A digital pathologist support tool for accurate detection of PD-L1 in head and neck squamous cell carcinoma.
Authors: Roberto Gianani, Will Paces, Elliott Ergon, Vitria Adisetiyo, Charles Caldwell
Journal: Journal of Clinical Oncology (2021)

e18031 Background: The programmed death-ligand 1 (PD-L1) (22C3) antibody is a companion diagnostic to the check-point inhibitor drug pembrolizumab for head and neck squamous cell carcinoma (HNSCC). Specifically, PD-L1 expression, as determined by a Combined Positive Score (CPS) ≥ 1, defines the clinical decision point for treatment. However, to calculate the CPS, pathologists must account for PD-L1+ tumor cells (TC) and immune cells (IC, i.e., lymphocytes, macrophages) – a cumbersome and time-consuming task that is prone to intra/inter-pathologist variability. We developed a digital image analysis (IA) based solution as a pathologist support tool that accurately and precisely scores images of PD-L1 stained HNSCC tissues. Here we compared the performance of the digital method with manual pathology. To address the literature on the differential regulation of PD-L1 by IC and TC in treatment response, we also examined how PD-L1+ IC contribute to the CPS as compared to PD-L1+ TC. Methods: 19 HNSCC samples were immunohistochemically stained for PD-L1 (22C3). Slides were assessed for CPS by manual pathology: CPS = (# PD-L1 staining cells [TC, lymphocytes, macrophages]/total # viable TC) x 100; wherein CPS &lt; 1 (no PD-L1 expression; no treatment) and CPS ≥ 1 (PD-L1 expression; treatment). Slides were scanned at 20X and analyzed using proprietary IA solutions that identify and separate TC from IC and quantify their PD-L1 expression. Resulting IA markups of cell detection and PD-L1 expression were reviewed by an MD pathologist for accurate cell recognition and stain classification. Upon pathologist approval of the IA performance, PD-L1 staining was evaluated by digital IA for CPS, % PD-L1+ TC, and % PD-L1+ IC. Pearson’s correlation analyses were conducted to assess the concordance between manual and digital CPS along with the association between CPS and PD-L1+ IC versus PD-L1+ TC. Results: Digital and manual CPS were significantly correlated ( r = 0.76, p = 0.00016) and concordant in treatment binning for 14/19 samples; for the 5 discordant samples, manual CPS binned them for no treatment while digital CPS binned them for treatment. Digital CPS significantly correlated with the % PD-L1+ IC ( r = 0.90, p &lt; 0.00001) along with the % PD-L1+ TC ( r = 0.98, p &lt; 0.00001). All 6 samples binned for no treatment by digital CPS had &lt; 1% PD-L1+ IC, whereas all but 2/13 samples binned for treatment by digital CPS had ≥ 1% PD-L1+ IC. Conclusions: The digital method for assessing PD-L1 expression in HNSCC performs similarly to manual pathology with improved accuracy for detecting and quantifying IC versus TC in the CPS calculation. Moreover, the % of PD-L1+ IC seems to contribute substantially to the CPS, to a similar degree as the % of PD-L1+ TC. Given the concordance with manual pathology, this digital method can support pathologists as a clinical diagnostic tool. As such, a clinical validation study of this digital method in a larger HNSCC cohort is underway.