Integration of NGS and CNV analysis in prognostic evaluation of MDS in a resource-limited country: insights from Uruguay

• NGS improves MDS diagnosis, detecting somatic mutations in 75% and CNVs in 38% of patients. • Adding CNV data to IPSS models enhances risk stratification and guides personalized care. Myelodysplastic syndromes (MDS) are clonal disorders of the bone marrow characterized by ineffective hematopoiesis, cytopenia, and an increased risk of progression to acute myeloid leukemia (AML). The phenotypic variability of the disease, driven by genetic abnormalities, poses challenges to its management and prognosis. While traditional cytogenetic analysis detects chromosomal abnormalities in about 50% of cases, next-generation sequencing (NGS) identifies genetic mutations in 70–90% of patients, providing critical prognostic insights. However, data on the genetic landscape of MDS in Latin America, including Uruguay, remain limited. This study investigates the role of NGS in detecting copy number variations (CNVs) alongside somatic mutations to refine prognostic evaluations. Among 52 patients analyzed, NGS-based CNV detection identified abnormalities in 38% of cases, including deletions on chromosomes 5 and 7 and gains on chromosome 8, many of which were missed by conventional karyotyping in real-world settings. Integrating CNV data with karyotyping into established risk models significantly improved prognostic accuracy. Furthermore, somatic mutation profiling revealed mutations in 75% of patients, with TP53, DNMT3A, TET2, ASXL1, and SF3B1 being the most frequently identified. Notably, TP53 mutations were strongly correlated with poor clinical outcomes. The IPSS-M model re-stratified 42.3% of patients, offering enhanced risk assessment and prognostic precision. By integrating CNV analysis with somatic mutation profiling, this study highlights the feasibility and utility of a NGS single-run approach that improves diagnostic accuracy and optimizes workflows, particularly in resource-constrained settings.