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Cancer Treatment with Anti-PD-1/PD-L1 Agents: Is PD-L1 Expression a Biomarker for Patient Selection?
Lucia Festino1, Gerardo Botti1, Paul Lorigan2
1Istituto Nazionale Tumori Fondazione "G. Pascale", Via Mariano Semmola, 80131, Naples, Italy.
Abstract:
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.
Insights
Anti-programmed death (PD)-1/PD-ligand 1 (L1) antibodies show promise in cancer treatment. However, PD-L1 expression alone is not a reliable biomarker for patient selection, necessitating complementary diagnostic approaches.
Area of Science:
- Immunology
- Oncology
- Biomarker Discovery
Background:
- Anti-programmed death (PD)-1/PD-ligand 1 (L1) monoclonal antibodies represent a significant advancement in cancer immunotherapy.
- These agents have demonstrated clinical activity across various malignancies, including melanoma and lung cancer.
Purpose of the Study:
- To evaluate the role of programmed death-ligand 1 (PD-L1) expression as a predictive biomarker for anti-PD-1/PD-L1 therapy.
- To explore the limitations of PD-L1 expression in patient selection and identify potential complementary biomarkers.
Main Methods:
- Review of clinical trial data and existing literature on anti-PD-1/PD-L1 therapies.
- Analysis of the utility of programmed death-ligand 1 (PD-L1) expression, including immunohistochemistry, antibody variations, and threshold definitions.
- Investigation into the role of tumor microenvironment characteristics, such as inflammatory cell composition and functional status.
Main Results:
- While programmed death-ligand 1 (PD-L1) expression is often associated with treatment response, its predictive value is limited.
- Many patients with low or negative PD-L1 expression benefit from anti-PD-1/PD-L1 therapy.
- Current methods for determining PD-L1 positivity lack standardization, with varying antibodies and thresholds yielding inconsistent results.
Conclusions:
- Programmed death-ligand 1 (PD-L1) expression should not be used as a sole biomarker for guiding anti-PD-1/PD-L1 therapy.
- Complementary biomarkers, including histopathologic features and inflammatory cell characteristics within the tumor microenvironment, are crucial for accurate patient selection.
- Multi-parameter algorithms hold promise for improving the reliability of patient stratification for immunotherapy.
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