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      Therapy-related myelodysplastic syndromes deserve specific diagnostic sub-classification and risk-stratification—an approach to classification of patients with t-MDS

      research-article
      1 , , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 2 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 13 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 5 , 35 , 1 , 36 , 37 , 38 , 39 , 40 , 40 , 41 , 1 , 42 , 1 , 43 , 35 , 44
      Leukemia
      Nature Publishing Group UK
      Risk factors, Diagnosis

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          Abstract

          In the current World Health Organization (WHO)-classification, therapy-related myelodysplastic syndromes (t-MDS) are categorized together with therapy-related acute myeloid leukemia (AML) and t-myelodysplastic/myeloproliferative neoplasms into one subgroup independent of morphologic or prognostic features. Analyzing data of 2087 t-MDS patients from different international MDS groups to evaluate classification and prognostication tools we found that applying the WHO classification for p-MDS successfully predicts time to transformation and survival (both p < 0.001). The results regarding carefully reviewed cytogenetic data, classifications, and prognostic scores confirmed that t-MDS are similarly heterogeneous as p-MDS and therefore deserve the same careful differentiation regarding risk. As reference, these results were compared with 4593 primary MDS (p-MDS) patients represented in the International Working Group for Prognosis in MDS database (IWG-PM). Although a less favorable clinical outcome occurred in each t-MDS subset compared with p-MDS subgroups, FAB and WHO-classification, IPSS-R, and WPSS-R separated t-MDS patients into differing risk groups effectively, indicating that all established risk factors for p-MDS maintained relevance in t-MDS, with cytogenetic features having enhanced predictive power. These data strongly argue to classify t-MDS as a separate entity distinct from other WHO-classified t-myeloid neoplasms, which would enhance treatment decisions and facilitate the inclusion of t-MDS patients into clinical studies.

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          Most cited references54

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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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            Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors.

            Multivariable regression models are powerful tools that are used frequently in studies of clinical outcomes. These models can use a mixture of categorical and continuous variables and can handle partially observed (censored) responses. However, uncritical application of modelling techniques can result in models that poorly fit the dataset at hand, or, even more likely, inaccurately predict outcomes on new subjects. One must know how to measure qualities of a model's fit in order to avoid poorly fitted or overfitted models. Measurement of predictive accuracy can be difficult for survival time data in the presence of censoring. We discuss an easily interpretable index of predictive discrimination as well as methods for assessing calibration of predicted survival probabilities. Both types of predictive accuracy should be unbiasedly validated using bootstrapping or cross-validation, before using predictions in a new data series. We discuss some of the hazards of poorly fitted and overfitted regression models and present one modelling strategy that avoids many of the problems discussed. The methods described are applicable to all regression models, but are particularly needed for binary, ordinal, and time-to-event outcomes. Methods are illustrated with a survival analysis in prostate cancer using Cox regression.
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              Age-related clonal hematopoiesis associated with adverse outcomes.

              The incidence of hematologic cancers increases with age. These cancers are associated with recurrent somatic mutations in specific genes. We hypothesized that such mutations would be detectable in the blood of some persons who are not known to have hematologic disorders. We analyzed whole-exome sequencing data from DNA in the peripheral-blood cells of 17,182 persons who were unselected for hematologic phenotypes. We looked for somatic mutations by identifying previously characterized single-nucleotide variants and small insertions or deletions in 160 genes that are recurrently mutated in hematologic cancers. The presence of mutations was analyzed for an association with hematologic phenotypes, survival, and cardiovascular events. Detectable somatic mutations were rare in persons younger than 40 years of age but rose appreciably in frequency with age. Among persons 70 to 79 years of age, 80 to 89 years of age, and 90 to 108 years of age, these clonal mutations were observed in 9.5% (219 of 2300 persons), 11.7% (37 of 317), and 18.4% (19 of 103), respectively. The majority of the variants occurred in three genes: DNMT3A, TET2, and ASXL1. The presence of a somatic mutation was associated with an increase in the risk of hematologic cancer (hazard ratio, 11.1; 95% confidence interval [CI], 3.9 to 32.6), an increase in all-cause mortality (hazard ratio, 1.4; 95% CI, 1.1 to 1.8), and increases in the risks of incident coronary heart disease (hazard ratio, 2.0; 95% CI, 1.2 to 3.4) and ischemic stroke (hazard ratio, 2.6; 95% CI, 1.4 to 4.8). Age-related clonal hematopoiesis is a common condition that is associated with increases in the risk of hematologic cancer and in all-cause mortality, with the latter possibly due to an increased risk of cardiovascular disease. (Funded by the National Institutes of Health and others.).
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                Author and article information

                Contributors
                kuendgen@med.uni-duesseldorf.de
                Journal
                Leukemia
                Leukemia
                Leukemia
                Nature Publishing Group UK (London )
                0887-6924
                1476-5551
                29 June 2020
                29 June 2020
                2021
                : 35
                : 3
                : 835-849
                Affiliations
                [1 ]GRID grid.14778.3d, ISNI 0000 0000 8922 7789, Department of Hematology, Oncology, and Clinical Immunology, , University Hospital Duesseldorf, ; Duesseldorf, Germany
                [2 ]Department of Laboratory Hematology, Institut Català d’Oncologia Hospital GermansTrias I Pujol, Badalona, Spain
                [3 ]GRID grid.413662.4, ISNI 0000 0000 8987 0344, Boltzmann Institute for Leukemia Research, Hanusch Hospital, ; Vienna, Austria
                [4 ]GRID grid.240145.6, ISNI 0000 0001 2291 4776, Department of Leukemia, , MD Anderson Cancer Center, ; Houston, TX USA
                [5 ]GRID grid.468198.a, ISNI 0000 0000 9891 5233, Department of Malignant Hematology, , H Lee Moffitt Cancer Center, ; Tampa, FL USA
                [6 ]GRID grid.239578.2, ISNI 0000 0001 0675 4725, Leukemia Program, Department of Hematology and Medical Oncology, , Taussig Cancer Institute, Cleveland Clinic, ; Cleveland, OH USA
                [7 ]Cancer Center - IRCCS Humanitas Research Hospital & Humanitas University, Rozzano - Milan, Italy
                [8 ]GRID grid.419425.f, ISNI 0000 0004 1760 3027, Department of Hematology Oncology, , IRCCS Policlinico San Matteo Foundation, ; Pavia, Italy
                [9 ]GRID grid.21107.35, ISNI 0000 0001 2171 9311, Sidney Kimmel Comprehensive Cancer Center, , Johns Hopkins University, ; Baltimore, MD USA
                [10 ]GRID grid.413734.6, ISNI 0000 0000 8499 1112, Weill Cornell Medicine and The New York Presbyterian Hospital, ; New York, NY USA
                [11 ]GRID grid.65499.37, ISNI 0000 0001 2106 9910, Dana-Farber Cancer Institute, ; Boston, MA USA
                [12 ]GRID grid.12380.38, ISNI 0000 0004 1754 9227, Amsterdam UMC, Vrije Universiteit Amsterdam, ; Amsterdam, Netherlands
                [13 ]GRID grid.410712.1, Department of Internal Medicine III, , University Hospital Ulm, ; Ulm, Germany
                [14 ]GRID grid.7497.d, ISNI 0000 0004 0492 0584, National Center of Tumor Diseases-Trial Center, National Center of Tumor Diseases, , German Cancer Research Center, ; Heidelberg, Germany
                [15 ]GRID grid.5253.1, ISNI 0000 0001 0328 4908, Department of Internal Medicine V, , Heidelberg University Hospital, ; Heidelberg, Germany
                [16 ]GRID grid.411142.3, ISNI 0000 0004 1767 8811, Hematological Citology Laboratory, Pathology Department, , Hospital del Mar, GRETNHE, IMIM Hospital del Mar Research Institute, ; Barcelona, Spain
                [17 ]GRID grid.8515.9, ISNI 0000 0001 0423 4662, Service of Hematology, , University Hospital Lausanne, ; Lausanne, Switzerland
                [18 ]GRID grid.410458.c, ISNI 0000 0000 9635 9413, Hemotherapy and Hemostasis Department, , Hospital Clínic de Barcelona IDIBAPS, ; Barcelona, Spain
                [19 ]GRID grid.22937.3d, ISNI 0000 0000 9259 8492, Department of Internal Medicine I, Division of Hematology & Hemostaseology and Ludwig Boltzmann Institute for Hematology and Oncology, , Medical University of Vienna, ; Vienna, Austria
                [20 ]GRID grid.410458.c, ISNI 0000 0000 9635 9413, Hematopathology Section, , Hospital Clínic de Barcelona IDIBAPS, ; Barcelona, Spain
                [21 ]GRID grid.459730.c, ISNI 0000 0004 0558 4607, Department of Oncology, Hematology and Palliative Care, , Marienhospital Duesseldorf, ; Duesseldorf, Germany
                [22 ]GRID grid.7080.f, Clinical Hematology Department, Institut Català d’Oncologia, Hospital Germans Trias i Pujol, Badalona, Josep Carreras Leukemia Research Institute, , Universitat Autònoma de Barcelona, ; Bellaterra, Spain
                [23 ]GRID grid.411339.d, ISNI 0000 0000 8517 9062, University Hospital Leipzig, ; Leipzig, Germany
                [24 ]GRID grid.411142.3, ISNI 0000 0004 1767 8811, Clinical Hematology Department, , Hospital del Mar, ; Barcelona, Spain
                [25 ]GRID grid.7708.8, ISNI 0000 0000 9428 7911, Department of Hematology, Oncology and Stem Cell Transplantation, , University Medical Center Freiburg, Faculty of Medicine, ; Freiburg, Germany
                [26 ]GRID grid.468902.1, ISNI 0000 0004 1773 0974, Clinical Hematology Department, , Hospital Universitario Araba, ; Vitoria-Gasteiz, Spain
                [27 ]GRID grid.411258.b, Clinical Hematology Department, , Hospital Universitario de Salamanca (HUSA), ; Salamanca, Spain
                [28 ]GRID grid.144756.5, ISNI 0000 0001 1945 5329, Clinical Hematology Department, , Hospital Universitario 12 de Octubre, ; Madrid, Spain
                [29 ]GRID grid.414473.1, 1st. Internal Department – Hematology with stem cell transplants, , Hemostaseology and Medical Oncology, Elisabethinen Hospital, ; Linz, Austria
                [30 ]GRID grid.106023.6, ISNI 0000 0004 1770 977X, Clinical Hematology Department, , Hospital General Universitari de València, ; Valencia, Spain
                [31 ]GRID grid.412468.d, ISNI 0000 0004 0646 2097, Department of Hematology and Oncology, , University Hospital Schleswig-Holstein, Campus Kiel, ; Kiel, Germany
                [32 ]GRID grid.414560.2, ISNI 0000 0004 0506 7757, Clinical Hematology Department, , Hospital Parc Taulí, ; Sabadell, Spain
                [33 ]GRID grid.5361.1, ISNI 0000 0000 8853 2677, Department of Internal Medicine V (Hematology and Oncology), , Innsbruck Medical University, ; Innsbruck, Austria
                [34 ]GRID grid.411083.f, ISNI 0000 0001 0675 8654, Department of Hematology, , University Hospital Vall d´Hebrón, ; Barcelona, Spain
                [35 ]GRID grid.411984.1, ISNI 0000 0001 0482 5331, Department of Hematology and Medical Oncology, , University Medical Center Göttingen, ; Göttingen, Germany
                [36 ]GRID grid.6530.0, ISNI 0000 0001 2300 0941, Department of Biomedicine and Prevention, , Tor Vergata University, ; Rome, Italy
                [37 ]GRID grid.413662.4, ISNI 0000 0000 8987 0344, Hanusch Krankenhaus Wien, ; Vienna, Austria
                [38 ]GRID grid.11598.34, ISNI 0000 0000 8988 2476, Medizinische Universität Graz, ; Graz, Austria
                [39 ]GRID grid.411327.2, ISNI 0000 0001 2176 9917, Institute of Human Genetics, , University Duesseldorf, ; Duesseldorf, Germany
                [40 ]Clinical Hematology Department, Hospital Clínic de Barcelona, IDIBAPS, Barcelona, Spain
                [41 ]GRID grid.416936.f, ISNI 0000 0004 1769 0319, Clinical Hematology Department, , Hospital Quirón Teknon, ; Barcelona, Spain
                [42 ]MDS Group, Josep Carreras Leukemia Research Institute, Barcelona, Spain
                [43 ]GRID grid.168010.e, ISNI 0000000419368956, Stanford University Cancer Center, ; Stanford, CA USA
                [44 ]GRID grid.84393.35, ISNI 0000 0001 0360 9602, Clinical Hematology Department, , Hospital Universitari I Politècnic la Fe, ; Valencia, Spain
                Author information
                http://orcid.org/0000-0002-1833-1890
                http://orcid.org/0000-0002-1568-3436
                http://orcid.org/0000-0001-6984-8817
                http://orcid.org/0000-0001-5130-9284
                http://orcid.org/0000-0003-2215-2059
                http://orcid.org/0000-0001-7934-9130
                http://orcid.org/0000-0003-2275-3081
                http://orcid.org/0000-0002-7566-5985
                http://orcid.org/0000-0002-6164-4761
                http://orcid.org/0000-0002-2767-8191
                Article
                917
                10.1038/s41375-020-0917-7
                7932916
                32595214
                7e6ef92e-be75-4b99-9fcd-2ebaea2faa81
                © The Author(s) 2020

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 15 May 2020
                : 23 May 2020
                : 5 June 2020
                Categories
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                © The Author(s), under exclusive licence to Springer Nature Limited 2021

                Oncology & Radiotherapy
                risk factors,diagnosis
                Oncology & Radiotherapy
                risk factors, diagnosis

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