| 1. |
- Hautaniemi, Sampsa, et al.
(författare)
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A Strategy for Identifying Putative Causes of Gene Expression Variation in Human Cancer
- 2002
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Ingår i: Workshop on Genomic Signal Processing and Statistics (GENSIPS).
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Konferensbidrag (refereegranskat)abstract
- There is often a need to predict the impact of alterations in one variable on another variable. This is especially the case in cancer research, where much effort has been made to carry out large-scale gene expression screening by microarray techniques. However, the causes of this variability from one cancer to another and from one gene to another often remain unknown. In this study we present a systematic procedure for finding genes whose expression is altered by an intrinsic or extrinsic explanatory phenomenon. The procedure has three stages: preprocessing, data integration and statistical analysis. We tested and verified the utility of this approach in a study, where expression and copy number of 13,824 genes were determined in 14 breast cancer samples. The expression of 270 genes could be explained by the variability of gene copy number. These genes may represent an important set of primary, genetically “damaged” genes that drive cancer progression.
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| 2. |
- Cunliffe, HE, et al.
(författare)
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The gene expression response of breast cancer to growth regulators: Patterns and correlation with tumor expression profiles
- 2003
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Ingår i: Cancer Research. - 1538-7445. ; 63:21, s. 7158-7166
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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.
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| 3. |
- Gruvberger, Sofia, et al.
(författare)
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Estrogen receptor status in breast cancer is associated with remarkably distinct gene expression patterns
- 2001
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Ingår i: Cancer Research. - 1538-7445. ; 61:16, s. 5979-5984
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Tidskriftsartikel (refereegranskat)abstract
- To investigate the phenotype associated with estrogen receptor alpha (ER) expression in breast carcinoma, gene expression profiles of 58 node-negative breast carcinomas discordant for ER status were determined using DNA microarray technology. Using artificial neural networks as well as standard hierarchical clustering techniques, the tumors could be classified according to ER status, and a list of genes which discriminate tumors according to ER status was generated. The artificial neural networks could accurately predict ER status even when excluding top discriminator genes, including ER itself. By reference to the serial analysis of gene expression database, we found that only a small proportion of the 100 most important ER discriminator genes were also regulated by estradiol in MCF-7 cells. The results provide evidence that ER+ and ER- tumors display remarkably different gene-expression phenotypes not solely explained by differences in estrogen responsiveness.
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| 4. |
- Gruvberger, Sofia, et al.
(författare)
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Expression profiling to predict outcome in breast cancer: the influence of sample selection
- 2003
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Ingår i: Breast Cancer Research. - : Springer Science and Business Media LLC. - 1465-5411 .- 1465-542X. ; 5:1, s. 23-26
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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.
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| 5. |
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| 6. |
- Hautaniemi, S, et al.
(författare)
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A strategy for identifying putative causes of gene expression variation in human cancers
- 2004
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Ingår i: Journal of the Franklin Institute. - : Elsevier BV. - 0016-0032. ; 341:1-2, s. 77-88
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Tidskriftsartikel (refereegranskat)abstract
- The majority of microarray studies focus on analysis of gene expression differences between various specimens or conditions. However, the causes of this variability from one cancer to another, from one sample to another and from one gene to another often remain unknown. In this study, we present a systematic procedure for finding genes whose expression levels are altered due to an intrinsic or extrinsic explanatory phenomenon. The procedure consists of three stages: preprocessing, data integration and statistical analysis. We tested and verified the utility of this approach in a case study, where expression and copy number levels of 13,824 genes were determined in 14 breast cancer cell lines. The procedure resulted in identification of 92 genes whose expression levels could be explained by the variability of gene copy number. This set includes several genes that are known to be both overexpressed and amplified in breast cancer. Thus, these genes may represent an important set of primary, genetically altered genes that drive cancer progression. (C) 2003 The Franklin Institute.
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| 7. |
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| 8. |
- Hedenfalk, Ingrid, et al.
(författare)
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Molecular classification of familial non-BRCA1/BRCA2 breast cancer
- 2003
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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
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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.
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| 9. |
- Pavey, S, et al.
(författare)
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Microarray expression profiling in melanoma reveals a BRAF mutation signature
- 2004
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Ingår i: Oncogene. - : Springer Science and Business Media LLC. - 1476-5594 .- 0950-9232. ; 23:23, s. 4060-4067
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Tidskriftsartikel (refereegranskat)abstract
- We have used microarray gene expression pro. ling and machine learning to predict the presence of BRAF mutations in a panel of 61 melanoma cell lines. The BRAF gene was found to be mutated in 42 samples (69%) and intragenic mutations of the NRAS gene were detected in seven samples (11%). No cell line carried mutations of both genes. Using support vector machines, we have built a classifier that differentiates between melanoma cell lines based on BRAF mutation status. As few as 83 genes are able to discriminate between BRAF mutant and BRAF wild-type samples with clear separation observed using hierarchical clustering. Multidimensional scaling was used to visualize the relationship between a BRAF mutation signature and that of a generalized mitogen-activated protein kinase ( MAPK) activation ( either BRAF or NRAS mutation) in the context of the discriminating gene list. We observed that samples carrying NRAS mutations lie somewhere between those with or without BRAF mutations. These observations suggest that there are gene-specific mutation signals in addition to a common MAPK activation that result from the pleiotropic effects of either BRAF or NRAS on other signaling pathways, leading to measurably different transcriptional changes.
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