Classification and Personalized Prognostic Assessment on the Basis of Clinical and Genomic Features in Myelodysplastic Syndromes

Abstract

PURPOSE Recurrently mutated genes and chromosomal abnormalities have been identified in myelodysplastic syndromes (MDS). We aim to integrate these genomic features into disease classification and prognostication. METHODS We retrospectively enrolled 2,043 patients. Using Bayesian networks and Dirichlet processes, we combined mutations in 47 genes with cytogenetic abnormalities to identify genetic associations and subgroups. Random-effects Cox proportional hazards multistate modeling was used for developing prognostic models. An independent validation on 318 cases was performed. RESULTS We identify eight MDS groups (clusters) according to specific genomic features. In five groups, dominant genomic features include splicing gene mutations ( SF3B1, SRSF2, and U2AF1) that occur early in disease history, determine specific phenotypes, and drive disease evolution. These groups display different prognosis (groups with SF3B1 mutations being associated with better survival). Specific co-mutation patterns account for clinical heterogeneity within SF3B1- and SRSF2-related MDS. MDS with complex karyotype and/or TP53 gene abnormalities and MDS with acute leukemia–like mutations show poorest prognosis. MDS with 5q deletion are clustered into two distinct groups according to the number of mutated genes and/or presence of TP53 mutations. By integrating 63 clinical and genomic variables, we define a novel prognostic model that generates personally tailored predictions of survival. The predicted and observed outcomes correlate well in internal cross-validation and in an independent external cohort. This model substantially improves predictive accuracy of currently available prognostic tools. We have created a Web portal that allows outcome predictions to be generated for user-defined constellations of genomic and clinical features. CONCLUSION Genomic landscape in MDS reveals distinct subgroups associated with specific clinical features and discrete patterns of evolution, providing a proof of concept for next-generation disease classification and prognosis.

The Genetics of Myelodysplastic Syndromes: Clinical Relevance

“…In myeloid neoplasms, classifications based on clinical and morphologic criteria are being complemented by the introduction of specific genomic features, which may capture better clinical-pathological entities [1]. Recently, a first example of molecular classification of MDS was proposed, on the basis of a retrospective study of 2043 patients [41]. In the study, both gene mutations and cytogenetic abnormalities were combined, identifying eight MDS subgroups that shared specific genomic and clinical features.…”

“…Mutations in these genes occur early in disease history and they determine specific clinical phenotypes driving different disease evolution patterns. MDS categories defined by splicing gene mutations display different prognosis (groups with SF3B1 mutations being associated with better probability of survival) [41] (Table 1). More in details, SF3B1 mutations define a specific MDS subgroup characterized by ring sideroblasts, a low percentage of bone marrow blasts and with favourable outcome.…”

“…Another subgroup includes patients carrying SF3B1 mutations with co-existing mutations in other genes (more frequently RUNX1 and ASXL1), and it is usually characterized by multilineage dysplasia, higher bone marrow blast count, and poorer outcome. SRSF2 and U2AF1 mutations characterize distinct disease categories with specific co-mutation patterns, clinical phenotype, and with reduced survival with respect to SF3B1-defined categories [41].…”

Myelodysplastic Syndromes in the Postgenomic Era and Future Perspectives for Precision Medicine

“…More recently, genomic features of cytogenetics and NGS have been integrated for nextgeneration disease classification and prognostication based on biological information [58]. Sequential NGS analysis may gain importance for the assessment of clonal composition during treatment and for the genetically inferred selection of targeting compounds to refractory subclones in the near future.…”

“…Secondary prophylaxis with antibiotics, antimycotics or antiviral substances can be made on an individual basis. Even though it has not been systematically investigated, vaccinations against COVID-19, influenza and pneumococci are generally recommended in MDS patients, although the individual immunological response may be very variable [58].…”