Your browser has javascript turned off or blocked. This will lead to some parts of our website to not work properly or at all. Turn on javascript for best performance.

The browser you are using is not supported by this website. All versions of Internet Explorer are no longer supported, either by us or Microsoft (read more here: https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Please use a modern browser to fully experience our website, such as the newest versions of Edge, Chrome, Firefox or Safari etc.

Picture of Patrik Edén

Patrik Edén

Senior Lecturer

Picture of Patrik Edén

Expression profiling to predict outcome in breast cancer: the influence of sample selection

Author

  • Sofia Gruvberger
  • Markus Ringnér
  • Patrik Edén
  • Åke Borg
  • Mårten Fernö
  • Carsten Peterson
  • Paul S Meltzer

Summary, in English

Gene expression profiling of tumors using DNA microarrays is a promising method for predicting prognosis and treatment response in cancer patients. It was recently reported that expression profiles of sporadic breast cancers could be used to predict disease recurrence better than currently available clinical and histopathological prognostic factors. Having observed an overlap in those data between the genes that predict outcome and those that predict estrogen receptor- status, we examined their predictive power in an independent data set. We conclude that it may be important to define prognostic expression profiles separately for estrogen receptor--positive and estrogen receptor--negative tumors.

Department/s

  • Breastcancer-genetics
  • Computational Biology and Biological Physics

Publishing year

2003

Language

English

Pages

23-26

Publication/Series

Breast Cancer Research

Volume

5

Issue

1

Document type

Journal article

Publisher

BioMed Central (BMC)

Topic

  • Cancer and Oncology

Status

Published

ISBN/ISSN/Other

  • ISSN: 1465-5411