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Abstract
Background The tumor microenvironment has recently become important in cancer and immune phenotype (IP) has been proposed as a way to assess it. A variety of methods to assess IP in tumor tissue were proposed, mostly from histology slides of IHC including CD3 and CD8 or hematoxylin and eosin (H&E). However, methodology based on spatial tumor-infiltrating lymphocyte (TIL) analysis in PD-L1 IHC has been rarely investigated. Here, we performed AI-based IP classification in TNBC using PD-L1 22C3 IHC whole slide images (WSIs) as well as PD-L1 combined positive score (CPS) and its positivity.
Methods We employed Lunit SCOPE IO, an AI-powered H&E analyzer for spatial TIL analysis, identifying and quantifying TIL within cancer or stromal areas in H&E slides. For PD-L1 IHC analysis, Lunit SCOPE PD-L1 CPS, an AI-powered PD-L1 CPS analyzer was used. This model detects and quantifies PD-L1 status in tumor and immune cells and was developed with 3.35x105 tumor cells and 3.45x105 immune cells from PD-L1 IHC-stained WSI of breast cancer. To validate, 180 pairs of PD-L1 IHC and H&E WSIs from a cohort of TNBC were analyzed. The IPs were classified as inflamed (high TIL density in the cancer area, IIP) or non-inflamed (non-IIP) in H&E and PD-L1 IHC WSIs, using a standardized 0.5x0.5mm2 grid for both and a lymphocyte cutoff of 130/mm2 for AI-based PD-L1 IHC analysis.
Results IPs were classified as inflamed in 69 cases (38.3%) in PD-L1 and 40 cases (22.2%) in H&E. The agreement in IPs between two models was 73.9% (table 1). The median PD-L1 CPS was 10 (interquartile range 2 - 25). AI-based PD-L1 IHC analysis revealed significant differences in median CPS levels between IIP and non-IIP, with values of 32.5 (15.6 - 77.2) and 2.9 (0.6 - 8.6), respectively (p<0.001). Moreover, median values of PD-L1 positivity in IIP were significantly higher than in the non-IIP across cell types: tumor cells (14.9% vs. 1.4%, p<0.001), lymphocytes (36.2% vs. 14.1%, p<0.001), and macrophages (14.9% vs. 9.7%, p=0.001).
Conclusions The IP determined by the AI-powered PD-L1 IHC analyzer showed a high concordance rate with the IP determined by the AI-powered H&E analyzer. Moreover, high PD-L1 expression of each respective cell type of tumor, lymphocyte, and macrophage was observed in the IIP. Consistent with previous knowledge of the IP, our PD-L1 results support use of immune-oncology approaches in this phenotype.
Analysis of IPs by two AI-powered analyzers
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