Biology subjects

Spinella, J.-F.

Publications and source records attributed to Spinella, J.-F..

5 recordsLinked to original sources

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↗

bioRxiv2025

CD33 Epitope Editing Unlocks UM171-Expanded Cord Blood Grafts for AML Immunotherapy

Immunotherapies in acute myeloid leukemia (AML) are limited by shared antigen expression between leukemic and healthy hematopoietic cells, leading to on-target toxicity. Here we developed a clinically scalable strategy to engineer cord blood (CB)-derived hematopoietic stem and progenitor cell (HSPC) grafts resistant to CD33-directed therapies. Leveraging UM171-mediated expansion and adenine base editing, we precisely disrupted a critical epitope in CD33 required for gemtuzumab ozogamicin (GO) binding, centered on phenylalanine 21, while preserving CD33 expression and its sialic acid binding function. Ex vivo edited HSPCs maintained robust multilineage engraftment, T-cell output, and conferred protection from GO-induced myelotoxicity in xenograft models, without impairing anti-leukemic efficacy. Editing was efficient across multiple donors, enriched in primitive subsets, and exhibited minimal off-target activity by ultra-deep exome sequencing. Our work establishes base editor-driven epitope engineering as an improved approach to CD33-targeted immunotherapy-compatible HSPC grafts, enabling safe integration of currently available agents into post-transplant care.

immunology↗

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↗

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↗