Biology subjects

Cutillas, P. R.

Publications and source records attributed to Cutillas, P. R..

14 recordsLinked to original sources

bioRxiv2026

Neuro-mimetic reprogramming and epigenetic unlocking drive lung cancer stem cell adaptation to the brain microenvironment

Brain metastasis represents a lethal complication of non-small cell lung cancer, driven by the ability of disseminated tumor cells to colonize the central nervous system microenvironment. Although cancer stem cells (CSCs) are recognized as key drivers of metastatic initiation, whether their contact with the brain parenchyma induces terminal differentiation or adaptive plasticity remains controversial. Here, using an in vivo model of lung-derived CSCs injected intracranially, combined with quantitative total proteomics and phosphoproteomics, we demonstrate that contact with the brain niche triggers a neuro-mimetic reprogramming rather than terminal differentiation. Lung CSCs desensitize their systemic metastatic program while epigenetically unlocking neural stem cell networks, characterized by the upregulation of Ngfr, Fabp7, Pbx3, Vgf, and S100b, concurrent with the downregulation of the DNA methyltransferase Dnmt3a and the cytosolic exclusion of Id1. Phosphoproteomic profiling revealed post-translational adaptations, highlighted by hyperphosphorylation of Nestin at S957 and S963. Furthermore, kinase substrate enrichment analysis uncovered high activity of Prkcq (PKC{theta}) and Map3k7 (TAK1), shifting the cellular state toward structural remodelling and microenvironmental stress tolerance. Functional validation assays using selective inhibitors confirmed that these kinase networks represent adaptive liabilities within tissue constraints rather than basic in vitro survival requirements. Together, our findings demonstrate that lung CSCs exploit a phenotypic plasticity to adapt to the brain niche, acquiring a neural-glial disguise that supports vascular co-option, immune evasion, and metastatic colonization.

cancer biology↗

bioRxiv2026

Greatwall Kinase regulates Acute Myeloid Leukaemia Cell Division through a Non-Canonical Mechanism

Greatwall kinase regulates mitotic progression by phosphorylating ENSA and ARPP19, thereby inhibiting PP2A-B55. Moreover, Greatwall has been implicated in oncogenesis, particularly in solid tumours, but the mechanisms by which Greatwall regulates the cell cycle in other malignancies remain unclear. Here, we show that Greatwall regulates cytokinesis and cell cycle progression in acute myeloid leukaemia (AML) cells through a pathway distinct from ENSA-PP2A-B55. AML cells require Greatwall expression and activity to proliferate, as revealed by pharmacological and systematic genetic perturbation experiments. Mechanistically, Greatwall inactivation or genetic depletion does not measurably affect the ENSA-PP2A-B55 pathway. Instead, loss of Greatwall function alters cytokinesis, and the phosphorylation of proteins involved in cytoskeletal organisation and cytokinesis, including MARK3, which we identify as a direct Greatwall substrate in AML cells. Together, these findings reveal that the Greatwall kinase signalling network is wired differently in leukemic cells, thus uncovering a novel of cell cycle regulation.

cell biology↗

bioRxiv2025

Quantitative phosphoproteomics uncovers the signalling dynamics of hallucinogenic psychedelics

Psychedelic drugs can induce intense changes in perception and thought, and some also promote long-lasting adaptations in brain circuits that are being explored for treatment of mood and anxiety disorders. How these compounds differ at the level of intracellular signalling, and how hallucinogenic drugs diverge from related non-hallucinogenic forms, is poorly understood. A central question is whether a shared molecular fingerprint distinguishes hallucinogenic psychedelic action from other forms of receptor activation in neurons. Here, we show that chemically diverse psychedelics trigger a coordinated reorganisation of phosphorylation patterns across many proteins in neural cells, and that this global signalling response contains a distinct signature that separates hallucinogenic compounds from non-hallucinogenic counterparts of similar structure. We use a glycolysis-regulating transcription factor as an example of the signatures functional relevance to show that hallucinogenic psychedelics, but not their non-hallucinogenic analogues, enhance markers of glycolytic metabolism. These findings reveal that hallucinogenic and non-hallucinogenic psychedelics engage separable intracellular architectures, and establish a framework for understanding how different psychoactive compounds couple receptor activation to specific cellular states. More broadly, this work opens a path to using signalling fingerprints to guide the design of psychedelic-inspired therapeutics with tailored behavioural and metabolic profiles.

neuroscience↗

bioRxiv2025

The Balance between B55α and Greatwall expression levels predicts sensitivity to Greatwall inhibition in cancer cells

The Greatwall kinase inhibits PP2A-B55 phosphatase activity during mitosis to stabilise critical Cdk1-driven mitotic phosphorylation. Although Greatwall represents a potential oncogene and prospective therapeutic target, our understanding of cellular and molecular consequences of chemical Greatwall inactivation remains limited. To address this, we introduce C-604, a highly selective Greatwall inhibitor, and characterise both immediate and long-term cellular responses to the chemical attenuation of Greatwall activity. We demonstrate that Greatwall inhibition causes systemic destabilisation of the mitotic phosphoproteome, premature mitotic exit and pleiotropic cellular pathologies. Importantly, we demonstrate that the cellular and molecular abnormalities linked to reduced Greatwall activity are specifically dependent on the B55 isoform rather than other B55 variants, underscoring PP2A-B55 phosphatases as key mediators of cytotoxic effects of Greatwall-targeting agents in human cells. Additionally, we show that sensitivity to Greatwall inhibition varies in different cell line models and that dependency on Greatwall activity reflects the balance between Greatwall and B55 expression levels. Our findings highlight Greatwall dependency as a cell-specific vulnerability and propose the B55-to-Greatwall expression ratio as a predictive biomarker of cellular responses to Greatwall-targeted therapeutics.

cell biology↗

bioRxiv2025

DEK::NUP214 acts as an XPO1-dependent transcriptional activator of essential leukemia genes

The t(6;9)(p22.3;q34.1) translocation/DEK::NUP214 fusion protein defines a distinct subgroup of younger AML patients classified as a separate disease entity by the World Health Organization. DEK is a nuclear factor with multifunctional roles, including gene regulation, while its fusion partner, NUP214, plays a pivotal role in nuclear export by interacting with transport receptors such as XPO1. However, the precise mechanism by which DEK::NUP214 drives leukemia remains unclear. A comprehensive multi-omics comparison of 57 AML primary samples (including whole genome sequencing, targeted sequencing, transcriptomics, and drug screening with > 500 compounds) revealed that t(6;9) cases display a selective response to XPO1 inhibitors (Selinexor & Eltanexor) and a distinct transcriptomic signature characterized by the overexpression of FOXC1 and HOX genes that are key leukemia mediators. CUT&RUN experiments demonstrated the direct binding of DEK::NUP214 to the promoters of FOXC1 and HOXA/B clusters. Strikingly, the expression of these genes and the binding of DEK::NUP214 to their regulatory regions were selectively reduced upon XPO1 inhibition in t(6;9) cells. Altogether, these results identified a novel function of DEK::NUP214 as an XPO1-dependent transcriptional activator of key leukemia drivers and provide a rationale to explore the use of XPO1 inhibitors in this patient population.

cancer biology↗

bioRxiv2025

High levels of DNA replication initiation factors indicate ATR inhibitor sensitivity via excessive origin firing

Inhibitors of ATR, a central kinase controlling DNA replication origin firing and cellular checkpoint activity, are currently in multiple clinical trials, yet mechanisms underpinning sensitivity and robust patient stratification biomarkers are lacking. We used functional genomics approaches to identify molecular mechanisms driving sensitivity to the ATR inhibitor (ATRi) ceralasertib. Replication stress-associated patterns of DNA copy number alterations identified a subset of sensitive breast cancer cell lines. In parallel, we performed proteomics, phosphoproteomics and gene expression analyses and discovered that sensitive cell lines had higher expression of DNA replication origin firing factors, and massively increased origin firing in response to ATRi. ATRi sensitivity was partly rescued upon co-treatment with XL-413, a CDC7 inhibitor that decreases origin firing. High expression of replication initiation factors correlated with ATRi sensitivity across multiple cancer types, and in acute myeloid leukemia patient samples. Together, this study reveals a novel contribution of lethal origin firing capacity in determining the sensitivity of cancer cells to ATR inhibition and demonstrates the predictive potential of mechanism-specific copy number alterations, providing key steps towards developing a multimodal clinically applicable biomarker for ATR inhibitors.

cancer biology↗

bioRxiv2024

Maternal Immune Activation imprints translational dysregulation and differential MAP2 phosphorylation in descendant neural stem cells

Alterations induced by maternal immune activation (MIA) during gestation impact the subsequent neurodevelopment of progeny, a process that in humans, has been linked to the development of several neuropsychiatric conditions. To undertake a comprehensive examination of the molecular mechanisms governing MIA, we have devised an in vitro model based on neural stem cells (NSCs) sourced from fetuses carried by animals subjected to Poly I:C treatment. These neural progenitors demonstrate proliferative capacity and can be effectively differentiated into both neurons and glial cells. Transcriptomic, proteomic, and phosphoproteomic analyses conducted on these cellular models, in conjunction with counterparts from control treatments, revealed discernible shifts in the expression levels of a specific subset of proteins implicated in neuronal function. Noteworthy, we found an absence of congruence between these alterations at the transcriptomic level, suggesting that differences in protein translation contribute to the observed dysregulation. Furthermore, the phosphoproteomic data highlighted a discernible discrepancy in the basal phosphorylation of proteins between differentiated cells from both experimental groups, particularly within proteins associated with cytoskeletal architecture and synaptic functionality, notably those belonging to the MAP family. Observed alterations in MAP phosphorylation were found to potentially have functional consequences as they correlate with changes in neuronal plasticity and the establishment of neuronal synapses. Our data agrees with previous published observations and further underscore the importance of MAP2 phosphorylation state on its function and the impact that this protein has in neuronal structure and function.

neuroscience↗

bioRxiv2024

Understanding the Sources of Performance in Deep Learning Drug Response Prediction Models

Anti-cancer drug response prediction (DRP) using cancer cell lines plays a vital role in stratified medicine and drug discovery. Recently there has been a surge of new deep learning (DL) models for DRP that improve on the performance of their predecessors. However, different models use different input data types and neural network architectures making it hard to find the source of these improvements. Here we consider multiple published DRP models that report state-of-the-art performance in predicting continuous drug response values. These models take the chemical structures of drugs and omics profiles of cell lines as input. By experimenting with these models and comparing with our own simple benchmarks we show that no performance comes from drug features, instead, performance is due to the transcriptomics cell line profiles. Furthermore, we show that, depending on the testing type, much of the current reported performance is a property of the training target values. To address these limitations we create novel models (BinaryET and BinaryCB) that predict binary drug response values, guided by the hypothesis that this reduces the noise in the drug efficacy data. Thus, better aligning them with biochemistry that can be learnt from the input data. BinaryCB leverages a chemical foundation model, while BinaryET is trained from scratch using a transformer-type model. We show that these models learn useful chemical drug features, which is the first time this has been demonstrated for multiple DRP testing types to our knowledge. We further show binarising the drug response values is what causes the models to learn useful chemical drug features. We also show that BinaryET improves performance over BinaryCB, and over the published models that report state-of-the-art performance.

bioinformatics↗

bioRxiv2022

Integrative phosphoproteomics defines two biologically distinct groups of KMT2A rearranged acute myeloid leukaemia with different drug response phenotypes

Acute myeloid leukaemia (AML) patients harbouring certain chromosome abnormalities have particularly adverse prognosis. For these patients, targeted therapies have not yet made a significant clinical impact. To understand the molecular landscape of poor prognosis AML we profiled 74 patients from two different centres (in UK and Finland) at the proteomic, phosphoproteomic and drug response phenotypic levels. These data were complemented with transcriptomics analysis for 39 cases. Data integration highlighted a phosphoproteomics signature that define two biologically distinct groups of KMT2A rearranged leukaemia, which we term MLLGA and MLLGB. MLLGA presented increased DOT1L phosphorylation, HOXA gene expression, CDK1 activity and phosphorylation of proteins involved in RNA metabolism, replication and DNA damage when compared to MLLGB and no KMT2A rearranged samples. MLLGA was particularly sensitive to 15 compounds including genotoxic drugs and inhibitors of mitotic kinases and inosine-5-monosphosphate dehydrogenase (IMPDH) relative to other cases. Intermediate-risk KMT2A-MLLT3 cases were mainly represented in a third group closer to MLLGA than to MLLGB. The expression of IMPDH2 and multiple nucleolar proteins was higher in MLLGA and correlated with the response to IMPDH inhibition in KMT2A rearranged leukaemia, suggesting a role of the nucleolar activity in sensitivity to treatment. In summary, our multilayer molecular profiling of AML with poor prognosis and KMT2A-MLLT3 karyotypes identified a phosphoproteomics signature that defines two biologically and phenotypically distinct groups of KMT2A rearranged leukaemia. These data provide a rationale for the potential development of specific therapies for AML patients characterised by the MLLGA phosphoproteomics signature identified in this study.

molecular biology↗

bioRxiv2022

Low HER2 enables dedifferentiation and transformation of normal breast epithelial cells via chromatin opening

Overexpression of the human epidermal growth factor 2 (HER2) protein in breast cancer patients is a predictor of poor prognosis and resistance to therapies. Despite significant advances in the development of targeted therapies and improvements in the 5-year survival rate of metastatic HER2-positive breast cancer patients, a better understanding of the disease at an early stage is needed to prevent its progression. Here, we used an inducible breast cancer transformation system that allows investigation of early molecular changes at high temporal resolution. HER2 overexpression to similar levels as those observed in a subtype of HER2 positive breast cancer patients induced transformation of MCF10A cells and resulted in gross morphological changes, increased anchorage-independent growth of cells, and altered transcriptional programme of genes associated with oncogenic transformation. Global phosphoproteomic analysis during the first few hours of HER2 induction predominantly detected an increase in protein phosphorylation. Intriguingly, this correlated with a wave of chromatin opening, as measured by ATAC-seq on acini isolated from 3D cell culture. We observed that HER2 overexpression leads to reprogramming of many distal regulatory regions and promotes reprogramming-associated heterogeneity. We found that a subset of cells acquired a dedifferentiated breast stem-like phenotype, making them likely candidates for malignant transformation. Our data show that this population of cells, which counterintuitively enriches for relatively low HER2 protein abundance and increased chromatin accessibility, possesses transformational drive, resulting in increased anchorage-independent growth in vitro compared to cells not displaying a stem-like phenotype. Our data provide a discovery platform for signalling to chromatin pathways in HER2-driven cancers, offering an opportunity for biomarker discovery and identification of novel drug targets.

cancer biology↗

bioRxiv2022

Community detection in empirical kinase networks identifies new members of signaling pathways

Phosphoproteomics allows one to measure the activity of kinases that drive the fluxes of signal transduction pathways involved in biological processes such as immune function, senescence and growth. However, deriving knowledge of signaling network circuitry from these data is challenging due to a scarcity of phosphorylation sites that define kinase-kinase relationships. To address this issue, we previously identified around 6,000 phosphorylation sites markers of kinase-kinase relationships (that may be conceptualised as network edges), from which empirical cell-model-specific weighted kinase networks may be reconstructed. Here, we assess whether the application of community detection algorithms to such networks can identify new components linked to canonical signaling pathways.Phosphoproteomics data from acute myeloid leukaemia (AML) cells treated separately with PI3K, ATK, MEK and ERK inhibitors were used to reconstruct individual kinase networks. In each network, we applied the community detection method modularity maximization and selected the community containing the main target of the inhibitor the cells were treated with. These analyses returned communities that contained known canonical signaling components. Interestingly, in addition to canonical PI3K/AKT/MTOR members, the community assignments returned TTK (also known as MPS1) as a likely component of PI3K/AKT/mTOR signaling. We confirmed this observation with wet-lab laboratory experiments showing that TTK phosphorylation was decreased in AML cells treated with AKT and MTOR inhibitors. This study illustrates the application of community detection algorithms to the analysis of empirical kinase networks to uncover new members linked to canonical signaling pathways. Author summaryKinases are key enzymes that regulate the transduction of extracellular signals from cell surface receptors to changes in gene expression via a set of kinase-kinase interactions and signalling cascades. Inhibiting hyperactive kinases is a viable therapeutic strategy to treat different cancer types. Unfortunately, kinase signalling networks are robust to external perturbations, thus allowing tumour cells to orchestrate mechanisms that compensate for inhibition of specific kinases. Therefore, there is a need to better understand kinase network structure and to identify new therapeutic targets. Here, we reconstructed kinase networks from phosphoproteomics data, and compared the activity of its kinase interactions in acute myeloid leukaemia (AML) cells. We then tested community detection algorithms to identify kinase components associated to PI3K/AKT/MTOR signalling, a paradigmatic oncogenic signalling cascade. We found that TTK was usually grouped with networks derived for PI3K, AKT and MTOR kinases. Wet-lab experiments confirmed that TTK is likely to act downstream of AKT and MTOR. We thus show that our methods can be used to identify potential new members of canonical kinase signalling cascades.

systems biology↗

bioRxiv2021

Targeted therapy for LIMD1-deficient non-small cell lung cancer subtypes.

An early event in lung oncogenesis is loss of the tumour suppressor gene LIMD1 (LIM domains containing 1); this encodes a scaffold protein, which suppresses tumourigenesis via a number of different mechanisms. Approximately 45% of non-small cell lung cancers (NSCLC) are deficient in LIMD11, yet this subtype of NSCLC has been overlooked in preclinical and clinical investigations. Defining therapeutic targets in these LIMD1 loss-of-function patients is difficult due to a lack of druggable targets, thus alternative approaches are required. To this end, we performed the first drug repurposing screen to identify compounds that confer synthetic lethality with LIMD1 loss in NSCLC cells. PF-477736 was shown to selectively target LIMD1 deficient cells in vitro through inhibition of multiple kinases, inducing cell death via apoptosis. Furthermore, PF-477736 was effective in treating LIMD1-/- tumors in subcutaneous xenograft models, with no significant effect in LIMD1+/+ cells. We have identified a novel drug tool with significant preclinical characterization that serves as an excellent candidate to explore and define LIMD1-deficient cancers as a new therapeutic subgroup of critical unmet need. Significance StatementHere we provide the first proof-of-concept data validating the scope for development of a targeted therapy against the non-small cell lung cancers (NSCLC) subtypes deficient in expression of the LIMD1 tumor suppressor gene. Approximately 45% of NSCLC are deficient in LIMD11 representing at least 1.2 million lung cancer patients worldwide; yet this subtype has been ignored in preclinical and clinical investigations with no targeted therapies available. This seminal study applied synthetic lethality drug screening to target the loss/reduction of LIMD1 in lung cancer and normal cell lines, identifying and validating the multi-kinase inhibitor PF-477736 as a selectively cytotoxic compound towards LIMD1 deficient cells. This study provides rationale for further investigation into targeting LIMD1 loss in lung cancer, thereby addressing a critical unmet need for therapeutic approached to targeting LIMD1-deficent cancer subtypes.

cancer biology↗

bioRxiv2018

Integrin αVβ6-EGFR crosstalk regulates bidirectional force transmission and controls breast cancer invasion

The mechanical properties of the extracellular matrix within tumours control multiple cellular functions that drive cancer invasion and metastasis. However, the mechanisms controlling microenvironmental force sensation and transmission, and how these regulate transcriptional reprogramming and invasion, are unclear. Our aim was to understand how mechanical inputs are transmitted bidirectionally and translated into biochemical and transcriptional outputs to drive breast cancer progression. We reveal that adhesion receptor and growth factor receptor crosstalk regulates a bidirectional feedback mechanism co-ordinating force-dependent transcriptional regulation and invasion.\n\nIntegrin V{beta}6 drives invasion in a range of carcinomas and is a potential therapeutic target. V{beta}6 exhibits unique biophysical properties that promote force-generation and increase matrix rigidity. We employed an inter-disciplinary approach incorporating proteomics, biophysical techniques and multi-modal live-cell imaging to dissect the role of V{beta}6-EGFR crosstalk on transmission of mechanical signals bidirectionally between the extracellular matrix and nucleus.\n\nWe show that V{beta}6 expression correlates with poor prognosis in triple-negative breast cancer (TNBC) and drives invasion of TNBC cells. Moreover, our data show that a complex regulatory mechanism exists involving crosstalk between V{beta}6 integrin and EGFR that impacts matrix stiffness, force transmission to the nucleus, transcriptional reprogramming and microenvironment rigidity. V{beta}6 engagement triggers EGFR & MAPK signalling and V{beta}6-EGFR crosstalk regulates mutual receptor trafficking mechanisms. Consequently, EGF stimulation suppresses V{beta}6-mediated force-application on the matrix and nuclear shuttling of force-dependent transcriptional co-activators YAP/TAZ. Finally, we show that crosstalk between V{beta}6 & EGFR regulates TNBC invasion.\n\nWe propose a model whereby V{beta}6-EGFR crosstalk regulates matrix stiffening, but also the transmission of extracellular forces into the cell in order to co-ordinate transcriptional reprogramming and invasion. To exploit adhesion receptors and receptor tyrosine kinases therapeutically, it will be essential to understand the integration of their signalling functions and how crosstalk mechanisms influence invasion and the response of tumours to molecular therapeutics.

cancer biology↗

bioRxiv2017

Global analysis of specificity determinants in eukaryotic protein kinases

Protein kinases lie at the heart of cell signalling processes, constitute one of the largest human domain families and are often mutated in disease. Kinase target recognition at the active site is in part determined by a few amino acids around the phosphoacceptor residue. These preferences vary across kinases and despite the increased knowledge of target substrates little is known about how most preferences are encoded in the kinase sequence and how these preferences evolve. Here, we used alignment-based approaches to identify 30 putative specificity determinant residues (SDRs) for 16 preferences. These were studied using structural models and were validated by activity assays of mutant kinases. Mutation data from patient cancer samples revealed that kinase specificity is often targeted in cancer to a greater extent than catalytic residues. Throughout evolution we observed that kinase specificity is strongly conserved across orthologs but can diverge after gene duplication as illustrated by the evolution of the G-protein coupled receptor kinase family. The identified SDRs can be used to predict kinase specificity from sequence and aid in the interpretation of evolutionary or disease-related genomic variants.

cell biology↗