Biology subjects

Blanc, E.

Publications and source records attributed to Blanc, E..

13 recordsLinked to original sources

bioRxiv2024

Reporter-based screening identifies RAS-RAF stabilizing mutations as drivers of resistance to broad-spectrum RAS inhibition in colorectal cancer

Therapy-induced acquired resistance limits the clinical effectiveness of mutation-specific RAS inhibitors in colorectal cancer. It is unknown whether broad-spectrum active-state RAS inhibitors meet similar limitations. Here, we identify and categorize mechanisms of resistance to the broad-spectrum active-state RAS inhibitor RMC-7977 in colorectal cancer cell lines. We found that KRAS-mutant colorectal cancer cell lines are universally sensitive to RMC-7977, inhibiting the RAS-RAF-MEK-ERK axis, halting proliferation and in some cases inducing apoptosis. To monitor KRAS downstream effector pathway activity, we developed a compartment-specific dual-color ERK activity reporter. RMC-7977 treatment reduced reporter activity. However, long-term dose escalation with RMC-7977 revealed multiple patterns of reporter reactivation in emerging resistant cell populations that correlated with phosphorylation states of compartment-specific ERK targets. Cells sorted for high, low, or cytoplasmic reporter activity exhibited distinct patterns of genomic mutations, phospho-protein, and transcriptional activities. Notably, all resistant subpopulations showed dynamic ERK regulation in the presence of the RAS inhibitor, unlike the parental sensitive cell lines. High levels of RAS downstream activities were observed in cells characterized by a KRAS Y71H resistance mutation. In contrast, RAS inhibitor-resistant populations with low, or cytoplasmic ERK reporter reactivation displayed different genetic alterations, among them RAF1 S257L and S259P mutations. Colorectal cancer cells resistant to RMC-7977 and harboring the RAF1 mutation specifically exhibited synergistic sensitivity to concurrent RAS and RAF inhibition. Our findings endorse reporter-assisted screening together with single-cell analyses as a powerful approach for dissecting the complex landscape of therapy resistance. The strategy offers opportunities to develop clinically relevant combinatorial treatments to counteract emergence of resistant cancer cells.

cancer biology↗

bioRxiv2024

High-confidence calling of normal epithelial cells allows identification of a novel stem-like cell state in the colorectal cancer microenvironment

Single-cell analyses can be confounded by assigning unrelated groups of cells to common developmental trajectories. For instance, cancer cells and admixed normal epithelial cells could potentially adopt similar cell states thus complicating analyses of their developmental potential. Here, we develop and benchmark CCISM (for Cancer Cell Identification using Somatic Mutations) to exploit genomic single nucleotide variants for the disambiguation of cancer cells from genomically normal non-cancer epithelial cells in single-cell data. In colorectal cancer datasets, we find that our method and others based on gene expression or allelic imbalances identify overlapping sets of cancer versus normal epithelial cells, depending on molecular characteristics of individual cancers. Further, we define consensus cell identities of normal and cancer epithelial cells with higher transcriptome cluster homogeneity than those derived using existing tools. Using the consensus identities, we identify significant shifts of cell state distributions in genomically normal epithelial cells developing in the cancer microenvironment, with immature states increased at the expense of terminal differentiation throughout the colon, and a novel stem-like cell state arising in the left colon. Trajectory analyses show that the new cell state extends the pseudo-time range of normal colon stem-like cells in a cancer context. We identify cancer-associated fibroblasts as sources of WNT and BMP ligands potentially contributing to increased plasticity of stem cells in the cancer microenvironment. Our analyses advocate careful interpretation of cell heterogeneity and plasticity in the cancer context and the consideration of genomic information in addition to gene expression data when possible. Novelty and ImpactSingle-cell analyses have become standard to assess cell heterogeneity and developmental hierarchies in cancer tissues. However, these datasets are complex and contain cancer and non-cancer lineage cells. Here, we develop and systematically benchmark tools to distinguish between cancer and non-cancer single-cell transcriptomes, based on gene expression or different levels of genomic information. We provide strategies to combine results of different tools into consensus calls tailored to the biology and genetic characteristics of the individual cancer.

cancer biology↗

bioRxiv2023

IL-33 drives polyfunctionality and antitumor activity of a unique ST2+ NK cell population

Despite increasing recognition of NK cell diversity, their functional heterogeneity and its relevance to cancer remain incompletely understood. Here, we show that IL-12 promotes the emergence of a distinct ST2/IL33R+ NK cell state poised for robust activation by IL-33. Human ST2 NK cells exhibit a unique transcriptional program, an intermediate differentiation state between CD56bright and CD56dim NK cells, and enhanced polyfunctionality combining proliferation, cytokine production, and cytotoxicity. ST2 NK cells were identified across human and murine tumor datasets. In murine breast cancer models, IL-33 and IL-12 cooperated to elicit potent antitumor responses dependent on IFN-{gamma} and ST2 NK cells. In cancer patients, IL-12 unleashed tumor-infiltrating ST2 NK cells' potential to produce IFN-g in response to IL-33, an IL33hi-NKhi score was associated with prolonged survival, and ST2 NK cells correlated with improved immunotherapy response. Collectively, these findings identify ST2 NK cells as a therapeutically actionable NK cell state promoting antitumor immunity.

cancer biology↗

bioRxiv2023

A proteomics analysis of 5xFAD mouse brain regions reveals the lysosome-associated protein Arl8b as a candidate biomarker for Alzheimer's disease

BackgroundAlzheimers disease (AD) is characterized by the accumulation of amyloid-{beta} (A{beta}) peptides in intra- and extracellular deposits. How A{beta} aggregates perturb the proteome in brains of patients and AD transgenic mouse models, however, remains largely unclear. State-of-the-art mass spectrometry (MS) methods can comprehensively detect proteomic alterations in neurodegenerative disorders, providing relevant insights unobtainable with transcriptomics investigations. Analyses of the relationship between progressive A{beta} aggregation and protein abundance changes in brains of 5xFAD transgenic mice have not been reported previously. MethodsWe quantified progressive A{beta} aggregation in hippocampus and cortex of 5xFAD mice and controls with immunohistochemistry and biochemical membrane filter assays. Protein changes in different mouse tissues were analysed by MS-based proteomics using label-free quantification (LFQ); resulting MS data were processed using an established pipeline. Results were contrasted with existing proteomic data sets from postmortem AD patient brains. Finally, abundance changes in the candidate marker Arl8b were validated in CSF from AD patients and controls using ELISAs. ResultsExperiments revealed a more rapid accumulation of A{beta}42 peptides in hippocampus than in cortex of 5xFAD mice, accompanied by many more protein abundance changes in hippocampus than in cortex, indicating that A{beta}42 aggregate deposition is associated with brain region-specific proteome perturbations. Generating time-resolved data sets, we defined A{beta} aggregate-correlated and anticorrelated proteome changes, a fraction of which was conserved in postmortem AD patient brain tissue, suggesting that proteome changes in 5xFAD mice mimic disease relevant changes in human AD. We detected a positive correlation between A{beta}42 aggregate deposition in the hippocampus of 5xFAD mice and the abundance of the lysosome-associated small GTPase Arl8b, which accumulated together with axonal lysosomal membranes in close proximity of extracellular A{beta} plaques in 5xFAD brains. Abnormal aggregation of Arl8b was observed in AD brain tissue. Arl8b protein levels were significantly increased in cerebrospinal fluid (CSF) of AD patients, a clinically accessible body fluid. ConclusionsWe report a comprehensive biochemical and proteomic investigation of hippocampal and cortical brain tissue derived from 5xFAD transgenic mice, providing a valuable resource to the neuroscientific community. We identified Arl8b, with significant abundance changes in 5xFAD and AD patient brains. Arl8b might enable the measurement of progressive lysosome accumulation in AD patients and have clinical utility as a candidate biomarker. Data are available via ProteomeXchange with identifier PXD030348.

molecular biology↗

bioRxiv2022

The HDAC inhibitor trichostatin A impairs pancreatic β-cell function through an epigenome-wide reprogramming

ObjectiveThe pancreatic islets of Langerhans contain distinct cell subtypes including insulin-producing {beta} cells. Although their cell-specific gene expression pattern defines their identity, the underlying molecular network driving this transcriptional specificity is not fully understood. Among the numerous transcriptional regulators, histone deacetylases (HDAC) enzymes are potent chromatin modifiers which directly regulate gene expression through deacetylation of lysine residues within specific histone proteins. The precise molecular mechanisms underlying HDAC effects on cellular plasticity and {beta}-cell identity are currently unknown. MethodsThe pharmacological inhibition of HDAC activity by trichostatin A (TSA) was studied in the mouse Min6 and human EndocBH1 cell lines, as well as primary mouse sorted {beta} cells and human pancreatic islets. The molecular and functional effects of treating these complementary {beta}-cell models with TSA was explored at the epigenomic and transcriptomic level through next-generation sequencing of chromatin immunoprecipitation (ChIP) assays (ChIP-seq) and RNA sequencing (RNA-seq) experiments, respectively. ResultsWe showed that TSA alters insulin secretion associated with {beta}-cell specific transcriptome programming in both mouse and human {beta}-cell lines, as well as on human pancreatic islets. We also demonstrated that this alternative {beta}-cell transcriptional program in response to HDAC inhibition is related to an epigenome-wide remodeling at both promoters and enhancers. ConclusionsTaken together, our data indicate that full HDAC activity is required to safeguard the epigenome, to protect against loss of {beta}-cell identity with unsuitable expression of genes associated with alternative cell fates.

genomics↗

bioRxiv2022

Copy-number dosage regulates telomere maintenance and disease-associated pathways in neuroblastoma

Telomere maintenance in neuroblastoma is linked to poor outcome and caused by either TERT activation or through alternative lengthening of telomeres (ALT). In contrast to TERT activation, commonly caused by genomic rearrangements or MYCN amplification, ALT is less well understood. Alterations at the ATRX locus are key drivers of ALT but only present in [~]50% of ALT tumors. To identify potential new pathways to telomere maintenance, we investigate allele-specific gene dosage effects from whole genomes and transcriptomes in 115 primary neuroblastomas. We show that copy-number dosage deregulates telomere maintenance, genomic stability, and neuronal pathways and identify upregulation of variants of histone H3 and H2A as a potential alternative pathway to ALT. We investigate the interplay between TERT activation, overexpression and copy-number dosage and reveal loss of imprinting at the RTL1 gene associated with poor clinical outcome. These results highlight the importance of gene dosage in key oncogenic mechanisms in neuroblastoma.

genomics↗

bioRxiv2021

Preclinical models of human multiple myeloma subgroups

Multiple myeloma (MM), a tumor of germinal center (GC)-experienced plasma cells, comprises distinct genetic subgroups, such as the t(11;14)/CCND1 and the t(4;14)/MMSET subtype. We have generated subgroup-specific MM models by the GC B cell-specific co-activation of Ccnd1 or MMSET with a constitutively active Ikk2 mutant, mimicking the secondary NF{kappa}B activation frequently seen in human MM. Ccnd1/Ikk2ca and MMSET/Ikk2ca mice developed a pronounced, clonally restricted plasma cell outgrowth with age, accompanied by serum M spikes, bone marrow insufficiency and bone lesions. The transgenic plasma cells could be propagated in vivo and showed transcriptional profiles resembling their human counterparts. Thus, we show that Ccnd1 and MMSET cooperate with NF{kappa}B in MM pathogenesis, considering for the first time the genetic heterogeneity of MM for the generation of preclinical models.

cancer biology↗

bioRxiv2021

Automatic calibration of a functional-structural wheat model using an adaptive design and a metamodelling approach

O_LIBackground and Aims Functional-structural plant models are increasingly being used by plant scientists to address a wide variety of questions. However, the calibration of these complex models is often challenging, mainly because of their high computational cost. In this paper, we applied an automatic method to the calibration of WALTer: a functional-structural wheat model that simulates the plasticity of tillering in response to competition for light. C_LIO_LIMethods We used a Bayesian calibration method to estimate the values of 5 parameters of the WALTer model by fitting the model outputs to tillering dynamics data. The method presented in this paper is based on the Efficient Global Optimisation algorithm. It involves the use of Gaussian process metamodels to generate fast approximations of the model outputs. To account for the uncertainty associated with the metamodels approximations, an adaptive design was used. The efficacy of the method was first assessed using simulated data. The calibration was then applied to experimental data. C_LIO_LIKey Results The method presented here performed well on both simulated and experimental data. In particular, the use of an adaptive design proved to be a very efficient method to improve the quality of the metamodels predictions, especially by reducing the uncertainty in areas of the parameter space that were of interest for the fitting. Moreover, we showed the necessity to have a diversity of field data in order to be able to calibrate the parameters. C_LIO_LIConclusions The method presented in this paper, based on an adaptive design and Gaussian process metamodels, is an efficient approach for the calibration of WALTer and could be of interest for the calibration of other functional-structural plant models. C_LI

bioinformatics↗

bioRxiv2021

Neuroblastoma signalling models unveil combination therapies targeting feedback-mediated resistance

Very high risk neuroblastoma is characterised by increased MAPK signalling, and targeting MAPK signalling is a promising therapeutic strategy. We used a deeply characterised panel of neuroblastoma cell lines and found that the sensitivity to MEK inhibitors varied drastically between these cell lines. By generating quantitative perturbation data and mathematical modelling, we determined potential resistance mechanisms. We found that negative feedbacks within MAPK signalling and to the IGF receptor mediate re-activation of MAPK signalling upon treatment in resistant cell lines. By using cell-line specific models, we predict that combinations of MEK inhibitors with RAF or IGFR inhibitors can overcome resistance, and tested these predictions experimentally. In addition, phospo-proteomics profiles confirm the cell-specific feedback effects and synergy of MEK and IGFR targeted treatements. Our study shows that a quantitative understanding of signalling and feedback mechanisms facilitated by models can help to develop and optimise therapeutic strategies, and our findings should be considered for the planning of future clinical trials introducing MEKi in the treatment of neuroblastoma.

cancer biology↗

bioRxiv2020

A census of cell types and paracrine interactions in colorectal cancer

In colorectal cancer, oncogenic mutations transform a hierarchically organized and homeostatic epithelium into invasive cancer tissue lacking visible organization. We sought to define colorectal cancer cell types and signals controlling their development. More than 30,000 epithelial single cell transcriptomes of tumors and matched non-cancerous tissues of twelve colorectal cancer patients were clustered into six patient-overarching groups defined by differential activities of oncogenic signaling pathways such as mitogen-activated protein kinase and oncogenic traits such as replication stress. RNA metabolic labeling and assessment of RNA velocity in patient-derived organoids revealed developmental trajectories of colorectal cancer cells organized along a mitogen-activated protein kinase activity gradient. This was in contrast to normal colon organoid cells developing along graded Wnt activity. Experimental targeting of EGFR-BRAF-MEK in cancer organoids affected signaling and gene expression contingent on predictive KRAS/BRAF mutations and induced cell plasticity overriding default developmental trajectories, providing a basis for non-genetic resistance to targeted therapies.

cancer biology↗

bioRxiv2019

WiPP: Workflow for improved Peak Picking for Gas Chromatography-Mass Spectrometry (GC-MS) data

Lack of reliable peak detection impedes automated analysis of large scale GC-MS metabolomics datasets. Performance and outcome of individual peak-picking algorithms can differ widely depending on both algorithmic approach and parameters as well as data acquisition method. Comparing and contrasting between algorithms is thus difficult. Here we present a workflow for improved peak picking (WiPP), a parameter optimising, multi-algorithm peak detection for GC-MS metabolomics. WiPP evaluates the quality of detected peaks using a machine learning-based classification scheme based on seven peak classes. The quality information returned by the classifier for each individual peak is merged with results from different peak detection algorithms to create one final high quality peak set for immediate down stream analysis. Medium and low quality peaks are kept for further inspection. By applying WiPP to standard compound mixes and a complex biological dataset we demonstrate that peak detection is improved through the novel way to assign peak quality, an automated parameter optimisation, and results integration across different embedded peak picking algorithms. Furthermore, our approach can provide an impartial performance comparison of different peak picking algorithms. WiPP is freely available on GitHub (https://github.com/bihealth/WiPP) under MIT licence.

bioinformatics↗

bioRxiv2018

Identification and Ranking of Recurrent Neo-Epitopes in Cancer

Neo-epitopes are emerging as attractive targets for cancer immunotherapy and new strategies for rapid identification of relevant candidates have become a priority. We propose a method for in silico selection of candidates which have a high potential for neo-antigen generation and are likely to appear in multiple patients. This is achieved by carefully screening 33 TCGA data sets for recurrent somatic amino acid exchanges and, for the 1,055 resulting recurrent variants, applying MHC class I binding prediction algorithms. A preliminary confirmation of epitope binding and recognition by CD8 T cells has been carried out for a couple of candidates in humanized mice. Recurrent neo-epitopes may be suitable to supplement existing personalized T cell treatment approaches with precision treatment options.

genomics↗

bioRxiv2017

Neurexin and Neuroligin-based adhesion complexes drive axonal arborisation growth independent of synaptic activity

Building arborisations of the right size and shape is fundamental for neural network function. Live imaging studies in vertebrate brains strongly suggest that nascent synapses are critical for branch growth during the development of axonal and dendritic arborisations. The molecular mechanisms underlying such synaptotropic events are largely unknown.\n\nHere we present a novel system in Drosophila for studying the development of complex axonal arborisations live, in vivo during metamorphosis. In these growing axonal arborisations we see a relationship between the punctate localisations of presynaptic components and branch dynamics that is very similar to synaptotropic growth described in fish and frogs. These presynaptic components however do not appear to represent functional presynaptic release sites and are not paired with clusters of neurotransmitter receptors. Pharmacological and genetic knockdowns of evoked and spontaneous neurotransmission do not impact the outgrowth of these neurons. Instead, we find that axonal branch growth is regulated by the dynamic focal localisations of synaptic adhesion proteins Neurexin and Neuroligin. These adhesion complexes provide selective stability for filopodia by a stick and grow-based mechanism wholly independent of synaptic activity.

neuroscience↗