Using Random Peptide Phage Display Libraries for early Breast cancer detection

Thousands of people beat cancer every year. Doing so is easier when cancer is diagnosed at an early stage as treatment is often simpler and more likely to be effective. Cancer cells starts out as normal body cells, but they begin to grow out of control because of an abnormal gene expression. The immune system plays a major role in limiting the development of these abnormalities. It elicits a detectable humoral immune response to changes in antigen profiles caused by growing cancer cells. Circulating autoantibodies produced by the patient’s own immune system after exposure to cancer proteins are promising biomarkers for the early detection of cancer. Since an antibody recognizes not the whole antigen but 4-7 critical amino acids within the antigenic determinant (epitope), the whole proteome can be represented by random peptide phage display libraries (RPPDL). To solve cancer detection problem we propose a new method based on RPPDL. We determined that peptides assigned to breast cancer serum samples better correlate with each other than peptides assigned to control serum samples. Thus, the cancer samples had common features in immune response. We tested our method on the serum antibody repertoire profiles for 5 stage 0 breast cancer patients and for 5 cancer-free women. As result all samples were predicted correctly except one cancer which gave sensitivity equaled 0.8, specificity equaled 1 and accuracy equaled 0.9.