Biology subjects

Le Quesne, J.

Publications and source records attributed to Le Quesne, J..

18 recordsLinked to original sources

bioRxiv2026

mTORC2 stabilizes HIF-1β to coordinate metabolic adaptation in lung cancer

Despite extensive genetic heterogeneity, lung tumors frequently converge on shared signaling dependencies that remain therapeutically underexploited. Here, we identify mTORC2 signaling as a convergent dependency across genetically distinct lung cancer subtypes and uncover HIF-1{beta} as a selective metabolic effector downstream of mTORC2 that promotes lung tumor progression. Elevated mTORC2 signaling in lung adenocarcinoma was associated with poor overall survival, metastatic dissemination and metabolic rewiring. Using complementary genetically engineered mouse models of Rictor deletion or overexpression in Kras-driven lung tumors, we show that mTORC2 activity is dispensable for normal lung homeostasis but required for tumor progression and metabolic adaptation in vivo. Mechanistically, mTORC2 stabilized HIF-1{beta} by preventing its ubiquitin-independent proteasomal degradation through a non-canonical PKC-CK2 signaling axis, independently of AKT. Integrated multi-omics analyses identified extensive metabolic rewiring downstream of the mTORC2-HIF-1{beta} axis, with sphingolipid metabolism emerging as a prominent and therapeutically exploitable vulnerability. Accordingly, pharmacological targeting of sphingolipid metabolism markedly impaired the growth of mTORC2-driven lung tumors in vivo. Together, our findings establish a non-canonical mTORC2-HIF-1{beta} signaling axis that couples oncogenic signaling to metabolic adaptation and defines therapeutically actionable metabolic vulnerabilities in lung cancer.

cancer biology↗

bioRxiv2026

Self-Supervised AI Discovery of Histomorphological Phenotypes from Routine Mesothelioma Biopsies

Accurate subtype diagnosis is essential for guiding therapy and predicting patient outcome in malignant mesothelioma. Most computational pathology models are trained on large tissue images from resection specimens, which maximises information for training but limits model relevance in real-world diagnostic settings where small biopsies are the most usual tissue source. In this work, we assembled a large multicentre cohort of HES- and HPS-stained mesothelioma biopsy slides. We used a self-supervised learning model to evaluate the associations of biopsy-driven morphology patterns with histological subtype, molecular markers, and survival. The discovered histomorphology patterns captured a continuum of tissue phenotypes spanning epithelioid, sarcomatoid, and non-tumour morphologies. Also, patient-level HPC representations achieved excellent performance for distinguishing epithelioid from non-epithelioid mesothelioma (AUC = 0.94) and demonstrated predictive value for immunohistochemistry (IHC) markers. Additionally, HPC-derived features alone achieved performance comparable to established clinical and molecular variables (C-index = 0.65), while integration of HPCs with clinical and marker information improved performance to a C-index of 0.69. Several HPCs were significantly associated with favourable or adverse prognosis and reflected known subtype-specific biological patterns. In conclusion, self-supervised learning can discover interpretable histomorphological phenotypes directly from routine mesothelioma biopsies without further training. These AI-derived phenotypes capture clinically and biologically relevant information, linking tissue architecture to molecular characteristics, histological subtypes, and patient outcomes. The proposed framework provides a thorough evaluation of real-world biopsy data using a pre-trained model, without the need for computationally intensive retraining, and addresses the question of whether SSL-based AI can be deployed out of the box in clinical settings.

cancer biology↗

bioRxiv2026

Glandular architecture and malignant behaviour in colorectal cancer is regulated by the sialomucin Podocalyxin.

Glandular architecture-the coordination of lumen-containing structures by an apical-basal polarised epithelium-is frequently maintained in colorectal cancer (CRC), yet whether it actively contributes to tumour progression or metastatic competence remains unclear. Here, we identify Podocalyxin (PODXL), a developmental regulator of epithelial lumen formation, as a key determinant of glandular tumour architecture in CRC. PODXL is upregulated in CRC, particularly in poor-prognosis Consensus Molecular Subtype 4 (CMS4) tumours, where high expression predicts reduced survival. Using genetically engineered mouse models, matched organoids, human cell lines and xenografts, we show that PODXL promotes organisation of CRC cells into gland-like, lumen-containing structures. Loss of PODXL disrupts glandular architecture in both primary tumours and liver metastases, reducing tumour growth and metastatic colonisation. Mechanistically, TGF-{beta} signalling drives PODXL upregulation. Together, these findings establish glandular architecture as an active determinant of CRC progression and identify PODXL as a functional contributor rather than merely a prognostic biomarker.

cancer biology↗

bioRxiv2026

Phenotype-dependent subtyping exposes high MYC activity as a targetable dependency in LuAd

C-MYC (MYC) occupies a critical nexus of oncogenic signalling and deregulated expression of MYC is widespread across most human cancer types, suggesting that MYC should be an attractive target for therapeutic intervention. Although 30-40% of human Non-Small Cell lung cancers show low level amplification of c-MYC and genetic evidence has shown that c-Myc is a key downstream effector of KRas-driven lung tumourigenesis in mouse models, the functional contribution of MYC to human lung cancer remains unclear. We applied a phenotype-based classifier to the TCGA Lung Adenocarcinoma (LuAd) cohort and found that high MYC transcriptional activity identifies a subset of LuAd with significantly reduced survival. Application of the same methodology to a panel of genetically engineered mouse models identified multiple genotypes that give rise to the high MYC activity phenotype, disease positioning such models as reflective of a distinct subset of human LuAd. We show that high MYC activity predicts sensitivity to a small molecule dual-inhibitor of the MYC co-factors, EZH2 and G9A, HKMTi-1-005, and that treatment with HKMTi-1-005 strongly reduced MYC protein expression, induced B cell-mediated immune surveillance and suppressed growth of autochthonous KRasG12D-driven lung tumours. Statement of significanceThis work establishes the principle of indirectly targeting MYC in LuAd, via inhibition of associated enzymatic cofactors, EZH2 and G9A, and identifies a large subset of aggressive human LuAd with a high MYC activity signature that may benefit from this approach.

cancer biology↗

bioRxiv2026

Morphospatial profiling of cancer-associated fibroblasts reveals architectural subtypes of pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy with an urgent need for biomarkers to predict prognosis and guide treatment. Understanding the complex spatial biology of pancreatic cancer-associated fibroblasts (CAFs) and the broader architecture of the PDAC tumour microenvironment is central to this challenge. Using a multi-omics approach across multiple spatial resolutions in a large human PDAC cohort, we integrate geometry and shape to define discrete morphological CAF subtypes, expanding CAF phenotyping beyond conventional proteomics. We then reveal an architectural and molecular axis of PDAC at tissue level, suggestive of epithelial-stromal co-evolution, with translational implications and prioritisation of stromal targets. Finally, we recapitulate this axis by introducing four unique, internally validated architectural subtypes of PDAC, each characterised by a common microenvironment and CAF enrichment profile. These archetypes outperform conventional pathology in prognostication, and predict response to adjuvant chemotherapy. Collectively, this study establishes a novel morphological paradigm for spatial biology, illuminates the architectural landscape of PDAC, and provides a framework for spatial biomarker discovery to close the translational gap in this devastating disease.

cancer biology↗

bioRxiv2025

Self-supervised AI reveals a lethal discohesive phenotype in lung adenocarcinoma

Applications of artificial intelligence (AI) to histopathology are now common, but most require supervision which inherently limits their scope. By using self-supervised learning (SSL), we discover and quantify the full range of histopathological appearances in a disease, and associate them with clinicopathological ground truths such as prognosis. We used this approach to discover under-appreciated morphologies of lung adenocarcinoma (LUAD), using a highly characterised resected tumour cohort of over 4000 slides from over 1000 patients. By constructing an authoritative lexicon of recurrent LUAD appearances, we ab initio discovered several stromal morphologies strongly predictive of outcome. With multimodal data integration and external dataset validation, we propose that epithelial discohesion is lethal, but only in the context of immunologically cold stroma. Both these morphological features are independent of current prognostic schema. Crucially, we describe these features in the context of real-world diagnostic histopathology, giving them immediate clinical translatability. Statement of SignificanceHistopathology relies heavily on epithelial morphology, often neglecting the stroma. We use self-supervised AI to identify under-appreciated morphologies in LUAD linked to poor outcome and validate these observations in an external cohort, demonstrating the utility of self-supervised AI as a powerful biological discovery tool.

cancer biology↗

bioRxiv2025

Self-Supervised AI Reveals a Hidden Landscape of Prognostic Spatial Patterns in Multiplex Immunofluorescence Images

Modern spatial proteomic methods, such as multiplex immunofluorescence (mIF) imaging, offer a data-rich view of spatial biology in intact tissues. However, interpreting its complexity is a major bottleneck, limiting its potential for biological discovery and clinical translation. Current computational methods often rely on segmentation-based approaches that discard crucial morphological information and are limited to testing pre-defined hypotheses. Here, we introduce a self-supervised learning (SSL) framework that enables hypothesis-agnostic, context-aware discovery of biomarkers directly from mIF images. Our approach extracts rich feature representations that capture holistic architectural patterns, which integrate cellular morphology, marker interactions, and microenvironmental context without human supervision. Applying this framework to over 7,000 mIF tissue images from over 1,800 patients in two distinct cancer types, we demonstrate superior prognostic performance over conventional segmentation analyses. The method autonomously identified previously unknown and potentially clinically actionable biological patterns. In lung adenocarcinoma, these include a Ki67-mediated immune evasion phenotype, a sub-cellular pattern of GLB1 expression which aligns with low-grade EGFR-driven tumours, and distinct modes of tumour-immune interaction in PD-L1+ patients. We also find a regulatory T-cell-mediated immunosupressive environment promoting tumour budding in colorectal carcinoma. Our work establishes SSL as a powerful, scalable, and unbiased platform to decode tissue ecosystems while being fully explainable without pre-defined hypotheses. This paradigm shift transforms high-plex imaging from a hypothesistesting tool into a hypothesis-generating engine that can accelerate the discovery of next-generation spatial biomarkers.

cancer biology↗

bioRxiv2025

Lineage plasticity of the integrated stress response is a hallmark of cancer evolution.

The link between the "stress phenotype"-a well-established hallmark of cancer-and its role in tumor progression and intratumor heterogeneity remains poorly defined. The integrated stress response (ISR) is a key adaptive pathway that enables tumor survival under oncogenic stress. While ISR has been implicated in promoting tumor growth, its precise role in driving tumor evolution and heterogeneity has not been elucidated. In this study, using a genetically engineered mouse models, we demonstrate that ISR activation--indicated by elevated levels of phosphorylated eIF2 (p-eIF2) and ATF4--is essential for the emergence of dedifferentiated, therapy-resistant cell states. ISR, through the coordinated actions of ATF4 and MYC, facilitates the development of tumor cell populations characterized by high plasticity, stemness, and an epithelial-mesenchymal transition (EMT)-prone phenotype. This process is driven by ISR-mediated expression of genes that maintain mitochondrial integrity and function, critical for sustaining tumor progression. Importantly, genetic, or pharmacological inhibition of the p-eIF2-ATF4 signaling axis leads to mitochondrial dysfunction and significantly impairs tumor growth in mouse models of lung adenocarcinoma (LUAD). Moreover, ISR-driven dedifferentiation is associated with poor prognosis and therapy resistance in advanced human LUAD, underscoring ISR inhibition as a promising therapeutic strategy to disrupt tumor evolution and counteract disease progression.

cancer biology↗

bioRxiv2025

PixlMap: A generalisable pixel classifier for cellular phenotyping in multiplex immunofluorescence images

Multiplexed methods for the detection of protein expression generate extremely data-rich images of intact tissue sections. These images are invaluable for the quantification and analysis of complex biology and biomarker development. However, their interpretation presents a considerable analytical challenge. Cell segmentation from images is a key bottleneck and a major focus of research activity in artificial intelligence. Most current methods depend initially on the use of a nuclear counterstain to identify nuclear boundaries, which is a relatively straightforward task. The cellular boundary is then assigned either by expansion of the nuclear outline, or by the use of membrane or cytoplasm-specific stains to delineate cell boundaries, or by some combination of the two. The task is critical, as inaccurate segmentation leads to information loss and data contamination from neighbouring cells. Increasingly sophisticated methods are being developed to address these issues, but each has its own shortcomings. We present an alternative method which is inspired by the fact that the assignation of a cellular phenotype by eye does not depend upon the accurate identification of cell boundaries. We present an easy-to-use deep learning-based cellular phenotyping method which leverages this human capacity to assign phenotypes without segmenting the entire cell, and which can accurately phenotype cells based on nuclear segmentation alone. Using human ground truth annotations of entire cellular regions, we developed a classifier leveraging the U-Net architecture within a commercially available deep learning image analysis platform, but the principle is transferrable to any deep-learning framework. Crucially, training requires only a single example of each compartmental stain (nuclear/cytoplasmic/membranous). The resulting algorithm assigns class identities to cells with nuclear labelling alone, without the need for whole cell expansion. The method is highly novel, broadly generalisable, and comparable in accuracy to intensity-based phenotyping methods, bridging the gap between inaccurate cellular segmentation and accurate phenotype generation.

pathology↗

bioRxiv2024

A histomorphological atlas of resected mesothelioma from 3446 whole-slide images discovered by self-supervised learning

1Mesothelioma is a highly lethal and poorly biologically understood disease which presents diagnostic challenges due to its morphological complexity. This study uses self-supervised AI (Artificial Intelligence) to map the histomorphological landscape of the disease. The resulting atlas consists of recurrent patterns identified from 3446 Hematoxylin and Eosin (H&E) stained images scanned from resected tumour slides. These patterns generate highly interpretable predictions, achieving state-of-the-art performance with 0.65 concordance index (c-index) for outcomes and 88% AUC in subtyping. Their clinical relevance is endorsed by comprehensive human pathological assessment. Furthermore, we characterise the molecular underpinnings of these diverse, meaningful, predictive patterns. Our approach both improves diagnosis and deepens our understanding of mesothelioma biology, highlighting the power of this self-learning method in clinical applications and scientific discovery.

pathology↗

bioRxiv2024

Copper induces p53 unfolding in cancer cells, resulting in invasion and chemoresistance

Copper has been shown to play an important role in cancers, enhancing cell proliferation, remodelling the microenvironment and enhancing the function of oncogenes. Here we show that copper turns the tumour suppressor p53 protein into a pro-oncogenic protein. Mutations in the TP53 gene often lead to expression of various mutant p53 proteins. Mutant proteins often lose some or all wild type function and gain novel pro-tumourigenic functions, which can be partially attributed to protein unfolding. Here we show that copper accumulates in tumours, unfolds WTp53 and promotes chemoresistance. Unfolding of WT p53 results in interaction with mutant p53-specific interacting proteins TAp63 and Ago2 and promotes invasion. Interestingly, partially-functional p53 mutants that are frequently observed in lung cancers, are more affected by copper than WTp53. Our results suggest that small increases in copper impair WT p53 function and augment mutant p53 function, which could partially be restored by the chelator Trien.

cancer biology↗

bioRxiv2024

Spatial resolution of transcriptomic plasticity states underpinning lethal morphologies in lung adenocarcinoma

Adenocarcinoma of the lung (LUAD) is a common and highly lethal disease. Clinical grading of disease strongly predicts recurrence and survival after surgery and is determined by morphological assessment of histological growth patterns in resected tumours. The molecular basis of growth pattern is poorly understood at present, as are the mechanisms linking growth pattern to recurrence and death. Interestingly, the two archetypal lethal morphologies, solid and micropapillary patterns, are characterised by their biphasic appearance. Both have an epithelial fraction which is in direct stromal contact, and a fraction which is not. This morphological variance seems likely to represent plasticity, and to be causally linked to mechanisms of virulence. To investigate the gene expression changes related to growth pattern both intra- and intertumoral, we applied spatial transcriptomics (Nanostring GeoMx DSP) to tissue microarray specimens of primary resected human lung adenocarcinoma. Using a variety of region-of-interest (ROI) selection strategies, we sampled 160 pure epithelial ROIs across 7 distinct morphological features of LUAD from 51 patients. Analyses of gene expression reveal fundamental trajectories connecting growth patterns, and crucial modes of plasticity which underly high-risk morphologies. These modes suggest mechanisms for the origins of growth pattern and mechanisms of virulence. Our work highlights dramatic divergence in gene expression programmes between highly lethal but morphologically diverse modes of tumour growth. Furthermore, it provides an explanation for how microscopically localised hypoxia in the primary tumour helps to establish and maintain survival strategies which ultimately determine morphology-specific mechanisms of tumour metastasis, suggesting new therapeutic vulnerabilities.

cancer biology↗

bioRxiv2023

eIF4A1 is essential for reprogramming the translational landscape of Wnt-driven colorectal cancers

Dysregulated translation is a hallmark of cancer. Targeting the translational machinery represents a therapeutic avenue which is being actively explored. eIF4A inhibitors target both eIF4A1, which promotes translation as part of the eIF4F complex, and eIF4A2, which can repress translation via the CCR4-NOT complex. While high eIF4A1 expression is associated with poor patient outcome, the role of eIF4A2 in cancer remains unclear. Furthermore, the on-target toxicity of targeting specific eIF4A paralogues in healthy tissue is under-explored. We show that while loss of either paralogue is tolerated in the wild-type intestine, eIF4A1 is specifically required to support the translational demands of oncogenic Wnt signalling. Intestinal tumourigenesis is suppressed in colorectal cancer models following loss of eIF4A1 but accelerated following loss of eIF4A2, while eIF4A inhibition with eFT226 mimics loss of eIF4A1 in these models.

cancer biology↗

bioRxiv2022

Spatial regulation of the glycocalyx component Podocalyxin is a switch for pro-metastatic function

The glycocalyx component and sialomucin Podocalyxin (PODXL) is required for normal tissue development by promoting apical membranes to form between cells, triggering lumen formation. Elevated PODXL expression is also associated with metastasis and poor clinical outcome in multiple tumour types. How PODXL presents this duality in effect remains unknown. We identify an unexpected function of PODXL as a decoy receptor for Galectin-3 (GAL3), whereby the PODXL-GAL3 interaction releases GAL3 repression of integrin-based invasion. Differential cortical targeting of PODXL, regulated by ubiquitination, is the molecular mechanism controlling alternate fates. Both PODXL high versus low surface levels occur in parallel subpopulations within cancer cells. Orthotopic intraprostatic xenograft of PODXL-manipulated cells or those with different surface levels of PODXL define that this axis controls metastasis in vivo. Clinically, interplay between PODXL-GAL3 stratifies prostate cancer patients with poor outcome. Our studies define the molecular mechanisms and context in which PODXL promotes invasion and metastasis.

cell biology↗

bioRxiv2022

Purine-rich RNA sequences in the 5'UTR site-specifically regulate eIF4A1-unwinding through eIF4A1-multimerisation to facilitate translation

Oncogenic translational programmes underpin cancer development and are often driven by dysregulation of oncogenic signalling pathways that converge on the eukaryotic translation initiation (eIF) 4F complex. Altered eIF4F activity promotes translation of oncogene mRNAs that typically contain highly structured 5UTRs rendering their translation strongly dependent on RNA unwinding by the DEAD-box helicase eIF4A1 subunit of the eIF4F complex. While eIF4A1-dependent mRNAs have been widely investigated, it is still unclear how highly structured mRNAs recruit and activate eIF4A1 unwinding specifically to facilitate their preferential translation. Here, we show that RNA sequence motifs regulate eIF4A1 unwinding activity in cells. Our data demonstrate that eIF4A1-dependent mRNAs contain AG-rich motifs within their 5UTR which recruit and stimulate eIF4A1 unwinding of localised RNA structure to facilitate mRNA translation. This mode of eIF4A1 regulation is used by mRNAs encoding components of mTORC-signalling and cell cycle progression and renders these mRNAs particularly sensitive to eIF4A1-inhibition. Mechanistically, we show that binding of eIF4A1 to AG-rich sequences leads to multimerization of eIF4A1 with eIF4A1 subunits performing distinct enzymatic activities. Our structural data suggest that RNA-binding of multimeric eIF4A1 induces conformational changes in the RNA substrate resulting in an optimal positioning of eIF4A1 proximal to the RNA duplex region that supports efficient unwinding. Hence, we conclude a model in which mRNAs utilise AG-rich sequences to specifically recruit eIF4A1, enabling assembly of the helicase-active multimeric eIF4A1 complex, and positioning these complexes proximal to stable localised RNA structure allowing ribosomal subunit scanning.

molecular biology↗

bioRxiv2020

The role of SOX9 in non-small cell lung cancer progression is histopathology-selective.

The transcription factor SOX9 is a key regulator of multiple developmental processes, and is frequently re-expressed in non-small cell lung cancer (NSCLC). Its precise role in the progression of NSCLC histopathologies has however remained elusive. We show that SOX9 expression relates to poor outcome and invasive histopathology in human adenocarcinomas, and is absent in murine early minimally invasive and human in situ adenocarcinoma. Interestingly, despite wide SOX9 expression across advanced NSCLC histotypes, its genetic deletion in the murine KrasG12D;Lkb1-/- model selectively disrupted only the growth of papillary NSCLC, without affecting the initiation of precursor lesions or growth of mucinous or squamous tissue. Spatial tissue phenotyping indicated a requirement of SOX9 expression for the progression of surfactant protein C-expressing progenitor cells, which gave rise to papillary tumours. Intriguingly, while SOX9 expression was dispensable for squamous tissue formation, its loss in fact led to enhanced squamous tumour metastasis, which was associated with altered collagen IV deposition in the basement membrane. Our work therefore demonstrates histopathology-selective roles for SOX9 in NSCLC progression, namely a requirement for papillary adenocarcinoma progression, but opposing metastasis-suppressing function in squamous histotype tissue. This attests to a pleiotropic SOX9 function, linked to the cell of origin and microenvironmental tissue contexts.

cancer biology↗

bioRxiv2020

Asbestos accelerates disease onset in a genetic model of Malignant Pleural Mesothelioma

HypothesisAsbestos-driven inflammation contributes to malignant pleural mesothelioma beyond the acquisition of rate-limiting mutations. MethodsGenetically modified conditional allelic mice that were previously shown to develop mesothelioma in the absence of exposure to asbestos were induced with lentiviral vector expressing Cre recombinase with and without intrapleural injection of amosite asbestos and monitored until symptoms required euthanasia. Resulting tumours were examined histologically and by immunohistochemistry for expression of lineage markers and immune cell infiltration. ResultsInjection of asbestos dramatically accelerated disease onset and end-stage tumour burden. Tumours initiated in the presence of asbestos showed increased macrophage infiltration. Single-agent suppression of macrophages in mice with established tumours failed to extend survival. ConclusionAsbestos-driven inflammation contributes to the severity of mesothelioma beyond the acquisition of rate-limiting mutations, however, targeted suppression of macrophages alone in established disease showed no therapeutic benefit.

cancer biology↗

bioRxiv2017

BCL11A interacts with SOX2 to control the expression of epigenetic regulators in lung squamous cell carcinoma

Patients diagnosed with lung squamous cell carcinoma (LUSC) have limited targeted therapeutic options. We report here the identification and characterisation of the transcriptional regulator, BCL11A, as a LUSC oncogene. Analysis of cancer genomics datasets revealed BCL11A to be upregulated in LUSC but not lung adenocarcinoma (LUAD). We demonstrated that knockdown of BCL11A in LUSC cell lines abolished xenograft tumour growth and its overexpression in vivo led to lung airway hyperplasia and the development of reserve cell hyperplastic lesions. In addition, deletion of Bcl11a in the tracheal basal cells abolished the development of tracheosphere organoids while its overexpression led to solid tracheospheres with a squamous phenotype. At the molecular level we found BCL11A to be a target of SOX2 and we show that it is required for the oncogenic role of SOX2 in LUSC. Furthermore, we showed that BCL11A and SOX2 interact at the protein level and that together they co-regulated the expression of several transcription factors. We demonstrate that pharmacological inhibition of SETD8, a gene co-regulated by BCL11A and SOX2, alone or in combination with cisplatin treatment, shows significant selectivity to LUSC in comparison to LUAD cells. Collectively, these results indicate that the disruption of the BCL11A-SOX2 transcriptional program provides a future framework for the development of targeted therapeutic intervention for LUSC patients.

cancer biology↗