For the 30 million Americans living with a rare disease, 95 percent of those diseases do not currently have an identified therapeutic option. Advances in genetics and omics technologies coupled with increased availability of health data present an opportunity to make precise personalized patient care broadly a clinical reality. However, the lack of rare disease clinical samples and suitable preclinical models for research and development often makes it difficult to even nominate, let alone test, therapeutic options for these patients. To aid rare disease research, our long term goal is to develop and apply approaches leveraging multi-omics data to nominate and prioritize drug targets and repurposing candidates. In this project, our main objective is to conduct a feasibility study based on analyses across NIH Common Fund and other publicly available data, developing research methods to support data integration.
In Aim 1, we will pursue bioinformatics analysis to identify and improve optimal preclinical rare disease models by piloting approaches for identifying the best cell line as an avatar for a given patient (Aim 1a) and for analyzing patient induced pluripotent stem cell (iPSC) profiles in the context of the most clinically relevant tissue types (Aim 1b).
In Aim 2, we will determine and test prioritized drug repurposing candidates for rare diseases by implementing transfer learning to project data on to cell line-by-perturbation data and identifying drug candidates that might rescue cell physiology deficits (Aim 2a). We will test top drug repurposing candidates for each phenotype in either patient-derived iPSC cell lines or xenograft mouse models and generate and analyze RNA-seq profiles pre- and post-treatment for future use in refining computational models (Aim 2b). We focus here on two rare diseases which both desperately need improved therapeutic options: Friedreich?s ataxia (FRDA) and rare brain tumors including glioblastoma multiforme (GBM). We will use NIH Common Fund data sets specified in this Funding Opportunity Announcement (GTEx, Kids First, LINCS, and PHAROS), other NIH-supported data sets (TCGA and CCLE/DepMap), and RNA-seq data generated in our lab at UAB. Because we are advancing this methodology in two disease systems simultaneously, we will demonstrate broad utility of these approaches and ensure a high chance of success in the one year timeframe. These approaches will be the basis of a conceptual framework for subsequent R01-level funding regarding genome-guided precision medicine approaches and computational methods development, as well as generating hypothesis for future collaborative GBM and FRDA research projects. The interdisciplinary approaches described here are crucial for advancing bench-to-bedside rare disease studies both at UAB, a leader in rare disease diagnosis, as well as in the broader scientific community. Upon successful completion of this proposal, we expect our contribution to be advancements to both preclinical modeling of, and prioritizing drug repurposing candidates for rare diseases as well as demonstrate how Common Fund data can be used to accelerate rare disease research.
The proposed research is relevant to public health because it focuses on developing and applying approaches leveraging multi-omics data to nominate and prioritize drug targets and repurposing candidates for rare diseases. Once these approaches have been developed, there is the potential for significant advancement of preclinical models, prioritization of drug repurposing candidates, and ultimately, rare disease treatment. Therefore, this project is relevant to NIH?s mission because the innovative research strategies will contribute to improvement of human health.
