On The Use of Antibody Microarrays for Cancer Biomarker Discovery.

摘要

The discovery of novel early detection biomarkers of cancer offers one of the best approaches to decrease the morbidity and mortality of this disease. This study reports on the development, production, and implementation of high-density antibody microarrays for discovering novel ovarian cancer serum biomarkers. Antibody microarrays are arrangements of thousands of antibodies printed on chemically-modified glass slides. The attached antibodies retain their ability to interact with and bind to proteins in contact with the slide. The slide can be used to simultaneously quantify binding of thousands of proteins to the antibodies as a measure of the amount of a protein present in a complex biological fluid such as serum. In this project, first, antibody microarrays were probed with serum from ovarian cancer patients and healthy controls to establish that the antibody microarray platform could measure the levels of known ovarian biomarkers in a reproducible manner. Second, the antibody microarray was used on a larger serum sample set to find novel ovarian cancer serum biomarkers. The microarrays contained two types of antibodies: recombinant single-chain variable fragment antibodies and commercially available full length antibodies. To identify the binding targets of the recombinant antibodies, they were probed with a functional protein microarray. In this method, thousands of known proteins are printed on a slide and one recombinant antibody is incubated on the array to determine which protein it binds. Third, once target markers were identified, the measurements with the antibody microarray platform were validated using complimentary methods. Using these methods, several novel ovarian cancer serum biomarkers were discovered. Further validation is required before these are used in clinical applications. The results reported here constitute an innovative technique of high throughput screening of biological fluids for the discovery of novel cancer biomarkers.