HPV16 increases the number of migratory cancer stem cells and modulates their miRNA expression profile in oropharyngeal cancer.

Human papillomavirus type 16 (HPV16) is a major risk for development of oropharyngeal squamous-cell-carcinoma (OPSCC). Although HPV+ OPSCC metastasize faster than HPV- tumors, they have a better prognosis. The molecular and cellular alterations underlying this pathobiology of HPV+ OPSCC remain elusive. In this study, we examined whether expression of HPV16-E6E7 targets the number of migratory and stationary cancer stem cells (CSC). Furthermore, we wanted to elucidate if aberrantly expressed miRNAs in migratory CSC may be responsible for progression of OPSCCs and whether they may serve as potential novel biomarkers for increased potential of metastasis. Our studies revealed that HPV16-E6E7 expression leads to an increase in the number of stationary (CD44high /EpCAMhigh ) stem cells in primary keratinocyte cultures. Most importantly, expression of E6E7 in the cell line H357 increased the migratory (CD44high /EpCAMlow ) CSC pool. This increase in migratory CSCs could also be confirmed in HPV+ OPSCC. Differentially expressed miRNAs from HPV16-E6E7 positive CD44high /EpCAMlow CSCs were validated by RT-qPCR and in situ hybridization on HPV16+ OPSCCs. These experiments led to the identification of miR-3194-5p, which is upregulated in primary HPV16+ OPSCC and matched metastasis. MiR-1281 was also found to be highly expressed in HPV+ and HPV- metastasis. As inhibition of this miRNA led to a markedly reduction of CD44high /EpCAMlow cells, it may prove to be a promising drug target. Taken together, our findings highlight the capability of HPV16 to modify the phenotype of infected stem cells and that miR-1281 and miR3194-5p may represent promising targets to block metastatic spread of OPSCC.

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

PURPOSE: Human papillomavirus (HPV) in oropharyngeal squamous cell carcinoma (OPSCC) is tumorigenic and has been associated with a favorable prognosis compared with 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 hematoxylin and eosin (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 interrater reliability was minimal (Light Kappa = 0.37; P = 0.129), as compared with 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). CONCLUSIONS: 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 patients with OPSCC with a favorable prognosis, potentially outperforming combined HPV-DNA/p16 status as a biomarker for patient stratification.