Abstract
PurposeMinimal residual disease (MRD) and genetic abnormalities are important risk factors for outcome in acute lymphoblastic leukemia. Current risk algorithms dichotomize MRD data and do not assimilate genetics when assigning MRD risk, which reduces predictive accuracy. The aim of our study was to exploit the full power of MRD by examining it as a continuous variable and to integrate it with genetics.Patients and MethodsWe used a population-based cohort of 3,113 patients who were treated in UKALL2003, with a median follow-up of 7 years. MRD was evaluated by polymerase chain reaction analysis of Ig/TCR gene rearrangements, and patients were assigned to a genetic subtype on the basis of immunophenotype, cytogenetics, and fluorescence in situ hybridization. To examine response kinetics at the end of induction, we log-transformed the absolute MRD value and examined its distribution across subgroups.ResultsMRD was log normally distributed at the end of induction. MRD distributions of patients with distinct genetic subtypes were different (P < .001). Patients with good-risk cytogenetics demonstrated the fastest disease clearance, whereas patients with high-risk genetics and T-cell acute lymphoblastic leukemia responded more slowly. The risk of relapse was correlated with MRD kinetics, and each log reduction in disease level reduced the risk by 20% (hazard ratio, 0.80; 95% CI, 0.77 to 0.83; P < .001). Although the risk of relapse was directly proportional to the MRD level within each genetic risk group, absolute relapse rate that was associated with a specific MRD value or category varied significantly by genetic subtype. Integration of genetic subtype–specific MRD values allowed more refined risk group stratification.ConclusionA single threshold for assigning patients to an MRD risk group does not reflect the response kinetics of the different genetic subtypes. Future risk algorithms should integrate genetics with MRD to accurately identify patients with the lowest and highest risk of relapse.
Highlights
The assessment of treatment response via the measurement of minimal residual disease (MRD) is recognized as the most powerful prognostic factor in acute lymphoblastic leukemia (ALL).[1,2,3,4] The integration of Minimal residual disease (MRD) monitoring into riskadapted protocols has been used to successfully guide therapy intensification and reduction[2,5,6,7]; MRD alone is not sufficient to fully predict outcome
Patients and Methods We used a population-based cohort of 3,113 patients who were treated in UKALL2003, with a median follow-up of 7 years
MRD was log normally distributed at the end of induction
Summary
The assessment of treatment response via the measurement of minimal residual disease (MRD) is recognized as the most powerful prognostic factor in acute lymphoblastic leukemia (ALL).[1,2,3,4] The integration of MRD monitoring into riskadapted protocols has been used to successfully guide therapy intensification and reduction[2,5,6,7]; MRD alone is not sufficient to fully predict outcome. Integrating MRD and Genetics in Pediatric ALL retained its significance in the multivariable model.[8] In addition, studies by the Children’s Oncology Group (COG) and St Jude Children’s Research Hospital have noted significant associations between genetic abnormalities and MRD.[9,10] Previous studies of specific genetic subgroups have led to different conclusions. The United Kingdom and COG study groups assign patients with iAMP21 (intrachromosomal amplification of chromosome 21) to high-risk (HR) regimens irrespective of MRD,[11,12] whereas the BFM study group relies on MRD to assign risk in these patients.[13] Other studies of low hypodiploidy and Philadelphia chromosome– like ALL have proposed that treatment response can refine risk for patients with these abnormalities, but most protocols still allocate these patients to HR therapy.[14,15]
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