“Good Old” clinical markers have similar power in breast cancer prognosis as microarray gene expression profilers

Edén, Patrik; Ritz, Cecilia; Rose, Carsten; Fernö, Mårten; Peterson, Carsten

doi:10.1016/j.ejca.2004.02.025

Cited by 144 publications

(90 citation statements)

References 8 publications

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“…The use of conventional factors thus challenges the use of gene profiles for this purpose. This has also been demonstrated in previous studies, showing that conventional factors seem to give similar prognostic information as MammaPrint ® and Oncotype DX ® [28,29].…”

Section: Discussionsupporting

confidence: 83%

Combination of the proliferation marker cyclin A, histological grade, and estrogen receptor status in a new variable with high prognostic impact in breast cancer

Strand

Ahlin²,

Bendahl

et al. 2011

Breast Cancer Res Treat

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Section: Discussionsupporting

confidence: 83%

Combination of the proliferation marker cyclin A, histological grade, and estrogen receptor status in a new variable with high prognostic impact in breast cancer

Strand

Ahlin²,

Bendahl

et al. 2011

Breast Cancer Res Treat

Self Cite

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“…So far, most prediction rules using gene expression have not provided a substantially and significantly improved prognostic classification when compared to conventional prognostic factors (Eden et al, 2004;Dunkler et al, 2007). These results could be interpreted as disproving the initial assumption.…”

Section: Resultsmentioning

confidence: 95%

Interpretation of microarray data in cancer

2007

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“…Although the study only profiled 20 patients, several proteins associated with distant recurrence were revealed, including thioredoxin domaincontaining protein 5 and ferritin [12]. As reported in numerous studies, the investigators in this study found it easier to predict clinical outcome for the estrogen-receptor (ER)-positive than for the ER-negative cohort, based on gene expression data [13]. A possible explanation for this could be that the ER-positive tumors are a more homogenous group than ERnegative tumors.…”

Section: Proteomic Profiling Of Human Tissues and Fluidsmentioning

confidence: 82%

Breast tumor metastasis: analysis via proteomic profiling

Goodison¹,

Urquidi²

2008

Expert Review of Proteomics

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The ability to predict the metastatic behavior of a patient's cancer, as well as to detect and eradicate such recurrences, remain major clinical challenges in oncology. While many potential molecular biomarkers have been identified and tested previously, none have greatly improved the accuracy of specimen evaluation over routine histopathological criteria and, to date, they predict individual outcomes poorly. The ongoing development of high-throughput proteomic profiling technologies is opening new avenues for the investigation of cancer and, through application in tissue-based studies and animal models, will facilitate the identification of molecular signatures that are associated with breast tumor cell phenotype. The appropriate use of these approaches has the potential to provide efficient biomarkers, and to improve our knowledge of tumor biology. This, in turn, will enable the development of targeted therapeutics aimed at ameliorating the lethal dissemination of breast cancer. In this review, we focus on the accumulating proteomic signatures of breast tumor progression, particularly those that correlate with the occurrence of distant metastases, and discuss some of the expected future developments in the field.

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“Good Old” clinical markers have similar power in breast cancer prognosis as microarray gene expression profilers

Cited by 144 publications

References 8 publications

Combination of the proliferation marker cyclin A, histological grade, and estrogen receptor status in a new variable with high prognostic impact in breast cancer

Combination of the proliferation marker cyclin A, histological grade, and estrogen receptor status in a new variable with high prognostic impact in breast cancer

Interpretation of microarray data in cancer

Breast tumor metastasis: analysis via proteomic profiling

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