Arpad Danos is involved with CIViC (Clinical Interpretation of Variants in Cancer, www.civicdb.org) as an editor, and works on training and development of the CIViC data model to integrate new cancer variant classification guidelines, and keep pace with the rapidly evolving field of clinical cancer variant interpretation.
Arpad Danosa, Kilannin Krysiaka, Jason Salibaa, Susanna Kiwalaa, Joshua Mcmichaela, Adam Coffmana, Erika Barnella, Kana Shetaa, Nicholas Spiesa, Cameron Grisdaleb, Alex Wagnerc, Malachi Griffitha, Obi L. Griffitha
aWashington University, St. Louis, MO, USA; bCanada’s Michael Smith Genome Sciences Centre, Vancouver, BC, CA; cNationwide Children’s Hospital, Columbus, OH, USA
Somatic cancer variant oncogenicity holds great importance. Evidence demonstrating oncogenic or benign variant effects may have clinical implications. For example, in colorectal cancer, an activating KRAS mutation will preclude EGFR inhibitor treatment, but guidelines may allow EGFR administration if KRAS mutation is benign.Therefore it is clear that a rigorous method for assessing variant oncogenicity is crucial.
A method exists for assessing pathogenicity of germline variants developed between ACMG and AMP, and guidelines from AMP/ASCO/CAP classifying somatic variants are widely used for therapeutic, prognostic and diagnostic classification. To fill a gap in systematic classification of somatic oncogenicity, joint recommendations from ClinGen, CGC, and VICC for classification or somatic variant oncogenicity were recently developed and published.
The CIViC platform is dedicated to free and open distribution of knowledge along with a curation and data model emphasizing transparency and provenance of all curated information.
CIViC will implement a system to capture the ClinGen/CGC/VICC oncogenicity standards by updating curation and data models as well as CIViC client and server.
To accomplish this, CIViC will leverage the existing Oncogenic and Functional Evidence Item types, to form collections of evidence to underpin a new Oncogenicity Assertion, which is a natural extension of the existing Assertion data model. This will classify variants as oncogenic, likely oncogenic, benign, likely benign, or uncertain, with oncogenicity codes added to the interface.
CIViC curators will generate a set of Oncogenic Evidence Items and Assertions, working with expert panels to develop gene specific modifications to these guidelines