First, we applied a main effect logistic regression for all possible models that included age, and 4 antigens out of the 15. Each model consisted of samples with full data U0126 EtOH only (all 4 antigens present). The models were sorted according to the sensitivity at 50% specificity, conditioned upon the fact that the model can be applied for a sufficient number of the samples (no less than 80 samples per model). Next, we established a combined decision rule whereby for each sample, the final decision as to ��patient�� or ��control�� was accepted according to the highest ranked model that could be used (ie, that all antigens in the highest sorted model were simultaneously ��not-missing�� for this sample, otherwise, the next highest model, with all ��not-missing�� values was applied to this subject).

Results Theoretical considerations of the assay and data analysis approach Current diagnostic methods generally rely upon observing one TAA against which the amount of AAbs in patients is higher than in controls. Such a method uses a ��cut-off�� criterion with subjects above the cut-off designated as ��patients�� and those below the cut-off designated as ��healthy��. This premise is typically true for external antigens such as bacteria and viruses. When an individual is infected, there is an immune response and a specific antibody response. In such a scenario, using a specific cut-off to score positive or negative or ��infected�� or ��uninfected�� is applicable. However, when examining AAbs, the situation is different because AAbs are found in serum in the absence of overt disease among all populations.

The constitutive or ��natural�� levels of AAbs differ among individuals, which has no correlation to specific diseases. Using a cut-off criterion for AAbs will result in a distortion of the diagnostic results, as many false-positives (those with high amounts of AAbs), and false negatives (those with low amounts of AAbs), will occur. An example is shown in Figure 1A. Alternatively, if absolute values are not considered and if the ratio in the amount of cancer-specific AAbs relative to the presence of non-cancer specific AAbs is calculated, a more accurate distinction can be made between patients and healthy subjects (Fig. 1B). As illustrated in Figure 1B, to determine and analyze the ratio between normal AAbs and cancer-specific AAbs, at least two AAbs should be used.

This would include one normal occurring AAb unique to the individual (AAbA), and a second cancer-specific AAb (AAbB). Comparing the amount of the ��non-relevant�� AV-951 normal occurring AAbs (AAb A) to the amount of cancer-related AAbs (AAb B), whose amounts are higher than the normal amounts of AAbs (AAb A), produces the following decision rule: a cancer patient is defined when AAb B > AAb A, and a healthy individual is defined when AAb B < AAb A.