Jae-Kyung Won1,2,3, Su Jong Yu4, Chae Young Hwang1, Sung-Hwan Cho1, Sang-Min Park1, Kwangsoo Kim5, Won-Mook Choi4, Hyeki Cho4, Eun Ju Cho4, Jeong-Hoon Lee4, Kyung Bun Lee3, Yoon Jun Kim4, Kyung-Suk Suh6, Ja-June Jang3, Chung Yong Kim4, Jung-Hwan Yoon4, Kwang-Hyun Cho1,2. 1. Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea. 2. Graduate School of Medical Science and Engineering, KAIST, Daejeon, Korea. 3. Department of Pathology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea. 4. Department of Internal Medicine and Liver Research Institute, Seoul National University College of Medicine, Seoul, Korea. 5. Division of Clinical Bioinformatics, Biomedical Research Institute, Seoul National University Hospital, Seoul, Korea. 6. Department of Surgery, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea.
Abstract
Sorafenib is the only approved targeted drug for hepatocellular carcinoma (HCC), but its effect on patients' survival gain is limited and varies over a wide range depending on pathogenetic conditions. Thus, enhancing the efficacy of sorafenib and finding a reliable predictive biomarker are crucial to achieve efficient control of HCCs. In this study, we utilized a systems approach by combining transcriptome analysis of the mRNA changes in HCC cell lines in response to sorafenib with network analysis to investigate the action and resistance mechanism of sorafenib. Gene list functional enrichment analysis and gene set enrichment analysis revealed that proteotoxic stress and apoptosis modules are activated in the presence of sorafenib. Further analysis of the endoplasmic reticulum stress network model, combined with in vitro experiments, showed that introducing an additional stress by treating the orally active protein disulfide isomerase (PDI) inhibitor (PACMA 31) can synergistically increase the efficacy of sorafenib in vitro and in vivo, which was confirmed using a mouse xenograft model. We also found that HCC patients with high PDI expression show resistance to sorafenib and poor clinical outcomes, compared to the low-PDI-expression group. CONCLUSION: These results suggest that PDI is a promising therapeutic target for enhancing the efficacy of sorafenib and can also be a biomarker for predicting sorafenib responsiveness. (Hepatology 2017;66:855-868).
Sorafenib is the only approved targeted drug for hepatocellular carcinoma (HCC), but its effect on patients' survival gain is limited and varies over a wide range depending on pathogenetic conditions. Thus, enhancing the efficacy of sorafenib and finding a reliable predictive biomarker are crucial to achieve efficient control of HCCs. In this study, we utilized a systems approach by combining transcriptome analysis of the mRNA changes in HCC cell lines in response to sorafenib with network analysis to investigate the action and resistance mechanism of sorafenib. Gene list functional enrichment analysis and gene set enrichment analysis revealed that proteotoxic stress and apoptosis modules are activated in the presence of sorafenib. Further analysis of the endoplasmic reticulum stress network model, combined with in vitro experiments, showed that introducing an additional stress by treating the orally active protein disulfide isomerase (PDI) inhibitor (PACMA 31) can synergistically increase the efficacy of sorafenib in vitro and in vivo, which was confirmed using a mouse xenograft model. We also found that HCC patients with high PDI expression show resistance to sorafenib and poor clinical outcomes, compared to the low-PDI-expression group. CONCLUSION: These results suggest that PDI is a promising therapeutic target for enhancing the efficacy of sorafenib and can also be a biomarker for predicting sorafenib responsiveness. (Hepatology 2017;66:855-868).
Authors: Jeffrey I Zwicker; Benjamin L Schlechter; Jack D Stopa; Howard A Liebman; Anita Aggarwal; Maneka Puligandla; Thomas Caughey; Kenneth A Bauer; Nancy Kuemmerle; Ellice Wong; Ted Wun; Marilyn McLaughlin; Manuel Hidalgo; Donna Neuberg; Bruce Furie; Robert Flaumenhaft Journal: JCI Insight Date: 2019-02-21
Authors: Anna Fishbein; Weicang Wang; Haixia Yang; Jun Yang; Victoria M Hallisey; Jianjun Deng; Sanne M L Verheul; Sung Hee Hwang; Allison Gartung; Yuxin Wang; Diane R Bielenberg; Sui Huang; Mark W Kieran; Bruce D Hammock; Dipak Panigrahy Journal: Proc Natl Acad Sci U S A Date: 2020-08-14 Impact factor: 11.205