Novel insights in the genetics of HCC recurrence and advances in transcriptomic data integration
Authors: Andreas Teufel, Jens U. Marquardt, Peter R. Galle
Journal: Journal of Hepatology (2011)

COMMENTARY ON: Combining clinical, pathology, and gene expression data to predict recurrence of hepatocellular carcinoma. 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 JB, Roayaie S, Mazzaferro V, Schwartz ME, Llovet JM. Gastroenterology 2011 May;140(5):1501–1512. Copyright (2011). Abstract reprinted with permission from the American Gastroenterological Association. http://www.ncbi.nlm.nih.gov/pubmed/21320499 Abstract: Background & Aims: In approximately 70% of patients with hepatocellular carcinoma (HCC) treated by resection or ablation, disease recurs within 5years. 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 (Barcelona-Clinic 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 non-tumor, cirrhotic tissue (n=226). We evaluated gene expression signatures with reported prognostic ability generated from tumor or cirrhotic tissue in 18 and four 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 non-tumor, 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. Large scale biological data capturing and analysis has revolutionized biomedical research over the past decade [[1]Ioannidis J.P. Expectations, validity, and reality in omics.J Clin Epidemiol. 2010; 63: 945-949Abstract Full Text Full Text PDF PubMed Scopus (49) Google Scholar]. Among the diverse omics technologies transcriptome profiling by means of microarray analysis is one of the most advanced technologies, where expression levels of thousands of genes are measured at once [[2]Kim K. Zakharkin S.O. Allison D.B. Expectations, validity, and reality in gene expression profiling.J Clin Epidemiol. 2010; 63: 950-959Abstract Full Text Full Text PDF PubMed Scopus (48) Google Scholar]. High expectations exist that the analysis of large scale gene expression pattern will result in a better understanding of the molecular basis of multifactorial disorders, such as hepatocellular carcinoma (HCC). A common goal of large scale transcriptome profiling is the stratification of patients eventually leading to personalized prognostic predictions and therapeutic strategies. In particular, the approach to biomarker discovery and development in clinical research made considerable use of microarray technologies. In breast cancer this vision was forcefully pursued. The 70-gene MammaPrint profile [[3]Glas A.M. Floore A. Delahaye L.J. et al.Converting a breast cancer microarray signature into a high-throughput diagnostic test.BMC Genomics. 2006; 7: 278Crossref PubMed Scopus (411) Google Scholar] and the 21-gene Oncotype DX [[4]Cobleigh M.A. Tabesh B. Bitterman P. et al.Tumor gene expression and prognosis in breast cancer patients with 10 or more positive lymph nodes.Clin Cancer Res. 2005; 11: 8623-8631Crossref PubMed Scopus (188) Google Scholar] signature represent pioneering signatures aimed to determine an individualized benefit of chemotherapy and an individualized risk for metastasis. Although commercially available these genetic testing platforms have not entered clinical routine applications in cancer treatment. Currently most promising is a 76-gene outcome clinical profile (Rotterdam signature [[5]Foekens J.A. Atkins D. Zhang Y. et al.Multicenter validation of a gene expression-based prognostic signature in lymph node-negative primary breast cancer.J Clin Oncol. 2006; 24: 1665-1671Crossref PubMed Scopus (314) Google Scholar]), demonstrated to be of prognostic value in patients with node-negative (N0) breast cancer. This signature was confirmed in a larger cohort and is now further investigated in an ongoing European clinical trial (MINDACT, Microarray In Node negative Disease may Avoid ChemoTherapy, EORTC Trial 10041). However, despite all technological advances and progress in technology and bioinformatics algorithms for a sophisticated analysis, still major challenges in microarray analysis exist in all the biological, technological, statistical, and informatic aspects of this technology. Among the most discussed difficulties are: sample collection, sampling issues, technical variation, validation of results, and bioinformatics approaches to analysis [[2]Kim K. Zakharkin S.O. Allison D.B. Expectations, validity, and reality in gene expression profiling.J Clin Epidemiol. 2010; 63: 950-959Abstract Full Text Full Text PDF PubMed Scopus (48) Google Scholar]. These challenges still prevent a rapid introduction of gene expression profile in clinical decision processes. Over the past several years multiple signatures of a few genes supposed to be driver genes in particular biological phenomena have been published addressing HCC development, HCC recurrence, metastasis of HCC, vascular invasion of HCC, and others. Although all of these signatures had significant predictive ability, the successful translation and rigorous evaluation for a clinical application is yet to be generated [[6]Maass T. Sfakianakis I. Staib F. Krupp M. Galle P.R. Teufel A. Microarray-based gene expression analysis of hepatocellular carcinoma.Curr Genomics. 2010; 11: 261-268Crossref PubMed Scopus (32) Google Scholar]. In the May issue of Gastroenterology Villaneuva et al. published a comprehensive analysis of transcriptomic changes predicting the recurrence of HCC after surgical resection [[7]Villanueva A. Hoshida Y. Battiston C. et al.Combining clinical, pathology, and gene expression data to predict recurrence of hepatocellular carcinoma.Gastroenterology. 2011; 140: 1618-1628Abstract Full Text Full Text PDF PubMed Scopus (203) Google Scholar]. This work evaluated gene expression signatures associated with outcome in a large cohort of patients with early stage (Barcelona-Clinic Liver Cancer 0/A), single-nodule HCC and heterogeneity of signatures within tumor tissues. In conclusion the authors 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. As a major contribution to the progress of discovery and evaluation of genetic signatures, Villaneuva et al. provide a comprehensive evaluation of 22 previously published genetic signatures. These data will certainly be valuable on the way to establishing robust and meaningful signatures with a clear impact on clinical decision making. Interestingly most signatures previously reported could be confirmed in this independent data set. Seventeen out of these signatures were able to adequately allocate patients in a poor or good prognosis group. Of note, two of these signatures were obtained from adjacent non-tumoral tissue, emphasizing the critical role for the adjacent non-tumoral tissue in HCC development. Also, these signatures obtained from non-tumor adjacent tissue were not significantly associated with the signatures from the tumor. Thus, integrating this complimentary information into a comprehensive predictive signature may hold even more accuracy for the future. The signatures to be verified have initially not necessarily been described in comparable early stages of HCC. This may explain the fact that some signatures could not be confirmed. However, it is noteworthy that none of the investigated signatures identifying a progenitor cell origin, EpCAM, hepatoblastoma-C2, CK19-rat, and CK19-human signature could be confirmed to be of prognostic value in the presented analysis of early tumor stages. Although a potential contribution of stem/progenitor cells to the development of HCC is still a matter of debate, evidence for the concept of cancer stem/progenitor cells has been accumulated over the past several years. With respect to epithelial cell adhesion molecule (EpCAM), EpCAM-positive cells were demonstrated to possess stem cell characteristics as transplantation of human derived EpCAM+ cells into NOD/SCID mice after propagation in tissue culture results in mature liver tissue expressing human-specific proteins. Recently, several xenograft transplantation experiments using stem cell/progenitor cell exhibiting markers suggested a contribution of these progenitor cells to HCC development. Among them EpCAM+ HCC cells from alpha-fetoprotein AFP+ tumors with cancer stem/progenitor cell features exhibited the abilities to self-renew, differentiate, and initiate aggressive tumors in vivo. Since these tumor initiating capabilities of EpCAM+ cells should be of importance especially in early stages of tumor initiation and development, one would expect respective changes in gene expression signatures to be present in the early stages of HCC investigated in the present study [[8]Teufel A. Galle P.R. Collecting evidence for a stem cell hypothesis in HCC.Gut. 2010; 59: 870-871Crossref PubMed Scopus (9) Google Scholar]. Nevertheless, the authors were at least able to demonstrate the significance of these signatures in a subgroup analysis of patients with HCC closer to the scenarios where these signatures were originally identified. These included patients with more advanced stages of HCC, larger tumors or vascular invasion. Finally, the authors addressed the issue of sample variation between repetitive biopsies taken from the same tumor nodule. This issue is of significant interest as sample variation in liver fibrosis is well established. In addition, Pawlik et al. have recently reported the inaccuracy of needle core biopsies in predicting the histologic grade of a tumor [[9]Pawlik T.M. Gleisner A.L. Anders R.A. Assumpcao L. Maley W. Choti M.A. Preoperative assessment of hepatocellular carcinoma tumor grade using needle biopsy: implications for transplant eligibility.Ann Surg. 2007; 245: 435-442Crossref PubMed Scopus (180) Google Scholar]. Villanueva et al. have, therefore, investigated the reproducibility of the investigated signatures on paired biopsies from 15 tumor specimens beyond 4 cm size. In more than 80% of these paired biopsies the investigated signatures were comparable between the biopsy obtained from the tumor's center and the periphery. Although this does not necessarily hold true for smaller samples, which were the main focus of the presented work, and the number of samples was rather low, it provides at least some evidence for stability of genetic signatures in paired biopsies and suggests a low influence of sampling error. Together, summarizing the results provided by Villaneuva et al. these data provide a significant step ahead not only in the identification of genetic signatures predicting HCC recurrence but also in the evaluation of reported signatures in an independent data set. Thus, this work is certainly a milestone in investigating the genetics of HCC. Furthermore, this work presents the direction of future development of genetic biomarker identification in HCC, as one of the major challenges will be the integration of the available vast amount of data and the assembly of cumulative hypotheses in translational science. Therefore, the development of powerful bioinformatics resources able to cope with the multiple challenges of data integration (annotation, interaction, technical platforms, etc.) will be essential. An integration of transcriptomic genomic and epigenomic changes will be an extremely complex challenge, even more considering the fact that in normal liver tissue of model organisms approximately 25% of changes in gene expression are not accompanied by simultaneous changes in protein abundance, particularly of low expressed genes. However, unraveling the tight and complex interactions between the diverse biological levels in HCC development will certainly result in a much better understanding of the underlying biology. Thus, the pursuit of biomarkers in HCC will lead to a whole new perception of HCC development and subsequently disclose novel strategies for the diagnosis and treatment of HCC [[10]Marquardt J.U. Galle P.R. Teufel A. Molecular diagnosis and therapy of hepatocellular carcinoma (HCC); an emerging field for advanced technologies.J Hepatol. 2011; 56 ([Epub ahead of print]): 267-275Abstract Full Text Full Text PDF PubMed Scopus (141) Google Scholar]. Therefore, tight integration of expertise and knowledge from clinicians, biologists and bioinformaticians will be essential to achieve this ambitious goal. Thus, the presented work by Villaneuva et al. is a valuable step ahead in this direction. The authors declared that they do not have anything to disclose regarding funding or conflict of interest with respect to this manuscript.