The Evolving Landscape of Myelodysplastic Syndrome Prognostication

Treatment of myelodysplastic syndromes in the era of precision medicine and immunomodulatory drugs: a focus on higher-risk disease

Myelodysplastic syndromes (MDS) are a heterogeneous clonal disease of myeloid neoplasms characterized by ineffective hematopoiesis, variable degree of cytopenias, and an increased risk of progression to acute myeloid leukemia (AML). Molecular and genetic characterization of MDS has led to a better understanding of the disease pathophysiology and is leading to the development of novel therapies. Targeted and immune therapies have shown promising results in different hematologic malignancies. However, their potential use in MDS is yet to be fully defined. Here, we review the most recent advances in therapeutic approaches in MDS, focusing on higher-risk disease. Allogeneic hematopoietic cell transplantation is beyond the scope of this article.

From Immune Dysregulations to Therapeutic Perspectives in Myelodysplastic Syndromes: A Review

The pathophysiology of myelodysplastic syndromes (MDSs) is complex and often includes immune dysregulation of both the innate and adaptive immune systems. Whereas clonal selection mainly involves smoldering inflammation, a cellular immunity dysfunction leads to increased apoptosis and blast proliferation. Addressing immune dysregulations in MDS is a recent concept that has allowed the identification of new therapeutic targets. Several approaches targeting the different actors of the immune system have therefore been developed. However, the results are very heterogeneous, indicating the need to improve our understanding of the disease and interactions between chronic inflammation, adaptive dysfunction, and somatic mutations. This review highlights current knowledge of the role of immune dysregulation in MDS pathophysiology and the field of new drugs.

A machine learning approach to predicting risk of myelodysplastic syndrome

BACKGROUND

Early myelodysplastic syndrome (MDS) diagnosis can allow physicians to provide early treatment, which may delay advancement of MDS and improve quality of life. However, MDS often goes unrecognized and is difficult to distinguish from other disorders. We developed a machine learning algorithm for the prediction of MDS one year prior to clinical diagnosis of the disease.

METHODS

Retrospective analysis was performed on 790,470 patients over the age of 45 seen in the United States between 2007 and 2020. A gradient boosted decision tree model (XGB) was built to predict MDS diagnosis using vital signs, lab results, and demographics from the prior two years of patient data. The XGB model was compared to logistic regression (LR) and artificial neural network (ANN) models. The models did not use blast percentage and cytogenetics information as inputs. Predictions were made one year prior to MDS diagnosis as determined by International Classification of Diseases (ICD) codes, 9th and 10th revisions. Performance was assessed with regard to area under the receiver operating characteristic curve (AUROC).

RESULTS

On a hold-out test set, the XGB model achieved an AUROC value of 0.87 for prediction of MDS one year prior to diagnosis, with a sensitivity of 0.79 and specificity of 0.80. The XGB model was compared against LR and ANN models, which achieved an AUROC of 0.838 and 0.832, respectively.

CONCLUSIONS

Machine learning may allow for early MDS diagnosis MDS and more appropriate treatment administration.

Allogeneic stem cell transplantation for AML patients with RUNX1 mutation in first complete remission: a study on behalf of the acute leukemia working party of the EBMT

Acute myeloid leukemia with runt-related transcription factor 1 gene mutation (RUNX1+ AML) is associated with inferior response rates and outcome after conventional chemotherapy. We performed a retrospective, registry-based analysis to elucidate the prognostic value of RUNX1 mutation after allogeneic stem cell transplantation (alloSCT). All consecutive adults undergoing alloSCT for AML in first complete remission (CR1) between 2013 and 2019 with complete information on conventional cytogenetics and RUNX1 mutational status were included. Endpoints of interest were cumulative relapse incidence, non-relapse mortality, overall and leukemia-free survival (OS/LFS), and GvHD-free/relapse-free survival. A total of 674 patients (183 RUNX1+, 491 RUNX1−) were identified, with >85% presenting as de novo AML. Median follow-up was 16.4 (RUNX1+) and 21.9 (RUNX1−) months. Survival rates showed no difference between RUNX1+ and RUNX1− patients either in univariate or multivariate analysis (2-year OS: 67.7 vs. 66.1%, p = 0.7; 2-year LFS: 61.1 vs. 60.8%, p = 0.62). Multivariate analysis identified age, donor type and poor cytogenetics as risk factors for inferior outcome. Among patients with RUNX+ AML, older age, reduced intensity conditioning and minimal residual disease at alloSCT predicted inferior outcome. Our data provide evidence that the negative influence of RUNX1 mutations in patients with AML can be overcome by transplantation in CR1.

Navigating Myelodysplastic and Myelodysplastic/Myeloproliferative Overlap Syndromes

Myelodysplastic syndromes (MDS) and MDS/myeloproliferative neoplasms (MPNs) are clonal diseases that differ in morphologic diagnostic criteria but share some common disease phenotypes that include cytopenias, propensity to acute myeloid leukemia evolution, and a substantially shortened patient survival. MDS/MPNs share many clinical and molecular features with MDS, including frequent mutations involving epigenetic modifier and/or spliceosome genes. Although the current 2016 World Health Organization classification incorporates some genetic features in its diagnostic criteria for MDS and MDS/MPNs, recent accumulation of data has underscored the importance of the mutation profiles on both disease classification and prognosis. Machine-learning algorithms have identified distinct molecular genetic signatures that help refine prognosis and notable associations of these genetic signatures with morphologic and clinical features. Combined geno-clinical models that incorporate mutation data seem to surpass the current prognostic schemes. Future MDS classification and prognostication schema will be based on the portfolio of genetic aberrations and traditional features, such as blast count and clinical factors. Arriving at these systems will require studies on large patient cohorts that incorporate advanced computational analysis. The current treatment algorithm in MDS is based on patient risk as derived from existing prognostic and disease classes. Luspatercept is newly approved for patients with MDS and ring sideroblasts who are transfusion dependent after erythropoietic-stimulating agent failure. Other agents that address red blood cell transfusion dependence in patients with lower-risk MDS and the failure of hypomethylating agents in higher-risk disease are in advanced testing. Finally, a plethora of novel targeted agents and immune checkpoint inhibitors are being evaluated in combination with a hypomethylating agent backbone to augment the depth and duration of response and, we hope, improve overall survival.