Fuzzy Logic Selection as a New Reliable Tool to Identify Gene Signatures in Breast Cancer - the INNODIAG Study

Based on fuzzy logic selection and classification algorithms, our selection method measures the contribution of each gene for each of two pre-defined classes in order to find the best discrimination. This algorithm extracts and ranks the most pertinent markers, since it is based on feature weighting according to optimal error rate, sensitivity and specificity. We applied the fuzzy logic selection on four breast cancer microarray databases to obtain new gene signatures based on histological grade. To validate these gene signatures, we designed probes for the selected genes on Nimblegen custom microarrays and tested them on a series of 151 consecutive invasive breast carcinomas displaying clinicopathological features similar to those observed in routine practice. 151 frozen breast cancer tumors from the tumor bank of the Claudius Regaud Institute (ICR Toulouse, France) were selected. This cohort consisted of consecutive invasive breast carcinoma patients treated at Claudius Regaud Institute between 2009 and 2011. All patients included in this cohort signed an informed consent. Clinico-pathological characteristics of the series were similar to those observed in routine clinical practice (i.e. majority of pre-menopausal patients presenting with T1c, node negative, ER+ invasive ductal carcinoma of intermediate grade).