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      The prognostic contribution of CBL , NRAS , KRAS , RUNX1 , and TP53 mutations to mutation‐enhanced international prognostic score systems (MIPSS70/plus/plus v2.0) for primary myelofibrosis

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          Abstract

          Contemporary risk models in primary myelofibrosis (PMF) include the mutation (MIPSS70) and mutation/karyotype enhanced (MIPSS70 plus/v2.0) international prognostic scoring systems. High molecular risk (HMR) mutations incorporated in one or both of these models include ASXL1, SRSF2, EZH2, IDH1/2, and U2AF1Q157; the current study examines additional prognostic contribution from more recently described HMR mutations, including CBL, NRAS, KRAS, RUNX1, and TP53. In a cohort of 363 informative cases (median age 58 years; 60% males), mutations included JAK2 61%, CALR 24%, MPL 6%, ASXL1 29%, SRSF2 10%, U2AF1Q157 5%, EZH2 10%, IDH1/2 4%, TP53 5%, CBL 5%, NRAS 7%, KRAS 4%, and RUNX1 4%. At a median follow‐up of 4.6 years, 135 (37%) deaths and 42 (11.6%) leukemic transformations were recorded. Univariate analysis confirmed significant survival impact from the original MIPSS70/plus/v2.0 HMR mutations as well as CBL (HR 2.8; p < .001), NRAS (HR 2.4; p < .001), KRAS (HR 2.1; p = .01), and TP53 (HR 2.4; p = .004), but not RUNX1 mutations (HR 1.8; p = .08). Multivariate analysis (MVA) that included both the original and more recently described HMR mutations confirmed independent prognostic contribution from ASXL1 (HR 1.8; p = .007), SRSF2 (HR 4.3; p < .001), U2AF1Q157 (HR 2.9, p = .004), and EZH2 (HR 2.4; p < .001), but not from IDH1/ 2 ( p = .3), TP53 ( p = .2), CBL ( p = .3), NRAS ( p = .8) or KRAS ( p = .2) mutations. The lack of additional prognostic value from CBL, NRAS, KRAS, RUNX1, and TP53 was further demonstrated in the setting of (i) MVA of mutations and karyotype, (ii) MVA of MIPSS70/plus/v2.0 composite scores and each one of the recently described HMR mutations, except TP53, and iii) modified MIPSS70/plus/plus v2.0 that included CBL, NRAS, KRAS, and TP53 as part of the HMR constituency, operationally referred to as “HMR+” category. Furthermore, “HMR+” enhancement of MIPSS70/plus/plus v2.0 did not result in improved model performance, as measured by C‐statistics. We conclude that prognostic integrity of MIPSS70/plus/plus v2.0, as well as their genetic components, was sustained and their value not significantly upgraded by the inclusion of more recently described HMR mutations, including CBL, NRAS, KRAS, and RUNX1. Additional studies are needed to clarify the apparent additional prognostic value of TP53 mutation and its allelic state.

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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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            Genomic Classification and Prognosis in Acute Myeloid Leukemia

            New England Journal of Medicine, 374(23), 2209-2221
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              The 5th edition of the World Health Organization Classification of Haematolymphoid Tumours: Myeloid and Histiocytic/Dendritic Neoplasms

              The upcoming 5th edition of the World Health Organization (WHO) Classification of Haematolymphoid Tumours is part of an effort to hierarchically catalogue human cancers arising in various organ systems within a single relational database. This paper summarizes the new WHO classification scheme for myeloid and histiocytic/dendritic neoplasms and provides an overview of the principles and rationale underpinning changes from the prior edition. The definition and diagnosis of disease types continues to be based on multiple clinicopathologic parameters, but with refinement of diagnostic criteria and emphasis on therapeutically and/or prognostically actionable biomarkers. While a genetic basis for defining diseases is sought where possible, the classification strives to keep practical worldwide applicability in perspective. The result is an enhanced, contemporary, evidence-based classification of myeloid and histiocytic/dendritic neoplasms, rooted in molecular biology and an organizational structure that permits future scalability as new discoveries continue to inexorably inform future editions.
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                Author and article information

                Contributors
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                Journal
                American Journal of Hematology
                American J Hematol
                Wiley
                0361-8609
                1096-8652
                January 2024
                October 17 2023
                January 2024
                : 99
                : 1
                : 68-78
                Affiliations
                [1 ] Department of Experimental and Clinical Medicine, CRIMM, Center of Research and Innovation of Myeloproliferative Neoplasms Azienda Ospedaliero‐Universitaria Careggi, University of Florence Florence Italy
                [2 ] Doctorate School GenOMec University of Siena Siena Italy
                Article
                10.1002/ajh.27136
                37846894
                56626dae-796b-4a2b-8adf-4ad4bc761c5c
                © 2024

                http://creativecommons.org/licenses/by-nc-nd/4.0/

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