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Abstract 2478: Cancer genome interpretation with CIViCpy

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Abstract The promise of precision medicine–in which a cancer patient’s treatment is tailored to their specific cancer variants–requires concise, standardized, and searchable clinical interpretations. We recently developed the Clinical Interpretations for Variants in Cancer (CIViC; civicdb.org) crowd-sourced, open-access knowledgebase, providing a structured framework for evaluating genomic variants of various types (e.g., fusions, SNVs) for their therapeutic, prognostic, predisposing or diagnostic utility. CIViC is fully open-source, and has a documented API for accessing records of interpretation assertions, evidence, variants, and genes. However, this API was built around the framework for which the CIViC web tool was designed, with a focus on search and display of the interpretation of individual genomic variants in cancer. Third-party tools to analyze or access the contents of this database programmatically must therefore construct methods to leverage this API, often reimplementing similar functionality in the pursuit of common analysis tasks that are not within the scope of the CIViC web interface. To address this shortfall, we have developed CIViCpy (civicpy.org), an analysis toolkit and Software Developer Kit (SDK) for extracting and exploring the content of the CIViC knowledgebase. CIViCpy lets you pull data from CIViC using the civic module and interact with records as dynamic objects in Python. With the aid of automatic caching, CIViCpy provides fully-documented data objects for CIViC records (e.g. assertions, genes, variants) without worrying about querying CIViC more than once for the same data. Thus, repeated requests for a given CIViC record in the same session will load quickly, enabling rapid identification of records matching desired characteristics. All CIViCpy objects are directly comparable for ease of data analysis. CIViCpy also performs sensible pre-caching, e.g. requests for variants pre-load related gene and evidence records. Like CIViC, CIViCpy is fully documented, open-source, and permissively (MIT) licensed. CIViCpy is also easily and freely installable from the python package index (PyPI). In this study, we illustrate how the CIViCpy SDK is used to enable the analysis and export of CIViC entries. We highlight the use of the CIViCpy SDK to extract variants from CIViC and export them into the VCFv4.2 format through the built-in export tools, enabling their use in annotation pipelines or software that expects this common variant format. We also assess the viability of CIViCpy as a tool for advancing individualized patient care by examining how the CIViCpy interface can be used to systematically generate reports for the interpretation of observed variants in patient cancers. We evaluate this through the analysis of the 48,447 sequenced tumors of the AACR Project GENIE dataset, revealing clinical actionability at the patient level and addressing ongoing challenges in the programmatic analysis of genomic variants of cancers. Citation Format: Alex H. Wagner, Malachi Griffith, Obi L. Griffith. Cancer genome interpretation with CIViCpy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2478.
American Association for Cancer Research (AACR)
Title: Abstract 2478: Cancer genome interpretation with CIViCpy
Description:
Abstract The promise of precision medicine–in which a cancer patient’s treatment is tailored to their specific cancer variants–requires concise, standardized, and searchable clinical interpretations.
We recently developed the Clinical Interpretations for Variants in Cancer (CIViC; civicdb.
org) crowd-sourced, open-access knowledgebase, providing a structured framework for evaluating genomic variants of various types (e.
g.
, fusions, SNVs) for their therapeutic, prognostic, predisposing or diagnostic utility.
CIViC is fully open-source, and has a documented API for accessing records of interpretation assertions, evidence, variants, and genes.
However, this API was built around the framework for which the CIViC web tool was designed, with a focus on search and display of the interpretation of individual genomic variants in cancer.
Third-party tools to analyze or access the contents of this database programmatically must therefore construct methods to leverage this API, often reimplementing similar functionality in the pursuit of common analysis tasks that are not within the scope of the CIViC web interface.
To address this shortfall, we have developed CIViCpy (civicpy.
org), an analysis toolkit and Software Developer Kit (SDK) for extracting and exploring the content of the CIViC knowledgebase.
CIViCpy lets you pull data from CIViC using the civic module and interact with records as dynamic objects in Python.
With the aid of automatic caching, CIViCpy provides fully-documented data objects for CIViC records (e.
g.
assertions, genes, variants) without worrying about querying CIViC more than once for the same data.
Thus, repeated requests for a given CIViC record in the same session will load quickly, enabling rapid identification of records matching desired characteristics.
All CIViCpy objects are directly comparable for ease of data analysis.
CIViCpy also performs sensible pre-caching, e.
g.
requests for variants pre-load related gene and evidence records.
Like CIViC, CIViCpy is fully documented, open-source, and permissively (MIT) licensed.
CIViCpy is also easily and freely installable from the python package index (PyPI).
In this study, we illustrate how the CIViCpy SDK is used to enable the analysis and export of CIViC entries.
We highlight the use of the CIViCpy SDK to extract variants from CIViC and export them into the VCFv4.
2 format through the built-in export tools, enabling their use in annotation pipelines or software that expects this common variant format.
We also assess the viability of CIViCpy as a tool for advancing individualized patient care by examining how the CIViCpy interface can be used to systematically generate reports for the interpretation of observed variants in patient cancers.
We evaluate this through the analysis of the 48,447 sequenced tumors of the AACR Project GENIE dataset, revealing clinical actionability at the patient level and addressing ongoing challenges in the programmatic analysis of genomic variants of cancers.
Citation Format: Alex H.
Wagner, Malachi Griffith, Obi L.
Griffith.
Cancer genome interpretation with CIViCpy [abstract].
In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA.
Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2478.

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