Collections / Spatially resolved gene expression and tumour-microenvironment interactions across malignant transformation in IDH-mutant glioma

Spatially resolved gene expression and tumour-microenvironment interactions across malignant transformation in IDH-mutant glioma

Isocitrate dehydrogenase (IDH)-mutant gliomas represent most lower-grade diffuse gliomas in young adults. Although IDH-mutant gliomas initially behave indolently relative to IDH-wildtype counterparts, they undergo inevitable malignant transformation. The mechanisms driving this progression remain poorly defined. We analyzed a rare longitudinal cohort of two patients with IDH-mutant diffuse gliomas, each with matched WHO grade 2, 3, and 4 tumours. Spatial transcriptomics (NanoString GeoMx and 10X Visium), combined with differential expression, spatially variable gene analysis, and gene ontology enrichment, were used to define transcriptional programs underlying progression. Across both patients, higher-grade tumours demonstrated reduced proliferation and a shift toward a migratory, invasive phenotype. Grade 4 samples showed upregulation of genes involved in cell adhesion, axon guidance, cytoskeletal remodeling, and neural development. Spatial analyses localized these programs to tumour invasive margins. GO enrichment highlighted pathways related to neuron projection development, focal adhesion, and lamellipodium organization. Findings were clinically validated using Cox proportional hazards models with LASSO and Elastic Net regularization in a Pan-Cancer Atlas cohort. Elevated expression of invasion-associated genes correlated with shorter progression-free survival and an invasiveness gene signature predicted overall survival . These findings identify candidate molecular drivers of progression and potential targets for timepoint-specific therapeutic intervention.

Datasets3
Size~131 MB
AssayVisium
Samplehuman · brain
KindOther
www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE318238 ↗
Python · lamindb
import lamindb as ln
import spatialdata as sd

coll = ln.Collection.get(key="geo-gse318238")
coll.ordered_artifacts.df()                 # the 3 members, ordered

# download every member .zarr to the local cache:
paths = [a.cache() for a in coll.ordered_artifacts.all()]

# …or load each straight into memory as a SpatialData object:
sdatas = {a.key: sd.read_zarr(a.cache()) for a in coll.ordered_artifacts.all()}
Open in Vitessce
Visium
Human brain
GSM9490660
human brain astrocytoma (excluding glioblastoma) 80 MB 4,451 obs silver H&E NCBI GEO policy
TechnologyVisium
Organismhuman
Tissuebrain
Conditionastrocytoma (excluding glioblastoma)
Size80 MB
Obs4,451
silver H&E NCBI GEO policy Open in Vitessce
Open in Vitessce
Visium
Human brain
GSM9490659
human brain astrocytoma (excluding glioblastoma) 29 MB 1,079 obs silver H&E NCBI GEO policy
TechnologyVisium
Organismhuman
Tissuebrain
Conditionastrocytoma (excluding glioblastoma)
Size29 MB
Obs1,079
silver H&E NCBI GEO policy Open in Vitessce
Open in Vitessce
Visium
Human brain
GSM9490661
human brain astrocytoma (excluding glioblastoma) 22 MB 729 obs silver H&E NCBI GEO policy
TechnologyVisium
Organismhuman
Tissuebrain
Conditionastrocytoma (excluding glioblastoma)
Size22 MB
Obs729
silver H&E NCBI GEO policy Open in Vitessce

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