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Sorlie, T., Perou, C., Brown, P., Botstein, D. & Borresen-Dale, A. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc. Natl Acad. Sci. USA 98, 10869-10874

Department of Genetics, The Norwegian Radium Hospital, Montebello, N-0310 Oslo, Norway.
Proceedings of the National Academy of Sciences (Impact Factor: 9.81). 10/2001; 98(19):10869-74. DOI: 10.1073/pnas.191367098
Source: PubMed

ABSTRACT The purpose of this study was to classify breast carcinomas based on variations in gene expression patterns derived from cDNA microarrays and to correlate tumor characteristics to clinical outcome. A total of 85 cDNA microarray experiments representing 78 cancers, three fibroadenomas, and four normal breast tissues were analyzed by hierarchical clustering. As reported previously, the cancers could be classified into a basal epithelial-like group, an ERBB2-overexpressing group and a normal breast-like group based on variations in gene expression. A novel finding was that the previously characterized luminal epithelial/estrogen receptor-positive group could be divided into at least two subgroups, each with a distinctive expression profile. These subtypes proved to be reasonably robust by clustering using two different gene sets: first, a set of 456 cDNA clones previously selected to reflect intrinsic properties of the tumors and, second, a gene set that highly correlated with patient outcome. Survival analyses on a subcohort of patients with locally advanced breast cancer uniformly treated in a prospective study showed significantly different outcomes for the patients belonging to the various groups, including a poor prognosis for the basal-like subtype and a significant difference in outcome for the two estrogen receptor-positive groups.

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    • "Additionally, least-squares estimates of regression coefficients may be highly unstable, especially in cases of correlated predictor variables, which lead to low prediction accuracy. In genomic settings {such as the prediction of cancer patient survival from tumor gene expression (Beer et al., [2]; Shedden et al., [25]; Sørlie et al., [27]; van de Vijver et al., [32] and Wigle et al., [34]}, where collinear predictors, say p, typically outnumber available sample of size n (i.e. p > n); OLS regression is subject to overfitting and instability of coefficients and as well stepwise variable selection methods do not scale well as observed in the research conducted by Fan and Li [13]. "
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    • "If a sample shows more than 1% staining for one of these receptors, it is classified as hormone receptor-positive and hormone treatment is applicable (Hammond et al. 2010). Comprehensive gene analysis has allowed breast cancers to be categorized according to their intrinsic subtype and Luminal A, a group with high expression of the ER, is considered to be highly responsive to hormone therapy (Sorlie et al. 2001). Therefore, ER and PgR status are very important for the application of hormone therapy, especially ER status. "
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    • "The simplest and most commonly used approach is hierarchical clustering where patients are iteratively grouped by using a distance metric based upon expression values. This approach has been used in many previous molecular subtyping studies (Prat et al., 2010; Rouzier et al., 2005; Sørlie et al., 2001). Iossifov and colleagues (2014) clustered functional classes to determine enrichment of LGDs in individuals with ASD and their siblings in the following functional domains: Fragile-X mental FIG. 2. Approach to molecular and disease subtyping in ASD. "
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