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      Micro-RNA networks in T-cell prolymphocytic leukemia reflect T-cell activation and shape DNA damage response and survival pathways

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          Abstract

          T-cell prolymphocytic leukemia (T-PLL) is a poor-prognostic mature T-cell malignancy. It typically presents with exponentially rising lymphocyte counts, splenomegaly, and bone marrow infiltration. Effective treatment options are scarce and a better understanding of TPLL’s pathogenesis is desirable. Activation of the TCL1 proto-oncogene and loss-of-function perturbations of the tumor suppressor ATM are TPLL’s genomic hallmarks. The leukemic cell reveals a phenotype of active T-cell receptor (TCR) signaling and aberrant DNA damage responses. Regulatory networks based on the profile of microRNA (miR) have not been described for T-PLL. In a combined approach of small-RNA and transcriptome sequencing in 46 clinically and moleculary well-characterized T-PLL, we identified a global T-PLL-specific miR expression profile that involves 34 significantly deregulated miR species. This pattern strikingly resembled miR-ome signatures of TCR-activated T cells. By integrating these T-PLL miR profiles with transcriptome data, we uncovered regulatory networks associated with cell survival signaling and DNA damage response pathways. Despite a miR-ome that discerned leukemic from normal T cells, there were also robust subsets of T-PLL defined by a small set of specific miR. Most prominently, miR-141 and the miR- 200c-cluster separated cases into two major subgroups. Furthermore, increased expression of miR-223-3p as well as reduced expression of miR-21 and the miR-29 cluster were associated with more activated Tcell phenotypes and more aggressive disease presentations. Based on the implicated pathobiological role of these miR deregulations, targeting strategies around their effectors appear worth pursuing. We also established a combinatorial miR-based overall survival score for T-PLL (miROS-T-PLL), that might improve current clinical stratifications.

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          Most cited references49

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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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            The Molecular Signatures Database (MSigDB) hallmark gene set collection.

            The Molecular Signatures Database (MSigDB) is one of the most widely used and comprehensive databases of gene sets for performing gene set enrichment analysis. Since its creation, MSigDB has grown beyond its roots in metabolic disease and cancer to include >10,000 gene sets. These better represent a wider range of biological processes and diseases, but the utility of the database is reduced by increased redundancy across, and heterogeneity within, gene sets. To address this challenge, here we use a combination of automated approaches and expert curation to develop a collection of "hallmark" gene sets as part of MSigDB. Each hallmark in this collection consists of a "refined" gene set, derived from multiple "founder" sets, that conveys a specific biological state or process and displays coherent expression. The hallmarks effectively summarize most of the relevant information of the original founder sets and, by reducing both variation and redundancy, provide more refined and concise inputs for gene set enrichment analysis.
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              miRBase: annotating high confidence microRNAs using deep sequencing data

              We describe an update of the miRBase database (http://www.mirbase.org/), the primary microRNA sequence repository. The latest miRBase release (v20, June 2013) contains 24 521 microRNA loci from 206 species, processed to produce 30 424 mature microRNA products. The rate of deposition of novel microRNAs and the number of researchers involved in their discovery continue to increase, driven largely by small RNA deep sequencing experiments. In the face of these increases, and a range of microRNA annotation methods and criteria, maintaining the quality of the microRNA sequence data set is a significant challenge. Here, we describe recent developments of the miRBase database to address this issue. In particular, we describe the collation and use of deep sequencing data sets to assign levels of confidence to miRBase entries. We now provide a high confidence subset of miRBase entries, based on the pattern of mapped reads. The high confidence microRNA data set is available alongside the complete microRNA collection at http://www.mirbase.org/. We also describe embedding microRNA-specific Wikipedia pages on the miRBase website to encourage the microRNA community to contribute and share textual and functional information.
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                Author and article information

                Journal
                Haematologica
                Haematologica
                HAEMA
                Haematologica
                Fondazione Ferrata Storti
                0390-6078
                1592-8721
                19 November 2020
                01 January 2022
                : 107
                : 1
                : 187-200
                Affiliations
                [1 ]Department I of Internal Medicine, Center for Integrated Oncology (CIO), Aachen-Bonn- Cologne-Duesseldorf, Excellence Cluster for Cellular Stress Response and Aging- Associated Diseases (CECAD), Center for Molecular Medicine Cologne (CMMC), University of Cologne (UoC) , Cologne
                [2 ]Institute of Molecular Medicine , Section for Molecular Cell Biology, Faculty of Medicine, Martin Luther University Halle-Wittenberg , Charles Tanford Protein Center, Halle
                [3 ]Cologne Center for Genomics, Center for Molecular Medicine Cologne (CMMC), University of Cologne , Cologne, Germany
                Author notes
                *TB, MG and LW contributed equally as co-first authors.
                #SH, AS and MH contributed equally as co-senior authors

                Disclosures

                No conflicts of interest to disclose.

                Contributions

                TB, AS, and MH developed the concept of the research; TB, PM, MF, MH and JA acquired the data; TB, MG, LW and MO performed the formal analysis; MG and LW acquired and analyzed the data; TB, LW and AS prepared the original draft and wrote the manuscript; SH and MH wrote, reviewed and edited the mansucript; AS, SH, MH and MHa supervised the work; TB and MO did the project administration. All authors reviewed and approved the final version of the manuscript.

                Article
                10.3324/haematol.2020.267500
                8719084
                33543866
                8123b9f1-7159-495d-a0f4-3db8ae20a649
                Copyright© 2022 Ferrata Storti Foundation

                This article is distributed under the terms of the Creative Commons Attribution Noncommercial License ( by-nc 4.0) which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

                History
                : 20 July 2020
                : 06 November 2020
                Page count
                Figures: 6, Tables: 2, Equations: 0, References: 49, Pages: 14
                Funding
                Funding : This research was funded by the DFG Research Unit FOR1961 (Control-T; HE3553/4-2), the Köln Fortune program, and the Fritz Thyssen Foundation (10.15.2.034MN). This work was also funded by the EU Transcan-2 consortium ‘ERANET-PLL’ and by the ERAPerMed consortium ‘JAKSTAT-TARGET’. A.S. was supported by a scholarship of the German José Carreras Leukemia Foundation (DJCLS 03 F/2016). We gratefully acknowledge patients with their families for their invaluable contributions.
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