Cancer Treatment with Anti-PD-1/PD-L1 Agents: Is PD-L1 Expression a Biomarker for Patient Selection?

Lucia Festino, Gerardo Botti, Paul Lorigan, Giuseppe V. Masucci, Jason D. Hipp, Christine E. Horak, Ignacio Melero, Paolo A. Ascierto

Strategies to help improve the efficacy of the immune system against cancer represent an important innovation, with recent attention having focused on anti-programmed death (PD)-1/PD-ligand 1 (L1) monoclonal antibodies. Clinical trials have shown objective clinical activity of these agents (e.g., nivolumab, pembrolizumab) in several malignancies, including melanoma, non-small-cell lung cancer, bladder cancer, squamous head and neck cancer, renal cell cancer, ovarian cancer, microsatellite-unstable colorectal cancer, and Hodgkin's lymphoma. Expression of PD-L1 in the tumor microenvironment appears to be crucial for therapeutic activity, and initial trials suggested positive PD-L1 tumor expression was associated with higher response rates. However, subsequent observations have questioned the prospect of using PD-L1 expression as a biomarker for selecting patients for therapy, especially since many patients considered PD-L1-negative experience a benefit from treatment. Importantly, there is not yet a definitive test for determination of PD-L1 and a cut-off reference for PD-L1-positive status has not been established. Immunohistochemistry with different antibodies and different thresholds has been used to define PD-L1 positivity (1-50 %), with no clear superiority of one threshold over another for identifying which patients respond. Moreover, the type of cells on which PD-L1 expression is most relevant is not yet clear, with immune infiltrate cells and tumor cells both being used. In conclusion, while PD-L1 expression is often a predictive factor for treatment response, it must be complemented by other biomarkers or histopathologic features, such as the composition and amount of inflammatory cells in the tumor microenvironment and their functional status. Multi-parameter quantitative or semi-quantitative algorithms may become useful and reliable tools to guide patient selection.