Comparison of continuous measures across diagnostic PD-L1 assays in non-small cell lung cancer using automated image analysis

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.

Improving the Diagnostic Accuracy of the PD-L1 Test with Image Analysis and Multiplex Hybridization

“…It is important to recognize that a sensitivity and specificity analysis used to assess the suitability of a test (multiplex) to a gold standard (PD-L1 DAB IHC) can only be as reliable as the reference test is capable of determining sample status without error [15]. A recent publication assessed the sensitivity and specificity of image analysis to the pathologist gold standard in 100 cases and their findings showed, as have other studies, that automated scoring was no worse than the concordance between pathologists [14,16]. As PD-L1 IHC is an imperfect test, where no other reference test or standards are available that fully confirm the pathologist's subjective score [9], a full comprehensive validation is required to verify multiplexing accuracy.…”

Tumor‐derived exosomes in the PD‐1/PD‐L1 axis: Significant regulators as well as promising clinical targets

“…With the development of “point‐of‐care” known as precision medical therapy which requires accurate diagnosis and prediction of tumor progression, an exponentially increasing number of studies have found that TEXs and exosomal PD‐L1 could act as a tumor diagnostic and prognostic marker (Cordonnier et al, 2020; Hansen & Siu, 2016; Hernandez‐Martinez, Zatarain‐Barron, Cardona, & Arrieta, 2018; Liu & Liu, 2020; Lux, Kahlert, Grutzmann, & Pilarsky, 2019; Munari et al, 2019; Widmaier et al, 2019; Yu et al, 2017). We summarize the clinical proofs of TEX‐PD‐L1 acting as biomarkers in different tumors in Table 1.…”

Automated tumor proportion scoring for PD-L1 expression based on multistage ensemble strategy in non-small cell lung cancer

“…Previous studies demonstrated inter-pathologist variability could be even higher than assay variability due to the subjective nature of IHC reporting [ 9 , 17 , 18 ]. Accurate PD-L1 scoring was even more difficult in tissue samples with low expression (< 10%) and in assays with 1%, 25%, or 50% cutoff value [ 17 , 19 ], and further obstacles include weak-staining TCs, PD-L1–positive immune cells (ICs; lymphocytes and macrophages), and cytoplasm-staining TCs in PD-L1 scoring [ 20 , 21 ]. These staining result in false positive signals and unfaithful PD-L1 scoring which cannot be rectified by experienced pathologists.…”

“…These staining result in false positive signals and unfaithful PD-L1 scoring which cannot be rectified by experienced pathologists. In summary, IHC-based PD-L1 scoring is hindered by tedious, subjective, and time consuming process of manual scoring and the inconsistence of results amony pathologists [ 15 , 21 ].…”