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김주연Juyeon Kim

GIST

Brief. Bioinform., Jan 22 2024, 25 (2) bbae088 | https://doi.org/10.1093/bib/bbae088 PANCDR: precise medicine prediction using an adversarial network for cancer drug response

Juyeon Kim¹, Sung-Hye Park ^2,3, Hyunju Lee ^1,4

¹School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, 61005, Gwangju, South Korea.

²Department of Pathology, Seoul National University Hospital, Seoul National University College of Medicine, 03080, Seoul, South Korea.

³Neuroscience Research Institute, Seoul National University College of Medicine, 03080, Seoul, South Korea.

⁴Artificial Intelligence Graduate School, Gwangju Institute of Science and Technology, 61005, Gwangju, South Korea.

Corresponding author: Hyunju Lee

Abstract

Pharmacogenomics aims to provide personalized therapy to patients based on their genetic variability. However, accurate prediction of cancer drug response (CDR) is challenging due to genetic heterogeneity. Since clinical data are limited, most studies predicting drug response use preclinical data to train models. However, such models might not be generalizable to external clinical data due to differences between the preclinical and clinical datasets. In this study, a Precision Medicine Prediction using an Adversarial Network for Cancer Drug Response (PANCDR) model is proposed. PANCDR consists of two sub-models, an adversarial model and a CDR prediction model. The adversarial model reduces the gap between the preclinical and clinical datasets, while the CDR prediction model extracts features and predicts responses. PANCDR was trained using both preclinical data and unlabeled clinical data. Subsequently, it was tested on external clinical data, including The Cancer Genome Atlas and brain tumor patients. PANCDR outperformed other machine learning models in predicting external test data. Our results demonstrate the robustness of PANCDR and its potential in precision medicine by recommending patient-specific drug candidates. The PANCDR codes and data are available at https://github.com/DMCB-GIST/PANCDR.

논문정보

형식Research article
게재일2024년 01월 (BRIC 등록일 2024-03-18)
연구진국내 연구진
분야 생명과학 > 분자생물학

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