This is a K23 career development award resubmission for Dr. Ryan Stidham, a gastroenterologist at the University of Michigan. Dr. Stidham's long-term career goal is to develop objective prognostics to personalize treatment decisions for patients with inflammatory bowel disease. The immediate goals of the proposal are to evaluate both analytic morphomics, an image analysis platform to quantify CT scan findings, and serum glycoproteomic profiles to predict therapeutic response in patients with Crohn's disease. Dr. Stidham's integrated research and career development plan align with goals of developing himself as a collaborative translational investigator, skilled in statistical analysis o large datasets, applying novel prognostics to improve clinical outcomes in Crohn's disease. Crohn's disease (CD) affects over 700,000 patients in the United States. Nearly 60% of CD patients require surgical resection for medically-unresponsive disease within 10 years of diagnosis. Frequently, patients present with deep bowel injury composed of both medically-responsive inflammatory and non- responsive fibrotic injury. Despite the presence of an inflammatory target, medical therapy may be futile and timely surgical management should be pursued. Further, strategies using early high-intensity immunosuppression to prevent future fibrostenotic disease are likely to over treat portions of the CD population. Therefore, therapeutic decisions are predicated on (1) the probability that existing disease activity will respond to medical therapy and (2) the probability of developing future fibrostenotic complications. Assessment using colonoscopy and surrogates of inflammation including imaging, blood, and stool-based biomarkers fail to provide comprehensive and quantitative assessments of bowel injury, including fibrosis. Despite its importance, there are no measures to account for deep bowel wall injury and intestinal fibrosis. This research proposal will utilize quantitative imaging findings of deep bowel injury in conjunction with blood-based biomarkers reflecting disease phenotypes to predict medical failure, defined as requiring future bowel resection, hospitalization, and prolonged steroid use. To achieve our imaging aims (Aim 1) we will use analytic morphomics, a computer image analysis method, to quantify body and bowel composition from over 4,000 CT-enterographies in a longitudinal 1200 patient CD cohort.
In Aim 2, serum glycoproteome variance will be characterized in carefully phenotyped inflammatory and stricturing subjects to refine our preexisting biomarkers of fibrosis. We have demonstrated that variations of the serum glycoproteome reflect the degree of intestinal fibrosis present and expect that serum glycoproteomics can differentiate inflammatory from fibrotic phenotypes in CD. Optimized serum glycoproteome profiles will be used to predict future conversion from B1- inflammatory disease to B2/3 fibrostenotic disease in a pediatric prospective-inception cohort (RISK study).
Aim 3 entails a prospective study combining analytic morphomics and serum glycoproteome profiles to predict therapeutic response and clinical outcomes; providing preliminary data for future R03/R01 studies. Dr. Stidham is a Lecturer of Medicine at the University of Michigan Division of Gastroenterology. He earned his medical degree from the University of Virginia (AOA), completed an internal medicine residency at the University of Pennsylvania, and was a T-32 research fellow at the University of Michigan (2011). His research background combines laboratory and clinical experience with Dr. Peter Higgins (primary mentor), focusing on novel biomarker development. Dr. Stidham has been awarded several prior grants for pilot work, including a MICHR-CTSA T32 Pilot Grant for Proteomics and a Crohn's and Colitis Foundation Career Development Award for ultrasound imaging research. He is in the process of completing a Masters program in Clinical Research Design and Statistical Analysis at the University of Michigan School of Public Health (matriculation in April 2015). He has published in several gastroenterology journals, presented research internationally, and has built the independent collaborations featured in this proposal. Central to this career development award, the candidate will leverage the expertise of several mentors and collaborators to develop deep foundational skillsets in analytic imaging, translational proteomics, bioinformatics, and machine learning methods. The University of Michigan houses a nationally recognized Inflammatory Bowel Disease Program, internationally recognized proteomics expertise, and a dedicated Analytic Morphomics Group focused on computational image analysis of organs. The mentored research training will be supplemented with focused graduate-level coursework in bioinformatics, medical image analysis, machine learning methodologies, and decision support systems provided through the University of Michigan Center for Computational Medicine and Bioinformatics and the School of Public Health. The Division of Gastroenterology and Department of Medicine have a history of strong support for Dr. Stidham and will provide the time, resources, and mentorship necessary to achieve his professional and research goals. The training provided through this career development award will be pivotal for the candidate's development as an independent translational investigator focused on individualizing therapeutic management. This research will provide the foundation for future collaborative studies to further develop these and other prognostic tools for the inflammatory bowel diseases.
The proposed work evaluates the capabilities of quantitative image analysis and serum glycoproteome profiles to predict the probability of medical response in Crohn's disease. Improved prediction of therapeutic response would tailor Crohn's disease management such that patients unlikely to benefit from medical therapy can proceed to timely surgical management and be spared the risks and costs of modern immunosuppressive treatments.
|Waljee, Akbar K; Sauder, Kay; Patel, Anand et al. (2018) Response to 'The end of the dosage of 6 Thioguanine nucleotides? Not so sure…'. J Crohns Colitis 12:127|
|Ballengee, Cortney R; Stidham, Ryan W; Liu, Chunyan et al. (2018) Association Between Plasma Level of Collagen Type III alpha 1 Chain and Development of Strictures in Pediatric Patients With Crohn's Disease. Clin Gastroenterol Hepatol :|
|Govani, Shail M; Noureldin, Mohamed; Higgins, Peter D R et al. (2018) Defining an Optimal Adherence Threshold for Patients Taking Subcutaneous Anti-TNFs for Inflammatory Bowel Diseases. Am J Gastroenterol 113:276-282|
|Waljee, A K; Liu, B; Sauder, K et al. (2018) Predicting corticosteroid-free endoscopic remission with vedolizumab in ulcerative colitis. Aliment Pharmacol Ther 47:763-772|
|Limsrivilai, Julajak; Rao, Krishna; Stidham, Ryan W et al. (2018) Systemic Inflammatory Responses in Ulcerative Colitis Patients and Clostridium difficile Infection. Dig Dis Sci 63:1801-1810|
|Reutemann, Bethany A; Turkeltaub, Joshua A; Al-Hawary, Mahmoud et al. (2017) Endoscopic Balloon Dilation Size and Avoidance of Surgery in Stricturing Crohn's Disease. Inflamm Bowel Dis 23:1803-1809|
|Waljee, Akbar K; Lipson, Rachel; Wiitala, Wyndy L et al. (2017) Predicting Hospitalization and Outpatient Corticosteroid Use in Inflammatory Bowel Disease Patients Using Machine Learning. Inflamm Bowel Dis 24:45-53|
|Stidham, Ryan W; Wu, Jing; Shi, Jiaqi et al. (2017) Serum Glycoproteome Profiles for Distinguishing Intestinal Fibrosis from Inflammation in Crohn's Disease. PLoS One 12:e0170506|
|Waljee, Akbar K; Sauder, Kay; Patel, Anand et al. (2017) Machine Learning Algorithms for Objective Remission and Clinical Outcomes with Thiopurines. J Crohns Colitis 11:801-810|
|Wright, Andrew P; Fontana, Robert J; Stidham, Ryan W (2017) Vedolizumab is safe and effective in moderate-to-severe inflammatory bowel disease following liver transplantation. Liver Transpl 23:968-971|
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