Report & query settings

The report is generated with PCGR version 0.8.4.10, using the following key settings:

  • Genome assembly: grch38
  • Tumor type: Cancer_NOS
  • Sequencing mode input (VCF): Tumor vs. control
  • Coding target size (VCF): 34 Mb
  • Minimum sequencing depth (DP) tumor (SNV + InDels): 0
  • Minimum allelic fraction (AF) tumor (SNV + InDels): 0
  • Minimum sequencing depth (DP) control (SNV + InDels): 0
  • Maximum allelic fraction (AF) control (SNV + InDels): 1
  • Tier system (VCF): pcgr_acmg
  • MSI prediction: ON
  • Mutational burden estimation: ON
  • Mutational signatures estimation: OFF
  • Report theme (Bootstrap): default

Main results

Somatic SNVs/InDels

Tumor mutational burden (TMB)

The size of the targeted (coding) genomic region has been defined as: 34 Mb. For estimation of TMB, PCGR employs the same approach as was outlined in a recently published large-scale study of TMB (Chalmers et al. 2017), i.e. counting all coding, somatic base substitutions and indels in the targeted regions, including synonymous alterations.

Estimated mutational burden: 11.38 mutations/Mb (TMB - Intermediate (5 - 20 mutations/Mb))



TMB reference distributions - TCGA


The plot below indicates how the mutational burden estimated for the query tumor sample (red dotted line) compares with the distributions observed for tumor samples in The Cancer Genome Atlas (TCGA). The grey area indicates the upper TMB tertile as defined by the user. Please note the following characteristics of the TCGA dataset presented here, which must be taken into account during TMB interpretation of the query sample:

  • The TCGA tumor samples are sequenced with a mean coverage of approximately 100X
  • The TCGA somatic mutation calls are based on a consensus among variant callers (each variant is supported by a minimum of two variant calling algorithms)
  • The TCGA somatic mutation calls are based on paired tumor-control sequencing (tumor-only sequencing may produce higher numbers due to more noise)





Tier & variant statistics

  • Number of SNVs: 34357
  • Number of InDels: 1993
  • Number of protein-coding variants: 313

The prioritization of SNV/InDels is here done according to a four-tiered structure, adopting the joint consensus recommendation by AMP/ACMG (Li et al. 2017).

  • Tier 1 - variants of strong clinical significance: 0
  • Tier 2 - variants of potential clinical significance: 1
  • Tier 3 - variants of unknown clinical significance: 17
  • Tier 4 - other coding variants: 295
  • Noncoding variants: 36037



Global distribution - allelic support


Global variant browser

The table below permits filtering of the total SNV/InDel set by various criteria.

NOTE 1: The filtering applies to this table only, and not to the tier-specific tables below.

NOTE 2: Filtering on sequencing depth/allelic fraction depends on input specified by user (VCF INFO tags).





Tier 1 - Variants of strong clinical significance



Predictive biomarkers


No variant-evidence item associations found.



Prognostic biomarkers


No variant-evidence item associations found.



Diagnostic biomarkers


No variant-evidence item associations found.



Tier 2 - Variants of potential clinical significance

  • Tier 2 considers evidence items of i) strong evidence levels (A & B) in other tumor types, and ii) weak evidence levels (C, D & E) in the query tumor type (Cancer_NOS). Using the database for clinical interpretations of variants in cancer (CIViC) and Cancer Biomarkers database, a total of 1 unique, somatic variants were found in the tumor sample:
    • Tier 2 - Predictive/Therapeutic: 0 evidence items
    • Tier 2 - Prognostic: 1 evidence items
    • Tier 2 - Diagnostic: 0 evidence items



Predictive biomarkers


No variant-evidence item associations found.



Prognostic biomarkers


The table below lists all variant-evidence item associations:



Diagnostic biomarkers


No variant-evidence item associations found.



Tier 3 - Variants of unknown clinical significance

  • A total of 17 unique, somatic variant(s) in the tumor sample are of unknown clinical significance, as found within known proto-oncogenes or tumor suppressor genes.

Tumor suppressor gene mutations


The table below lists all variants:



Proto-oncogene mutations


The table below lists all variants:



Tier 4 - Other coding mutations

  • A total of 295 unique, coding somatic variant(s) are also found in the tumor sample.





Noncoding mutations

  • A total of 36037 unique, somatic variant(s) that do not alter any encoded protein sequence(s) are also found in the tumor sample.

NOTE - listing top 2000 variants (ranked according to Open Targets phenotype association score)



MSI status

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 98.9% and a negative predictive value of 98.8% on an independent test set of 427 samples. Details of the MSI classification approach can be found here.


  • _Predicted MSI status for SEQC-II-50pc__SEQC-II_Tumor_50pc-somatic_: MSS (Microsatellite stable)

Supporting evidence: indel fraction among somatic calls

The plot below illustrates the fraction of indels among all calls in _SEQC-II-50pc__SEQC-II_Tumor_50pc-somatic_ (black dashed line) along with the distribution of indel fractions for TCGA samples (colorectal, endometrial, ovarian, stomach) with known MSI status assayed from mononucleotide markers ( MSI.H = high microsatellite instability, MSS = microsatellite stable):



Somatic coding mutations in MSI-associated genes




Documentation

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 (v98.3)
    • 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)
    • deconstructSigs - Assessment of contribution of known mutational processes in a tumor sample (v1.8.0)
    • vcf2maf - VCF to MAF conversion (v1.6.16)
    • vcf-validator - Validation suite for Variant Call Format (VCF) files, implemented using C++11 (v0.6)
    • maftools - Summary, analysis and visualization of MAF files (v1.8.0)
    • 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 31/19)
    • dbNSFP - Database of non-synonymous functional predictions (v4.0 (May 2019))
    • Pfam - Collection of protein families/domains (v32 (September 2018))
    • TCGA - The Cancer Genome Atlas - somatic mutations (release 18 (July 8th 2019))
    • ICGC-PCAWG - ICGC-Pancancer Analysis of Whole Genomes - somatic mutations (release 28 (March 2019))
    • TCGA-PCDM - Putative Cancer Driver Mutations based on multiple discovery approaches (release 15)
    • UniProtKB - Comprehensive resource of protein sequence and functional information (release 2019_10)
    • CORUM - The comprehensive resource of mammalian protein complexes (release 3.0 (20180903))
    • gnomAD - Germline variant frequencies exome-wide (r2.1 (October 2018))
    • COSMIC - Catalogue of somatic mutations in cancer (89/86)
    • dbSNP - Database of short genetic variants (152/152)
    • 1000Genomes - Germline variant frequencies genome-wide (phase 3 (20130502))
    • DisGenet - Database of gene-disease associations (v6.0 (January 2019))
    • 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 (release 20191105)
    • CancerMine - Literature-mined database of tumor suppressor genes/proto-oncogenes (v18 - 20191101)
    • DiseaseOntology - Standardized ontology for human disease (20191030)
    • OncoScore - Literature-based ranking of gene-cancer association (20190127)
    • OpenTargetPlatform - Comprehensive and robust data integration for access to potential drug targets associated with disease (2019_09)
    • ChEMBL - Manually curated database of bioactive molecules (v25 (March 2019))
    • KEGG - Knowledge base on the molecular interaction, reaction and relation networks (20191002)
    • CIViC - Clinical interpretations of variants in cancer (November 5th 2019)
    • CBMDB - Cancer Biomarkers database (January 17th 2018)

References

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.

Alexandrov, Ludmil B, Serena Nik-Zainal, David C Wedge, Peter J Campbell, and Michael R Stratton. 2013. “Deciphering Signatures of Mutational Processes Operative in Human Cancer.” Cell Rep. 3 (1): 246–59.

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.

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.

Dong, Fei, Phani K Davineni, Brooke E Howitt, and Andrew H Beck. 2016. “A BRCA1/2 Mutational Signature and Survival in Ovarian High-Grade Serous Carcinoma.” Cancer Epidemiol. Biomarkers Prev. 25 (11): 1511–6.

Kim, Jaegil, Kent W Mouw, Paz Polak, Lior Z Braunstein, Atanas Kamburov, Grace Tiao, David J Kwiatkowski, et al. 2016. “Somatic ERCC2 Mutations Are Associated with a Distinct Genomic Signature in Urothelial Tumors.” Nat. Genet. 48 (6): 600–606.

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.

Rosenthal, Rachel, Nicholas McGranahan, Javier Herrero, Barry S Taylor, and Charles Swanton. 2016. “DeconstructSigs: Delineating Mutational Processes in Single Tumors Distinguishes DNA Repair Deficiencies and Patterns of Carcinoma Evolution.” Genome Biol. 17 (1): 31.

Secrier, Maria, Xiaodun Li, Nadeera de Silva, Matthew D Eldridge, Gianmarco Contino, Jan Bornschein, Shona MacRae, et al. 2016. “Mutational Signatures in Esophageal Adenocarcinoma Define Etiologically Distinct Subgroups with Therapeutic Relevance.” Nat. Genet. 48 (10): 1131–41.




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