A six gene expression signature defines aggressive subtypes and predicts outcome in childhood and adult acute lymphoblastic leukemia

Wang, Jin; Mi, Jian-Qing; Debernardi, Alexandra; Vitte, Anne-Laure; Emadali, Anouk; Meyer, Julia; Charmpi, Konstantina; Ycart, Bernard; Callanan, Mary; Carroll, William L.; Khochbin, Saadi; Rousseaux, Sophie

doi:10.18632/oncotarget.4113

Cited by 40 publications

(44 citation statements)

References 30 publications

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“…It has recently been demonstrated that STARD4 expression could be used as poor prognosis gene in a six genes signature that defines aggressive subtypes in adult acute lymphoblastic leukemia [84]. …”

Section: Discussionmentioning

confidence: 99%

Single-Cell-Based Analysis Highlights a Surge in Cell-to-Cell Molecular Variability Preceding Irreversible Commitment in a Differentiation Process

et al. 2016

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In some recent studies, a view emerged that stochastic dynamics governing the switching of cells from one differentiation state to another could be characterized by a peak in gene expression variability at the point of fate commitment. We have tested this hypothesis at the single-cell level by analyzing primary chicken erythroid progenitors through their differentiation process and measuring the expression of selected genes at six sequential time-points after induction of differentiation. In contrast to population-based expression data, single-cell gene expression data revealed a high cell-to-cell variability, which was masked by averaging. We were able to show that the correlation network was a very dynamical entity and that a subgroup of genes tend to follow the predictions from the dynamical network biomarker (DNB) theory. In addition, we also identified a small group of functionally related genes encoding proteins involved in sterol synthesis that could act as the initial drivers of the differentiation. In order to assess quantitatively the cell-to-cell variability in gene expression and its evolution in time, we used Shannon entropy as a measure of the heterogeneity. Entropy values showed a significant increase in the first 8 h of the differentiation process, reaching a peak between 8 and 24 h, before decreasing to significantly lower values. Moreover, we observed that the previous point of maximum entropy precedes two paramount key points: an irreversible commitment to differentiation between 24 and 48 h followed by a significant increase in cell size variability at 48 h. In conclusion, when analyzed at the single cell level, the differentiation process looks very different from its classical population average view. New observables (like entropy) can be computed, the behavior of which is fully compatible with the idea that differentiation is not a “simple” program that all cells execute identically but results from the dynamical behavior of the underlying molecular network.

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Section: Discussionmentioning

confidence: 99%

Single-Cell-Based Analysis Highlights a Surge in Cell-to-Cell Molecular Variability Preceding Irreversible Commitment in a Differentiation Process

et al. 2016

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“…lists of genes able to predict clinical outcome [41] or for molecular subtyping, i.e. list of genes able to classify different subtypes of a disease [42][43]. However, even if markers performed well, gene signatures derived from studies on the same treatments and diseases often resulted in gene lists with little overlap [44].…”

Section: Discussionmentioning

confidence: 99%

Comparison of multiple transcriptomes exposes unified and divergent features of quiescent and activated skeletal muscle stem cells

Pietrosemoli

Mella

Yennek

et al. 2017

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BackgroundSkeletal muscle stem cells (MuSCs) are quiescent in adult mice and can undergo multiple rounds of proliferation and self-renewal following muscle injury. Several labs have profiled transcripts of myogenic cells during developmental and adult myogenesis with the aim of identifying quiescent markers. Here, we focused on the quiescent cell state and generated new transcriptome profiles that include subfractionations of adult MuSC populations, and an artificially induced prenatal quiescent state, to identify core signatures for quiescent and proliferating MuSCs.MethodsComparison of available data offered challenges related to the inherent diversity of datasets and biological conditions. We developed a standardized workflow to homogenize the normalization, filtering, quality control steps for the analysis of gene expression profiles allowing the identification up- and down-regulated genes and the subsequent gene set enrichment analysis. To share the analytical pipeline of this work, we developed Sherpa, an interactive Shiny server that allows multiscale comparisons for extraction of desired gene sets from the analysed datasets. This tool is adaptable to cell populations in other contexts and tissues.ResultsA multiscale analysis comprising eight datasets of quiescent MuSCs had 207 and 542 genes commonly up- and down-regulated, respectively. Shared up-regulated gene sets include an over-representation of the TNFa pathway via NFKb signaling, Il6-Jak-Stat3 signaling, and the apical surface processes, while shared down-regulated gene sets exhibited an over-representation of Myc and E2F targets, and genes associated to the G2M checkpoint and oxidative phosphorylation. However, virtually all datasets contained genes that are associated with activation or cell cycle entry, such as the immediate early stress response genes Fos and Jun. Empirical examination of fixed and isolated MuSCs showed that these and other genes were absent in vivo, but activated during procedural isolation of cells.ConclusionsThrough the systematic comparison and individual analysis of diverse transcriptomic profiles, we identified genes that were consistently differentially expressed among the different datasets and common underlying biological processes key to the quiescent cell state. Our findings provide impetus to define and distinguish transcripts associated with true in vivo quiescence from those that are first responding genes due to disruption of the stem cell niche.

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“…42 Oxidized low-density lipoprotein receptor-1 (OLR-1), is a type II transmembrane receptor of the C-type lectin family that induces oxidative stress, activates tumor necrosis factor a TNFa/nuclear factor-kB (NFkB), upregulates the expression of monocyte chemoattractant protein-1 (MCP1), a key mediator of inflammatory processes, and has a proinflammatory role in cardiovascular disease. [43][44][45] calcium/calmodulin-dependent protein kinase kinase 2 (CAMKK2) transcripts were downregulated in CMML CD14 1 monocytes. As previous studies have demonstrated, a crucial role of the CAMKK2-59-AMP-activated protein kinase catalytic subunit-1 (PRKAA1) axis in the differentiation of monocytes into macrophages, our data are consistent with the observation that the differentiation from monocytes to macrophages is compromised in CMML.…”

Section: Identification Of Gene Expression Differences Between Cmml Pmentioning

confidence: 99%

The transcriptome of CMML monocytes is highly inflammatory and reflects leukemia-specific and age-related alterations

et al. 2019

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A six gene expression signature defines aggressive subtypes and predicts outcome in childhood and adult acute lymphoblastic leukemia

Cited by 40 publications

References 30 publications

Single-Cell-Based Analysis Highlights a Surge in Cell-to-Cell Molecular Variability Preceding Irreversible Commitment in a Differentiation Process

Single-Cell-Based Analysis Highlights a Surge in Cell-to-Cell Molecular Variability Preceding Irreversible Commitment in a Differentiation Process

Comparison of multiple transcriptomes exposes unified and divergent features of quiescent and activated skeletal muscle stem cells

The transcriptome of CMML monocytes is highly inflammatory and reflects leukemia-specific and age-related alterations

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