Impact of primary cancer history and molecular landscape in therapy-related myeloid neoplasms

Therapy-related myeloid neoplasms (t-MN) are aggressive hematologic malignancies with poor prognosis and high-risk clinical features. Recent advances have highlighted the role of molecular data in refining prognostic models. This study aims to analyze a monocentric cohort of t-MN patients, focusing on the clinical and prognostic impact of prior malignancies and their associated molecular landscape.

A retrospective analysis was conducted on 61 patients diagnosed with t-MN from an Oncology Hospital and referred to a hematology Unit. Diagnoses were based on established criteria for therapy-related myelodysplastic syndrome (t-MDS) and therapy-related acute myeloid leukemia (t-AML), with a history of prior exposure to cytotoxic therapy. Cytogenetic and molecular analyses supported the diagnoses. Risk stratification was performed using the revised International Prognostic Scoring System (IPSS-R) and molecular IPSS (IPSS-M) for t-MDS and the 2022 European LeukemiaNet (ELN) classification for t-AML.

Overall, 61 patients with t-MN were diagnosed: 38 (62.3%) with t-MDS, and 23 (37.7%) with t-AML. The median latency from primary cancer to t-MN diagnosis was 5.8 years (IQR: 2.6-12.5). Risk stratification identified 63.2% of t-MDS cases as IPSS-R very-low to intermediate risk, while 57.9% were reclassified as IPSS-M moderate-high to very high risk. Patients with prior hematologic cancer showed a greater tendency toward higher IPSS-R (p=0.021) and IPSS-M (p=0.015) risk compared to solid cancer. The IPSS-M, more accurately than R-IPSS, demonstrated predictive value for survival in both univariate and multivariate analyses and effectively predicted leukemic progression in t-MDS. TP53-mutated cases were more prevalent in patients with prior hematologic cancer (p=0.043) and associated with longer latency (8.2 years) compared to TP53 wild type (6.1 years, p=0.044). Allogeneic transplantation proved beneficial, significantly improving survival outcomes in eligible t-MDS and t-AML patients.

t-MN exhibits distinct clinical and molecular profiles according to prior malignancy type. Intriguingly, our analysis reveals a distinct latency pattern in TP53-mutated cases, suggesting unique leukemogenic dynamics. Moreover, IPSS-M proved highly accurate in predicting t-MDS survival. Integrating molecular data into prognostic models enhances risk stratification and informs therapeutic strategies, potentially improving outcomes for t-MN patients. Further studies are needed to validate these findings and refine tailored treatment approaches.