Biology subjects

Esposito, C.

Publications and source records attributed to Esposito, C..

9 recordsLinked to original sources

bioRxiv2026

An RFX-driven ciliogenic cell state defines ex vivo FGFR-inhibitor sensitivity in colorectal cancer

Genomic stratification leaves most patients with colorectal cancer (CRC) without opportunities for targeted therapy. Seeking tumour state-linked drug sensitivities, we combined image-based ex vivo drug profiling with matched transcriptomics in 127 prospectively collected patient biopsies across CRC stages and molecular subtypes. Network-level integration identified a ciliogenic cell state defined by upregulation of cilia related genes with selective sensitivity to the FGFR inhibitor erdafitinib. This ciliogenic signature was associated with poor progression-free survival (hazard ratio 2.28; adjusted p=0.0098), even in early-stage colorectal cancer. Furthermore, knockdown of the transcriptional regulators RFX2 and RFX3, upstream of the ciliogenic cell state, attenuated erdafitinib sensitivity. Orthogonal bulk and single-cell analyses across normal, primary and metastatic tissues contextualised the ciliogenic programme along a transition from canonical epithelial states towards fetal and non-canonical tumour states, concomitant with Wnt-related remodelling, FGFR1/FGFR2 upregulation, and loss of intestinal epithelial identity. Together, these findings define a tumour-intrinsic ciliogenic cell state as a feature of CRC progression, revealing a patient-stratified repurposing opportunity for targeted therapy beyond the current CRC treatment paradigms.

cancer biology↗

bioRxiv2025

Multiple instance learning with spatial transcriptomics for interpretable patient-level predictions: application in glioblastoma

Accurate prediction of patient outcomes remains a major challenge in oncology. While recent machine learning (ML) approaches often rely on bulk omics lacking spatial resolution or histology-based multiple instance learning (MIL), spatial transcriptomics (SpT) provides a unique opportunity to capture both molecular content and tissue architecture. However, no generalizable ML framework has yet been established to exploit SpT for patient-level outcomes. We present SpaMIL, a flexible and interpretable MIL framework designed for SpT, with a distillation strategy that enables deployment for hematoxylin and eosin (H&E) slides alone. We evaluate the framework by predicting survival from glioblastoma (GBM) patients, a clinically compelling setting given its aggressiveness with a median survival of only 15 months and the lack of prognostic clinical variables. We analyzed 76 GBM cases from the MOSAIC dataset: 43 with matched SpT, H&E, single-nucleus RNA-seq (scRNA-seq), bulk RNA-seq, and clinical variables, and 33 with H&E for external validation. We developed two main architectures: abMIL, tailored to SpTs spatial molecular structure, and MabMIL, which distills SpT-derived representations into H&E. Model interpretability was achieved through a Shapley-based framework linking prognostic predictions to cell-type compositions via SpT deconvolution. In benchmarking across the five GBM MOSAIC modalities, SpT-based abMIL achieved unprecedented prognostic accuracy (median C-index: 0.72, standard deviation: 0.04), outperforming all other modalities, including established clinical predictors. PCA and deconvolution-based SpT representations surpassed recent foundation models, suggesting the need for further research on SpT foundation models. Our interpretability analysis highlighted malignant and non-malignant cell subpopulations associated with favorable or poor prognosis, consistent with recent reports. Finally, MabMIL maintained strong performance while enabling H&E-only deployment, with improved condorance index over H&E-only baselines in both internal (0.59 vs. 0.57) and external (0.62 vs. 0.55) cohorts.

cancer biology↗

bioRxiv2025

Histology and spatial transcriptomic integration revealed infiltration zone with specific cell composition as a prognostic hotspot in glioblastoma

BackgroundGlioblastoma (GBM), the most aggressive primary brain tumor, has a median survival of approximately 15 months. Twenty percent of patients survive beyond three years, but known clinical factors like age, performance status, resection extent, and MGMT promoter methylation status do not fully explain the observed outcomes. ObjectiveOur objective was to identify novel histology derived biomarkers associated with end-of-spectrum overall survival (OS) to provide novel biological insight with a translational potential. MethodsWe analyzed a total of 748 GBM patients from 3 different cohorts, uniquely enriched in long survivors (n=98 with overall survival (OS) > 5y including n=196 with OS[≥]3y), with clinical data and H&E slides obtained from the primary tumor at baseline. We propose an interpretable machine learning (ML) methodology for the discovery of histological biomarkers. Our method learned to segment each H&E slide into three distinct regions associated with long-term survival, short-term survival, and non-informative tissue. We characterized these regions by integrating unsupervised learning, nuclei segmentation, blood vessels detection, pathologist annotations, and multimodal data including spatial transcriptomics from n=31 patients of the GBM MOSAIC dataset to discover fully interpretable biomarkers. ResultsOur OS prediction model using histology and clinical data as input achieved an area under the curve (AUC) of 0.85 for the classification of patients between OS<2 and OS[≥]3y in external cohort validation, outperforming significantly models trained on clinical data or on histology alone (AUC of 0.76; 0.73, respectively). Two novel biomarkers were predicting poor survival: the presence of regions of lowly infiltrated white matter enriched in malignant cells with a mesenchymal-like phenotype, and lower levels of angiogenesis associated with higher hypoxia response in the main tumor regions. We also found that a subtype of immunosuppressive tumor macrophages - defined by high PLIN2 expression and lipid accumulation- is consistently enriched in histological areas predictive of poor prognosis. ConclusionOur interpretable ML methodology identified a novel prognostic impact of biological processes and cell types according to distinct tumor regions of GBM. These results pave the way for spatially-informed biomarkers to improve risk stratification and for personalized spatially-targeted therapeutic strategies. Key highlightsO_LIOur ML model identified histological biomarkers predicting prognosis independently from known clinical factors C_LIO_LIThe region of lowly infiltrated white matter enriched in malignant cells including a mesenchymal-like phenotype is predictive of poor prognosis C_LIO_LIAngiogenesis is increased in areas predictive of long survival in main non-necrotic tumor regions. C_LIO_LIThe subtype of macrophages expressing PLIN2 and associated with increased lipid metabolism was associated with poor prognosis in all GBM regions. C_LI Highlights O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=116 SRC="FIGDIR/small/681087v4_ufig1.gif" ALT="Figure 1"> View larger version (43K): org.highwire.dtl.DTLVardef@45e619org.highwire.dtl.DTLVardef@105a4b0org.highwire.dtl.DTLVardef@17f257dorg.highwire.dtl.DTLVardef@7669b4_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2025

Integrated cell atlas and tumoroids chart pancreatic cancer therapeutic targets

Pancreatic ductal adenocarcinoma (PDAC) is characterized by dense, fibroblast-rich stroma that actively shapes the tumor microenvironment. Most PDAC cases arise from conserved genetic transformations initiated by oncogenic KRAS mutations, developing into metastatic disease with high mortality rates. To chart universal PDAC cell states and identify therapeutic inroads, we integrated published single-cell transcriptomes from 200 patient samples, and used the atlas to define prevalent cancer cell and cancer-associated fibroblast (CAF) states, gene expression programs, and ligand-receptor interactions. We established modular tumoroids incorporating patient-derived cancer cells and CAFs that recapitulate aspects of ductal architecture and desmoplastic stroma. Single-cell and spatial transcriptomic profiling confirmed preservation of key cellular states and signaling networks in vitro. We identified Syndecan-1 (SDC1) as a CAF-responsive cancer cell receptor correlating with poor patient survival. Functional SDC1 blockade disrupted cancer growth in tumoroids, highlighting therapeutic relevance. This study provides a framework for dissecting cancer-stroma dynamics and identifying actionable targets using patient-derived tumoroid models.

cancer biology↗

bioRxiv2024

Eosinophils restrict CRC metastasis by inhibiting pro-tumorigenic SPP1+ macrophage differentiation

Eosinophils, traditionally associated with allergic responses, have emerged as critical immune modulators in colorectal cancer (CRC). Here, we reveal that eosinophils actively shape the tumor microenvironment and influence metastatic progression. Using comprehensive transcriptomics analysis of human CRC and a murine orthotopic tumor model, we identify a conserved tumor-specific eosinophil signature and activation profile. Despite their declining presence in advanced CRC, eosinophils suppress metastatic dissemination by counteracting the pro-tumorigenic functions of SPP1+ macrophages - a subset linked to immune exclusion and tumor metastasis. Mechanistically, eosinophils respond to tumor-derived signals and inhibit macrophage differentiation into SPP1+ cells. Eosinophil depletion exacerbates peritoneal tumor spread. These findings highlight the pivotal role of eosinophils in restraining late-stage CRC progression and unveil a novel eosinophil-macrophage axis as potential therapeutic targets.

cancer biology↗

bioRxiv2024

PIRATE: Plundering AlphaFold Predictions to Automate Protein Engineering

An important characteristic for many proteins is the presence of flexible loops or linkers known as intrinsically disordered regions. One downside to many of the state-of-the-art disorder prediction approaches is that they require significant computational time for each inference. Here, we introduce three novel surrogate models trained on AlphaFold2 predictions that rapidly encode local, regional, and global structural properties directly from primary sequence. We combined the outputs from these surrogate models, in an approach we term PIRATE, and show that this approach approximates the performance of AlphaFold2 for disorder prediction. Additionally, we show that PIRATE is much more sensitive to the effects of point mutants on disorder at distal sites than many current disorder prediction methods. Furthermore, we show that in the context of a greedy exploration algorithm, PIRATEs ability to evaluate differences between point mutants makes it ideal for automating disorder-related protein engineering tasks.

biochemistry↗

bioRxiv2024

Robust Evaluation of Deep Learning-based Representation Methods for Survival and Gene Essentiality Prediction on Bulk RNA-seq Data

Deep learning (DL) has shown potential to provide powerful representations of bulk RNA-seq data in cancer research. However, there is no consensus regarding the impact of design choices of DL approaches on the performance of the learned representation, including the model architecture, the training methodology and the various hyperparameters. To address this problem, we evaluate the performance of various design choices of DL representation learning methods using TCGA and DepMap pan-cancer datasets, and assess their predictive power for survival and gene essentiality predictions. We demonstrate that non DL-based baseline methods achieve comparable or superior performance compared to more complex models on survival predictions tasks. DL representation methods, however, are the most efficient to predict the gene essentiality of cell lines. We show that auto-encoders (AE) are consistently improved by techniques such as masking and multi-head training. Our results suggest that the impact of DL representations and of pre-training are highly task- and architecture-dependent, highlighting the need for adopting rigorous evaluation guidelines. These guidelines for robust evaluation are implemented in a pipeline made available to the research community.

bioinformatics↗

bioRxiv2019

CHCHD4 regulates a proliferation-EMT switch in tumour cells, through respiratory complex I mediated metabolism.

BACKGROUNDMitochondrial metabolism involves oxidative phosphorylation (OXPHOS) via the respiratory chain and is required for the maintenance of tumour cell proliferation and regulation of epithelial-mesenchymal transition (EMT)-related phenotypes through mechanisms that are not fully understood. The essential mitochondrial import protein coiled-coil helix coiled-coil helix domain-containing protein 4 (CHCHD4) controls respiratory chain complex activity and oxygen consumption, and regulates the growth of tumours in vivo. In this study we interrogate the role of CHCHD4-regulated respiratory chain activity and metabolism in tumour cell proliferation and EMT-related phenotypes.\n\nRESULTSWe show that CHCHD4 is essential for the proliferation of tumour cells irrespective of their aetiology. In human tumours, elevated CHCHD4 expression is correlated with a mitochondrial OXPHOS gene signature and with a proliferative gene signature associated with the mTORC1 signalling pathway. Elevated CHCHD4 increases tumour cell proliferation, in a manner that is dependent on complex I (CI) activity, glutamine consumption and mTORC1 activation. CHCHD4 expression is inversely correlated with EMT gene expression both in vitro and in vivo. Finally, we show CHCHD4 regulates the intracellular distribution of the EMT marker vimentin, in a CI-mediated manner.\n\nCONCLUSIONSCHCHD4 regulates tumour cell proliferation and metastatic (EMT-related) phenotypes through its control of CI-mediated mitochondrial metabolism.

cell biology↗

bioRxiv2019

CHCHD4 confers metabolic vulnerabilities to tumour cells through its control of the mitochondrial respiratory chain.

BACKGROUNDTumour cells rely on glycolysis and mitochondrial oxidative phosphorylation (OXPHOS) to survive. Thus mitochondrial OXPHOS has become an increasingly attractive area for therapeutic exploitation in cancer. However, mitochondria are required for intracellular oxygenation and normal physiological processes, and it remains unclear which mitochondrial molecular mechanisms might provide therapeutic benefit. Previously, we discovered that coiled-coil helix coiled-coil helix domain-containing protein 4 (CHCHD4) is critical for maintaining intracellular oxygenation and required for the cellular response to hypoxia (low oxygenation) in tumour cells through molecular mechanisms that we do not yet fully understand. Overexpression of CHCHD4 in human cancers, correlates with increased tumour progression and poor patient survival.\n\nRESULTSHere, we show that elevated CHCHD4 expression provides a proliferative and metabolic advantage to tumour cells in normoxia and hypoxia. Using stable isotope labelling with amino acids in cell culture (SILAC) and analysis of the whole mitochondrial proteome, we show that CHCHD4 dynamically affects the expression of a broad range of mitochondrial respiratory chain subunits from complex I-V, including multiple subunits of complex I (CI) required for complex assembly that are essential for cell survival. We found that loss of CHCHD4 protects tumour cells from respiratory chain inhibition at CI, while elevated CHCHD4 expression in tumour cells leads to significantly increased sensitivity to CI inhibition, in part through the production of mitochondrial reactive oxygen species (ROS).\n\nCONCLUSIONSOur study highlights an important role for CHCHD4 in regulating tumour cell metabolism, and reveals that CHCHD4 confers metabolic vulnerabilities to tumour cells through its control of the mitochondrial respiratory chain and CI biology.

cell biology↗