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      Phase I First-in-Human Dose Escalation Study of the oral SF3B1 modulator H3B-8800 in myeloid neoplasms

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      1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 25 , 1 , 26 , 26 , 26 , 26 , 27 , 26 , 26 , 26 , 26 , 26 , 26 , 26 , , 28 ,
      Leukemia
      Nature Publishing Group UK
      Health sciences, Medical research, Drug development

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          Abstract

          We conducted a phase I clinical trial of H3B-8800, an oral small molecule that binds Splicing Factor 3B1 (SF3B1), in patients with MDS, CMML, or AML. Among 84 enrolled patients (42 MDS, 4 CMML and 38 AML), 62 were red blood cell (RBC) transfusion dependent at study entry. Dose escalation cohorts examined two once-daily dosing regimens: schedule I (5 days on/9 days off, range of doses studied 1–40 mg, n = 65) and schedule II (21 days on/7 days off, 7–20 mg, n = 19); 27 patients received treatment for ≥180 days. The most common treatment-related, treatment-emergent adverse events included diarrhea, nausea, fatigue, and vomiting. No complete or partial responses meeting IWG criteria were observed; however, RBC transfusion free intervals >56 days were observed in nine patients who were transfusion dependent at study entry (15%). Of 15 MDS patients with missense SF3B1 mutations, five experienced RBC transfusion independence (TI). Elevated pre-treatment expression of aberrant transcripts of Transmembrane Protein 14C ( TMEM14C), an SF3B1 splicing target encoding a mitochondrial porphyrin transporter, was observed in MDS patients experiencing RBC TI. In summary, H3B-8800 treatment was associated with mostly low-grade TAEs and induced RBC TI in a biomarker-defined subset of MDS.

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          Most cited references34

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          The meaning and use of the area under a receiver operating characteristic (ROC) curve.

          A representation and interpretation of the area under a receiver operating characteristic (ROC) curve obtained by the "rating" method, or by mathematical predictions based on patient characteristics, is presented. It is shown that in such a setting the area represents the probability that a randomly chosen diseased subject is (correctly) rated or ranked with greater suspicion than a randomly chosen non-diseased subject. Moreover, this probability of a correct ranking is the same quantity that is estimated by the already well-studied nonparametric Wilcoxon statistic. These two relationships are exploited to (a) provide rapid closed-form expressions for the approximate magnitude of the sampling variability, i.e., standard error that one uses to accompany the area under a smoothed ROC curve, (b) guide in determining the size of the sample required to provide a sufficiently reliable estimate of this area, and (c) determine how large sample sizes should be to ensure that one can statistically detect differences in the accuracy of diagnostic techniques.
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            Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine.

            The clinical performance of a laboratory test can be described in terms of diagnostic accuracy, or the ability to correctly classify subjects into clinically relevant subgroups. Diagnostic accuracy refers to the quality of the information provided by the classification device and should be distinguished from the usefulness, or actual practical value, of the information. Receiver-operating characteristic (ROC) plots provide a pure index of accuracy by demonstrating the limits of a test's ability to discriminate between alternative states of health over the complete spectrum of operating conditions. Furthermore, ROC plots occupy a central or unifying position in the process of assessing and using diagnostic tools. Once the plot is generated, a user can readily go on to many other activities such as performing quantitative ROC analysis and comparisons of tests, using likelihood ratio to revise the probability of disease in individual subjects, selecting decision thresholds, using logistic-regression analysis, using discriminant-function analysis, or incorporating the tool into a clinical strategy by using decision analysis.
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              Revised international prognostic scoring system for myelodysplastic syndromes.

              The International Prognostic Scoring System (IPSS) is an important standard for assessing prognosis of primary untreated adult patients with myelodysplastic syndromes (MDS). To refine the IPSS, MDS patient databases from international institutions were coalesced to assemble a much larger combined database (Revised-IPSS [IPSS-R], n = 7012, IPSS, n = 816) for analysis. Multiple statistically weighted clinical features were used to generate a prognostic categorization model. Bone marrow cytogenetics, marrow blast percentage, and cytopenias remained the basis of the new system. Novel components of the current analysis included: 5 rather than 3 cytogenetic prognostic subgroups with specific and new classifications of a number of less common cytogenetic subsets, splitting the low marrow blast percentage value, and depth of cytopenias. This model defined 5 rather than the 4 major prognostic categories that are present in the IPSS. Patient age, performance status, serum ferritin, and lactate dehydrogenase were significant additive features for survival but not for acute myeloid leukemia transformation. This system comprehensively integrated the numerous known clinical features into a method analyzing MDS patient prognosis more precisely than the initial IPSS. As such, this IPSS-R should prove beneficial for predicting the clinical outcomes of untreated MDS patients and aiding design and analysis of clinical trials in this disease.
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                Author and article information

                Contributors
                antonio_gualberto@h3biomedicine.com
                uwe.platzbecker@medizin.uni-leipzig.de
                Journal
                Leukemia
                Leukemia
                Leukemia
                Nature Publishing Group UK (London )
                0887-6924
                1476-5551
                25 June 2021
                25 June 2021
                2021
                : 35
                : 12
                : 3542-3550
                Affiliations
                [1 ]GRID grid.65499.37, ISNI 0000 0001 2106 9910, Dana–Farber Cancer Institute, ; Boston, MA USA
                [2 ]GRID grid.412282.f, ISNI 0000 0001 1091 2917, University Hospital Dresden, ; Dresden, Germany
                [3 ]GRID grid.51462.34, ISNI 0000 0001 2171 9952, Memorial Sloan Kettering Cancer Center, ; New York, NY USA
                [4 ]GRID grid.240952.8, ISNI 0000000087342732, Stanford University Medical Center, ; Stanford, CA USA
                [5 ]GRID grid.410526.4, ISNI 0000 0001 0277 7938, Hospital General Universitario Gregorio Marañon, ; Madrid, Spain
                [6 ]GRID grid.468198.a, ISNI 0000 0000 9891 5233, H. Lee Moffitt Cancer Center and Research Institute, ; Tampa, FL USA
                [7 ]GRID grid.477517.7, ISNI 0000 0004 0396 4462, Karmanos Cancer Institute, ; Detroit, MI USA
                [8 ]GRID grid.32224.35, ISNI 0000 0004 0386 9924, Massachusetts General Hospital, ; Boston, MA USA
                [9 ]GRID grid.239578.2, ISNI 0000 0001 0675 4725, Taussig Cancer Institute, , Cleveland Clinic, ; Cleveland, OH USA
                [10 ]GRID grid.413328.f, ISNI 0000 0001 2300 6614, Hopital Saint Louis, ; Paris, France
                [11 ]GRID grid.66875.3a, ISNI 0000 0004 0459 167X, Mayo Clinic, ; Rochester, MN USA
                [12 ]GRID grid.419651.e, ISNI 0000 0000 9538 1950, Hospital Universitario Fundacion Jimenez Diaz- IISFJD-UAM, ; Madrid, Spain
                [13 ]GRID grid.411730.0, ISNI 0000 0001 2191 685X, Clinica Universidad Navarra, ; Pamplona, Spain
                [14 ]GRID grid.10698.36, ISNI 0000000122483208, University of North Carolina at Chapel Hill, ; Chapel Hill, NC USA
                [15 ]GRID grid.270240.3, ISNI 0000 0001 2180 1622, Fred Hutchinson Cancer Research Center, ; Seattle, WA USA
                [16 ]GRID grid.492963.3, ISNI 0000 0004 0480 9560, Tennessee Oncology/Sarah Cannon Research Institute, ; Nashville, TN USA
                [17 ]GRID grid.417467.7, ISNI 0000 0004 0443 9942, Mayo Clinic, ; Jacksonville, FL USA
                [18 ]GRID grid.240145.6, ISNI 0000 0001 2291 4776, University of Texas MD Anderson Cancer Center, ; Houston, TX USA
                [19 ]GRID grid.488768.d, Colorado Blood Cancer Institute, ; Denver, CO USA
                [20 ]GRID grid.38142.3c, ISNI 000000041936754X, Beth Israel Deaconess Medical Center, , Harvard Medical School, ; Boston, MA USA
                [21 ]GRID grid.14925.3b, ISNI 0000 0001 2284 9388, Gustave Roussy Cancer Center, ; Villejuif, France
                [22 ]GRID grid.488453.6, ISNI 0000000417724902, Hospital Universitario Sanchinarro, ; Madrid, Spain
                [23 ]GRID grid.10423.34, ISNI 0000 0000 9529 9877, Hannover Medical School, ; Hannover, Germany
                [24 ]GRID grid.240614.5, ISNI 0000 0001 2181 8635, Roswell Park Comprehensive Cancer Center, ; Buffalo, NY USA
                [25 ]GRID grid.26790.3a, ISNI 0000 0004 1936 8606, University of Miami, Sylvester Comprehensive Cancer Center, ; Miami, FL USA
                [26 ]H3 Biomedicine, Cambridge, MA USA
                [27 ]GRID grid.418767.b, ISNI 0000 0004 0599 8842, Eisai, Inc, ; Woodcliff Lake, NJ USA
                [28 ]GRID grid.411339.d, ISNI 0000 0000 8517 9062, University Hospital Leipzig, Clinic for Hematology, Cellular Therapy and Hemostaseology, ; Leipzig, Germany
                Author information
                http://orcid.org/0000-0001-5130-9284
                http://orcid.org/0000-0002-4844-4087
                http://orcid.org/0000-0002-1876-5269
                http://orcid.org/0000-0002-2169-4115
                http://orcid.org/0000-0002-9020-8766
                http://orcid.org/0000-0002-0824-3715
                http://orcid.org/0000-0002-4426-9023
                http://orcid.org/0000-0001-6724-290X
                http://orcid.org/0000-0003-1673-1708
                http://orcid.org/0000-0002-3631-2482
                http://orcid.org/0000-0003-0934-294X
                http://orcid.org/0000-0002-8644-0968
                http://orcid.org/0000-0003-4407-6325
                http://orcid.org/0000-0002-8469-6134
                http://orcid.org/0000-0002-6590-839X
                http://orcid.org/0000-0003-1863-3239
                Article
                1328
                10.1038/s41375-021-01328-9
                8632688
                34172893
                bb5703a9-79a4-4248-8526-fe8498ce6b53
                © The Author(s) 2021

                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
                : 10 April 2021
                : 25 May 2021
                : 11 June 2021
                Funding
                Funded by: H3 Biomedicine Inc
                Categories
                Article
                Custom metadata
                © The Author(s), under exclusive licence to Springer Nature Limited 2021

                Oncology & Radiotherapy
                health sciences,medical research,drug development
                Oncology & Radiotherapy
                health sciences, medical research, drug development

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