Can machine learning models predict asparaginase-associated pancreatitis in childhood acute lymphoblastic leukemia
Nielsen, Rikke L.; Wolthers, Benjamin O.; Helenius, Marianne; Albertsen, Birgitte K.; Clemmensen, Line; Nielsen, Kasper; Kanerva, Jukka; Niinimäki, Riitta; Frandsen, Thomas L.; Attarbaschi, Andishe; Barzilai, Shlomit; Colombini, Antonella; Escherich, Gabriele; Aytan-Aktug, Derya; Liu, Hsi-Che; Möricke, Anja; Samarasinghe, Sujith; van der Sluis, Inge M.; Stanulla, Martin; Tulstrup, Morten; Yadav, Rachita; Zapotocka, Ester; Schmiegelow, Kjeld; Gupta, Ramneek (2022-04-01)
Nielsen, Rikke L. PhD*,†,‡; Wolthers, Benjamin O. MD, PhD‡; Helenius, Marianne MSc*; Albertsen, Birgitte K. MD, PhD§; Clemmensen, Line PhD∥; Nielsen, Kasper PhD¶; Kanerva, Jukka MD, PhD#; Niinimäki, Riitta MD, PhD**; Frandsen, Thomas L. MD, PhD‡; Attarbaschi, Andishe MD, PhD††; Barzilai, Shlomit MD‡‡; Colombini, Antonella MD§§; Escherich, Gabriele MD∥∥; Aytan-Aktug, Derya MSc¶¶; Liu, Hsi-Che MD##; Möricke, Anja MD***; Samarasinghe, Sujith MD, PhD†††; van der Sluis, Inge M. MD, PhD‡‡‡; Stanulla, Martin MD, PhD§§§; Tulstrup, Morten MD‡; Yadav, Rachita PhD¶; Zapotocka, Ester MD, PhD∥∥∥; Schmiegelow, Kjeld MD, PhD‡,¶¶¶; Gupta, Ramneek PhD* Can Machine Learning Models Predict Asparaginase-associated Pancreatitis in Childhood Acute Lymphoblastic Leukemia, Journal of Pediatric Hematology/Oncology: April 2022 - Volume 44 - Issue 3 - p e628-e636 doi: 10.1097/MPH.0000000000002292
© 2021 The Author(s). Published by Wolters Kluwer Health, IncThis is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc-nd/4.0/
https://creativecommons.org/licenses/by-nc-nd/4.0/
https://urn.fi/URN:NBN:fi-fe2022050633294
Tiivistelmä
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.
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