Spatial UMAP and Image Cytometry for Topographic Immuno-oncology Biomarker Discovery

Détails

ID Serval
serval:BIB_B645EB19D1AF
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Spatial UMAP and Image Cytometry for Topographic Immuno-oncology Biomarker Discovery
Périodique
Cancer Immunol Res
Auteur⸱e⸱s
Giraldo N. A., Berry S., Becht E., Ates D., Schenk K. M., Engle E. L., Green B., Nguyen P., Soni A., Stein J. E., Succaria F., Ogurtsova A., Xu H., Gottardo R., Anders R. A., Lipson E. J., Danilova L., Baras A. S., Taube J. M.
ISSN
2326-6074 (Electronic)
ISSN-L
2326-6066
Statut éditorial
Publié
Date de publication
11/2021
Volume
9
Numéro
11
Pages
1262-1269
Langue
anglais
Notes
Giraldo, Nicolas A
Berry, Sneha
Becht, Etienne
Ates, Deniz
Schenk, Kara M
Engle, Elizabeth L
Green, Benjamin
Nguyen, Peter
Soni, Abha
Stein, Julie E
Succaria, Farah
Ogurtsova, Aleksandra
Xu, Haiying
Gottardo, Raphael
Anders, Robert A
Lipson, Evan J
Danilova, Ludmila
Baras, Alexander S
Taube, Janis M
eng
P30 CA006973/CA/NCI NIH HHS/
R01 CA142779/CA/NCI NIH HHS/
R50 CA243627/CA/NCI NIH HHS/
Research Support, Non-U.S. Gov't
Research Support, N.I.H., Extramural
Cancer Immunol Res. 2021 Nov;9(11):1262-1269. doi: 10.1158/2326-6066.CIR-21-0015. Epub 2021 Aug 25.
Résumé
Multiplex immunofluorescence (mIF) can detail spatial relationships and complex cell phenotypes in the tumor microenvironment (TME). However, the analysis and visualization of mIF data can be complex and time-consuming. Here, we used tumor specimens from 93 patients with metastatic melanoma to develop and validate a mIF data analysis pipeline using established flow cytometry workflows (image cytometry). Unlike flow cytometry, spatial information from the TME was conserved at single-cell resolution. A spatial uniform manifold approximation and projection (UMAP) was constructed using the image cytometry output. Spatial UMAP subtraction analysis (survivors vs. nonsurvivors at 5 years) was used to identify topographic and coexpression signatures with positive or negative prognostic impact. Cell densities and proportions identified by image cytometry showed strong correlations when compared with those obtained using gold-standard, digital pathology software (R(2) > 0.8). The associated spatial UMAP highlighted "immune neighborhoods" and associated topographic immunoactive protein expression patterns. We found that PD-L1 and PD-1 expression intensity was spatially encoded-the highest PD-L1 expression intensity was observed on CD163(+) cells in neighborhoods with high CD8(+) cell density, and the highest PD-1 expression intensity was observed on CD8(+) cells in neighborhoods with dense arrangements of tumor cells. Spatial UMAP subtraction analysis revealed numerous spatial clusters associated with clinical outcome. The variables represented in the key clusters from the unsupervised UMAP analysis were validated using established, supervised approaches. In conclusion, image cytometry and the spatial UMAPs presented herein are powerful tools for the visualization and interpretation of single-cell, spatially resolved mIF data and associated topographic biomarker development.
Pubmed
Création de la notice
28/02/2022 11:45
Dernière modification de la notice
06/05/2022 5:35
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