Independent and functional validation of a multi-tumour-type proliferation signature

Over the last decade, gene expression microarray technology has had a profound impact on cancer research. The ability to analyse the expression of thousands of genes in a single experiment has been systematically used to derive prognostic and predictive markers for many cancer types (Shedden et al, 2008; Sotiriou and Pusztai, 2009; Gomez-Raposo et al, 2010; Oberthuer et al, 2010). Numerous of these ‘signatures' show good prognostic power, but surprisingly gene-wise overlap between them has been minimal (Ein-Dor et al, 2006; Fan et al, 2006; Chen et al, 2007; Lau et al, 2007), which increases the difficulty of introducing microarrays in clinical practice. Moreover, studies comparing data originating from different microarray platforms have reported poor inter-platform correlations (Kuo et al, 2002; Tan et al, 2003). Nevertheless, multiple studies in breast and non-small-cell lung cancer (NSCLC) have shown that most of these signatures exhibit similar prognostic performance and identify identical patients (Fan et al, 2006; Haibe-Kains et al, 2008). These data suggest that, although gene-wise overlap is small, the signatures track common underlying biology that determine patient outcome. Among others, Weigelt et al (2010) have suggested that proliferation genes drive the prognostic power of these signatures (Whitfield et al, 2006; Desmedt et al, 2008; Haibe-Kains et al, 2008). A large meta-analysis by Wirapati et al (2008) supports this concept. To determine if this result could be clinically useful, we previously developed a signature based on 104 proliferation genes (Starmans et al, 2008). This signature was derived from two in vitro gene expression data sets. Genes were selected that showed a cycling pattern after synchronisation in one data set and responded to serum stimulation in the other. Our proliferation signature exhibited strong prognostic power in several large transcriptome data sets representing different cancer types (Starmans et al, 2008). Further, the proliferation signature and multiple other signatures identified similar patients as having good or poor prognosis (Starmans et al, 2008). These results substantiate the hypothesis that many published signatures act as surrogates of proliferation. The clinical applicability of gene expression signatures remains controversial; studies seem to lack consistency and external validation is not straightforward (Michiels et al, 2005; Ein-Dor et al, 2006; Dupuy and Simon, 2007; Boulesteix and Slawski, 2009; Boutros et al, 2010; Subramanian and Simon, 2010). Many gene expression signatures were developed since the introduction of gene expression microarray technology, however, so far only in breast cancer two prognostic gene profiles are tested in large prospective trials (Bogaerts et al, 2006; Sparano, 2006; Wirapati et al, 2008; Weigelt et al, 2010). The dimensionality of gene expression microarrays makes statistical analysis complex, and large numbers of samples are required for reproducible results (Zien et al, 2003; Ein-Dor et al, 2006). An approach to only evaluate a select number of transcripts may therefore provide an efficient alternative to high-throughput expression profiling. The use of a PCR-based test to evaluate the proliferation signature would assist in the application to a clinical setting (Zhou et al, 2010). Furthermore, a PCR-based technique does not necessitate the availability of fresh-frozen tissue, whereas this is recommended for gene expression microarrays (Tumour Analysis Best Practices Working Group, 2004). Many more samples might thus be available to validate classifiers with a PCR-based technique. Initially, we examined whether it was possible to reduce the number of genes in the proliferation signature, without deteriorating its prognostic value. The original signature consisted of 104 genes, and so reducing this number would make data collection, analysis and transfer to a PCR-based approach simpler and more transparent. To further facilitate translation of this reduced proliferation signature to a PCR-platform a series of in vitro and ex vivo validation experiments were performed. We reduced the proliferation signature to 10 genes and validated it in 1820 breast cancer and 862 NSCLC patients. Lastly, the reduced proliferation signature was applied to another independent, 129-patient breast cancer cohort with qPCR to demonstrate clinical utility.