Reproducible and Accurate PD-L1 Immunohistochemistry Biomarker Quantification Using Virtual Multiplex Immunofluorescence Restaining - Project Summary Groundbreaking immunotherapy (IO) drugs offer durable response and improved survival in cancer patients who previously had limited treatment options. The semi-quantitative assessment of PD-L1 protein expression on tumor and/or immune cells (lymphocytes, macrophage) by a certified pathologist (“positive”, “negative”, “low”, “medium”, “high”) is the most prevalent biomarker for guiding clinical decision-making in IO. However, PD-L1 expression is difficult to score on standard immunohistochemistry (IHC) slides. The disagreement among pathologists for immune cell PD-L1 scoring is greater than 50%, which can lead to a high percentage of patients receiving IO when they are unlikely to benefit. This discordance could explain why most cancer patients do not benefit from these groundbreaking yet expensive Ios (costs > $200K / year per patient). Thus, there is an urgent unmet clinical need to identify patients who will not benefit from IO. As opposed to standard single-plex IHC, multiplex immunofluorescence (mpIF) staining, though expensive, provides the opportunity to examine panels of several markers (including tumor- and immune-specific markers) individually or simultaneously as a composite while permitting stain standardization, objective scoring, and cut-offs for all the markers; mpIF also has higher sensitivity and diagnostic prediction accuracy than IHC PD-L1 scoring. This opens up unique opportunities to leverage mpIF-stained images (with objective ground truth tumor/immune cell annotations and absolute PD-L1 intensities) coupled with recent deep learning methods to improve the explainability and interpretability of the conventional IHCs broadly. In previous work, restained and co-registered IHC/mpIF whole-slide images (WSIs) were used to create a deep learning virtual mpIF restaining algorithm, DeepLIIF (Deep Learning Inferred IF), for scoring IHC Ki67 and other nuclear markers. DeepLIIF is the only IHC scoring globally available as a public/free cloud-native platform with a user-friendly web interface and AI-ready IHC/mpIF datasets; it has been extensively validated in low- and high-resource settings. Recently, DeepLIIF was extended for more reproducible and accurate visual IHC PD-L1 scoring in tumor cells for lung cancer. The work proposed under this NIH R01 will further improve DeepLIIF PD-L1 scoring by incorporating mpIF immune cell (lymphocytes and macrophage) markers, whole-cell (rather than simple nuclear) segmentation, and large/diverse datasets across lung and bladder cancers. Additionally, the team will (1) validate DeepLIIF PD-L1 tumor and immune cell scoring on thousands of IHC PD-L1 whole-slide images (with manual readouts and ground truth IHC/mpIF for a subset) spanning different antibodies, platforms, and scoring systems, and (2) validate DeepLIIF-derived spatial biomarkers and PD-L1 scores on lung and bladder cancer datasets with clinical outcomes. Successful validation will establish DeepLIIF as an interpretable, tissue non-destructive, and cost-efficient solution to accurate IO patient stratification.
