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Microarray-based gene expression data from BPLER tumor explants

ABSTRACT: The gene expression of 6 different mouse xenografts initiated by BPLER cells analyzed by microarray. Basal-like triple negative breast cancers (TNBC) have poor prognosis. To study the basal-like transcriptional profile of tumors transformed by defined genetic elements, the human breast epithelial cell line BPLER was injected into NOD/SCID mice. The resulting tumors were excised for expression analysis. Keywords: breast cancer, BPLER, metastasis, tumor stem cells, tumor initiating cells, breast adenocarcinoma BPLER cells were derived from human primary breast epithelial cells propagated in WIT medium (Ince et al, Cancer Cell, 2007). These cells were transformed with hTERT, SV40 early region and hRASV12 by retroviral transduction. BPLER have a TNBC-like phenotype in vitro and are highly enriched for tumor-initiating cells. To define the phenotype of BPLER-derived tumors in vivo, BPLER cells were injected in the mammary fat-pad of 6 NOD/SCID mice. Once tumors were palpable, mice were sacrificed and tumors excised. Total RNA was extracted using Trizol. The gene expression profile of each tumor was assessed by mRNA microarray analysis. Based on histology and principal component analysis of microarray data, BPLER tumors were remarkably similar to human primary TNBC encountered in the clinic (Petrocca et al, Cancer Cell, 2013).

ORGANISM(S): Homo sapiens

SUBMITTER: Ince Tan

PROVIDER: S-ECPF-GEOD-48444 | biostudies-other |

REPOSITORIES: biostudies-other

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Project description:BACKGROUND: An important use of data obtained from microarray measurements is the classification of tumor types with respect to genes that are either up or down regulated in specific cancer types. A number of algorithms have been proposed to obtain such classifications. These algorithms usually require parameter optimization to obtain accurate results depending on the type of data. Additionally, it is highly critical to find an optimal set of markers among those up or down regulated genes that can be clinically utilized to build assays for the diagnosis or to follow progression of specific cancer types. In this paper, we employ a mixed integer programming based classification algorithm named hyper-box enclosure method (HBE) for the classification of some cancer types with a minimal set of predictor genes. This optimization based method which is a user friendly and efficient classifier may allow the clinicians to diagnose and follow progression of certain cancer types. METHODOLOGY/PRINCIPAL FINDINGS: We apply HBE algorithm to some well known data sets such as leukemia, prostate cancer, diffuse large B-cell lymphoma (DLBCL), small round blue cell tumors (SRBCT) to find some predictor genes that can be utilized for diagnosis and prognosis in a robust manner with a high accuracy. Our approach does not require any modification or parameter optimization for each data set. Additionally, information gain attribute evaluator, relief attribute evaluator and correlation-based feature selection methods are employed for the gene selection. The results are compared with those from other studies and biological roles of selected genes in corresponding cancer type are described. CONCLUSIONS/SIGNIFICANCE: The performance of our algorithm overall was better than the other algorithms reported in the literature and classifiers found in WEKA data-mining package. Since it does not require a parameter optimization and it performs consistently very high prediction rate on different type of data sets, HBE method is an effective and consistent tool for cancer type prediction with a small number of gene markers.

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Microarray-based gene expression data from BPLER tumor explants

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