The role of new technologies in myeloproliferative neoplasms: Application of next-generation sequencing in myelofibrosis
Language English Country England, Great Britain Media print-electronic
Document type Journal Article
Grant support
IGA-LF-UP-2020_002
Palacky University Olomouc
PubMed
33734589
DOI
10.1111/ijlh.13504
Knihovny.cz E-resources
- Keywords
- myelofibrosis, next-generation sequencing, prognostic stratification,
- MeSH
- Dioxygenases genetics MeSH
- DNA-Binding Proteins genetics MeSH
- Adult MeSH
- Phosphoproteins genetics MeSH
- Middle Aged MeSH
- Humans MeSH
- Mutation MeSH
- Primary Myelofibrosis genetics MeSH
- Repressor Proteins genetics MeSH
- Aged MeSH
- RNA Splicing Factors genetics MeSH
- High-Throughput Nucleotide Sequencing methods MeSH
- Check Tag
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Male MeSH
- Aged MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Names of Substances
- ASXL1 protein, human MeSH Browser
- Dioxygenases MeSH
- DNA-Binding Proteins MeSH
- Phosphoproteins MeSH
- Repressor Proteins MeSH
- RNA Splicing Factors MeSH
- SF3B1 protein, human MeSH Browser
- TET2 protein, human MeSH Browser
INTRODUCTION: Driver mutations in Philadelphia chromosome-negative myeloproliferative neoplasms are well known. In the past, whole-genome sequencing identified nondriver mutations in other genes, potentially contributing to evolution of malignant clones. METHODS: Next-generation sequencing was used to assess the presence of any mutations in 14 candidate genes at the point of diagnosis and the resultant impact on the clinical course of the disease. RESULTS: The study analysed 63 patients with myelofibrosis (MF). Nondriver mutations were detected in 44% of them. The most frequently affected genes were ASXL1 (27%), TET2 (11%) and SF3B1 (6%). The frequency of such mutations was highest in primary MF (59%) and lowest in the prefibrotic phase of primary MF (21%). Patients with prognostically unfavourable sequence variants in genes had significantly worse overall survival (53 vs 71 months; HR = 2.77; 95% CI 1.17-6.56; P = .017). CONCLUSION: In our study, multivariate analysis proved DIPSS to be the only significant factor to predict patient survival. DIPSS contains all of the important clinical and laboratory factors except genetic changes. Stratification of patients according to DIPSS is still beneficial although there are newer and improved scoring systems like GIPSS or MIPSS70. Assessing subclonal mutations in candidate genes during diagnosis may aid in the identification of high-risk MF patients and is therefore relevant for making a prediction for overall survival more accurate.
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