한빛사 논문
Kiwon Jang1†, Min Ji Park2†, Jae Soon Park1, Haeun Hwangbo1, Min Kyung Sung1, Sinae Kim2, Jaeyun Jung2, Jong Won Lee3, Sei-Hyun Ahn3, Suhwan Chang2* and Jung Kyoon Choi1,4*
1Department of Bio and Brain Engineering, KAIST, Daejeon 34141, Republic of Korea.
2Department of Biomedical Sciences, University of Ulsan College of Medicine, Asan Medical Center, Seoul 05505, Republic of Korea.
3Department of Surgery, University of Ulsan College of Medicine, Asan Medical Center, Seoul 05505, Republic of Korea.
4Penta Medix Co., Ltd., Seongnam-si, Gyeongi-do 13449, Republic of Korea.
†Kiwon Jang and Min Ji Park contributed equally to this work.
*Corresponding author
Abstract
Background
Systematic in vitro loss-of-function screens provide valuable resources that can facilitate the discovery of drugs targeting cancer vulnerabilities.
Results
We develop a deep learning-based method to predict tumor-specific vulnerabilities in patient samples by leveraging a wealth of in vitro screening data. Acquired dependencies of tumors are inferred in cases in which one allele is disrupted by inactivating mutations or in association with oncogenic mutations. Nucleocytoplasmic transport by Ran GTPase is identified as a common vulnerability in Her2-positive breast cancers. Vulnerability to loss of Ku70/80 is predicted for tumors that are defective in homologous recombination and rely on nonhomologous end joining for DNA repair. Our experimental validation for Ran, Ku70/80, and a proteasome subunit using patient-derived cells shows that they can be targeted specifically in particular tumors that are predicted to be dependent on them.
Conclusion
This approach can be applied to facilitate the development of precision therapeutic targets for different tumors.
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