1. ¹Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA
2. ²Department of Pathology, University of Michigan Medical School, Ann Arbor, MI, USA
3. ³Department of Urology, University of Michigan Medical School, Ann Arbor, MI, USA
4. ⁴Department of Human Genetics, University of Michigan Medical School, Ann Arbor, MI, USA

Correspondence: CS Duckett, Department of Pathology and Internal Medicine, University of Michigan, BSRB, Room 2057, 109 Zina Pitcher Place, Ann Arbor, MI 48109-2200, USA. Tel.: +734 615 6414; Fax: +734 763 2162; E-mail: colind@umich.edu

⁵These authors contributed equally to this work.

Received 12 September 2007; Revised 20 December 2007; Accepted 7 January 2008; Published online 8 February 2008.

Abstract

Deregulation of apoptotic pathways plays a central role in cancer pathogenesis. X-linked inhibitor of apoptosis protein (XIAP), is an antiapoptotic molecule, whose elevated expression has been observed in tumor specimens from patients with prostate carcinoma. Studies in human cancer cell culture models and xenograft tumor models have demonstrated that loss of XIAP sensitizes cancer cells to apoptotic stimuli and abrogates tumor growth. In view of these findings, XIAP represents an attractive antiapoptotic therapeutic target for prostate cancer. To examine the role of XIAP in an immunocompetent mouse cancer model, we have generated transgenic adenocarcinoma of the mouse prostate (TRAMP) mice that lack XIAP. We did not observe a protective effect of Xiap deficiency in TRAMP mice as measured by tumor onset and overall survival. In fact, there was an unexpected trend toward more aggressive disease in the Xiap-deficient mice. These findings suggest that alternative mechanisms of apoptosis resistance are playing a significant oncogenic role in the setting of Xiap deficiency. Our study has implications for XIAP-targeting therapies currently in development. Greater understanding of these mechanisms will aid in combating resistance to XIAP-targeting treatment, in addition to optimizing selection of patients who are most likely to respond to such treatment.