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)
Genetic abnormalities provide vital diagnostic and prognostic information in pediatric acute lymphoblastic leukemia (ALL) and are increasingly used to assign patients to risk groups. We recently proposed a novel classifier based on the copy-number alteration (CNA) profile of the 8 most commonly deleted genes in B-cell precursor ALL. This classifier defined 3 CNA subgroups in consecutive UK trials and was able to discriminate patients with intermediate-risk cytogenetics. In this study, we sought to validate the United Kingdom ALL (UKALL)–CNA classifier and reevaluate the interaction with cytogenetic risk groups using individual patient data from 3239 cases collected from 12 groups within the International BFM Study Group. The classifier was validated and defined 3 risk groups with distinct event-free survival (EFS) rates: good (88%), intermediate (76%), and poor (68%) (P < .001). There was no evidence of heterogeneity, even within trials that used minimal residual disease to guide therapy. By integrating CNA and cytogenetic data, we replicated our original key observation that patients with intermediate-risk cytogenetics can be stratified into 2 prognostic subgroups. Group A had an EFS rate of 86% (similar to patients with good-risk cytogenetics), while group B patients had a significantly inferior rate (73%, P < .001). Finally, we revised the overall genetic classification by defining 4 risk groups with distinct EFS rates: very good (91%), good (81%), intermediate (73%), and poor (54%), P < .001. In conclusion, the UKALL-CNA classifier is a robust prognostic tool that can be deployed in different trial settings and used to refine established cytogenetic risk groups.
Deletions in IKZF1 are found in ~15% of children with B-cell precursor acute lymphoblastic leukemia (BCP-ALL). There is strong evidence for the poor prognosis of IKZF1 deletions affecting exons 4-7 and exons 1-8, but evidence for the remaining 33% of cases harboring other variants of IKZF1 deletions is lacking. In an international multicenter study we analyzed the prognostic value of these rare variants in a case-control design. Each IKZF1-deleted case was matched to three IKZF1 wild-type controls based on cytogenetic subtype, treatment protocol, risk stratification arm, white blood cell count and age. Hazard ratios for the prognostic impact of rare IKZF1 deletions on event-free survival were calculated by matched pair Cox regression. Matched pair analysis for all 134 cases with rare IKZF1 deletions together revealed a poor prognosis (P<0.001) that was evident in each risk stratification arm. Rare variant types with the most unfavorable event-free survival were DEL 2-7 (P=0.03), DEL 2-8 (P=0.002) and DEL-Other (P<0.001). The prognosis of each type of rare variant was equal or worse compared with the well-known major DEL 4-7 and DEL 1-8 IKZF1 deletion variants. We therefore conclude that all variants of rare IKZF1 deletions are associated with an unfavorable prognosis in pediatric BCP-ALL.
IGH@ translocations define a genetic feature that is frequent among adolescents and young adults with ALL. Although associated with an adverse outcome in adults, it is not an independent prognostic factor in children and adolescents.
These data are encouraging and demonstrate that artificial intelligence systems may have a role in providing prognostic and predictive data for patients. The performance of these systems likely will improve with increasing data set size, and this needs further investigation.
Risk stratification is essential for the delivery of optimal treatment in childhood acute lymphoblastic leukemia. However, current risk stratification algorithms dichotomize variables and apply risk factors independently, which may incorrectly assume identical associations across biologically heterogeneous subsets and reduce statistical power. Accordingly, we developed and validated a prognostic index (PIUKALL) that integrates multiple risk factors and uses continuous data. We created discovery (n = 2405) and validation (n = 2313) cohorts using data from 4 recent trials (UKALL2003, COALL-03, DCOG-ALL10, and NOPHO-ALL2008). Using the discovery cohort, multivariate Cox regression modeling defined a minimal model including white cell count at diagnosis, pretreatment cytogenetics, and end-of-induction minimal residual disease. Using this model, we defined PIUKALL as a continuous variable that assigns personalized risk scores. PIUKALL correlated with risk of relapse and was validated in an independent cohort. Using PIUKALL to risk stratify patients improved the concordance index for all end points compared with traditional algorithms. We used PIUKALL to define 4 clinically relevant risk groups that had differential relapse rates at 5 years and were similar between the 2 cohorts (discovery: low, 3% [95% confidence interval (CI), 2%-4%]; standard, 8% [95% CI, 6%-10%]; intermediate, 17% [95% CI, 14%-21%]; and high, 48% [95% CI, 36%-60%; validation: low, 4% [95% CI, 3%-6%]; standard, 9% [95% CI, 6%-12%]; intermediate, 17% [95% CI, 14%-21%]; and high, 35% [95% CI, 24%-48%]). Analysis of the area under the curve confirmed the PIUKALL groups were significantly better at predicting outcome than algorithms employed in each trial. PIUKALL provides an accurate method for predicting outcome and more flexible method for defining risk groups in future studies.
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