ABSTRACT
Antiestrogen-resistant and triple-negative breast tumors pose a serious clinical challenge because of limited treatment options. We assessed global gene expression changes in antiestrogen-sensitive compared with antiestrogen-resistant (two tamoxifen resistant and two fulvestrant resistant) MCF-7 breast cancer cell lines. The branched-chain amino acid transaminase 1 (BCAT1), which catalyzes the first step in the breakdown of branched-chain amino acids, was among the most upregulated transcripts in antiestrogen-resistant cells. Elevated BCAT1 expression was confirmed in relapsed tamoxifen-resistant breast tumor specimens. High intratumoral BCAT1 levels were associated with a reduced relapse-free survival in adjuvant tamoxifen-treated patients and overall survival in unselected patients. On a tissue microarray (n=1421), BCAT1 expression was detectable in 58% of unselected primary breast carcinomas and linked to a higher Ki-67 proliferation index, as well as histological grade. Interestingly, BCAT1 was predominantly expressed in estrogen receptor-α-negative/human epidermal growth factor receptor-2-positive (ERα-negative/HER-2-positive) and triple-negative breast cancers in independent patient cohorts. The inverse relationship between BCAT1 and ERα was corroborated in various breast cancer cell lines and pharmacological long-term depletion of ERα induced BCAT1 expression in vitro. Mechanistically, BCAT1 indirectly controlled expression of the cell cycle inhibitor p27Kip1 thereby affecting pRB. Correspondingly, phenotypic analyses using a lentiviral-mediated BCAT1 short hairpin RNA knockdown revealed that BCAT1 sustains proliferation in addition to migration and invasion and that its overexpression enhanced the capacity of antiestrogen-sensitive cells to grow in the presence of antiestrogens. Importantly, silencing of BCAT1 in an orthotopic triple-negative xenograft model resulted in a massive reduction of tumor volume in vivo, supporting our findings that BCAT1 is necessary for the growth of hormone-independent breast tumors.
Subject(s)
Breast Neoplasms/metabolism , Estrogen Antagonists/pharmacology , Estrogen Receptor alpha/metabolism , Transaminases/genetics , Animals , Breast Neoplasms/drug therapy , Breast Neoplasms/genetics , Breast Neoplasms/pathology , Cell Line, Tumor , Cell Movement/physiology , Cell Proliferation/physiology , Drug Resistance, Neoplasm , Female , Gene Expression Profiling , Heterografts , Humans , MCF-7 Cells , Mice , Mice, Inbred BALB C , Tamoxifen/pharmacology , Transaminases/antagonists & inhibitors , Transaminases/biosynthesis , Transaminases/metabolism , Up-RegulationABSTRACT
The use of existing devices for object orientation in 3D CAD applications requires some practice and familiarization. In this article, the development of Cercon move, a navigation aid for intuitive control of object movements in dental 3D CAD, is presented. Its navigation concept and ergonomics are tailored for the requirements of dental technicians in computer-aided manufacturing of dental restorations.
Subject(s)
Computer-Aided Design/instrumentation , Dental Prosthesis Design/instrumentation , Computer Peripherals , Humans , Motor Skills , Movement , Space Perception , User-Computer InterfaceABSTRACT
This paper presents two new methods for robust parameter estimation of mixtures in the context of magnetic resonance (MR) data segmentation. The head is constituted of different types of tissue that can be modeled by a finite mixture of multivariate Gaussian distributions. Our goal is to estimate accurately the statistics of desired tissues in presence of other ones of lesser interest. These latter can be considered as outliers and can severely bias the estimates of the former. For this purpose, we introduce a first method, which is an extension of the expectation-maximization (EM) algorithm, that estimates parameters of Gaussian mixtures but incorporates an outlier rejection scheme which allows to compute the properties of the desired tissues in presence of atypical data. The second method is based on genetic algorithms and is well suited for estimating the parameters of mixtures of different kind of distributions. We use this property by adding a uniform distribution to the Gaussian mixture for modeling the outliers. The proposed genetic algorithm can efficiently estimate the parameters of this extended mixture for various initial settings. Also, by changing the minimization criterion, estimates of the parameters can be obtained by histogram fitting which considerably reduces the computational cost. Experiments on synthetic and real MR data show that accurate estimates of the gray and white matters parameters are computed.
