Biology subjects

Hebert, J.

Publications and source records attributed to Hebert, J..

10 recordsLinked to original sources

bioRxiv2026

Glucocorticoids reprogram human AML leukemic stem cells to promote elimination through differentiation and apoptosis

Acute myeloid leukemia (AML) is sustained by leukemic stem cells (LSCs) that can evade standard therapies and drive relapse. Targeting LSC-specific vulnerabilities is therefore essential for durable remission. Here we demonstrate that glucocorticoids (GCs) induce potent depletion of AML LSCs by promoting terminal differentiation and apoptosis. This effect is observable within 24 hours and is conserved across multiple LSC-enriched models and primary patient samples. Mechanistically, we establish that GC targeting of LSCs is mediated through the glucocorticoid receptor (NR3C1), with higher receptor binding affinity correlating with greater anti-LSC activity. We performed structure activity relationship (SAR) modeling of 24 corticosteroids and identified key features, including bulky D-ring substituents, associated with enhanced anti-LSC efficacy. Bulk and single-cell transcriptomic data revealed that GC treatment of LSCs suppresses NF-{kappa}B inflammatory signaling and disrupts stemness and quiescence programs while inducing transcriptional signatures associated with transient proliferation, metabolic stress, and terminal differentiation. Notably, GC sensitivity was associated with the expression of pre-existing inflammatory or extracellular matrix (ECM) signatures. Finally, we found that FLT3 ligand (FLT3L) is required for GC-induced proliferation of CD34- blasts but not for LSC depletion, suggesting that FLT3L levels may serve as a biomarker for blast expansion in patients receiving GC therapy. These findings support the clinical development of GC-based therapies in AML and provide mechanistic insights into how GCs target inflammatory and metabolic programs required for LSC survival.

cancer biology↗

bioRxiv2026

Discovery of compounds targeting human acute myeloid leukemia stem cells via a novel high-throughput screen

Acute myeloid leukemia (AML) is sustained by leukemic stem cells (LSCs), which must be eradicated for durable remission yet remain therapy-resistant. Here, we present a scalable platform to identify compounds that eliminate LSCs by performing the first large high-throughput drug screen directly on human LSC-enriched cells. Using this system, we screened 11 142 compounds and identified 20 inhibitors selective for LSCs. For three candidates, BIO-acetoxime, SJB2-043 and UMxxxxx03, we confirmed anti-LSC activity across multiple primary patient samples and validated efficacy through xenotransplantation assays. Single-cell RNA sequencing uncovered convergent and distinct anti-LSC mechanisms of action, including USP1 inhibition, which suppressed stemness and cell-cycle programs while promoting metabolic and inflammatory stress responses. Collectively, the LSC-enriched fraction was eliminated through apoptosis or differentiation. Together, this work establishes the first platform to directly screen LSCs at scale and identifies novel vulnerabilities and therapeutics capable of eliminating the root of AML.

cancer biology↗

bioRxiv2026

Combination of indomethacin and temperature produces reliable strobilation in Cassiopea xamachana but batch effects create high variability in ephyra outcomes

Indomethacin and temperature shocks are both widely used to induce ephyra production in Cassiopea polyps, yet the combined effect of these stimuli on strobilation rate, ephyra viability, and polyp survivorship has not been systematically examined. We designed a full factorial experiment crossing five indomethacin concentrations (0, 10, 25, 50, and 75 {micro}M) with four temperature treatments (22, 25, 28, and 31{degrees}C) across three independent experimental trials, exposing a total of 288 individual Cassiopea xamachana polyps for 29 days. Overall, 207 of 288 polyps (71.9%) released at least one ephyra. Temperature was the stronger predictor of strobilation: higher temperatures substantially increased the probability of producing a healthy ephyra (log-odds: +0.327 per {degrees}C; {chi}{superscript 2} p = 6.5 x 10-5), while indomethacin had a smaller but significant positive effect (log-odds: +0.024 per {micro}M; {chi}{superscript 2} p = 0.035). The two stimuli acted independently without significant interaction on healthy ephyra production ({chi}{superscript 2} p = 0.87), although their interaction on total ephyra production (including unhealthy releases) was significant ({chi}{superscript 2} p = 1.0 x 10-9). Notably, low indomethacin concentrations (10 and 25 {micro}M) significantly increased the proportion of unhealthy ephyrae relative to seawater controls (paired t-tests: p = 0.035 and p = 0.006, respectively), while higher concentrations (50-75 {micro}M) did not. Polyp survivorship declined significantly at 28{degrees}C and 31{degrees}C but was unaffected by indomethacin. Trial replicate was the most statistically significant predictor of every outcome measured with the third trial producing near-universal strobilation but zero viable ephyrae, a result of an unidentified perturbation. These results indicate that temperature elevation to 28-31{degrees}C is the most reliable induction strategy for C. xamachana and that low indomethacin concentrations should be avoided when ephyra quality is paramount. We also note that cryptic batch-level variables can overwhelm controlled factors and should be addressed in future experimental designs.

zoology↗

bioRxiv2025

TP53-mutant AML with ribosomal gene loss exhibits impaired protein translation and sensitivity to HSP90 inhibition

TP53-mutated acute myeloid leukemia (AML) represents a particularly aggressive and therapeutically refractory subtype of the disease. While recurrent chromosomal abnormalities such as -5/del(5q), -7/del(7q), and del(17p) are well studied in this context, additional co-occurring events remain less well defined. Using the multi-dimensional Leucegene dataset ([~]700 primary AML specimens), we identified and comprehensively characterized a distinct subset of TP53-altered AML marked by recurrent deletions on the short arm of chromosome 3 (del(3p), > 20% TP53-mutated cases). These deletions frequently co-occur with del(5q) and encompass several ribosomal protein genes (RPGs), leading to a global downregulation of the ribosomal network and reduced protein synthesis. We show that this ribosomopathy-like phenotype is most pronounced in TP53-mutated cases with combined RPG deletions on chromosomes 3p and 5q, suggesting a cooperative oncogenic mechanism. Importantly, chemical screening identified HSP90 inhibition as a selective vulnerability in AML with low RPG expression. These findings highlight a previously unappreciated TP53-altered AML subset characterized by converging genomic and translational defects, and suggest that ribosomal stress may serve as a therapeutic entry point for targeted intervention of this patient subgroup.

cancer biology↗

bioRxiv2025

Predictive Gene Discovery with EPCY: A Density-Based Alternative to DE analysis

Identifying predictive genes from high-throughput data remains a key challenge in biomedical research. Most current approaches rely on statistical tests to select differentially expressed genes (DEGs), which may not align with the goal of predicting outcomes. We present EPCY, a method that ranks genes based on their predictive power using cross-validated classifiers and density estimation, without relying on null hypothesis testing. Applied to both bulk and single-cell RNA sequencing datasets, EPCY consistently outperforms benchmark DEG-based methods in selecting robust candidate genes. It also demonstrates greater stability across varying cohort sizes, enabling reproducible gene prioritization even in large, heterogeneous datasets. EPCY provides interpretable predictive scores, facilitating candidate selection aligned with downstream validation goals.

bioinformatics↗

bioRxiv2023

SPAT: Surface Protein Annotation Tool

Given the particular attractivity of antibody-based immunotherapies, in vitro experimental approaches aiming to identify and quantify proteins directly located at the cell surface, such as the surfaceome, have been recently developed and improved. However, the "surface" enriched, yet noisy output obtained from available methods makes it challenging to accurately evaluate which proteins are more likely to be located at the surface of the plasma membrane and which are simple contaminants. To that purpose, we developed the in silico Surface Protein Annotation Tool (SPAT), which unifies established annotations to grade proteins according to the chance they have to be located at the cell surface. SPAT accuracy was tested using in-house acute myeloid leukemia data, as well as public datasets, and despite using publicly available annotations, showed good performances when compared to more complex surfaceome predictors. Given its simple input requirement, SPAT is easily usable for the annotation of any gene/protein lists. Its output, in addition to the "surface" score, provides additional annotations including a "secretion" flag, references to verified antibodies targeting annotated proteins, as well as expression data and protein levels in essential human organs, making it a user-friendly tool for the community.

bioinformatics↗

bioRxiv2022

Baseline Acute Myeloid Leukemia Prognosis Models using Transcriptomic and Clinical Profiles by Studying the Impacts of Dimensionality Reductions and Gene Signatures on Cox-Proportional Hazard

Gene marker extraction to evaluate risk in cancer can refine the diagnosis process and lead to adapted therapies and better survival. These survival analyses can be done through computer systems and Machine Learning (ML) algorithms such as the Cox-Proportional-Hazard model from gene expression (GE) RNA-Seq data. However, optimal tuning of CPH from genome-wide GE data is challenging and poorly assessed so far. In this work we propose to interrogate an Acute Myeloid Leukemia (AML) dataset (Leucegene) to derive key components of the CPH driving down its performance and discovering its sensitivity to various factors in hoping to ameliorate the system. In this study, we compare the projection and selection data reduction techniques, mainly the PCA and LSC17 gene signature in combination with the CPH in a ML framework. Results reveals that CPH performs better with a combination of clinical and genetic expression features. We determine that projections performs better than selections without clinical information. We ascertain that CPH is affected by overfitting and that this overfitting is linked to the number and the content of input covariables. We show that PCA links clinical features via ability to learn from the input data directly and generalizes better than LSC17 on Leucegene. We postulate that projection are preferred than selection on harder task such as assessing risk in the intermediate subset of Leucegene. We extrapolate that these findings apply in the more general context of risk detection via machine learning in cancer. We see that higher capacity models such as CPH-DNNs systems can be improved via survival-derived projections and combat overfitting through heavy regularization. Author summaryThis study aims to investigate the feasibility of using gene expression to evaluate risk in cancer, and to compare the projection and selection data reduction techniques. The study used the Leucegene dataset to compare the PCA method and a previously published 17 genes signature in combination with the Cox-Proportional-Hazard model in a machine learning framework. Results showed that CPH was affected by overfitting and that this overfitting was linked to the number and the content of input covariables. The study found that PCA links clinical features via ability to learn from the input data directly and generalizes better than LSC17 on Leucegene. The study concluded that projections are preferred than selection on harder task such as assessing risk in the intermediate subset of Leucegene and can be tuned to improve their performance. Data availability statementSource code for pipelines and algorithms, as well as gene expression matrices, are available here: https://github.com/lemieux-lab/dimensions_coxph. Access to the Leucegene cohorts survival times can be granted upon request and following ethical review.

bioinformatics↗

bioRxiv2021

Donor-derived vasculature is required to support neocortical cell grafts after stroke

Neural precursor cells (NPCs) transplanted into the adult neocortex generate neurons that synaptically integrate with host neurons, supporting the possibility of achieving functional tissue repair. However, poor survival of transplanted NPCs greatly limits efficient engraftment. Here, we test the hypothesis that combining blood vessel-forming vascular cells with neuronal precursors improves engraftment. By transplanting mixed embryonic neocortical cells into adult mice with neocortical strokes, we show that transplant-derived neurons synapse with appropriate targets while donor vascular cells form vessels that fuse with the host vasculature to perfuse blood within the graft. Although all grafts became vascularized, larger grafts had greater contributions of donor-derived vessels that increased as a function of their distance from the host-graft border. Moreover, excluding vascular cells from the donor cell population strictly limited graft size. Thus, inclusion of vessel-forming vascular cells with NPCs is required for more efficient engraftment and ultimately for tissue repair.

neuroscience↗

bioRxiv2019

Genetic Landscape of Electron Transport Chain Complex I Dependency in Acute Myeloid Leukemia

Inhibition of oxidative phosphorylation (OXPHOS) is a promising therapeutic strategy in Acute Myeloid Leukemia (AML), but patients respond heterogeneously. Through chemically interrogation of 200 sequenced specimens, we identified Mubritinib as a strong in vitro and in vivo anti-leukemic compound, acting through ubiquinone-dependent inhibition of Electron Transport Chain complex I (ETC1). ETC1 targeting showed selective toxicity against a subgroup of chemotherapy-resistant leukemias exhibiting OXPHOS hyperactivity, high expression of mitochondrial activity-related genes, and mutations affecting NPM1, FLT3 and DNMT3A. Altogether, our work thus identifies a novel ETC1 inhibitor with high clinical potential and reveals the landscape of OXPHOS dependency in AML.

cancer biology↗

bioRxiv2018

Target variant detection in leukemia using unaligned RNA-Seq reads

Mutations identified in each Acute Myeloid Leukemia (AML) patients are useful for prognosis and to select targeted therapies. Detection of such mutations by the analysis of Next-Generation Sequencing (NGS) data requires a computationally intensive read mapping step and application of several variant calling methods. Targeted mutation identification drastically shifts the usual tradeoff between accuracy and performance by concentrating all computations over a small portion of sequence space. Here, we present km, an efficient approach leveraging k-mer decomposition of reads to identify targeted mutations. Our approach is versatile, as it can detect single-base mutations, several types of insertions and deletions, as well as fusions. We used two independent AML cohorts (The Cancer Genome Atlas and Leucegene), to show that mutation detection by km is fast, accurate and mainly limited by sequencing depth. Therefore, km allows to establish fast diagnostics from NGS data, and could be suitable for clinical applications.

bioinformatics↗