Current technologies have been disappointing and have not resulted in diagnostic test suitable for clinical practice. The goal of this project is to detect antibodies that are produced by patients in reaction to proteins expressed in their ovarian tumors and use them as diagnostic biomarkers. The core of this project rests on research from the PI's lab in which he has developed a high throughput method to identify large numbers of epitopes that can be used to identify the presence of ovarian cancer by detecting the presence of auto-antibodies to tumor proteins in the serum of the test subject. These biomarkers are cloned without a preconceived notion of their function. The essential features of the approach are acknowledging the heterogeneous nature of any specific kind of cancer, departing from the reliance on any single marker for disease detection, and using specialized bioinformatics techniques to interpret the results. The concept employs pattern recognition of multiple markers as a diagnostic rather than any single marker. In the discovery phase, the serum antibodies have been detected by screening of large numbers of potential epitope targets on protein microarrays. We developed an effective screening test for early detection of ovarian cancer (OVCA) using cDNA T7 phage display libraries to isolate cDNAs coding for epitopes reacting with antibodies present specifically in the sera of patients with ovarian cancer. The isolation of these epitopes is achievable by our differential biopanning technology using human sera collected both from healthy controls and patients having ovarian cancer. We screen phage display tumor cell libraries from OVCA fro cDNAs of genes that bind immunoglobulin-G molecules (IgGs) present in OVCA patients' sera and do not bind to IgGs from normal sera. The serum reaction with large numbers of these epitopes is be detected in a highly parallel assay on protein microarrays. The principle is that we clone epitopes reacting with IgG in patients sera and use them to detect antibodies in sera to discriminate cancer and healthy subjects and whether we can detect disease prior to standard diagnosis.
| Chatterjee, Madhumita; Dyson, Greg; Levin, Nancy K et al. (2012) Tumor autoantibodies as biomarkers for predicting ovarian cancer recurrence. Cancer Biomark 11:59-73 |
| Dudas, Steven P; Chatterjee, Madhumita; Tainsky, Michael A (2010) Usage of cancer associated autoantibodies in the detection of disease. Cancer Biomark 6:257-70 |
| Ali-Fehmi, Rouba; Chatterjee, Madhumita; Ionan, Alexei et al. (2010) Analysis of the expression of human tumor antigens in ovarian cancer tissues. Cancer Biomark 6:33-48 |
| Chatterjee, Madhumita; Tainsky, Michael A (2010) Autoantibodies as biomarkers for ovarian cancer. Cancer Biomark 8:187-201 |
| Chatterjee, Madhumita; Wojciechowski, Jerzy; Tainsky, Michael A (2009) Discovery of antibody biomarkers using protein microarrays of tumor antigens cloned in high throughput. Methods Mol Biol 520:21-38 |
| Tarca, Adi Laurentiu; Draghici, Sorin; Khatri, Purvesh et al. (2009) A novel signaling pathway impact analysis. Bioinformatics 25:75-82 |
| Tainsky, Michael A (2009) Genomic and proteomic biomarkers for cancer: a multitude of opportunities. Biochim Biophys Acta 1796:176-93 |
| Draghici, Sorin; Tarca, Adi L; Yu, Longfei et al. (2008) KUTE-BASE: storing, downloading and exporting MIAME-compliant microarray experiments in minutes rather than hours. Bioinformatics 24:738-40 |
| Tarca, Adi L; Carey, Vincent J; Chen, Xue-wen et al. (2007) Machine learning and its applications to biology. PLoS Comput Biol 3:e116 |
| Hassan, Sonia S; Romero, Roberto; Tarca, Adi L et al. (2007) Signature pathways identified from gene expression profiles in the human uterine cervix before and after spontaneous term parturition. Am J Obstet Gynecol 197:250.e1-7 |
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