CancerVar: a web server for improved evidence-based clinical interpretation of cancer somatic mutations and copy number abnormalities

Several knowledgebases, such as CIViC, CGI and OncoKB, have been manually curated to support clinical interpretations of somatic mutations and copy number abnormalities (CNAs) in cancer. However, these resources focus on known hotspot mutations, and discrepancies or even conflicting interpretations have been observed between these knowledgebases. To standardize clinical interpretation, AMP/ASCO/CAP/ACMG/CGC jointly published consensus guidelines for the interpretations of somatic mutations and CNAs in 2017 and 2019, respectively. Based on these guidelines, we developed a standardized, semi-automated interpretation tool called CancerVar (Cancer Variants interpretation), with a user-friendly web interface to assess the clinical impacts of somatic variants. Using a semi-supervised method, CancerVar interpret the clinical impacts of cancer variants as four tiers: strong clinical significance, potential clinical significance, unknown clinical significance, benign/likely benign. CancerVar also allows users to specify criteria or adjust scoring weights as a customized interpretation strategy, and allows phenotype-driven scoring for specific types of cancer. Importantly, CancerVar generates automated texts to summarize clinical evidence on somatic variants, which greatly reduces manual workload to write interpretations that include relevant information from harmonized knowledgebases. CancerVar can be accessed at http://cancervar.wglab.org and it is open to all users without login requirements. The command line tool is also available at https://github.com/WGLab/CancerVar.

[1]  Tom R. Gaunt,et al.  Predicting the Functional, Molecular, and Phenotypic Consequences of Amino Acid Substitutions using Hidden Markov Models , 2012, Human mutation.

[2]  N. Schultz,et al.  American Association for Cancer Research Project Genomics Evidence Neoplasia Information Exchange: From Inception to First Data Release and Beyond—Lessons Learned and Member Institutions’ Perspectives , 2018, JCO clinical cancer informatics.

[3]  B. Taylor,et al.  AKT Inhibition in Solid Tumors With AKT1 Mutations. , 2017, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[4]  G. Mills,et al.  Phase II trial of AKT inhibitor MK-2206 in patients with advanced breast cancer who have tumors with PIK3CA or AKT mutations, and/or PTEN loss/PTEN mutation , 2019, Breast Cancer Research.

[5]  Vanessa L. Horner,et al.  Technical laboratory standards for interpretation and reporting of acquired copy-number abnormalities and copy-neutral loss of heterozygosity in neoplastic disorders: a joint consensus recommendation from the American College of Medical Genetics and Genomics (ACMG) and the Cancer Genomics Consort , 2019, Genetics in Medicine.

[6]  Dmitriy Sonkin,et al.  TP53 Variations in Human Cancers: New Lessons from the IARC TP53 Database and Genomics Data , 2016, Human mutation.

[7]  Steven J. M. Jones,et al.  Comprehensive molecular portraits of human breast tumours , 2013 .

[8]  Steven J. M. Jones,et al.  Comprehensive Characterization of Cancer Driver Genes and Mutations , 2018, Cell.

[9]  Quan Li,et al.  InterVar: Clinical Interpretation of Genetic Variants by the 2015 ACMG-AMP Guidelines. , 2017, American journal of human genetics.

[10]  Jie Zhou,et al.  Akt1 governs breast cancer progression in vivo , 2007, Proceedings of the National Academy of Sciences.

[11]  Steven Henikoff,et al.  SIFT: predicting amino acid changes that affect protein function , 2003, Nucleic Acids Res..

[12]  Renjie Jin,et al.  The role of hepatocyte nuclear factor-3 alpha (Forkhead Box A1) and androgen receptor in transcriptional regulation of prostatic genes. , 2003, Molecular endocrinology.

[13]  Steven J. M. Jones,et al.  CIViC is a community knowledgebase for expert crowdsourcing the clinical interpretation of variants in cancer , 2017, Nature Genetics.

[14]  Anandita Rajpurohit,et al.  PIK3CA and AKT1 Mutations Have Distinct Effects on Sensitivity to Targeted Pathway Inhibitors in an Isogenic Luminal Breast Cancer Model System , 2013, Clinical Cancer Research.

[15]  I. Adzhubei,et al.  Predicting Functional Effect of Human Missense Mutations Using PolyPhen‐2 , 2013, Current protocols in human genetics.

[16]  Steven J. M. Jones,et al.  Comprehensive molecular portraits of human breast tumors , 2012, Nature.

[17]  Francis Lévi,et al.  Home-Based e-Health Platform for Multidimensional Telemonitoring of Symptoms, Body Weight, Sleep, and Circadian Activity: Relevance for Chronomodulated Administration of Irinotecan, Fluorouracil-Leucovorin, and Oxaliplatin at Home-Results From a Pilot Study. , 2018, JCO clinical cancer informatics.

[18]  Moriah H Nissan,et al.  OncoKB: A Precision Oncology Knowledge Base. , 2017, JCO precision oncology.

[19]  Xiaoming Liu,et al.  iCAGES: integrated CAncer GEnome Score for comprehensively prioritizing driver genes in personal cancer genomes , 2016, Genome Medicine.

[20]  A. Jemal,et al.  Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries , 2018, CA: a cancer journal for clinicians.

[21]  G. Giaccone,et al.  Targeting HER2 aberrations as actionable drivers in lung cancers: phase II trial of the pan-HER tyrosine kinase inhibitor dacomitinib in patients with HER2-mutant or amplified tumors. , 2015, Annals of oncology : official journal of the European Society for Medical Oncology.

[22]  Marcin Imielinski,et al.  The cancer precision medicine knowledge base for structured clinical-grade mutations and interpretations , 2016, J. Am. Medical Informatics Assoc..

[23]  Hui Yang,et al.  Phenolyzer: phenotype-based prioritization of candidate genes for human diseases , 2015, Nature Methods.

[24]  Marilyn M. Li,et al.  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. , 2017, The Journal of molecular diagnostics : JMD.

[25]  Chunhua Weng,et al.  Phen2Gene: rapid phenotype-driven gene prioritization for rare diseases , 2019, bioRxiv.

[26]  Serafim Batzoglou,et al.  Identifying a High Fraction of the Human Genome to be under Selective Constraint Using GERP++ , 2010, PLoS Comput. Biol..

[27]  Jana Marie Schwarz,et al.  MutationTaster evaluates disease-causing potential of sequence alterations , 2010, Nature Methods.

[28]  Christina S. Leslie,et al.  FOXA1 mutations alter pioneering activity, differentiation, and prostate cancer phenotypes , 2019, Nature.

[29]  Larissa V Furtado,et al.  Multi-Institutional Evaluation of Interrater Agreement of Variant Classification Based on the 2017 Association for Molecular Pathology, American Society of Clinical Oncology, and College of American Pathologists Standards and Guidelines for the Interpretation and Reporting of Sequence Variants in Ca , 2020 .

[30]  R. Gibbs,et al.  Comparison and integration of deleteriousness prediction methods for nonsynonymous SNVs in whole exome sequencing studies. , 2015, Human molecular genetics.

[31]  Steven J. M. Jones,et al.  The Molecular Taxonomy of Primary Prostate Cancer , 2015, Cell.

[32]  Marilyn M. Li Standards and Guidelines for the Interpretation and Reporting of Sequence Variants in Cancer , 2019 .

[33]  Michael P. Schroeder,et al.  Cancer Genome Interpreter annotates the biological and clinical relevance of tumor alterations , 2017, Genome Medicine.

[34]  Ken Chen,et al.  Systematic Functional Annotation of Somatic Mutations in Cancer. , 2018, Cancer cell.

[35]  Yue Hu,et al.  Variant Interpretation for Cancer (VIC): a computational tool for assessing clinical impacts of somatic variants , 2019, Genome Medicine.

[36]  Donavan T. Cheng,et al.  Mutational Landscape of Metastatic Cancer Revealed from Prospective Clinical Sequencing of 10,000 Patients , 2017, Nature Medicine.

[37]  M. Kris,et al.  HER2 Amplification and HER2 Mutation Are Distinct Molecular Targets in Lung Cancers , 2016, Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer.

[38]  A. Chinnaiyan,et al.  Distinct structural classes of activating FOXA1 alterations in advanced prostate cancer , 2019, Nature.

[39]  C. Sander,et al.  Predicting the functional impact of protein mutations: application to cancer genomics , 2011, Nucleic acids research.

[40]  Anuradha Lakshminarayana,et al.  The clinical trial landscape in oncology and connectivity of somatic mutational profiles to targeted therapies , 2016, Human Genomics.