CIViC is a community knowledgebase for expert crowdsourcing the clinical interpretation of variants in cancer

Griffith, Malachi; Spies, Nicholas C.; Krysiak, Kilannin; McMichael, Joshua F.; Coffman, Adam; Danos, Arpad; Ainscough, Benjamin J.; Ramirez, Cody; Rieke, Damian; Kujan, Lynzey; Barnell, Erica K.; Wagner, Alex H.; Skidmore, Zachary L.; Wollam, Amber; Liu, Connor J.; Jones, Steven J.M.; Bilski, Rachel L.; Lesurf, Robert; Feng, Yuan; Shah, Nakul; Bonakdar, Melika; Trani, Lee; Matlock, Matthew K.; Ramu, Avinash; Campbell, Katie M.; Spies, Gregory C.; Graubert, Aaron; Gangavarapu, Karthik; Eldred, James M.; Larson, David E.; Walker, Jason; Good, Benjamin M.; Wu, Chunlei; Su, Andrew I.; Dienstmann, Rodrigo; Margolin, Adam A.; Tamborero, David; López‐Bigas, Núria; Bose, Ron; Spencer, David H.; Wartman, Lukas D.; Wilson, Richard K.; Mardis, Elaine R.; Griffith, Obi L.

doi:10.1038/ng.3774

Cited by 528 publications

(489 citation statements)

References 24 publications

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“…For targeted sequencing, mutations were called using Mutect2 from GATK v3.8 (Van der Auwera et al., 2013). Mutations were prioritized after annotating with VEP (McLaren et al., 2016) as follows:Variants observed in the CIVIC (Griffith et al., 2017), Sanger, Genie (AACR Project GENIE Consortium, 2017) or Memorial Sloane Kettering Cancer Center cancer hotspots database (MSKCC) were categorized as “High confidence”.Variants overlapping the Encode Blacklist (Encode Project Consortium, 2012), or that were unidirectional, or were seen in >1.5% of germline reads were categorized as “Unreliable”.Silent variants were retained.Variants observed in the ExAC database (Lek et al., 2016) were categorized as “Unreliable”.Variants observed in >1 patient wass categorized as “Medium confidence”, otherwise “Low confidence”.“Medium confidence” variants were re-categorized as “Low confidence” if CADD score <20, IMPACT=LOW or IMPACT=MODIFIER, while “Low confidence” variants were re-categorized as “Medium confidence” if CADD score>20, IMPACT=HIGH or MODERATE or clinsig=pathogenic.…”

Section: Methodsmentioning

confidence: 99%

Undifferentiated Sarcomas Develop through Distinct Evolutionary Pathways

et al. 2019

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SummaryUndifferentiated sarcomas (USARCs) of adults are diverse, rare, and aggressive soft tissue cancers. Recent sequencing efforts have confirmed that USARCs exhibit one of the highest burdens of structural aberrations across human cancer. Here, we sought to unravel the molecular basis of the structural complexity in USARCs by integrating DNA sequencing, ploidy analysis, gene expression, and methylation profiling. We identified whole genome duplication as a prevalent and pernicious force in USARC tumorigenesis. Using mathematical deconvolution strategies to unravel the complex copy-number profiles and mutational timing models we infer distinct evolutionary pathways of these rare cancers. In addition, 15% of tumors exhibited raised mutational burdens that correlated with gene expression signatures of immune infiltration, and good prognosis.

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Section: Methodsmentioning

confidence: 99%

Undifferentiated Sarcomas Develop through Distinct Evolutionary Pathways

et al. 2019

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“…These include web tools that provide data and text summaries of the frequency, mechanisms, and druggable targets of known driver mutations [110]. Multiple tools now include “interpretations” or summaries of the driver mutations written by clinicians – including the Precision Medicine Knowledgebase (at Weill Cornell) and the Personalized Cancer Therapy knowledge base (at MD Anderson) – or by the “crowd” [111, 112] (see list of references in Table S2C). A related approach recently explored leveraging existing ‘omics datasets for the interpretation of variants in newly sequenced samples, in acute myeloid leukemia[113].…”

Section: Analysis Approaches To Determine Molecular Subtypes and Cancmentioning

confidence: 99%

Precision Oncology: The Road Ahead

Senft

Leiserson

Ruppin

et al. 2017

Trends in Molecular Medicine

150

123

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Current efforts in precision oncology largely focus on the benefit of genomics-guided therapy. Yet, advances in sequencing techniques provide an unprecedented view of the complex genetic and non-genetic heterogeneity within individual tumors. Herein, we outline the benefits of integrating genomic and transcriptomic analyses for advanced precision oncology. We summarize relevant computational approaches to detect novel drivers and genetic vulnerabilities, suitable for therapeutic exploration. Clinically relevant platforms to functionally test predicted drugs/drug combinations for individual patients are reviewed. Finally, we highlight the technological advances in single-cell analysis of tumor specimens. These may ultimately lead to the development of next generation cancer drugs, capable of tackling the hurdles imposed by genetic and phenotypic heterogeneity on current anticancer therapies.

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“…Several knowledge base efforts exist including MyCancerGenome, CIViC 3 , the IMP Knowledgebase 4 , the JAX-Clinical Knowledgebase (CKB) 5 , Cancer Genome Interpreter (CGI), CANDL 6 , TumorPortal 7 , Targeted Cancer Care, and the Personalized Cancer Medicine Knowledge Base 8 . Some of these databases are in their early stages of development, do not yet contain sufficient breadth or detail to be used in clinical decision support and vary in the methods by which data are collected, stored, or accessed.…”

Section: Introductionmentioning

confidence: 99%

OncoKB: A Precision Oncology Knowledge Base

Chakravarty¹,

Gao²,

Phillips³

et al. 2017

JCO Precision Oncology

1,839

1,697

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Executive Summary PURPOSE With prospective clinical sequencing of tumors emerging as a mainstay in cancer care, there is an urgent need for a clinical support tool that distills the clinical implications associated with specific mutation events into a standardized and easily interpretable format. To this end, we developed OncoKB, an expert-guided precision oncology knowledge base. METHODS OncoKB annotates the biological and oncogenic effect and the prognostic and predictive significance of somatic molecular alterations. Potential treatment implications are stratified by the level of evidence that a specific molecular alteration is predictive of drug response based on US Food and Drug Administration (FDA) labeling, National Comprehensive Cancer Network (NCCN) guidelines, disease-focused expert group recommendations and the scientific literature. RESULTS To date, over 3000 unique mutations, fusions, and copy number alterations in 418 cancer-associated genes have been annotated. To test the utility of OncoKB, we annotated all genomic events in 5983 primary tumor samples in 19 cancer types. Forty-one percent of samples harbored at least one potentially actionable alteration, of which 7.5% were predictive of clinical benefit from a standard treatment. OncoKB annotations are available through a public web resource (http://oncokb.org/) and are also incorporated into the cBioPortal for Cancer Genomics to facilitate the interpretation of genomic alterations by physicians and researchers. CONCLUSION OncoKB, a comprehensive and curated precision oncology knowledge base, offers oncologists detailed, evidence-based information about individual somatic mutations and structural alterations present in patient tumors with the goal of supporting optimal treatment decisions.

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CIViC is a community knowledgebase for expert crowdsourcing the clinical interpretation of variants in cancer

Cited by 528 publications

References 24 publications

Undifferentiated Sarcomas Develop through Distinct Evolutionary Pathways

Undifferentiated Sarcomas Develop through Distinct Evolutionary Pathways

Precision Oncology: The Road Ahead

OncoKB: A Precision Oncology Knowledge Base

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