Comparison of prognostic gene expression signatures for breast cancer

BMC Genomics. 2008 Aug 21;9:394. doi: 10.1186/1471-2164-9-394.

Abstract

Background: During the last years, several groups have identified prognostic gene expression signatures with apparently similar performances. However, signatures were never compared on an independent population of untreated breast cancer patients, where risk assessment was computed using the original algorithms and microarray platforms.

Results: We compared three gene expression signatures, the 70-gene, the 76-gene and the Gene expression Grade Index (GGI) signatures, in terms of predicting distant metastasis free survival (DMFS) for the individual patient. To this end, we used the previously published TRANSBIG independent validation series of node-negative untreated primary breast cancer patients. We observed agreement in prediction for 135 of 198 patients (68%) when considering the three signatures. When comparing the signatures two by two, the agreement in prediction was 71% for the 70- and 76-gene signatures, 76% for the 76-gene signature and the GGI, and 88% for the 70-gene signature and the GGI. The three signatures had similar capabilities of predicting DMFS and added significant prognostic information to that provided by the classical parameters.

Conclusion: Despite the difference in development of these signatures and the limited overlap in gene identity, they showed similar prognostic performance, adding to the growing evidence that these prognostic signatures are of clinical relevance.

Publication types

  • Comparative Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Algorithms
  • Breast Neoplasms / genetics*
  • Breast Neoplasms / mortality
  • Breast Neoplasms / therapy
  • Disease-Free Survival
  • Female
  • Gene Expression Profiling
  • Gene Expression*
  • Genomics / statistics & numerical data
  • Humans
  • Middle Aged
  • Oligonucleotide Array Sequence Analysis
  • Prognosis
  • Proportional Hazards Models
  • Risk Factors
  • Sensitivity and Specificity
  • Survival Analysis