Combining clinical, pathology, and gene expression data to predict recurrence of hepatocellular carcinoma

Augusto Villanueva, Yujin Hoshida, Carlo Battiston, Victoria Tovar, Daniela Sia, Clara Alsinet, Helena Cornella, Arthur Liberzon, Masahiro Kobayashi, Hiromitsu Kumada, Swan N. Thung, Jordi Bruix, Philippa Newell, Craig April, Jianbing Fan, Sasan Roayaie, Vincenzo Mazzaferro, Myron E. Schwartz, Josep M. Llovet

Research output: Contribution to journalArticle

Abstract

Background & Aims: In approximately 70% of patients with hepatocellular carcinoma (HCC) treated by resection or ablation, disease recurs within 5 years. Although gene expression signatures have been associated with outcome, there is no method to predict recurrence based on combined clinical, pathology, and genomic data (from tumor and cirrhotic tissue). We evaluated gene expression signatures associated with outcome in a large cohort of patients with early stage (BarcelonaClinic Liver Cancer 0/A), single-nodule HCC and heterogeneity of signatures within tumor tissues. Methods: We assessed 287 HCC patients undergoing resection and tested genome-wide expression platforms using tumor (n = 287) and adjacent nontumor, cirrhotic tissue (n = 226). We evaluated gene expression signatures with reported prognostic ability generated from tumor or cirrhotic tissue in 18 and 4 reports, respectively. In 15 additional patients, we profiled samples from the center and periphery of the tumor, to determine stability of signatures. Data analysis included Cox modeling and random survival forests to identify independent predictors of tumor recurrence. Results: Gene expression signatures that were associated with aggressive HCC were clustered, as well as those associated with tumors of progenitor cell origin and those from nontumor, adjacent, cirrhotic tissues. On multivariate analysis, the tumor-associated signature G3-proliferation (hazard ratio [HR], 1.75; P = .003) and an adjacent poor-survival signature (HR, 1.74; P = .004) were independent predictors of HCC recurrence, along with satellites (HR, 1.66; P = .04). Samples from different sites in the same tumor nodule were reproducibly classified. Conclusions: We developed a composite prognostic model for HCC recurrence, based on gene expression patterns in tumor and adjacent tissues. These signatures predict early and overall recurrence in patients with HCC, and complement findings from clinical and pathology analyses.

Original languageEnglish
JournalGastroenterology
Volume140
Issue number5
DOIs
Publication statusPublished - May 2011

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Clinical Pathology
Hepatocellular Carcinoma
Gene Expression
Recurrence
Neoplasms
Transcriptome
Survival
Liver Neoplasms
Stem Cells
Multivariate Analysis
Genome

Keywords

  • Liver Cancer
  • Microarray
  • Prognosis
  • Relapse

ASJC Scopus subject areas

  • Gastroenterology

Cite this

Combining clinical, pathology, and gene expression data to predict recurrence of hepatocellular carcinoma. / Villanueva, Augusto; Hoshida, Yujin; Battiston, Carlo; Tovar, Victoria; Sia, Daniela; Alsinet, Clara; Cornella, Helena; Liberzon, Arthur; Kobayashi, Masahiro; Kumada, Hiromitsu; Thung, Swan N.; Bruix, Jordi; Newell, Philippa; April, Craig; Fan, Jianbing; Roayaie, Sasan; Mazzaferro, Vincenzo; Schwartz, Myron E.; Llovet, Josep M.

In: Gastroenterology, Vol. 140, No. 5, 05.2011.

Research output: Contribution to journalArticle

Villanueva, A, Hoshida, Y, Battiston, C, Tovar, V, Sia, D, Alsinet, C, Cornella, H, Liberzon, A, Kobayashi, M, Kumada, H, Thung, SN, Bruix, J, Newell, P, April, C, Fan, J, Roayaie, S, Mazzaferro, V, Schwartz, ME & Llovet, JM 2011, 'Combining clinical, pathology, and gene expression data to predict recurrence of hepatocellular carcinoma', Gastroenterology, vol. 140, no. 5. https://doi.org/10.1053/j.gastro.2011.02.006
Villanueva, Augusto ; Hoshida, Yujin ; Battiston, Carlo ; Tovar, Victoria ; Sia, Daniela ; Alsinet, Clara ; Cornella, Helena ; Liberzon, Arthur ; Kobayashi, Masahiro ; Kumada, Hiromitsu ; Thung, Swan N. ; Bruix, Jordi ; Newell, Philippa ; April, Craig ; Fan, Jianbing ; Roayaie, Sasan ; Mazzaferro, Vincenzo ; Schwartz, Myron E. ; Llovet, Josep M. / Combining clinical, pathology, and gene expression data to predict recurrence of hepatocellular carcinoma. In: Gastroenterology. 2011 ; Vol. 140, No. 5.
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AU - Villanueva, Augusto

AU - Hoshida, Yujin

AU - Battiston, Carlo

AU - Tovar, Victoria

AU - Sia, Daniela

AU - Alsinet, Clara

AU - Cornella, Helena

AU - Liberzon, Arthur

AU - Kobayashi, Masahiro

AU - Kumada, Hiromitsu

AU - Thung, Swan N.

AU - Bruix, Jordi

AU - Newell, Philippa

AU - April, Craig

AU - Fan, Jianbing

AU - Roayaie, Sasan

AU - Mazzaferro, Vincenzo

AU - Schwartz, Myron E.

AU - Llovet, Josep M.

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N2 - Background & Aims: In approximately 70% of patients with hepatocellular carcinoma (HCC) treated by resection or ablation, disease recurs within 5 years. Although gene expression signatures have been associated with outcome, there is no method to predict recurrence based on combined clinical, pathology, and genomic data (from tumor and cirrhotic tissue). We evaluated gene expression signatures associated with outcome in a large cohort of patients with early stage (BarcelonaClinic Liver Cancer 0/A), single-nodule HCC and heterogeneity of signatures within tumor tissues. Methods: We assessed 287 HCC patients undergoing resection and tested genome-wide expression platforms using tumor (n = 287) and adjacent nontumor, cirrhotic tissue (n = 226). We evaluated gene expression signatures with reported prognostic ability generated from tumor or cirrhotic tissue in 18 and 4 reports, respectively. In 15 additional patients, we profiled samples from the center and periphery of the tumor, to determine stability of signatures. Data analysis included Cox modeling and random survival forests to identify independent predictors of tumor recurrence. Results: Gene expression signatures that were associated with aggressive HCC were clustered, as well as those associated with tumors of progenitor cell origin and those from nontumor, adjacent, cirrhotic tissues. On multivariate analysis, the tumor-associated signature G3-proliferation (hazard ratio [HR], 1.75; P = .003) and an adjacent poor-survival signature (HR, 1.74; P = .004) were independent predictors of HCC recurrence, along with satellites (HR, 1.66; P = .04). Samples from different sites in the same tumor nodule were reproducibly classified. Conclusions: We developed a composite prognostic model for HCC recurrence, based on gene expression patterns in tumor and adjacent tissues. These signatures predict early and overall recurrence in patients with HCC, and complement findings from clinical and pathology analyses.

AB - Background & Aims: In approximately 70% of patients with hepatocellular carcinoma (HCC) treated by resection or ablation, disease recurs within 5 years. Although gene expression signatures have been associated with outcome, there is no method to predict recurrence based on combined clinical, pathology, and genomic data (from tumor and cirrhotic tissue). We evaluated gene expression signatures associated with outcome in a large cohort of patients with early stage (BarcelonaClinic Liver Cancer 0/A), single-nodule HCC and heterogeneity of signatures within tumor tissues. Methods: We assessed 287 HCC patients undergoing resection and tested genome-wide expression platforms using tumor (n = 287) and adjacent nontumor, cirrhotic tissue (n = 226). We evaluated gene expression signatures with reported prognostic ability generated from tumor or cirrhotic tissue in 18 and 4 reports, respectively. In 15 additional patients, we profiled samples from the center and periphery of the tumor, to determine stability of signatures. Data analysis included Cox modeling and random survival forests to identify independent predictors of tumor recurrence. Results: Gene expression signatures that were associated with aggressive HCC were clustered, as well as those associated with tumors of progenitor cell origin and those from nontumor, adjacent, cirrhotic tissues. On multivariate analysis, the tumor-associated signature G3-proliferation (hazard ratio [HR], 1.75; P = .003) and an adjacent poor-survival signature (HR, 1.74; P = .004) were independent predictors of HCC recurrence, along with satellites (HR, 1.66; P = .04). Samples from different sites in the same tumor nodule were reproducibly classified. Conclusions: We developed a composite prognostic model for HCC recurrence, based on gene expression patterns in tumor and adjacent tissues. These signatures predict early and overall recurrence in patients with HCC, and complement findings from clinical and pathology analyses.

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KW - Microarray

KW - Prognosis

KW - Relapse

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