Inflammatory bowel disease (IBD) affects more than 1 million Americans and has been increasing in prevalence in the last decade. IBD consists of two diseases, Crohn's disease (CD) and ulcerative colitis (UC), which are both characterized by chronic inflammation of the gastrointestinal tract and can manifest with similar symptoms. Despite their similarities, the diseases are very heterogeneous and consist of complex interactions between the immune system, the microbiome, and the affected tissue. Characterizing the two diseases at the molecular level has been met with limited success, and the lack of a clear mechanistic pathway has resulted in the lack of efficacy of existing treatments in subsets of IBD patients. Previous methods have focused primarily on gene enrichment analysis, but these studies have shown inconsistent results and do not offer mechanistic insights into disease development. Systems pharmacological methods offer several advantages over traditional gene-based approaches by using networks to characterize relationships between genes, which enables the application of powerful analyses using graph theory. These methods enable us to use the topology of protein interaction networks to elucidate disease mechanisms and characterize network perturbations caused by drugs. Furthermore, with the advent of cheaper and more accurate modalities to quantify molecular profiles and the increasing availability of molecular data, we now have the ability to leverage multiple data sources to create a multifaceted approach to understanding the mechanism of IBD. Thus, by combining protein-protein interaction data, drug target data, and drug perturbation data, we aim to (1) construct personalized molecular networks for UC and CD, (2) develop a supervised network method to model drug effects, and (3) identify and validate novel drugs for treatment responders and nonresponders. With success, we will have the ability to distinguish treatment responders from nonresponders and have identified potential novel drug candidates to treat IBD. Furthermore, we will have a more thorough understanding of the molecular mechanisms of IBD, which will enable the development of more effective therapies to alleviate the suffering of millions worldwide.
Inflammatory bowel disease affects over a million people in the United States, and many affected patients do not respond to current therapies. This proposal addresses this challenge by developing methods to discern patients unlikely to respond to these therapies and identifying novel therapeutic agents for drug responders and drug nonresponders. The results of this proposal have the potential to impact treatment strategies and is relevant to the improved treatment and clinical care of inflammatory bowel disease.
| Han, Lichy; Maciejewski, Mateusz; Brockel, Christoph et al. (2018) Mendelian Disease Associations Reveal Novel Insights into Inflammatory Bowel Disease. Inflamm Bowel Dis 24:471-481 |
| Han, Lichy; Maciejewski, Mateusz; Brockel, Christoph et al. (2018) A probabilistic pathway score (PROPS) for classification with applications to inflammatory bowel disease. Bioinformatics 34:985-993 |
| Han, Lichy; Kamdar, Maulik R (2018) MRI to MGMT: predicting methylation status in glioblastoma patients using convolutional recurrent neural networks. Pac Symp Biocomput 23:331-342 |
