In order to help predict how tumors will respond to anti–PD-1 (programmed cell death 1)/PD-L1 (programmed cell death ligand-1) therapies, doctors assess patients using PD-L1 immunohistochemistry (IHC), tumor mutational burden (TMB), gene expression profiling (GEP), and multiplex immunohistochemistry/immunofluorescence (mIHC/IF). However, the diagnostic performance of these tests has not been established. To bridge this education gap, researchers conducted a systematic review to determine the accuracy of these diagnostic tests in predicting tumor response to anti–PD-1/PD-L1 therapy.
For this study, published in JAMA Oncology, the investigators searched PubMed from its beginning to 2018 and annual meeting abstracts from 2013 to 2018 from the American Association for Cancer Research, American Society of Clinical Oncology, European Society for Medical Oncology, and Society for Immunotherapy of Cancer for studies that examined the use of PD-L1 IHC, TMB, GEP, and mIHC/IF tests to determine objective response to anti–PD-1/PD-L1 therapy.
Ten different solid tumor types in 8,135 patients were analyzed using these tests, and objective response to anti–PD1/PD-L1 therapy was also examined. Area under the curve (AUC), a performance measure used to determine which test more accurately predicts therapy response, was used.
The results revealed that mIHC/IF testing had a significantly higher AUC compared with PD-L1 IHC, GEP, and TMB. When various tests were used in conjunction, (eg, PD-L1 IHC and/or GEP + TMB) the AUC became closer to the AUC of mIHC/IF testing.
"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," the study authors conclude. "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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