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Research Article: Multi-omics modality completion and knowledge distillation for drug response prediction in cervical cancer

Date Published: 2025-08-27

Abstract:
In clinical practice, the development of personalized treatment strategies for cervical cancer is hindered by the limited accuracy of drug response prediction, partly due to missing modalities in multi-omics data. We present MKDR, a deep learning framework that integrates variational autoencoder-based modality completion with knowledge distillation to transfer information from complete omics data to incomplete samples. MKDR-Student achieves state-of-the-art performance On cervical cancer cell lines, with an MSE of 0.0034 (34% lower than Xgboost), R² of 0.8126, and MAE of 0.0431, while maintaining high Spearman (0.8647) and Pearson (0.9033) correlations. Data ablation experiments highlight the contributions of knowledge distillation and modality completion: removing the teacher increases MSE by 23%, and VAE reduces error by 15% with 40% missingness. Interpretability analysis shows balanced feature contributions from gene expression (38%), copy number variation (30%), and mutation data (32%), indicating effective multi-omics learning and integration by the student model. Under limited-input conditions, MKDR’s accuracy drops less than 5%, supporting its robustness and potential for clinical application.

Introduction:
Cervical cancer remains a major malignancy among women worldwide, with approximately 604,000 new cases and 341,000 deaths reported in 2020 ( 1 ). Despite progress in HPV vaccination and early screening, significant variability in treatment response persists due to the high heterogeneity of molecular subtypes and the tumor microenvironment. Precision medicine aims to tailor therapies based on individual molecular profiles, with drug response prediction (DRP) playing a key role in estimating treatment efficacy…

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