Literature DB >> 15288284

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

Patrik Edén1, Cecilia Ritz, Carsten Rose, Mårten Fernö, Carsten Peterson.   

Abstract

We compared the power of gene expression measurements with that of conventional prognostic markers, i.e., clinical, histopathological, and cell biological parameters, for predicting distant metastases in breast cancer patients using both established prognostic indices (e.g., the Nottingham Prognostic Index (NPI)) and novel combinations of conventional markers. We used publicly available data on 97 patients, and the performance of metastasis prediction was represented by receiver operating characteristic (ROC) areas and Kaplan-Meier plots. The gene expression profiler did not perform noticeably better than indices constructed from the clinical variables, e.g., the well established NPI. When analysing separately subgroups, according to the oestrogen receptor (ER) status both approaches could predict clinical outcome more easily for the ER-positive than for the ER-negative cohort. Given the time it may take before microarray processing is used worldwide, particularly due to the costs and the lack of standards, it is important to pursue research using conventional markers. Our analysis suggests that it might be possible to improve the combination of different conventional prognostic markers into one prognostic index.

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Year:  2004        PMID: 15288284     DOI: 10.1016/j.ejca.2004.02.025

Source DB:  PubMed          Journal:  Eur J Cancer        ISSN: 0959-8049            Impact factor:   9.162


  37 in total

Review 1.  Molecular basis for therapy resistance.

Authors:  Per E Lønning
Journal:  Mol Oncol       Date:  2010-04-24       Impact factor: 6.603

Review 2.  Genomic platforms for cancer research: potential diagnostic and prognostic applications in clinical oncology.

Authors:  Pedro Jares; Elías Campo
Journal:  Clin Transl Oncol       Date:  2006-03       Impact factor: 3.405

3.  Improved breast cancer prognosis through the combination of clinical and genetic markers.

Authors:  Yijun Sun; Steve Goodison; Jian Li; Li Liu; William Farmerie
Journal:  Bioinformatics       Date:  2006-11-26       Impact factor: 6.937

4.  A perspective on DNA microarrays in pathology research and practice.

Authors:  Jonathan R Pollack
Journal:  Am J Pathol       Date:  2007-06-28       Impact factor: 4.307

Review 5.  Molecular profiling in breast cancer.

Authors:  Shannon R Morris; Lisa A Carey
Journal:  Rev Endocr Metab Disord       Date:  2007-09       Impact factor: 6.514

6.  [Prognostic and predictive factors of invasive breast cancer: update 2009].

Authors:  T Decker; D Hungermann; W Böcker
Journal:  Pathologe       Date:  2009-02       Impact factor: 1.011

7.  Breast medical oncologists' use of standard prognostic factors to predict a 21-gene recurrence score.

Authors:  Arif H Kamal; Charles L Loprinzi; Carol Reynolds; Amylou C Dueck; Xochiquetzal J Geiger; James N Ingle; Robert W Carlson; Timothy J Hobday; Eric P Winer; Matthew P Goetz
Journal:  Oncologist       Date:  2011-09-20

8.  Testing the additional predictive value of high-dimensional molecular data.

Authors:  Anne-Laure Boulesteix; Torsten Hothorn
Journal:  BMC Bioinformatics       Date:  2010-02-08       Impact factor: 3.169

Review 9.  Cancer gene discovery in mouse and man.

Authors:  Jenny Mattison; Louise van der Weyden; Tim Hubbard; David J Adams
Journal:  Biochim Biophys Acta       Date:  2009-03-12

10.  Bridging the gap between systems biology and medicine.

Authors:  Gilles Clermont; Charles Auffray; Yves Moreau; David M Rocke; Daniel Dalevi; Devdatt Dubhashi; Dana R Marshall; Peter Raasch; Frank Dehne; Paolo Provero; Jesper Tegner; Bruce J Aronow; Michael A Langston; Mikael Benson
Journal:  Genome Med       Date:  2009-09-29       Impact factor: 11.117

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