Deep Learning Predicts HPV Association in Oropharyngeal Squamous Cell Carcinomas and Identifies Patients with a Favorable Prognosis Using Regular H&E Stains

Abstract

Purpose: Human papillomavirus (HPV) in oropharyngeal squamous cell carcinoma (OPSCC) is tumorigenic and has been associated with a favorable prognosis compared to OPSCC caused by tobacco, alcohol, and other carcinogens. Meanwhile, machine learning has evolved as a powerful tool to predict molecular- and cellular alterations of medical images of various sources. Experimental Design: We generated a deep learning-based HPV prediction score (HPV-ps) on regular H&E stains and assessed its performance to predict HPV-association using 273 patients from two different sites (OPSCC; Giessen, n=163; Cologne, n=110). Then, the prognostic relevance in a total of 594 patients (Giessen, Cologne, HNSCC TCGA) was evaluated. In addition, we investigated whether four board-certified pathologists could identify HPV-association (n=152) and compared the results to the classifier. Results: Although pathologists were able to diagnose HPV-association from H&E stained slides, (AUC=0.74, median of four observers) the inter-rater reliability was minimal (Light's Kappa=0.37; p=0.129), as compared to AUC=0.8 using the HPV-ps within two independent cohorts (n=273). The HPV-ps identified individuals with a favorable prognosis in a total of 594 patients from three cohorts (Giessen, OPSCC, HR=0.55, p<0.0001; Cologne, OPSCC, HR=0.44, p=0.0027; TCGA, non-OPSCC Head and Neck, HR=0.69, p=0.0073). Interestingly, the HPV-ps further stratified patients when combined with p16-status (Giessen, HR=0.06, p<0.0001; Cologne, HR=0.3, p=0.046). Conclusion: Detection of HPV-association in OPSCC using deep learning with help of regular H&E stains may either be used as a single biomarker-or in combination with p16-status-to identify OPSCC patients with a favorable prognosis potentially outperforming combined HPV-DNA/p16-status as a biomarker for patient stratification.