Can machine learning models predict asparaginase-associated pancreatitis in childhood acute lymphoblastic leukemia

Abstract Asparaginase-associated pancreatitis (AAP) frequently affects children treated for acute lymphoblastic leukemia (ALL) causing severe acute and persisting complications. Known risk factors such as asparaginase dosing, older age and single nucleotide polymorphisms (SNPs) have insufficient odds ratios to allow personalized asparaginase therapy. In this study, we explored machine learning strategies for prediction of individual AAP risk. We integrated information on age, sex, and SNPs based on Illumina Omni2.5exome-8 arrays of patients with childhood ALL (N=1564, 244 with AAP 1.0 to 17.9 yo) from 10 international ALL consortia into machine learning models including regression, random forest, AdaBoost and artificial neural networks. A model with only age and sex had area under the receiver operating characteristic curve (ROC-AUC) of 0.62. Inclusion of 6 pancreatitis candidate gene SNPs or 4 validated pancreatitis SNPs boosted ROC-AUC somewhat (0.67) while 30 SNPs, identified through our AAP genome-wide association study cohort, boosted performance (0.80). Most predictive features included rs10273639 (PRSS1-PRSS2), rs10436957 (CTRC), rs13228878 (PRSS1/PRSS2), rs1505495 (GALNTL6), rs4655107 (EPHB2) and age (1 to 7 y). Second AAP following asparaginase re-exposure was predicted with ROC-AUC: 0.65. The machine learning models assist individual-level risk assessment of AAP for future prevention trials, and may legitimize asparaginase re-exposure when AAP risk is predicted to be low

Thiopurine Enhanced ALL Maintenance (TEAM):study protocol for a randomized study to evaluate the improvement in disease-free survival by adding very low dose 6-thioguanine to 6-mercaptopurine/methotrexate-based maintenance therapy in pediatric and adult patients (0–45 years) with newly diagnosed B-cell precursor or T-cell acute lymphoblastic leukemia treated according to the intermediate risk-high group of the ALLTogether1 protocol

Abstract Background: A critical challenge in current acute lymphoblastic leukemia (ALL) therapy is treatment intensification in order to reduce the relapse rate in the subset of patients at the highest risk of relapse. The year-long maintenance phase is essential in relapse prevention. The Thiopurine Enhanced ALL Maintenance (TEAM) trial investigates a novel strategy for ALL maintenance. Methods: TEAM is a randomized phase 3 sub-protocol to the ALLTogether1 trial, which includes patients 0–45 years of age with newly diagnosed B-cell precursor or T-cell ALL, and stratified to the intermediate risk-high (IR-high) group, in 13 European countries. In the TEAM trial, the traditional methotrexate (MTX)/6-mercaptopurine (6MP) maintenance backbone (control arm) is supplemented with low dose (2.5–12.5 mg/m²/day) oral 6-thioguanine (6TG) (experimental arm), while the starting dose of 6MP is reduced from 75 to 50 mg/m²/day. A total of 778 patients will be included in TEAM during ~ 5 years. The study will close when the last included patient has been followed for 5 years from the end of induction therapy. The primary objective of the study is to significantly improve the disease-free survival (DFS) of IR-high ALL patients by adding 6TG to 6MP/MTX-based maintenance therapy. TEAM has 80% power to detect a 7% increase in 5-year DFS through a 50% reduction in relapse rate. DFS will be evaluated by intention-to-treat analysis. In addition to reducing relapse, TEAM may also reduce hepatotoxicity and hypoglycemia caused by high levels of methylated 6MP metabolites. Methotrexate/6MP metabolites will be monitored and low levels will be reported back to clinicians to identify potentially non-adherent patients. Discussion: TEAM provides a novel strategy for maintenance therapy in ALL with the potential of improving DFS through reducing relapse rate. Potential risk factors that have been considered include hepatic sinusoidal obstruction syndrome/nodular regenerative hyperplasia, second cancer, infection, and osteonecrosis. Metabolite monitoring can potentially increase treatment adherence in both treatment arms. Trial registration: EudraCT, 2018–001795-38. Registered 2020-05-15