Article

SPARC: A mechanism-aware spatial representation from routine histology predicts cancer survival and therapy response

2026-05-06

Abstract excerpt

Understanding the molecular mechanisms that drive treatment response is central to personalized cancer care, but assays such as spatial transcriptomics are not yet scalable in routine clinical practice. A critical question, then, is whether this deeper molecular insight can be extracted directly from routine histology. Here, we introduce SPARC, a framework that infers spatially resolved activity maps for 40 gene e...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 0c4622bd-a4cd-597c-9575-821480f5e59f
DOI: 10.64898/2026.05.04.26352410

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

SPARC: A mechanism-aware spatial representation from routine histology predicts cancer survival and therapy responseDOI 10.64898/2026.05.04.26352410Read

AI-Driven Spatial Transcriptomics Unlocks Large-Scale Breast Cancer Biomarker Discovery from HistopathologySemantic proximity Read

Virtual spatial transcriptomics from histopathology enables prognostic and therapeutic response prediction in cancerSemantic proximity Read

HOPE: Interpretable Histology Analysis with Spatial Omics-Derived Signatures for Precision OncologySemantic proximity Read

SpaPheno: Linking Spatial Transcriptomics to Clinical Phenotypes with Interpretable Machine LearningSemantic proximity Read

SpaPheno: linking spatial transcriptomics to clinical phenotypes with interpretable machine learning.Semantic proximity Read

A deep learning-based multiscale integration of spatial omics with tumor morphologySemantic proximity Read

Towards interpretable molecular and spatial analysis of the tumor microenvironment from digital histopathology images with HistoTME-v2Semantic proximity Read

Digital Spatial Pathway Mapping Reveals Prognostic Tumor States in Head and Neck CancerSemantic proximity Read

MOSAIC: Intra-tumoral heterogeneity characterization through large-scale spatial and cell-resolved multi-omics profilingSemantic proximity Read

A deep learning-based multiscale integration of spatial omics with tumor morphologySemantic proximity Read

Integrating Histology with Spatial Molecular Programs Using a Multimodal Foundation ModelSemantic proximity Read

X-SPATIO: An Explanatory Deep Learning Pipeline for the Prediction and Visualization of Spatially Resolved Biomarker Expression in Triple-Negative Breast CancerSemantic proximity Read