Age-related dynamics in acute myeloid leukemia: Implications for prognosis, risk stratification, and treatment response

Abstract

Abstract BACKGROUND: Acute myeloid leukemia (AML) is a complex, heterogeneous disease driven by acquired somatic mutations. The presence of specific mutations advances stratification, treatment, and prognosis. Linear accumulation of mutations over time is a crucial factor in cancer development, particularly among elderly patients. Our recent study on gene rearrangement in AML revealed a significant association between age and adverse risk cases. AIM: The aim of this study was to examine the distribution of age, molecular characteristics, risk stratification, and treatment response based on age among patients with de novo AML in Iraq. PATIENTS AND METHODS: A prospective cohort study enrolled 115 Iraqi adult patients diagnosed with de novo AML using morphology and flow cytometry from December 2020 to May 2022. The Leukemia Q-Fusion Screening Kit, employing multiplex reverse transcription–real-time quantitative polymerase chain reaction with 30 gene rearrangements, was employed for the identification of gene rearrangement. The patients received care and follow-up at the Hematology Unit of Baghdad Teaching Hospital in Medical City. Ethical approval from the College of Medicine’s Ethical Committee at the University of Baghdad was secured before commencing the research, ensuring adherence to ethical standards throughdout the study. RESULTS: The age distribution exhibited a bimodal pattern, with a mean of 45.1 ± 17.5 years, ranging from 18 to 84 years, and a median of 46 years. A total of 39.1% of patients were diagnosed with AML before the age of 35 years, while 43% were diagnosed after the age of 51 years. AML patients with RARA mutations, RUNX1:: RUNX1T1 alterations, and NPM1 mutations were predominantly observed in younger individuals, as well as those diagnosed with AML defined by differentiation. Conversely, KMT2A rearrangements were more prevalent among older age groups, with a statistically significant difference in the distribution of AML classifications according to the World Health Organization (WHO) by age categories (P = 0.001). The risk stratification based on age and response assessment showed a notable higher risk profile observed among elderly patients that was associated with adverse risk and poorer response and mortality (P < 0.05). The prediction of treatment response accuracy rate was improved by adding age to the WHO classification and ELN 2022 risk stratification (73.5%–87.9%). CONCLUSION: Age significantly influences AML prognosis and treatment response. Incorporating age into risk stratification improves accuracy. Tailored approaches considering age are vital for optimizing AML management and outcomes.