학술논문
Multidimensional biomarker predicts disease control in response to immunotherapy in recurrent or metastatic head and neck squamous-cell carcinoma.
Document Type
article
Author
Flanagan, Kevin; Earls, Jon; Schillebeeckx, Ian; Hiken, Jeffrey; Wellinghoff, Rachel; LaFranzo, Natalie; Bradley, Zachary; Babbitt, Joey; Westra, William; Hsu, Raymond; Nadauld, Lincoln; Mcleod, Howard; Firth, Sean; Sharp, Brittany; Fuller, Josh; Vavinskaya, Vera; Sutton, Leisa; Deichaite, Ida; Bailey, Samuel; Sandulache, Vlad; Rendo, Matthew; Macdonald, Orlan; Welaya, Karim; Wade, James; Pippas, Andrew; Slim, Jennifer; Bank, Bruce; Saccaro, Steven; Sui, Xingwei; Akhtar, Adil; Balaraman, Savitha; Kossman, Steven; Sonnier, Scott; Shenkenberg, Todd; Alexander, Warren; Price, Katherine; Bane, Charles; Ley, Jessica; Messina, David; Glasscock, Jarret; Adkins, Douglas; Duncavage, Eric; Cohen, Ezra
Source
Journal of Cancer Research and Clinical Oncology. 149(15)
Subject
Language
Abstract
PURPOSE: Anti-PD-1 therapy provides clinical benefit in 40-50% of patients with relapsed and/or metastatic head and neck squamous cell carcinoma (RM-HNSCC). Selection of anti- PD-1 therapy is typically based on patient PD-L1 immunohistochemistry (IHC) which has low specificity for predicting disease control. Therefore, there is a critical need for a clinical biomarker that will predict clinical benefit to anti-PD-1 treatment with high specificity. METHODS: Clinical treatment and outcomes data for 103 RM-HNSCC patients were paired with RNA-sequencing data from formalin-fixed patient samples. Using logistic regression methods, we developed a novel biomarker classifier based on expression patterns in the tumor immune microenvironment to predict disease control with monotherapy PD-1 inhibitors (pembrolizumab and nivolumab). The performance of the biomarker was internally validated using out-of-bag methods. RESULTS: The biomarker significantly predicted disease control (65% in predicted non-progressors vs. 17% in predicted progressors, p