Gene Expression and Proteomics Technologies Used for Development of Prognostic Biomarkers in Liver Disease

Zobair M. Younossi, MD, MPH, FACP, FACG


November 07, 2007

Boston, Massachusetts; Tuesday, November 6, 2007 -- The Human Genome Project has led to a revolution in translational research, resulting in a significant expansion in our knowledge about the role of the genome, transcriptome, and proteome in complex human diseases, including chronic liver disease. Indeed, slight variations in our DNA sequences can have an impact on whether we develop a disease as well as on our responses to environmental factors or response to treatment. In general, genomics technology can use single nucleotide polymorphisms (SNPs) or gene expression profiling in blood or a specific tissue. SNPs are DNA sequence variations of a single nucleotide that can highlight the differences between individuals with different diseases or disease states. The application of this technology usually requires a large number of patients as well as their associated clinical data. Gene expression profiling, which is also known as the study of transcriptomics, is a global assessment of gene expression patterns, providing a collection of molecular "snapshots" reflecting the relative levels of the mRNAs (the transcriptome is the complete set of RNA transcripts) in specific human tissues. Differences seen in the concentrations of specific mRNAs in tissue specimens from different patients may provide clues to the pathogenesis or progression of a specific disease. Finally, proteomics refers to the "universe of proteins" -- that is, all the proteins expressed by a genome. Proteomics technology assesses the protein profiles associated with a specific disease, the stage of its progression, or its response to treatment.

Although a number of proteomic technologies (such as surface-enhanced laser desorption/ionization [SELDI] or matrix-assisted laser desorption/ionization [MALDI]) have become "popular," protein chips/microarrays such as reverse phase protein microarray (RPPA) can provide information about protein network profiling and intracellular signaling pathways contained within tissue specimens. These genomic and proteomic technologies have increasingly been applied to study a number of liver diseases and to develop diagnostic or prognostic biomarkers for hepatitis C and nonalcoholic fatty liver disease (NAFLD).[1] A number of studies presented during this year's meeting of the American Association for the Study of Liver Diseases (AASLD) used these technologies to develop "prognostic" biomarkers to predict treatment response in hepatitis C or disease recurrence in patients with liver cancer. This report will highlight some of these key studies, with a view toward the potential clinical implications.


Predicting Response to Treatment in Hepatitis C Patients Using Gene Expression in Liver Tissue

Two studies presented during AASLD 2007 assessed gene expression ("gene signatures") in the pretreatment liver tissue of patients with hepatitis C virus (HCV) infection to predict response to the current standard of care, combination treatment with pegylated interferon and ribavirin.

In the first of these studies,[2] investigators from Canada obtained liver biopsies from 78 HCV-infected patients to validate their "18 gene signature profile," which was previously shown to be associated with treatment response to the combination regimen for hepatitis C. Gene expression was studied in the pretreatment liver specimens using microarray technology. All patients were treated with combination therapy, and response patterns (sustained virologic response [SVR] or nonresponse [NR]) were determined after the completion of therapy. The study authors confirmed the ability of their pretreatment liver tissue gene expression profile to differentiate between responders (SVR) and nonresponders (NR).

In a second study, investigators from France[3] set out to identify a liver gene signature to predict SVR in patients with hepatitis C. They used pretreatment liver tissue from 69 HCV-infected patients (40 patients comprised the training set and 29 comprised the validation set) to assess for a gene expression profile associated with the pattern of response to antiviral treatment with combination pegylated interferon plus ribavirin. Real-time polymerase chain reaction (RT-PCR) assay was used to determine the expression profile of 58 genes associated with liver gene dysfunction during hepatitis C in the pretreatment liver specimens. The study authors identified 3 genes that significantly differed between responders and nonresponders to combination therapy.

In summary, these studies reported exciting data using gene expression technology, suggesting a potential role for this technology in predicting response to antiviral therapy in hepatitis C. Thus, the application of these gene signatures may help clinicians optimize and tailor therapy. Nevertheless, both studies used pretreatment liver tissue, which requires an invasive procedure (ie, liver biopsy); this may prove to be a deterrent for patients or clinicians.


Assessment of Gene Expression in the Peripheral Blood Mononuclear Cells of Patients With Chronic Hepatitis C

In 2 studies presented at this year's AASLD meeting,[4,5] investigators reported the gene expression profile of 160 interferon-inducible, interferon pathway, immune response, and housekeeping-related genes from 60 HCV-infected patients (28 treatment-naive patients and 32 patients who had failed previous combination treatment) receiving combination pegylated interferon plus ribavirin therapy. Gene expression profiling was done by RT-PCR of the peripheral blood samples collected from these patients prior to treatment, as well as 1 day, 1 week, 4 weeks, and 8 weeks after initiation of combination therapy. Multiple regression analysis was performed to develop models predicting SVR at different time points. Using gene expression data and clinical data, investigators were able to develop gene expression panels associated with SVR for both categories of HCV-infected patients (treatment-naive group and previous treatment failure group). They suggested that after further validation, these gene expression models can be used to predict response to combination therapy as early as 24 hours after the initiation of treatment. Obviously, the advantage of this approach to using gene expression technology to develop a biomarker profile predicting SVR is that it uses blood samples rather than requiring liver biopsy specimens.


Gene Expression to Predict Recurrence of Hepatocellular Carcinoma in Patients With Hepatitis B

Hepatitis B is one of the most common causes of chronic liver diseases worldwide. Patients infected with the hepatitis B virus (HBV) are at increased risk of developing hepatocellular carcinoma (HCC). Those with a previous history of HCC are at higher risk for HCC recurrence. Thus, development of an effective prognostic prediction model would have good utility. Investigators from South Korea and The National Institutes of Health[6] generated gene expression profiles for 65 patients with HBV-related HCC. They used microarray technology to develop and validate a gene expression signature that could accurately predict recurrence of HCC in this group of high-risk patients. If further validated, this type of prognostic gene expression biomarker could be very useful in the management of patients with HCC.


Reverse Phase Phosphoproteomic Arrays for Predicting Weight Loss After Bariatric Surgery in Patients With NAFLD

Bariatric surgery for morbid obesity has become one of the fastest growing surgeries in the United States. Over 90% of patients undergoing bariatric surgery have biopsy-proven NAFLD.[7] In most patients, weight loss after the surgery has been associated with improvement in obesity-related complications, including NAFLD. In a study presented at this year's AASLD meeting, investigators set out to develop a prognostic biomarker to predict successful weight loss after this operative intervention.[8] They used white adipose tissue (WAT) obtained from 111 patients undergoing bariatric surgery to predict weight loss and resolution of obesity complications. Protein lysate was extracted from WAT and then used for reverse phase protein microarray analysis to quantify the relative phosphorylation of 80 specific signaling molecules. Results showed that signal pathway profiling using reverse phase protein microarrays of WAT was able to predict successful weight loss in obese patients with NAFLD after bariatric surgery. In fact, nearly all of these proteins were within apoptosis control pathways or insulin/AKT/growth factor signaling pathways. These pathways have been known to be important in the pathogenesis of NAFLD as well as important targets for drug development. Again, this technology can be applied not only to develop prognostic biomarkers for liver diseases such as NAFLD, but also to pinpoint important pathways that may be involved in disease pathogenesis and to develop targeted therapy.

Supported by an independent educational grant from Bristol-Myers Squibb


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