Using synthetic lethal based approach to predict drug and biomarker pairs for personalized medicine

• Main Authors: Nai-Chia Yu, 游乃嘉
• Other Authors: Chi-Ying F. Huang
• Format: Others
• Language: en_US
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/67148904774180717372

碩士 === 國立陽明大學 === 生物藥學研究所 === 104 === Recently, synthetic lethality (SL) has emerged as a novel anti-cancer strategy. SL is a type of genetic interaction between two genes such that simultaneous perturbations of two genes result in cell death or a dramatic decrease in cell viability, while a perturbation of either gene alone is not lethal. The successful application of SL concept in the drug development is the approval of olaparib (a PARP inhibitor) by FDA in 2014 for the treatment of advanced ovarian cancer with BRCA1/2 mutations. There is a pressing need to systematically discover and understand SL interactions specific to human cancer. Non-small cell lung cancer (NSCLC) is the most common type of lung cancer which is a driver gene-related cancer. Most advance stage lung cancer patients receiving front-line chemotherapy for EGFR-wild type patients and EGFR-TKI for EGFR-mutant patients. However, other abnormalities in NSCLC do not have corresponding treatments. We establish an integrated platform to link the genetic alteration and drug sensitivity in silico. This platform systematically detects drug-gene-cancer type pairs for prediction of drug response. We selected two genes, including CTNNB1 and KRAS, are used as examples to predict the corresponding potential drugs. Besides, we can utilize the platform to choose the optimal treatment for different cancer types. Our integrated analysis not only highlights genetic dependency with anti-cancer drug sensitivity, but also offers the possible cancer therapy for precision medicine at the systems level.