Biology subjects

Tessem, M.-B.

Publications and source records attributed to Tessem, M.-B..

7 recordsLinked to original sources

bioRxiv2026

Integrated multi-omics profiling uncovers the epigenetic, transcriptional, and metabolic landscape of prostate cancer progression

A comprehensive understanding of the underlying molecular mechanisms of prostate cancer is essential for the development of precise diagnostic biomarkers. In this study, we applied unsupervised multi-omics factor analysis (MOFA) to integrate DNA methylation, gene expression, and metabolic profiles derived from the same individuals, aiming to characterize the biological landscape of normal, malignant, and aggressive prostate tissue. Our analysis identified distinct molecular pathways associated with aggressive disease, specifically those involved in zinc metabolism, cell cycle regulation, smooth muscle architecture, immune activation, and tissue morphology. Key metabolites within the TCA cycle, amino acid metabolism, and lipid pathways were central to these signatures. Furthermore, we observed a consistent co-enrichment of SP1 and CTCFL binding regions among factor-associated CpGs, suggesting a model of global epigenetic reprogramming. These findings indicate a novel interplay between Polycomb deregulation, CTCFL-mediated chromatin remodeling, and SP1-driven transcriptional activation in shaping the prostate cancer epigenome. Apart from immune activation, the identified molecular signatures were validated in the TCGA cohort and demonstrated significant predictive value for disease recurrence. Overall, these results underscore the power of multi-omics integration in providing a holistic understanding of prostate cancer biology and its potential for clinical translation into prognostic biomarkers.

cancer biology↗

bioRxiv2024

Spatial Integration of Multi-Omics Data using the novel Multi-Omics Imaging Integration Toolset

To truly understand the cancer biology of heterogenous tumors in the context of precision medicine, it is crucial to use analytical methodology capable of capturing the complexities of multiple omics levels, as well as the spatial heterogeneity of cancer tissue. Different molecular imaging techniques, such as mass spectrometry imaging (MSI) and spatial transcriptomics (ST) achieve this goal by spatially detecting metabolites and mRNA, respectively. To take full analytical advantage of such multi-omics data, the individual measurements need to be integrated into one dataset. We present MIIT (Multi-Omics Imaging Integration Toolset), a Python framework for integrating spatially resolved multi-omics data. MIITs integration workflow consists of performing a grid projection of spatial omics data, registration of stained serial sections, and mapping of MSI-pixels to the spot resolution of Visium 10x ST data. For the registration of serial sections, we designed GreedyFHist, a registration algorithm based on the Greedy registration tool. We validated GreedyFHist on a dataset of 245 pairs of serial sections and reported an improved registration performance compared to a similar registration algorithm. As a proof of concept, we used MIIT to integrate ST and MSI data on cancer-free tissue from 7 prostate cancer patients and assessed the spot-wise correlation of a gene signature activity for citrate-spermine secretion derived from ST with citrate, spermine, and zinc levels obtained by MSI. We confirmed a significant correlation between gene signature activity and all three metabolites. To conclude, we developed a highly accurate, customizable, computational framework for integrating spatial omics technologies and for registration of serial tissue sections.

bioinformatics↗

bioRxiv2024

Deep phenotyping of the prostate tumor microenvironment reveals molecular stratifiers of relapse and links inflammatory chemokine expression to aberrant metabolism

Understanding the molecular characteristics and changes of the tumor microenvironment (TME) associated with aggressive prostate cancer (PCa) is essential for precise diagnosis and treatment. We interrogated spatially resolved integrated transcriptomics and metabolomics data to build molecular strafiers discriminating patients with aggressive, potentially relapsing, and metastasizing PCa. We report a relapse associated (RA) gene expression signature characterized by upregulated immune response related gene expression scoring high in cancer, stroma, and glandular tissue of relapsing patients. Further, we identified a signature specific to a distinct sub-group of morphologically non-cancerous glands in prostate tissue from patients with relapsing cancer. This signature, named chemokine-enriched-gland (CEG) signature, was characterized by upregulated gene expression of pro-inflammatory chemokines. Glands with a high CEG score were enriched for club-like cells and surrounding stroma was infiltrated by immune cells. Tissue regions scoring high for both CEG and RA signatures were associated with reduced levels of citrate and zinc and loss of normal prostate secretory gland functions via reduced expression of genes necessary for citrate secretion. In summary we report that aggressive PCa is associated with an increased inflammatory status linked to chemokine production and club-like cell enrichment in potentially pre-cancerous prostate glands displaying an aberrant metabolism.

cancer biology↗

bioRxiv2024

Androgen deprivation therapy-resistant club cells are linked to myeloid cell-driven immunosuppression in the prostate tumor microenvironment

Prostate cancer treatment resistance is a significant challenge facing the field. Genomic and transcriptomic profiling have partially elucidated the mechanisms through which cancer cells escape treatment, but their relation toward the tumor microenvironment (TME) remains elusive. Here we present a comprehensive transcriptomic landscape of the prostate TME at multiple points in the standard treatment timeline employing single-cell RNA-sequencing and spatial transcriptomics data from 110 patients. We identify club-like cells as a key epithelial cell subtype that acts as an interface between the prostate and the immune system. Tissue areas enriched with club-like cells have depleted androgen signaling and upregulated expression of luminal progenitor cell markers. Club-like cells display a senescence-associated secretory phenotype and their presence is linked to increased polymorphonuclear myeloid-derived suppressor cell (PMN-MDSC) activity. Our results indicate that club-like cells partake in inducing myeloid inflammation previously associated with androgen deprivation therapy resistance, providing a rationale for their therapeutic targeting.

cancer biology↗

bioRxiv2022

FunHoP analysis reveals upregulation of mitochondrial genes in prostate cancer

Mitochondrial activity in cancer cells has been central to cancer research since Otto Warburg first published his thesis on the topic in 1956. Although Warburg proposed that oxidative phosphorylation in the tricarboxylic acid (TCA) cycle was perturbed in cancer, later research has shown that oxidative phosphorylation is activated in most cancers, including prostate cancer (PCa). However, more detailed knowledge on mitochondrial metabolism and metabolic pathways in cancers is still lacking. In this study we expand our previously developed method for analyzing functional homologous proteins (FunHoP), which can provide a more detailed view of metabolic pathways. FunHoP uses results from differential expression analysis of RNA-Seq data to improve pathway analysis. By adding information on subcellular localization based on experimental data and computational predictions we can use FunHoP to differentiate between mitochondrial and non-mitochondrial processes in cancerous and normal prostate cell lines. Our results show that mitochondrial pathways are upregulated in PCa and that splitting metabolic pathways into mitochondrial and non-mitochondrial counterparts using FunHoP adds to the interpretation of the metabolic properties of PCa cells.

bioinformatics↗

bioRxiv2021

The genes controlling normal function of citrate and spermine secretion is lost in aggressive prostate cancer and prostate model systems

BackgroundSecretion of the metabolites citrate and spermine into prostate lumen is a unique hallmark for normal prostate epithelial cells. However, the identity of the genes controlling citrate and spermine secretion remains mostly unknown despite their obvious relevance for progression to aggressive prostate cancer. Materials & MethodsIn this study, we have correlated simultaneous measurement of citrate/spermine and transcriptomics data. We have refined these gene correlations in 12 prostate cancer cohorts containing 2915 tissue samples to create a novel gene signature of 150 genes connected with citrate and spermine secretion. We further explored the signature in public data, interrogating over 18 000 samples from various tissues and model systems, including 3826 samples from prostate and prostate cancer. ResultsIn prostate cancer, the expression of this gene signature is gradually lost in tissue from normal epithelial cells through PIN, low grade (Gleason <= 7), high grade cancer (Gleason >= 8) and metastatic lesions. The accuracy of the signature is validated by its unique enrichment in prostate compared to other tissues, and its strong enrichment in epithelial tissue compartments compared to stroma. Several zinc-binding proteins that are not previously investigated in the prostate are present in the gene signature, suggesting new mechanisms for controlling zinc homeostasis in citrate/spermine secretion. However, the absence of the gene signature in all common prostate normal and cancer cell-lines, as well as prostate organoids, underlines the challenge to study the role of these genes during prostate cancer progression in model systems. ConclusionsA large collection of transcriptomics data integrated with metabolomics identifies the genes related to citrate and spermine secretion in the prostate, and show that the expression of these genes gradually decreases on the path towards aggressive prostate cancer. In addition, the study questions the relevance of currently available model systems to study metabolism in prostate cancer development.

bioinformatics↗

bioRxiv2017

Cholesterol Synthesis Pathway Genes in Prostate Cancer are consistently downregulated when tissue confounding is minimized.

The relationship between cholesterol and prostate cancer has been extensively studied for decades, where high levels of cellular cholesterol are generally associated with cancer progression and less favorable outcomes. However, the role of in vivo cellular cholesterol synthesis in this process is unclear, and data on the transcriptional activity of cholesterol synthesis pathway genes in tissue from prostate cancer patients are inconsistent. A common problem with cancer tissue data from patient cohorts is the presence of heterogeneous tissue which confounds molecular analysis of the samples. In this study we present a method to minimize systematic confounding from stroma tissue in seven patient cohorts consisting of 1713 prostate cancer and 230 normal tissue samples. When confounding was minimized, differential gene expression analysis over all cohorts showed robust and consistent downregulation of nearly all genes in the cholesterol synthesis pathway. Additional analysis also identified cholesterol synthesis as the most significantly altered metabolic pathway in prostate cancer. This surprising observation is important for our understanding of how prostate cancer cells regulate cholesterol levels in vivo. Moreover, we show that tissue heterogeneity explains the lack of consistency in previous expression analysis of cholesterol synthesis genes in prostate cancer.

cancer biology↗