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The Tumor and Host Immune Signature, and the Gut Microbiota as Predictive Biomarkers for Immune Checkpoint Inhibitor Response in Melanoma Patients

There are various melanoma treatment strategies that are based on immunological responses, among which immune checkpoint inhibitors (ICI) are relatively novel form. Nowadays, anti-cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) and anti-programmed death-1 (PD-1) antibodies represent a standard...

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Detalles Bibliográficos
Autores principales: Tomela, Katarzyna, Pietrzak, Bernadeta, Schmidt, Marcin, Mackiewicz, Andrzej
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7600343/
https://www.ncbi.nlm.nih.gov/pubmed/32992737
http://dx.doi.org/10.3390/life10100219
Descripción
Sumario:There are various melanoma treatment strategies that are based on immunological responses, among which immune checkpoint inhibitors (ICI) are relatively novel form. Nowadays, anti-cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) and anti-programmed death-1 (PD-1) antibodies represent a standard treatment for metastatic melanoma. Although there are remarkable curative effects in responders to ICI therapy, up to 70% of melanoma patients show resistance to this treatment. This low response rate is caused by innate as well as acquired resistance, and some aspects of treatment resistance are still unknown. Growing evidence shows that gut microbiota and bacterial metabolites, such as short-chain fatty acids (SCFAs), affect the efficacy of immunotherapy. Various bacterial species have been indicated as potential biomarkers of anti-PD-1 or anti-CTLA-4 therapy efficacy in melanoma, next to biomarkers related to molecular and genetic tumor characteristics or the host immunological response, which are detected in patients’ blood. Here, we review the current status of biomarkers of response to ICI melanoma therapies, their pre-treatment predictive values, and their utility as on-treatment monitoring tools in order to select a relevant personalized therapy on the basis of probability of the best clinical outcome.