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      Kinome expression profiling improves risk stratification and therapeutic targeting in myelodysplastic syndromes

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          Key Points

          • Through transcriptomic analysis, we identified 7 kinases whose expressions were strongly predictive of compromised survival in MDS.

          • The KISS could improve risk stratification and imply novel therapeutic opportunities in MDS.

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          Abstract

          The human kinome, which comprises >500 kinases, plays a critical role in regulating numerous essential cellular functions. Although the dysregulation of kinases has been observed in various human cancers, the characterization and clinical implications of kinase expressions in myelodysplastic syndromes (MDS) have not been systematically investigated. In this study, we evaluated the kinome expression profiles of 341 adult patients with primary MDS and identified 7 kinases ( PTK7, KIT, MAST4, NTRK1, PAK6, CAMK1D, and PRKCZ) whose expression levels were highly predictive of compromised patient survival. We then constructed the kinase stratification score (KISS) by combining the weighted expressions of the 7 kinases and validated its prognostic significance in 2 external MDS cohorts. A higher KISS was associated with older age, higher peripheral blood and marrow blast percentages, higher Revised International Prognostic Scoring System (IPSS-R) risks, complex karyotype, and mutations in several adverse-risk genes in MDS, such as ASXL1, EZH2, NPM1, RUNX1, STAG2, and TP53. Multivariate analysis confirmed that a higher KISS was an independent unfavorable risk factor in MDS. Mechanistically, the KISS-high patients were enriched for gene sets associated with hematopoietic and leukemic stem cell signatures. By investigating the Genomics of Drug Sensitivity in Cancer database, we identified axitinib and taselisib as candidate compounds that could potentially target the KISS-high myeloblasts. Altogether, our findings suggest that KISS holds the potential to improve the current prognostic scheme of MDS and inform novel therapeutic opportunities.

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          Most cited references57

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          STAR: ultrafast universal RNA-seq aligner.

          Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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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 2016 revision to the World Health Organization classification of myeloid neoplasms and acute leukemia.

              The World Health Organization (WHO) classification of tumors of the hematopoietic and lymphoid tissues was last updated in 2008. Since then, there have been numerous advances in the identification of unique biomarkers associated with some myeloid neoplasms and acute leukemias, largely derived from gene expression analysis and next-generation sequencing that can significantly improve the diagnostic criteria as well as the prognostic relevance of entities currently included in the WHO classification and that also suggest new entities that should be added. Therefore, there is a clear need for a revision to the current classification. The revisions to the categories of myeloid neoplasms and acute leukemia will be published in a monograph in 2016 and reflect a consensus of opinion of hematopathologists, hematologists, oncologists, and geneticists. The 2016 edition represents a revision of the prior classification rather than an entirely new classification and attempts to incorporate new clinical, prognostic, morphologic, immunophenotypic, and genetic data that have emerged since the last edition. The major changes in the classification and their rationale are presented here.
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                Author and article information

                Contributors
                Journal
                Blood Adv
                Blood Adv
                Blood Advances
                The American Society of Hematology
                2473-9529
                2473-9537
                27 March 2024
                28 May 2024
                27 March 2024
                : 8
                : 10
                : 2442-2454
                Affiliations
                [1 ]Division of Hematology, Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
                [2 ]Department of Laboratory Medicine, National Taiwan University Hospital, Taipei, Taiwan
                [3 ]Graduate Institute of Clinical Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
                [4 ]Division of Cancer Sciences, University of Manchester, Manchester, United Kingdom
                [5 ]Department of Medical Research, National Taiwan University Hospital, Taipei, Taiwan
                [6 ]Graduate Institute of Oncology, College of Medicine, National Taiwan University, Taipei, Taiwan
                [7 ]Graduate Institute of Medical Genomics and Proteomics, College of Medicine, National Taiwan University, Taipei, Taiwan
                Author notes
                []Correspondence: Wen-Chien Chou, Department of Laboratory Medicine, National Taiwan University Hospital, No. 7, Chung-Shan South Rd, Taipei City 10002, Taiwan; wchou@ 123456ntu.edu.tw
                [∗∗ ]Chia-Lang Hsu, Department of Medical Research, National Taiwan University Hospital, No. 7, Chung Shan South Rd, Taipei City 10002, Taiwan; chialanghsu@ 123456ntuh.gov.tw
                Article
                S2473-9529(24)00182-4
                10.1182/bloodadvances.2023011512
                11112608
                38527292
                36d68a13-7454-4597-9006-6467efed487d
                © 2024 by The American Society of Hematology. Licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0), permitting only noncommercial, nonderivative use with attribution. All other rights reserved.

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 23 August 2023
                : 12 February 2024
                Categories
                Myeloid Neoplasia

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