A Gene-Expression Signature as a Predictor of Survival in Breast Cancer

Vijver, Marc J. van de; He, Yudong; Veer, Laura J. van ’t; Dai, Hongyue; Hart, A. A. M.; Voskuil, D.W.; Schreiber, George J.; Peterse, Johannes L.; Roberts, Chris; Marton, Matthew J.; Parrish, Mark L.; Atsma, Douwe E.; Witteveen, Anke; Glas, Annuska M.; Delahaye, Leonie; Velde, Tony van der; Bartelink, Harry; Rodenhuis, S.; Rutgers, Ejt; Friend, Stephen H.; Bernards, René

doi:10.1056/nejmoa021967

Cited by 5,639 publications

(4,460 citation statements)

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“…To perform this analysis, we leveraged four large patient cohorts with accompanying gene expression profiling data from studies of breast cancer patients with extensive clinical follow-up. These represent four of the largest data sets in the public domain in which microarray profiles and long-term patient outcomes are available: (i) 103 patients from the Norway/Stanford study of response to chemotherapy of locally advanced cancer (33); (ii) 295 consecutive breast cancer patients from the Netherlands Cancer Institute (NKI) (34); (iii) 251 consecutive breast cancer patients from Uppsala, Sweden (35); and (iv) 159 surgically resected breast cancer patients from the Karolinska Institute in Stockholm, Sweden (31). Patient outcomes measured in these studies were either disease-specific survival (DSS) (death due to breast cancer) or distant metastasis–free survival (DMFS) (recurrence of cancer at a distant organ site).…”

Section: Resultsmentioning

confidence: 99%

“…Correlations between ferroportin expression in primary breast tumors and metastatic recurrence in patients were assessed with gene expression profiles from publicly accessible microarray data sets: (i) the Norway/Stanford study (33) (http://genome-www.stanford.edu/breast_cancer/mopo_clinical/data.shtml), (ii) the NKI study (34) (http://www.rii. com/publications/2002/nejm.html), (iii) the Uppsala study (35) [Gene Expression Omnibus (GEO) accession number GSE3494], and (iv) the Stockholm study (31) (GEO accession number GSE1456).…”

Section: Methodsmentioning

confidence: 99%

“…Breast cancer patients were ranked according to ferroportin expression levels, and DSS or DMFS of patients with below-mean expression was compared to that of patients with above-mean expression. ( A to D ) Kaplan-Meier plots are shown for (A) the Norway/Stanford cohort (33) (included were 103 tumors with reported expression values for ferroportin; data for 19 tumors were reported as “missing” in the original data set and these were excluded from the analysis), (B) the NKI cohort (34), (C) the Uppsala cohort (35), and (D) the Stockholm cohort (31). Log-rank tests were used to compare the survival curves between groups and to generate the P values for these comparisons.…”

Section: Figmentioning

confidence: 99%

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Ferroportin and Iron Regulation in Breast Cancer Progression and Prognosis

Pinnix¹,

Miller²,

Wang³

et al. 2010

Science Translational Medicine

245

278

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Ferroportin and hepcidin are critical proteins for the regulation of systemic iron homeostasis. Ferroportin is the only known mechanism for export of intracellular non–heme-associated iron; its stability is regulated by the hormone hepcidin. Although ferroportin profoundly affects concentrations of intracellular iron in tissues important for systemic iron absorption and trafficking, ferroportin concentrations in breast cancer and their influence on growth and prognosis have not been examined. We demonstrate here that both ferroportin and hepcidin are expressed in cultured human breast epithelial cells and that hepcidin regulates ferroportin in these cells. Further, ferroportin protein is substantially reduced in breast cancer cells compared to nonmalignant breast epithelial cells; ferroportin protein abundance correlates with metabolically available iron. Ferroportin protein is also present in normal human mammary tissue and markedly decreased in breast cancer tissue, with the highest degree of anaplasia associated with lowest ferroportin expression. Transfection of breast cancer cells with ferroportin significantly reduces their growth after orthotopic implantation in the mouse mammary fat pad. Gene expression profiles in breast cancers from >800 women reveal that decreased ferroportin gene expression is associated with a significant reduction in metastasis-free and disease-specific survival that is independent of other breast cancer risk factors. High ferroportin and low hepcidin gene expression identifies an extremely favorable cohort of breast cancer patients who have a 10-year survival of >90%. Ferroportin is a pivotal protein in breast biology and a strong and independent predictor of prognosis in breast cancer.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Figmentioning

confidence: 99%

See 1 more Smart Citation

Ferroportin and Iron Regulation in Breast Cancer Progression and Prognosis

Pinnix¹,

Miller²,

Wang³

et al. 2010

Science Translational Medicine

245

278

View full text Add to dashboard Cite

show abstract

“…Currently, four subtypes (luminal A and luminal B, HER2‐enriched and basal like/triple negative) are firmly established but more recent data even suggest a finer molecular classification in ten subtypes 9 whose clinical implications, however, still need to be explored. Complementing these advances, several multigene predictors have been developed to aid prediction of response towards adjuvant therapy and risk stratification of recurrence 10, 11, 12.…”

Section: Introductionmentioning

confidence: 99%

Classical pathology and mutational load of breast cancer – integration of two worlds

Budczies

Bockmayr

Denkert

et al. 2015

The Journal of Pathology CR

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Breast cancer is a complex molecular disease comprising several biological subtypes. However, daily routine diagnosis is still based on a small set of well‐characterized clinico‐pathological variables. Here, we try to link the two worlds of surgical pathology and multilayered molecular profiling by analyzing the relationships between clinico‐pathological phenotypes and mutational loads of breast cancer. We evaluated the number of mutated genes with somatic non‐silent mutations in different subgroups of breast cancer based on clinico‐pathological, including immunohistochemical and tumour characteristics. The analysis was performed for a cohort of 687 primary breast cancer patients with mutational profiling, gene expression and clinico‐pathological data available from The Cancer Genome Atlas (TCGA) project. The number of mutated genes was strongly positively associated with higher tumour grade (p = 1.4e−14) and with the different immunohistochemical and PAM50 molecular subtypes of breast cancer (p = 1.4e−10 and p = 4.3e−10, respectively). We observed significant associations (|R| > 0.4) between the abundance of mutated genes and expression levels of genes related to proliferation in the overall cohort and hormone receptor positive cohort, including the Recurrence Score gene signature (e.g., MYBL2 and BIRC5). Specific mutated genes (TP53, NCOR1, NF1, PTPRD and RB1) were highly significantly associated with high loads of mutated genes. Multivariate analysis for overall survival (OS) revealed a worse survival for patients with high numbers of mutated genes (hazard ratio = 4.6, 95% CI: 1.0 – 20.0, p = 0.044). Here, we report a strong association of the number of mutated genes with immunohistochemical and PAM50 subtypes and tumour grade in breast cancer. We provide evidence that specific levels of the mutational load underlie different morphological and biological phenotypes, which collectively constitute the current basis of pathological diagnosis. Our study is a step towards genomics‐informed breast pathology and will provide a basis for future studies in this field bridging the gap between morphology, tumour biology and medical oncology.

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“…The latter three categories are defined by pathway signatures, which are currently being used as experimental biomarkers in clinical studies. The hope that these signatures will be used in clinical practice is encouraged by the 2007 Food and Drug Administration approval of an assay for another pathway signature, the prognostic Mammaprint assay[14]. This landmark achievement is evidence that a multi-analyte measurement of gene expression can be executed with rigor and reproducibility to meet clinical regulatory requirements.…”

Section: Pathway Signatures Capture Genetic Output and Epigenetic Feamentioning

confidence: 99%

A proposal: a comprehensive platform to characterize tumors in Chinese and improve success in cancer drug discovery and development

Huang

Ho²,

Zhang³

2011

Chin J Cancer

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Cancer is a collection of complex diseases in which cell proliferation and apoptosis are dysregulated due to the acquisition of genetic changes in cancer cells. These genetic changes, combined with the interrelated physiologic adaptations of neo-angiogenesis, recruitment of stromal support tissues, and suppression of immune recognition, are measurable characteristics in tumor gene expression profiles and biochemical pathways. These measures can lead to identification of disease drivers and, ultimately, can be used to assign therapy. With advances in RNA sequencing technologies, the ability to simultaneously measure all genetic and gene expression changes with a single technology is now possible. The ability to create a comprehensive catalog of genotypic and phenotypic changes in a collection of histologically similar but otherwise distinct tumors should allow for a more precise positioning of existing targeted therapies and identification of new targets for intervention.

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A Gene-Expression Signature as a Predictor of Survival in Breast Cancer

Cited by 5,639 publications

References 19 publications

Ferroportin and Iron Regulation in Breast Cancer Progression and Prognosis

Ferroportin and Iron Regulation in Breast Cancer Progression and Prognosis

Classical pathology and mutational load of breast cancer – integration of two worlds

A proposal: a comprehensive platform to characterize tumors in Chinese and improve success in cancer drug discovery and development

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