Collections / Cancer-induced nerve injury promotes resistance to anti-PD-1 therapy [Spatial Transcriptomics]

Cancer-induced nerve injury promotes resistance to anti-PD-1 therapy [Spatial Transcriptomics]

Peri-neural invasion (PNI) is a well-established poor prognostic factor in multiple cancer types. However, the mechanisms driving the PNI's detrimental clinical effect remain elusive. Here, we provide clinical and mechanistic insights into PNI and cancer-induced injury of tumor-associated nerves (TANs) and their role in resistance to anti-PD-1 therapy. Our work demonstrates that poor response to anti-PD-1 therapy in cutaneous squamous cell carcinoma (cSCC), melanoma, and gastric cancer is associated with PNI and TANs injury. Ultrastructural electron microscopy analysis reveals that direct contact between cancer cells and nerve fibers leads to cancer-induced nerve injury (CINI) via myelin degradation. Injured neurons respond by autonomously initiating an interleukin (IL)-6 and interferon (IFN) type I inflammatory response. This inflammatory response alters the immune activity in the peri-neural niche in melanoma, cSCC, and pancreatic adenocarcinoma, leading to an immuno-suppressive activity aimed at nerve healing and regeneration. As the tumor grows, the CINI burden increases, the inflammatory signal within the niche becomes chronic, and eventually skews the general immune tone within the tumor microenvironment to a suppressive and exhaustive state. The CINI-driven anti-PD-1 resistance can be reversed by targeting multiple steps in the CINI signaling process: denervating the tumor, conditional knockout of the transcription factor mediating the injury signal within neurons (cKO-Atf3), knockout of the IFN-a receptor signaling (Ifnar1-/-), or by combining anti-PD-1 and anti-IL-6-receptor blockade. Our findings demonstrate the direct immuno-regulatory roles of TANs and their therapeutic potential.

Datasets11
Size~448 MB
AssayVisium
Samplehuman · skin of body
KindOther
PMID:40836096 ↗
Python · lamindb
import lamindb as ln
import spatialdata as sd

coll = ln.Collection.get(key="geo-gse289745")
coll.ordered_artifacts.df()                 # the 11 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
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Open in Vitessce
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Open in Vitessce
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Open in Vitessce
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Open in Vitessce
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Open in Vitessce
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Open in Vitessce
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Open in Vitessce
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Open in Vitessce
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Open in Vitessce
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Open in Vitessce
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