title: Pretreatment prediction of interferon-alfa efficacy in chronic hepatitis C patients.
abstract: OBJECTIVE
  Interferon has been used widely to treat patients with chronic hepatitis C infections. Prediction of interferon efficacy before treatment has been performed mainly by using viral information, such as viral load and genotype. This information has allowed the successful prediction of sustained responders (SR) and non-SRs, which includes transient responders (TR) and nonresponders (NR). In the current study we examined whether liver messenger RNA expression profiles also can be used to predict interferon efficacy.
  
  
  METHODS
  RNA was isolated from 69 liver biopsy samples from patients receiving interferon monotherapy and was analyzed on a complementary DNA microarray. Of these 69 samples, 31 were used to develop an algorithm for predicting interferon efficacy, and 38 were used to validate the precision of the algorithm. We also applied our methodology to the prediction of the efficacy of interferon/ribavirin combination therapy using an additional 56 biopsy samples.
  
  
  RESULTS
  Our microarray analysis combined with the algorithm was 94% successful at predicting SR/TR and NR patients. A validation study confirmed that this algorithm can predict interferon efficacy with 95% accuracy and a P value of less than .00001. Similarly, we obtained a 93% prediction efficacy and a P value of less than .0001 for patients receiving combination therapy.
  
  
  CONCLUSIONS
  By using only host data from the complementary DNA microarray we are able to successfully predict SR/TR and NR patients for interferon therapy. Therefore, this technique can help determine the appropriate treatment for hepatitis C patients.
authors.affiliation: Medicine and Biosystemic Science, Kyushu University Graduate School of Medical Sciences, Higashi-ku, Fukuoka, Japan.
authors.forename: Kazuhiro
authors.initials: K
authors.lastname: Hayashida
authors.forename: Akito
authors.initials: A
authors.lastname: Daiba
authors.forename: Akito
authors.initials: A
authors.lastname: Sakai
authors.forename: Takeshi
authors.initials: T
authors.lastname: Tanaka
authors.forename: Kyosuke
authors.initials: K
authors.lastname: Kaji
authors.forename: Niro
authors.initials: N
authors.lastname: Inaba
authors.forename: Satoshi
authors.initials: S
authors.lastname: Ando
authors.forename: Naoki
authors.initials: N
authors.lastname: Kajiyama
authors.forename: Hiroshi
authors.initials: H
authors.lastname: Terasaki
authors.forename: Aki
authors.initials: A
authors.lastname: Abe
authors.forename: Masanori
authors.initials: M
authors.lastname: Ogasawara
authors.forename: Michinori
authors.initials: M
authors.lastname: Kohara
authors.forename: Mine
authors.initials: M
authors.lastname: Harada
authors.forename: Takeshi
authors.initials: T
authors.lastname: Okanoue
authors.forename: Satoru
authors.initials: S
authors.lastname: Ito
authors.forename: Shuichi
authors.initials: S
authors.lastname: Kaneko
mesh_terms: D000328:Adult; D000368:Aged; D000998:Antiviral Agents / Q000627:therapeutic use*; D001706:Biopsy; D004279:DNA, Viral / Q000235:genetics*; D005260:Female; D005500:Follow-Up Studies; D020869:Gene Expression Profiling / Q000379:methods; D016174:Hepacivirus / Q000235:genetics* / Q000302:isolation & purification; D019698:Hepatitis C, Chronic / Q000188:drug therapy* / Q000473:pathology / Q000821:virology; D006801:Humans; D016898:Interferon-alpha / Q000627:therapeutic use*; D008297:Male; D008875:Middle Aged; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D011237:Predictive Value of Tests; D012189:Retrospective Studies; D016896:Treatment Outcome
chemical_list: D000998:Antiviral Agents; D004279:DNA, Viral; D016898:Interferon-alpha