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      Overexpression of wild-type IL-7Rα promotes T-cell acute lymphoblastic leukemia/lymphoma

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          Abstract

          Tight regulation of IL-7Rα expression is essential for normal T-cell development. IL-7Rα gain-of-function mutations are known drivers of T-cell acute lymphoblastic leukemia (T-ALL). Although a subset of patients with T-ALL display high IL7R messenger RNA levels and cases with IL7R gains have been reported, the impact of IL-7Rα overexpression, rather than mutational activation, during leukemogenesis remains unclear. In this study, overexpressed IL-7Rα in tetracycline-inducible Il7r transgenic and Rosa26 IL7R knockin mice drove potential thymocyte self-renewal, and thymus hyperplasia related to increased proliferation of T-cell precursors, which subsequently infiltrated lymph nodes, spleen, and bone marrow, ultimately leading to fatal leukemia. The tumors mimicked key features of human T-ALL, including heterogeneity in immunophenotype and genetic subtype between cases, frequent hyperactivation of the PI3K/Akt pathway paralleled by downregulation of p27Kip1 and upregulation of Bcl-2, and gene expression signatures evidencing activation of JAK/STAT, PI3K/Akt/mTOR and Notch signaling. Notably, we also found that established tumors may no longer require high levels of IL-7R expression upon secondary transplantation and progressed in the absence of IL-7, but remain sensitive to inhibitors of IL-7R–mediated signaling ruxolitinib (Jak1), AZD1208 (Pim), dactolisib (PI3K/mTOR), palbociclib (Cdk4/6), and venetoclax (Bcl-2). The relevance of these findings for human disease are highlighted by the fact that samples from patients with T-ALL with high wild-type IL7R expression display a transcriptional signature resembling that of IL-7–stimulated pro-T cells and, critically, of IL7R-mutant cases of T-ALL. Overall, our study demonstrates that high expression of IL-7Rα can promote T-cell tumorigenesis, even in the absence of IL-7Rα mutational activation.

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          Most cited references78

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          Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

          Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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            Is Open Access

            limma powers differential expression analyses for RNA-sequencing and microarray studies

            limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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              Trinity: reconstructing a full-length transcriptome without a genome from RNA-Seq data

              Massively-parallel cDNA sequencing has opened the way to deep and efficient probing of transcriptomes. Current approaches for transcript reconstruction from such data often rely on aligning reads to a reference genome, and are thus unsuitable for samples with a partial or missing reference genome. Here, we present the Trinity methodology for de novo full-length transcriptome reconstruction, and evaluate it on samples from fission yeast, mouse, and whitefly – an insect whose genome has not yet been sequenced. Trinity fully reconstructs a large fraction of the transcripts present in the data, also reporting alternative splice isoforms and transcripts from recently duplicated genes. In all cases, Trinity performs better than other available de novo transcriptome assembly programs, and its sensitivity is comparable to methods relying on genome alignments. Our approach provides a unified and general solution for transcriptome reconstruction in any sample, especially in the complete absence of a reference genome.
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                Author and article information

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                Journal
                Blood
                American Society of Hematology
                0006-4971
                1528-0020
                September 23 2021
                May 10 2021
                September 23 2021
                May 10 2021
                : 138
                : 12
                : 1040-1052
                Affiliations
                [1 ]Institute of Immunity and Transplantation, Division of Infection and Immunity, University College London, London, United Kingdom;
                [2 ]Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal;
                [3 ]Vlaams Instituut voor Biotechnologie (VIB) Center for Cancer Biology;
                [4 ]Katholieke Universiteit (KU) Leuven Center for Human Genetics, Katholieke Universiteit (VIB-KU) Leuven, Leuven, Belgium;
                [5 ]Department of Pathology Erasmus Medical Center Rotterdam, Rotterdam, The Netherlands;
                [6 ]Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands.; and
                [7 ]Departamento de Ciências da Vida, Faculdade de Ciências e Tecnologia, Unidade de Ciências Biomoleculares Aplicadas (UCIBIO), Universidade NOVA de Lisboa, Caparica, Portugal
                Article
                10.1182/blood.2019000553
                cc94480f-1370-45d2-99fd-f3715debea9b
                © 2021
                History

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