From single H&E to virtual immunohistochemical biomarker staining in the lung tumor microenvironment

Profiling the tumor microenvironment (TME) is fundamental to understanding immune, stromal, and tumor interactions influencing cancer diagnosis, progression, and therapeutic response. Physical immunohistochemistry (IHC) remains essential but is limited by reagent dependency, labor-intensive workflows, and tissue exhaustion. This study aimed to develop and validate a virtual IHC platform to reproduce biomarker staining directly from hematoxylin and eosin (H&E) slides, enabling high-fidelity TME characterization while conserving tissue. Using the ViewsML deep learning platform, a virtual TME panel was trained to predict five key biomarkers: CD31 (endothelial/vasculature), CD45 (pan-leukocyte), CD68 (macrophage), smooth muscle actin/SMA (activated fibroblasts and pericytes), and cytokeratin AE1/3 (tumor epithelium). 316 digitized lung cancer biopsy slides (Aperio GT450) encompassing over 150 million annotated cells were used. Data were split into training (70%), validation (15%), and test (15%) sets. Neural networks were optimized for biomarker-specific predictions, and model performance was evaluated using ROC AUC metrics and blinded pathologist review. Virtual biomarkers showed strong concordance with physical IHC, achieving AUCs of CD31=0.91, CD45=0.90, CD68=0.93, SMA=0.91, and CK AE1/3=0.90. Pearson's correlations were 0.73, 0.72, 0.76, 0.73, and 0.76 (p<0.0001). Virtual stains preserved spatial and morphological features, including CD31-positive vascular frameworks at tumor-stroma boundaries, CD45-positive immune infiltration, CD68-positive macrophage aggregates, SMA-positive stromal reaction patterns, and CK-positive tumor epithelium. Quantitative per-cell biomarker expression enabled automated precise cell fraction, cell ratio, spatial proximity, and immune and macrophage clustering analysis. These findings demonstrate that ViewsML's virtual IHC technology can reproduce physical staining from standard H&E slides, offering a scalable solution for digital TME profiling and conserving tissue. This approach supports translational research, virtual biomarker screening in drug development, and clinical application by enabling immune versus stroma-dominant tumor stratification to guide immunotherapy. Future work will extend this platform to more tumor types and incorporate multiplex virtual staining for broader digital pathology integration.