Patients Selection for Immunotherapy in Solid Tumors: Overcome the Naïve Vision of a Single Biomarker

Immunotherapy, and in particular immune-checkpoints blockade therapy (ICB), represents a new pillar in cancer therapy. Antibodies targeting Cytotoxic T-Lymphocyte Antigen 4 (CTLA-4) and Programmed Death 1 (PD-1)/Programmed Death Ligand-1 (PD-L1) demonstrated a relevant clinical value in a large numb...
Ausführliche Beschreibung

Immunotherapy, and in particular immune-checkpoints blockade therapy (ICB), represents a new pillar in cancer therapy. Antibodies targeting Cytotoxic T-Lymphocyte Antigen 4 (CTLA-4) and Programmed Death 1 (PD-1)/Programmed Death Ligand-1 (PD-L1) demonstrated a relevant clinical value in a large number of solid tumors, leading to an improvement of progression free survival and overall survival in comparison to standard chemotherapy. However, across different solid malignancies, the immune-checkpoints inhibitors efficacy is limited to a relative small number of patients and, for this reason, the identification of positive or negative predictive biomarkers represents an urgent need. Despite the expression of PD-L1 was largely investigated in various malignancies, (i.e., melanoma, head and neck malignancies, urothelial and renal carcinoma, metastatic colorectal cancer, and pancreatic cancer) as a biomarker for ICB treatment-patients selection, it showed an important, but still imperfect, role as positive predictor of response only in nonsmall cell lung cancer (NSCLC). Importantly, other tumor and/or microenvironments related characteristics are currently under clinical evaluation, in combination or in substitution of PD–L1 expression. In particular, tumor-infiltrating immune cells, gene expression analysis, mismatch- repair deficiency, and tumor mutational landscape may play a central role in predicting clinical benefits of CTLA-4 and/or PD-1/PD-L1 checkpoint inhibitors. In this review, we will focus on the clinical evaluation of emerging biomarkers and how these may improve the naïve vision of a single- feature patients-based selection. Ausführliche Beschreibung