Personalized treatment in HPV⁺ oropharynx cancer using genomic adjusted radiation dose

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Abstract

BACKGROUND A key objective in managing HPV+ oropharyngeal squamous cell carcinoma (OPSCC) is reducing radiation therapy (RT) doses without compromising cure rates. A recent phase II/III HN005 trial revealed that clinical factors alone are insufficient to guide safe RT dose de-escalation. Our prior research demonstrated that the genomic adjusted radiation dose (GARD) predicts RT benefit and may inform dose selection. We hypothesize that GARD can guide personalized RT de-escalation in HPV+ OPSCC patients.METHODS Gene expression profiles were analyzed in 191 HPV+ OPSCC patients enrolled in an international, multi-institutional observational study (AJCC Eighth Edition, stages I–III). Most patients received 70 Gy in 35 fractions or 69.96 Gy in 33 fractions (median dose: 70 Gy; range: 51.0–74.0 Gy). Overall survival (OS) was 94.1% at 36 months and 87.3% at 60 months. A Cox proportional hazards model assessed association between GARD and OS, and time-dependent receiver operating characteristic analyses compared GARD with traditional clinical predictors.RESULTS Despite uniform RT dosing, GARD showed wide heterogeneity, ranging from 15.4 to 71.7. Higher GARD values were significantly associated with improved OS in univariate (HR = 0.941, P = 0.041) and multivariable analyses (HR = 0.943, P = 0.046), while T and N stages were not. GARD demonstrated superior predictive performance at 36 months (AUC = 78.26) versus clinical variables (AUC = 71.20). Two GARD-based RT de-escalation strategies were identified, offering potential survival benefits while reducing radiation exposure.CONCLUSION GARD predicts OS and outperforms clinical variables, supporting its integration into the diagnostic workflow for personalized RT in HPV+ OPSCC.FUNDING This work was supported by the National Cancer Institute through the Cleveland Clinic/Emory ROBIN center (U54-CA274513, project 2), the European Union Horizon 2020 Framework Programme (grant/award 689715), the Italian Association for Cancer Research (AIRC project ID 23573), and the European Research Area Network ERA PerMed JTC2019/Fondazione Regionale per la Ricerca Biomedica project SuPerTreat (Supporting Personalized Treatment Decisions in Head and Neck Cancer through Big Data).

Authors

Emily Ho, Loris De Cecco, Steven A. Eschrich, Stefano Cavalieri, Geoffrey Sedor, Frank Hoebers, Ruud H. Brakenhoff, Kathrin Scheckenbach, Tito Poli, Kailin Yang, Jessica A. Scarborough, Shivani Nellore, Shauna Campbell, Neil Woody, Tim Chan, Jacob Miller, Natalie Silver, Shlomo Koyfman, James Bates, Jimmy J. Caudell, Michael W. Kattan, Lisa Licitra, Javier F. Torres-Roca, Jacob G. Scott