Center for Gastrointestinal Research, Baylor Scott & White Research Institute and Charles A. Sammons Cancer Center, Baylor University Medical Center, Dallas, TX.
Department of Surgery, Tokushima University, Tokushima, Japan.
Hepatology. 2021 Sep;74(3):1371-1383. doi: 10.1002/hep.31803. Epub 2021 Jun 15.
Tumor recurrence is frequent even in intrahepatic cholangiocarcinoma (ICC), and improved strategies are needed to identify patients at highest risk for such recurrence. We performed genome-wide expression profile analyses to discover and validate a gene signature associated with recurrence in patients with ICC.
For biomarker discovery, we analyzed genome-wide transcriptomic profiling in ICC tumors from two public data sets: The Cancer Genome Atlas (n = 27) and GSE107943 (n = 28). We identified an eight-gene panel (BIRC5 [baculoviral IAP repeat containing 5], CDC20 [cell division cycle 20], CDH2 [cadherin 2], CENPW [centromere protein W], JPH1 [junctophilin 1], MAD2L1 [mitotic arrest deficient 2 like 1], NEIL3 [Nei like DNA glycosylase 3], and POC1A [POC1 centriolar protein A]) that robustly identified patients with recurrence in the discovery (AUC = 0.92) and in silico validation cohorts (AUC = 0.91). We next analyzed 241 specimens from patients with ICC (training cohort, n = 64; validation cohort, n = 177), followed by Cox proportional hazard regression analysis, to develop an integrated transcriptomic panel and establish a risk-stratification model for recurrence in ICC. We subsequently trained this transcriptomic panel in a clinical cohort (AUC = 0.89; 95% confidence interval [CI] = 0.79-0.95), followed by evaluating its performance in an independent validation cohort (AUC = 0.86; 95% CI = 0.80-0.90). By combining our transcriptomic panel with various clinicopathologic features, we established a risk-stratification model that was significantly superior for the identification of recurrence (AUC = 0.89; univariate HR = 6.08, 95% CI = 3.55-10.41, P < 0.01; and multivariate HR = 3.49, 95% CI = 1.81-6.71, P < 0.01). The risk-stratification model identified potential recurrence in 85% of high-risk patients and nonrecurrence in 76% of low-risk patients, which is dramatically superior to currently used pathological features.
We report a transcriptomic signature for risk-stratification and recurrence prediction that is superior to currently used clinicopathological features in patients with ICC.
即使在肝内胆管癌（ICC）中，肿瘤也经常复发，因此需要改进策略来识别复发风险最高的患者。我们进行了全基因组表达谱分析，以发现和验证与 ICC 患者复发相关的基因特征。
为了进行生物标志物的发现，我们分析了来自两个公共数据集的 ICC 肿瘤的全基因组转录组谱：癌症基因组图谱（n=27）和 GSE107943（n=28）。我们确定了一个由八个基因组成的面板（BIRC5[杆状病毒 IAP 重复包含 5]、CDC20[细胞分裂周期 20]、CDH2[钙粘蛋白 2]、CENPW[着丝粒蛋白 W]、JPH1[连接蛋白 1]、MAD2L1[有丝分裂阻滞缺陷 2 样 1]、NEIL3[Nei 样 DNA 糖苷酶 3]和 POC1A[POC1 中心粒蛋白 A]），该面板可在发现队列（AUC=0.92）和计算机模拟验证队列（AUC=0.91）中可靠地区分复发患者。接下来，我们分析了 241 例 ICC 患者的标本（训练队列，n=64；验证队列，n=177），然后进行 Cox 比例风险回归分析，以开发一种综合转录组面板并建立 ICC 复发的风险分层模型。随后，我们在临床队列中对该转录组面板进行训练（AUC=0.89；95%置信区间[CI]为 0.79-0.95），然后在独立的验证队列中评估其性能（AUC=0.86；95%CI 为 0.80-0.90）。通过将我们的转录组面板与各种临床病理特征相结合，我们建立了一种风险分层模型，该模型在识别复发方面的表现明显优于其他模型（AUC=0.89；单因素 HR=6.08，95%CI=3.55-10.41，P<0.01；多因素 HR=3.49，95%CI=1.81-6.71，P<0.01）。风险分层模型在高危患者中识别出 85%的潜在复发患者，在低危患者中识别出 76%的非复发患者，这明显优于目前使用的病理特征。
我们报告了一种用于风险分层和复发预测的转录组特征，与 ICC 患者目前使用的临床病理特征相比，该特征具有更好的性能。
