Оценка надежности предикторов при плоскоклеточной карциноме ротоглотки, ассоциированной с вирусом папилломы человека

Abstract

Introduction. The progressive epidemic of the human papillomavirus (HPV) has led to an increase in the incidence of oropharyngeal squamous cell carcinoma (OPSCC). Positive HPV status is not always a reliable predictor of risk stratification in patients with OPSCC. Therefore, in order to improve the quality of the repertoire of prognostic predictors, it is necessary to identify additional indicators canonically related to the pathogenesis of HPV+OPSCC.
Methods. The study included 62 patients treated with OPSCC T1-4N0-3M0 (7th edition, AJCC) in 2015-2020 at the Republican Specialized Scientific and Practical Medical Center of Oncology and Radiology and in the Tashkent and Samarkand city branches. An immunohistochemical analysis was performed for p16INK4a, PD-L1, and p53 proteins. In the study, the correlation between predictors and HPV status was assessed by the Pearson coefficient, and their predictive potential was determined by the method of logistic regression.
Results. The most suitable predictors for establishing a positive HPV status in OPSCC are considered to be a relatively young age (up to 60 years) of patients, localization of the tumor in the palatine tonsils, negative expression of p53mutant, positive expression of p53wild regardless of intensity, low and medium levels (L and M) of PD-L1 expression, primary tumors on T1 and T2, early tumor stages according to the 8th edition of the TNM classification. Clinical demographic and molecular prognostic models based on these parameters have excellent predictive potential (AUC>0.9) and can be a reliable tool in the diagnosis of HPV+OPSCC (p<0.001).
Conclusion. Modern oncology certainly contributes to the effective assessment of toxicity, while facilitating the continuous evaluation of the effectiveness of ongoing therapy, which leads to more logical design of clinical trials than standard approaches. Therefore, it is very important to understand the impact of predictors with excellent predictive power on HPV+ patients when planning treatment and testing deintensification strategies.