1.
Cunliffe, HE, et al.
(författare)
The gene expression response of breast cancer to growth regulators: Patterns and correlation with tumor expression profiles
2003
Ingår i: Cancer Research. - 1538-7445. ; 63:21, s. 7158-7166
Tidskriftsartikel (refereegranskat) abstract
The effects of hormone and growth factor signaling on gene expression contribute significantly to breast tumorigenesis and disease progression; however, the targets of signaling networks associated with deregulated growth are not well understood. We defined the dynamic transcriptional effects elicited in MCF7, T-47D, and MDA-MB-436 breast cancer cell lines by nine regulators of growth and differentiation (17beta-estradiol, antiestrogens fulvestrant and tamoxifen, progestin R5020, antiprogestin RU486, all-trans-retinoic acid, epidermal growth factor, mitogen-activated protein/extracellular signal-regulated kinase 1/2 inhibitor U0126 and phorbol ester 12-O-tetradecanoylphorbol-13-acetate) and compared the patterns of gene regulation to published tumor expression profiles. The complex pattern of response to these agents revealed unexpected relationships between their effects, including a profound overlap in genes regulated by both steroids and epidermal growth factor, and striking overlaps between fulvestrant and all-trans-retinoic acid. Estrogen-responsive genes could be divided into two major clusters, only one of which is associated with cell proliferation. Gene ontology analysis was used to highlight functionally distinct biological responses to different mitogens. Significant correlations were identified between several clusters of drug-responsive genes and genes that discriminate estrogen receptor status or disease outcome in patient samples. The majority of estrogen receptor status discriminators were not responsive in our dataset and are therefore likely to reflect underlying differences in histogenesis and disease progression rather than growth factor signaling. This article highlights the overall impact at the gene expression level of diverse regulators of breast cancer growth and links the behavior of breast cancer cells in culture to important clinical properties of human breast tumors.
2.
Gruvberger, Sofia, et al.
(författare)
Expression profiling to predict outcome in breast cancer: the influence of sample selection
2003
Ingår i: Breast Cancer Research. - : Springer Science and Business Media LLC. - 1465-5411 .- 1465-542X. ; 5:1, s. 23-26
Tidskriftsartikel (refereegranskat) abstract
Gene expression profiling of tumors using DNA microarrays is a promising method for predicting prognosis and treatment response in cancer patients. It was recently reported that expression profiles of sporadic breast cancers could be used to predict disease recurrence better than currently available clinical and histopathological prognostic factors. Having observed an overlap in those data between the genes that predict outcome and those that predict estrogen receptor- status, we examined their predictive power in an independent data set. We conclude that it may be important to define prognostic expression profiles separately for estrogen receptor--positive and estrogen receptor--negative tumors.
3.
Hedenfalk, Ingrid, et al.
(författare)
Molecular classification of familial non-BRCA1/BRCA2 breast cancer
2003
Ingår i: Proceedings of the National Academy of Sciences. - : Proceedings of the National Academy of Sciences. - 1091-6490 .- 0027-8424. ; 100:5, s. 2532-2537
Tidskriftsartikel (refereegranskat) abstract
In the decade since their discovery, the two major breast cancer susceptibility genes BRCA1 and BRCA2, have been shown conclusively to be involved in a significant fraction of families segregating breast and ovarian cancer. However, it has become equally clear that a large proportion of families segregating breast cancer alone are not caused by mutations in BRCA1 or BRCA2. Unfortunately, despite intensive effort, the identification of additional breast cancer predisposition genes has so far been unsuccessful, presumably because of genetic heterogeneity, low penetrance, or recessive/polygenic mechanisms. These non-BRCA1/2 breast cancer families (termed BRCAx families) comprise a histopathologically heterogeneous group, further supporting their origin from multiple genetic events. Accordingly, the identification of a method to successfully subdivide BRCAx families into recognizable groups could be of considerable value to further genetic analysis. We have previously shown that global gene expression analysis can identify unique and distinct expression profiles in breast tumors from BRCA1 and BRCA2 mutation carriers. Here we show that gene expression profiling can discover novel classes among BRCAx tumors, and differentiate them from BRCA1 and BRCA2 tumors. Moreover, microarray-based comparative genomic hybridization (CGH) to cDNA arrays revealed specific somatic genetic alterations within the BRCAx subgroups. These findings illustrate that, when gene expression-based classifications are used, BRCAx families can be grouped into homogeneous subsets, thereby potentially increasing the power of conventional genetic analysis.
