Combining clinical, pathology, and gene expression data to predict recurrence of hepatocellular carcinoma.
Combining clinical, pathology, and gene expression data to predict recurrence of hepatocellular carcinoma.
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DOI：
10.1053/j.gastro.2011.02.006
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发表时间：
2011-05
期刊：
影响因子：
29.4
通讯作者：
Llovet JM
中科院分区：
文献类型：
--
作者：
Villanueva A;Hoshida Y;Battiston C;Tovar V;Sia D;Alsinet C;Cornella H;Liberzon A;Kobayashi M;Kumada H;Thung SN;Bruix J;Newell P;April C;Fan JB;Roayaie S;Mazzaferro V;Schwartz ME;Llovet JM
在大约70%的肝细胞癌（HCC）患者接受切除或消融治疗后，疾病在5年内复发。尽管基因表达特征与预后相关，但目前还没有基于临床、病理学和基因组数据（来自肿瘤和胰腺组织）预测复发的方法。我们评估了一个大的早期（BCLC 0/A）单结节HCC患者队列中与结果相关的基因表达特征以及肿瘤组织中特征的异质性。我们评估了287例接受切除术的HCC患者，并使用肿瘤（n=287）和邻近非肿瘤组织（n=226）测试了全基因组表达平台。我们分别在18份和4份报告中评估了来自肿瘤或结肠组织的具有报告预后能力的基因表达特征。在另外15名患者中，我们分析了来自肿瘤中心和周边的样本，以确定签名的稳定性。数据分析包括考克斯模型和随机生存森林，以确定肿瘤复发的独立预测因子。与侵袭性HCC相关的基因表达特征被聚类，以及与祖细胞来源的肿瘤相关的基因表达特征和来自非肿瘤、邻近的癌组织的基因表达特征。在多变量分析中，肿瘤相关标志“G3-增殖”（风险比[HR]=1.75，P=0.003）和相邻的“生存差”标志（HR=1.74，P=0.004）是HCC复发的独立预测因子，沿着卫星标志（HR=1.66，P=0.04）。来自同一肿瘤结节中不同部位的样品可重复地分类。我们基于肿瘤和邻近组织中的基因表达模式开发了一个肝癌复发的复合预后模型。这些特征可预测HCC患者的早期和总体复发，并补充临床和病理学分析的结果。
In approximately 70% of patients with hepatocellular carcinoma (HCC) treated by resection or ablation, disease recurs within 5 years. Although gene expression signatures have been associated with outcome, there is no method to predict recurrence based on combined clinical, pathology, and genomic data (from tumor and cirrhotic tissue). We evaluated gene expression signatures associated with outcome in a large cohort of patients with early-stage (BCLC 0/A), single-nodule HCC and heterogeneity of signatures within tumor tissues. We assessed 287 HCC patients undergoing resection and tested genome-wide expression platforms using tumor (n=287) and adjacent non-tumor, cirrhotic tissue (n=226). We evaluated gene expression signatures with reported prognostic ability generated from tumor or cirrhotic tissue in 18 and 4 reports, respectively. In 15 additional patients, we profiled samples from the center and periphery of the tumor, to determine stability of signatures. Data analysis included Cox modeling and random survival forests to identify independent predictors of tumor recurrence. Gene expression signatures that were associated with aggressive HCC were clustered, as well as those associated with tumors of progenitor cell origin and those from non-tumor, adjacent, cirrhotic tissues. On multivariate analysis, the tumor-associated signature “G3-proliferation” (hazard ratio [HR]=1.75, P=0.003) and an adjacent “poor-survival” signature (HR=1.74, P=0.004) were independent predictors of HCC recurrence, along with satellites (HR=1.66, P=0.04). Samples from different sites in the same tumor nodule were reproducibly classified. We developed a composite prognostic model for HCC recurrence, based on gene expression patterns in tumor and adjacent tissues. These signatures predict early and overall recurrence in patients with HCC, and complement findings from clinical and pathology analyses.
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影响因子：
254.7
作者：
Jemal, Ahmedin;Siegel, Rebecca;Thun, Michael J.
通讯作者：
Thun, Michael J.
影响因子：
11.2
作者：
Hoshida Y;Nijman SM;Kobayashi M;Chan JA;Brunet JP;Chiang DY;Villanueva A;Newell P;Ikeda K;Hashimoto M;Watanabe G;Gabriel S;Friedman SL;Kumada H;Llovet JM;Golub TR
通讯作者：
Golub TR
影响因子：
51.1
作者：
Faivre, Sandrine;Raymond, Eric;Cheng, Ann Lii
通讯作者：
Cheng, Ann Lii
影响因子：
0.8
作者：
Ishwaran H;Kogalur UB
通讯作者：
Kogalur UB
DOI：
10.1056/nejmoa0804525
发表时间：
2008-11-06
期刊：
The New England journal of medicine
影响因子：
--
作者：
Hoshida Y;Villanueva A;Kobayashi M;Peix J;Chiang DY;Camargo A;Gupta S;Moore J;Wrobel MJ;Lerner J;Reich M;Chan JA;Glickman JN;Ikeda K;Hashimoto M;Watanabe G;Daidone MG;Roayaie S;Schwartz M;Thung S;Salvesen HB;Gabriel S;Mazzaferro V;Bruix J;Friedman SL;Kumada H;Llovet JM;Golub TR
通讯作者：
Golub TR