Alex H. Wagner, Ph.D. - US grants

Project Summary / Abstract The proposed research strategy is to identify novel recurrent biomarkers of lung cancers through collaborative interpretation of genomic data from patients. The project will first focus on developing a web platform that will allow sequence analysts, research scientists, and clinicians (collectively, a genomics tumor board) to collaborate in the detection and interpretation of genomic events. We will develop the Genome Interpretation Explorer (GenIE) framework for uploading, viewing, discussing, and annotating genomic events from lung cancers. Genomic visualization software will be developed to create interactive visualizations of complex genomic events, including structural variations (SVs), copy number variations (CNVs), and RNA fusions. These visualizations will be integrated into GenIE, enabling discussion over the visualized genomic events. As part of this process, critical events from each of these datasets will be discussed and annotated, with the discussions and annotations automatically stored in GenIE. Novel complex genome mutations (events) will be validated by targeted sequencing, and the results associated with the parent dataset (the patient case). A query interface will be developed to enable advanced searches of the patient cases stored in GenIE, to facilitate review and discovery of cases with specific molecular characteristics. Next, the platform will be used to build a knowledgebase characterizing the genomic events of lung cancers, to be used as a resource for clinical decision making in the practice of precision oncology. Several cases describing lung adenocarcinomas and squamous cell carcinomas, in addition to other cases characterizing non-small cell lung cancers, will be collected and imported into GenIE. From there, the sequence data associated with these datasets will be reviewed using the GenIE cohort visualization tools to identify significantly recurrent complex genomic events that were not reported in the original datasets. The resulting knowledgebase and web tools resulting from this study will then be used in the evaluation of expert identified clinical cases to inform clinical decision-making, improve diagnosis, or advance our understanding of lung cancers. This research strategy is well-aligned with the mission of the National Cancer Institute (NCI) to support research with respect to the cause, diagnosis, and treatment of cancer. In addition, the NCI facilitates translational research that can inform standard clinical practice and medical decision making, which is the intent of the proposed research.

PROJECT SUMMARY/ABSTRACT The availability of high-throughput, low-cost sequencing has transformed the landscape of biomedical research by dramatically expanding our capacity to interrogate the sequence of the human genome. Consequently, there has been an explosion of biomedical literature describing the role of specific genomic variants and their impact on human diseases. These advances are bringing sequencing into the clinic to shape clinical practice from the patient?s genomic content, a paradigm colloquially referred to as genomic or precision medicine. There remain many obstacles to fully realizing our potential in the era of precision medicine. Among them is a recognized need for robust, well-engineered systems that provide knowledge about genomic variants and their role in disease. Ideally, such systems would provide a comprehensive summary of all knowledge that is relevant to the patient?s unique genomic content. An early bottleneck to realizing precision medicine was that, despite the substantial literature and several established knowledgebases that define interactions between drugs and genes, querying across them was extremely challenging. In response to this need, the Drug-Gene Interaction database (DGIdb, dgidb.org) was developed. Through a combination of automated processing and manual curation, drug-gene interaction information was collected, structured, and connected (normalized) from these diverse sources of data and entered into a database with a user-friendly search interface and an application programming interface (API). However, linking drug and drug-gene interaction concepts across resources remains an extremely challenging task, and aggregated drug-gene interactions are also challenging to represent in a way that highlights the utility of the collected knowledge for precision medicine efforts. This proposal seeks to improve our ability to normalize and interpret drug-gene interactions corresponding to patient genomic variants. We will achieve this goal through two specific aims. First, the DGIdb normalization routines will be improved through incorporation of new content and features. Among these, the DGIdb will support collections of drugs, including combination therapies and drug classes. Also, the DGIdb will have new community submission and curation features, allowing users to incorporate new knowledge into the database. Second, the Variant Interpretation Aggregator database (VIAdb) will be created to normalize knowledge across several disparate sources focused on the clinical interpretations of genomic variants. The VIAdb will operate as a stand-alone web tool and API and will behave as a source of relevant interpretations to DGIdb. Finally, we will develop techniques for automated identification of drug-gene interactions and variant interpretation consensus to assist community curation efforts. If successful, this research will improve breadth and consistency of variant interpretations and drug-gene interactions for precision medicine efforts.