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Open AccessJournal ArticleDOI

Gene Expression Profiling in Breast Cancer: Understanding the Molecular Basis of Histologic Grade To Improve Prognosis

TLDR
Gene expression grade index appeared to reclassify patients with histologic grade 2 tumors into two groups with high versus low risks of recurrence, which may improve the accuracy of tumor grading and thus its prognostic value.
Abstract
Background: Histologic grade in breast cancer provides clinically important prognostic information. However, 30% – 60% of tumors are classifi ed as histologic grade 2. This grade is associated with an intermediate risk of recurrence and is thus not informative for clinical decision making. We examined whether histologic grade was associated with gene expression profi les of breast cancers and whether such profi les could be used to improve histologic grading. Methods: We analyzed microarray data from 189 invasive breast carcinomas and from three published gene expression datasets from breast carcinomas. We identifi ed differentially expressed genes in a training set of 64 estrogen receptor (ER) – positive tumor samples by comparing expression profi les between histologic grade 3 tumors and histologic grade 1 tumors and used the expression of these genes to defi ne the gene expression grade index. Data from 597 independent tumors were used to evaluate the association between relapse-free survival and the gene expression grade index in a Kaplan – Meier analysis. All statistical tests were two-sided. Results: We identifi ed 97 genes in our training set that were associated with histologic grade; most of these genes were involved in cell cycle regulation and proliferation. In validation datasets, the gene expression grade index was strongly associated with histologic grade 1 and 3 status; however, among histologic grade 2 tumors, the index spanned the values for histologic grade 1 – 3 tumors. Among patients with histologic grade 2 tumors, a high gene expression grade index was associated with a higher risk of recurrence than a low gene expression grade index (hazard ratio = 3.61, 95% confi dence interval = 2.25 to 5.78; P <.001, log-rank test). Conclusions: Gene expression grade index appeared to reclassify patients with histologic grade 2 tumors into two groups with high versus low risks of recurrence. This approach may improve the accuracy of tumor grading and thus its prognostic value. [J Natl Cancer Inst 2006;98:262 – 72]

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Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies

TL;DR: Gen expression profiles from 21 breast cancer data sets and identified 587 TNBC cases may be useful in biomarker selection, drug discovery, and clinical trial design that will enable alignment of TNBC patients to appropriate targeted therapies.
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An online survival analysis tool to rapidly assess the effect of 22,277 genes on breast cancer prognosis using microarray data of 1,809 patients.

TL;DR: An online tool to draw survival plots, which can be used to assess the relevance of the expression levels of various genes on the clinical outcome both in untreated and treated breast cancer patients, and which validated the capability of microarrays to determine estrogen receptor status in 1,231 patients.
Journal ArticleDOI

An embryonic stem cell–like gene expression signature in poorly differentiated aggressive human tumors

TL;DR: The results reveal a previously unknown link between genes associated with ES cell identity and the histopathological traits of tumors and support the possibility that these genes contribute to stem cell–like phenotypes shown by many tumors.
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American Society of Clinical Oncology 2007 Update of Recommendations for the Use of Tumor Markers in Breast Cancer

TL;DR: Thirteen categories of breast tumor markers were considered, six of which were new for the guideline, and certain multiparameter gene expression assays not all applications for these markers were supported, however.
References
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Journal ArticleDOI

Molecular portraits of human breast tumours

TL;DR: Variation in gene expression patterns in a set of 65 surgical specimens of human breast tumours from 42 different individuals were characterized using complementary DNA microarrays representing 8,102 human genes, providing a distinctive molecular portrait of each tumour.
Journal ArticleDOI

Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications

TL;DR: 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.
Book

Statistical Methods for Meta-Analysis

TL;DR: In this article, the authors present a model for estimating the effect size from a series of experiments using a fixed effect model and a general linear model, and combine these two models to estimate the effect magnitude.
Journal ArticleDOI

Gene expression profiling predicts clinical outcome of breast cancer

TL;DR: DNA microarray analysis on primary breast tumours of 117 young patients is used and supervised classification is applied to identify a gene expression signature strongly predictive of a short interval to distant metastases (‘poor prognosis’ signature) in patients without tumour cells in local lymph nodes at diagnosis, providing a strategy to select patients who would benefit from adjuvant therapy.
Journal ArticleDOI

A comparison of normalization methods for high density oligonucleotide array data based on variance and bias

TL;DR: Three methods of performing normalization at the probe intensity level are presented: a one number scaling based algorithm and a method that uses a non-linear normalizing relation by comparing the variability and bias of an expression measure and the simplest and quickest complete data method is found to perform favorably.
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