ArticleNature communications2020
Spatial domain analysis predicts risk of colorectal cancer recurrence and infers associated tumor microenvironment networks.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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
24 citing papers in PubMed.
- The circMTCL1/miR-145-5p/BMP3 axis suppresses colorectal cancer cell growth by mediating the MAPK signaling pathway.Medical oncology (Northwood, London, England) · 2026Article
- Decoding spatial mTOR signalling reveals distinct roles in the tumour microenvironment.NPJ precision oncology · 2026Article
- Informatics at the Frontier of Cancer Research.Cancer research · 2025Review
- A novel multislice framework for precision 3D spatial domain reconstruction and disease pathology analysis.Genome research · 2025Article
- Integrative spatial omics reveals distinct tumor-promoting multicellular niches and immunosuppressive mechanisms in Black American and White American patients with TNBC.Nature communications · 2025Article
- Patient-Derived Colorectal Cancer Extracellular Matrices Modulate Cancer Cell Stemness Markers.International journal of molecular sciences · 2025Article
- The T cell receptor landscape of childhood brain tumors.Science translational medicine · 2025Article
- From morphology to single-cell molecules: high-resolution 3D histology in biomedicine.Molecular cancer · 2025Review
- An initial game-theoretic assessment of enhanced tissue preparation and imaging protocols for improved deep learning inference of spatial transcriptomics from tissue morphology.Briefings in bioinformatics · 2024Article
- Cell-cell communication: new insights and clinical implications.Signal transduction and targeted therapy · 2024Review
- Advancements and applications of single-cell multi-omics techniques in cancer research: Unveiling heterogeneity and paving the way for precision therapeutics.Biochemistry and biophysics reports · 2024Review
- Using tumor habitat-derived radiomic analysis during pretreatmentCancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Article
- Deriving spatial features from in situ proteomics imaging to enhance cancer survival analysis.Bioinformatics (Oxford, England) · 2023Article
- Identification of Spatial Proteomic Signatures of Colon Tumor Metastasis: A Digital Spatial Profiling Approach.The American journal of pathology · 2023Article
- Immune landscape in invasive ductal and lobular breast cancer reveals a divergent macrophage-driven microenvironment.Nature cancer · 2023Article
- Functional biomaterials for biomimetic 3D in vitro tumor microenvironment modeling.In vitro models · 2023Review
- Inferring spatial transcriptomics markers from whole slide images to characterize metastasis-related spatial heterogeneity of colorectal tumors: A pilot study.Journal of pathology informatics · 2023Article
- Artificial intelligence in clinical and translational science: Successes, challenges and opportunities.Clinical and translational science · 2022Review
- Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
An unmet clinical need in solid tumor cancers is the ability to harness the intrinsic spatial information in primary tumors that can be exploited to optimize prognostics, diagnostics and therapeutic strategies for precision medicine. Here, we develop a transformational spatial analytics computational and systems biology platform (SpAn) that predicts clinical outcomes and captures emergent spatial biology that can potentially inform therapeutic strategies. We apply SpAn to primary tumor tissue samples from a cohort of 432 chemo-naïve colorectal cancer (CRC) patients iteratively labeled with a highly multiplexed (hyperplexed) panel of 55 fluorescently tagged antibodies. We show that SpAn predicts the 5-year risk of CRC recurrence with a mean AUROC of 88.5% (SE of 0.1%), significantly better than current state-of-the-art methods. Additionally, SpAn infers the emergent network biology of tumor microenvironment spatial domains revealing a spatially-mediated role of CRC consensus molecular subtype features with the potential to inform precision medicine.
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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.