An Antibody-based Blood Test Utilizing a Panel of Biomarkers as a New Method for Improved Breast Cancer Diagnosis

Abstract

In order to develop a new tool for diagnosis of breast cancer based on autoantibodies against a panel of biomarkers, a clinical trial including blood samples from 507 subjects was conducted. All subjects showed a breast abnormality on exam or breast imaging and final biopsy pathology of either breast cancer patients or healthy controls. Using an enzyme-linked immunosorbent assay, the samples were tested for autoantibodies against a predetermined number of biomarkers in various models that were used to determine a diagnosis, which was compared to the clinical status. Our new assay achieved a sensitivity of 95.2% [CI = 92.8-96.8%] at a fixed specificity of 49.5%. Receiver-operator characteristic curve analysis showed an area under the curve of 80.1% [CI = 72.6-87.6%]. These results suggest that a blood test which is based on models comprising several autoantibodies to specific biomarkers may be a new and novel tool for improving the diagnostic evaluation of breast cancer.

Keywords: autoantibodies; biomarkers; breast cancer; diagnostic testing.

Figures

Figure 1

(A) Using “cut-off” criteria (AAb = 150) results with false-positive and false-negative results because different immune systems have different AAb levels. (B) Measuring relative amounts of antibodies (AAb A relative to AAb B) eliminates both false-positive and false-negative results that emerge when using a “cut-off” criterion. Using a ratio of AAb B > AAb A as a criteria for patients eliminates all false results.

Figure 2

An example of the smoothing procedure. Each graph shows data corresponding to antigens of sample B2404, with the raw data shown as the thin line and data after smoothing as the thick line. In most cases, the raw data dilution curves yielded high linear correlation (R2 > 0.95). When data could not be replaced by a straight line with good fitting (such as antigen #16 and antigen #41), the specific antigen was replaced by a missing value for this specific sample. All other antigens of the sample received a value corresponding to the value at a specific reference point of dilution in the middle of the theoretical line.

Figure 3

Box plots of average of log10 [RLU] of all antigens after the smoothing procedure. The two clinical groups are represented in the graph are breast cancer (filled bars) and healthy (empty bars). No statistically significant separation could be achieved between the groups using any one of these antigens.

Figure 4

(A) ROC curve (sensitivity versus 1—specificity) of the 507 samples in the data set. The AUC is 80.1% (CI = 72.6%–87.6%). At specificity of 49.5%, sensitivity is 95.2% (CI = 92.8–96.8%). (B) ROC curve (sensitivity versus 1—specificity) of the 193 samples in the data set of post-menopausal women. The AUC is 84% (CI = 66.1–93.4%). At specificity of 52.8%, sensitivity is 96.2% (CI = 92.6–98.5%).

Figure 5

(A) ROC curve (sensitivity versus 1—specificity) of the 152 samples in the training set. The AUC is 84.5% (CI = 78.6–89.0%). At specificity of 61.8%, sensitivity is 94.7% (CI = 88.0–98.3%). (B) ROC curve (sensitivity versus 1—specificity) of the 48 samples in the data set of post-menopausal women. The AUC is 80.4% (CI = 67.4–91.0%). At specificity of 52.8%, sensitivity is 96.2% (CI = 78.2–100.0%).