Biology subjects

Karkampouna, S.

Publications and source records attributed to Karkampouna, S..

9 recordsLinked to original sources

bioRxiv2026

Spatial single-cell proteomics defines multicellular niches in the primary prostate cancer microenvironment

Prostate cancer displays substantial clinical and histopathological heterogeneity which is not fully captured by conventional Gleason grading. To resolve the spatial and phenotypic complexity of the prostate tumor microenvironment, we performed imaging mass cytometry using a prostate-tailored 34-plex antibody panel on a clinically annotated tissue microarray cohort of 195 patients of primary stage disease after radical prostatectomy (523 regions of interest; 2.19 million cells). We identified 34 distinct cell types spanning epithelial, endothelial, stromal and immune compartments, and further organized into 18 epithelial-dominated, cancer associated fibroblast-dominated, and immune-rich spatial niches. Within the epithelial compartment, we detected an ERGp53 luminal population whose abundance is independently associated with poor overall and progression-free survival. In the stroma, we defined extracellular matrix remodeling-related cancer associated fibroblast and smooth muscle cell lineages, including a periglandular CD105high niche with strong stromal-immune connectivity that is selectively associated with worse clinical outcome. Finally, cumulative immune niche burden correlated with histological inflammation and stratifies for worse patient survival. Together, these data provide a spatially resolved single-cell atlas of primary PCa and reveal stromal-immune-epithelial niches with prognostic relevance beyond Gleason grade.

cancer biology↗

bioRxiv2026

Iron Metabolism as a Therapeutic Vulnerability in Stem Cell-Like Castration-Resistant Prostate Cancer

Prostate cancer is the second most common malignancy among men, with androgen deprivation therapy (ADT) serving as the standard treatment due to the hormone sensitivity of prostate tumors. However, therapeutic resistance frequently develops, leading to castration-resistant prostate cancer (CRPC), an aggressive and lethal disease. A recently defined subtype, stem cell-like CRPC (CRPC-SCL), accounts for approximately 25% of CRPC cases and demonstrates poor responsiveness to ADT. CRPC-SCL is characterized by the expression of Cluster of Differentiation 44 (CD44), a glycoprotein that promotes hyaluronic acid binding and uptake. Within CRPC-SCL patient-derived xenograft (PDX) model, CD44 high (CD44hi) cells exhibit enhanced tumorigenicity and proliferative capacity. Importantly, iron metabolism emerges as a critical regulator of this population: CD44hi cells maintain elevated intracellular iron, which sustains CD44 expression and stem cell-like properties by modulating H3K9me2 modification. Leveraging this vulnerability, inhibition of the iron-regulatory factor NRF2 was shown to increase intracellular free iron and selectively induce ferroptosis in CD44hi cells. These findings highlight the therapeutic potential of targeting iron metabolism to induce ferroptosis as a novel treatment strategy for CRPC-SCL.

cancer biology↗

bioRxiv2025

Unraveling the YAP1-TGFβ1 axis: a key driver of androgen receptor loss in prostate cancer-associated fibroblasts

Due to their pivotal roles in tumor progression and therapy resistance, cancer-associated fibroblasts (CAF) are considered key therapeutic targets with loss of stromal androgen receptor (AR) a poorly understood hallmark of aggressive prostate cancer (PCa). A paucity of pre-clinical models however has hampered functional studies of CAF heterogeneity. We demonstrate that our newly-generated CAF biobank contains three FAP+-fibroblast subtypes, each with unique molecular and functional traits. Cultures with an early-activated phenotype expressed the highest levels of AR and exhibited AR-dependent growth. Consistently, stromal cells expressing early-activation markers co-expressed nuclear AR in clinical specimens and were enriched in pre-neoplastic lesions/low-grade PCa. Conversely, myofibroblastic CAF (myCAF), which expressed low AR levels in vitro and in vivo and were proliferatively-insensitive to AR signaling modulation, constituted the predominant CAF subpopulation in stromogenic high-grade PCa and castration-resistant LACP9 patient-derived xenografts. Exacerbation of the myCAF state upon castration of LAPC9-bearing hosts underscored these findings. Mechanistically, AR loss in myCAF was driven by an NF{kappa}B-TGF{beta}1-YAP1 axis, whose combined targeting synergistically repressed myofibroblastic hallmarks and impaired autophagic flux, effects that were potentiated by enzalutamide resulting in myCAF cell death. Collectively, these findings provide a mechanistic rationale for adjuvant targeting of the YAP1-TGF{beta} signaling axis to improve patient outcomes.

cancer biology↗

bioRxiv2023

Identifying drug sensitivity of multifocal primary prostate cancer

The pronounced intra-patient variability in multifocal primary prostate cancer (PCa) has curtailed the efficacy of current treatment options. Patient-derived organoids (PDOs) have emerged as a pivotal model for functional testing due to their capability to retain the histopathological and molecular characteristics of parental tissues, allowing timely acquisition of drug response outcomes. In our study, employing twin biopsies from multiple lesions with matched PDO models in vitro, we investigated the molecular heterogeneity of PCa, and how it is linked to in vitro PDO pharmacological heterogeneity. Our functional testing approach leverages PDOs to screen standard-of-care treatment and FDA-approved compounds for other malignancies, aiming to repurpose their use in PCa and explore alternatives to androgen deprivation therapy. By integrating gene expression data from parental tissue with drug response results from PDOs, we have established a transcriptomics-based drug prediction models. The machine learning-based prediction model can predict the experimental PDO response to a specific drug, for the majority of screened drugs. This study offers a preclinical approach to potentially procure drug prediction outcomes and validate them in PDO models, as a prior step to clinical trial investigations or for selection of targeted therapeutic options.

cancer biology↗

bioRxiv2023

Multiplexed tumor profiling with generative AI accelerates histopathology workflows and improves clinical predictions

Understanding the spatial heterogeneity of tumors and its links to disease is a cornerstone of cancer biology. Emerging spatial technologies offer unprecedented capabilities towards this goal, but several limitations hinder their clinical adoption. To date, histopathology workflows still heavily depend on hematoxylin & eosin (H&E) and serial immunohistochemistry (IHC) staining, a cumbersome and tissue-exhaustive process that yields unaligned tissue images. We propose the VirtualMultiplexer, a generative AI toolkit that translates real H&E images to matching IHC images for several markers based on contrastive learning. The VirtualMultiplexer learns from unpaired H&E and IHC images and introduces a novel multi-scale loss to ensure consistent and biologically reliable stainings. The virtually multiplexed images enabled training a Graph Transformer that simultaneously learns from the joint spatial distribution of several markers to predict clinically relevant endpoints. Our results indicate that the VirtualMultiplexer achieves rapid, robust and precise generation of virtually multiplexed imaging datasets of high staining quality that are indistinguishable from the real ones. We successfully employed transfer learning to generate realistic virtual stainings across tissue scales, patient cohorts, and cancer types with no need for model fine-tuning. Crucially, the generated images are not only realistic but also clinically relevant, as they greatly improved the prediction of different clinical endpoints across patient cohorts and cancer types, speeding up histopathology workflows and accelerating spatial biology.

bioinformatics↗

bioRxiv2022

Bladder cancer organoids as a functional system to model different disease stages and therapy response

Bladder Cancer (BLCa) inter-patient heterogeneity is considered the primary cause of tumor reoccurrence and treatment failure, suggesting that BLCa patients could benefit from a more personalized treatment approach. Patient-derived organoids (PDOs) have been successfully used as a functional model for predicting drug response in different cancer types. In our study, we established BLCa PDO cultures from different BLCa stages. BLCa PDOs preserve the histological and molecular heterogeneity of the parental tumors, including their multiclonal genetic landscapes. BLCa PDOs consistently share key genetic alterations detected in parental tumors, mirroring tumor evolution in longitudinal sampling. Our drug screening pipeline was implemented using BLCa PDOs, testing both standard-of-care and additional FDA-approved compounds for other solid tumors. Integrative analysis of drug response profiles with matched PDO genomic analysis was used to determine enrichment thresholds for candidate markers of therapy resistance and sensitivity. By assessing the clinical history of longitudinally sampled cases, the clonal evolution of the disease could be determined and matched with drug response profiles. In conclusion, we have developed a clinically relevant pipeline for drug response profile assessment and discovery of candidate markers of therapy resistance.

cancer biology↗

bioRxiv2021

Oncofetal protein CRIPTO regulates wound healing and fibrogenesis in regenerating liver and is associated with the initial stages of cardiac fibrosis

BackgroundOncofetal protein, Cripto, is silenced during postnatal life and often re-expressed in different neoplastic processes. In the present study we investigated the potential role of Cripto in hepatic and cardiac fibrosis. In this study, the aim was to explore whether Cripto is expressed during liver fibrogenesis and whether this is related to the disease severity and pathogenesis of fibrogenesis. Furthermore, we aimed to identify the impact of Cripto expression on fibrogenesis in organs with high versus low regenerative capacity, represented by murine liver fibrogenesis and adult murine heart fibrogenesis MethodsCirculating CRIPTO levels were measured in plasma samples of patients with cirrhosis registered at the waitlist for liver transplantation (LT) and one year after LT. The expression of Cripto and fibrotic markers (aSMA, collagen I) were determined in human liver tissues of patients with cirrhosis (on a basis of viral hepatitis or alcoholic disease), in cardiac tissue samples of patients with end-stage heart failure and of mice with experimental liver and heart fibrosis using immuno-histochemical stainings and qPCR. Mouse models with experimental chronic liver fibrosis, induced with multiple shots of carbon tetrachloride (CCl4) and acute liver fibrosis (one shot of CCl4) were evaluated for Cripto expression and fibrotic markers. Cripto was overexpressed in vivo (Adenoviral delivery) or functionally sequestered by ALK4Fc ligand trap in the acute liver fibrosis mouse model. Murine heart tissues were evaluated for Cripto and fibrotic markers, in three models of heart injury; following myocardial infarction, pressure overload and ex vivo induced fibrosis. ResultsPatients with end-stage liver cirrhosis showed elevated Cripto levels in plasma, which had decreased one year after LT. Cripto expression was observed in fibrotic tissues of patients with end-stage liver cirrhosis and in patients with heart failure. The expression of Cripto in the liver was found specifically in the hepatocytes and was positively correlated with the Model for End-stage Liver Disease (MELD) score for end-stage liver disease. Cripto expression in the samples of cardiac fibrosis was limited and mostly observed in the interstitial cells. In the chronic and acute mouse models of liver fibrosis, Cripto-positve cells were observed in damaged liver areas around the central vein, which preceded the expression of aSMA-positive stellate cells, i.e. mediators of fibrosis. Whereas in the chronic mouse models the fibrosis and Cripto expression was still present after 11 weeks, in the acute model the liver regenerated and the fibrosis and Cripto expression resolved. In vivo overexpression of Cripto in this model, led to an increase in fibrotic markers while blockage of Cripto secreted function inhibited the extend of fibrotic areas and marker expression (SMA, Collagen type I and III) and induced higher proliferation of residual healthy hepatocytes. Cripto expression was also upregulated in several mouse models of cardiac fibrosis. During myocardial infarction Cripto is upregulated initially in cardiac interstitial cells, followed by expression in SMA-positive myofibroblasts throughout the infarct area. After the scar formation, Cripto expression decreased concomitantly with the aSMA expression. Temporal expression of Cripto in SMA-positive myofibroblasts was also observed surrounding the coronary arteries in the pressure overload model of cardiac fibrosis. Furthermore, Cripto expression was upregulated in interstitial myofibroblasts in hearts cultured in an ex vivo model for cardiac fibrosis. ConclusionOur results are indicative for a functional role of Cripto in induction of fibrogenesis and potential applications in antifibrotic treatments and stimulation of tissue regeneration.

cell biology↗

bioRxiv2020

Stroma transcriptomic and proteomic profile of prostate cancer metastasis xenograft models reveals conservation of bonemicroenvironment signatures

Prostate cancer (PCa) is the second leading cause of cancer-associated death in men with therapy resistance acquisition to androgen deprivation treatment and metastasis progression. Understanding the mechanisms of tumor progression to metastatic stage is necessary for the design of therapeutic and prognostic schemes. The main objective of the current study is to determine, using transcriptomic and proteomic analyses on patient derived-xenograft models, whether differentially aggressive PCa tumors predispose their microenvironment (stroma) to a metastatic gene expression pattern, and how this information could be applied in prognostics. Transcriptomic profiling (RNA Sequencing) was performed on PCa PDX models representing different disease stages; BM18 (androgen dependent bone metastasis) and LAPC9 (androgen independent bone metastasis). Using organism-specific reference databases, the human-specific transcriptome, representing the tumor, was identified and separated from the mouse-specific transcriptome (representing the contributing stroma counterpart) from the same PDX tumor samples. To identify proteome changes in the tumor (human) versus the stroma (mouse), we performed human and mouse cell separation using the MACS mouse depletion sorting kit, and subjected protein lysates to quantitative TMT labeling and mass spectrometry. We show that tenascin C is one of the most abundant stromal genes in bone metastasis PCa PDXs, is modulated by androgen levels in vivo and is highly expressed in castration resistant LAPC9 PDX compared to castration sensitive BM18 PDX. Tissue microarray of primary PCa samples (N=210) was used to evaluate the potential of TNC to act as a metastasis prognosis marker. Low number of TNC-positive cells were associated with statistically significant clinical progression to local recurrence or metastasis, compared to high TNC-positive group. Our data showed that metastatic PCa PDXs that differ in androgen sensitivity trigger a differential stroma response suggesting that stroma was influenced by tumor cues. Selected stromal markers of osteoblastic PCa induced bone metastases, were induced in the microenvironment of the host organism in metastatic xenografts, although implanted in a non-bone site, indicating a conserved mechanism of tumor cells to induce a stromal pre-metastatic signature with high potential prognostic or diagnostic value.

cancer biology↗

bioRxiv2020

Patient-derived xenografts and organoids model therapy response in prostate cancer

Therapy resistance and metastatic processes in prostate cancer (PCa) remain undefined, due to lack of experimental models that mimic different disease stages. We describe a novel androgen-dependent PCa patient-derived xenograft (PDX) model from treatment-naive, soft tissue metastasis (PNPCa). RNA and whole-exome sequencing of the PDX tissue and organoids confirmed transcriptomic and genomic similarity to primary tumor. PNPCa harbours BRCA2 and CHD1 somatic mutations, shows an SPOP/FOXA1-like transcriptomic signature and microsatellite instability, which occurs in 3% of advanced PCa and has never been modelled in vivo. Comparison of the treatment-naive PNPCa with additional metastatic PDXs (BM18, LAPC9), in a medium-throughput organoid screen of FDA-approved compounds, revealed differential drug sensitivities. Multikinase inhibitors (ponatinib, sunitinib, sorafenib) were broadly effective on all PDX- and patient-derived organoids from advanced cases with acquired resistance to standard-of-care compounds. This proof-of-principle study may provide a preclinical tool to screen drug responses to standard-of-care and newly identified, repurposed compounds.

cancer biology↗