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
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
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:
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.
Inclusion Criteria:
Newly diagnosed patients affected with MDS:
Exclusion Criteria: