A Transcriptomic Signature for Risk‐Stratification and Recurrence Prediction in Intrahepatic Cholangiocarcinoma. Issue 3 (15th June 2021)

• Record Type: Journal Article
• Authors: Wada, Yuma; Shimada, Mitsuo; Yamamura, Kensuke; Toshima, Takeo; Banwait, Jasjit K; Morine, Yuji; Ikemoto, Tetsuya; Saito, Yu; Baba, Hideo; Mori, Masaki; Goel, Ajay
• Is Part Of: Hepatology. Volume 74:Issue 3(2021)
• Page Start: 1371
• Page End: 1383
• Publication Date: 2021-06-15
• Subjects: Heart -- Diseases -- Nursing -- Periodicals; Lungs -- Diseases -- Nursing -- Periodicals; Intensive care nursing -- Periodicals; Foie -- Maladies -- Périodiques; 616.362
• DOI: 10.1002/hep.31803
• Languages: English
• ISSNs: 0270-9139

Abstract

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.