Biology subjects

Kruithof-de Julio, M.

Publications and source records attributed to Kruithof-de Julio, M..

17 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

A Human Alveolus-on-Chip Recapitulates SARS-CoV-2-mediated Lung Injury in an Organ-relevant Context for Pre-clinical Applications

Respiratory viruses pose a constant threat to public health as highlighted by the pandemic outbreak of COVID-19. The most severe manifestation of COVID-19 is observed in the distal lung, where SARS-CoV-2 infection can result in massive inflammation and barrier breakdown. To date, few therapies are approved for clinical use in COVID-19 patients partially due to the lack of highly translational pre-clinical models of the alveoli. Human-derived microphysiological systems pose a promising new class of in vitro models to study viral infection in a relevant context. Therefore, we aimed to develop a Lung-on-Chip (LOC) model for studying SARS-CoV-2 infection at the alveolar barrier. Using an immortalized alveolar epithelial cell line, AXiAEC, and human lung microvascular endothelial cells (hLMVEC) we established a SARS-CoV-2 infection model on a LOC system. The LOC models the alveolar epithelial/endothelial barrier under physiological breathing motion. Our results demonstrated that AXiAEC maturation at the air-liquid interface (ALI) is essential for SARS-CoV-2 infection. SARS-CoV-2 infected the breathing LOC model and induced breakdown of the air-blood barrier. Finally, we evaluated the application of the LOC SARS-CoV-2 infection model for efficacy testing and demonstrated the antiviral effect of remdesivir. Drug treatment not only inhibited viral replication but also protected the alveolar barrier from damage and partially reverted SARS-CoV-2-mediated transcriptional dysregulation in AXiAEC. This novel LOC infection model recapitulates aspects of COVID-19. Our results highlight the importance of physiological cues such as ALI and stretch to accurately model host-pathogen interactions in the distal lung. Application of LOC models in pre-clinical drug testing may facilitate candidate compound selection at an early stage, allowing the allocation of resources to a few promising candidate compounds raising the potential to accelerate drug development and reduce costs and animal testing.

cell biology↗

bioRxiv2025

Optimised dissociation and multimodal profiling of prostate cancer stroma reveal fibromuscular cell heterogeneity with clinical correlates

BackgroundDynamic remodelling of the tumour microenvironment (TME) plays a central role in prostate cancer (PCa) progression, immune evasion and therapy resistance. However, the co-existence of both tumour-promoting and tumour-restraining stromal elements necessitates extensive characterisation of the TME for effective targeting. Fibromuscular cell heterogeneity in PCa remains poorly characterised, in part due to challenges in isolating cells embedded within the desmoplastic stroma. This study therefore aimed to better characterise fibroblast and smooth muscle cell (SMC) populations as the major tissue-resident stromal cell subtypes within the PCa TME. MethodsA PCa single-cell RNA sequencing (scRNA-seq) dataset was re-analysed to define fibromuscular subtypes. Due to low fibroblast yields, an optimised tissue dissociation protocol was developed and benchmarked against two commercial kits via flow cytometry, immunostaining of clinical specimens and ex vivo culture. Dimensionality reduction and clustering were applied to the CD31- stromal fraction using a multiparameter surface marker panel. Annotation of the resulting clusters based on their surface marker profile was supported by integrating scRNA-seq and immuno-histological findings. ResultsThe optimised protocol yielded over twice the viable cells/mg tissue compared to two commercial kits, preserved surface marker integrity, enhanced successful cultivation of mesenchymal cells and recovered diverse stromal subpopulations from benign and malignant samples. Dimensionality reduction and clustering of flow cytometry counts identified 11 distinct CD31- stromal populations. Integration with transcriptomic data and immunofluorescence of clinical specimens identified spatially- and prognostically-distinct fibroblast subtypes, including inflammatory and myofibroblastic cancer-associated fibroblasts, pericytes linked to poor prognosis and a novel SMC subset associated with stromal activation. ConclusionThis study presents a robust workflow for improved isolation and characterisation of fibromuscular stromal cells in PCa. The multimodal approach enabled refined characterisation of phenotypically distinct and clinically-relevant stromal subpopulations within their spatial context providing a foundation for future TME-targeted therapies.

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↗

bioRxiv2023

Mechano-signaling of prostate tumor initiating cells facilitates their tropism to stiff metastatic niche

Analysis of clinical datasets indicate that cancer stem-like cells/tumor-initiating cells (CSCs/TICs) derived from prostate cancer (PCa) patients display an elevated expression of genes for cell-matrix interactions, cell adhesion proteins and of the putative mechanotransducer TAZ. Here we combined measurements on the cellular mechano-responses to matrix stiffness, including cell-generated forces, zebrafish and PDX-derived organoid models, to show that mechanotransduction serves as a key determinant for PCa CSC maintenance during metastatic onset. The {beta}1-integrin-ILK-CDC42-N-Wasp dependent cytoskeletal tension and TAZ nucleus-translocation mediate this mechano-signaling axis. As a result, expression of the stemness genes NANOG and OCT4 are induced, leading to metastatic tumor initiation. It is further demonstrated that pharmaceutical perturbation of this mechano-signaling using a novel YAP/TAZ inhibitor K975 constrains PCa metastasis in zebrafish, and development of PDX-derived organoids. Our data highlights the essential role of mechanotransduction in PCa aggressiveness, thereby underlying this pathway as a therapeutic target for future studies.

cancer biology↗

bioRxiv2023

MicroCT-based imaging of microvasculature within the bone tissue

Angiogenesis is essential for skeletal development, bone healing, and regeneration. Improved non-destructive, three-dimensional (3D) imaging of the vasculature within bone tissue benefits many research areas, especially implantology and tissue engineering. X-ray microcomputed tomography (microCT) is a well-suited non-destructive 3D imaging technique for bone morphology. For microCT-based detection of vessels, it is paramount to use contrast enhancement. Limited differences in radiopacity between perfusion agents and mineralized bone make their distinct segmentation problematic and have been a major drawback of this approach. A decalcification step resolves this issue but inhibits the simultaneous assessment of bone microstructure and vascular morphology. The problem of contrasting becomes further complicated in samples with metal implants. This study describes contrast-enhanced microCT-based visualization of vasculature within bone tissue in small and large animal models, also in the vicinity of the metal implants. We present simultaneous microvascular and bone imaging in murine tibia, a murine bone metastatic model, the pulp chamber, gingiva, and periodontal ligaments. In a large animal model (minipig), we perform visualization and segmentation of different tissue types and vessels in the hemimandible containing metal implants. We further demonstrate the potential of dual-energy imaging in distinguishing bone tissue from the applied contrast agents. This work introduces a non-destructive approach for 3D imaging of vasculature within soft and hard tissues near metal implants in a large animal model.

physiology↗

bioRxiv2022

TransCistor reveals the landscape of cis-regulatory long noncoding RNAs

A key attribute of some long noncoding RNAs (lncRNAs) is their ability to regulate expression of neighbouring genes in cis. However, such cis-lncRNAs are presently defined using ad hoc criteria that, we show, are prone to false-positive predictions. The resulting lack of cis-lncRNA catalogues hinders our understanding of their extent, characteristics and mechanisms. Here, we introduce TransCistor, a framework for defining and identifying cis-lncRNAs based on enrichment of targets amongst proximal genes. TransCistors simple and conservative statistical models are compatible with functionally-defined target gene maps generated by existing and future technologies. Using transcriptome-wide perturbation experiments for 268 human and 134 mouse lncRNAs, we provide the first large-scale survey of cis-lncRNAs. Known cis-lncRNAs are correctly identified, including XIST, LINC00240 and UMLILO, and predictions are consistent across analysis methods, perturbation types and independent experiments. Our results indicate that cis-activity is detected in a minority of lncRNAs, primarily involving activators over repressors. Cis-lncRNAs are detected by both RNA interference and antisense oligonucleotide perturbations. Mechanistically, cis-lncRNA transcripts are observed to physically associate with their target-genes, and are weakly enriched with enhancer-elements. In summary, TransCistor establishes a quantitative foundation for cis-lncRNAs, opening a path to elucidating their molecular mechanisms and biological significance.

genomics↗

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

Minor intron splicing efficiency increases with the development of lethal prostate cancer

Here we explored the role of minor spliceosome (MiS) function and minor intron-containing gene (MIG) expression in prostate cancer (PCa). We show MIGs are enriched as direct interactors of cancer-causing genes and their expression discriminates PCa progression. Increased expression of MiS U6atac snRNA, including others, and 6x more efficient minor intron splicing was observed in castration-resistant PCa (CRPC) versus primary PCa. Notably, androgen receptor signalling influenced MiS activity. Inhibition of MiS through siU6atac in PCa caused minor intron mis-splicing and aberrant expression of MIG transcripts and encoded proteins, which enriched for MAPK activity, DNA repair and cell cycle. Single cell-RNAseq confirmed cell cycle defects and lineage dependency on the MiS from primary to CRPC and neuroendocrine PCa. siU6atac was [~]50% more efficient in lowering tumor burden of CRPC cells and organoids versus current state-of-the-art combination therapy. In all, MiS is a strong therapeutic target for lethal PCa and potentially other cancers. Graphical AbstractU6atac expression, MiS activity, and minor intron splicing correlate with PCa therapy resistance and PCa progression to CRPC-adeno and transdifferentiation to CRPC-NE. One major MiS regulator during that process is the AR-axis, which is re-activated during CRPC-adeno and blocked in CRPC-NE. Molecularly, an increase in MiS dependent splicing promotes changes of transcriptome and proteome. This results in cell cycle activation, increased MAPK signalling and increased DNA repair. U6atac mediated MiS inhibition renders MiS splicing error-prone through increased intron retention and alternative splicing events, which results in cell cycle block and decreased MAPK signalling and DNA repair. MiS inhibition blocks all stages of PCa. Figure created with BioRender.com. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=154 SRC="FIGDIR/small/471104v1_ufig1.gif" ALT="Figure 1"> View larger version (61K): org.highwire.dtl.DTLVardef@1c1c296org.highwire.dtl.DTLVardef@1ce1416org.highwire.dtl.DTLVardef@88d842org.highwire.dtl.DTLVardef@2ddf96_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2021

Network-based identification and pharmacological targeting of host cell master regulators induced by SARS-CoV-2 infection

Precise characterization and targeting of host cell transcriptional machinery hijacked by SARS-CoV-2 remains challenging. To identify therapeutically targetable mechanisms that are critical for SARS-CoV-2 infection, here we elucidated the Master Regulator (MR) proteins representing mechanistic determinants of the gene expression signature induced by SARS-CoV-2. The analysis revealed coordinated inactivation of MR-proteins linked to regulatory programs potentiating efficiency of viral replication (detrimental host MR-signature) and activation of MR-proteins governing innate immune response programs (beneficial MR-signature). To identify MR-inverting compounds capable of rescuing activity of inactivated host MR-proteins, with-out adversely affecting the beneficial MR-signature, we developed the ViroTreat algorithm. Overall, >80% of drugs predicted to be effective by this methodology induced significant reduction of SARS-CoV-2 infection, without affecting cell viability. ViroTreat is fully generalizable and can be extended to identify drugs targeting the host cell-based MR signatures induced by virtually any pathogen.

microbiology↗

bioRxiv2020

GATA3 and MDM2 are synthetic lethal in estrogen receptor-positive breast cancers

Synthetic lethal interactions, where the simultaneous but not individual inactivation of two genes is lethal to the cell, have been successfully exploited to treat cancer. GATA3 is frequently mutated in estrogen receptor (ER)-positive breast cancers and its deficiency defines a subset of patients with poor response to hormonal therapy and poor prognosis. However, GATA3 is not targetable. Here we show that GATA3 and MDM2 are synthetically lethal in ER-positive breast cancer. Depletion and pharmacological inhibition of MDM2 induce apoptosis in GATA3-deficient models in vitro, in vivo and in patient-derived organoids (PDOs) harboring GATA3 somatic mutation. The synthetic lethality requires p53 and acts via the PI3K/Akt/mTOR pathway. Our results present MDM2 as a novel therapeutic target in the substantial cohort of ER-positive, GATA3-mutant breast cancer patients. With MDM2 inhibitors widely available, our findings can be rapidly translated into clinical trials to evaluate in-patient efficacy.

cancer 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↗

bioRxiv2020

Role of Specialized mSWI/SNF Complexes in Prostate Cancer Lineage Plasticity

Advanced prostate cancer initially responds to hormonal treatment, but ultimately becomes resistant and requires more potent therapies. One mechanism of resistance observed in [~]10% of these patients is through lineage plasticity, which manifests in a partial or complete small cell or neuroendocrine prostate cancer (NEPC) phenotype. Here, we investigate the role of the mammalian SWI/SNF (mSWI/SNF) chromatin remodeling complex in NEPC. Using large patient datasets, patient-derived organoids and cancer cell lines, we identify mSWI/SNF subunits that are deregulated in NEPC and demonstrate that SMARCA4 (BRG1) overexpression is associated with aggressive disease. We also show that SWI/SNF complexes interact with different lineage-specific factors in NEPC compared to prostate adenocarcinoma. These data suggest a role for mSWI/SNF complexes in therapy-related lineage plasticity, which may be relevant for other solid tumors.

cancer biology↗