Dataset Information

Genomic predictors for recurrence patterns of hepatocellular carcinoma: model derivation and validation.

ABSTRACT:

Background

Typically observed at 2 y after surgical resection, late recurrence is a major challenge in the management of hepatocellular carcinoma (HCC). We aimed to develop a genomic predictor that can identify patients at high risk for late recurrence and assess its clinical implications.

Methods and findings

Systematic analysis of gene expression data from human liver undergoing hepatic injury and regeneration revealed a 233-gene signature that was significantly associated with late recurrence of HCC. Using this signature, we developed a prognostic predictor that can identify patients at high risk of late recurrence, and tested and validated the robustness of the predictor in patients (n = 396) who underwent surgery between 1990 and 2011 at four centers (210 recurrences during a median of 3.7 y of follow-up). In multivariate analysis, this signature was the strongest risk factor for late recurrence (hazard ratio, 2.2; 95% confidence interval, 1.3-3.7; p = 0.002). In contrast, our previously developed tumor-derived 65-gene risk score was significantly associated with early recurrence (p = 0.005) but not with late recurrence (p = 0.7). In multivariate analysis, the 65-gene risk score was the strongest risk factor for very early recurrence (<1 y after surgical resection) (hazard ratio, 1.7; 95% confidence interval, 1.1-2.6; p = 0.01). The potential significance of STAT3 activation in late recurrence was predicted by gene network analysis and validated later. We also developed and validated 4- and 20-gene predictors from the full 233-gene predictor. The main limitation of the study is that most of the patients in our study were hepatitis B virus-positive. Further investigations are needed to test our prediction models in patients with different etiologies of HCC, such as hepatitis C virus.

Conclusions

Two independently developed predictors reflected well the differences between early and late recurrence of HCC at the molecular level and provided new biomarkers for risk stratification. Please see later in the article for the Editors' Summary.

SUBMITTER: Kim JH

PROVIDER: S-EPMC4275163 | biostudies-literature | 2014 Dec

REPOSITORIES: biostudies-literature

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Publications

Genomic predictors for recurrence patterns of hepatocellular carcinoma: model derivation and validation.

Kim Ji Hoon JH Sohn Bo Hwa BH Lee Hyun-Sung HS Kim Sang-Bae SB Yoo Jeong Eun JE Park Yun-Yong YY Jeong Woojin W Lee Sung Sook SS Park Eun Sung ES Kaseb Ahmed A Kim Baek Hui BH Kim Wan Bae WB Yeon Jong Eun JE Byun Kwan Soo KS Chu In-Sun IS Kim Sung Soo SS Wang Xin Wei XW Thorgeirsson Snorri S SS Luk John M JM Kang Koo Jeong KJ Heo Jeonghoon J Park Young Nyun YN Lee Ju-Seog JS

PLoS medicine 20141223 12

<h4>Background</h4>Typically observed at 2 y after surgical resection, late recurrence is a major challenge in the management of hepatocellular carcinoma (HCC). We aimed to develop a genomic predictor that can identify patients at high risk for late recurrence and assess its clinical implications.<h4>Methods and findings</h4>Systematic analysis of gene expression data from human liver undergoing hepatic injury and regeneration revealed a 233-gene signature that was significantly associated with ...[more]

PMID: 25536056

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Project description:BackgroundIn recent years, nomogram prediction models have been widely used to evaluate the prognosis of various diseases. However, studies in primary hepatocellular carcinoma (HCC) are limited. This study sought to explore the risk factors of recurrence of patients with primary HCC after surgical resection and establish a nomogram prediction model.MethodsThe data of 424 patients with primary HCC who had been admitted to the Wuhan Third Hospital were retrospectively collected. The patients were followed-up for 5 years after surgery. The patients were divided into the recurrence group (n=189) and control group (n=235) according to whether the cancer recurred after surgery. The differences in the clinical characteristics between the two groups were analyzed. The risk factors of recurrence after surgical resection of primary HCC were also analyzed, and a prediction model was then established using R4.0.3 statistical software.ResultsThere were significant statistical differences between the two groups in terms of the tumor size, systemic immune-inflammation (SII) index, the number of lesions, tumor differentiation degree, ascites, vascular invasion, and portal vein tumor thrombus (P<0.05). The multivariate regression analysis showed that multiple foci, poorly differentiated tumors, ascites, vascular invasion, and portal vein tumor thrombus were risk factors for the recurrence of primary HCC in patients after surgical resection (P<0.05). The data set was randomly divided into a training set and verification set. The sample size of the training set was 297, and the sample size of the verification set was 127. The area under the receiver operating characteristic (ROC) curve of the training set was 0.866 [95% confidence interval (CI): 0.824-0.907], and the area under the ROC curve of the validation set was 0.812 (95% CI: 0.734-0.890). The Hosmer-Lemeshow Goodness-of-Fit Test was used to test the model with the validation set (χ2=11.243, P=0.188), which indicated that the model had high value in predicting the recurrence of primary HCC after surgical resection.ConclusionsThis model had high value in predicting the recurrence of primary HCC in patients after surgical resection. This model could assist clinicians to assess the prognosis of patients. Intensive treatment for high-risk patients might improve the prognosis of patients.

Project description:BackgroundAlthough long-term outcomes may be comparable between laparoscopic liver resection (LLR) and open liver resection (OLR) for hepatocellular carcinoma (HCC), there has been little discussion regarding the patterns of recurrence after LLR.MethodsPatients with HCC who underwent hepatectomy between April 2015 and November 2018 were included in this study. The recurrence patterns were analyzed in detail. The recurrence outcomes following laparoscopic versus OLR for HCC were compared after 1:2 propensity score matching. Potential risk factors for recurrence were also assessed with Cox proportional risk models.ResultsAmong 425 patients after LLR, 144 (33.8%) experienced recurrence at the last follow-up, with a median recurrence-free survival (RFS) of 10.0 months (range 1-58 months). The most frequent recurrence site was the liver (n = 99, 68.8%), followed by the surgical margin (n = 15, 10.4%) and distant metastases (n = 12, 8.3%). Liver recurrence with distant metastasis (n = 10, 6.9%) tended to occur early (median 8.0 months), while peritoneal recurrence (n = 8, 5.6%) occurred later (median 14.0 months). A total of 120 (83.3%) patients had recurrence within 2 years after LLR. No trocar site recurrence was observed in this study. The recurrence patterns, timing, and treatment did not show significant differences between the LLR and OLR. The independent risk factors for recurrence included ALBI grade, postoperative α-fetoprotein > 8 ng/ml, tumor size > 5 cm, surgical margin ≤ 1 cm, and multiple tumors. Patients with recurrence had 1- and 5-year overall survival rates of 81.1% and 60.7%, respectively, compared with rates of 95.8% and 92.9% for patients without recurrence (P < 0.000).ConclusionThis study suggested that intrahepatic recurrence was still the most common recurrence pattern for HCC after LLR and that LLR did not increase the risk of trocar hole recurrence or implantation. Most cases of recurrence occurred within 2 years after LLR, suggesting that surveillance should be targeted to early recurrence.

Project description:ImportanceBecause of tumor heterogeneity, traditional clinical variables remain insufficient to predict recurrence, which impairs long-term survival among patients undergoing radical hepatectomy for hepatocellular carcinoma (HCC). Vessels encapsulating tumor clusters (VETC) constitute a novel vascular pattern distinct from microvascular invasion (MVI), representing biological aggressiveness of HCC.ObjectiveTo establish a model to estimate individualized recurrence-free survival (RFS) in HCC by integrating VETC and MVI.Design, setting, and participantsThis prognostic study included 498 patients undergoing radical hepatectomy for HCC from 5 academic centers in China from January 1, 2013, to December 31, 2016, and consisted of 3 cohorts: training (243 [48.8%]), internal validation (122 [24.5%]), and external validation (133 [26.7%]). Follow-up was completed on March 30, 2020, and the data were analyzed from December 1 to 31, 2020.ExposuresVETC, MVI, tumor number, and maximum tumor size.Main outcomes and measuresThe primary end point was RFS. The risk score for relative recurrence and nomogram for absolute RFS probability were derived from the final model, which contained variables recommended by multivariate least absolute shrinkage and selection operator Cox proportional hazards regression analysis. Their performance was quantified using the Harrell concordance index (C index), the time-dependent area under the receiver operating characteristic curve, and calibration curves and was compared with 6 prognostic systems. Recurrence-free survival was estimated by the Kaplan-Meier method, and RFS curves were compared using a log-rank test.ResultsAmong the 498 patients, 432 (86.7%) were men; the mean (SD) age at diagnosis was 51.4 (11.3) years. Independent predictors for RFS identified included VETC, MVI, tumor number, and maximum tumor size, which were incorporated into the multivariate model (VMNS model). The C index (0.702; 95% CI, 0.653-0.752) for the VMNS score of the training cohort was significantly higher than those of 6 conventional systems (0.587 [95% CI, 0.535-0.638] to 0.657 [95% CI, 0.606-0.708]). Different recurrence risk groups defined by the VMNS score showed significantly different 2-year RFS (low-risk group, 81.4% [SE, 0.036]; medium-risk group, 62.1% [SE, 0.054]; high-risk group, 30.1% [SE, 0.079]; P < .001). Calibration curves of the VMNS nomogram showed good agreement between the nomogram-predicted RFS probability and actual RFS proportion. The internal and external validation cohorts confirmed the results.Conclusions and relevanceThe VMNS model enabled individualized prognostication of RFS in patients with HCC undergoing curative resection.

Dataset Information

Genomic predictors for recurrence patterns of hepatocellular carcinoma: model derivation and validation.

Background

Methods and findings

Conclusions

Publications

Genomic predictors for recurrence patterns of hepatocellular carcinoma: model derivation and validation.

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