J Cancer 2019; 10(15):3323-3332. doi:10.7150/jca.29693

Research Paper

Molecular Decision Tree Algorithms Predict Individual Recurrence Pattern for Locally Advanced Nasopharyngeal Carcinoma

Hongmin Cai1,2*, Xiaolin Pang1*, Dong Dong3*, Yan Ma1*, Yan Huang4, Xinjuan Fan4, Peihuang Wu4, Haiyang Chen1, Fang He1, Yikan Cheng1, Shuai Liu1, Yizhen Yu1, Minghuang Hong5, Jian Xiao6, Xiangbo Wan1, Yanchun Lv7✉, Jian Zheng1✉

1. Department of Radiation Oncology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou 510655, China
2. School of Computer Science and Engineering, South China University of Technology, Guangzhou 510641, China
3. Department of Rhinology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
4. Department of Pathology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou 510060, China
5. Department of Nasopharyngeal Carcinoma, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
6. Department of Medical Oncology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou 510060, China
7. Department of Medical Radiology, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China
*These authors contributed equally to this work.

Abstract

Background: Recurrence remains one of the key reasons of relapse after the radical radiation for locally advanced nasopharyngeal carcinoma (NPC). Here, the multiple molecular and clinical variables integrated decision tree algorithms were designed to predict individual recurrence patterns (with VS without recurrence) for locally advanced NPC.

Methods: A total of 136 locally advanced NPC patients retrieved from a randomized controlled phase III trial, were included. For each patient, the expression levels of 33 clinicopathological biomarkers in tumor specimen, 3 Epstein-Barr virus related serological antibody titer and 5 clinicopathological variables, were detected and collected to construct the decision tree algorithm. The expression level of 33 clinicopathological biomarkers in tumor specimen was evaluated by immunohistochemistry staining.

Results: Three algorithm classifiers, augmented by the adaptive boosting algorithm for variable selection and classification, were designed to predict individual recurrence pattern. The classifiers were trained in the training subset and further tested using a 10-fold cross-validation scheme in the validation subset. In total, 13 molecules expression level in tumor specimen, including AKT1, Aurora-A, Bax, Bcl-2, N-Cadherin, CENP-H, HIF-1α, LMP-1, C-Met, MMP-2, MMP-9, Pontin and Stathmin, and N stage were selected to construct three 10-fold cross-validation decision tree classifiers. These classifiers showed high predictive sensitivity (87.2-93.3%), specificity (69.0-100.0%), and overall accuracy (84.5-95.2%) to predict recurrence pattern individually. Multivariate analyses confirmed the decision tree classifier was an independent prognostic factor to predict individual recurrence (algorithm 1: hazard ration (HR) 0.07, 95% confidence interval (CI) 0.03-0.16, P < 0.01; algorithm 2: HR 0.13, 95% CI 0.04-0.44, P < 0.01; algorithm 3: HR 0.13, 95% CI 0.03-0.68, P = 0.02).

Conclusion: Multiple molecular and clinicopathological variables integrated decision tree algorithms may individually predict the recurrence pattern for locally advanced NPC. This decision tree algorism provides a potential tool to select patients with high recurrence risk for intensive follow-up, and to diagnose recurrence at an earlier stage for salvage treatment in the NPC endemic region.

Keywords: nasopharyngeal carcinoma, decision tree algorithms, classifiers, recurrence pattern

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How to cite this article:
Cai H, Pang X, Dong D, Ma Y, Huang Y, Fan X, Wu P, Chen H, He F, Cheng Y, Liu S, Yu Y, Hong M, Xiao J, Wan X, Lv Y, Zheng J. Molecular Decision Tree Algorithms Predict Individual Recurrence Pattern for Locally Advanced Nasopharyngeal Carcinoma. J Cancer 2019; 10(15):3323-3332. doi:10.7150/jca.29693. Available from http://www.jcancer.org/v10p3323.htm