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Sujobert, P.

Publications and source records attributed to Sujobert, P..

5 recordsLinked to original sources

Single-cell genotype-phenotype mapping identifies therapeutic vulnerabilities in VEXAS syndrome

Somatic evolution leads to the emergence of clonal diversity across tissues with broad implications for human health. A striking example of somatic evolution is the VEXAS (Vacuoles E1 enzyme X-linked Autoinflammatory Somatic) syndrome, caused by somatic UBA1 mutations in hematopoietic stem cells (HSCs), inducing treatment-refractory, systemic inflammation. However, the mechanisms that lead to survival and expansion of mutant HSCs are unknown, limiting the development of effective therapies. The lack of animal or cellular models of UBA1-mutant HSCs has hindered such mechanistic understanding, mandating analysis of primary human VEXAS samples, which harbor admixtures of wild-type and UBA1-mutant HSCs. To address these challenges, we applied single-cell multi-omics to comprehensively define mutant UBA1-induced transcriptome, chromatin accessibility and signaling pathway alterations in VEXAS patients, allowing for the direct comparison of mutant versus wild-type cells within the same environment. We confirmed the expected enrichment of UBA1M41V/T mutations in myeloid cells, and additionally discovered that these mutations were also prevalent in Natural Killer (NK) cells in VEXAS patients, providing new insights into disease phenotypes. Through mapping genotypes to molecular phenotypes, including transcriptome, chromatin accessibility, cell surface protein or intracellular protein profiles, in HSCs, we found that UBA1M41V/T-mutant cells showed an increased inflammation signature (interferon alpha and gamma response pathways), as well as activation of unfolded protein response (UPR) via pro-survival, but not pro-apoptotic, mediators of the PERK pathway, compared to UBA1 wild-type HSCs. Ex vivo validation experiments showed that inhibiting UBA1 in normal CD34+ or using UBA1-mutant HSCs led to PERK pathway up-regulation, increased myeloid differentiation and cell survival, which was reversed by PERK inhibition. Thus, we demonstrated that human VEXAS HSCs show cell-intrinsic inflammatory phenotypes and survive the proteomic stress caused by compromised ubiquitination through PERK-mediated activation of the UPR. Together, these analyses nominate PERK pathway inhibition as a potential new therapeutic strategy for eradicating the VEXAS-inducing clone, demonstrating the power of single-cell multi-omics profiling of primary patient sample to enable genotype-to-phenotype somatic mapping for the discovery of novel candidates for clinical intervention.

genomics↗

Functional precision oncology for follicular lymphoma with patient-derived xenograft in avian embryos

BackgroundFollicular lymphoma (FL) is an incurable B-cell malignancy that constitutes a quarter of all lymphomas. Although RCHOP immuno-chemotherapy induces high rates of complete remission, almost all FL patients experience multiple relapses post-treatment. The limited understanding of treatment response heterogeneity is due to the absence of in vitro or in vivo experimental models, primarily because tumor cells heavily rely on their microenvironment to survive. In this study, we present an innovative xenograft model of primary FL cells in avian embryos, circumventing these limitations. MethodsWe developed the FL-AVI-PDX model by transplanting 20 biopsy FL samples, including good (n=11) and poor clinical responders (POD24, n=9), into chicken embryos. Each set of embryos was treated with RCHOP or vehicle intravenously. We evaluated the effect of immuno-chemotherapy on tumor volume by light sheet microscopy and on tumor biology by transcriptomic analysis at the single-cell level. ResultsWe successfully engrafted all samples in avian embryos. We found that RCHOP treatment in ovo led to tumor volume reduction, which predicted progression-free survival in multivariate analysis, demonstrating the models capacity to capture clinical heterogeneity at the patient level. The FL-AVI-PDX model also provided a unique opportunity to analyze the transcriptomic impact of RCHOP on FL cells using single-cell RNA sequencing. We identified a signature of 21 genes upregulated after RCHOP exposure, displaying significant intra-tumoral heterogeneity. As a proof of concept, we validated the functional involvement of BAX, a gene from the RCHOP-induced signature, as a critical effector of immuno- chemotherapy in vitro and in avian embryos. ConclusionsThe FL-AVI-PDX model is a platform for functional precision oncology in primary FL cells that captures both inter- and intra-patient heterogeneity of clinical response to a complex therapeutic regimen. It offers a unique opportunity to better understand FL biology, opening perspectives for the development of new drugs.

cancer biology↗

Panel Informativity Optimizer (PIO): an R package to improve cancer NGS panel informativity

Mutation detection by next generation sequencing (NGS) is routinely used for cancer diagnosis. Selecting an optimal set of genes for a given cancer is not trivial as it has to optimize informativity (i.e. the number of patients with at least one mutation in the panel), while minimizing panel length in order to reduce sequencing costs and increase sensitivity. We propose herein Panel Informativity Optimizer (PIO), an open-source software developed as an R package with a user-friendly graphical interface to help optimize cancer NGS panel informativity. Using patient-level mutational data from either private datasets or preloaded dataset of 91 independent cohort from 31 different cancer type, PIO selects an optimal set of genomic intervals to maximize informativity and panel size in a given cancer type. Different options are offered such as the definition of genomic intervals at the gene or exon level, and the use of optimization strategy at the patient or patient per kilobase level. PIO can also propose an optimal set of genomic intervals to increase informativity of custom panels. A panel tester function is also available for panel benchmarking. Using public databases, as well as data from real-life settings, we demonstrate that PIO allows panel size reduction of up to 1000kb, and accurately predicts the performance of custom or commercial panels. PIO is available online at https://vincentalcazer.shinyapps.io/Panel_informativity_optimizer/ or can be set on a locale machine from https://github.com/VincentAlcazer/PIO.

bioinformatics↗

Hematopoietic differentiation is characterized by a transient peak of cell-to-cell gene expression variability in normal and pathological conditions.

Hematopoietic differentiation has been metaphorically represented as linear trajectories with discrete steps from hematopoietic stem cells to mature cells. While the transcriptional state of cells at the beginning or at the end of these trajectories are well described from bulk analysis, what happens in the intermediate states has remained elusive until the use of single cell approaches. Applying Shannon entropy to measure cell-to-cell variability among cells at the same stage of differentiation, we observed a transient peak of gene expression variability in all the hematopoietic differentiation pathways. Strikingly, genes with the highest entropy variation in a given differentiation pathway matched genes known as pathway-specific, whereas genes with the highest expression variation were common to all pathways. Finally, we showed that the level of cell-to-cell variation is increased in the most immature compartment of hematopoiesis in myelodysplastic syndromes. These data suggest that differentiation could be better conceptualized as a dynamical stochastic process with a transient stage of cellular indetermination.

cell biology↗

Performances of targeted RNA-sequencing for the analysis of fusion transcripts, gene mutation and expression in haematological malignancies.

RNA sequencing holds great promise to improve the diagnostic of haematological malignancies, because this technique enables to detect fusion transcripts, to look for somatic mutations in oncogenes, and to capture transcriptomic signatures nosological entities. However, the analytical performances of targeted RNA sequencing have not been extensively described in diagnostic samples. Using a targeted panel of 1385 cancer-related genes in a series of 100 diagnosis samples and 8 controls, we detected all the already known fusion transcripts, and also discovered unknown and/or unsuspected fusion transcripts in 12 samples. Regarding the analysis of transcriptomic profiles, we show that targeted RNA sequencing is performant to discriminate acute lymphoblastic leukemia entities driven by different oncogenic translocations. Additionally, we show that 86% of the mutations identified at the DNA level are also detectable at the mRNA level, except for nonsense mutations which are subjected to mRNA decay. We conclude that targeted RNA sequencing might improve the diagnosis of haematological malignancies. Standardization of the preanalytical steps and further refinements of the panel design and of the bioinformatical pipelines will be an important step towards its use in standard diagnostic procedures.

genomics↗