Evidence map›Paper›PMID 41040398›Full record

ArticlebioRxiv : the preprint server for biology2025

A Multi-Layered Atlas of Spatial Regulatory Programs and Therapeutic Vulnerabilities in Glioblastoma.

Linan Zhang, Matthew Lu, Xiaojun Ma, Hatice Osmanbeyoglu

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Linan ZhangDepartment of Applied Mathematics, School of Mathematics and Statistics, Ningbo University, Ningbo, Zhejiang 315211, China.
Matthew LuDepartment of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA 15206, USA.
Xiaojun MaDepartment of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA 15206, USA.
Hatice OsmanbeyogluDepartment of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA 15206, USA.ORCID 0000-0002-4972-4347

Funding

Computational methods for delineating cell context-specific regulatory programsR35GM146989 · NIGMS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI Hatice Ulku Osmanbeyoglu · 2022 to 2026
$1.9M
NIGMS NIH HHS R35 GM146989
6 · The paper itself

Abstract

Glioblastoma (GBM) is marked by profound spatial heterogeneity. This complexity fuels tumor progression and therapy resistance. Although recent spatial transcriptomics (ST) studies have defined transcriptional niches, or metaprograms, the systems level regulatory logic linking upstream signaling, transcriptional programs, and therapeutic vulnerabilities remains unresolved. Here, we introduce an integrated, multi layered framework that unifies transcription factor and pathway activity inference (STAN and SPAN) with ligand/receptor and drug target mapping, thereby constructing a systems-level regulatory atlas of GBM across 26 tumors. This atlas uncovers spatial niches organized around transcriptional hubs and signaling programs, including hypoxic mesenchymal regions coordinated by HIF1A and SOX2 with mTORC1 signaling, macrophage rich areas driven by STAT3 mediated immune modulation, and astrocyte-like states shaped by lipid metabolic regulators such as SREBF2. By coupling this regulatory atlas to drug target predictions, we identify niche specific therapeutic vulnerabilities, such as VEGF blockade in HIF1A high regions and MAO-B inhibition in mesenchymal states. Finally, we provide an open, interactive web resource to make these data broadly accessible. This work provides a generalizable blueprint for linking spatial regulation to therapeutic hypotheses across diverse human diseases.

Identifiers

PMID41040398
PMCPMC12485955

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Registered trials

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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.