PD-L1 Immunohistochemistry in Gastric Cancer: Comparison of Combined Positive Score and Tumor Area Positivity Across 28-8, 22C3, and SP263 Assays.
JCO precision oncology(2024)
Abstract
PURPOSE:The clinical application of PD-L1 immunohistochemistry (IHC) testing is complicated by the availability of multiple IHC assays, scoring algorithms, and cutoffs. This study assessed the analytical comparability of three commercially available PD-L1 assays and two scoring algorithms used to assess PD-L1 status in gastric cancer (GC) samples.
METHODS:Serial sections of 100 resected GC samples, with PD-L1 expression levels across the dynamic range, were stained with three in vitro diagnostic-grade PD-L1 assays (28-8, 22C3, and SP263). Three trained pathologists blindly and independently scored slides using combined positive score (CPS) and tumor area positivity (TAP) algorithms. Comprehensive statistical analyses were performed to evaluate analytical concordance. Digital image analysis (DIA) was used to objectively compare the technical performance of each assay by simulating CPS and TAP.
RESULTS:Comparable staining patterns were observed with these three PD-L1 assays. Despite discernible variation in staining intensity, reproducible evaluations of PD-L1 positivity were observed. Inter- and intra-assay assessments of all three assays, using either CPS or TAP and the same PD-L1 cutoffs, demonstrated moderate to almost-perfect (interassay Cohen's kappa [κ] range, 0.47-0.83) and substantial to almost-perfect (intra-assay κ range, 0.77-1.00) agreement. Interpathologist assessment exhibited a significant level of concordance (intraclass correlation coefficient ≥0.92). No difference in technical performance was observed using DIA.
CONCLUSION:This study highlights analytical concordance in PD-L1 testing between three major PD-L1 assays when TAP and CPS are applied. Comparability of the technical assay performance was further supported by independent DIA. These observations support cross-application flexibility of the different PD-L1 assays and scoring algorithms to characterize PD-L1 expression in GC.
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