Genotype-Specific Minimal Residual Disease Interpretation Improves Stratification in Pediatric Acute Lymphoblastic
David O'Connor1, Amir Enshaei1, Jack Bartram1
1David O'Connor, Jack Bartram, Sujith Samarasinghe, Ajay Vora, and Nick Goulden, Great Ormond Street Hospital; Rachael Hough, University College Hospital, London; Amir Enshaei, Christine J. Harrison, Claire Schwab, and Anthony V. Moorman, Northern Institute for Cancer Research, Newcastle University, Newcastle upon Tyne; Jeremy Hancock, North Bristol National Health Service Trust; John Moppett, Royal Hospital for Sick Children, Bristol; Ajay Vora, Sheffield Children's Hospital, Sheffield; Rachel Wade, Medical Research Council, University of Oxford, Oxford, United Kingdom; and Nick Goulden, Trapehade, Monferran-Plavès, France.
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Purpose Minimal 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 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 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. Results MRD 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. Conclusion A 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.
