期刊名称：Proceedings of the National Academy of Sciences
印刷版ISSN：0027-8424
电子版ISSN：1091-6490
出版年度：2016
卷号：113
期号：48
页码：E7769-E7777
DOI：10.1073/pnas.1607836113
语种：English
出版社：The National Academy of Sciences of the United States of America
摘要：SignificanceThe Cancer Genome Atlas datasets were used in the current study to explore the relationship of programmed death ligand-1 (PD-L1) expression, a cytotoxic T-cell gene signature, and mutational load to each other, to immunoactive factors, such as programmed cell death protein-1 (PD-1), PD-L2, and other checkpoint molecules, and to survival across multiple solid tumor types. We found that PD-L2 expression is more closely related to an ongoing host immune response in certain tumor types than PD-L1. Notably, mutational load was not immediately related to inflammation in any tumor type studied, and was inferior to an inflamed tumor microenvironment for predicting survival in patients with metastatic melanoma. Our findings also indicate the need for biomarker assays that are tumor-type-specific and include both expression studies and genomic profiling. Programmed cell death protein-1 (PD-1)/programmed death ligand-1 (PD-L1) checkpoint blockade has led to remarkable and durable objective responses in a number of different tumor types. A better understanding of factors associated with the PD-1/PD-L axis expression is desirable, as it informs their potential role as prognostic and predictive biomarkers and may suggest rational treatment combinations. In the current study, we analyzed PD-L1, PD-L2, PD-1, and cytolytic activity (CYT) expression, as well as mutational density from melanoma and eight other solid tumor types using The Cancer Genome Atlas database. We found that in some tumor types, PD-L2 expression is more closely linked to Th1/IFNG expression and PD-1 and CD8 signaling than PD-L1. In contrast, mutational load was not correlated with a Th1/IFNG gene signature in any tumor type. PD-L1, PD-L2, PD-1, CYT expression, and mutational density are all positive prognostic features in melanoma, and conditional inference modeling revealed PD-1/CYT expression (i.e., an inflamed tumor microenvironment) as the most impactful feature, followed by mutational density. This study elucidates the highly interdependent nature of these parameters, and also indicates that future biomarkers for anti-PD-1/PD-L1 will benefit from tumor-type-specific, integrated, mRNA, protein, and genomic approaches.
