PURPOSE: There is evidence that IFN gamma plays an important role in ovarian cancer development. IFN gamma produces numerous antitumor effects and it may be evasion of these effects which allows tumor progression. We postulate that genetic instability in tumor cells may lead to modulation of expression of the IFN gamma receptor, thus leading to altered tumor biology and patient prognosis. This hypothesis would support the theory of immunoediting in ovarian cancer. EXPERIMENTAL DESIGN: Using tissue microarray technology of 339 primary ovarian cancers, the expression of IFN gamma receptor was assessed immunohistochemically. Coupled to a comprehensive database of clinicopathologic variables, its effect on these factors was studied. RESULTS: Tumors expressing high levels of IFN gamma receptor had significantly improved survival (P=0.017) compared with tumors expressing low levels of the receptor; this was also seen with complete receptor loss (P=0.014). Factors shown to predict prognosis independently of each other were the following: age, International Federation of Gynecologists and Obstetricians stage, and the absence of macroscopic disease after surgery. The level of IFN gamma receptor expression and complete receptor loss were independently predictive of prognosis on multivariate analysis. There was no correlation between receptor status and any of the standard clinicopathologic variables. CONCLUSIONS: Loss of IFN gamma receptor independently predicts poor prognosis in ovarian cancer. Loss of receptor expression may be responsible for the limited success in the therapeutic use of IFN gamma in ovarian cancer trials and highlights a subgroup of high expressing IFN gamma receptor tumors which are more likely to be susceptible to such treatments.
RCT Entities:
PURPOSE: There is evidence that IFN gamma plays an important role in ovarian cancer development. IFN gamma produces numerous antitumor effects and it may be evasion of these effects which allows tumor progression. We postulate that genetic instability in tumor cells may lead to modulation of expression of the IFN gamma receptor, thus leading to altered tumor biology and patient prognosis. This hypothesis would support the theory of immunoediting in ovarian cancer. EXPERIMENTAL DESIGN: Using tissue microarray technology of 339 primary ovarian cancers, the expression of IFN gamma receptor was assessed immunohistochemically. Coupled to a comprehensive database of clinicopathologic variables, its effect on these factors was studied. RESULTS:Tumors expressing high levels of IFN gamma receptor had significantly improved survival (P=0.017) compared with tumors expressing low levels of the receptor; this was also seen with complete receptor loss (P=0.014). Factors shown to predict prognosis independently of each other were the following: age, International Federation of Gynecologists and Obstetricians stage, and the absence of macroscopic disease after surgery. The level of IFN gamma receptor expression and complete receptor loss were independently predictive of prognosis on multivariate analysis. There was no correlation between receptor status and any of the standard clinicopathologic variables. CONCLUSIONS: Loss of IFN gamma receptor independently predicts poor prognosis in ovarian cancer. Loss of receptor expression may be responsible for the limited success in the therapeutic use of IFN gamma in ovarian cancer trials and highlights a subgroup of high expressing IFN gamma receptor tumors which are more likely to be susceptible to such treatments.
Authors: Hannelore I Bax; Alexandra F Freeman; Victoria L Anderson; Per Vesterhus; Dan Laerum; Stefania Pittaluga; Wyndham H Wilson; Steven M Holland Journal: J Clin Immunol Date: 2013-06-26 Impact factor: 8.317
Authors: Christine M Fam; Stephen P Eisenberg; Sharon J Carlson; Elizabeth A Chlipala; George N Cox; Mary S Rosendahl Journal: J Interferon Cytokine Res Date: 2014-05-19 Impact factor: 2.607
Authors: A V Sosnina; A I Autenshlyus; E S Mikhailova; D V Morozov; A V Vankhalsky; L A Shpagina; N A Varaksin; V V Lyakhovich Journal: Dokl Biol Sci Date: 2016-05-20
Authors: Caroline Soliman; Jia Xin Chua; Mireille Vankemmelbeke; Richard S McIntosh; Andrew J Guy; Ian Spendlove; Lindy G Durrant; Paul A Ramsland Journal: J Biol Chem Date: 2019-12-12 Impact factor: 5.157
Authors: Katy Milne; Martin Köbel; Steven E Kalloger; Rebecca O Barnes; Dongxia Gao; C Blake Gilks; Peter H Watson; Brad H Nelson Journal: PLoS One Date: 2009-07-29 Impact factor: 3.240
Authors: Katy Milne; Rebecca O Barnes; Adam Girardin; Melanie A Mawer; Nancy J Nesslinger; Alvin Ng; Julie S Nielsen; Robert Sahota; Eric Tran; John R Webb; May Q Wong; Darin A Wick; Andrew Wray; Elissa McMurtrie; Martin Köbel; Steven E Kalloger; C Blake Gilks; Peter H Watson; Brad H Nelson Journal: PLoS One Date: 2008-10-15 Impact factor: 3.240