clinical Relevance of Tumor Alterations.” Genome Med. 10 (1): 25. http://dx.doi.org/10.1186/s13073-018-0531-8.
Yin, Xia, Rui Bi, Pengfei Ma, Shengzhe Zhang, Yang Zhang, Yunheng Sun, Yi Zhang, et al. 2020. “Multiregion Whole-Genome Sequencing Depicts Intratumour Heterogeneity and Punctuated Evolution in Ovarian Clear Cell Carcinoma.” J. Med. Genet. 57 (9): 605–9. http://dx.doi.org/10.1136/jmedgenet-2019-106418.