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Whole-genome sequencing reveals the evolutionary trajectory of HBV-related hepatocellular carcinoma early recurrence.

ABSTRACT: Patients with hepatocellular carcinoma (HCC) have poor long-term survival following curative resection because of the high rate of tumor early recurrence. Little is known about the trajectory of genomic evolution from primary to early-recurrent HCC. In this study, we performed whole-genome sequencing (WGS) on 40 pairs of primary and early-recurrent hepatitis B virus (HBV)-related HCC tumors from patients who received curative resection, and from four patients whose primary and recurrent tumor were extensively sampled. We identified two recurrence patterns: de novo recurrence (18/40), which developed genetically independently of the primary tumor and carried different HCC drivers, and ancestral recurrence (22/40), which was clonally related to the primary tumor and progressed more rapidly than de novo recurrence. We found that the recurrence location was predictive of the recurrence pattern: distant recurrence tended to display the de novo pattern, whereas local recurrence tended to display the ancestral pattern. We then uncovered the evolutionary trajectories based on the subclonal architecture, driver-gene mutations, and mutational processes observed in the primary and recurrent tumors. Multi-region WGS demonstrated spatiotemporal heterogeneity and polyclonal, monophyletic dissemination in HCC ancestral recurrence. In addition, we identified recurrence-specific mutations and copy-number gains in BCL9, leading to WNT/β-catenin signaling activation and an immune-excluded tumor microenvironment, which suggests that BCL9 might serve as a new therapeutic target for recurrent HCC. Collectively, our results allow us to view with unprecedented clarity the genomic evolution during HBV-related HCC early recurrence, providing an important molecular foundation for enhanced understanding of HCC with implications for personalized therapy to improve patient survival.

SUBMITTER: Zhou SL

PROVIDER: S-EPMC8789859 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

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Whole-genome sequencing reveals the evolutionary trajectory of HBV-related hepatocellular carcinoma early recurrence.

Zhou Shao-Lai SL Zhou Zheng-Jun ZJ Song Cheng-Li CL Xin Hao-Yang HY Hu Zhi-Qiang ZQ Luo Chu-Bin CB Luo Yi-Jie YJ Li Jia J Dai Zhi Z Yang Xin-Rong XR Shi Ying-Hong YH Wang Zheng Z Huang Xiao-Wu XW Fan Jia J Zhou Jian J

Signal transduction and targeted therapy 20220126 1

Patients with hepatocellular carcinoma (HCC) have poor long-term survival following curative resection because of the high rate of tumor early recurrence. Little is known about the trajectory of genomic evolution from primary to early-recurrent HCC. In this study, we performed whole-genome sequencing (WGS) on 40 pairs of primary and early-recurrent hepatitis B virus (HBV)-related HCC tumors from patients who received curative resection, and from four patients whose primary and recurrent tumor we ...[more]

PMID: 35078970

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Project description:Background and aimsCurrent prediction models for early recurrence of hepatocellular carcinoma (HCC) after surgical resection remain unsatisfactory. The aim of this study was to develop evolutionary learning-derived prediction models with interpretability using both clinical and radiomic features to predict early recurrence of HCC after surgical resection.MethodsConsecutive 517 HCC patients receiving surgical resection with available contrast-enhanced computed tomography (CECT) images before resection were retrospectively enrolled. Patients were randomly assigned to a training set (n = 362) and a test set (n = 155) in a ratio of 7:3. Tumor segmentation of all CECT images including noncontrast phase, arterial phase, and portal venous phase was manually performed for radiomic feature extraction. A novel evolutionary learning-derived method called genetic algorithm for predicting recurrence after surgery of liver cancer (GARSL) was proposed to design prediction models for early recurrence of HCC within 2 years after surgery.ResultsA total of 143 features, including 26 preoperative clinical features, 5 postoperative pathological features, and 112 radiomic features were used to develop GARSL preoperative and postoperative models. The area under the receiver operating characteristic curves (AUCs) for early recurrence of HCC within 2 years were 0.781 and 0.767, respectively, in the training set, and 0.739 and 0.741, respectively, in the test set. The accuracy of GARSL models derived from the evolutionary learning method was significantly better than models derived from other well-known machine learning methods or the early recurrence after surgery for liver tumor (ERASL) preoperative (AUC = 0.687, p < 0.001 vs. GARSL preoperative) and ERASL postoperative (AUC = 0.688, p < 0.001 vs. GARSL postoperative) models using clinical features only.ConclusionThe GARSL models using both clinical and radiomic features significantly improved the accuracy to predict early recurrence of HCC after surgical resection, which was significantly better than other well-known machine learning-derived models and currently available clinical models.

Dataset Information

Whole-genome sequencing reveals the evolutionary trajectory of HBV-related hepatocellular carcinoma early recurrence.

Publications

Whole-genome sequencing reveals the evolutionary trajectory of HBV-related hepatocellular carcinoma early recurrence.

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