Pre-treatment prediction of response to pegylated-interferon plus ribavirin for chronic hepatitis C using genetic polymorphism in IL28B and viral factors

[Guest guest]

http://www.sciencedirect.com/science?_ob=ArticleURL & _udi=B6W7C-5125R15-D & _user=1\

0 & _coverDate=03%2F31%2F2011 & _rdoc=14 & _fmt=high & _orig=browse & _origin=browse & _zone\

=rslt_list_item & _srch=doc-info(%23toc%236623%232011%23999459996%232908743%23FLA%\

23display%23Volume) & _cdi=6623 & _sort=d & _docanchor= & _ct=41 & _acct=C000050221 & _versi\

on=1 & _urlVersion=0 & _userid=10 & md5=e7377efd7dbb0e2877fbbb529ede986e & searchtype=a

Journal of Hepatology

Volume 54, Issue 3, March 2011, Pages 439-448

--------------------------------------------------------------------------------

doi:10.1016/j.jhep.2010.07.037 | How to Cite or Link Using DOI

European Association for the Study of the Liver Published by

Elsevier Ireland Ltd.

Permissions & Reprints

Research Article

Pre-treatment prediction of response to pegylated-interferon plus ribavirin for

chronic hepatitis C using genetic polymorphism in IL28B and viral factors

References and further reading may be available for this article. To view

references and further reading you must purchase this article.

Masayuki Kurosaki1, Yasuhito Tanaka2, Nao Nishida3, Naoya Sakamoto4, Nobuyuki

Enomoto5, Masao Honda6, Masaya Sugiyama2, Kentaro Matsuura2, Fuminaka Sugauchi2,

Yasuhiro Asahina1, Mina Nakagawa4, Mamoru Watanabe4, Minoru Sakamoto5, Shinya

Maekawa5, Akito Sakai6, Shuichi Kaneko6, Kiyoaki Ito7, Naohiko Masaki7, Katsushi

Tokunaga3, Namiki Izumi1, , and Masashi Mizokami2, 7

1 Division of Gastroenterology and Hepatology, Musashino Red Cross Hospital,

Tokyo, Japan

2 Department of Virology, Liver Unit, Nagoya City University, Graduate School of

Medical Sciences, Nagoya, Japan

3 Department of Human Genetics, Graduate School of Medicine, University of

Tokyo, Tokyo, Japan

4 Department of Gastroenterology and Hepatology, Tokyo Medical and Dental

University, Tokyo, Japan

5 First Department of Internal Medicine, University of Yamanashi, Yamanashi,

Japan

6 Department of Gastroenterology, Kanazawa University, Graduate School of

Medicine, Kanazawa, Japan

7 Research Center for Hepatitis and Immunology, International Medical Center of

Japan, Konodai Hospital, Ichikawa, Japan

Received 14 March 2010; revised 22 June 2010; accepted 7 July 2010. Available

online 19 September 2010.

Background & Aims

Pegylated interferon and ribavirin (PEG-IFN/RBV) therapy for chronic hepatitis C

virus (HCV) genotype 1 infection is effective in 50% of patients. Recent studies

revealed an association between the IL28B genotype and treatment response. We

aimed to develop a model for the pre-treatment prediction of response using host

and viral factors.

Methods

Data were collected from 496 patients with HCV genotype 1 treated with

PEG-IFN/RBV at five hospitals and universities in Japan. IL28B genotype and

mutations in the core and IFN sensitivity determining region (ISDR) of HCV were

analyzed to predict response to therapy. The decision model was generated by

data mining analysis.

Results

The IL28B polymorphism correlated with early virological response and predicted

null virological response (NVR) (odds ratio = 20.83, p <0.0001) and sustained

virological response (SVR) (odds ratio = 7.41, p <0.0001) independent of other

covariates. Mutations in the ISDR predicted relapse and SVR independent of

IL28B. The decision model revealed that patients with the minor IL28B allele and

low platelet counts had the highest NVR (84%) and lowest SVR (7%), whereas those

with the major IL28B allele and mutations in the ISDR or high platelet counts

had the lowest NVR (0–17%) and highest SVR (61–90%). The model had high

reproducibility and predicted SVR with 78% specificity and 70% sensitivity.

Conclusions

The IL28B polymorphism and mutations in the ISDR of HCV were significant

pre-treatment predictors of response to PEG-IFN/RBV. The decision model,

including these host and viral factors may support selection of optimum

treatment strategy for individual patients.