Determining the molecular landscape and impact on prognosis in HPV-associated head and neck cancer

Abstract

Abstract Background Human papillomavirus (HPV) associated head and neck squamous cell carcinoma (HNSCC) has a better prognosis than HNSCC due to other risk factors. However, there is significant heterogeneity within HPV-associated HNSCC and 25% of these patients still do poorly despite receiving aggressive therapy. We currently have no good molecular tools to differentiate and exclude this “high-risk” sub-population and focus on “low-risk” patients for clinical trials. This has been a potential barrier to identifying successful de-escalation treatment strategies in HPV-associated HNSCC. We conducted an analysis of molecular markers with a well-known role in the pathogenesis of HPV-associated HNSCC and hypothesized that these markers could help independently predict recurrence and prognosis in these patients and therefore help identify at the molecular level “low-risk” patients suitable for de-escalation trials. Methods We analyzed 24 tumor specimens of patients with p16+ HNSCC who underwent definitive resection as primary treatment. Tissue microarray (TMA) was generated from the 24 pathology blocks and immunohistochemistry (IHC) was performed using highly specific antibodies for our chosen biomarkers (PI3K-PTEN, AKT pathway, mTOR, 4EBP1, S6, and pAMPK, ERCC-1). Transcriptome data was also obtained for 7 p16+ HNSCC patients from The Cancer Genome Atlas (TCGA). Data from the TMA and TCGA were analyzed for association of relapse-free survival (RFS) and overall survival (OS) with protein and gene expression of the chosen biomarkers. Results Increased pAMPK protein activity by IHC and AMPK gene expression by TCGA gene expression data was correlated with improved RFS with a trend towards statistical significance. Conclusions This data suggests that increased pAMPK activity and expression may portend a better prognosis in HPV-associated HNSCC undergoing primary definitive resection. However, these findings require validation in larger studies.