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Large-scale pharmacogenomic studies and drug response prediction for personalized cancer medicine
- Source :
- Journal of Genetics and Genomics. 48:540-551
- Publication Year :
- 2021
- Publisher :
- Elsevier BV, 2021.
-
Abstract
- The response rate of most anti-cancer drugs is limited because of the high heterogeneity of cancer and the complex mechanism of drug action. Personalized treatment that stratifies patients into subgroups using molecular biomarkers is promising to improve clinical benefit. With the accumulation of preclinical models and advances in computational approaches of drug response prediction, pharmacogenomics has made great success over the last 20 years and is increasingly used in the clinical practice of personalized cancer medicine. In this article, we first summarize FDA-approved pharmacogenomic biomarkers and large-scale pharmacogenomic studies of preclinical cancer models such as patient-derived cell lines, organoids, and xenografts. Furthermore, we comprehensively review the recent developments of computational methods in drug response prediction, covering network, machine learning, and deep learning technologies and strategies to evaluate immunotherapy response. In the end, we discuss challenges and propose possible solutions for further improvement.
- Subjects :
- Response rate (survey)
0303 health sciences
business.industry
Mechanism (biology)
Cancer
Drug action
Computational biology
Biology
medicine.disease
03 medical and health sciences
0302 clinical medicine
Cancer Medicine
Pharmacogenomics
Genetics
medicine
Drug response
Personalized medicine
Precision Medicine
business
Molecular Biology
030217 neurology & neurosurgery
030304 developmental biology
Subjects
Details
- ISSN :
- 16738527
- Volume :
- 48
- Database :
- OpenAIRE
- Journal :
- Journal of Genetics and Genomics
- Accession number :
- edsair.doi.dedup.....5a2e11207b360f95d102e8f9d768ca0a