Biology subjects

Giustacchini, A.

Publications and source records attributed to Giustacchini, A..

5 recordsLinked to original sources

bioRxiv2026

Recurrent leukaemia stem cell states define the antigenic landscape of paediatric Acute Myeloid Leukaemia

Paediatric acute myeloid leukaemia (pAML) remains a leading cause of childhood cancer mortality, with disease heterogeneity representing a major obstacle to effective immunotherapy. To enable rational immunotherapy design accounting for such heterogeneity, we combined single-cell transcriptomics and surface proteomics (CITE-seq). Using an integrated framework for malignant cell identification and unbiased gene program identification, we resolved three conserved leukaemia stem cell (LSC) subtypes, primitive (P-LSC), monocytic (M-LSC), and erythroid-like (E-LSC), that frequently coexist within individual patients in varying proportions and represent distinct stem-cell states with characteristic immunophenotypes and transcriptional programmes. Quantitative surface antigen profiling revealed that no single target achieved full LSC coverage. CLEC12A, an antigen currently being evaluated clinically, was expressed across P-LSC and M-LSC but was largely absent from E-LSC. In contrast, CD84 provided complementary coverage in the P and M groups and served as the primary target in the E-LSC population. The combination of CLEC12A and CD84 provided broad coverage across all three LSC subtypes, while sparing healthy haematopoietic stem cells. Validation across independent single-cell and bulk cohorts confirmed the robustness of this combination. These findings establish a paediatric-specific framework for antigen prioritisation in AML, identify CLEC12A/CD84 as a robust dual-targeting strategy across heterogeneous LSC states, and provide a blueprint for the rational design of next-generation CAR T-cell combinations.

cancer biology↗

bioRxiv2026

scINTILLA: Single-Cell Integrated Inference, Labelling, and Landscape Analysis for Cell-Type Annotation Quality Assessment

Single-cell RNA sequencing has enabled the construction of comprehensive cell atlases, yet the quality and coherence of the cell-type annotations within these atlases remain largely unexamined. When a label is applied to a transcriptionally heterogeneous population, the downstream analyses that depend on it, and automated label transfer in particular, become unreliable. We present scINTILLA (Single-Cell Integrated Inference, Labelling, and Landscape Analysis), a computational framework that combines supervised and unsupervised machine learning to score the learnability and internal consistency of cell-type labels in single-cell datasets. The unsupervised arm benchmarks a broad panel of clustering algorithms and derives a neighbourhood confusion score for every cell, whilst the supervised arm trains up to twelve classifiers and extracts prediction agreement, entropy, and confidence. These signals are normalised and aggregated into a single composite score per cell type, where a low score flags label ambiguity or concealed heterogeneity. As a by-product, scIN-TILLA also reports which clustering and classification algorithms perform best on a given dataset, offering practical guidance for downstream label transfer. We applied it to five Human Cell Atlas datasets spanning the adult brain, lung, eye, and two organoid atlases, and recovered clear differences in the learnability and internal consistency of annotations across atlases that were not driven by the number of annotated cell types. Focused re-analysis of lowscoring populations in the lung and endoderm-organoid atlases resolved biologically coherent sub-populations, in some cases with context-specific enrichment, much of it recovered from cells that had been assigned broad or catch-all labels. scINTILLA is advisory rather than prescriptive, guiding principled, data-driven re-annotation at atlas scale.

bioinformatics↗

bioRxiv2025

Integrated in silico and in vitro approaches identify SNX.2112 as a drug vulnerability in t(7;12) AML stem-like cells

The t(7;12) translocation is a chromosomal rearrangement characteristic of infant Acute Myeloid Leukemia (AML). It arises in utero and results in ectopic overexpression of homeobox gene MNX1. Using a 3-dimensional (3D) model of blood development, we recently showed that t(7;12)-AML originates at the endothelial-to-hematopoietic transition, explaining its characteristic gene expression signature. Herein, we employ that signature to interrogate the transcriptional profiles of hundreds of human cell lines against the GDSC database of drug sensitivities to identify candidate drugs against t(7;12)-AML. We employ a cell line in which we engineered t(7;12) and systematically test the candidate drugs by cell surface phenotype and clonogenic assays. Importantly, we identify HSP90 inhibitor SNX.2112 as a potential therapeutic agent against t(7;12)-AML. SNX.2112 selectively eliminates colony-initiating leukemia progenitors in vitro and decreases MNX1 expression, effects recapitulated by other HSP90 inhibitors. SNX.2112 acts at least partly through destabilisation of STAT5 signalling. Critically, SNX.2112-differential signatures uniquely map to progenitors with hemato-endothelial characteristics in t(7;12)-AML patient blasts, suggesting targeting of leukemia-initiating cells. Combinatorial treatment with chemotherapeutic agents indicates synergy, suggesting SNX.2112 potential as a targeted and cytotoxicity-sparing therapeutic approach. Overall, we successfully use an integrated computational and multi-model experimental approach to identify a drug vulnerability of t(7;12)-AML. Key pointsO_LIClassifier-based in silico drug screening identifies vulnerabilities of t(7;12)-infant leukemia C_LIO_LI2D and 3D models of t(7;12)-leukemia match HSP90 inhibition cellular and molecular responses to candidate leukemia stem cells in t(7;12) patient analysis. C_LI

cancer biology↗

bioRxiv2020

Enhanced functionality of low-affinity CD19 CAR T-cells is associated with activation priming and a polyfunctional cytokine phenotype

We recently described a low-affinity second-generation CD19 chimeric antigen receptor (CAR) CAT that showed enhanced expansion, cytotoxicity, and anti-tumour efficacy compared to the high-affinity (FMC63 based) CAR used in Tisagenlecleucel, in pre-clinical models. Furthermore, CAT demonstrated an excellent toxicity profile, enhanced in vivo expansion, and long-term persistence in a Phase I clinical study. To understand the molecular mechanisms behind these properties of CAT CAR T-cells, we performed a systematic in vitro characterization of the transcriptomic (RNA-seq) and protein (CyTOF) changes occurring in T-cells expressing low-affinity vs high-affinity CD19 CARs following stimulation with CD19-expressing cells. Our results show that CAT CAR T-cells exhibit enhanced activation to CD19 stimulation and a distinct transcriptomic and protein profile, with increased activation and cytokine polyfunctionality compared to FMC63 CAR T-cells. We demonstrate that the enhanced functionality of low-affinity CAT CAR T-cells is a consequence of an antigen-dependent priming induced by residual CD19-expressing B-cells present in the manufacture.

cancer biology↗

bioRxiv2018

Unraveling intratumoral heterogeneity through high-sensitivity single-cell mutational analysis and parallel RNA-sequencing

Single-cell RNA-sequencing has emerged as a powerful tool to resolve transcriptional heterogeneity. However, its application to study cancerous tissues is currently hampered by the lack of coverage across key mutation hotspots in the vast majority of cells, which prevents correlation of genetic and transcriptional readouts from the same single cell. To overcome this, we developed TARGET-seq, a method for the high-sensitivity detection of multiple mutations within single-cells from both genomic and coding DNA, in parallel with unbiased, high-depth whole transcriptome analysis. We demonstrate how this technique uniquely resolves transcriptional and genetic tumor heterogeneity in myeloproliferative neoplasm stem/progenitor cells, providing insights into deregulated pathways of mutant and non-mutant cells. TARGET-seq provides a powerful tool to resolve molecular signatures of genetically distinct subclones of tumor cells.

genomics↗