Exploratory immunomonitoring during radiochemotherapy in HNSCC and machine-learning reveal immune parameters...

Immunological biomarkers are increasingly relevant for personalized cancer treatment, but peripheral blood-derived biomarkers are not yet used to guide therapy in head and neck squamous cell carcinoma (HNSCC). The prospective non-randomized DIREKHT study (ClinicalTrials.gov: NCT02528955, 2015-08-19) therefore integrated immune monitoring into postoperative radio(chemo)therapy (R(C)T) to explore blood-based biomarkers. In 70 oral cavity and oropharyngeal cancer patients receiving curative R(C)T, the peripheral immune status was assessed before and after therapy and during follow-up by flow cytometry-based immunophenotyping of 45 immune parameters. A machine learning workflow identified predictors of disease-free survival (DFS), using Repeated Elastic Net Technique (RENT) feature selection within repeated stratified K-fold cross-validation and nested cross-validation for tuning and assessment. This approach identified a 29-parameter immune signature from pre- and post-therapeutic profiles, with key contributors including HLA-DR + T cells, HLA-DR+ monocytes, and basophils. The best model achieved a Matthews correlation coefficient of 0.681, with pre- and post-therapeutic parameters contributing equally, highlighting immune dynamics during R(C)T. Adding clinical parameters did not improve performance (MCC = 0.678), but yielded a comparable model integrating immune and clinical variables. Blood-based immune signatures may have prognostic relevance for DFS after R(C)T in HNSCC. Validation in larger cohorts is required to confirm clinical applicability and reduce the signature.