Clinical, biological, and prognostic implications of SF3B1 co-occurrence mutations in very low/low- and intermediate-risk MDS patients.

SF3B1 is a highly mutated gene in myelodysplastic syndrome (MDS) patients, related to a specific subtype and parameters of good prognosis in MDS without excess blasts. More than 40% of MDS patients carry at least two myeloid-related gene mutations but little is known about the impact of concurrent mutations on the outcome of MDS patients. In applying next-generation sequencing (NGS) with a 117 myeloid gene custom panel, we analyzed the co-occurrence of SF3B1 with other mutations to reveal their clinical, biological, and prognostic implications in very low/low- and intermediate-risk MDS patients. Mutations in addition to those of SF3B1 were present in 80.4% of patients (median of 2 additional mutations/patient, range 0-5). The most frequently mutated genes were as follows: TET2 (39.2%), DNMT3A (25.5%), SRSF2 (10.8%), CDH23 (5.9%), and ASXL1, CUX1, and KMT2D (4.9% each). The presence of at least two mutations concomitant with that of SF3B1 had an adverse impact on survival compared with those with the SF3B1 mutation and fewer than two additional mutations (median of 54 vs. 87 months, respectively: p = 0.007). The co-occurrence of SF3B1 mutations with specific genes is also linked to a dismal prognosis: SRSF2 mutations were associated with shorter overall survival (OS) than SRSF2wt (median, 27 vs. 75 months, respectively; p = 0.001), concomitant IDH2 mutations (median OS, 11 [mut] vs. 75 [wt] months; p = 0.001), BCOR mutations (median OS, 11 [mut] vs. 71 [wt] months; p = 0.036), and NUP98 and STAG2 mutations (median OS, 27 and 11 vs. 71 months, respectively; p = 0.008 and p = 0.002). Mutations in CHIP genes (TET2, DNMT3A) did not significantly affect the clinical features or outcome. Our results suggest that a more comprehensive NGS study in low-risk MDS SF3B1mut patients is essential for a better prognostic evaluation.

Genome-wide transcriptomics leads to the identification of deregulated genes after deferasirox therapy in low-risk MDS patients.

The iron chelator deferasirox is widely used in patients with iron overload. Patients with low-grade myelodysplastic syndromes (MDS) get transfusion dependency and need to be treated with deferasirox to avoid iron overload. Moreover, in some patients an increase in both erythroid and platelets have been observed after deferasirox therapy. However, the mechanisms involved in these clinical findings are poorly understood. The aim of this work was to analyze, in patients treated with deferasirox, the changes in the gene-expression profile after receiving the treatment. A total of 15 patients with the diagnosis of low-grade MDS were studied. Microarrays were carried out in RNA from peripheral blood before and after 14 weeks of deferasirox therapy. Changes in 1457 genes and 54 miRNAs were observed: deferasirox induced the downregulation of genes related to the Nf kB pathway leading of an overall inactivation of this pathway. In addition, the iron chelator also downregulated gamma interferon. Altogether these changes could be related to the improvement of erythroid response observed in these patients after therapy. Moreover, the inhibition of NFE2L2/NRF2, which was predicted in silico, could be playing a critical role in the reduction of reactive oxygen species (ROS). Of note, miR-125b, overexpressed after deferasirox treatment, could be involved in the reduced inflammation and increased hematopoiesis observed in the patients after treatment. In summary this study shows, for the first time, the mechanisms that could be governing deferasirox impact in vivo.

Circulating microbial content in myeloid malignancy patients is associated with disease subtypes and patient outcomes.

Although recent work has described the microbiome in solid tumors, microbial content in hematological malignancies is not well-characterized. Here we analyze existing deep DNA sequence data from the blood and bone marrow of 1870 patients with myeloid malignancies, along with healthy controls, for bacterial, fungal, and viral content. After strict quality filtering, we find evidence for dysbiosis in disease cases, and distinct microbial signatures among disease subtypes. We also find that microbial content is associated with host gene mutations and with myeloblast cell percentages. In patients with low-risk myelodysplastic syndrome, we provide evidence that Epstein-Barr virus status refines risk stratification into more precise categories than the current standard. Motivated by these observations, we construct machine-learning classifiers that can discriminate among disease subtypes based solely on bacterial content. Our study highlights the association between the circulating microbiome and patient outcome, and its relationship with disease subtype.