ArticleNPJ precision oncology2024
Integrating histopathology and transcriptomics for spatial tumor microenvironment profiling in a melanoma case study.
Article in NPJ precision oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed.
- Targeting cancer-associated fibroblasts: therapeutic strategies, translational challenges, and future perspectives.Journal of hematology & oncology · 2026Review
- Informing agent-based models with spatial data using convolutional autoencoders.Bioinformatics (Oxford, England) · 2026Article
- Review
- Artificial Intelligence for Spatial Immunometabolic Analysis of the Tumor Microenvironment: Current Evidence and Future Directions.Current issues in molecular biology · 2026Review
- Deep learning-driven recognition of panoramic tumor microenvironment features in H&E sections and its application.Journal for immunotherapy of cancer · 2026Review
- Tumor-on-chip's alliance with molecular pathology against metastatic disease.Journal of biomedical science · 2026Review
- Multimodal artificial intelligence and machine learning in oncology: from data integration to precision cancer care.Frontiers in digital health · 2026Review
- Multiscale tumor characterization in histopathology via self-distilled transformers and topology-aware visual encoding.Scientific reports · 2025Article
- From Slide to Insight: The Emerging Alliance of Digital Pathology and AI in Melanoma Diagnostics.Cancers · 2025Review
- Artificial Intelligence in Histopathological Analysis for Predicting Immunotherapy Response in Cutaneous Melanoma.International journal of molecular sciences · 2025Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Local structures formed by cells in the tumor microenvironment (TME) play an important role in tumor development and treatment response. This study introduces SPoTLIghT, a computational framework providing a quantitative description of the tumor architecture from hematoxylin and eosin (H&E) slides. We trained a weakly supervised machine learning model on melanoma patients linking tile-level imaging features extracted from H&E slides to sample-level cell type quantifications derived from RNA-sequencing data. Using this model, SPoTLIghT provides spatial cellular maps for any H&E image, and converts them in graphs to derive 96 interpretable features capturing TME cellular organization. We show how SPoTLIghT's spatial features can distinguish microenvironment subtypes and reveal nuanced immune infiltration structures not apparent in molecular data alone. Finally, we use SPoTLIghT to effectively predict patients' prognosis in an independent melanoma cohort. SPoTLIghT enhances computational histopathology providing a quantitative and interpretable characterization of the spatial contexture of tumors.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.