scholarly journals Splicing factor gene mutations in the myelodysplastic syndromes: impact on disease phenotype and therapeutic applications

2017 ◽  
Vol 63 ◽  
pp. 59-70 ◽  
Author(s):  
Andrea Pellagatti ◽  
Jacqueline Boultwood
Keyword(s):  
Myelodysplastic Syndromes ◽  
Gene Mutations ◽  
Splicing Factor ◽  
Disease Phenotype ◽  
Factor Gene ◽  
Therapeutic Applications

Dysregulation of RUNX1 Plays a Critical Role in the Progression of Myelodysplastic Syndromes

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 4108-4108
Author(s):  
Hiroko Sakurai ◽  
Yuka Harada ◽  
Hirotaka Matsui ◽  
Hideaki Nakajima ◽  
Toshio Kitamura ◽  
...  
Keyword(s):  
Splice Site ◽  
Myelodysplastic Syndromes ◽  
Critical Role ◽  
Gene Mutations ◽  
Splicing Factor ◽  
Expression Level ◽  
Dominant Negative Effect ◽  
Rapid Progression ◽  
Myeloid Neoplasms ◽  
Cd34 Cells

Abstract RUNX1/AML1 mutations have been frequently detected in patients with myeloid neoplasms, especially myelodysplastic syndromes (MDS) and chronic monocytic leukemia (CMML). Although the mutations have been analyzed thoroughly, its expression level has not been investigated. Therefore, we attempt to clarify the expression of RUNX1 in the pathogenesis of myeloid neoplasms. The study was approved by the institutional review board and patients gave written informed consent for the study, according to the Declaration of Helsinki. Several isoforms of RUNX1 mRNA are known and we analyzed RUNX1a (including exon 7a which has stop codon) and RUNX1b (skipping exon 7a and including exon 7b and 8). Expression levels of full length isoform (RUNX1b) and short isoform (RUNX1a which has a dominant negative effect on RUNX1b) in CD34+ cells from patients with myeloid neoplasms were examined. A part of patients with MDS or myelodysplastic syndrome / myeloproliferative neoplasms (MDS/MPN) including CMML showed RUNX1a overexpression. Average of relative RUNX1a expression level in MDS patients (n=34) and MDS/MPN patients (n=20) was 7.4-fold and 8.6-fold of the level in normal bone marrow (BM), respectively, whereas most of these patients showed almost same or slight increase of expression level of RUNX1b compared with normal BM. Interestingly, some patients showed high expression of RUNX1a and repression of RUNX1b. In both disease categories, patients with excess blasts displayed a significantly higher expression level of RUNX1a compared with normal BM and patients without excess blasts. During the disease progression in a single patient with MDS or MDS/MPN, the expression of RUNX1a became higher, while azacitidine treatment reduced RUNX1a expression. Genomic mutations of RUNX1 were also examined. RUNX1 mutations were detected in 16% of MDS and 35% of MDS/MPN. Surprisingly, a part of patients had both RUNX1 gene mutation and RUNX1a overexpression, and they showed rapid progression of disease. To evaluate the effects of RUNX1a overexpression, RUNX1a was transduced into CD34+ cells from MDS patients with low expression level of RUNX1a. RUNX1a-transduction resulted in cell proliferation on MS5 stromal cells. These results indicate that overexpression of RUNX1a may add growth advantage to CD34+ cells in patients with MDS or MDS/MPN. We next analyzed the mechanism of RUNX1a overexpression. Gene mutations affecting exon recognition were examined in the patients. Splicing factor mutations, SRSF2 and U2AF1, were detected frequently in MDS (15%) and MDS/MPN (50%). Patients with splicing factor mutations showed higher RUNX1a expression than patients without the mutations. To confirm that the splicing factor mutations affect the expression of RUNX1a, we performed enforced expression of SRSF2 p.P95H mutant using pMYs.IRES.EGFP retrovirus vector in a MDS-derived cell line, TF-1. After a single cell sorting, independent 13 expanding clones were analyzed. Most of the clones demonstrated higher expression of RUNX1a than mock cells, whereas RUNX1b expression was reduced in all clones. Increase of RUNX1a expression in SRSF2 mutant-transduced TF-1 cells was also confirmed by Western blot. Moreover, the clones with higher GFP intensity showed higher expression level of RUNX1a, suggesting that SRSF2 p.P95H expression level may affect the expression level of RUNX1a. Furthermore, SRSF2 mutant-transduced TF-1 cells showed phenotypic changes of higher CD11b and CD14 than mock TF-1 cells, suggesting that SRSF2 mutant may induce monocytic differentiation via RUNX1a overexpression. Gene mutations of RUNX1 in intron 6 and exon 7a were also analyzed. A 5' splice site change just after exon 6 was detected in a CMML patient with RUNX1a overexpression, which may be another mechanism of RUNX1a overexpression. Mutations of exon 7a or changes in 3' splice site just before exon 7a have not been detected yet. In conclusion, our data suggest that overexpression of RUNX1a may play a critical role in the progression of MDS and MDS/MPN, in addition to RUNX1 mutations. Splicing factor mutations are suspected to contribute to the mechanism of the dysregulation of RUNX1. Disclosures No relevant conflicts of interest to declare.

Identification of Aberrant Splicing Events in Myelodysplastic Syndrome Patients with Splicing Factor Gene Mutations

2017 ◽  
Vol 55 ◽  
pp. S9 ◽  
Author(s):  
A. Pellagatti ◽  
V. Steeples ◽  
E. Sharma ◽  
E. Repapi ◽  
B.H. Yip ◽  
...  
Keyword(s):  
Myelodysplastic Syndrome ◽  
Gene Mutations ◽  
Splicing Factor ◽  
Aberrant Splicing ◽  
Factor Gene

scholarly journals ETV6 and signaling gene mutations are associated with secondary transformation of myelodysplastic syndromes to chronic myelomonocytic leukemia

Blood ◽  
2014 ◽  
Vol 123 (23) ◽  
pp. 3675-3677 ◽  
Author(s):  
Eric Padron ◽  
Sean Yoder ◽  
Sateesh Kunigal ◽  
Tania Mesa ◽  
Jamie K. Teer ◽  
...  
Keyword(s):  
Myelodysplastic Syndromes ◽  
Gene Mutations ◽  
Chronic Myelomonocytic Leukemia ◽  
Myelomonocytic Leukemia

scholarly journals NHERF (Na+/H+ Exchanger Regulatory Factor) gene mutations in human breast cancer

Oncogene ◽  
2004 ◽  
Vol 23 (53) ◽  
pp. 8681-8687 ◽  
Author(s):  
Jia Le Dai ◽  
Lei Wang ◽  
Aysegul A Sahin ◽  
Lyle D Broemeling ◽  
Mieke Schutte ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Human Breast Cancer ◽  
Gene Mutations ◽  
Human Breast ◽  
Regulatory Factor ◽  
Factor Gene

scholarly journals Splicing factor gene mutations in hematologic malignancies

Blood ◽  
2017 ◽  
Vol 129 (10) ◽  
pp. 1260-1269 ◽  
Author(s):  
Borja Saez ◽  
Matthew J. Walter ◽  
Timothy A. Graubert
Keyword(s):  
Animal Models ◽  
Messenger Rna ◽  
Gene Mutations ◽  
Hematologic Malignancies ◽  
Splicing Factor ◽  
Hematopoietic Malignancy ◽  
Healthy Elderly ◽  
Increased Risk ◽  
Recurrent Mutations ◽  
Clinical Consequences

Abstract Alternative splicing generates a diversity of messenger RNA (mRNA) transcripts from a single mRNA precursor and contributes to the complexity of our proteome. Splicing is perturbed by a variety of mechanisms in cancer. Recurrent mutations in splicing factors have emerged as a hallmark of several hematologic malignancies. Splicing factor mutations tend to occur in the founding clone of myeloid cancers, and these mutations have recently been identified in blood cells from normal, healthy elderly individuals with clonal hematopoiesis who are at increased risk of subsequently developing a hematopoietic malignancy, suggesting that these mutations contribute to disease initiation. Splicing factor mutations change the pattern of splicing in primary patient and mouse hematopoietic cells and alter hematopoietic differentiation and maturation in animal models. Recent developments in this field are reviewed here, with an emphasis on the clinical consequences of splicing factor mutations, mechanistic insights from animal models, and implications for development of novel therapies targeting the precursor mRNA splicing pathway.

Personalized Prediction Model to Risk Stratify Patients With Myelodysplastic Syndromes

2021 ◽  
pp. JCO.20.02810
Author(s):  
Aziz Nazha ◽  
Rami Komrokji ◽  
Manja Meggendorfer ◽  
Xuefei Jia ◽  
Nathan Radakovich ◽  
...  
Keyword(s):  
Prediction Model ◽  
Myelodysplastic Syndromes ◽  
Gene Mutations ◽  
Molecular Data ◽  
Machine Learning Techniques ◽  
Prognostic Models ◽  
Prognostic Impact ◽  
Hematopoietic Stem ◽  
Training Cohort ◽  
Time Points

PURPOSE Patients with myelodysplastic syndromes (MDS) have a survival that can range from months to decades. Prognostic systems that incorporate advanced analytics of clinical, pathologic, and molecular data have the potential to more accurately and dynamically predict survival in patients receiving various therapies. METHODS A total of 1,471 MDS patients with comprehensively annotated clinical and molecular data were included in a training cohort and analyzed using machine learning techniques. A random survival algorithm was used to build a prognostic model, which was then validated in external cohorts. The accuracy of the proposed model, compared with other established models, was assessed using a concordance (c)index. RESULTS The median age for the training cohort was 71 years. Commonly mutated genes included SF3B1, TET2, and ASXL1. The algorithm identified chromosomal karyotype, platelet, hemoglobin levels, bone marrow blast percentage, age, other clinical variables, seven discrete gene mutations, and mutation number as having prognostic impact on overall and leukemia-free survivals. The model was validated in an independent external cohort of 465 patients, a cohort of patients with MDS treated in a prospective clinical trial, a cohort of patients with paired samples at different time points during the disease course, and a cohort of patients who underwent hematopoietic stem-cell transplantation. CONCLUSION A personalized prediction model on the basis of clinical and genomic data outperformed established prognostic models in MDS. The new model was dynamic, predicting survival and leukemia transformation probabilities at different time points that are unique for a given patient, and can upstage and downstage patients into more appropriate risk categories.

scholarly journals Recurrent mutations in the U2AF1 splicing factor in myelodysplastic syndromes

2011 ◽  
Vol 44 (1) ◽  
pp. 53-57 ◽  
Author(s):  
Timothy A Graubert ◽  
Dong Shen ◽  
Li Ding ◽  
Theresa Okeyo-Owuor ◽  
Cara L Lunn ◽  
...  
Keyword(s):  
Myelodysplastic Syndromes ◽  
Splicing Factor ◽  
Recurrent Mutations
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