Abstract
BackgroundPrecision oncology involves analysis of individual cancer samples to understand the genes and pathways involved in the development and progression of a cancer. To improve patient care, knowledge of diagnostic, prognostic, predisposing, and drug response markers is essential. Several knowledgebases have been created by different groups to collate evidence for these associations. These include the open-access Clinical Interpretation of Variants in Cancer (CIViC) knowledgebase. These databases rely on time-consuming manual curation from skilled experts who read and interpret the relevant biomedical literature.MethodsTo aid in this curation and provide the greatest coverage for these databases, particularly CIViC, we propose the use of text mining approaches to extract these clinically relevant biomarkers from all available published literature. To this end, a group of cancer genomics experts annotated sentences that discussed biomarkers with their clinical associations and achieved good inter-annotator agreement. We then used a supervised learning approach to construct the CIViCmine knowledgebase.ResultsWe extracted 121,589 relevant sentences from PubMed abstracts and PubMed Central Open Access full-text papers. CIViCmine contains over 87,412 biomarkers associated with 8035 genes, 337 drugs, and 572 cancer types, representing 25,818 abstracts and 39,795 full-text publications.ConclusionsThrough integration with CIVIC, we provide a prioritized list of curatable clinically relevant cancer biomarkers as well as a resource that is valuable to other knowledgebases and precision cancer analysts in general. All data is publically available and distributed with a Creative Commons Zero license. The CIViCmine knowledgebase is available at http://bionlp.bcgsc.ca/civicmine/.
Highlights
Precision oncology involves analysis of individual cancer samples to understand the genes and pathways involved in the development and progression of a cancer
Despite not having used Clinical Interpretation of Variants in Cancer (CIViC) publications in training CIViCmine, we find that a substantial number of papers cited in CIViC (294/1474) were identified automatically by CIViCmine
The high percentage of intermediate for the usability of predisposing biomarkers was due to the general variant terms identified where the exact variant was unclear and further curation would be needed. These results show that CIViCmine provides valuable data that can be curated into CIViC and other knowledgebases
Summary
Precision oncology involves analysis of individual cancer samples to understand the genes and pathways involved in the development and progression of a cancer. Several knowledgebases have been created by different groups to collate evidence for these associations These include the open-access Clinical Interpretation of Variants in Cancer (CIViC) knowledgebase. A growing number of biomarkers have been developed to select patients who are more likely to respond to certain treatments. These biomarkers have been valuable for prognostic purposes and for understanding the underlying biology of. The high genomic variability observed in cancers means that each patient sample includes a large number of new mutations, many of which may have never been documented before [6]. An analyst trying to understand a patient sample typically performs a literature review for each gene and specific variant which is needed to understand its relevance in a cancer type, characterize the driver/passenger role of its observed mutations, and gauge the relevance for clinical decision making
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