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Can Machine Learning Models Predict Asparaginase-associated Pancreatitis in Childhood Acute Lymphoblastic Leukemia
RL. Nielsen, BO. Wolthers, M. Helenius, BK. Albertsen, L. Clemmensen, K. Nielsen, J. Kanerva, R. Niinimäki, TL. Frandsen, A. Attarbaschi, S. Barzilai, A. Colombini, G. Escherich, D. Aytan-Aktug, HC. Liu, A. Möricke, S. Samarasinghe, IM. van der...
Language English Country United States
Document type Journal Article
- MeSH
- Precursor Cell Lymphoblastic Leukemia-Lymphoma * drug therapy genetics MeSH
- Asparaginase * adverse effects MeSH
- Genome-Wide Association Study MeSH
- Child MeSH
- Humans MeSH
- Pancreatitis * chemically induced genetics MeSH
- Antineoplastic Agents * adverse effects MeSH
- Machine Learning MeSH
- Check Tag
- Child MeSH
- Humans MeSH
- Publication type
- Journal Article MeSH
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.
Center for Biological Sequence Analysis Technical University of Denmark
Children's Hospital Helsinki University Central Hospital University of Helsinki Helsinki
Clinic of Pediatric Hematology and Oncology University Medical Center Eppendorf Hamburg
Department of Applied Mathematics and Computer Science Kgs Lyngby
Department of Pediatric Hematology and Oncology Hannover Medical School Hannover Germany
Department of Pediatric Hematology Oncology University Hospital Motol Prague Czech Republic
Department of Pediatrics and Adolescent Medicine Aarhus University Hospital Aarhus Denmark
Department of Pediatrics and Adolescent Medicine University Hospital Rigshospitalet
Departments of Health Technology
Division of Pediatric Hematology Oncology Mackay Memorial Hospital Taipei Taiwan
Great Ormond Street Hospital for Children London UK
Institute of Clinical Medicine Faculty of Health and Medical Sciences University of Copenhagen
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