HELIOS: A spatial omics-informed multi-organ cell atlas enables AI-based tumour microenvironment profiling and biomarker discovery from routine histopathology

Spatial omics defines tumour microenvironments with high biological resolution but remains difficult to scale across large clinical cohorts. We developed HELIOS (H&E Learning Informed by Omics and Spatial Profiling), which transfers spatial-omics-derived cell identities into routine haematoxylin and eosin histology for interpretable single-cell tumour microenvironment profiling. HELIOS integrates 12.9 million cells from 60 specimens across 13 tissue types and four spatial profiling platforms, spanning eight epithelial, immune, stromal, and vascular populations. The resulting multiclass detector generalised across independent histopathology benchmarks. Applied to colorectal, breast, and lung cancers, HELIOS recapitulated molecular subtype-associated microenvironment states and identified prognostic immune and stromal phenotypes. Tumour-infiltrating lymphocytes were consistently associated with favourable outcomes, whereas neutrophil and fibroblast phenotypes showed cancer-specific associations. HELIOS-derived features remained prognostic after adjustment for clinicopathological and molecular variables. These findings demonstrate that spatial omics can provide scalable biological supervision for extracting clinically relevant tumour microenvironment features from routine H&E, supporting scalable risk stratification and biomarker development across clinical cohorts and retrospective pathology archives.

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Publication Details

Journal
medRxiv
Published
2026-10-29
DOI
https://doi.org/10.64898/2026.10.26.26366087
Primary Topic
AI in cancer detection
Type
preprint

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preprint

HELIOS: A spatial omics-informed multi-organ cell atlas enables AI-based tumour microenvironment profiling and biomarker discovery from routine histopathology

Manuel Rodriguez‐Justo, M. Hawkins, Huiyan Hu, Zhuoyan Shen et al.
3 links
medRxiv
AI in cancer detection
preprint

HELIOS: A spatial omics-informed multi-organ cell atlas enables AI-based tumour microenvironment profiling and biomarker discovery from routine histopathology

Manuel Rodriguez‐Justo, M. Hawkins, Huiyan Hu, Zhuoyan Shen, Charles-Antoine Collins Fekete, Konstantin Braeutigam, Jinyi Wang
preprint en

Abstract

Spatial omics defines tumour microenvironments with high biological resolution but remains difficult to scale across large clinical cohorts. We developed HELIOS (H&E Learning Informed by Omics and Spatial Profiling), which transfers spatial-omics-derived cell identities into routine haematoxylin and eosin histology for interpretable single-cell tumour microenvironment profiling. HELIOS integrates 12.9 million cells from 60 specimens across 13 tissue types and four spatial profiling platforms, spanning eight epithelial, immune, stromal, and vascular populations. The resulting multiclass detector generalised across independent histopathology benchmarks. Applied to colorectal, breast, and lung cancers, HELIOS recapitulated molecular subtype-associated microenvironment states and identified prognostic immune and stromal phenotypes. Tumour-infiltrating lymphocytes were consistently associated with favourable outcomes, whereas neutrophil and fibroblast phenotypes showed cancer-specific associations. HELIOS-derived features remained prognostic after adjustment for clinicopathological and molecular variables. These findings demonstrate that spatial omics can provide scalable biological supervision for extracting clinically relevant tumour microenvironment features from routine H&E, supporting scalable risk stratification and biomarker development across clinical cohorts and retrospective pathology archives.

medRxiv
University Hospital of Basel (CH), University College London (GB)
National Institute for Health and Care Research
Good health and well-being
AI in cancer detection
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