alexa Novel Use of Proteomic Profiles in a Convex-Hull Ensemble Classifier to Predict Gynecological Cancer Patients’ Susceptibility to Gastrointestinal Mucositis as Side Effect of Radiation Therapy
ISSN: 0974-276X

Journal of Proteomics & Bioinformatics
Open Access

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Research Article

Novel Use of Proteomic Profiles in a Convex-Hull Ensemble Classifier to Predict Gynecological Cancer Patients’ Susceptibility to Gastrointestinal Mucositis as Side Effect of Radiation Therapy

Ralph L Kodell1*, Randy S Haun2,3*, Eric R Siegel1, Chuanlei Zhang4, Angela B Trammel5, Martin Hauer–Jensen2 and Alexander F Burnett5

1Department of Biostatistics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA

2Department of Pharmaceutical Sciences, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA

3Central Arkansas Veterans Healthcare System, Little Rock, AR 72205, USA

4Department of Applied Mathematics and Computer Science, Philander Smith College, Little Rock, AR 72202, USA

5Department of Obstetrics and Gynecology, Division of Gynecology Oncology, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA

*Corresponding Author:
Ralph L. Kodell
Department of Biostatistics
University of Arkansas for Medical Sciences
Little Rock, AR 72205, USA
Tel: 5 0 1 - 6 8 6 -5353
Fax: 501-526-6729
E-mail: [email protected]
 
Randy S. Haun
Department of Pharmaceutical Sciences
University of Arkansas for Medical Sciences
Little Rock, AR 72205, USA
Tel: 501-686-8594
Fax: 501-686-6517
E-mail: [email protected]

Received date: May 15, 2015; Accepted date: June 19, 2015; Published date: June 25, 2015

Citation: Kodell RL, Haun RS, Siegel ER, Zhang C, Trammel AB, et al. (2015) Novel Use of Proteomic Profiles in a Convex-Hull Ensemble Classifier to Predict Gynecological Cancer Patients’ Susceptibility to Gastrointestinal Mucositis as Side Effect of Radiation Therapy. J Proteomics Bioinform 8: 149-154. doi: 10.4172/jpb.1000363

Copyright: © 2015 Kodell RL, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

 

Abstract

Background: Whole-pelvis radiation therapy is common practice in the post-surgical treatment of cervical and endometrial cancer. Gastrointestinal mucositis is an adverse side effect of radiation therapy, and is a primary concern in patient management. We investigate whether proteomic information obtained from blood samples drawn from patients scheduled to receive radiation therapy for gynecological cancers could be used to predict which patients are most susceptible to radiation-induced gastrointestinal mucositis, in order to improve the individualization of radiation therapy. Methods: We use 132 proteins measured on 17 gynecological cancer patients in a convex-hull-based, selectivevoting ensemble classifier to classify each patient into one of two classes: patients who would not (class 1) or would (class 2) develop gastrointestinal mucositis. We employ 20 repetitions of 10-fold cross-validation to measure classification accuracy. Results: We achieved a 95% confidence interval on average prediction accuracy of (0.711, 0.771) using preradiation proteomic profiles to predict which patients would experience gastrointestinal mucositis. Pathway analysis of the 12 most prominent proteins indicated that they could be assembled into a single interaction network with direct associations. The function associated with the highest number of these 12 proteins was cell-to-cell signaling and interaction. Conclusions: Pre-radiation proteomic profiles have the potential to classify cervical/endometrial cancer patients with high accuracy as to their susceptibility to gastrointestinal mucositis following radiation therapy. Further study of the network of 12 identified proteins is warranted with a larger patient sample to confirm that these proteins are predictive of gastrointestinal mucositis in this patient population.

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