Personalized Medicine Program on Myelodysplastic Syndromes: Characterization of the Patient's Genome for Clinical Decision Making and Systematic Collection of Real World Data to Improve Quality of Health Care (FISiM-NGS-MDS)

• ClinicalTrials.gov Identifier: NCT04212390
• Recruitment Status: Recruiting
• First Posted: December 27, 2019
• Last Update Posted: February 22, 2024
• Sponsor: Fondazione Italiana Sindromi Mielodisplastiche-ETS
• Information provided by (Responsible Party): Fondazione Italiana Sindromi Mielodisplastiche-ETS

Tracking Information

• First Submitted Date: December 20, 2019
• First Posted Date: December 27, 2019
• Last Update Posted Date: February 22, 2024
• Actual Study Start Date: June 3, 2020
• Estimated Primary Completion Date: March 31, 2024 (Final data collection date for primary outcome measure)

Current Primary Outcome Measures (submitted: January 3, 2020)

• Type and frequency of recurrent gene mutations in MDS patients at diagnosis [ Time Frame: 0-24 months ]
  Polymorphonuclear granulocytes (PMN)/mononucleated cells (MNC) will be isolated from peripheral blood (PB) and/or bone marrow (BM) samples. Separated cells will be frozen locally and sent to the central lab to perform DNA extraction and NGS screening every 4 months.In all cases, a NGS screening will be performed by targeted approach to sequence all coding exons of 60 candidate genes. As a result of this approach, the investigators will describe type and frequency of recurrent gene mutations in MDS patients.
• Prognostic significance of gene mutations in MDS patients [ Time Frame: 12-36 months ]
  A research platform will be developped by combining FISiM data on genetic and molecular characterization of hematological malignancies together with the national platform (REDCAP database), where clinical data are kept in a protected environment . The investigators will analyze the prognostic effect of gene mutations on MDS patients' outcome (overall survival) by multivariable analysis.
• Measure of quality of life (QoL) in MDS patients [ Time Frame: 0-24 months ]
  The investigators will use QOL-E questionnaire to measure QoL in MDS patients. QOL-E is a specific tool to evaluate patient reported outcomes in patients with Myelodysplastic Syndromes. It evaluates the impact of the disease and treatment on 4 general dimensions (physical, functional, social and sexual well-being) and on one specific MDS-related dimension and also fatigue (https://qol-e.it/). Each item is rescaled so that a better response corresponds to a higher numerical value and better QoL.Transformation of raw scores into a 0-100 scale will be carried out to generate the standardized scores for each domain. Questionnaire will be completed by the patients upon study entry. Follow-up measurements will be performed every 6 months for patients receiving supportive care (including RBC transfusions), before and after disease modifying treatments (every 4 months) and at the time of disease progression.
• Measure of patient-reported outcomes (PRO) in MDS patients. [ Time Frame: 0-24 months ]
  The investigators will use HM-PRO questionnaire (Hematology specific patient-reported outocome measure) to measure PRO in MDS patients. The HM-PRO is a composite measure consisting of two scales: Part A (mesures the impact of MDS and its treatment on a patient's quality of life; Part B (signs and symptoms, SS) captures the severity of different disease symptoms and treatment side effects (https://www.futuremedicine.com/doi/10.2217/cer-2018-0108?url_ver=Z39.88-2003&rfr_id=ori%3Arid%3Acrossref.org&rfr_dat=cr_pub%3Dwww.ncbi.nlm.nih.gov&). Both scales have linear scoring system ranging from 0 to 100, with higher scores representing greater impact on QoL and symptom burden. Questionnaire will be completed by the patients upon study entry. Follow-up measurements will be performed every 6 months for patients receiving supportive care (including RBC transfusions), before and after disease modifying treatments (every 4 months) and at the time of disease progression.

Original Primary Outcome Measures (submitted: December 23, 2019)

• Type and frequency of recurrent gene mutations in MDS patients at diagnosis [ Time Frame: 0-24 months ]
  Polymorphonuclear granulocytes (PMN)/mononucleated cells (MNC) will be isolated from peripheral blood (PB) and/or bone marrow (BM) samples. Separated cells will be frozen locally and sent to the central lab to perform DNA extraction and NGS screening every 4 months.In all cases, a NGS screening will be performed by targeted approach to sequence all coding exons of 60 candidate genes. As a result of this approach, the investigators will describe type and frequency of recurrent gene mutations in MDS patients.
• Prognostic significance of gene mutations in MDS patients [ Time Frame: 12-36 months ]
  A research platform will be developped by combining FISiM data on genetic and molecular characterization of hematological malignancies together with the national platform (REDCAP database), where clinical data are kept in a protected environment . The investigators will analyze the prognostic effect of gene mutations on MDS patients' outcome (overall survival) by multivariable analysis.
• Measure of quality of life (QoL) in MDS patients [ Time Frame: 0-24 months ]
  The investigators will use QOL-E questionnaire to measure QoL in MDS patients. QOL-E is a specific tool to evaluate patient reported outcomes in patients with Myelodysplastic Syndromes. It evaluates the impact of the disease and treatment on 4 general dimensions (physical, functional, social and sexual well-being) and on one specific MDS-related dimension and also fatigue (https://qol-e.it/). Questionnaire will be completed by the patients upon study entry. Follow-up measurements will be performed every 6 months for patients receiving supportive care (including RBC transfusions), before and after disease modifying treatments (every 4 months) and at the time of disease progression.
• Measure of patient-reported outcomes (PRO) in MDS patients. [ Time Frame: 0-24 months ]
  The investigators will use HM-PRO questionnaire (Hematology specific patient-reported outocome measure) to measure PRO in MDS patients. The HM-PRO is a composite measure consisting of tqo scales: Part A (impact mesures the impact of MDS and its treatment on a patient's quality of life; Part B (signs and symptoms, SS) captures the severity of different disease symptoms and treatment side effects (https://www.futuremedicine.com/doi/10.2217/cer-2018-0108?url_ver=Z39.88-2003&rfr_id=ori%3Arid%3Acrossref.org&rfr_dat=cr_pub%3Dwww.ncbi.nlm.nih.gov&). Questionnaire will be completed by the patients upon study entry. Follow-up measurements will be performed every 6 months for patients receiving supportive care (including RBC transfusions), before and after disease modifying treatments (every 4 months) and at the time of disease progression.

• Current Secondary Outcome Measures: Not Provided
• Original Secondary Outcome Measures: Not Provided
• Current Other Pre-specified Outcome Measures: Not Provided
• Original Other Pre-specified Outcome Measures: Not Provided

Descriptive Information

• Brief Title: Personalized Medicine Program on Myelodysplastic Syndromes: Characterization of the Patient's Genome for Clinical Decision Making and Systematic Collection of Real World Data to Improve Quality of Health Care
• Official Title: Personalized Medicine Program on Myelodysplastic Syndromes: Characterization of the Patient's Genome for Clinical Decision Making and Systematic Collection of Real World Data to Improve Quality of Health Care

Brief Summary

BACKGROUND Myelodysplastic syndromes (MDS) typically occur in elderly people and with time, a portion of the patients evolve into acute myeloid leukemia (AML). Therefore a risk-adapted treatment strategy is mandatory. Current prognostic scores present limitations, and in most cases fail to capture reliable prognostic information at individual level.

STATE OF THE ART Important steps forward have been made in defining the molecular architecture of MDS and gene mutations have been reported to influence survival and risk of disease progression in MDS. Evaluation of the mutation status may add significant information to currently used prognostic scores and a comprehensive analyses of large, prospective patient populations is warranted to correctly estimate the independent effect of each mutation on clinical outcome and response to treatments.

AIMS In this project, the investigators will develop a research platform by integrating genomic mutations, clinical variables and patient outcome derived from real-world data obtained from FISiM (Fondazione Italiana Sindromi Mielodisplastiche) clinical network, including 72 hematological centers.

This will allow the investigators to:

1. define the clinical utility of mutational screening in the diagnostic work-up and classification of MDS
2. assess the implementation of diagnostic and therapeutic guidelines (appropriateness) in the real-life
3. evaluate the impact of specific interventions (treatments) on clinical outcomes, long-term complications and costs
4. identify predictors of response to specific treatments, and develop precision medicine programs in hematology based on Real World Evidence RWD
5. measure patient-reported outcomes (PRO) and quality of life (QoL) in a real world MDS setting

Detailed Description

Myelodysplastic syndromes (MDS) typically occur in elderly people. Patients present peripheral blood cytopenia, and with time a portion of these subjects evolve into acute myeloid leukemia (AML). MDS are heterogeneous ranging from conditions with a near-normal life expectancy to forms close to AML and therefore a risk-adapted treatment strategy is mandatory. Current prognostic scores present limitations, and in most cases fail to capture reliable prognostic information at individual level. Several therapeutic tools have been proposed for MDS but only few survived the evidence-based criteria of efficacy. Lenalidomide improves anemia in patients with 5q deletion. Allogeneic transplantation (HSCT) is the only potentially curative treatment for high risk patients; however, an accurate selection of candidate patients is needed. Hypomethylating agents (HMA) may improve survival in MDS not eligible HSCT, while predictive factors for clinical response remain to be defined.

Important steps forward have been made in defining the molecular architecture of MDS. The MDS associated with 5q deletion derives from the haploinsufficiency of RPS14 gene. The investigators and others identified genes encoding for spliceosome components in a high proportion of MDS. The investigators found a close relationship between ring sideroblasts and SF3B1 mutations, which is consistent with a causal relationship. In addition, an increasing number of genes have been found to carry recurrent mutations in MDS, involved in DNA methylation (DNMT3A, TET2, IDH1/2), chromatin modification (EZH2, ASXL1), transcriptional regulation (RUNX1), signal transduction. Gene mutations have been reported to influence survival and risk of disease progression in MDS, and the evaluation of the mutation status may add significant information to currently used prognostic scores. Moreover, mutation screening may affect clinical decision making : a) in MDS with 5q-, subjects carrying TP53 mutations have a higher risk of leukemic progression and a lower probability of response to lenalidomide; b) in patients receiving HSCT, TP53 mutations predict high probability of relapse; c) SF3B1 mutations are associated with increased probability of erythroid response to TGFb inhibitors

Despite these findings, caution is needed against immediately adopting such mutational testing in clinical practice. Most of scientific evidence derive from retrospective analyses of selected patient populations. In addition, in patients with MDS genetic abnormalities explain only a proportion of the total hazard for overall survival and outcome associated with specific treatments, meaning that a large percentage is still associated with clinical and non-mutational factors. Comprehensive analyses of large, prospective patient populations are warranted to correctly estimate the independent effect of each mutation on clinical outcome and response to treatments.

Real World Evidence (RWE) is information on health care that is derived from multiple sources outside typical clinical research settings, including electronic health records (EHRs), claims and billing data, product and disease registries, and data gathered through personal devices and health applications National healthcare systems of advanced countries, including Italy, widely agree on the approach whereby public healthcare decisions should be driven by available evidence on effectiveness and safety of therapeutics. It is equally accepted that randomized controlled clinical trials (RCTs), although universally recognized as the most robust "evidence generators", are insufficient for guiding the decision-making process since they are intrinsically unsuited to capture the impact of treatments in routine clinical practice. The complexity of treatments, as well as the demographic and clinical heterogeneity of patients receiving the treatments, and the long period of many treatments, explain the gap between the evidence generated in the controlled, but artificial, setting of RCTs and their current impact in the real world.

This explains the growing interest in the development of methods able to produce evidence on the real-world impact of care pathways (i.e., real-world evidence). Among them, those based on the Electronic Healthcare Records (EHRs), are becoming established and receiving increasing attention from the scientific community and healthcare decision-makers. In addition, real world data (RWD) are currently used during drug development to examine aspects such as the natural history of a disease, delineating treatment pathways in clinical practice, and determining the costs and resource use associated with treatment interventions

In this project, the investigators will develop a research platform by integrating genomic mutations, clinical variables and patient outcome derived from real-world data obtained from FISiM (Fondazione Italiana Sindromi Mielodisplastiche) clinical network, including 72 hematological centers. In this context, there is clearly a need to develop effective solutions to analyze and integrate molecular and clinical data of large patient populations, in order to fully understand the relationship between genotype and the clinical expression of a disease. In this area, a solution of excellence has been developed by the research center i2b2 (Informatics for Integrating Biology and the Bedside, University of Harvard, Boston - www.i2b2.org). This center developed an open-source software based on a data-warehouse able to integrate and to exploit all data coming from clinical practice and hospital admissions, making them available and easily accessible by researchers. FISiM network is based on a platform to specifically support hematological research, called i2b2Hematology (www.biomeris.com/index.php/it/tasks/i2b2-hematology-pv-it), allowing researchers to explore and analyze three types of data: (i) the clinical data available in all hematological centers belonging to the clinical network, (ii) the information related to the samples stored in biobanks, and (iii) NGS sequencing data in terms of genomic variants. Relying on this national clinical network and on an innovative informatics infrastructure, in this project the investigators will analyze the interactions among driver mutations clinical variables and patient outcome of specific treatments. At the same time the investigators will render NGS analysis of somatic mutations available for the FISiM centers that need support for this technique.

The investigators will address strategical needs in MDS (i.e., standardization and improvement of diagnostic work-up, clinical relevance of mutational screening, adherence to evidence-based guidelines, drug safety and efficacy, clinical relevance of patient-reported outcomes, PRO and quality of life,QoL) in a real world MDS setting with the final objective to propose a personalized approach for the individual patient.

• Study Type: Observational
• Study Design: Observational Model: Cohort; Time Perspective: Prospective
• Target Follow-Up Duration: Not Provided

Biospecimen

Retention:   Samples With DNA

Description:

Peripheral blood and marrow samples

• Sampling Method: Non-Probability Sample
• Study Population: Newly diagnosed patients affected with myelodysplastic syndromes (defined according to 2016 WHO classifcation criteria and stratified according to revised IPSS risk) prospectively enrolled in the FISiM registry
• Condition: MDS (Myelodysplastic Syndrome)
• Intervention: Not Provided

Study Groups/Cohorts

FISiM MDS patients

Patients receiving a diagnosis of MDS and prospectively enrolled in the FISiM registry.

Publications *

• https://www.ctti-clinicaltrials.org/files/recommendations/registrytrials-recs.pdf
• Ades L, Itzykson R, Fenaux P. Myelodysplastic syndromes. Lancet. 2014 Jun 28;383(9936):2239-52. doi: 10.1016/S0140-6736(13)61901-7. Epub 2014 Mar 21.
• Jaiswal S, Fontanillas P, Flannick J, Manning A, Grauman PV, Mar BG, Lindsley RC, Mermel CH, Burtt N, Chavez A, Higgins JM, Moltchanov V, Kuo FC, Kluk MJ, Henderson B, Kinnunen L, Koistinen HA, Ladenvall C, Getz G, Correa A, Banahan BF, Gabriel S, Kathiresan S, Stringham HM, McCarthy MI, Boehnke M, Tuomilehto J, Haiman C, Groop L, Atzmon G, Wilson JG, Neuberg D, Altshuler D, Ebert BL. Age-related clonal hematopoiesis associated with adverse outcomes. N Engl J Med. 2014 Dec 25;371(26):2488-98. doi: 10.1056/NEJMoa1408617. Epub 2014 Nov 26.
• Cazzola M, Della Porta MG, Malcovati L. The genetic basis of myelodysplasia and its clinical relevance. Blood. 2013 Dec 12;122(25):4021-34. doi: 10.1182/blood-2013-09-381665. Epub 2013 Oct 17.
• Greenberg PL, Tuechler H, Schanz J, Sanz G, Garcia-Manero G, Sole F, Bennett JM, Bowen D, Fenaux P, Dreyfus F, Kantarjian H, Kuendgen A, Levis A, Malcovati L, Cazzola M, Cermak J, Fonatsch C, Le Beau MM, Slovak ML, Krieger O, Luebbert M, Maciejewski J, Magalhaes SM, Miyazaki Y, Pfeilstocker M, Sekeres M, Sperr WR, Stauder R, Tauro S, Valent P, Vallespi T, van de Loosdrecht AA, Germing U, Haase D. Revised international prognostic scoring system for myelodysplastic syndromes. Blood. 2012 Sep 20;120(12):2454-65. doi: 10.1182/blood-2012-03-420489. Epub 2012 Jun 27.
• Della Porta MG, Alessandrino EP, Bacigalupo A, van Lint MT, Malcovati L, Pascutto C, Falda M, Bernardi M, Onida F, Guidi S, Iori AP, Cerretti R, Marenco P, Pioltelli P, Angelucci E, Oneto R, Ripamonti F, Bernasconi P, Bosi A, Cazzola M, Rambaldi A; Gruppo Italiano Trapianto di Midollo Osseo. Predictive factors for the outcome of allogeneic transplantation in patients with MDS stratified according to the revised IPSS-R. Blood. 2014 Apr 10;123(15):2333-42. doi: 10.1182/blood-2013-12-542720. Epub 2014 Feb 20.
• Fenaux P, Mufti GJ, Hellstrom-Lindberg E, Santini V, Finelli C, Giagounidis A, Schoch R, Gattermann N, Sanz G, List A, Gore SD, Seymour JF, Bennett JM, Byrd J, Backstrom J, Zimmerman L, McKenzie D, Beach C, Silverman LR; International Vidaza High-Risk MDS Survival Study Group. Efficacy of azacitidine compared with that of conventional care regimens in the treatment of higher-risk myelodysplastic syndromes: a randomised, open-label, phase III study. Lancet Oncol. 2009 Mar;10(3):223-32. doi: 10.1016/S1470-2045(09)70003-8. Epub 2009 Feb 21.
• Della Porta MG, Galli A, Bacigalupo A, Zibellini S, Bernardi M, Rizzo E, Allione B, van Lint MT, Pioltelli P, Marenco P, Bosi A, Voso MT, Sica S, Cuzzola M, Angelucci E, Rossi M, Ubezio M, Malovini A, Limongelli I, Ferretti VV, Spinelli O, Tresoldi C, Pozzi S, Luchetti S, Pezzetti L, Catricala S, Milanesi C, Riva A, Bruno B, Ciceri F, Bonifazi F, Bellazzi R, Papaemmanuil E, Santoro A, Alessandrino EP, Rambaldi A, Cazzola M. Clinical Effects of Driver Somatic Mutations on the Outcomes of Patients With Myelodysplastic Syndromes Treated With Allogeneic Hematopoietic Stem-Cell Transplantation. J Clin Oncol. 2016 Oct 20;34(30):3627-3637. doi: 10.1200/JCO.2016.67.3616.
• Gerstung M, Papaemmanuil E, Martincorena I, Bullinger L, Gaidzik VI, Paschka P, Heuser M, Thol F, Bolli N, Ganly P, Ganser A, McDermott U, Dohner K, Schlenk RF, Dohner H, Campbell PJ. Precision oncology for acute myeloid leukemia using a knowledge bank approach. Nat Genet. 2017 Mar;49(3):332-340. doi: 10.1038/ng.3756. Epub 2017 Jan 16.
• Grinfeld J, Nangalia J, Baxter EJ, Wedge DC, Angelopoulos N, Cantrill R, Godfrey AL, Papaemmanuil E, Gundem G, MacLean C, Cook J, O'Neil L, O'Meara S, Teague JW, Butler AP, Massie CE, Williams N, Nice FL, Andersen CL, Hasselbalch HC, Guglielmelli P, McMullin MF, Vannucchi AM, Harrison CN, Gerstung M, Green AR, Campbell PJ. Classification and Personalized Prognosis in Myeloproliferative Neoplasms. N Engl J Med. 2018 Oct 11;379(15):1416-1430. doi: 10.1056/NEJMoa1716614.
• Anglemyer A, Horvath HT, Bero L. Healthcare outcomes assessed with observational study designs compared with those assessed in randomized trials. Cochrane Database Syst Rev. 2014 Apr 29;2014(4):MR000034. doi: 10.1002/14651858.MR000034.pub2.
• Ball R, Robb M, Anderson SA, Dal Pan G. The FDA's sentinel initiative--A comprehensive approach to medical product surveillance. Clin Pharmacol Ther. 2016 Mar;99(3):265-8. doi: 10.1002/cpt.320. Epub 2016 Jan 12.
• Benson K, Hartz AJ. A comparison of observational studies and randomized, controlled trials. N Engl J Med. 2000 Jun 22;342(25):1878-86. doi: 10.1056/NEJM200006223422506.
• Berger ML, Sox H, Willke RJ, Brixner DL, Eichler HG, Goettsch W, Madigan D, Makady A, Schneeweiss S, Tarricone R, Wang SV, Watkins J, Daniel Mullins C. Good practices for real-world data studies of treatment and/or comparative effectiveness: Recommendations from the joint ISPOR-ISPE Special Task Force on real-world evidence in health care decision making. Pharmacoepidemiol Drug Saf. 2017 Sep;26(9):1033-1039. doi: 10.1002/pds.4297.
• Ford I, Norrie J. Pragmatic Trials. N Engl J Med. 2016 Aug 4;375(5):454-63. doi: 10.1056/NEJMra1510059. No abstract available.
• Fralick M, Kesselheim AS, Avorn J, Schneeweiss S. Use of Health Care Databases to Support Supplemental Indications of Approved Medications. JAMA Intern Med. 2018 Jan 1;178(1):55-63. doi: 10.1001/jamainternmed.2017.3919.
• Franklin JM, Schneeweiss S. When and How Can Real World Data Analyses Substitute for Randomized Controlled Trials? Clin Pharmacol Ther. 2017 Dec;102(6):924-933. doi: 10.1002/cpt.857. Epub 2017 Sep 25.
• Hemkens LG, Contopoulos-Ioannidis DG, Ioannidis JP. Agreement of treatment effects for mortality from routinely collected data and subsequent randomized trials: meta-epidemiological survey. BMJ. 2016 Feb 8;352:i493. doi: 10.1136/bmj.i493. Erratum In: BMJ. 2018 Aug 17;362:k3210.
• Wang SV, Schneeweiss S, Berger ML, Brown J, de Vries F, Douglas I, Gagne JJ, Gini R, Klungel O, Mullins CD, Nguyen MD, Rassen JA, Smeeth L, Sturkenboom M; joint ISPE-ISPOR Special Task Force on Real World Evidence in Health Care Decision Making. Reporting to Improve Reproducibility and Facilitate Validity Assessment for Healthcare Database Studies V1.0. Pharmacoepidemiol Drug Saf. 2017 Sep;26(9):1018-1032. doi: 10.1002/pds.4295. Erratum In: Pharmacoepidemiol Drug Saf. 2017 Dec;26(12 ):1570.

Recruitment Information

• Recruitment Status: Recruiting
• Estimated Enrollment (submitted: December 23, 2019): 1000
• Original Estimated Enrollment: Same as current
• Estimated Study Completion Date: March 31, 2025
• Estimated Primary Completion Date: March 31, 2024 (Final data collection date for primary outcome measure)

Eligibility Criteria

Inclusion Criteria:

Newly diagnosed patients affected with MDS:

• age ≥ 18 years
• written informed consent

Exclusion Criteria:

• Lack of written informed consent
• Lack of biological samples (peripheral blood, bone marrow aspirate)

Sex/Gender

• Sexes Eligible for Study: All

• Ages: 18 Years to 100 Years (Adult, Older Adult)
• Accepts Healthy Volunteers: No

Contacts

Contact: Matteo Della Porta, MD; +39 02 8224 7668; matteo.della_porta@hunimed.eu

Contact: Maria Elena Bicchieri, PhD; marilena.bicchieri@cancercenter.humanitas.it

• Listed Location Countries: Italy

Administrative Information

• NCT Number: NCT04212390
• Other Study ID Numbers: FISIMMDS2020
• Has Data Monitoring Committee: No

U.S. FDA-regulated Product

• Studies a U.S. FDA-regulated Drug Product: No
• Studies a U.S. FDA-regulated Device Product: No

• IPD Sharing Statement: Not Provided
• Current Responsible Party: Fondazione Italiana Sindromi Mielodisplastiche-ETS
• Original Responsible Party: Istituto Clinico Humanitas
• Current Study Sponsor: Fondazione Italiana Sindromi Mielodisplastiche-ETS
• Original Study Sponsor: Istituto Clinico Humanitas
• Collaborators: Not Provided

Investigators

• Principal Investigator: Matteo Della Porta, MD; Humanitas Hospital, Italy
• Study Director: Valeria Santini, MD; AOU Careggi-Università di Firenze
• Study Director: Emanuele Angelucci, MD; AOU San Martino IST - Genova
• Study Director: Enrico Balleari, MD; AOU San Martino IST - Genova
• Study Director: Elena Crisà, MD; l'AOU Maggiore della Carità di Novara
• Study Director: Pellgrino Musto, MD; IRCCS Centro di Riferimento Oncologico della Basilicata Rionero in Vulture PZ
• Study Director: Antonella Poloni, MD; Ospedali Riuniti - Università Politecnica delle Marche Ancona
• Study Director: Renato Zambello, MD; U.O. Ematologia, Azienda Ospedale - Università di Padova
• Study Director: Lorenza Borin, MD; ASST San Gerardo, Monza
• Study Director: Gastone Castellani, Physics; University of Bologna
• Study Director: Pasquale Niscola, MD; Ospedale S.Eugenio-CTO (ASL Roma 2), Roma
• Study Director: Esther Oliva, MD; Ospedale Metropolitano Bianchi Melacrino Morelli di Reggio Calabria
• Study Director: Paolo Giorgio Sergio Pasini, Presidente AIPaSiM; AIPaSiM, Associazione Italiana Pazienti con Sindrome Mielodisplastica
• Study Director: Francesco Passamonti, MD; ASST Sette Laghi, Varese
• Study Director: Federica Pilo, MD; Azienda Ospedaliera Brotzu, Cagliari

• PRS Account: Fondazione Italiana Sindromi Mielodisplastiche-ETS
• Verification Date: February 2024