Periodic Reporting for period 5 - ProstOmics ('Tissue is the issue': a multi-omics approach to improve prostate cancer diagnosis)
Periodo di rendicontazione: 2023-08-01 al 2024-09-30
With the global population aging, a significant rise in prostate-specific antigen (PSA) testing and prostate cancer diagnoses is anticipated. Current diagnostic tools lack the precision to distinguish between aggressive and indolent forms of prostate cancer, often resulting in over-diagnosis and unnecessary treatments. These interventions can lead to serious side effects such as impotence and incontinence, diminished quality of life, and increased healthcare costs. ProstOmics addresses this critical challenge by integrating novel and established “omics” technologies to uncover new biomarkers and deepen our understanding of the molecular mechanisms underlying prostate cancer progression. The project has developed a pioneering spatial integration pipeline for multi-omics data derived from prostate tissue, yielding valuable insights into aggressive cancer phenotypes.
As part of ProstOmics, we have pioneered a cutting-edge spatial integration pipeline that fuses multi-omics data from prostate tissue to unveil insights into the biology of aggressive prostate cancer. This innovative approach has led to the discovery and validation of novel gene signatures associated with tumor aggressiveness—particularly linked to club-like epithelial cells and heightened inflammatory activity. Our team has also developed a suite of advanced omics protocols tailored specifically for prostate tissue, alongside a powerful new tool for the spatial integration of complex molecular datasets. In parallel, we have established a unique and richly characterized patient cohort, laying the groundwork for future translational research and biomarker discovery.
As part of ProstOmics, we have pioneered a cutting-edge spatial integration pipeline that fuses multi-omics data from prostate tissue to unveil insights into the biology of aggressive prostate cancer. This innovative approach has led to the discovery and validation of novel gene signatures associated with tumor aggressiveness—particularly linked to club-like epithelial cells and heightened inflammatory activity. Our team has also developed a suite of advanced omics protocols tailored specifically for prostate tissue, alongside a powerful new tool for the spatial integration of complex molecular datasets. In parallel, we have established a unique and richly characterized patient cohort, laying the groundwork for future translational research and biomarker discovery.
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
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
The ProstOmics project has significantly contributed to the field of precision medicine by developing a novel multi-omics protocol capable of extracting and integrating spatially resolved molecular data from a single prostate cancer tissue sample. This approach provides an efficient integration of spatial transcriptomics, proteomics, metabolomics (MALDI-TOF MSI), DNA methylation profiling, and bulk omics, into a unified analytical framework—surpassing existing methodologies. A significant advancement was the creation of the Multi-Omics Imaging Integration Toolset (MIIT), a flexible Python-based platform that enables the integration of complex spatial omics datasets across tissue types. This tool facilitated the discovery of new gene expression signatures predictive of aggressive disease, relapse and revealed previously uncharacterized epithelial-immune interactions involving club-like cells. Additionally, the project achieved the world-first detection of zinc trichloride in prostate tissue using targeted MALDI-TOF MSI, enabling simultaneous spatial mapping of zinc and its metabolites. This innovation has opened new avenues for exploring zinc metabolism as a prognostic marker in prostate cancer. Together, these advancements represent a leap beyond the current state of the art, offering powerful new tools and insights for understanding and managing prostate cancer.