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OBJECTIVES: The majority of patients with locally advanced larynx or hypopharynx squamous cell carcinoma are treated with organ-preserving chemoradiotherapy (CRT). Clinical outcome following CRT varies greatly. We hypothesized that tumor microRNA (miRNA) expression is predictive for outcome following CRT. METHODS: Next-generation sequencing (NGS) miRNA profiling was performed on 37 formalin-fixed paraffin-embedded (FFPE) tumor samples. Patients with a recurrence-free survival (RFS) of less than 2 years and patients with late/no recurrence within 2 years were compared by differential expression analysis. Tumor-specific miRNAs were selected based on normal mucosa miRNA expression data from The Cancer Genome Atlas database. A model was constructed to predict outcome using group-regularized penalized logistic ridge regression. Candidate miRNAs were validated by RT-qPCR in the initial sample set as well as in 46 additional samples. RESULTS: Thirteen miRNAs were differentially expressed (p < 0.05, FDR < 0.1) according to outcome group. Initial class prediction in the NGS cohort (n = 37) resulted in a model combining five miRNAs and disease stage, able to predict CRT outcome with an area under the curve (AUC) of 0.82. In the RT-qPCR cohort (n = 83), 25 patients (30%) experienced early recurrence (median RFS 8 months; median follow-up 42 months). Class prediction resulted in a model combining let-7i-5p, miR-192-5p and disease stage, able to discriminate patients with good versus poor clinical outcome (AUC:0.80). CONCLUSION: The combined miRNA expression and disease stage prediction model for CRT outcome is superior to using either factor alone. This study indicates NGS miRNA profiling using FFPE specimens is feasible, resulting in clinically relevant biomarkers.
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PURPOSE: Due to the established role of the human papillomavirus (HPV), the optimal treatment for oropharyngeal carcinoma is currently under debate. We evaluated the most important determinants of treatment outcome to develop a multifactorial predictive model that could provide individualized predictions of treatment outcome in oropharyngeal carcinoma patients. METHODS: We analyzed the association between clinico-pathological factors and overall and progression-free survival in 168 OPSCC patients treated with curative radiotherapy or concurrent chemo-radiation. A multivariate model was validated in an external dataset of 189 patients and compared to the TNM staging system. This nomogram will be made publicly available at www.predictcancer.org. RESULTS: Predictors of unfavorable outcomes were negative HPV-status, moderate to severe comorbidity, T3-T4 classification, N2b-N3 stage, male gender, lower hemoglobin levels and smoking history of more than 30 pack years. Prediction of overall survival using the multi-parameter model yielded a C-index of 0.82 (95% CI, 0.76-0.88). Validation in an independent dataset yielded a C-index of 0.73 (95% CI, 0.66-0.79. For progression-free survival, the model's C-index was 0.80 (95% CI, 0.76-0.88), with a validation C-index of 0.67, (95% CI, 0.59-0.74). Stratification of model estimated probabilities showed statistically different prognosis groups in both datasets (p<0.001). CONCLUSION: This nomogram was superior to TNM classification or HPV status alone in an independent validation dataset for prediction of overall and progression-free survival in OPSCC patients, assigning patients to distinct prognosis groups. These individualized predictions could be used to stratify patients for treatment de-escalation trials.