Abstract 2536: Multimodal stratification of predictive biomarkers in head and neck cancers: A focus on cytokine-based immunotherapy

Abstract

Abstract Background Immunotherapy, particularly cytokine-based therapy, has been gaining traction in the treatment of head and neck cancers. Cytokines are small proteins that play a crucial role in the immune response to cancer. However, not all patients respond to cytokine-based immunotherapies, and some may experience severe side effects. The ability to predict which patients are most likely to benefit from these therapies could greatly improve the efficacy and tolerability of treatment. In this context, the study of predictive biomarkers, such as the expression of immune checkpoint molecules like PD-L1 and TIGIT, and the profiling of cytokines within the tumor microenvironment, has become crucial. These biomarkers could provide valuable information about the patient's immune response to the tumor and their likelihood of responding to cytokine-based immunotherapies. Method A multimodal approach to stratify predictive biomarkers in head and neck cancers was used. The methodology was founded on combining metabolic, transcriptomic, and proteomic data to offer a comprehensive understanding of potential biomarkers. The data from different modalities were integrated using bioinformatics and machine learning algorithms. This comprehensive dataset was analyzed to identify multimodal biomarker signatures that could predict patient responses to cytokine-based therapies. Result Preliminary finding showed significant heterogeneity in the expression of PD-L1, TIGIT, and various cytokines across the tumor microenvironment. This underscored the complexity of head and neck cancers. Certain multimodal biomarker signatures, which included specific patterns of immune checkpoint molecule expression, cytokine profiles, and immune cell infiltration, correlated robustly with patient responses to cytokine-based therapies. Conclusion Our study demonstrates the considerable potential of a multimodal stratification approach in deciphering the complexity of head and neck cancers and predicting responses to cytokine-based immunotherapies. By integrating metabolomic, transcriptomic, and proteomic data, we identified unique biomarker signatures that strongly correlated with therapy responses. This work underscores the invaluable role of integrated predictive biomarker profiles in enhancing the precision of immunotherapy. Citation Format: Corinne Ramos, Richard Ruez, Sandra Delebecq, Victor Senechal. Multimodal stratification of predictive biomarkers in head and neck cancers: A focus on cytokine-based immunotherapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2536.