Search

Purpose: The aim of this study was to identify and independently validate a novel gene signature predicting locoregional tumor control (LRC) for treatment individualization of patients with locally advanced HPV-negative head and neck squamous cell carcinomas (HNSCC) who are treated with postoperative radio(chemo)therapy (PORT-C).Experimental Design: Gene expression analyses were performed using NanoString technology on a multicenter training cohort of 130 patients and an independent validation cohort of 121 patients. The analyzed gene set was composed of genes with a previously reported association with radio(chemo)sensitivity or resistance to radio(chemo)therapy. Gene selection and model building were performed comparing several machine-learning algorithms.Results: We identified a 7-gene signature consisting of the three individual genes HILPDA, CD24, TCF3, and one metagene combining the highly correlated genes SERPINE1, INHBA, P4HA2, and ACTN1. The 7-gene signature was used, in combination with clinical parameters, to fit a multivariable Cox model to the training data (concordance index, ci = 0.82), which was successfully validated (ci = 0.71). The signature showed improved performance compared with clinical parameters alone (ci = 0.66) and with a previously published model including hypoxia-associated genes and cancer stem cell markers (ci = 0.65). It was used to stratify patients into groups with low and high risk of recurrence, leading to significant differences in LRC in training and validation (P < 0.001).Conclusions: We have identified and validated the first hypothesis-based gene signature for HPV-negative HNSCC treated by PORT-C including genes related to several radiobiological aspects. A prospective validation is planned in an ongoing prospective clinical trial before potential application in clinical trials for patient stratification. Clin Cancer Res; 24(6); 1364–74. ©2018 AACR.

Table S1 Analyzed genes of the hypothesis-driven gene set. Table S2 Programs and packages used for evaluation. Figure S1 Results of repeated 3-fold cross validation for LRC. Figure S2 Mean oob ci depending of the signature size for LRC. Figure S3 Importance score for genes associated with LRC. Table S3 Increasing robustness by including highly correlated genes. Figure S4 Patient stratification by the 7-gene signature for LRC. Figure S5 Patient stratification by the 7-gene signature and clinical parameters for LRC. Table S4 Multivariable Cox regression of OS. Figure S6 Patient stratification by the 7-gene signature and clinical parameters for OS. Table S5 Multivariable Cox regression of DM. Figure S7 Patient stratification by the 7-gene signature and clinical parameters for DM. Table S6 Means and standard deviations of the 7-gene signature gene expressions. Table S7 Comparison of gene expressions between patient groups. Table S8 Hyper-parameters for feature selection algorithms and predictive models. Table S9 Patient characteristics of all patients. Figure S8 Identification of the final gene signature.