Determining and Interpreting New Predictive Rules for Breast Cancer Familial Inheritance

Abstract

DNA copy number alterations have been discovered to be key genetic events in development and progression of cancer. No clear data of familial and sporadic breast cancer are available. We focused on looking for an independent platform as a tool to identify the chromosomal profile in familial versus sporadic breast cancer patients. A total of 124 breast cancer patients were studied utilizing aCGH. The dataset was analyzed using Gaussian Mixture Models to determine the thresholds in order to assess gene copy number changes and to minimize the impact of noise on further data analyses. The identification of regions of consistent aberration across samples was carried out with statistical approaches and machine learning tools to draw profiles for familial and sporadic groups. Familial and sporadic cases resulted with a chromosome imbalance of 15% [false discovery rate (FDR): q=718E-5] and 18% (FDR: q=632E-13), respectively. The differential map evidenced two cytogenetic bands (8p23 and 11q13-11q14) significantly altered in familial versus sporadic cases (FDR: q=7E-4). The application of a new bioinformatics tool that discovers fuzzy classification rules (IFRAIS) let to individualize association of genes alterations that identify familial or sporadic cases. These results are comparable to those of the other systems used and are consistent from the biological point of view.