bioRxivCancer BiologyPreprint
Spatial single-cell proteomics defines multicellular niches in the primary prostate cancer microenvironment
Adriano Luca MartinelliiD, Andrea BrunelloiD, Melissa Ensmenger, Sofia KarkampounaiD, Francesco BonolloiD, Éva CompératiD, Andrea LunardiiD, Beat Roth, Martin SpahniD, George Niklaus ThalmanniD, Elena BrunneriD, Elisabeth DamischiD, Lukas NommenseniD, Natalie SampsoniD, Marianna RapsomanikiiD, Marianna Kruithof‐de JulioiD
Abstract
Prostate cancer displays substantial clinical and histopathological heterogeneity which is not fully captured by conventional Gleason grading. To resolve the spatial and phenotypic complexity of the prostate tumor microenvironment, we performed imaging mass cytometry using a prostate-tailored 34-plex antibody panel on a clinically annotated tissue microarray cohort of 195 patients of primary stage disease after radical prostatectomy (523 regions of interest; 2.19 million cells). We identified 34 distinct cell types spanning epithelial, endothelial, stromal and immune compartments, and further organized into 18 epithelial-dominated, cancer associated fibroblast-dominated, and immune-rich spatial niches. Within the epithelial compartment, we detected an ERGp53 luminal population whose abundance is independently associated with poor overall and progression-free survival. In the stroma, we defined extracellular matrix remodeling-related cancer associated fibroblast and smooth muscle cell lineages, including a periglandular CD105high niche with strong stromal-immune connectivity that is selectively associated with worse clinical outcome. Finally, cumulative immune niche burden correlated with histological inflammation and stratifies for worse patient survival. Together, these data provide a spatially resolved single-cell atlas of primary PCa and reveal stromal-immune-epithelial niches with prognostic relevance beyond Gleason grade.
- organism: human
- assay: spatial transcriptomics
- assay: proteomics
- research focus: cell type annotation
- research focus: spatial domain detection
- approach: experimental assay
- Citations
- 0
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