Sagmeister Paula, Daza Jimmy, Ofner Andrea, Ziesch Andreas, Ye Liangtao, Ben Khaled Najib, Ebert Matthias, Mayerle Julia, Teufel Andreas, De Toni Enrico N, Munker Stefan
Department of Medicine II, LMU Munich, Munich, Bavaria, Germany.
Department of Medicine II, Medical Faculty Mannheim, Heidelberg University, Mannheim, Baden Wurttenberg, Germany.
J Hepatocell Carcinoma. 2022 Jul 9;9:595-607. doi: 10.2147/JHC.S356333. eCollection 2022.
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.
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 (GR). Subgroups discriminated by GR values underwent differential expression and gene set enrichment analysis (GSEA).
The nine cell lines showed broadly different sensitivities to different TKIs. GR values of sorafenib and regorafenib clustered closer in all cell lines, whereas treatments with lenvatinib and cabozantinib showed diversified GR 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.
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.
尽管肝细胞癌（HCC）的治疗模式最近已转向支持基于检查点抑制剂（CPI）的治疗方案，但目前批准用于治疗HCC的酪氨酸激酶抑制剂（TKI）预计仍将是HCC单独治疗或与CPI联合治疗的基石。尽管进行了大量研究，但尚未验证能够预测对特定TKI反应的生物标志物。因此，HCC的个性化治疗方法可能有助于确定二线和三线的最佳治疗方案。为了识别新的生物标志物，我们研究了差异敏感性并调查了潜在的转录组预测因子。
为此，通过延长治疗方案评估了9种HCC细胞系对索拉非尼、仑伐替尼、瑞戈非尼和卡博替尼的敏感性，以确定它们各自的生长速率抑制浓度（GR）。根据GR值区分的亚组进行差异表达和基因集富集分析（GSEA）。
这9种细胞系对不同的TKI表现出广泛不同的敏感性。在所有细胞系中，索拉非尼和瑞戈非尼的GR值聚类更接近，而仑伐替尼和卡博替尼治疗显示出多样化的GR值。GSEA显示敏感和非敏感细胞系中特定途径的激活。由14种生物标志物（GAGE12H、GJB6、PTCHD3、PRH1-PRR4、C6orf222、HBB、C17orf99、GOLGA6A、CRYAA、CCL23、RP11-347C12.3、RP11-514O12.4、FAM180B和TMPRSS4）组成的特征将细胞系的反应分为三种不同的治疗模式：1）对索拉非尼、瑞戈非尼和卡博替尼同样敏感；2）对仑伐替尼敏感；3）对瑞戈非尼比对索拉非尼更敏感。
我们观察到对四种TKI中的任何一种都有不同的反应。TKI有效性的亚组分析显示了与反应性相关的不同转录组特征和信号通路。这促使进行更广泛的研究，以探索和验证用于个性化治疗方法的药物基因组学和转录组学策略，特别是在CPI治疗失败后。
