University of Oulu

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

Can machine learning models predict asparaginase-associated pancreatitis in childhood acute lymphoblastic leukemia

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Author: Nielsen, Rikke L.1,2,3; Wolthers, Benjamin O.4; Helenius, Marianne1;
Organizations: 1Health Technology, Technical University of Denmark
2Bioinformatics, Technical University of Denmark
3Center for Biological Sequence Analysis, Technical University of Denmark
4Department of Pediatrics and Adolescent Medicine, University Hospital Rigshospitalet
5Department of Pediatrics and Adolescent Medicine, Aarhus University Hospital, Aarhus, Denmark
6Department of Applied Mathematics and Computer Science, Kgs. Lyngby
7Children’s Hospital, Helsinki University Central Hospital, University of Helsinki, Helsinki
8Oulu University Hospital, Department of Children and Adolescents, and University of Oulu, PEDEGO Research Unit, Oulu, Finland
9Department of Pediatric Hematology and Oncology, St Anna Children’s Hospital and Department of Pediatric and Adolescent Medicine, Medical University of Vienna, Wien, Austria
10Pediatric Hematology and Oncology, Schneider Children’s Medical Center of Israel, Petah-Tikva, Israel and Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv-Yafo, Israel
11Department of Pediatrics, Ospedale San Gerardo, University of Milano-Bicocca, Fondazione MBBM, Monza, Italy
12Clinic of Pediatric Hematology and Oncology, University Medical Center Eppendorf, Hamburg
13Division of Pediatric Hematology- Oncology, Mackay Memorial Hospital, Taipei, Taiwan; †††Great Ormond Street Hospital for Children, London, UK
14Department of Pediatrics, Christian-Albrechts-University Kiel and University Medical Center Schleswig-Holstein, Kiel
15Great Ormond Street Hospital for Children, London, UK
16Dutch Childhood Oncology Group, The Hague and Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands
17§Department of Pediatric Hematology and Oncology, Hannover Medical School, Hannover, Germany
18Department of Pediatric Hematology/Oncology, University Hospital Motol, Prague, Czech Republic
19Institute of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 0.4 MB)
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Language: English
Published: Wolters Kluwer, 2022
Publish Date: 2022-09-08


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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Series: Journal of pediatric hematology/oncology
ISSN: 1077-4114
ISSN-E: 1536-3678
ISSN-L: 1077-4114
Volume: 44
Issue: 3
Pages: e628 - e636
DOI: 10.1097/MPH.0000000000002292
Type of Publication: A1 Journal article – refereed
Field of Science: 3122 Cancers
3123 Gynaecology and paediatrics
Funding: Funded by the Kirsten and Freddy Johansen Foundation, the Danish Childhood Cancer Foundation, the Swedish Childhood Cancer Foundation, the Danish Cancer Society, The Nordic Cancer Union, The Otto Christensen Foundation, University Hospital Rigshospitalet, the European Union’s Interregional Öresund–Kattegat–Skagerrak interregional Childhood Oncology Precision Medicine (iCOPE) grant and The Novo Nordisk Foundation. R.L.N. was supported by a grant from the Sino-Danish Center for Education and Research and a grant from the Poul V Andersen Foundation.
Copyright information: © 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.