Abstract 2463: Prototype open-access biomarker knowledgebase for genetic tests for breast cancer

Cancer biomarkers have become integral components of many clinical protocols and research studies. Nonetheless there still is an urgent need for more accurate, less invasive, and cost-effective biomarker tests to advance precision oncology. Despite considerable success in the preclinical setting, most candidate biomarker tests have yet to receive regulatory clearance or approval and gain market acceptance. To better understand the challenges facing modern biomarker research and facilitate access to biomarker-related information, this study aims to delineate the paths to approval of genetic tests for breast cancer. Public databases were searched to identify approved tests and respective biomarkers. A prototype format for unified biomarker information was developed iteratively due to the highly variable presentation of this data. A knowledgebase of FDA-approved genetic biomarkers for breast cancer was built as a pilot implementation of this format. This model is extensible to a larger database of biomarkers to be hosted in OncoMX (https://oncomx.org), which integrates cancer mutation, differential expression, literature mining, and pathway information. Fields in the prototype database were populated based on findings from the FDA website, PubMed, and other open-source databases. The resulting dataset is structured with the corresponding readme following the BioCompute Object (BCO) model. This BCO-compliant approach enables provenance capture and transparency of data processing, both of which are critical to large-scale data integration efforts. The adherence to BCO standards is expected to enhance the usability of the resulting data and to streamline the subsequent integration of multiple biomarker datasets. In the rapidly evolving field of oncologic biomarker research, this open-source biomarker knowledgebase offers clinicians and researchers a streamlined access to genetic tests information including corresponding genes, guides to evidence, clinical trial data, links to archival databases, and context for the behavior of the implicated gene in other disease and normal processes.

Citation Format: Dara Baker, Natalie Fedorova Abrams, Amanda Bell, Maureen Colbert, Hayley Dingerdissen, Evan Holmes, Samir Gupta, Robel Kahsay, Heather Kincaid, David Liu, A. S. M. Ashique Mahmood, Frédéric B. Bastian, Marc Robinson-Rechavi, Elena Schwartz, K. Vijay-Shanker, Daniel Crichton, Raja Mazumder. Prototype open-access biomarker knowledgebase for genetic tests for breast cancer [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 2463.

eGARD: Extracting associations between genomic anomalies and drug responses from text

Tumor molecular profiling plays an integral role in identifying genomic anomalies which may help in personalizing cancer treatments, improving patient outcomes and minimizing risks associated with different therapies. However, critical information regarding the evidence of clinical utility of such anomalies is largely buried in biomedical literature. It is becoming prohibitive for biocurators, clinical researchers and oncologists to keep up with the rapidly growing volume and breadth of information, especially those that describe therapeutic implications of biomarkers and therefore relevant for treatment selection. In an effort to improve and speed up the process of manually reviewing and extracting relevant information from literature, we have developed a natural language processing (NLP)-based text mining (TM) system called eGARD (extracting Genomic Anomalies association with Response to Drugs). This system relies on the syntactic nature of sentences coupled with various textual features to extract relations between genomic anomalies and drug response from MEDLINE abstracts. Our system achieved high precision, recall and F-measure of up to 0.95, 0.86 and 0.90, respectively, on annotated evaluation datasets created in-house and obtained externally from PharmGKB. Additionally, the system extracted information that helps determine the confidence level of extraction to support prioritization of curation. Such a system will enable clinical researchers to explore the use of published markers to stratify patients upfront for 'best-fit' therapies and readily generate hypotheses for new clinical trials.

WebGIVI: a web-based gene enrichment analysis and visualization tool

BACKGROUND

A major challenge of high throughput transcriptome studies is presenting the data to researchers in an interpretable format. In many cases, the outputs of such studies are gene lists which are then examined for enriched biological concepts. One approach to help the researcher interpret large gene datasets is to associate genes and informative terms (iTerm) that are obtained from the biomedical literature using the eGIFT text-mining system. However, examining large lists of iTerm and gene pairs is a daunting task.

RESULTS

We have developed WebGIVI, an interactive web-based visualization tool ( http://raven.anr.udel.edu/webgivi/ ) to explore gene:iTerm pairs. WebGIVI was built via Cytoscape and Data Driven Document JavaScript libraries and can be used to relate genes to iTerms and then visualize gene and iTerm pairs. WebGIVI can accept a gene list that is used to retrieve the gene symbols and corresponding iTerm list. This list can be submitted to visualize the gene iTerm pairs using two distinct methods: a Concept Map or a Cytoscape Network Map. In addition, WebGIVI also supports uploading and visualization of any two-column tab separated data.

CONCLUSIONS

WebGIVI provides an interactive and integrated network graph of gene and iTerms that allows filtering, sorting, and grouping, which can aid biologists in developing hypothesis based on the input gene lists. In addition, WebGIVI can visualize hundreds of nodes and generate a high-resolution image that is important for most of research publications. The source code can be freely downloaded at https://github.com/sunliang3361/WebGIVI . The WebGIVI tutorial is available at http://raven.anr.udel.edu/webgivi/tutorial.php .

DiMeX: A Text Mining System for Mutation-Disease Association Extraction

The number of published articles describing associations between mutations and diseases is increasing at a fast pace. There is a pressing need to gather such mutation-disease associations into public knowledge bases, but manual curation slows down the growth of such databases. We have addressed this problem by developing a text-mining system (DiMeX) to extract mutation to disease associations from publication abstracts. DiMeX consists of a series of natural language processing modules that preprocess input text and apply syntactic and semantic patterns to extract mutation-disease associations. DiMeX achieves high precision and recall with F-scores of 0.88, 0.91 and 0.89 when evaluated on three different datasets for mutation-disease associations. DiMeX includes a separate component that extracts mutation mentions in text and associates them with genes. This component has been also evaluated on different datasets and shown to achieve state-of-the-art performance. The results indicate that our system outperforms the existing mutation-disease association tools, addressing the low precision problems suffered by most approaches. DiMeX was applied on a large set of abstracts from Medline to extract mutation-disease associations, as well as other relevant information including patient/cohort size and population data. The results are stored in a database that can be queried and downloaded at http://biotm.cis.udel.edu/dimex/. We conclude that this high-throughput text-mining approach has the potential to significantly assist researchers and curators to enrich mutation databases.