SYDNEY: A genetic variant in hepatitis C patients could be responsible for inhibiting current treatments, an Australian-led research team has discovered.

Researchers and clinicians say they can now predict how well patients will respond to current treatment options simply by looking at their genotype.

The findings, published this week in the open access journal PLoS Medicine, could improve the clinical management of patients diagnosed with the virus and could lead to improved therapies in the future.

"This research tells us we can use someone's genetic make up to predict their responses to drug treatments," said molecular geneticist and co-author David Booth from the Westmead Millennium Institute for Medical Research in Sydney.

"If an analysis of their genes tells us the treatment won't work, we can advise them to wait for better drugs," he said.

The chronic conundrum

Hepatitis C is a very common virus that affects approximately 170 million people per year, or about 3% of the world's population.

Between 20% and 30% of those who contract the virus have the infection cleared naturally, something known as spontaneous clearance, while the bulk of patients develop chronic hepatitis.

Initial diagnosis of hepatitis C is difficult because the symptoms are generally mild and vague. But left untreated, it can result in liver failure, scarring of the liver and liver cancer.

At the moment, the current standard treatment options are to use an injected drug, known as pegylated interferon-alpha or a pill, known as ribavirin (PegIFN/R). Both are antiviral drugs, which stops the virus from spreading.

Treatment limitations

Previous research in 2009 showed that certain types of genetic variants, known as single nucleotide polymorphisms (SNP), utilised in Hepatitis C research are near the IL28B gene, which encodes a protein in the immune system to fight viral infections.

This gene also strongly influences treatment outcomes and spontaneous clearing of the hepatitis C virus in infected people.

The antiviral-by-injection treatment is only successful for 40% to 50% of patients diagnosed with hepatitis C type 1 - one of the six types of the disease.

Even then, treatment is expensive and associated with numerous side effects. In some cases, patients require dose reduction and premature treatment termination, increasing the risk of treatment failure.

Variants more common in unresponsive patients

The Australian-led study looked at whether using two additional genotypes, HLA-C and the KIR gene, can improve the predictive value of the IL28B gene in potential treatment failure rates.

The researchers looked at the genotypes of 417 patients who were chronically infected with the hepatitis C virus and had the virus cleared by the PegIFN/R treatment.

They then compared them to 493 patients whose infection did not respond to treatment and 234 patients whose infections had cleared spontaneously.

They found that the genetic variants of the IL28B SNP were more commonly found in patients who did not respond to treatment, than patients who did.

In addition, these same patients were less likely to have their infection cleared spontaneously. The combination of HLA-C and IL28B genotyping increased the correct prediction rate of treatment failure, from 66% to 80%.

Prediction helps patient management

Michael Beard, a molecular virologist from the University of Adelaide, supports the conclusions the study highlights about the role of 'natural killer' cells in the pathogenesis and clearance of hepatitis C.

"This work strongly suggests that in some cases, our genetic background can have a significant outcome on how we respond to viral infection," he said.

"The use of the IL28B gene to predict success may sway patients and clinicians to either treat with PegIFN/R or wait until the new combination therapies come on board." Beard said.

"Considering treatment is costly in the long term and has significant side effects in some patients," he commented, "any method to predict treatment response is essential to target therapies to those that have a high chance of success."