The ProstOmics project has successfully developed a comprehensive and unique multi-omics protocol capable of analyzing multiple layers of molecular data—including spatial omics—from a single prostate cancer tissue sample. This integrated approach represents a significant advancement in precision oncology. To optimize the analytical pipeline, we published several methodological innovations, particularly in spatial metabolomics and proteomics, as well as in tissue processing, storage, and transport (Buchholz et al., Metallomics, 2022; Høiem et al., Proteomics, 2021; Andersen et al., Methods Mol Biol, 2023). The project has also incorporated cutting-edge technologies such as spatial transcriptomics, MALDI imaging-based spatial proteomics, and DNA methylation profiling into a unified multi-omics framework—surpassing the original scope and pushing the boundaries of current state-of-the-art methodologies. A major milestone has been the integration of spatial data from MALDI-TOF MSI and spatial transcriptomics into a cohesive dataset. This complex task was essential for exploring the interplay between different molecular layers within the tumor microenvironment. To facilitate this, we developed the Multi-Omics Imaging Integration Toolset (MIIT)—a flexible Python-based framework designed to integrate spatial multi-omics data across diverse tissue types (Wess et al., Gigascience, 2025).
Using our Multi-Omics Imaging Integration Toolset (MIIT), we have successfully analyzed complex spatial multi-omics datasets to uncover key molecular mechanisms driving aggressive and metastatic prostate cancer. This integrative approach has been applied across several studies in our group, yielding novel biological insights with potential significant clinical relevance. In a large validation cohort comprising 1,588 patients, we identified and validated a novel gene expression signature predictive of relapse and metastatic progression in aggressive prostate cancer based on our spatial multi-omics data. Additionally, we discovered a chemokine-enriched glandular gene signature specific to a distinct subset of non-cancerous glands within aggressive tumors. These glands are characterized by elevated expression of pro-inflammatory chemokines, enrichment of club-like epithelial cells and immune infiltrates, and signs of metabolic dysregulation (bioRxiv, 2024). In collaboration with Finnish partners, we also conducted a pan-European multi-cohort study involving spatial transcriptomics data from 120 prostate cancer samples, including those generated within this project. This study revealed club-like epithelial cells as a pivotal interface between the prostate epithelium and the immune system, highlighting their potential role in tumor-immune interactions (Kiviaho et al., Nature Communications, 2024). Further analyses of our integrated spatial omics datasets are ongoing and will continue to advance our understanding of prostate cancer biology and progression.
As part of the ProstOmics project, we made a novel discovery of zinc trichloride in prostate tissue using targeted matrix-assisted laser desorption/ionization time-of-flight mass spectrometry imaging (MALDI-TOF MSI). This innovative approach enabled the simultaneous spatial mapping of zinc and its key metabolites—citrate and aspartate—within a single MALDI analysis (Andersen et al., Analytical Chemistry, 2020). Zinc plays a pivotal role in prostate physiology and pathology, and its spatial distribution is now being explored in large-scale sample analyses to assess its prognostic value in prostate cancer. By aligning MALDI-TOF MSI data with histopathological annotations, we have been able to pinpoint cell type-specific metabolic profiles within intact tissue architecture. This allows us to study prostate cancer in its native microenvironment, offering critical insights into the cellular interactions that drive disease progression. Our analyses have uncovered several differential metabolites and lipid species associated with distinct cell populations, many of which show promise as diagnostic and prognostic biomarkers (Andersen et al., Cancer and Metabolism, 2021). These findings underscore the power of spatial metabolomics in advancing precision oncology and deepening our understanding of prostate cancer biology.
Image multi-omics: Maria Karoline Andersen
Image zink: Andersen et al 2020 analytical chemistry
