Distinctive gene expression patterns in human mammary epithelial cells and breast cancers

Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.
Proceedings of the National Academy of Sciences (Impact Factor: 9.67). 09/1999; 96(16):9212-7.
Source: PubMed


cDNA microarrays and a clustering algorithm were used to identify patterns of gene expression in human mammary epithelial cells growing in culture and in primary human breast tumors. Clusters of coexpressed genes identified through manipulations of mammary epithelial cells in vitro also showed consistent patterns of variation in expression among breast tumor samples. By using immunohistochemistry with antibodies against proteins encoded by a particular gene in a cluster, the identity of the cell type within the tumor specimen that contributed the observed gene expression pattern could be determined. Clusters of genes with coherent expression patterns in cultured cells and in the breast tumors samples could be related to specific features of biological variation among the samples. Two such clusters were found to have patterns that correlated with variation in cell proliferation rates and with activation of the IFN-regulated signal transduction pathway, respectively. Clusters of genes expressed by stromal cells and lymphocytes in the breast tumors also were identified in this analysis. These results support the feasibility and usefulness of this systematic approach to studying variation in gene expression patterns in human cancers as a means to dissect and classify solid tumors.

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    • "With this abundance of gene expression data nowadays, the researchers have the opportunity to do cancer classification using gene expression data. In recent years, a lot of machine learning methods have been proposed to do cancer classification using gene expression data such as clustering-based methods [1], [2], k-nearest neighbor method [3], artificial neural network method [4], and support vector machine method [5], to name a few. However, there still exist a lot of issues needed to be identified and understood. "
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    ABSTRACT: A successful classification of different tumor types is essential for successful treatment of cancer. However, most prior cancer classification methods are clinical-based and have inadequate diagnostic ability. Cancer classification using gene expression data is very important in cancer diagnosis and drug discovery. The introduction of DNA microarray techniques has made simultaneous monitoring of thousands of gene expression probable. With this abundance of gene expression data nowadays, the researchers have the opportunity to do cancer classification using gene expression data. In recent years, a lot of machine learning methods have been proposed to do cancer classification using gene expression data such as clustering-based methods, k-nearest neighbor method, artificial neural network method, and support vector machine method, to name a few. In this paper, we present the un-normalized graph p-Laplacian semi-supervised learning methods. These methods will be applied to the patient-patient network constructed from the gene expression data to predict the tumor types of all patients in the network. These methods are based on the assumption that the labels of two adjacent patients in the network are likely to be the same. The experiments show that that the un-normalized graph p-Laplacian semi-supervised learning methods are at least as good as the current state of the art network-based method (the un-normalized graph Laplacian based semi-supervised learning method) but often lead to better classification accuracy performance measures.
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    • "Luminal B breast cancer is classified as higher nuclear grade [2], lower expression of ER-related genes [11], [12], and higher expression of proliferative genes [13], [14] compared to luminal A, thus representing a heterogeneous disease. IHC classification defines Luminal B tumors as ER- and/or PR-positive, HER2-negative, and Ki-67 labeling index (Ki-67 index) ≥14%, or as ER- and/or PR-positive, HER2 over-expressed or amplified, any and Ki-67 index [7], although this is still a heterogeneous group. "
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    ABSTRACT: Purpose Few studies has documented early relapse in luminal B/HER2-negative breast cancer. We examined prognostic factors for early relapse among these patients to improve treatment decision-making. Patients and Methods A total 398 patients with luminal B/HER2-negative breast cancer were included. Kaplan-Meier curves were applied to estimate disease-free survival and Cox regression to identify prognostic factors. Results Progesterone receptor (PR) negative expression was associated with higher tumor grade (p<.001) and higher Ki-67 index (p = .010). PR-negative patients received more chemotherapy than the PR-positive group (p = .009). After a median follow-up of 28 months, 17 patients (4.3%) had early relapses and 8 patients (2.0%) died of breast cancer. The 2-year disease-free survival was 97.7% in the PR-positive and 90.4% in the PR-negative groups (Log-rank p = .002). Also, patients with a high Ki-67 index (defined as >30%) had a reduced disease-free survival (DFS) when compared with low Ki-67 index group (≤30%) (98.0% vs 92.4%, respectively, Log-rank p = .013). In multivariate analysis, PR negativity was significantly associated with a reduced DFS (HR = 3.91, 95% CI 1.29–11.88, p = .016). Conclusion In this study, PR negativity was a prognostic factor for early relapse in luminal B/HER2-negative breast cancer, while a high Ki-67 index suggested a higher risk of early relapse.
    PLoS ONE 03/2014; 9(8):e95629. DOI:10.1371/journal.pone.0095629 · 3.23 Impact Factor
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    • "Current data imply that BC is a heterogeneous group of diseases with complex and distinctive underlying molecular pathogenesis (Beckmann et al, 1997; Ellis et al, 1999; Lishman and Lakhani, 1999). Further support for this hypothesis is provided by gene expression profiling (GEP) that have identified distinct molecular tumour groups with direct clinical relevance (Perou et al, 1999; Sorlie et al, 2001; van de Vijver et al, 2002; van&apos;t Veer et al, 2003; Darb-Esfahani et al, 2009; Parker et al, 2009; Nielsen et al, 2010). While this provides further compelling evidence that tumour biology is a key variable required for decision making in personalised BC management, the heterogeneity within these groups and its incorporation with the currently validated variables and prognostic indices add complexity. "
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    ABSTRACT: Background: Current management of breast cancer (BC) relies on risk stratification based on well-defined clinicopathologic factors. Global gene expression profiling studies have demonstrated that BC comprises distinct molecular classes with clinical relevance. In this study, we hypothesised that molecular features of BC are a key driver of tumour behaviour and when coupled with a novel and bespoke application of established clinicopathologic prognostic variables can predict both clinical outcome and relevant therapeutic options more accurately than existing methods. Methods: In the current study, a comprehensive panel of biomarkers with relevance to BC was applied to a large and well-characterised series of BC, using immunohistochemistry and different multivariate clustering techniques, to identify the key molecular classes. Subsequently, each class was further stratified using a set of well-defined prognostic clinicopathologic variables. These variables were combined in formulae to prognostically stratify different molecular classes, collectively known as the Nottingham Prognostic Index Plus (NPI+). The NPI+ was then used to predict outcome in the different molecular classes. Results: Seven core molecular classes were identified using a selective panel of 10 biomarkers. Incorporation of clinicopathologic variables in a second-stage analysis resulted in identification of distinct prognostic groups within each molecular class (NPI+). Outcome analysis showed that using the bespoke NPI formulae for each biological BC class provides improved patient outcome stratification superior to the traditional NPI. Conclusion: This study provides proof-of-principle evidence for the use of NPI+ in supporting improved individualised clinical decision making.
    British Journal of Cancer 03/2014; 110(7). DOI:10.1038/bjc.2014.120 · 4.84 Impact Factor
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