DataSheet_1_CTNNB1 Alternation Is a Potential Biomarker for Immunotherapy Prognosis in Patients With Hepatocellular Carcinoma.pdf

BackgroundThe emergence of immune checkpoint inhibitors (ICIs) marks the beginning of a new era of immunotherapy for hepatocellular carcinoma (HCC), however, not all patients respond successfully to this treatment. A major challenge for HCC immunotherapy is the development of ways to screen for those patients that would benefit from this type of treatment and determine the optimal treatment plan for individual patients. Therefore, it is important to find a biomarker which allows for the stratification of HCC patients, which distinguishes responders from non-responders, thereby further improving the clinical benefits for those undergoing immunotherapy.
MethodsWe used univariate and multivariate Cox risk proportional regression models to evaluate the relationship between non-synonymous mutations with a mutation frequency greater than 10%. We made a prognosis of an immunotherapy HCC cohort using mutation and prognosis data. An additional three HCC queues from the cbioportal webtool were used for further verification. The CIBERSORT, IPS, quanTIseq, and MCPcounter algorithms were used to evaluate the immune cells. PCA and z-score algorithm were used to calculate immune-related signature with published gene sets. Gene set enrichment analysis (GSEA) was used to compare the differences in the pathway-based enrichment scores of candidate genes between mutant and wild types.
ResultsUnivariate and multivariate Cox results showed that only CTNNB1-Mutant(CTNNB1-MUT) was associated with progression-free survival (PFS) of HCC patients in the immunotherapy cohort. After excluding the potential bias introduced by other clinical features, it was found that CTNNB1-MUT served as an independent predictor of the prognosis of HCC patients after immunotherapy (P < 0.05; HR > 1). The results of the tumor immune microenvironment (TIME) analysis showed that patients with CTNNB1-MUT had significantly reduced activated immune cells [such as T cells, B cells, M1-type macrophages, and dendritic cells (DCs)], significantly increased M2-type macrophages, a significantly decreased expression of immunostimulating molecules, low activity of the immune activation pathways (cytokine pathway, immune cell activation and recruitment) and highly active immune depletion pathways (fatty acid metabolism, cholesterol metabolism, and Wnt pathway).
ConclusionsIn this study, we found CTNNB1-MUT to be a potential biomarker for HCC immunotherapy patients, because it identified those patients are less likely to benefit from ICIs.

研究背景：免疫检查点抑制剂（immune checkpoint inhibitors, ICIs）的出现，标志着肝细胞癌（hepatocellular carcinoma, HCC）免疫治疗新时代的开启。然而并非所有患者均能从该治疗中获得理想应答。肝细胞癌免疫治疗面临的核心挑战之一，是如何筛选出可从该类治疗中获益的患者，并为个体患者制定最优治疗方案。因此，寻找能够实现肝细胞癌患者分层、区分免疫治疗应答者与非应答者的生物标志物，对于进一步提升免疫治疗患者的临床获益至关重要。
研究方法：本研究采用单因素及多因素Cox风险比例回归模型，分析突变频率＞10%的非同义突变（non-synonymous mutations）与患者预后的关联；利用突变数据与预后数据，对肝细胞癌免疫治疗队列进行预后分析。另外从cbioportal数据库平台获取3个肝细胞癌队列，用于后续验证。采用CIBERSORT、IPS、quanTIseq及MCPcounter算法对免疫细胞浸润情况进行评估；结合已发表的基因集，通过主成分分析（principal component analysis, PCA）与z-score算法计算免疫相关特征评分。采用基因集富集分析（gene set enrichment analysis, GSEA），比较候选基因在突变型与野生型样本间的通路富集评分差异。
研究结果：单因素及多因素Cox回归分析结果显示，在免疫治疗队列中，仅CTNNB1突变型（CTNNB1-Mutant, CTNNB1-MUT）与肝细胞癌患者的无进展生存期（progression-free survival, PFS）存在显著关联。在排除其他临床特征引入的潜在偏倚后，结果表明CTNNB1-MUT可作为肝细胞癌患者免疫治疗后预后的独立预测因子（P < 0.05；风险比HR > 1）。肿瘤免疫微环境（tumor immune microenvironment, TIME）分析结果显示，CTNNB1-MUT阳性患者的活化免疫细胞（如T细胞、B细胞、M1型巨噬细胞及树突状细胞（dendritic cells, DCs））浸润水平显著降低，M2型巨噬细胞浸润水平显著升高；免疫刺激分子表达水平显著下调，免疫激活通路（包括细胞因子通路、免疫细胞激活与募集通路）活性低下，而免疫耗竭通路（包括脂肪酸代谢、胆固醇代谢及Wnt通路）活性显著升高。
研究结论：本研究发现，CTNNB1-MUT可作为肝细胞癌免疫治疗患者的潜在生物标志物，其能够识别出那些难以从免疫检查点抑制剂治疗中获益的患者。