A Transcriptomic Signature for Risk-Stratification and Recurrence Prediction in Intrahepatic Cholangiocarcinoma

Abstract

Background and aims: 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.

Approach and results: 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.

Conclusions: We report a transcriptomic signature for risk-stratification and recurrence prediction that is superior to currently used clinicopathological features in patients with ICC.

Figure 1.. Training and validation phase of a transcriptomic panel for the identification of recurrence in patients with ICC.

A) A ROC curve for a transcriptomic panel in tissue specimens from training cohort patients (with recurrence = 37, non-recurrence = 27, AUC = 0.89). B) Risk score distribution plot in training cohort patients. Modified risk scores were obtained from individual risk scores by using Youden’s index values from the risk model. C) A ROC curve for the transcriptomic panel in tissue specimens from validation cohort patients (with recurrence = 109, non-recurrence = 68, AUC = 0.86). D) Risk score distribution plot in validation cohort patients.

Figure 2.. Prognostic potential of the transcriptomic panel in patients with ICC.

A–B) A comparison of (A) RFS and (B) OS between high and low-risk group estimated by the panel in the training cohort. C–D) A comparison of (C) RFS and (D) OS between high and low-risk group estimated by the panel in the validation cohort. E) A nomogram illustrating the probability of RFS. For clinical purposes, the scores of each covariate are added, and the total score is depicted on the total score point axis.

Figure 3.. Clinical validation of the risk-stratification model in patients with ICC.

A) The risk-stratification model, which combines the transcriptomic panel and clinical risk factors, outperformed detection accuracy of the transcriptomic panel or risk factors alone in tissue specimens from validation cohort patients (AUC = 0.89). B–C) Forest plot with HRs of clinicopathological variables, transcriptomic panel, and risk-stratification model in univariate (B) and multivariate (C) Cox proportional hazard regression analysis in validation cohort patients. D) A comparison of RFS between high and low-risk group estimated by the risk-stratification model in the validation cohort (left panel). The risk-stratification model would have led to the 5-year recurrence rate of 24% patients with low-risk and 85% patients with high-risk ICC (right panel).