Gene expression in liver tumors and patient prognosis

Liver cancer genes cluster into two subsets that correlate to survival

An analysis of the gene expression patterns of 91 unrelated liver tumors revealed two distinctive subclasses highly associated with patient survival, according to a new study published in the September 2004 issue of Hepatology.
Hepatology, the official journal of the American Association for the Study of Liver Diseases (AASLD), published by John Wiley & Sons, Inc., is available online via Wiley InterScience at http://www.interscience.wiley.com/journal/hepatology.

The study is among the first to explore the complete molecular pathogenesis of hepatocellular carcinoma (HCC). It identified expression profiles of a limited number of genes that accurately predicted length of patient survival and these findings may open the door to the development of more targeted therapies for liver cancer patients.

While gene technology has previously been applied to some specific aspects of liver cancer, researchers led by Dr. Snorri Thorgeirsson of the NIH’s National Cancer Institute, sought to uncover molecular prognostic indices that could be applied to the overall patient population with HCC. They investigated variations in gene expression in HCC at diagnosis and compared them to patient survival over time.

The researchers examined 91 HCC tissues, 60 matched non-tumor surrounding liver tissues, and 18 normal liver samples. They isolated total RNA to derive complementary DNA and characterized each sample’s gene expression profile. They applied three independent approaches for data analysis to uncover subclasses of HCC and the underlying biological differences between the subclasses.

The study yielded two distinct subclasses of HCC that were highly associated with patient survival and provided new molecular insight into the pathogenesis of HCC. Tumors from the low survival subclass had a strong cell proliferation and antiapoptosis gene expression signatures. They also had higher expression of genes involved in ubiquitination and histone modification, which suggests an involvement of these processes in aggressive HCC.

“Our results indicate that HCC prognosis can be readily predicted from the gene expression profiles of primary tumors,” the authors report. Furthermore, they suggest that the unique molecular characteristics of each subclass could be useful in developing new therapies for liver cancer patients.

“Even if a curative therapy for HCC patients cannot be offered at this stage, it may be possible to identify therapeutic targets that can slow the course of disease progression,” the authors conclude. “For example, small molecules that inhibit activities of some transcription factors are already available and may provide opportunities to alter the course of HCC progression in both subclass A and B.”

An accompanying editorial by Joseph Locker, M.D. of Albert Einstein College of Medicine in New York, underscores the conclusions of the study by Lee et al. and points out that their work has produced an extremely large data set that is a valuable resource for many kinds of studies.

“Lee et al. focused on unsupervised clustering and prognosis, but there are numerous other important questions that could be approached with further analysis of their comprehensive data sets,” writes Locker. “Most important, expression profiling will reveal specific targets for rational therapy.”