Predictive biomarkers in PD-1/PD-L1 checkpoint blockade immunotherapy

Xiangjiao Meng1, Zhaoqin Huang2, Feifei Teng1

  • 1Department of Radiation Oncology and Shandong Key Laboratory of Radiation Oncology, Shandong Cancer Hospital and Institute, Jinan, Shandong, China.

Cancer Treatment Reviews
|November 22, 2015
PubMed

Insights

Predicting cancer immunotherapy response remains challenging. This review explores biomarkers beyond PD-L1 expression, including tumor-infiltrating cells and gene analysis, to identify patients who will benefit from programmed death 1 (PD-1)/programmed death ligand-1 (PD-L1) checkpoint blockades.

Area of Science:

  • Immunotherapy
  • Oncology
  • Biomarker Discovery

Background:

  • Programmed death 1 (PD-1)/programmed death ligand-1 (PD-L1) checkpoint blockade has revolutionized cancer immunotherapy, demonstrating significant clinical efficacy and durable responses.
  • Despite successes, a substantial proportion of cancer patients do not respond to these therapies, necessitating improved patient selection strategies.

Purpose of the Study:

  • To review and discuss predictive biomarkers for programmed death 1 (PD-1)/programmed death ligand-1 (PD-L1) pathway checkpoint blockades in cancer treatment.
  • To highlight the limitations of current biomarkers like PD-L1 expression and explore novel approaches.

Main Methods:

  • Literature review focusing on predictive biomarkers for PD-1/PD-L1 checkpoint blockade therapy.
  • Analysis of existing data on PD-L1 expression, tumor-infiltrating immune cells, and gene-based markers.

Main Results:

  • Programmed death ligand-1 (PD-L1) expression is a widely studied biomarker but suffers from low prediction accuracy and dynamic variability.
  • Tumor microenvironment factors, including infiltrating immune cells and molecular signatures, alongside gene analysis (e.g., mutational landscape, mismatch-repair deficiency), show promise in predicting response.
  • Current biomarkers are insufficient for accurate patient stratification, underscoring the need for further research.

Conclusions:

  • Accurate identification of patients who will benefit from PD-1/PD-L1 checkpoint blockades requires exploring biomarkers beyond PD-L1.
  • Tumor-infiltrating immune cells and genomic features represent promising avenues for improving predictive accuracy.
  • Further preclinical and clinical studies are essential to validate novel biomarkers and integrate them into clinical practice for personalized cancer immunotherapy.

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