ORIGINAL RESEARCH article
Front. Oncol.
Sec. Gynecological Oncology
Volume 15 - 2025 | doi: 10.3389/fonc.2025.1622600
This article is part of the Research TopicRefining Precision Medicine through AI and Multi-omics IntegrationView all 3 articles
Multi-Omics Modality Completion and Knowledge Distillation for Drug Response Prediction in Cervical Cancer
Provisionally accepted
- 1Department of Gynecology and Obstetrics,TheNingbo University Affiliated Women and Children's Hospital of Ningbo University, Ningbo, China
- 2The Affiliated Women and Children's Hospital of Ningbo University, Ning Bo City, China
- 3Fudan, shanghai, China
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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. response data were retrieved from the PRISM Repurposing dataset
Keywords: multi-omics, cervical cancer, Drug response prediction, Knowledge distillation, Modality Completion
Received: 04 May 2025; Accepted: 28 Jul 2025.
Copyright: © 2025 Li, Liao, Yan, Huang, Zhang, Chen, Zhu, Zhi and Chen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Pingli Chen, Department of Gynecology and Obstetrics,TheNingbo University Affiliated Women and Children's Hospital of Ningbo University, Ningbo, China
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