Login / Signup

Next-generation sequencing with a 54-gene panel identified unique mutational profile and prognostic markers in Chinese patients with myelofibrosis.

Harinder GillHo-Wan IpRita YimWing-Fai TangHerbert H PangPaul LeeGarret M K LeungJamilla LiKaren TangJason C C SoRock Y Y LeungJun LiGianni PanagioutouClarence C K LamYok Lam Kwong
Published in: Annals of hematology (2018)
Current prognostication in myelofibrosis (MF) is based on clinicopathological features and mutations in a limited number of driver genes. The impact of other genetic mutations remains unclear. We evaluated for mutations in a myeloid panel of 54 genes using next-generation sequencing. Multivariate Cox regression analysis was used to determine prognostic factors for overall survival (OS) and leukaemia-free survival (LFS), based on mutations of these genes and relevant clinical and haematological features. One hundred and one patients (primary MF, N = 70; secondary MF, N = 31) with a median follow-up of 49 (1-256) months were studied. For the entire cohort, inferior OS was associated with male gender (P = 0.04), age > 65 years (P = 0.04), haemoglobin < 10 g/dL (P = 0.001), CUX1 mutation (P = 0.003) and TP53 mutation (P = 0.049); and inferior LFS was associated with male gender (P = 0.03), haemoglobin < 10 g/dL (P = 0.04) and SRSF2 mutations (P = 0.008). In primary MF, inferior OS was associated with male gender (P = 0.03), haemoglobin < 10 g/dL (P = 0.002), platelet count < 100 × 109/L (P = 0.02), TET2 mutation (P = 0.01) and CUX1 mutation (P = 0.01); and inferior LFS was associated with haemoglobin < 10 g/dL (P = 0.02), platelet count < 100 × 109/L (P = 0.02), TET2 mutations (P = 0.01) and CUX1 mutations (P = 0.04). These results showed that clinical and haematological features and genetic mutations should be considered in MF prognostication.
Keyphrases
  • prognostic factors
  • genome wide
  • free survival
  • end stage renal disease
  • chronic kidney disease
  • newly diagnosed
  • acute myeloid leukemia
  • peripheral blood
  • data analysis