Comparison of DNA methylation based classification models for precision diagnostics of central nervous system tumors

As part of the advancement in therapeutic decision-making for brain tumor patients at St. Jude Children's Research Hospital (SJCRH), we developed three robust classifiers, a deep learning neural network (NN), k-nearest neighbor (kNN), and random forest (RF), trained on a reference series DNA-methylation profiles to classify central nervous system (CNS) tumor types. The models' performance was rigorously validated against 2054 samples from two independent cohorts. In addition to classic metrics of model performance, we compared the robustness of the three models to reduced tumor purity, a critical consideration in the clinical utility of such classifiers. Our findings revealed that the NN model exhibited the highest accuracy and maintained a balance between precision and recall. The NN model was the most resistant to drops in performance associated with a reduction in tumor purity, showing good performance until the purity fell below 50%. Through rigorous validation, our study emphasizes the potential of DNA-methylation-based deep learning methods to improve precision medicine for brain tumor classification in the clinical setting.

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Publication Details

Journal
npj Precision Oncology
Published
2024-10-02
DOI
https://doi.org/10.1038/s41698-024-00718-3
Citations
12
Primary Topic
Cancer Genomics and Diagnostics
Type
article
Field-Weighted Citation Impact
3.42

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article

Comparison of DNA methylation based classification models for precision diagnostics of central nervous system tumors

Michael R. Clay, Jinan Chen, Quynh T. Tran, Alex Breuer et al.
12 citations
npj Precision Oncology
Cancer Genomics and Diagnostics
3.42
article

Comparison of DNA methylation based classification models for precision diagnostics of central nervous system tumors

Michael R. Clay, Jinan Chen, Quynh T. Tran, Alex Breuer, Brent A. Orr, Amar Gajjar, Paul A. Northcott, Giles W Robinson, Elizabeth M. Azzato, Stanley Pounds, Larissa V. Furtado, Tylman Michael, David W. Ellison, Ruth Tatevossian, Sariah J. Allen, Tong Lin, Sheila Shurtleff, Dale J. Hedges
article en
12 citations

Abstract

As part of the advancement in therapeutic decision-making for brain tumor patients at St. Jude Children's Research Hospital (SJCRH), we developed three robust classifiers, a deep learning neural network (NN), k-nearest neighbor (kNN), and random forest (RF), trained on a reference series DNA-methylation profiles to classify central nervous system (CNS) tumor types. The models' performance was rigorously validated against 2054 samples from two independent cohorts. In addition to classic metrics of model performance, we compared the robustness of the three models to reduced tumor purity, a critical consideration in the clinical utility of such classifiers. Our findings revealed that the NN model exhibited the highest accuracy and maintained a balance between precision and recall. The NN model was the most resistant to drops in performance associated with a reduction in tumor purity, showing good performance until the purity fell below 50%. Through rigorous validation, our study emphasizes the potential of DNA-methylation-based deep learning methods to improve precision medicine for brain tumor classification in the clinical setting.

npj Precision OncologyVol. 8(1)
St. Jude Children's Research Hospital (US), Cleveland Clinic (US), Clinical Insights (US), University of Colorado Denver (US)
American Lebanese Syrian Associated Charities, National Cancer Institute
Openalex Percentile: Top 7%
Cancer Genomics and Diagnostics
3.42
DOIOpen Access PDFOpenAlex
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