Collections / Interleukin 7 therapy changes tumor immune environment to pro-inflammatory through myeloid and T cell interactions [Visium]

Interleukin 7 therapy changes tumor immune environment to pro-inflammatory through myeloid and T cell interactions [Visium]

Immune checkpoint inhibitors unleash inhibitory signals on effector T cells inflicted by tumor and surrounding stromal cells. Despite the great success of PD-1 inhibitors in a fraction of cancer patients, lack of checkpoint molecule expression and persistence of immunosuppressive cells limit the effects of the therapy. To overcome the limitations, combination therapies or direct stimulation of myeloid cells are being investigated. Nonetheless, combination therapies increase the incidence of side effects, and a single agent co-stimulating T cell and myeloid populations might be a better strategy. Exploring single cell RNA sequencing data from multiple human cancers and mouse tumor model, we found pleiotropic expression of interleukin 7 receptor on T cells, macrophages, and dendritic cells. Treatment of recombinant IL7 induced expansion of effector CD8 T cells along with pro-inflammatory activation of macrophages and dendritic cells. Spatial transcriptomic data revealed strengthen of anti-tumoral interactions between macrophages and T cells by IL7 treatment. Altogether, our results suggest that IL7 therapy is applicable to a broad range of cancer patients.

Datasets2
Size~70.2 MB
AssayVisium
Samplemouse · colon
KindOther
Flag a data issue
PMID:38424167 ↗
Python · sddb client
from sddb import Catalog

cohort = Catalog().collection("geo-gse205306")
cohort.members        # the 2 datasets as a table

print(cohort.licenses())              # check the terms before you download

sdatas = cohort.to_dict()             # {uid: SpatialData}, cached locally
# or keep the .zarr files in a folder of your choice:
# sdatas = cohort.to_dict(dest="data/")
Python · lamindb

Needs pip install lamindb and network access to the Lamin database, which is limited during the alpha. If it fails, use the Client tab.

import lamindb as ln

db = ln.DB("scverse/spatialdata-db")
coll = db.Collection.get(key="geo-gse205306")
coll.ordered_artifacts.to_dataframe()   # the 2 members, ordered

# download every member .zarr to the local cache:
paths = [a.cache() for a in coll.ordered_artifacts.all()]
Shell
# sddb client: download every member .zarr into data/
sddb-cli download --collection geo-gse205306 --dest data/

Licenses: 2 NCBI GEO policy

Open in Vitessce
Visium
Mouse colon
GSM6210023
mouse colon colorectal carcinoma 35.1 MB 2,221 spots bronze H&E NCBI GEO policy
TechnologyVisium
Organismmouse
Tissuecolon
Conditioncolorectal carcinoma
Size35.1 MB
Spots2,221
Magnificationother · 3.515 µm/px
bronze H&E NCBI GEO policy Open in Vitessce
Open in Vitessce
Visium
Mouse colon
GSM6210024
mouse colon colorectal carcinoma 35.1 MB 1,951 spots bronze H&E NCBI GEO policy
TechnologyVisium
Organismmouse
Tissuecolon
Conditioncolorectal carcinoma
Size35.1 MB
Spots1,951
Magnificationother · 3.437 µm/px
bronze H&E NCBI GEO policy Open in Vitessce

Cite this collection

doi.org/10.1038/s41416-024-02617-7 ↗
BibTeX
@article{Eum_2024,
  title = {Single-cell RNA sequencing reveals myeloid and T cell co-stimulation mediated by IL-7 anti-cancer immunotherapy},
  volume = {130},
  ISSN = {1532-1827},
  url = {http://dx.doi.org/10.1038/s41416-024-02617-7},
  DOI = {10.1038/s41416-024-02617-7},
  number = {8},
  journal = {British Journal of Cancer},
  publisher = {Springer Science and Business Media LLC},
  author = {Eum, Hye Hyeon and Jeong, Dasom and Kim, Nayoung and Jo, Areum and Na, Minsu and Kang, Huiram and Hong, Yourae and Kong, Jin-Sun and Jeong, Gi Heon and Yoo, Seung-Ah and Lee, Hae-Ock},
  year = {2024},
  month = Feb,
  pages = {1388–1401}
}