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

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          Abstract

          T-cell acute lymphoblastic leukemia (T-ALL) is often not cured by frontline chemotherapy, and efforts to improve treatment by targeting oncogenes such as NOTCH1 have been hampered by toxicity. Silva and colleagues studied primary patient samples to show that high-level interleukin 7 receptor α ( IL7Rα) gene expression correlates with ongoing, oncogenic IL7R-mediated signaling. Using new in vivo models, they characterized the impact of IL7Rα expression on the pathogenesis of T-ALL and its response to various targeted therapies that reduce IL7-related signaling.

          Key Points

          • Mice overexpressing IL-7Rα develop leukemia with features of human T-ALL and sensitivity to ruxolitinib, dactolisib, and venetoclax.

          • T-ALL patients with high levels of wild-type IL7R present with evidence of ongoing, oncogenic-like IL-7R–mediated activation of signaling.

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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 p27 Kip1 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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          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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            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

                Journal
                Blood
                Blood
                bloodjournal
                blood
                Blood
                Blood
                American Society of Hematology (Washington, DC )
                0006-4971
                1528-0020
                23 September 2021
                10 May 2021
                23 September 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
                Author notes
                [*]

                A.S. and A.A. are joint first authors.

                [†]

                A.C. and J.L.N. are joint second authors.

                [‡]

                B.S. and J.T.B. contributed equally to this study.

                Author information
                https://orcid.org/0000-0002-4292-8558
                https://orcid.org/0000-0003-0863-158X
                https://orcid.org/0000-0002-8193-5734
                https://orcid.org/0000-0002-4206-2854
                https://orcid.org/0000-0002-5697-1602
                https://orcid.org/0000-0002-6860-798X
                https://orcid.org/0000-0001-6974-4209
                https://orcid.org/0000-0003-4352-3373
                https://orcid.org/0000-0002-4826-8976
                Article
                2021/BLD2019000553
                10.1182/blood.2019000553
                8462360
                33970999
                cc94480f-1370-45d2-99fd-f3715debea9b
                © 2021 by The American Society of Hematology

                This article is made available via the PMC Open Access Subset for unrestricted reuse and analyses in any form or by any means with acknowledgment of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections.

                History
                : 11 March 2019
                : 15 April 2021
                Page count
                Pages: 13
                Categories
                39
                Lymphoid Neoplasia

                Hematology
                Hematology

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