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.
Cancers of the lung are highly mutated due to repeated exposure to the carcinogens that a smoker is exposed to. The large number of mutations makes it very difficult to understand the complexity of this disease, which is both the deadliest of cancers and the second leading cause of death in the United States. Here a web tool is proposed to make it easier for a team of experts to review, discuss, and learn about the genomic complexities of a patient's lung cancer when considering treatment strategies for that patient.
| Cotto, Kelsy C; Wagner, Alex H; Feng, Yang-Yang et al. (2018) DGIdb 3.0: a redesign and expansion of the drug-gene interaction database. Nucleic Acids Res 46:D1068-D1073 |
| Wagner, Alex H; Devarakonda, Siddhartha; Skidmore, Zachary L et al. (2018) Recurrent WNT pathway alterations are frequent in relapsed small cell lung cancer. Nat Commun 9:3787 |
| Griffith, Malachi; Spies, Nicholas C; Krysiak, Kilannin et al. (2017) CIViC is a community knowledgebase for expert crowdsourcing the clinical interpretation of variants in cancer. Nat Genet 49:170-174 |
