Abstract
Multiplexed protein imaging offers valuable insights into interactions between tumors and their surrounding tumor microenvironment (TME), but its widespread use is limited by cost, time, and tissue availability. We present HistoPlexer, a deep learning framework that generates spatially resolved protein multiplexes directly from standard hematoxylin and eosin (H&E) histopathology images. HistoPlexer jointly predicts multiple tumor and immune markers using a conditional generative adversarial architecture with custom loss functions designed to ensure pixel- and embedding-level similarity while mitigating slice-to-slice variations. A comprehensive evaluation on metastatic melanoma samples demonstrates that HistoPlexer-generated protein maps closely resemble real maps, as validated by expert assessment. They preserve crucial biological relationships by capturing spatial co-localization patterns among proteins. The spatial distribution of immune infiltration from HistoPlexer-generated protein multiplex enables stratification of tumors into immune subtypes. In an independent cohort, integration of HistoPlexer-derived features into predictive models enhances performance in survival prediction and immune subtype classification compared to models using H&E features alone. To assess broader applicability, we benchmarked HistoPlexer on publicly available pixel-aligned datasets from different cancer types. In all settings, HistoPlexer consistently outperformed baseline methods, demonstrating robustness across diverse tissue types and imaging conditions. By enabling whole-slide protein multiplex generation from routine H&E images, HistoPlexer offers a cost- and time-efficient approach to tumor microenvironment characterization with strong potential to advance precision oncology.