# Intratumor Heterogeneity

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Nature Nov 4, 2026
Glioblastoma's malignant states are not scattered at random: a developmental-to-injury-and-hypoxia trajectory layers around the necrotic core
Glioblastoma malignant states were treated as parallel subtypes, and a single biopsy could not represent the whole tumour. Pairing single-nucleus and spatial transcriptomics across 12 IDH-wild-type primary tumours and 57 sampling sites revealed a trajectory from developmental-like states toward glial injury response and hypoxia, layered in space from the infiltrating edge to the necrotic core; the trajectory appears across tumour subtypes and genetically distinct subclones and is recapitulated in patient-derived organoids.
Glioblastoma malignant states were treated as parallel subtypes, and a single biopsy could not represent the whole tumour. Pairing single-nucleus and spatial transcriptomics across 12 IDH-wild-type primary tumours and 57 sampling sites revealed a trajectory from developmental-like states toward glial injury response and hypoxia, layered in space from the infiltrating edge to the necrotic core; the trajectory appears across tumour subtypes and genetically distinct subclones and is recapitulated in patient-derived organoids.
University Hospital Heidelberg University of Cambridge Central Institute of Mental Health Medizinische Fakultät Mannheim Istanbul Medipol University Heidelberg University European Molecular Biology Laboratory Cambridge University Hospitals NHS Foundation Trust
Grant de Jong（First author）Grant de Jong（First author）
Glioblastoma SpaceTree Intratumor Heterogeneity Spatial transcriptomics RNA Velocity
medRxiv Oct 26, 2026
Ewing sarcoma survival splits by neighborhood, not mutation: a 135-patient spatial map yields four prognostic types, with interferon-reactive tumors faring best
Ewing sarcoma is genomically near-uniform yet patients fare very differently. Single-cell and spatial transcriptomics of tumors and their microenvironment in 135 patients identified five tumor-cell transcriptional states and eight niches defined by neighboring cell configurations, and niche composition sorted patients into Fusion Active, Proliferative, Immune Reactive and TME-enriched types. The Immune Reactive type, marked by interferon-activated tumor cells, had favorable survival, and the survival differences reproduced in two independent bulk transcriptomic cohorts.
Ewing sarcoma is genomically near-uniform yet patients fare very differently. Single-cell and spatial transcriptomics of tumors and their microenvironment in 135 patients identified five tumor-cell transcriptional states and eight niches defined by neighboring cell configurations, and niche composition sorted patients into Fusion Active, Proliferative, Immune Reactive and TME-enriched types. The Immune Reactive type, marked by interferon-activated tumor cells, had favorable survival, and the survival differences reproduced in two independent bulk transcriptomic cohorts.
Charité - Universitätsmedizin Berlin University Hospital Heidelberg University Hospital Münster Humboldt-Universität zu Berlin Hospital Universitario La Paz Heinrich Heine University Düsseldorf Essen University Hospital Heidelberg University
Heng Luo（First author）Moritz Gerstung（Corresponding author）
Spatial Niche Organization Spatial transcriptomics Ewing Sarcoma Intratumor Heterogeneity
researchsquare.com Oct 23, 2026
Tumor clones differ by more than mutations: regulatory networks separate dominant clones across 253 patients and 12 cancers
Whether tumor clones can be told apart by their gene regulatory networks had remained largely unmapped, since clones are usually defined by copy-number profiles and rarely characterized by regulatory activity. Clone-specific networks were inferred from single-cell RNA-seq and compared with a Wasserstein-distance measure of regulatory divergence. Across 253 patients and 12 cancer types, clone-clone networks resembled each other more than clone-normal pairs, and the largest clone diverged from normal cells significantly more than smaller clones.
Whether tumor clones can be told apart by their gene regulatory networks had remained largely unmapped, since clones are usually defined by copy-number profiles and rarely characterized by regulatory activity. Clone-specific networks were inferred from single-cell RNA-seq and compared with a Wasserstein-distance measure of regulatory divergence. Across 253 patients and 12 cancer types, clone-clone networks resembled each other more than clone-normal pairs, and the largest clone diverged from normal cells significantly more than smaller clones.
Rice University Indian Institute of Technology Kanpur
Narein Rao（First author）
Intratumor Heterogeneity Gene Regulatory Network Inference Wasserstein Distance-Based Regulatory Divergence Regulon Analysis Single-Cell RNA Sequencing
Cancer Research Oct 15, 2026
Bulk tumor RNA can now be split into malignant cell states: 193 states found across 33 cancer types
Malignant cell states coexist within a tumor, and single-cell RNA sequencing can resolve them but is costly and hard to scale, while bulk RNA sequencing is cheap but mixes their signals together. CDState uses nonnegative matrix factorization with a sum-to-one constraint on proportions and a cosine-similarity penalty to infer malignant cell states from bulk RNA without reference data; on simulated bulk data from five cancer types it outperformed existing unsupervised methods overall in estimating state expression and proportions. Applied to 9,669 samples across 33 TCGA cancer types, it yielded 193 malignant cell states, with epithelial–mesenchymal transition, MYC targets, and oxidative phosphorylation the most recurrent programs.
Malignant cell states coexist within a tumor, and single-cell RNA sequencing can resolve them but is costly and hard to scale, while bulk RNA sequencing is cheap but mixes their signals together. CDState uses nonnegative matrix factorization with a sum-to-one constraint on proportions and a cosine-similarity penalty to infer malignant cell states from bulk RNA without reference data; on simulated bulk data from five cancer types it outperformed existing unsupervised methods overall in estimating state expression and proportions. Applied to 9,669 samples across 33 TCGA cancer types, it yielded 193 malignant cell states, with epithelial–mesenchymal transition, MYC targets, and oxidative phosphorylation the most recurrent programs.
SIB Swiss Institute of Bioinformatics University Hospital Zurich
Agnieszka Kraft（First author）
CDState Nonnegative Matrix Factorization Cell-type deconvolution Intratumor Heterogeneity

You’re all caught up

Glioblastoma's malignant states are not scattered at random: a developmental-to-injury-and-hypoxia trajectory layers around the necrotic core

Ewing sarcoma survival splits by neighborhood, not mutation: a 135-patient spatial map yields four prognostic types, with interferon-reactive tumors faring best

Tumor clones differ by more than mutations: regulatory networks separate dominant clones across 253 patients and 12 cancers

Bulk tumor RNA can now be split into malignant cell states: 193 states found across 33 cancer types