Can Machine Learning Models Predict Asparaginase-associated Pancreatitis in Childhood Acute Lymphoblastic Leukemia

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.