Grimwade, David, Walker, Helen, Harrison, Georgina, Oliver, Fiona, Chatters, Stephen, Harrison, Christine J., Wheatley, Keith, Burnett, Alan K. and Goldstone, Anthony H.
The predictive value of hierarchical cytogenetic classification in older adults with acute myeloid leukemia (AML): analysis of 1065 patients entered into the United Kingdom Medical Research Council AML11 trial.
Blood, 98, (5), .
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Acute myeloid leukemia (AML) in older adults carries a poor prognosis, and the optimum treatment remains to be determined. In younger patients, treatment stratification is frequently based upon diagnostic karyotype, which was the most important prognostic factor in the UK Medical Research Council (MRC) AML10 trial. Considered here is whether karyotype is also predictive in older adults; this is done by studying 1065 cases from MRC AML11 (median age, 66 years). Three prognostic groups were distinguished on the basis of response to induction therapy and overall survival (OS). Those with t(15;17), t(8;21), or inv(16) composed the favorable risk group. Overall, these abnormalities predicted a superior complete remission (CR) rate (72%), reflecting relatively low levels of resistant disease (RD) (8%), and lower relapse risk (RR) (56%) associated with superior OS (34% at 5 years). Normal karyotype (CR, 63%; RD, 17%; RR, 78%; OS, 15%) and other noncomplex abnormalities (CR, 53%; RD, 32%; RR, 85%; OS, 10%) composed the intermediate group; while complex karyotype predicted an extremely poor prognosis (CR, 26%; RD, 56%; RR, 91%; OS, 2%). Combining MRC AML10 and AML11 (n = 2677) revealed that the most favorable changes were rarer in older patients (younger than 55 years, 24%; 55 years or older, 7%), while complex abnormalities were more common (6% vs 13%). This study suggests that hierarchical cytogenetic classification identifies biologically distinct subsets of AML that are represented in all age groups. Furthermore, it highlights the importance of karyotype as a critical independent determinant of outcome in older patients with AML, providing a potential framework for stratified treatment approaches.
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