Key Points• Integrating cytogenetic and genomic data in pediatric ALL reveals 2 subgroups with different outcomes independent of other risk factors.• A total of 75% of children on UKALL2003 had a good-risk genetic profile, which predicted an EFS and OS of 94% and 97% at 5 years.

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.

Key Points• Chromosomal abnormalities predict outcome after relapse in BCP-ALL, and high-risk cytogenetics takes precedence over clinical risk factors. • Patients with mutations or deletions targeting TP53, NR3C1, BTG1, and NRAS were associated with clinical high risk and an inferior outcome.Somatic genetic abnormalities are initiators and drivers of disease and have proven clinical utility at initial diagnosis. However, the genetic landscape and its clinical utility at relapse are less well understood and have not been studied comprehensively. We analyzed cytogenetic data from 427 children with relapsed B-cell precursor ALL treated on the international trial, ALLR3. Also we screened 238 patients with a marrow relapse for selected copy number alterations (CNAs) and mutations. Cytogenetic risk groups were predictive of outcome postrelapse and survival rates at 5 years for patients with good, intermediate-, and high-risk cytogenetics were 68%, 47%, and 26%, respectively (P < .001). TP53 alterations and NR3C1/BTG1 deletions were associated with a higher risk of progression: hazard ratio 2.36 (95% confidence interval, 1.51-3.70, P < .001) and 2.15 (1.32-3.48, P 5 .002). NRAS mutations were associated with an increased risk of progression among standard-risk patients with high hyperdiploidy: 3.17 (1.15-8.71, P 5 .026). Patients classified clinically as standard and high risk had distinct genetic profiles. The outcome of clinical standard-risk patients with high-risk cytogenetics was equivalent to clinical high-risk patients. Screening patients at relapse for key genetic abnormalities will enable the integration of genetic and clinical risk factors to improve patient stratification and outcome. This study is registered at www.clinicaltrials.org as #ISCRTN45724312. (Blood. 2016;128(7):911-922)