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

Sotiriou, Christos; Wirapati, Pratyaksha; Loi, Sherene; Harris, Adrian L.; Fox, Stephen B.; Smeds, Johanna; Nordgren, Hans; Farmer, Pierre; Praz, Viviane; Haibe‐Kains, Benjamin; Desmedt, Christine; Larsimont, Denis; Cardoso, Fátima; Peterse, Hans; Nuyten, Dimitry S.A.; Buyse, Marc; Vijver, Marc J. van de; Bergh, Jonas; Piccart, Martine; Delorenzi, Mauro

doi:10.1093/jnci/djj052

Cited by 1,815 publications

(1,743 citation statements)

References 23 publications

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“…We included 238 of our own samples (datasets Frankfurt, Frankfurt-2, and Frankfurt-3) which have been described previously (Ahr et al 2002 [9], [10], Rody et al 2008 [11], Ruckhäberle et al 2008 [12], and Rody et al 2007 [13], respectively) as well as 2792 samples from 22 different publicly available datasets (Table 1): Rotterdam [14][15][16], Mainz [17], Trans-BIG [18], Oxford-Untreated [19], London [20], London-2 [21], Oxford-Tamoxifen, Veridex-Tam [22], Stockholm [23], Uppsala [24,25], San Francisco [26], New York [27], MDA133 [28], EORTC [29], Edinburgh [30], ExpO [31], Signapore [32], Genentech [33], Boston [34], Berlin [35], Paris [36], and Tampa [37]. For comparability, only the ProbeSets from the Affymetrix HG-U133A microarray were used from seven datasets where HG-U133?…”

Section: Methodsmentioning

confidence: 99%

Data driven derivation of cutoffs from a pool of 3,030 Affymetrix arrays to stratify distinct clinical types of breast cancer

Karn

Metzler

Ruckhäberle

et al. 2009

Breast Cancer Res Treat

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Section: Methodsmentioning

confidence: 99%

Data driven derivation of cutoffs from a pool of 3,030 Affymetrix arrays to stratify distinct clinical types of breast cancer

Karn

Metzler

Ruckhäberle

et al. 2009

Breast Cancer Res Treat

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“…Two major types, basal and luminal, have been identified, each with the potential to be subdivided into additional subtypes [2][3][4][5]. Although histological grade can provide clinically important prognostic information, as many as 30-60% of tumors are classified as grade 2.…”

Section: Introductionmentioning

confidence: 99%

Meta-analysis of gene expression profiles related to relapse-free survival in 1,079 breast cancer patients

Győrffy

Schäfer

2008

Breast Cancer Res Treat

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The transcriptome of breast cancers have been extensively screened with microarrays and large sets of genes associated with clinical features have been established. The aim of this study was to validate original gene sets on a large cohort of raw breast cancer microarray data with known clinical follow-up. We recovered 20 publications and matched them to Affymetrix HGU133A annotations. Raw Affymetrix HGU133A microarray data were extracted from GEO and MAS5 normalized. For classifying patients using the selected gene sets, we applied prediction analysis of microarrays and constructed KaplanMeier plots. A new classification including all patients was generated using supervised principal components analysis. Seven studies including 1,470 patients were downloaded from GEO. Notably, we uncovered 641 microarrays representing 251 individual tumor specimens among them, which were repeatedly described under independent GEO identifiers. We excluded all redundant data and used the remaining 1,079 samples. Eight of the 20 gene sets were able to predict response at a significance of P \ 0.05. The discrimination of good and poor prognosis groups exclusively relying on gene expression data resulted in high significance (P = 1.8E-12). A model including genes fitted by both gene expression and clinical covariates (lymph node status and grade) contains 44 genes and can predict response at P = 9.5E-7. The outcome provides a ranking of the gene lists regarding applicability on an independent dataset. We established a consensus predictor combining the available clinical and gene expression data. The database comprising expression profiles of 1,079 breast cancers can be used to classify individual patients.

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“…Gene expression profiling has been used for classification of cancer outcome in several studies [1][2][3][4][5][6][7][8][9] showing that the overall gene expression pattern in primary tumors is associated with clinical outcome. However, from these studies it has not been possible to pinpoint the causal gene expression changes.…”

Section: Introductionmentioning

confidence: 99%

Gene expression meta-analysis identifies chromosomal regions and candidate genes involved in breast cancer metastasis

Thomassen

Tan

Kruse

2008

Breast Cancer Res Treat

116

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. Abstract Breast cancer cells exhibit complex karyotypic alterations causing deregulation of numerous genes. Some of these genes are probably causal for cancer formation and local growth whereas others are causal for the various steps of metastasis. In a fraction of tumors deregulation of the same genes might be caused by epigenetic modulations, point mutations or the influence of other genes. We have investigated the relation of gene expression and chromosomal position, using eight datasets including more than 1200 breast tumors, to identify chromosomal regions and candidate genes possibly causal for breast cancer metastasis. By use of ''Gene Set Enrichment Analysis'' we have ranked chromosomal regions according to their relation to metastasis. Overrepresentation analysis identified regions with increased expression for chromosome 1q41-42, 8q24, 12q14, 16q22, 16q24, 17q12-21.2, 17q21-23, 17q25, 20q11, and 20q13 among metastasizing tumors and reduced gene expression at 1p31-21, 8p22-21, and 14q24. By analysis of genes with extremely imbalanced expression in these regions we identified DIRAS3 at 1p31, PSD3, LPL, EPHX2 at 8p21-22, and FOS at 14q24 as candidate metastasis suppressor genes. Potential metastasis promoting genes includes RECQL4 at 8q24, PRMT7 at 16q22, GINS2 at 16q24, and AURKA at 20q13.

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

Abstract: 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.

Cited by 1,815 publications

References 23 publications

Data driven derivation of cutoffs from a pool of 3,030 Affymetrix arrays to stratify distinct clinical types of breast cancer

Data driven derivation of cutoffs from a pool of 3,030 Affymetrix arrays to stratify distinct clinical types of breast cancer

Meta-analysis of gene expression profiles related to relapse-free survival in 1,079 breast cancer patients

Gene expression meta-analysis identifies chromosomal regions and candidate genes involved in breast cancer metastasis

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