Incidence of HPV+ oropharyngeal squamous cell carcinoma (OPSCC) has been increasing dramatically. Although long-term survival rates for these patients are high, they often suffer from permanent radiotherapy-related morbidity. This has prompted the development of de-escalation clinical protocols to reduce morbidity. However, a subset of patients do not respond even to standard therapy and have poor outcomes. It is unclear how to properly identify and treat the high- and low-risk HPV+ OPSCC patients. Since HPV positivity drives radiotherapy sensitivity, we hypothesized that variations in HPV biology may cause differences in treatment response and outcome. By analyzing gene expression data, we identified variations in HPV-related molecules among HPV+ OPSCC. A subset of tumors presented a molecular profile distinct from that of typical HPV+ tumors and exhibited poor treatment response, indicating molecular and clinical similarities with HPV– tumors. These molecular changes were also observed in vitro and correlated with radiation sensitivity. Finally, we developed a prognostic biomarker signature for identification of this subgroup of HPV+ OPSCC and validated it in independent cohorts of oropharyngeal and cervical carcinomas. These findings could translate to improved patient stratification for treatment deintensification and new therapeutic approaches for treatment-resistant HPV-related cancer.
Frederico O. Gleber-Netto, Xiayu Rao, Theresa Guo, Yuanxin Xi, Meng Gao, Li Shen, Kelly Erikson, Nene N. Kalu, Shuling Ren, Guorong Xu, Kathleen M. Fisch, Keiko Akagi, Tanguy Seiwert, Maura Gillison, Mitchell J. Frederick, Faye M. Johnson, Jing Wang, Jeffrey N. Myers, Joseph Califano, Heath D. Skinner, Curtis R. Pickering
Usage data is cumulative from January 2019 through April 2019.
Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.
Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.