Integrated transcriptomic and immune-associated network analysis of breast cancer patient-derived organoids reveals...

Despite advances in targeted therapies, breast cancer remains one of the major challenges to global health. Although patient-derived organoids (PDOs) are physiologically relevant models, existing transcriptomic studies are limited by the poor integration of immune signals and the absence of shared biomarkers across subtypes. In this study, we hypothesised that an integrated transcriptomic and network-based analysis of PDOs could identify conserved transcriptional signatures and pathway interactions across breast cancer subtypes.

PDOs representative of multiple breast cancer subtypes were analysed and compared with non-tumour organoids. Differential gene expression analysis was performed to identify transcriptional alterations, followed by pathway enrichment analysis and protein-protein interaction network analysis to investigate functional pathways and molecular interactions. Candidate biomarkers identified through computational analyses were subsequently validated experimentally by assessing gene expression and cytokine secretion profiles in tumour PDOs.

A total of 646 differentially expressed genes were identified. Pathway enrichment analysis revealed translational machinery and ribosome biogenesis as the dominant statistically significant processes, consistent with hyperactivated protein synthesis in cancer cells. Although neurotrophin signaling pathways were not enriched at the transcriptome-wide level, network analysis identified a computationally predicted functional association between IL1RAP and NTRK3 in the STRING database. SLITRK3, IL1RAP and IRF2BP2 were consistently overexpressed in tumour PDOs and associated with activation of inflammatory pathways and increased secretion of IL-1 and IFN-γ. NTRK3 was identified as a direct network interactor of IL1RAP bridging inflammatory and neurotrophin signalling, though its transcriptional direction in BC PDOs could not be unambiguously resolved from the available data. Experimental validation confirmed the upregulation of SLITRK3, IL1RAP and IRF2BP2 together with elevated secretion of IL-1β and IFN-γ.

Our findings suggest that IL1RAP and IRF2BP2 may be important in immune-related and candidated biomarker identification in breast cancer PDOs. We integrated transcriptomic, enrichment and network-based analyses for a robust strategy for biomarker discovery. This methodologically transparent framework highlights potential targets in breast cancer. Rather than only a list of candidate genes, it provides an organoid-derived computational framework for risk stratification.