Abstract
Tumor programmed cell death ligand-1 (PD-L1) expression is a key biomarker to identify patients with non-small cell lung cancer who may have an enhanced response to anti-programmed cell death-1 (PD-1)/PD-L1 treatment. Such treatments are used in conjunction with PD-L1 diagnostic immunohistochemistry assays. We developed a computer-aided automated image analysis with customized PD-L1 scoring algorithm that was evaluated via correlation with manual pathologist scores and used to determine comparability across PD-L1 immunohistochemistry assays. The image analysis scoring algorithm was developed to quantify the percentage of PD-L1 positive tumor cells on scans of whole-slide images of archival tumor samples from commercially available non-small cell lung cancer cases, stained with four immunohistochemistry PD-L1 assays (Ventana SP263 and SP142 and Dako 22C3 and 28-8). The scans were co-registered and tumor and exclusion annotations aligned to ensure that analysis of each case was restricted to comparable tissue areas. Reference pathologist scores were available from previous studies. F1, a statistical measure of precision and recall, and overall percentage agreement scores were used to assess concordance between pathologist and image analysis scores and between immunohistochemistry assays. In total, 471 PD-L1-evalulable samples were amenable to image analysis scoring. Image analysis and pathologist scores were highly concordant, with F1 scores ranging from 0.8 to 0.9 across varying matched PD-L1 cutoffs. Based on F1 and overall percentage agreement scores (both manual and image analysis scoring), the Ventana SP263 and Dako 28-8 and 22C3 assays were concordant across a broad range of cutoffs; however, the Ventana SP142 assay showed very different characteristics. In summary, a novel automated image analysis scoring algorithm was developed that was highly correlated with pathologist scores. The algorithm permitted quantitative comparison of existing PD-L1 diagnostic assays, confirming previous findings that indicate a high concordance between the Ventana SP263 and Dako 22C3 and 28-8 PD-L1 immunohistochemistry assays.
Highlights
These authors contributed : Moritz Widmaier, Tobias Wiestler
Of the 493 slides previously rated by pathologists, 471 were amenable for automated image analysis scoring using the Ventana SP263 and Dako 28-8 and 22C3 immunohistochemistry assays (Fig. 1)
Both the manual and automated image analysis approaches showed that the Ventana SP263, Dako 28-8, and Dako 22C3 assays are highly concordant for a broad range of cutoffs on an analytical level, as reflected in both overall percentage agreement and F1 scoring, while the Ventana SP142 assay showed very different characteristics
Summary
Pembrolizumab, for example, is approved for use in metastatic non-small cell lung cancer as first-line treatment in patients with ≥ 50% of tumor cells expressing PD-L1, as determined by a Food and Drug Administration-approved test [11], such as the Dako IHC PD-L1 22C3 pharmDx companion assay used in the trial underlying approval [11, 12]. We report an extension of previous studies [24, 25] using archival tumor samples from commercially available non-small cell lung cancer cases in which automated image analysis with a customized PD-L1 scoring system was developed, evaluated via correlation with manual pathologist scores, and used to determine comparability across the four PD-L1 immunohistochemistry assays, based on a more quantitative comparison. The PD-L1 scoring used in this analysis was developed to detect stained tumor cells, owing to their use in commercially available companion tests [24, 25]
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