Multimodal Feature-Based Drug Response Prediction Using Light Gradient Boosting Machine and Gene Expression Analysis in Brain Tumors

Articles in Press

Document Type : Research Article

Authors

1 Pakistan Institute of Engineering & Applied Sciences

2 Pakistan Institute of Engineering & Applied Sciences

3 Department of Mechanical and Process Engineering, ETH Zurich

10.24200/sci.2025.67321.10542

Abstract

Due to their recurrence and complex biology, brain tumors remain among the most challenging cancers and significantly contribute to global cancer mortality. With continuous development in precision oncology, accurately predicting patient-specific drug responses is essential for effective treatment and drug design. In this study, we propose a novel multimodal framework utilizing a Light Gradient Boosting Machine (LightGBM) regressor for brain tumors drug response prediction. The model integrates both biological and chemical data features using three modality-specific Variational Autoencoders (VAEs) to encode gene expression, gene mutation, and drug molecular fingerprint features respectively. The integrated feature representations are used by the LightGBM regressor to predict the half-maximal inhibitory concentration IC50 of drugs. Reliable results are obtained using subject-level stratified nested cross-validation. Our model has yielded improved RMSE and correlation R² values 1.12 and 0.76, respectively. These results are statistically significant (p<0.05) as compared to several existing models. Furthermore, using proposed model five FDA-approved drugs with the most accurately predicted IC50 values were identified. Using the statistical analysis of Glioblastoma (GBM) cell lines, we explored several over-expressed genes: EGFR, MKI67, BIRC5, TOP2A, AURKA and under-expressed genes: GFAP, MBP, NEFL, SLC1A2, PLP1., highlighting their biological roles in tumor progression and suppression. For clinical perspective, we have carried out the Survival analysis that showed that highly expressed tumor genes did not significantly affect normal patient survival (p > 0.05). It is anticipated that this study would be useful in precision oncology for anticancer drug development.

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Scientia Iranica

Articles in Press, Accepted Manuscript
Available Online from 13 May 2026

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History

  • Receive Date: 20 July 2025
  • Revise Date: 10 December 2025
  • Accept Date: 27 December 2025

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