Abstract 451: A novel algorithm for prioritizing candidate genes driving malignant transformation of MCF10F cells and basal-like breast cancer
Authors: Lee Sangjun , Madahian Behrouz , Sutter Carrie , Dickens Charles , Russo Irma , Russo Jose , Homayouni Ramin , Sutter Thomas 
Published: May 2015

We have developed a model of malignant transformation of MCF10F cells to identify the temporal acquisition of changes in genome structure and gene expression that correspond to the progressive transformed phenotype culminating in tumorigenesis. Genomic DNA and total RNA were isolated from MCF10F, trMCF (MCF10F transformed by 70 nM 17-beta estradiol), bsMCF (trMCF selected by Boyden chamber, tumorigenic), bcMCF (clones of bsMCF), and caMCF (cells from tumors of bsMCF grown in SCID mice). The molecular changes accompanying this progression were interrogated using high resolution methods of analysis for loss of heretozygosity (LOH), chromosome copy number (CCN) and gene expression profiles. The observed genomic changes, along with the tumor phenotype of a poorly differentiated adenocarcinoma, indicate an underlying mechanism common to basal-like breast cancer development in women. As examples, similarities were found at chromosomes 4 and 5 between this ER negative cell model and the basal-like tumors, and progression was accompanied by changes in gene expression consistent with epithelial mesenchymal transition. Here we describe an algorithm for prioritizing candidate oncogenes and tumor suppressor genes. Venn analysis of CCN and gene expression (Venn_input, &gt; 1100 genes) was input into GeneIndexer, a text mining tool, and queried by key words or genes that were selected for relevance to breast cancer and then neutralized for gene-by-word correlation bias. The sum of the output cosine values for the Venn_input list was then used to prioritize candidate genes of interest in relation to breast cancer (Venn_output), and further, in relation to their first appearance during the progression of cell transformation. To determine the relevance of these genes to clinical breast cancer, the Venn_output list was used to perform hierarchical cluster analysis of several clinical studies of breast cancer. Of interest, the prioritized 113 genes identified only the basal-like subtype in four separate clinical sample sets and one set of 51 breast cancer cell lines. Further analysis using a support vector machine technique to train and query these same data predicted the tumor class (basal/non basal) with a sensitivity range of 71-87 percent, and a specificity range of 77-100 percent. These results support the use of this approach for combining complex data for the prioritization of candidate genes, and indicate that this model of malignant cell transformation recapitulates many key aspects of basal breast cancer.

Citation Format: Sangjun Lee, Behrouz Madahian, Carrie Sutter, Charles Dickens, Irma Russo, Jose Russo, Ramin Homayouni, Thomas Sutter. A novel algorithm for prioritizing candidate genes driving malignant transformation of MCF10F cells and basal-like breast cancer. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 451. doi:10.1158/1538-7445.AM2014-451