We aimed to evaluate the immunodiagnostic values of autoantibodies to a panel of six tumor-associated antigens (TAAs) in the detection of patients with breast cancer. This study determines whether a panel of multiple TAAs would enhance antibody detection and be a useful approach in breast cancer detection and diagnosis. The panel of multiple TAAs was composed of six TAAs including Imp1, p16, Koc, survivin, cyclin B1, and c-myc full-length recombinant proteins. Enzyme-linked immunosorbent assay (ELISA) was used to detect antibodies against these six TAAs in 49 sera from patients with breast cancer, 35 sera from patients with benign breast tumor, and 38 sera from normal individuals. Antibody frequency to any individual TAA in breast cancer was variable and ranged from 12.2 to 18.4 %. With the successive addition of TAAs to a final total of six antigens, there was a stepwise increase of positive antibody reactions reaching a sensitivity of 67.3 % and a specificity of 92.2 % in breast cancer. Positive and negative likelihood ratios were 8.52 and 0.36, respectively, which showed that the clinical diagnostic value of a parallel assay of six TAAs was high. Positive and negative predictive values were, respectively, 91.7 and 68.6 %, indicating that the parallel assay of six TAAs raised the diagnostic accuracy greatly. Agreement rate and kappa value were 78.1 % and 0.57, respectively, which indicated that the observed value of this assay had a middle range of coincidence with the actual value. The data from this study further support our previous hypothesis that the detection of autoantibodies for diagnosis of a certain type of cancer can be enhanced by using a panel of several carefully selected TAAs as target antigens and a panel of multiple TAAs would be a useful approach in the detection and diagnosis of breast cancer.
Autoantibody Tumor-associated antigen Breast cancer Immunodiagnosis
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The authors thank Dr. Eng M. Tan (The Scripps Research Institute) for his support. This work was supported by a grant (SC1CA166016) from the National Institutes of Health (NIH). We also thank the Border Biological Research Center (BBRC) Core Facilities at The University of Texas at El Paso (UTEP) for their support, which were funded by an NIH grant (5G12MD007592).
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