Collections / A targetable developmental program co-regulates angiogenesis and immune evasion [Spatial Transcriptomics]

A targetable developmental program co-regulates angiogenesis and immune evasion [Spatial Transcriptomics]

Ultraviolet (UV)-induced DNA mutations produce genetic drivers of cutaneous melanoma initiation and numerous neoantigens that can trigger anti-tumor immune responses in the host. Consequently, melanoma cells must rapidly evolve to evade immune detection by simultaneously modulating cell-autonomous epigenetic mechanisms and tumor-microenvironment interactions. Angiogenesis has been implicated in this process; although an increase of vasculature facilitates the immune response in normal tissue, solid tumors manage to somehow enhance blood flow while preventing immune cell infiltration. By comparing the expression of transcription factors (TFs) across early-stage melanoma, naevi, and other cancer types, we found the homeodomain-containing TF HOXD13 drives a melanoblast-like developmental program, which is upregulated in melanoma and strongly correlated with angiogenesis and immune cell exclusion. Using transcriptomics, 3D chromatin profiling, and in vivo models, we demonstrate that HOXD13 upregulation promotes tumor growth in vivo by concomitantly enhancing angiogenesis and suppressing T-cell infiltration. HOXD13 orchestrates 3D chromatin contacts between distal enhancers and promoters, simultaneously activating VEGFA, SEMA3A, and CD73. VEGFA and SEMA3A remodel the tumor vasculature and CD73 elevates extracellular levels of adenosine, a vasodilator and immune suppressor that binds adenosine receptors (AdR) on endothelial and T cells. In line with these findings, HOXD13-induced growth advantage in vivo was significantly reversed by the concomitant administration of VEGFR and AdR inhibitors. By revealing a dual pro-angiogenic and immunosuppressive HOXD13-CD73/VEGF gene regulatory axis, we identify a subset of patients who might benefit from combinations of AdR and VEGFR inhibitors which are currently being tested in clinical trials.

Datasets2
Size~179 MB
AssayVisium
Samplehuman · skin of body
KindOther
PMID:41631877 ↗
Python · lamindb
import lamindb as ln
import spatialdata as sd

coll = ln.Collection.get(key="geo-gse311909")
coll.ordered_artifacts.df()                 # the 2 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 skin of body
GSM9334385
human skin of body cutaneous melanoma 127 MB 4,795 obs bronze H&E
TechnologyVisium
Organismhuman
Tissueskin of body
Conditioncutaneous melanoma
Size127 MB
Obs4,795
bronze H&E Open in Vitessce
Open in Vitessce
Visium
Human skin of body
GSM9334384
human skin of body cutaneous melanoma 52 MB 4,619 obs bronze H&E
TechnologyVisium
Organismhuman
Tissueskin of body
Conditioncutaneous melanoma
Size52 MB
Obs4,619
bronze H&E Open in Vitessce

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