Cancer-associated fibroblast (CAF)s in the tumour microenvironment (TME) modulate the extracellular matrix, interact with cancer cells, and facilitate communication with infiltrating leukocytes, significantly contributing to cancer progression and therapeutic response. In prostate cancer (PCa), CAFs promote malignancy through metabolic rewiring, cancer stem cell regulation, and therapy resistance. Pre-clinical studies indicate that targeting amino acid metabolism, particularly glutamine (Gln) metabolism, reduces cancer proliferation and stemness. However, most studies lack the context of CAF–cancer interaction, focusing on monocultures. This study assesses the influence of CAFs on PCa growth by manipulating Gln metabolism using colour-labelled PCa cell lines (red) and fibroblast (green) in a co-culture system to evaluate CAFs’ effects on PCa cell proliferation and clonogenic potential. CAFs increased the proliferation of hormone-sensitive LNCaP cells, whereas the castration-resistant C4-2 cells were unaffected. However, clonogenic growth increased in both cell lines. Gln deprivation and GLS1 inhibition experiments revealed that the increased growth rate of LNCAP cells was associated with increased dependence on Gln, which was confirmed by proteomic analyses. Tissue analysis of PCa patients revealed elevated GLS1 levels in both the PCa epithelium and stroma, suggesting that GLS1 is a therapeutic target. Moreover, the median overall survival analysis of GLS1 expression in the PCa epithelium and stroma identified a “high-risk” patient group that may benefit from GLS1-targeted therapies. Therefore, GLS1 targeting appears promising in castration-resistant PCa patients with high GLS1 epithelium and low GLS1 stromal expression.

Rare tumor diseases are difficult to diagnose and there is a lack of routine diagnostic procedures. Approaches must be found that allow comprehensive identification and evaluation of prognostic relevant target proteins or transcripts. Analyzing rare ampullary cancer, respectively, to their prognosis and predictive factors by machine learning (ML) based matrix-assisted laser desorption/ionization (MALDI) time-of-flight (TOF) imaging is a first step towards providing new solutions for diagnostics of those cancer samples. In this study, we investigated a cohort of ampullary adenocarcinomas, including intestinal, pancreatic and cases of unknown subtypes, to identify differences in the proteome. Human formalin-fixed paraffin-embedded (FFPE) tissues were pathologically assessed, immunohistological stained, MALDI Imaging detected, and ML-related analyzed. We enable MALDI imaging as a diagnostic complement for immunohistochemical analysis and provide a MALDI Imaging neural network for broad application in tumor diagnostics. Moreover, using tools from ML model explainability, we determined a small subset of influential m/z -values from the trained models. The transformation of locally established ML networks dependent on one proteomic application source to other similar application sources (without peak picking or other pre-processing) is the basis for future rare cancer patient data collection.