Distinct Molecular Profiles in Younger vs Older Patients With MDS Identified in New Study

Myelodysplastic syndromes (MDS) are a heterogenous group of conditions that typically occur in elderly patients, but the relationship between aging and molecular profiles is not well understood. A recent study identified distinct genetic landscapes in younger and older patients with MDS, suggesting that risk estimation based on age and genomic profile may help personalize treatment for individuals with MDS.

MDS are characterized by bone marrow failure and potential transformation into acute myeloid leukemia (AML). Specific mutations have been associated with different clinical phenotypes and with prognosis in patients with MDS, and genomic findings have been incorporated into recent risk-stratification models.

Older age is a known risk factor for MDS, but the effect of aging on the genomic features of MDS is unclear, particularly in the Asian population. The study, published in Hematological Oncology, used next‐generation sequencing to characterize the genetic landscapes of Asian patients who have MDS who are 50 years or younger vs patients older than 50 years. Researchers also explored the impact of specific mutations in each age group and the potential prognostic implications of variant allele frequency (VAF) of individual mutations.

A total of 698 patients with primary MDS were included in the study, and the median patient age in the overall cohort was 66.5 years. Of the group, 138 patients (19.8%) were 50 years or younger and 560 (80.2%) were older than 50 years. The median overall follow-up was 54.7 months.

Older patients showed higher white blood cell counts, absolute neutrophil counts, hemoglobin levels, platelet counts, and bone marrow blast percentages than the younger cohort. However, the revised International Prognostic Scoring System ( distribution was similar between the groups. Younger patients were more likely to receive allogeneic hematopoietic stem cell transplantation and older patients were more likely to receive supportive care. Older patients were more likely to experience disease transformation to AML and were more likely to die within 3 months of their MDS diagnosis.

A total of 545 patients (78.1%) had chromosome abnormalities and/or molecular genetic alterations at the time of their MDS diagnosis, with patients averaging 1.7 mutations in the overall cohort. Older patients averaged 1.8 mutations compared with 1.2 in younger patients. Fifty-four genes were analyzed, and the most common mutations were ASXL1 (20.3%), TET2 (14.3%), SF3B1 (13.8%), RUNX1 (12.6%), STAG2 (12.5%), and TP53 (12.3%). In the overall cohort, 71.5% of patients had mutations in the genes analyzed and 42.0% had abnormal cytogenetics.

Younger patients more frequently had WT1and CBL mutations and less commonly had ASXL1, DNMT3A, TET2, SF3B1, SRSF2 STAG2, and TP53 alterations compared with the older cohort.

The frequencies of mutated DNMT3A, TET2, ASXL1, IDH2, RUNX1, SRSF2, SF3B1, STAG2, and TP53 were most correlated with aging, while WT1 mutations demonstrated a negative correlation with aging. A multivariable analysis showed DNMT3A, TET2, IDH2, CBL, and TP53 mutations to be independent risk factors for worse leukemia-free survival (LFS) and overall survival (OS). NRAS mutation was found to be a poor risk factor for LFS.

A multivariable survival analysis of individual mutations, including their VAF, in both age groups found that the mutational landscapes associated with clinical outcomes were different in each group. In younger patients, RUNX1 mutations with higher VAF, U2AF1, and TP53 mutations were independent negative prognostic factors. In the older cohort, DNMT3A and IDH2 mutations with higher VAF and TP53 mutations had negative prognostic implications.

Taken together, the findings showed differing genetic landscapes and risk factors for OS and LFS among younger vs patients with MDS. Although the results warrant validation in further research, they lay groundwork for future studies.

“The derivation of risk estimates on the basis of age and the genetic profile of each patient may help optimize risk stratification and ultimately deliver patient‐tailored therapeutic avenues,” the authors concluded. “Given the age dependence of the accumulation of mutations, longitudinal follow‐ups are warranted to elucidate the dynamic changes in molecular architecture.”

Reference

Lee WH, Lin CC, Wang YH, et al. Distinct genetic landscapes and their clinical implications in younger and older patients with myelodysplastic syndromes. Hematol Oncol. Published online November 24, 2022. doi:10.1002/hon.3109