Researchers have developed a deep learning framework called HEX that generates virtual spatial proteomics directly from standard, low-cost H&E-stained tissue images. While traditional spatial profiling is expensive and difficult to scale, this computational approach accurately predicts the distribution of 40 different protein biomarkers with single-cell resolution. By integrating these virtual maps with traditional pathology through a fusion model named MICA, the system significantly improves the ability to predict patient survival and immunotherapy response in lung cancer. This technology identifies complex cellular interactions within the tumor microenvironment, such as the proximity of specific immune cells, which serve as critical indicators of treatment success. Ultimately, the framework offers a scalable and interpretable tool for precision medicine by extracting sophisticated molecular insights from routine diagnostic slides.

References:

• Li Z, Li Y, Xiang J, et al. AI-enabled virtual spatial proteomics from histopathology for interpretable biomarker discovery in lung cancer[J]. Nature Medicine, 2026: 1-14.