Shuang Xin1,2, Wenfeng Fang2, Jianwen Li3, Delan Li4, Changzheng Wang5, Quanfei Huang3, Min Huang1, Wei Zhuang1, Xueding Wang6, Likun Chen7. 1. Institute of Clinical Pharmacology, School of Pharmaceutical Sciences, Sun Yat-Sen University, Guangzhou, 510060, People's Republic of China. 2. Department of Medical Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, People's Republic of China. 3. CapitalBio Genomics Co., Ltd., Dongguan, 523808, China. 4. Chemotherapy Department 2, Zhongshan City People's Hospital, Zhongshan, 528403, People's Republic of China. 5. Department of Computer Science, City University of Hong Kong, Hong Kong, China. 6. Institute of Clinical Pharmacology, School of Pharmaceutical Sciences, Sun Yat-Sen University, Guangzhou, 510060, People's Republic of China. wangxd@mail.sysu.edu.cn. 7. Department of Medical Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, People's Republic of China. chenlk@sysucc.org.cn.
Abstract
PURPOSE: Crizotinib is the first-line small molecule tyrosine kinase inhibitor for ALK-positive non-small cell lung cancer. In this study, a retrospective pharmacogenomics investigation was conducted to explore the relationship between genes related to RTK downstream signaling pathways and crizotinib-induced hepatic toxicity in ALK-positive NSCLC patients. METHODS: The variable importance analysis of random forest algorithm was applied to identify the significant features which contribute to the crizotinib sensitivity in Cancer Cell Line Encyclopedia (CCLE) database. The KEGG and reactome pathway enrichment analysis were conducted with EnrichR. The differential expression genes were identified with R package DESeq2 in CCLE liver derived cell lines between crizotinib sensitive and resistant groups. From 2012 to 2015, 42 NSCLC patients were enrolled in this study. 90 polymorphisms were genotyped using the Sequenom Massarray system. Sequencing of HGFR (c-Met) genes was carried out on the Ion Torrent Proton. RESULTS: In total, 66.7% NSCLC patients suffered from crizotinib-induced liver toxicity and 11.9% progressed to severe hepatic toxicity. The features with the top importance from classification and regression random forest model were enriched in RTK downstream signaling pathways (JAK/STAT, RAS/RAF/MAPK, PI3K/AKT pathways) and immune system-related pathways. Collagen family genes (COL1A1, COL1A2, COL6A1, COL5A1) and other extracellular matrix protein (TNC, TAGLN, TENM2, EDIL3, VCAN, CNN1, SH3BP4, TAGLN), which were closely related to MAPK-ERK signaling pathways, were significantly enriched in crizotinib resistant cell lines. In multiple logistic regression, STAT1 rs10208033 (T > C) was significantly associated with crizotinib-induced liver toxicity (OR = 6.733, 95% CI 1.406-32.24, P = 0.017). Compared with non-CC, OR is 5.5 (95% CI 1.219-24.81, P = 0.027) for STAT1 rs10208033 CC genotype to develop crizotinib-induced liver toxicity. Further cell viability test in human fetal hepatocyte line, L-02, reveals that the STAT1 inhibitor might protect hepatocyte cells from the toxicity caused by crizotinib. CONCLUSION: Polymorphism of rs10208033 is a potential biomarker for predicting crizotinib-induced hepatotoxicity. These results suggest that STAT1 plays an important role in crizotinib-induced hepatotoxicity. Further studies are needed to confirm our finding and understand the underlying mechanisms.
PURPOSE:Crizotinib is the first-line small molecule tyrosine kinase inhibitor for ALK-positive non-small cell lung cancer. In this study, a retrospective pharmacogenomics investigation was conducted to explore the relationship between genes related to RTK downstream signaling pathways and crizotinib-induced hepatic toxicity in ALK-positive NSCLCpatients. METHODS: The variable importance analysis of random forest algorithm was applied to identify the significant features which contribute to the crizotinib sensitivity in Cancer Cell Line Encyclopedia (CCLE) database. The KEGG and reactome pathway enrichment analysis were conducted with EnrichR. The differential expression genes were identified with R package DESeq2 in CCLE liver derived cell lines between crizotinib sensitive and resistant groups. From 2012 to 2015, 42 NSCLCpatients were enrolled in this study. 90 polymorphisms were genotyped using the Sequenom Massarray system. Sequencing of HGFR (c-Met) genes was carried out on the Ion Torrent Proton. RESULTS: In total, 66.7% NSCLCpatients suffered from crizotinib-induced liver toxicity and 11.9% progressed to severe hepatic toxicity. The features with the top importance from classification and regression random forest model were enriched in RTK downstream signaling pathways (JAK/STAT, RAS/RAF/MAPK, PI3K/AKT pathways) and immune system-related pathways. Collagen family genes (COL1A1, COL1A2, COL6A1, COL5A1) and other extracellular matrix protein (TNC, TAGLN, TENM2, EDIL3, VCAN, CNN1, SH3BP4, TAGLN), which were closely related to MAPK-ERK signaling pathways, were significantly enriched in crizotinib resistant cell lines. In multiple logistic regression, STAT1rs10208033 (T > C) was significantly associated with crizotinib-induced liver toxicity (OR = 6.733, 95% CI 1.406-32.24, P = 0.017). Compared with non-CC, OR is 5.5 (95% CI 1.219-24.81, P = 0.027) for STAT1rs10208033 CC genotype to develop crizotinib-induced liver toxicity. Further cell viability test in human fetal hepatocyte line, L-02, reveals that the STAT1 inhibitor might protect hepatocyte cells from the toxicity caused by crizotinib. CONCLUSION: Polymorphism of rs10208033 is a potential biomarker for predicting crizotinib-induced hepatotoxicity. These results suggest that STAT1 plays an important role in crizotinib-induced hepatotoxicity. Further studies are needed to confirm our finding and understand the underlying mechanisms.