Asparaginase enzyme activity levels and toxicity in childhood acute lymphoblastic leukemia:a NOPHO ALL2008 study

Abstract Asparaginase treatment is a mainstay in contemporary treatment of acute lymphoblastic leukemia (ALL), but substantial asparaginase-related toxicity may lead to jeopardized protocol compliance and compromises survival. We investigated the association between risk of asparaginase-associated toxicities (AspTox) and asparaginase enzyme activity (AEA) levels in 1155 children aged 1.0 to 17.9 years, diagnosed with ALL between July 2008 and March 2016, and treated according to the Nordic Society of Pediatric Hematology and Oncology (NOPHO) ALL2008 protocol. Patients with ≥2 blood samples for AEA measurement drawn 14 ± 2 days after asparaginase administration were included (6944 trough values). AEA was measurable (or >0 IU/L) in 955 patients, whereas 200 patients (17.3%) had asparaginase inactivation and few AspTox recorded. A time-dependent multiple Cox model of time to any first asparaginase-associated toxicity adjusted for sex and age was used. For patients with measurable AEA, we found a hazard ratio (HR) of 1.17 per 100 IU/L increase in median AEA (95% confidence interval [CI], 0.98–1.41; P = 0.09). For pancreatitis, thromboembolism, and osteonecrosis, the HRs were 1.40 (95% CI, 1.12–1.75; P = 0.002), 0.99 (95% CI, 0.70–1.40; P = 0.96), and 1.36 (95% CI, 1.04–1.77; P = 0.02) per 100 IU/L increase in median AEA, respectively. No significant decrease in the risk of leukemic relapse was found: HR 0.88 per 100 IU/L increase in AEA (95% CI, 0.66–1.16; P = 0.35). In conclusion, these results emphasize that overall AspTox and relapse are not associated with AEA levels, yet the risk of pancreatitis and osteonecrosis increases with increasing AEA levels

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