All authors substantially contributed not only to the conception and design but also participated in the acquisition of data, analysis, and interpretation of data and drafting the manuscript.
Proteomic patterns of colonic mucosal tissues delineate Crohn's colitis and ulcerative colitis
Article first published online: 8 MAY 2013
© 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
PROTEOMICS - Clinical Applications
How to Cite
Seeley, E. H., Washington, M. K., Caprioli, R. M. and M'Koma, A. E. (2013), Proteomic patterns of colonic mucosal tissues delineate Crohn's colitis and ulcerative colitis. Prot. Clin. Appl.. doi: 10.1002/prca.201200107
- Article first published online: 8 MAY 2013
- Accepted manuscript online: 4 FEB 2013 08:13AM EST
- Manuscript Accepted: 7 JAN 2013
- Manuscript Revised: 27 NOV 2012
- Manuscript Received: 9 SEP 2012
- MMC/VICC/TSU Partnership PIs: Harold L. Moses and Samuel E. Adunyah. Grant Number: 3U54 CA091405–09S1
- Vanderbilt CTSA. Grant Number: 5U54RR026140–03
- NCRR/NIH; Research Foundation, American Society of Colon and Rectal Surgeons. Grant Number: 1 UL1 RR024975
- DOD. Grant Number: NIH/NCI-CA068485
- ooperative Human Tissue Network (CHTN). Grant Number: 5U 01CA094664–09
- Vanderbilt SPORE in GI Cancer. Grant Number: P50CA095103
- Colon tissue profiling;
- Crohn's colitis;
- Mass spectrometry;
- Ulcerative colitis
Although Crohn's colitis (CC) and ulcerative colitis (UC) share several clinical features, they have different causes, mechanisms of tissue damage, and treatment options. Therefore, the accurate diagnosis is of paramount importance in terms of medical care. The distinction between CC/UC is made on the basis of clinical, radiologic, endoscopic, and pathologic interpretations but cannot be differentiated in up to 15% of inflammatory bowel disease patients. Correct management of this “indeterminate colitis” depends on the accuracy of future, and yet not known, destination diagnosis (CC/UC).
We have developed a proteomic methodology that has the potential to discriminate between UC/CC. The histologic layers of 62 confirmed UC/CC tissues were analyzed using MALDI-MS for proteomic profiling.
A Support Vector Machine algorithm consisting of 25 peaks was able to differentiate spectra from CC and UC with 76.9% spectral accuracy when using a leave-20%-out cross-validation. Application of the model to the entire dataset resulted in accurate classification of 19/26 CC patients and 36/36 UC patients when using a 2/3 correct cutoff. A total of 114 peaks were found to have Wilcoxin rank sum p-values of less than 0.05.
Conclusion and clinical relevance
This information may provide new avenues for the development of novel personalized therapeutic targets.