Paula Sagmeister1, Jimmy Daza2, Andrea Ofner1, Andreas Ziesch1, Liangtao Ye1,3,4, Najib Ben Khaled1,3, Matthias Ebert2,5, Julia Mayerle1, Andreas Teufel2,5, Enrico N De Toni1,3, Stefan Munker1,3. 1. Department of Medicine II, LMU Munich, Munich, Bavaria, Germany. 2. Department of Medicine II, Medical Faculty Mannheim, Heidelberg University, Mannheim, Baden Wurttenberg, Germany. 3. Liver Center Munich, LMU Munich, Munich, Bavaria, Germany. 4. Center of Digestive Disease, Sun Yat-Sen University, Shenzhen, Guangdong, People's Republic of China. 5. Center for Preventive Medicine and Digital Health Baden-Württenberg (CPDBW), Heidelberg University, Mannheim, Baden Wurttenberg, Germany.
Abstract
Introduction: Although the treatment paradigm for hepatocellular carcinoma (HCC) has recently shifted in favour of checkpoint inhibitor (CPI)-based treatment options, the tyrosine kinase inhibitors (TKI) currently approved for the treatment of HCC are expected to remain the cornerstone of HCC treatment alone or in combination with CPIs. Despite considerable research efforts, no biomarker capable of predicting the response to specific TKIs has been validated. Thus, personalized approaches to HCC may aid in determining optimal treatment lines for 2nd and 3rd lines. To identify new biomarkers, we examined differential sensitivity and investigated potential transcriptomic predictors. Methods: To this aim, the sensitivity of nine HCC cell lines to sorafenib, lenvatinib, regorafenib, and cabozantinib was evaluated by a prolonged treatment scheme to determine their respective growth rate inhibition concentrations (GR50). Subgroups discriminated by GR50 values underwent differential expression and gene set enrichment analysis (GSEA). Results: The nine cell lines showed broadly different sensitivities to different TKIs. GR50 values of sorafenib and regorafenib clustered closer in all cell lines, whereas treatments with lenvatinib and cabozantinib showed diversified GR50 values. GSEA showed the activation of specific pathways in sensitive vs non-sensitive cell lines. A signature consisting of 14 biomarkers (GAGE12H, GJB6, PTCHD3, PRH1-PRR4, C6orf222, HBB, C17orf99, GOLGA6A, CRYAA, CCL23, RP11-347C12.3, RP11-514O12.4, FAM180B, and TMPRSS4) discriminates the cell lines' response into three distinct treatment profiles: 1) equally sensible to sorafenib, regorafenib and cabozantinib, 2) sensible to lenvatinib, and 3) more sensible to regorafenib than sorafenib. Conclusion: We observed diverse responses to either of the four TKIs. Subgroup analysis of TKI effectiveness showed distinct transcriptomic profiles and signaling pathways associated with responsiveness. This prompts more extensive studies to explore and validate pharmacogenomic and transcriptomic strategies for a personalized treatment approach, particularly after the failure of CPI treatment.
Introduction: Although the treatment paradigm for hepatocellular carcinoma (HCC) has recently shifted in favour of checkpoint inhibitor (CPI)-based treatment options, the tyrosine kinase inhibitors (TKI) currently approved for the treatment of HCC are expected to remain the cornerstone of HCC treatment alone or in combination with CPIs. Despite considerable research efforts, no biomarker capable of predicting the response to specific TKIs has been validated. Thus, personalized approaches to HCC may aid in determining optimal treatment lines for 2nd and 3rd lines. To identify new biomarkers, we examined differential sensitivity and investigated potential transcriptomic predictors. Methods: To this aim, the sensitivity of nine HCC cell lines to sorafenib, lenvatinib, regorafenib, and cabozantinib was evaluated by a prolonged treatment scheme to determine their respective growth rate inhibition concentrations (GR50). Subgroups discriminated by GR50 values underwent differential expression and gene set enrichment analysis (GSEA). Results: The nine cell lines showed broadly different sensitivities to different TKIs. GR50 values of sorafenib and regorafenib clustered closer in all cell lines, whereas treatments with lenvatinib and cabozantinib showed diversified GR50 values. GSEA showed the activation of specific pathways in sensitive vs non-sensitive cell lines. A signature consisting of 14 biomarkers (GAGE12H, GJB6, PTCHD3, PRH1-PRR4, C6orf222, HBB, C17orf99, GOLGA6A, CRYAA, CCL23, RP11-347C12.3, RP11-514O12.4, FAM180B, and TMPRSS4) discriminates the cell lines' response into three distinct treatment profiles: 1) equally sensible to sorafenib, regorafenib and cabozantinib, 2) sensible to lenvatinib, and 3) more sensible to regorafenib than sorafenib. Conclusion: We observed diverse responses to either of the four TKIs. Subgroup analysis of TKI effectiveness showed distinct transcriptomic profiles and signaling pathways associated with responsiveness. This prompts more extensive studies to explore and validate pharmacogenomic and transcriptomic strategies for a personalized treatment approach, particularly after the failure of CPI treatment.
Authors: Josep M Llovet; Sergio Ricci; Vincenzo Mazzaferro; Philip Hilgard; Edward Gane; Jean-Frédéric Blanc; Andre Cosme de Oliveira; Armando Santoro; Jean-Luc Raoul; Alejandro Forner; Myron Schwartz; Camillo Porta; Stefan Zeuzem; Luigi Bolondi; Tim F Greten; Peter R Galle; Jean-François Seitz; Ivan Borbath; Dieter Häussinger; Tom Giannaris; Minghua Shan; Marius Moscovici; Dimitris Voliotis; Jordi Bruix Journal: N Engl J Med Date: 2008-07-24 Impact factor: 91.245
Authors: Liangtao Ye; Julia Mayerle; Andreas Ziesch; Florian P Reiter; Alexander L Gerbes; Enrico N De Toni Journal: Cell Death Discov Date: 2019-04-05