Comparison of Biomarker Modalities for Predicting Response to PD-1/PD-L1 Checkpoint Blockade

医学 免疫组织化学 肿瘤科 内科学 PD-L1 生物标志物 接收机工作特性 伴生诊断 免疫疗法 临床试验 癌症 生物化学 化学
作者
Steve Lu,Julie E. Stein,David L. Rimm,Daphne W. Wang,Michael A. Bell,Douglas B. Johnson,Jeffrey A. Sosman,Kurt A. Schalper,Robert A. Anders,Hao Wang,Clifford Hoyt,Drew M. Pardoll,Ludmila Danilova,Janis M. Taube
出处
期刊:JAMA Oncology [American Medical Association]
卷期号:5 (8): 1195-1195 被引量:517
标识
DOI:10.1001/jamaoncol.2019.1549
摘要

Importance

PD-L1 (programmed cell death ligand 1) immunohistochemistry (IHC), tumor mutational burden (TMB), gene expression profiling (GEP), and multiplex immunohistochemistry/immunofluorescence (mIHC/IF) assays have been used to assess pretreatment tumor tissue to predict response to anti–PD-1/PD-L1 therapies. However, the relative diagnostic performance of these modalities has yet to be established.

Objective

To compare studies that assessed the diagnostic accuracy of PD-L1 IHC, TMB, GEP, and mIHC/IF in predicting response to anti–PD-1/PD-L1 therapy.

Evidence Review

A search of PubMed (from inception to June 2018) and 2013 to 2018 annual meeting abstracts from the American Association for Cancer Research, American Society of Clinical Oncology, European Society for Medical Oncology, and Society for Immunotherapy of Cancer was conducted to identify studies that examined the use of PD-L1 IHC, TMB, GEP, and mIHC/IF assays to determine objective response to anti–PD-1/PD-L1 therapy. For PD-L1 IHC, only clinical trials that resulted in US Food and Drug Administration approval of indications for anti–PD-1/PD-L1 were included. Studies combining more than 1 modality were also included. Preferred Reporting Items for Systematic Reviews and Meta-analysis guidelines were followed. Two reviewers independently extracted the clinical outcomes and test results for each individual study.

Main Outcomes and Measures

Summary receiver operating characteristic (sROC) curves; their associated area under the curve (AUC); and pooled sensitivity, specificity, positive and negative predictive values (PPV, NPV), and positive and negative likelihood ratios (LR+ and LR−) for each assay modality.

Results

Tumor specimens representing over 10 different solid tumor types in 8135 patients were assayed, and the results were correlated with anti–PD-1/PD-L1 response. When each modality was evaluated with sROC curves, mIHC/IF had a significantly higher AUC (0.79) compared with PD-L1 IHC (AUC, 0.65,P < .001), GEP (AUC, 0.65,P = .003), and TMB (AUC, 0.69,P = .049). When multiple different modalities were combined such as PD-L1 IHC and/or GEP + TMB, the AUC drew nearer to that of mIHC/IF (0.74). All modalities demonstrated comparable NPV and LR−, whereas mIHC/IF demonstrated higher PPV (0.63) and LR+ (2.86) than the other approaches.

Conclusions and Relevance

In this meta-analysis, tumor mutational burden, PD-L1 IHC, and GEP demonstrated comparable AUCs in predicting response to anti–PD-1/PD-L1 treatment. Multiplex immunohistochemistry/IF and multimodality biomarker strategies appear to be associated with improved performance over PD-L1 IHC, TMB, or GEP alone. Further studies with mIHC/IF and composite approaches with a larger number of patients will be required to confirm these findings. Additional study is also required to determine the most predictive analyte combinations and to determine whether biomarker modality performance varies by tumor type.
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