2021 iTHRIV Pilot: Use of machine learning image analysis and tissue transcriptomics to define clinically actionable celiac disease sub-types
This proposal leverages the use of molecular and computational tools to query human tissue to discern patient-specific outcomes for Celiac Disease (CD). The proposal aims to address pressing questions regarding the role of enteroendocrine cells in CD severity and their potential links to patients who go on to develop autoimmune endocrinopathies, such as diabetes or hypothyroidism. More critically, this proposal sets the stage for the investigation of other clinically relevant CD sub-types, such as those with trisomy 21, who are more likely to develop CD.
To achieve these goals, we will train an existing image analysis platform to distinguish and predict CD sub-types (Aim 1). This will be complemented by gene expression profiling for the sub-types versus routine CD and refractory CD using Formalin-Fixed Paraffin-Embedded (FFPE) duodenal biopsy samples (Aim 2). The corroboration of image analysis and transcriptomic patterns will further enable us to explore distinguishing patterns of CD subtypes (Aim 3).
| Keywords | autoimmune endocrinopathies, formalin-fixed paraffin-embedded, ffpe, hypothyroidism, trisomy 21, ithriv pilot, transcriptomic patterns, diabetes, celiac disease, gene expression profiling |
| Storage Organization | University of Virginia |
| Partner Institution(s) | Inova, University of Virginia |
| Funding Source(s) | NCATS Award UL1TR003015 iTHRIV CTSA |