After applying this analysis, significant differences between the two populations (healthy and breast cancer patients) were not find more observed for any of the antigens, as shown in the box graph in Figure 3. These results indicate that a simple average RLU determination of the data after logarithmic transformation and smoothing did not reveal differences between patients and healthy controls. We therefore applied a more sophisticated method of data analysis using the ratio concept and ��separation models�� based on relevant clinical, demographic, or epidemiological parameters. 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 … Classification of different samples Before starting this second-tier analysis, we evaluated which relevant clinical or demographic parameter can be incorporated into the analysis. We checked the following parameters: age, menopausal status, and familial breast cancer history (as given by the patients verbally). We tested the distribution of these parameters between the two groups. Using the Mann-Whitney test for age gave a P < 0.0001, and Fisher��s exact test for menopause (P < 0.001) and for family history (P = 0.005) (see Table S7 B-D in Supplementary Data online Analysis of clinical variables as ��stand alone�� predictors, for detailed analysis). We only used age for the entire population and performed a separate analysis for post-menopausal women.
We did not use the family history parameter because this notion could not be rigorously defined, making it less reliable (the information is not always available to the subjects) and less significant. We also performed the logistic regression of the outcome (health status) on age and menopause. In this analysis, only age retained its significance (P < 0.001), while menopause became non-significant (P = 0.076) after age adjustment (see Table S7-A in Supplementary Data online��Analysis of clinical variables as ��stand alone�� predictors, for detailed analysis). To further use the AAbs results to discriminate between patient samples and control samples, we used logistic regression of the disease status (��patient�� or ��control��) on age and 4 antigens testing all possible combinations of 4 antigens out of 15.
A classification model is defined as the set of antigens, as well as clinical data (age), and their corresponding coefficients obtained after logistic regression is performed. All sub-sets of theoretical combinations of the antigens (ie, all classification Cilengitide models) were tested for their sensitivities at the level of 50% specificity. Models created with at least 80 samples, resulting in a specificity of at least 50%, were ranked according to their sensitivities.