Researchers in China have concluded that mutation of the SF3B1 gene is “significantly associated” with poorer progression-free survival (PFS) and overall survival (OS) in patients with chronic lymphocytic leukemia (CLL).
In results from a meta-analysis recently published online in the journal Oncotarget
, a research team from the First Affiliated Hospital of Xinjiang Medical University determined that the hazard ratio (HR) for pooled analysis on the effect of SF3B1
mutation was 1.81 (95% CI, 1.33-2.46; P
<.001) for PFS and 2.57 (95% CI, 1.68-3.94; P
<.001) for OS.
For the PFS analysis, researchers reviewed results from 11 studies involving 3505 patients with either wild-type SF3B1
(n = 3083) or mutant SF3B1
(n = 422) disease. Eleven studies involving 3175 patients with either wild-type SF3B1
(n = 2829) or mutant SF3B1
(n = 346) were included in the OS analysis.
“This meta-analysis indicated that SF3B1 mutation was significantly associated with poor PFS and OS in CLL,” wrote lead author Zhenghao Zhang and coinvestigators. “It suggests that SF3B1
mutation might be a predictive factor of poor prognosis in patients with CLL. However, more prospective studies with better standardized methods are needed to further confirm the results in this study.”
Results from some recent studies have shown that the SF3B1
mutation is associated with a relatively poor prognosis in patients with CLL, while others have found no significant prognostic value for the mutation. The researchers developed this meta-analysis to further clarify the relationship between the SF3B1
mutation and prognosis in patients with CLL.
Some patients with CLL show an indolent clinical course that does not require special therapy, while others develop an aggressive disease and have reduced survival despite following intensive treatment. As noted by Zhang et al, identifying prognostic factors and biomarkers for CLL is vital to accurately predict survival and disease progression, and to choose appropriate treatment and preventive methods.SF3B1
mutation was determined by high-resolution melt analysis, denaturing high performance liquid chromatography, Sanger sequencing, or next-generation sequencing. Researchers divided the studies into sequencing-based and PCR-based subgroups, and the subgroup analysis indicated that detection methods might also account for part of the heterogeneity. Pooled results from PCR-based methods showed SF3B1
mutation was not significantly correlated with PFS, but this was not the case in OS.
Researchers observed significant heterogeneity in the studies included in this meta-analysis, and the HRs for PFS and OS were pooled in different groups by random effects model to counteract the effects of this heterogeneity. Researchers performed a subgroup analysis based on country of origin, sample number, detection method, and data extraction to explain the heterogeneity.
Zhang et al suggested that some of the heterogeneity in PFS could be explained by sample number and detection methods. Country of origin, sample number, detection method, and data extraction method may explain the heterogeneity in OS.
For example, researchers observed no statistically significant association between the prognosis of CLL and the SF3B1
mutation in the small sample size subgroup for PFS (HR, 0.93; 95% CI, 0.48-1.81) or OS (HR, 1.53; 95% CI, 0.74-3.18). This was also true for PFS for the PCR-based methods group.
“Subgroup analysis indicated that sample size might account for part of included studies’ heterogeneity,” wrote Zhang et al. “Moreover, subgroup analysis indicated that SF3B1
mutation was significantly correlated with poor PFS and OS of patients in subgroup of sample size >100 while not in subgroup of sample size ≤100. SF3B1
mutation patients in studies of sample size ≤100 were too few, which could be a part of the reason for the discrepancy.”
Sensitivity analysis suggested that no individual study significantly affected the pooled hazard ratios.
Zhang Z, Chen S, Chen S, et al. SF3B1 mutation is a prognostic factor in chronic lymphocytic leukemia: a meta-analysis [published online July 22, 2017]. Oncotarget. doi:10.18632/oncotarget.19455.