---
title: "VISTA: Virtual spatial transcriptomics from H&E for prognostic and therapy response prediction in"
id: "biorxiv-21-virtual-spatial-transcriptomics-from-histopathology-enables-prognostic-and"
canonical_url: "https://medichelpline.com/clinical-feed/biorxiv-21-virtual-spatial-transcriptomics-from-histopathology-enables-prognostic-and"
content_type: "clinical_feed_article"
specialty: "Oncology"
source_name: "bioRxiv (Biomedical Preprints)"
source_url: "https://www.biorxiv.org/content/10.64898/2026.08.04.742671v1?rss=1"
published_at: "2026-08-09T12:00:00.000Z"
evidence_level: "Verified Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# VISTA: Virtual spatial transcriptomics from H&E for prognostic and therapy response prediction in
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/biorxiv-21-virtual-spatial-transcriptomics-from-histopathology-enables-prognostic-and
- **Specialty:** [Oncology](https://medichelpline.com/clinical-feed/oncology.md)
- **Primary Source:** bioRxiv (Biomedical Preprints)
- **Source URL:** [Original Journal Publication](https://www.biorxiv.org/content/10.64898/2026.08.04.742671v1?rss=1)
- **Published At:** 2026-08-09T12:00:00.000Z
- **Evidence Rating:** Verified Feed
## Executive GIST (TL;DR)
- Spatial transcriptomics provides cellular heterogeneity and tissue organization information but is limited clinically by cost and accessibility; the authors developed a virtual approach from routine histopathology. - The study introduces **VISTA**, a model that integrates multi-scale histological features and spatial context to infer spatial gene expression from standard **H&E**-stained images. - VISTA was evaluated using leave-one-section-out cross validation and independent validation, where it robustly predicted thousands of genes and outperformed existing state-of-the-art methods. - Beyond reconstructing expression, VISTA supported clinically relevant downstream analyses, including identification of survival-associated genes and stratification of prognostic risk groups in TCGA breast cancer samples. - In TCGA breast cancer, VISTA revealed adverse tumor-associated spatial subtypes linked to prognosis. - In an in-house intrahepatic cholangiocarcinoma cohort, VISTA preserved tumor–normal tissue organization and identified **CLDN4** and **CYP3A4** as complementary spatial biomarkers within that cancer type. - In HER2-positive breast cancer, VISTA predicted pathological response to neoadjuvant **trastuzumab**-based therapy and associated response-linked regions with immune and cytokine-related expression programs. - The findings indicate that virtual spatial transcriptomics from routine histopathology can enable prognostic and therapeutic response prediction in oncology and may expand access to spatially resolved molecular information without the cost of current spatial transcriptomics platforms. - The article is a preprint and has not been peer reviewed; methodological and dataset details beyond those summarized were not reported in the provided source text.
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Shaoqing Jiao 1 Northwestern Polytechnical University; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Shaoqing%2BJiao%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Jiao%20S&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AShaoqing%2BJiao%2B) * [ORCID record for Shaoqing Jiao](http://orcid.org/0000-0002-1037-7253 "Open in new tab") Zhen Yuan 1 Northwestern Polytechnical University; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Zhen%2BYuan%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Yuan%20Z&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AZhen%2BYuan%2B) Dazhi Lu 1 Northwestern Polytechnical University; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Dazhi%2BLu%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Lu%20D&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3ADazhi%2BLu%2B) Yunfei Xu 2 Department of General Surgery Qilu Hospital of Shandong University * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Yunfei%2BXu%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Xu%20Y&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AYunfei%2BXu%2B) Yunwei Dong 1 Northwestern Polytechnical University; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Yunwei%2BDong%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Dong%20Y&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AYunwei%2BDong%2B) Jiajie Peng 1 Northwestern Polytechnical University; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Jiajie%2BPeng%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Peng%20J&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AJiajie%2BPeng%2B) * [ORCID record for Jiajie Peng](http://orcid.org/0000-0002-3857-7927 "Open in new tab") * For correspondence: jiajiepeng@nwpu.edu.cn * [Abstract](https://www.biorxiv.org/content/10.64898/2026.08.04.742671v1)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_art/node:5691529/1) * [Info/History](https://www.biorxiv.org/content/10.64898/2026.08.04.742671v1.article-info)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_info/node:5691529/1) * [Metrics](https://www.biorxiv.org/content/10.64898/2026.08.04.742671v1.article-metrics)[](https://www.biorxiv.org/panels_ajax_tab/article_tab_metrics/node:5691529/1) * [ Preview PDF](https://www.biorxiv.org/content/10.64898/2026.08.04.742671v1.full.pdf+html)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_pdf/node:5691529/1) ![Loading](https://www.biorxiv.org/sites/all/modules/contrib/panels_ajax_tab/images/loading.gif) ## 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 inhouse 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. ### Competing Interest Statement The authors have declared no competing interest. Copyright The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a [CC-BY-ND 4.0 International license](http://creativecommons.org/licenses/by-nd/4.0/). bioRxiv and medRxiv thank the following for their generous financial support: > The Chan Zuckerberg Initiative, Cold Spring Harbor Laboratory, the Sergey Brin Family Foundation, California Institute of Technology, Centre National de la Recherche Scientifique, Fred Hutchinson Cancer Center, Imperial College London, Massachusetts Institute of Technology, Stanford University, The University of Edinburgh, University of Washington, and Vrije Universiteit Amsterdam. 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[ Download PDF](https://www.biorxiv.org/content/10.64898/2026.08.04.742671v1.full.pdf) Print/Save Options [Download PDF](https://www.biorxiv.org/content/biorxiv/early/2026/08/08/2026.08.04.742671.full.pdf)Full Text & In-line FiguresXML [More Info](https://www.biorxiv.org/about/FAQ#PrintOptions "More Information on Print/Save Options") [ Email](https://www.biorxiv.org/ "Email this Article") [ Share](https://www.biorxiv.org/) Virtual spatial transcriptomics from histopathology enables prognostic and therapeutic response prediction in cancer Shaoqing Jiao, Zhen Yuan, Dazhi Lu, Yunfei Xu, Yunwei Dong, Jiajie Peng bioRxiv 2026.08.04.742671; doi: https://doi.org/10.64898/2026.08.04.742671 This article is a preprint and has not been certified by peer review [[what does this mean?](https://www.biorxiv.org/about/FAQ#unrefereed)]. 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