IMMUNOTHERAPY PREDICTING IMMUNOTHERAPY RESPONSE IN RECURRENT HEAD AND NECK CANCER USING MACHINE LEARNING AND TUMOR MICROENVIRONMENT SIGNATURES

Abstract

Recurrent HNSCC is a very heterogeneous disease and a significant clinical challenge in treatment. Although there have been advances in the survival of some patients treated with immune checkpoint inhibitors, many do not respond, and many of the reasons for this failure of response include tumor heterogeneity, tumor escape mechanisms, and differences in the tumor microenvironment. The authors propose a new framework, named ImmunoNet, in this paper that is based on Machine Learning models, Clinical variables, Molecular biomarkers, and Signatures of Tumor microenvironment for predicting the response of recurrent Head and Neck Cancer (HNSCC) to Immunotherapy. The model incorporates immune-related parameters like tumor-infiltrating lymphocytes, mutational burden, inflammatory markers and microenvironmental patterns to distinguish patients who are likely responders with those who are likely non-responders. The idea is to combine biologically relevant immune signatures to predictive models to help choose immunotherapies and guide personalized treatments. Overall, the study shows the promise of AI in improving precision oncology, moving towards better patient selection for immune checkpoint combinations and minimizing unnecessary exposure to therapy for those who might not respond.