Prognostic Value of Inflammatory and Nutritional Biomarkers of Immune Checkpoint Inhibitor Treatment for Recurrent or Metastatic Squamous Cell Carcinoma of the Head and Neck

Simple Summary In recent years, various biomarkers have been developed to assist in the selection of anticancer agents. Inflammatory and nutritional biomarkers have been reported as useful for predicting prognosis after treatment with immune checkpoint inhibitor (ICI) treatment in head and neck cancers. Still, their prognostic value in recurrent or metastatic squamous cell carcinoma of the head and neck (HNSCC) has not been thoroughly investigated. Therefore, we evaluated the prognostic value of inflammatory and nutritional biomarkers of ICI treatment for RMHNSCC. We demonstrated that the lymphocyte-to-monocyte ratio (LMR) was the most important biomarker. This study suggests that LMR may be the most useful biomarker for predicting the prognosis of ICI treatment for RMHNSCC. Abstract This study aimed to determine the prognostic value of inflammatory and nutritional biomarkers of immune checkpoint inhibitor (ICI) therapy for recurrent or metastatic squamous cell carcinoma of the head and neck (RMHNSCC) and to identify the most useful factor for prognosis assessment. We retrospectively reviewed the medical records of patients with RMHNSCC who received ICI therapy. The response rate for ICI therapy and the relationship between inflammatory and nutritional biomarkers and overall survival were examined. The included biomarkers did not correlate with an objective response rate but were associated with a disease control rate. Univariate analysis showed significant correlations between the serum albumin level, C-reactive protein level, platelet to lymphocyte ratio, neutrophil to lymphocyte ratio, lymphocyte to monocyte ratio (LMR), systemic immune-inflammation index, and controlling the nutritional status score and overall survival; multivariate analysis showed that LMR was significantly correlated with overall survival. LMR was the most important biomarker according to the machine learning model. This study suggests that LMR may be the most useful biomarker for predicting the prognosis of ICI treatment for RMHNSCC.

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