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Acute lymphoblastic leukemia (ALL) is the most prevalent cancer in children, and despite considerable progress in treatment outcomes, relapses still pose significant risks of mortality and long-term complications. To address this challenge, we employed a supervised machine learning technique, specifically random survival forests, to predict the risk of relapse and mortality using array-based DNA methylation data from a cohort of 763 pediatric ALL patients treated in Nordic countries. The relapse risk predictor (RRP) was constructed based on 16 CpG sites, demonstrating c-indexes of 0.667 and 0.677 in the training and test sets, respectively. The mortality risk predictor (MRP), comprising 53 CpG sites, exhibited c-indexes of 0.751 and 0.754 in the training and test sets, respectively. To validate the prognostic value of the predictors, we further analyzed two independent cohorts of Canadian (n = 42) and Nordic (n = 384) ALL patients. The external validation confirmed our findings, with the RRP achieving a c-index of 0.667 in the Canadian cohort, and the RRP and MRP achieving c-indexes of 0.529 and 0.621, respectively, in an independent Nordic cohort. The precision of the RRP and MRP models improved when incorporating traditional risk group data, underscoring the potential for synergistic integration of clinical prognostic factors. The MRP model also enabled the definition of a risk group with high rates of relapse and mortality. Our results demonstrate the potential of DNA methylation as a prognostic factor and a tool to refine risk stratification in pediatric ALL. This may lead to personalized treatment strategies based on epigenetic profiling., (© 2024. The Author(s).)

Although bone fragility may already be present at diagnosis of pediatric acute lymphoblastic leukemia (ALL), routine performance of dual-energy X-ray absorptiometry (DXA) in every child is not universally feasible. The aim of this study was to develop and validate a risk prediction model for low lumbar spine bone mineral density (LS BMD Z-score ≤ -2.0) at diagnosis, as an important indicator for fracture risk and further treatment-related BMD aggravation. Children with ALL (4-18 years), treated according to the Dutch Childhood Oncology Group protocol (DCOG-ALL9; model development; n = 249) and children from the Canadian Steroid-Associated Osteoporosis in the Pediatric Population cohort (STOPP; validation; n = 99) were included in this study. Multivariable logistic regression analyses were used to develop the prediction model and to confirm the association of low LS BMD at diagnosis with symptomatic fractures during and shortly after cessation of ALL treatment. The area under the receiver operating characteristic curve (AUC) was used to assess model performance. The prediction model for low LS BMD at diagnosis using weight (β = -0.70) and age (β = -0.10) at diagnosis revealed an AUC of 0.71 (95% CI, 0.63-0.78) in DCOG-ALL9 and 0.74 (95% CI, 0.63-0.84) in STOPP, and resulted in correct identification of 71% of the patients with low LS BMD. We confirmed that low LS BMD at diagnosis is associated with LS BMD at treatment cessation (OR 5.9; 95% CI, 3.2-10.9) and with symptomatic fractures (OR 1.7; 95% CI, 1.3-2.4) that occurred between diagnosis and 12 months following treatment cessation. In meta-analysis, LS BMD at diagnosis (OR 1.6; 95% CI, 1.1-2.4) and the 6-month cumulative glucocorticoid dose (OR 1.9; 95% CI, 1.1-3.2) were associated with fractures that occurred in the first year of treatment. In summary, a prediction model for identifying pediatric ALL patients with low LS BMD at diagnosis, as an important indicator for bone fragility, was successfully developed and validated. This can facilitate identification of future bone fragility in individual pediatric ALL patients. © 2021 American Society for Bone and Mineral Research (ASBMR)., (© 2021 American Society for Bone and Mineral Research (ASBMR).)