Towards interpretable molecular and spatial analysis of the tumor microenvironment from digital histopathology images with HistoTME-v2.

The tumor microenvironment (TME) is a critical focus for biomarker discovery and therapeutic targeting in cancer. However, widespread clinical adoption of TME profiling is hindered by the high cost and technical complexity of current platforms such as spatial transcriptomics and proteomics. Artificial Intelligence (AI)-based analysis of the TME from routine Hematoxylin & Eosin (H&E)-stained pathology slides presents a promising alternative. Yet, most existing deep learning approaches depend on extensive high-quality single-cell or patch-level annotations, which are labor-intensive and costly to generate. To address these limitations, we previously introduced HistoTME, a weakly supervised deep learning framework that predicts the activity of cell type-specific transcriptomic signatures directly from whole slide H&E images of non-small cell lung cancer. This enables rapid, high throughput analysis of the TME composition from whole slide H&E images (WSI) without the need for segmenting and classifying individual cells. In this work, we present HistoTME-v2, a pan-cancer extension of HistoTME, applied across 25 solid tumor types, substantially broadening the scope of prior efforts. HistoTME-v2 demonstrates high accuracy for predicting cell type-specific transcriptomic signature activity from H&E images, achieving a median Pearson correlation of 0.61 with ground truth measurements in internal cross- validation on The Cancer Genome Atlas (TCGA), encompassing 7,586 WSIs, 6,901 patients, and 24 cancer types, and a median Pearson correlation of 0.53 on external validation datasets spanning 5,657 WSIs, 1,775 patients and 9 cancer types. Furthermore, HistoTME- v2 resolves the spatial distribution of key immune and stromal cell types, exhibiting strong spatial concordance with single-cell measurements derived from multiplex imaging (CODEX, IHC) as well as Visium spatial transcriptomics, spanning 259 WSI, 154 patients, and 7 cancer types. Overall, across both bulk and spatial settings, HistoTME-v2 significantly outperforms baselines, positioning it as a robust, interpretable and cost-efficient tool for TME profiling and advancing the integration of spatial biology into routine pathology workflows.