3 research outputs found
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
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Benefits for children with suspected cancer from routine whole-genome sequencing.
Acknowledgements: We thank S. Wakeling (Alice’s Arc), J. Miles (East Genomics Laboratory Hub), A. Sosinsky (Genomics England), P. Moss (Genomics England), A. Maartens (science writer, Wellcome Sanger Institute), G. Collord (University College Hospitals, London) and T. Treger (Wellcome Sanger Institute) for the discussion regarding the paper. We thank the scientific, technical and administrative staff of the North Thames and East Genomic Laboratory Hubs for making WGS possible. We acknowledge funding from the Wellcome Trust (personal fellowship to S. Behjati, institutional grant to the Wellcome Sanger Institute; references 220540/Z/20/A and 223135/Z/21/Z), the Pessoa de Araujo family (personal fellowship to A.H.) and NIHR (academic clinical fellowship to S.M.L.). This research was supported by the NIHR GOSH Biomedical Research Centre and NIHR Cambridge Biomedical Research Centre (NIHR203312). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the paper. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health. We are indebted to the children and families who participated in this study.Funder: NIHR Cambridge Biomedical Research Centre (NIHR203312)Funder: Coordenação de Aperfeiçoamento de Pessoal de NÃvel Superior (Brazilian Federal Agency for the Support and Evaluation of Graduate Education); doi: https://doi.org/10.13039/501100002322Funder: The Pessoa de Araujo family - Personal Fellowship to Dr Angus HodderFunder: DH | National Institute for Health Research (NIHR); doi: https://doi.org/10.13039/501100000272Funder: NIHR GOSH Biomedical Research CentreClinical whole-genome sequencing (WGS) has been shown to deliver potential benefits to children with cancer and to alter treatment in high-risk patient groups. It remains unknown whether offering WGS to every child with suspected cancer can change patient management. We collected WGS variant calls and clinical and diagnostic information from 281 children (282 tumors) across two English units (n = 152 from a hematology center, n = 130 from a solid tumor center) where WGS had become a routine test. Our key finding was that variants uniquely attributable to WGS changed the management in ~7% (20 out of 282) of cases while providing additional disease-relevant findings, beyond standard-of-care molecular tests, in 108 instances for 83 (29%) cases. Furthermore, WGS faithfully reproduced every standard-of-care molecular test (n = 738) and revealed several previously unknown genomic features of childhood tumors. We show that WGS can be delivered as part of routine clinical care to children with suspected cancer and can change clinical management by delivering unexpected genomic insights. Our experience portrays WGS as a clinically impactful assay for routine practice, providing opportunities for assay consolidation and for delivery of molecularly informed patient care