Multimodal detection of PD-L1: reasonable biomarkers for immune checkpoint inhibitor

• Published in: American journal of cancer research Vol. 8; no. 9; pp. 1689 - 1696
• Main Authors: Yang, Qiao, Xu, Zihan, Zheng, Linpeng, Zhang, Luping, You, Qiai, Sun, Jianguo
• Format: Journal Article
• Language: English
• Published: United States e-Century Publishing Corporation 01.01.2018
• Subjects: Review; PD-L1; multimodal; Immune checkpoint inhibitor; biomarker; liquid biopsy
• ISSN: 2156-6976

Immune checkpoint inhibitor (ICI) therapy had achieved significant clinical benefit in multiple malignant solid tumors, such as non-small cell lung cancer, melanoma and urothelial cancer. ICI therapy not only revolutionarily altered the treatment strategy of malignant solid tumors, but also dramatically prolonged overall survival. However, the objective response rate (ORR) of ICI therapy in second line treatment remains 20% or less. How to find patients eligible for ICI therapy by effective biomarkers became hot nowadays. High expression of PD-L1 protein in tumor cells or tumor microenvironment (TME) had been identified to be a logical biomarker for predicting efficacy of ICI therapy and approved by the U.S. Food and Drug Administration to be an indicator of initiating treatment for some solid tumors. Controversially, patients with low PD-L1 protein expression might also show clinical benefit. In this sense, tissue PD-L1 protein expression might not be a precise biomarker. Multimodal detection of PD-L1, such as PD-L1 protein, PD-L1 mRNA, and circulating PD-L1, might provide a comprehensive tumor profile and could find the patients who are more suitable for ICI therapy. Besides, dynamic monitoring of PD-L1 expression could shed light on efficacy assessment and drug resistance. ICI-based combination strategy had demonstrated better outcome than ICI alone. Single biomarker might not be efficient to precisely find advantage patients. Combined biomarkers could better instruct the consideration of therapeutic regimen. In addition, nomogram and artificial intelligence platform could integrate multiparameter information of biomarkers which might shed light on tumor profile and give a hint to treatment decision.