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.

Drugs
|May 28, 2016
PubMed

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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