title: Effects of sample size on robustness and prediction accuracy of a prognostic gene signature.
abstract: BACKGROUND
  Few overlap between independently developed gene signatures and poor inter-study applicability of gene signatures are two of major concerns raised in the development of microarray-based prognostic gene signatures. One recent study suggested that thousands of samples are needed to generate a robust prognostic gene signature.
  
  
  RESULTS
  A data set of 1,372 samples was generated by combining eight breast cancer gene expression data sets produced using the same microarray platform and, using the data set, effects of varying samples sizes on a few performances of a prognostic gene signature were investigated. The overlap between independently developed gene signatures was increased linearly with more samples, attaining an average overlap of 16.56% with 600 samples. The concordance between predicted outcomes by different gene signatures also was increased with more samples up to 94.61% with 300 samples. The accuracy of outcome prediction also increased with more samples. Finally, analysis using only Estrogen Receptor-positive (ER+) patients attained higher prediction accuracy than using both patients, suggesting that sub-type specific analysis can lead to the development of better prognostic gene signatures
  
  
  CONCLUSIONS
  Increasing sample sizes generated a gene signature with better stability, better concordance in outcome prediction, and better prediction accuracy. However, the degree of performance improvement by the increased sample size was different between the degree of overlap and the degree of concordance in outcome prediction, suggesting that the sample size required for a study should be determined according to the specific aims of the study.
authors.affiliation: Medical Genomics Research Center, KRIBB, Yuseong-Gu, Daejeon, Republic of Korea. kimsy@kribb.re.kr
authors.forename: Seon-Young
authors.initials: SY
authors.lastname: Kim
mesh_terms: D001943:Breast Neoplasms / Q000235:genetics; D019295:Computational Biology / Q000379:methods; D030541:Databases, Genetic; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods*; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D018401:Sample Size