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Updated: Nov 21, 2025

Identifying PD-1/PD-L1 Inhibitors with Surface Plasmon Resonance Technology
Published on: May 2, 2025
Targeting the PD-1/ PD-L1 interaction in nasopharyngeal carcinoma
David Johnson1, Brigette B Y Ma2
1Department of Clinical Oncology, Prince of Wales Hospital, Hong Kong Special Administrative Region.
Targeting the PD-1/PD-L1 pathway shows promise for Epstein-Barr virus-associated nasopharyngeal cancer (NPC). Clinical trials are evaluating PD-1 antibodies alone and in combination for recurrent, metastatic, and advanced NPC.
Area of Science:
- Immunology
- Oncology
- Virology
Background:
- Programmed cell death receptor-1 (PD-1) and its ligand (PD-L1) pathway upregulation is a key immune evasion mechanism in Epstein-Barr virus (EBV)-associated nasopharyngeal cancer (NPC).
- Targeting the PD-1/PD-L1 axis is a significant area of research for NPC treatment.
- At least 8 antibodies targeting this axis are in clinical evaluation for various NPC stages.
Purpose of the Study:
- To review the scientific rationale for targeting the PD-1/PD-L1 axis in NPC.
- To summarize current clinical trials investigating PD-1/PD-L1 inhibitors in NPC.
- To discuss predictive biomarkers for response to PD-1/PD-L1 therapies in NPC.
Main Methods:
- Review of scientific literature and clinical trial data.
- Analysis of therapeutic strategies involving PD-1/PD-L1 antibodies in NPC.
- Evaluation of combinatorial approaches with chemotherapy, radiotherapy, and other immunotherapies.
Main Results:
- PD-1 antibodies as monotherapy show objective response rates of 20-30% in patients with recurrent/metastatic (R/M) NPC in Phase II trials.
- The predictive role of PD-L1 expression in NPC is still under investigation.
- Combinatorial strategies are actively being evaluated in various clinical trial designs.
Conclusions:
- Targeting the PD-1/PD-L1 axis represents a promising therapeutic strategy for EBV-associated NPC.
- Ongoing clinical trials are exploring various treatment settings and combinations.
- Further research is needed to define predictive biomarkers for optimal patient selection.
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