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.