Predicting temozolomide response in low-grade glioma patients with large-scale machine learning
File(s)
Published version
Author(s)
Du, Hanqin
Piyawajanusorn, Chayanit
Ghislat, Ghita
Ballester, Pedro
Type
Journal Article
Abstract
Background
Temozolomide is the primary chemotherapeutic agent and first-line treatment for low-grade glioma. Although low-grade gliomas are generally less aggressive than high-grade gliomas, they can eventually progress into high-grade gliomas, making it crucial to maximise the efficacy of initial treatment.
Methods
We analysed data from 109 patients with low-grade gliomas in The Cancer Genome Atlas to evaluate the predictive performance of 12 machine learning classification algorithms for temozolomide response, using six types of omics data. Cross-validation and bootstrapping bias correction were applied to compare these models with a conventional biomarker-based model using promoter methylation status of O6-methylguanine-DNA methyltransferase. The Matthews Correlation Coefficient (MCC) was used as the primary evaluation metric.
Results
The microRNA-based model using the Extreme Gradient Boosting algorithm achieved the best performance (MCC = 0.447), outperforming both the automated machine learning method JADBio (MCC = 0.250) and the biomarker-based model (MCC = 0.331). Incorporating clinical variables, such as patient age and Karnofsky score, further improved predictive power, with the logistic regression model with optimal model complexity achieving the highest MCC (0.483). Feature importance analysis on the best model revealed six predictive microRNAs, including three tumour-related factors (miR-335, let-7f, and miR-7-2) and three potential biomarkers (miR-204, miR-6513, and miR-376).
Discussion
This study systematically demonstrates the potential of large-scale analyses combining machine learning and omics data to predict temozolomide response, offering superior predictive accuracy compared with standard biomarkers. However, validation in independent clinical datasets remains necessary before clinical translation.
Temozolomide is the primary chemotherapeutic agent and first-line treatment for low-grade glioma. Although low-grade gliomas are generally less aggressive than high-grade gliomas, they can eventually progress into high-grade gliomas, making it crucial to maximise the efficacy of initial treatment.
Methods
We analysed data from 109 patients with low-grade gliomas in The Cancer Genome Atlas to evaluate the predictive performance of 12 machine learning classification algorithms for temozolomide response, using six types of omics data. Cross-validation and bootstrapping bias correction were applied to compare these models with a conventional biomarker-based model using promoter methylation status of O6-methylguanine-DNA methyltransferase. The Matthews Correlation Coefficient (MCC) was used as the primary evaluation metric.
Results
The microRNA-based model using the Extreme Gradient Boosting algorithm achieved the best performance (MCC = 0.447), outperforming both the automated machine learning method JADBio (MCC = 0.250) and the biomarker-based model (MCC = 0.331). Incorporating clinical variables, such as patient age and Karnofsky score, further improved predictive power, with the logistic regression model with optimal model complexity achieving the highest MCC (0.483). Feature importance analysis on the best model revealed six predictive microRNAs, including three tumour-related factors (miR-335, let-7f, and miR-7-2) and three potential biomarkers (miR-204, miR-6513, and miR-376).
Discussion
This study systematically demonstrates the potential of large-scale analyses combining machine learning and omics data to predict temozolomide response, offering superior predictive accuracy compared with standard biomarkers. However, validation in independent clinical datasets remains necessary before clinical translation.
Date Issued
2025-09-30
Date Acceptance
2025-08-28
Citation
BMC Methods, 2025, 2 (1)
ISSN
3004-8729
Publisher
BioMed Central
Journal / Book Title
BMC Methods
Volume
2
Issue
1
Copyright Statement
© The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
License URL
Publication Status
Published
Article Number
25
Date Publish Online
2025-09-30