Predicting response to PD-1 inhibitors in head and neck squamous cell carcinomas using peripheral blood inflammatory...

Immune checkpoint inhibitor (ICI) treatment has the potential to induce durable disease remission. However, the current combined positive score (CPS) is insufficient accurate for predicting which patients will benefit from it. In the present study, a real-world retrospective study was conducted on 56 patients of HNSCC who received ICI treatment. Then the treatment that patient received and levels of pre-treatment blood inflammatory markers (NLR, MLR and PLR) were identified to develop a model for predicting immunotherapy response. Notably, the model achieved an area under the curve (AUC) of 0.877 (95 % CI 0.769-0.985) , providing a larger net benefit than the CPS marker (AUC=0.614, 95 % CI 0.466-0.762). Furthermore, the internal validation of the prediction model showed a C-index of 0.835. Patients with high score of the model would get improved PFS than those with low score. Therefore, the prediction model for patients with local advanced or R/M HNSCC receiving ICI treatment, which represented an better efficient prediction of immunotherapy response than CPS marker.