# Clinical Characteristics and Molecular Profiling of SF3B1-Mutated Myelodysplastic Syndrome (MDS) in a Real-World Practice

## TL;DR

This study analyzes SF3B1-mutated myelodysplastic syndrome patients in Singapore, finding that co-mutations, not variant types, affect disease progression.

## Contribution

The study provides real-world data on SF3B1-MDS, highlighting the role of co-mutations over variant types in prognosis.

## Key findings

- SF3B1-MDS patients had ten variants, with K700E being the most common.
- Co-mutations, not variant type or VAF, were linked to disease progression.
- Further research is needed to improve risk stratification for SF3B1-MDS.

## Abstract

SF3B1-mutated myelodysplastic syndrome (MDS) is a distinct entity associated with a favorable prognosis. Recent data suggest that certain SF3B1 variants portend a worse prognosis. Our study aims to (1) describe SF3B1-MDS patients from a single tertiary center in Singapore and (2) determine if variant type holds prognostic value. We identified MDS patients with SF3B1 variants via next-generation sequencing (NGS) performed from 1 November 2021 to 31 October 2025 at Singapore General Hospital. Extracted genomic material from marrow or blood samples was amplified. Libraries were prepared, sequenced, and analyzed, and the hematological parameters, mutation profiles, and outcomes were evaluated. Twenty-five patients had SF3B1-MDS. Ten SF3B1 variants were found, and the three most prevalent were K700E (42%), K666N (19%), and R625C (7.7%). The median variant allele frequency (VAF) was 30% (IQR: 11–36%). Twelve patients (48%) had ≥1 co-mutations. Variant type and VAF had no impact on disease progression; only the presence of ≥1 co-mutations increased the progression chances. In our study, the analysis of SF3B1 variant type was inconclusive and showed no demonstrable statistical association with disease progression. However, the number of co-mutations affected the prognosis of patients. As SF3B1-MDS is heterogenous, further studies are needed to capture its diversity and identify features required to improve risk stratification and personalized treatment.