TY - JOUR
T1 - Can Machine Learning Models Predict Asparaginase-associated Pancreatitis in Childhood Acute Lymphoblastic Leukemia
AU - Nielsen, Rikke L.
AU - Wolthers, Benjamin O.
AU - Helenius, Marianne
AU - Albertsen, Birgitte K.
AU - Clemmensen, Line
AU - Nielsen, Kasper
AU - Kanerva, Jukka
AU - Niinimäki, Riitta
AU - Frandsen, Thomas L.
AU - Attarbaschi, Andishe
AU - Barzilai, Shlomit
AU - Colombini, Antonella
AU - Escherich, Gabriele
AU - Aytan-Aktug, Derya
AU - Liu, Hsi-Che
AU - Möricke, Anja
AU - Samarasinghe, Sujith
AU - van der Sluis, Inge M.
AU - Stanulla, Martin
AU - Tulstrup, Morten
AU - Yadav, Rachita
AU - Zapotocka, Ester
AU - Schmiegelow, Kjeld
AU - Gupta, Ramneek
PY - 2022
Y1 - 2022
N2 - 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 aged 1.0 to 17.9 y) 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 ROCAUC 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.
AB - 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 aged 1.0 to 17.9 y) 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 ROCAUC 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.
KW - Pediatric hematology/oncology
KW - Acute lymphoblastic leukemia
KW - Treatment toxicity
KW - Translational research
KW - Artificial intelligence
U2 - 10.1097/MPH.0000000000002292
DO - 10.1097/MPH.0000000000002292
M3 - Journal article
C2 - 35226426
SN - 1077-4114
VL - 44
SP - e628-e636
JO - Journal of Pediatric Hematology/Oncology
JF - Journal of Pediatric Hematology/Oncology
IS - 3
ER -