Table of Contents Author Guidelines Submit a Manuscript
Journal of Biomedicine and Biotechnology
Volume 2011, Article ID 454861, 10 pages
http://dx.doi.org/10.1155/2011/454861
Research Article

IgG Responses to Tissue-Associated Antigens as Biomarkers of Immunological Treatment Efficacy

1Carbone Comprehensive Cancer Center, University of Wisconsin, 1111 Highland Avenue, Madison, WI 53705, USA
2DecisionQ Corporation, 1010 Wisconsin Avenue NW, Suite 310, Washington, DC 20007, USA
3Laboratory of Tumor Immunology and Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
47007 Wisconsin Institutes for Medical Research, 1111 Highland Avenue, Madison, WI 53705, USA

Received 1 September 2010; Accepted 12 November 2010

Academic Editor: Timothy M. Clay

Copyright © 2011 Heath A. Smith et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

We previously demonstrated that IgG responses to a panel of 126 prostate tissue-associated antigens are common in patients with prostate cancer. In the current report we questioned whether changes in IgG responses to this panel might be used as a measure of immune response, and potentially antigen spread, following prostate cancer-directed immune-active therapies. Sera were obtained from prostate cancer patients prior to and three months following treatment with androgen deprivation therapy ( 𝑛 = 3 4 ), a poxviral vaccine ( 𝑛 = 3 1 ), and a DNA vaccine ( 𝑛 = 2 1 ). Changes in IgG responses to individual antigens were identified by phage immunoblot. Patterns of IgG recognition following three months of treatment were evaluated using a machine-learned Bayesian Belief Network (ML-BBN). We found that different antigens were recognized following androgen deprivation compared with vaccine therapies. While the number of clinical responders was low in the vaccine-treated populations, we demonstrate that ML-BBN can be used to develop potentially predictive models.