Myelodysplastic syndromes (MDS) constitute a group of diverse, hematological malignancies. The prognosis for these patients varies considerably, ranging from a stable, slowly developing disease, to much more aggressive forms. About 30% of MDS patients progress to aggressive acute myeloid leukemia. For patients who are currently classified as "lower-risk MDS patients" using the current clinical and laboratory parameters , it is very hard to predict which patients will show rapid disease progression and which patients will show a much more stable disease. As a consequence, it is unclear which patients would benefit from early therapeutic intervention, and which patients would benefit more from a wait-and-see policy. This is particularly important as MDS patients tend to be of advanced age, and many of them have one or more co-morbidities, and therapy may have profound effects on quality of life.To better diagnose, prognosticate and stratify patients for intensive or targeted forms of therapy, a comprehensive analysis of all possible mutations in MDS would be of great help. This is now possible with the advent of novel, high throughput methodology, known as next generation sequencing.In the scope of the project „MDS-Triage" (Translational Implementation of Genetic Evidence in the management of MDS) (follow-up project: „MDS-RIGHT" (Providing the right care to the right patient with MyeloDysplastic Syndrome at the right time)) we wish to validate the latest next-generation sequencing technology for this purpose, allowing to analyze all known genetic mutations in MDS in a single experiment. We wish to analyze material of 1000 lower-risk MDS patients who have been entered in the ongoing EU-MDS registry, in which clinical status, follow-up and quality of life and age-related co-morbidities are registered. It is expected that this validation will have important consequences for the future disease management in MDS by the prevention of disease progression, and bringing the right therapy to the right patients.
Dugas, Martin | Institute of Medical Informatics |
Dugas, Martin | Institute of Medical Informatics |
Sandmann-Varghese, Sarah | Institute of Medical Informatics |