Can survival prediction be improved by merging gene expression data sets?
BACKGROUND
High-throughput gene expression profiling technologies generating a wealth of data, are increasingly used for characterization of tumor biopsies for clinical trials. By applying machine learning algorithms to such clinically documented data sets, one hopes to improve tumor diagnosis, prognosis, as well as prediction of treatment response. However, the limited number of patients enrolled in a single trial study limits the power of machine learning approaches due to over-fitting. One could partially overcome this limitation by merging data from different studies. Nevertheless, such data sets differ from each other with regard to technical biases, patient selection criteria and follow-up treatment. It is therefore not clear at all whether the advantage of increased sample size outweighs the disadvantage of higher heterogeneity of merged data sets. Here, we present a systematic study to answer this question specifically for breast cancer data sets. We use survival prediction based on Cox regression as an assay to measure the added value of merged data sets.


RESULTS
Using time-dependent Receiver Operating Characteristic-Area Under the Curve (ROC-AUC) and hazard ratio as performance measures, we see in overall no significant improvement or deterioration of survival prediction with merged data sets as compared to individual data sets. This apparently was due to the fact that a few genes with strong prognostic power were not available on all microarray platforms and thus were not retained in the merged data sets. Surprisingly, we found that the overall best performance was achieved with a single-gene predictor consisting of CYB5D1.


CONCLUSIONS
Merging did not deteriorate performance on average despite (a) The diversity of microarray platforms used. (b) The heterogeneity of patients cohorts. (c) The heterogeneity of breast cancer disease. (d) Substantial variation of time to death or relapse. (e) The reduced number of genes in the merged data sets. Predictors derived from the merged data sets were more robust, consistent and reproducible across microarray platforms. Moreover, merging data sets from different studies helps to better understand the biases of individual studies and can lead to the identification of strong survival factors like CYB5D1 expression.
Swiss Institute for Experimental Cancer Research (ISREC), Swiss Federal Institute of Technology (EPFL), School of Life Sciences, EPFL SV ISREC, Lausanne, Switzerland. Haleh.Yasrebi@epfl.ch
Haleh
H
Yasrebi
Peter
P
Sperisen
Viviane
V
Praz
Philipp
P
Bucher
D019540:Area Under Curve; D001943:Breast Neoplasms / Q000235:genetics* / Q000401:mortality*; D019295:Computational Biology / Q000379:methods*; D016208:Databases, Factual; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis; D025341:Principal Component Analysis; D011379:Prognosis; D016016:Proportional Hazards Models; D012372:ROC Curve; D012306:Risk; D016019:Survival Analysis