Key Points• Different driver mutations have distinct effects on phenotype of myelodysplastic syndromes (MDS) and myelodysplastic/ myeloproliferative neoplasms (MDS/MPN).• Accounting for driver mutations may allow a classification of these disorders that is considerably relevant for clinical decision-making. Irrespective of driver somatic mutations, a threshold of 5% bone marrow blasts retained a significant discriminant value for identifying cases with clonal evolution. Comutation of TET2 and SRSF2 was highly predictive of a myeloid neoplasm characterized by myelodysplasia and monocytosis, including but not limited to, chronic myelomonocytic leukemia. These results serve as a proof of concept that a molecular classification of myeloid neoplasms is feasible. (Blood. 2014;124(9):1513-1521

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 genetic basis of myelodysplastic syndromes (MDS) is heterogeneous, and various combinations of somatic mutations are associated with different clinical phenotypes and outcomes. Whether the genetic basis of MDS influences the outcome of allogeneic hematopoietic stem-cell transplantation (HSCT) is unclear. Patients and MethodsWe studied 401 patients with MDS or acute myeloid leukemia (AML) evolving from MDS (MDS/ AML). We used massively parallel sequencing to examine tumor samples collected before HSCT for somatic mutations in 34 recurrently mutated genes in myeloid neoplasms. We then analyzed the impact of mutations on the outcome of HSCT. ResultsOverall, 87% of patients carried one or more oncogenic mutations. Somatic mutations of ASXL1, RUNX1, and TP53 were independent predictors of relapse and overall survival after HSCT in both patients with MDS and patients with MDS/AML (P values ranging from .003 to .035). In patients with MDS/AML, gene ontology (ie, secondary-type AML carrying mutations in genes of RNA splicing machinery, TP53-mutated AML, or de novo AML) was an independent predictor of posttransplantation outcome (P = .013). The impact of ASXL1, RUNX1, and TP53 mutations on posttransplantation survival was independent of the revised International Prognostic Scoring System (IPSS-R).Combining somatic mutations and IPSS-R risk improved the ability to stratify patients by capturing more prognostic information at an individual level. Accounting for various combinations of IPSS-R risk and somatic mutations, the 5-year probability of survival after HSCT ranged from 0% to 73%. ConclusionSomatic mutation in ASXL1, RUNX1, or TP53 is independently associated with unfavorable outcomes and shorter survival after allogeneic HSCT for patients with MDS and MDS/AML. Accounting for these genetic lesions may improve the prognostication precision in clinical practice and in designing clinical trials.

Dendritic cells (DCs) play a crucial role in initiating and shaping immune responses. The effects of DCs on adaptive immune responses depend partly on functional specialization of distinct DC subsets, and partly on the activation state of DCs, which is largely dictated by environmental signals. Fully activated immunostimulatory DCs express high levels of costimulatory molecules, produce pro-inflammatory cytokines, and stimulate T cell proliferation, whereas tolerogenic DCs express low levels of costimulatory molecules, produce immunomodulatory cytokines and impair T cell proliferation. Relevant to the increasing use of immune checkpoint blockade in cancer treatment, signals generated from inhibitory checkpoint molecules on DC surface may also contribute to the inhibitory properties of tolerogenic DCs. Yet, our knowledge on the expression of inhibitory molecules on human DC subsets is fragmentary. Therefore, in this study, we investigated the expression of three immune checkpoints on peripheral blood DC subsets, in basal conditions and upon exposure to pro-inflammatory and anti-inflammatory stimuli, by using a flow cytometric panel that allows a direct comparison of the activatory/inhibitory phenotype of DC-lineage and inflammatory DC subsets. We demonstrated that functionally distinct DC subsets are characterized by differential expression of activatory and inhibitory molecules, and that cDC1s in particular are endowed with a unique immune checkpoint repertoire characterized by high TIM-3 expression, scarce PD-L1 expression and lack of ILT2. Notably, this unique cDC1 repertoire was subverted in a group of patients with myelodysplastic syndromes included in the study. Applied to the characterization of DCs in the tumor microenvironment, this panel has the potential to provide valuable information to be used for investigating the role of DC subsets in cancer, guiding DC-targeting treatments, and possibly identifying predictive biomarkers for clinical response to cancer immunotherapy.