# Prognostic impact of spatial niches in prostate cancer

## TL;DR

This study shows that combining protein expression data from the center and edge of prostate tumors can predict patient outcomes better than using either area alone.

## Contribution

The novel finding is that integrating spatial niche data improves prognostic accuracy in prostate cancer.

## Key findings

- Protein expression from tumor center or periphery alone was not prognostic.
- Combining center and periphery data revealed two patient subgroups with different progression-free survival.
- Spatially resolved protein expression using DSP can inform novel prognostic biomarkers.

## Abstract

The formation of intratumoral spatial niches has been reported for many human malignancies. However, the translational potential of such spatial niches is understudied. Herein, we utilize digital spatial profiling (DSP) to explore the prognostic relevance of spatially defined protein expression in high-risk prostate cancer. A total of 49 patient samples were analyzed for the expression of 46 proteins in 463 regions of interest (ROIs) from the tumor center (n = 198) and the tumor periphery (n = 265) resulting in 21,298 primary data points (mean per patient n = 9.4). Expression data from either the tumor center or the tumor periphery were not found to be prognostic. Protein expression of tumor center and periphery was then integrated into single datapoints by calculating the log2-transformed relative expression between the two niches for each protein and patient. Unsupervised hierarchical clustering of these data yielded two distinct patient subgroups. These clusters did not show a statistically significant correlation with known prognostic parameters yet significantly correlated with progression-free survival (p = 0.014, log-rank, HR 0.43; 95% CI, 0.22–0.86). Our results thus reveal that spatial protein expression contains prognostic information, however, only when expression data from both spatial niches are taken into account. In conclusion, our proof-of-concept study shows that DSP can be exploited for the development of novel prognostic biomarkers that rely on spatially resolved protein expression.

The online version contains supplementary material available at 10.1038/s41598-026-35720-1.