Immunotherapies targeting the programmed cell death 1 (PD-1) and cytotoxic T-lymphocyte antigen 4 (CTLA-4) checkpoint receptors have revolutionized the treatment of metastatic melanoma. However, half of the treated patients do not respond to or eventually progress on standard therapies and many experience adverse events as a result of drug toxicity. The identification of accurate biomarkers of clinical outcomes are required in order to move away from the one-size-fits-all treatment approach of standard clinical practice and toward a more personalized approach to enable the administration of the optimal therapy for any given patient and further improve patient outcomes. Recent clinical trials have proven the potential of multiomics analyses, including genomic, gene expression, and tumor immune profiling, of patients' tumor biopsies, to predict a patient's response to subsequently administered immunotherapies. However, reproducibility of such multiomics analyses, tissue requirements, and clinical validation have limited the practical application of these approaches in routine clinical workflows. In this review, we discuss several pivotal tissue-based profiling techniques that can be utilized to identify potential genomic, transcriptomic, and immune biomarkers predictive of clinical outcomes following treatment with immune checkpoint inhibitors in melanoma. Furthermore, we highlight the key opportunities and challenges associated with the use of each of these techniques. The development and implementation of multimodal predictive models that combine data derived from these various methods is the future for achieving precision medicine for patients with melanoma.
©2024 American Association for Cancer Research.