Recommendations for determining HPV status in patients with oropharyngeal cancers under TNM8 guidelines: a two-tier approach.

Authors:
Stephanie G Craig
Stephanie G Craig
Queen's University Belfast
Lesley A Anderson
Lesley A Anderson
Queen's University Belfast
Ireland
Andrew G Schache
Andrew G Schache
University of Liverpool
United Kingdom
Michael Moran
Michael Moran
University of Toronto
Canada
Laura Graham
Laura Graham
Virginia Commonwealth University
United States
Max Robinson
Max Robinson
Institute for Systems Biology
United States

Br J Cancer 2019 Apr 20;120(8):827-833. Epub 2019 Mar 20.

Centre for Cell Research and Cell Biology, Queen's University Belfast, Belfast, Northern Ireland, UK.

Background: TNM8 staging for oropharyngeal squamous cell carcinomas (OPSCC) surrogates p16 immunohistochemistry for HPV testing. Patients with p16+ OPSCC may lack HPV aetiology. Here, we evaluate the suitability of TNM8 staging for guiding prognosis in such patients.

Methods: HPV status was ascertained using p16 immunohistochemistry and high-risk HPV RNA and DNA in situ hybridisation. Survival by stage in a cohort of OPSCC patients was evaluated using TNM7/TNM8 staging. Survival of p16+/HPV- patients was compared to p16 status.

Results: TNM8 staging was found to improve on TNM7 (log rank p = 0·0190 for TNM8 compared with p = 0·0530 for TNM7) in p16+ patients. Patients who tested p16+ but were HPV- (n = 20) had significantly reduced five-year survival (33%) compared to p16+ patients (77%) but not p16- patients (35%). Cancer stage was reduced in 95% of p16+/HPV- patients despite having a mortality rate twice (HR 2.66 [95% CI: 1.37-5.15]) that of p16+/HPV+ patients under new TNM8 staging criteria.

Conclusion: Given the significantly poorer survival of p16+/HPV- OPSCCs, these data provide compelling evidence for use of an HPV-specific test for staging classification. This has particular relevance in light of potential treatment de-escalation that could expose these patients to inappropriately reduced treatment intensity as treatment algorithms evolve.

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http://dx.doi.org/10.1038/s41416-019-0414-9DOI Listing
April 2019
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