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bioRxiv · 10.64898/2026.08.04.742671

Virtual spatial transcriptomics from histopathology enables prognostic and therapeutic response prediction in cancer

Jiao, S.·Yuan, Z.·Lu, D.·Xu, Y.·Dong, Y.·Peng, J.
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Abstract

Spatial transcriptomics reveals cellular heterogeneity, intercellular communication, and tissue organization, but its cost and limited accessibility restrict clinical use. Here, we present VISTA, a model that integrates multi-scale histological features and spatial context to infer spatial gene expression from H&E-stained tissue images. Across leave-one-section-out cross-validation and independent validation, VISTA robustly predicted thousands of genes and outperformed state-of-the-art methods. Beyond expression reconstruction, VISTA enabled clinically relevant downstream analyses. In TCGA breast cancer samples, it identified survival-associated genes, stratified prognostic risk groups, and revealed adverse tumor-associated spatial subtypes. In our in-house intrahepatic cholangiocarcinoma cohort, it preserved tumor-normal organization and identified CLDN4 and CYP3A4 as complementary spatial biomarkers. In HER2+ breast cancer, it predicted pathological response to neoadjuvant trastuzumab-based therapy and linked response-associated regions to immune and cytokine-related programs. These results support virtual spatial transcriptomics from routine histopathology for oncology applications.

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Jiao, S., Yuan, Z., Lu, D., Xu, Y., Dong, Y., Peng, J.. 2026-08-09. Virtual spatial transcriptomics from histopathology enables prognostic and therapeutic response prediction in cancer. https://doi.org/10.64898/2026.08.04.742671

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