Trial reportNature communications2023
Estimation of cell lineages in tumors from spatial transcriptomics data.
Trial report in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 91 papers.
What it found
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
91 citing papers in PubMed.
- Decoding B-cell Signatures of Complete Pathologic Response to Perioperative Chemoimmunotherapy in Non-Small Cell Lung Cancer.Clinical cancer research : an official journal of the American Association for Cancer Research · 2026Trial
- Integrated spatial and single-cell transcriptomics uncover IFI6⁺ regulatory T-cells and SPP1⁺ macrophages immunosuppressive niches in colorectal cancer.British journal of cancer · 2026Article
- Integrated scRNA-Seq and Spatial Transcriptomics Reveal CHPF and GPC1 as Markers of Cancer-Associated Fibroblast-Dominant Domains in Colorectal Cancer.Cancer medicine · 2026Article
- Uncovering pan-cancer signatures of chemoresistance.Genome medicine · 2026Article
- Pan-tumor spatial transcriptomics reveals conserved properties of tumor organization.Cell reports. Medicine · 2026Article
- Benchmarking copy number alteration inference methods for spatial transcriptomics.Nature communications · 2026Article
- Spatial and single-cell profiling identify NDRG1⁺ macrophages as key hallmark of angiogenic remodeling and platinum resistance in HGSOC.Translational oncology · 2026Article
- Coordinated Expression of ADC TargetsCurrent issues in molecular biology · 2026Article
- Inference of secreted protein signaling activities in intercellular communication.Nature methods · 2026Article
- Integrative pan-cancer and COAD-focused multi-omics analysis of non-histone acetylation substrate-related transcriptional landscapes.NPJ precision oncology · 2026Article
- P4HA1⁺ collagen-modifying fibroblasts are associated with pro-tumor niches and therapeutic vulnerabilities in colorectal cancer.Journal of translational medicine · 2026Article
- Integrated bioinformatic evaluation unveils a cellular senescence gene signature as a poor prognostic factor in hepatocellular carcinoma.Clinical and experimental medicine · 2026Article
- A ZDHHC3-USP5-PTRF axis links palmitoylation to ferroptosis-associated phenotypes in melanoma.Cell death & disease · 2026Article
- Article
- Dissecting and steering cell dynamics using spatially-informed RNA velocity with veloAgent.Molecular systems biology · 2026Article
- NAT10-dependent NCell reports · 2026Article
- GraphTME: graph-based framework for predicting immunotherapy response by interpreting tumour microenvironment interactions using spatial transcriptomics.NPJ systems biology and applications · 2026Article
- Article
- Robust identification of cell-cell communication heterogeneity in single cells.bioRxiv : the preprint server for biology · 2026Article
- Multi-omic characterization of nasopharyngeal carcinoma delineates the subtype-specific landscape of response to induction chemotherapy.Nature cancer · 2026Article
31 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Spatial transcriptomics (ST) technology through in situ capturing has enabled topographical gene expression profiling of tumor tissues. However, each capturing spot may contain diverse immune and malignant cells, with different cell densities across tissue regions. Cell type deconvolution in tumor ST data remains challenging for existing methods designed to decompose general ST or bulk tumor data. We develop the Spatial Cellular Estimator for Tumors (SpaCET) to infer cell identities from tumor ST data. SpaCET first estimates cancer cell abundance by integrating a gene pattern dictionary of copy number alterations and expression changes in common malignancies. A constrained regression model then calibrates local cell densities and determines immune and stromal cell lineage fractions. SpaCET provides higher accuracy than existing methods based on simulation and real ST data with matched double-blind histopathology annotations as ground truth. Further, coupling cell fractions with ligand-receptor coexpression analysis, SpaCET reveals how intercellular interactions at the tumor-immune interface promote cancer progression.
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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.