Abstract
PURPOSEPrecision oncology depends on the matching of tumor variants to relevant knowledge describing the clinical significance of those variants. We recently developed the Clinical Interpretations for Variants in Cancer (CIViC; civicdb.org) crowd-sourced, expert-moderated, and open-access knowledgebase. CIViC provides a structured framework for evaluating genomic variants of various types (eg, fusions, single-nucleotide variants) for their therapeutic, prognostic, predisposing, diagnostic, or functional utility. CIViC has a documented application programming interface for accessing CIViC records: assertions, evidence, variants, and genes. Third-party tools that analyze or access the contents of this knowledgebase programmatically must leverage this application programming interface, often reimplementing redundant functionality in the pursuit of common analysis tasks that are beyond the scope of the CIViC Web application.METHODSTo address this limitation, we developed CIViCpy (civicpy.org), a software development kit for extracting and analyzing the contents of the CIViC knowledgebase. CIViCpy enables users to query CIViC content as dynamic objects in Python. We assess the viability of CIViCpy as a tool for advancing individualized patient care by using it to systematically match CIViC evidence to observed variants in patient cancer samples.RESULTSWe used CIViCpy to evaluate variants from 59,437 sequenced tumors of the American Association for Cancer Research Project GENIE data set. We demonstrate that CIViCpy enables annotation of > 1,200 variants per second, resulting in precise variant matches to CIViC level A (professional guideline) or B (clinical trial) evidence for 38.6% of tumors.CONCLUSIONThe clinical interpretation of genomic variants in cancers requires high-throughput tools for interoperability and analysis of variant interpretation knowledge. These needs are met by CIViCpy, a software development kit for downstream applications and rapid analysis. CIViCpy is fully documented, open-source, and available free online.
Highlights
The use of massively parallel sequencing to profile the molecular composition of human tissues has become increasingly commonplace in the clinical setting to inform diagnosis and therapeutic strategy for patients’ tumors.1,2 This has led to an ever-growing body of biomedical literature describing the impact of tumor variants on disease progression and response to therapy, creating a bottleneck of expert review of relevant literature to construct a clinical report.3 The Clinical Interpretations for Variants in Cancer (CIViC) community knowledgebase is a platform for expert crowdsourcing the clinical interpretation of variants in cancer.4 To date, CIViC contains 6,471 interpretation evidence records describing 2,312 variants in 402 genes
We demonstrate that CIViCpy enables annotation of . 1,200 variants per second, resulting in precise variant matches to CIViC level A or B evidence for 38.6% of tumors
The clinical interpretation of genomic variants in cancers requires high-throughput tools for interoperability and analysis of variant interpretation knowledge. These needs are met by CIViCpy, a software development kit for downstream applications and rapid analysis
Summary
The use of massively parallel sequencing to profile the molecular composition of human tissues has become increasingly commonplace in the clinical setting to inform diagnosis and therapeutic strategy for patients’ tumors. This has led to an ever-growing body of biomedical literature describing the impact of tumor variants on disease progression and response to therapy, creating a bottleneck of expert review of relevant literature to construct a clinical report. The Clinical Interpretations for Variants in Cancer (CIViC) community knowledgebase (civicdb.org) is a platform for expert crowdsourcing the clinical interpretation of variants in cancer. To date, CIViC contains 6,471 interpretation evidence records (ie, clinical significance statements extracted from biomedical literature) describing 2,312 variants in 402 genes. The use of massively parallel sequencing to profile the molecular composition of human tissues has become increasingly commonplace in the clinical setting to inform diagnosis and therapeutic strategy for patients’ tumors.1,2 This has led to an ever-growing body of biomedical literature describing the impact of tumor variants on disease progression and response to therapy, creating a bottleneck of expert review of relevant literature to construct a clinical report.. CIViC evidence and assertions are linked to data classes describing genes, drugs (if applicable), and diseases, in addition to the myriad supporting data for tracking the provenance and community activity surrounding these concepts and their relationships These data are released under a Creative Commons public domain attribution (CC0), promoting their redistribution and use in downstream applications
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