#3007 Background
 In breast cancer patients the level of expression of estrogen receptor (ER), progesterone receptor (PR) and HER2 is predictive for prognosis and/or treatment response. However, differences in assessment methods and interpretation can substantially affect the accuracy and reproducibility of the results. Previously, we have determined the association between immunohistochemistry (IHC) and mRNA levels for ER, PR and HER2, and have confirmed the accuracy of microarray readout on >400 samples. In the current study we describe the use of this microarray based readout on prospectively collected samples. We compared these readouts with multiple IHC and fluorescent in situ hybridization (FISH) assessments generated in various hospitals and a CLIA-certified reference laboratory and developed a microarray based test called TargetPrint™.
 Methods
 Gene expression data for ER, PR and HER2 were obtained by analysis of 100 breast carcinomas that have been collected prospectively within the RASTER study. Samples were stratified as receptor positive or negative using thresholds for ER, PR and HER2 mRNA levels. IHC assessment was performed (1) according to local standards of the hospital from where the sample originated, (2) by the central laboratory of the Netherlands Cancer Institute, and (3) at an independent reference laboratory using FDA-approved procedures and ASCO/CAP guidelines. A tumor was classified positive for ER and PR when ≥10% of tumor cells showed positive staining. HER2 IHC status was scored as 0, 1+, 2+ or 3+; a score of 3+ was considered positive. In case of 2+ samples, a FISH was performed to assess final HER2 amplification status. The cohort used in this study was pre-selected to include about two-third ER and PR positive samples and one-third HER2 positive samples.
 Results
 Multiple microarray readouts were highly reproducible (Pearson correlation 0.991) and resulted in 67, 61 and 39 percent positive samples for ER, PR and HER2, respectively. Comparison of microarray results with IHC (including FISH for HER2) performed at the three centers indicated highly similar results for receptor readout with a concordance of 92, 93 and 92% for ER; 84, 81 and 86% for PR; and 93, 95 and 94% for HER2. Overall misclassification rates between microarray and IHC readout were low for ER (0.08) and HER2 (0.06) and quite low for PR (0.14), and were comparable to the misclassification rates between the three IHC methods.
 Conclusion
 A microarray-based assessment of ER, PR and HER2 in relation to mRNA levels gives results comparable to multiple IHC methods and FISH and provides an objective and more quantitative assessment of tumor receptor status than IHC alone. Using TargetPrint™ for microarray readouts for hormone and HER2 receptor in addition to standard IHC will improve molecular characterization of breast cancer tissue. Citation Information: Cancer Res 2009;69(2 Suppl):Abstract nr 3007.
#2026 To determine the prognosis of breast cancer patients, clinical and pathological factors are currently employed. Gene expression micro-arrays offer new opportunities to determine individual prognosis. Publications have raised concerns about micro-arrays studies who have the potential to preclude their use in clinical routine. To improve the understanding of gene-expression classifiers we addressed the following issues: 1) Is the performance similar between independent classifiers? 2) Is proliferation a common biological theme that represents various signatures? 3) Are there other enriched pathways among signatures with prognostic ability?
 Methods:
 On 6 public datasets we applied the 76-gene signature; the Molecular subtypes; the Chromosomal Instability Signature; the Wound Signature; the Invasiveness Gene Signature; the Molecular Prognosis Index; and the Genomic Grade Index. Survival, predictive accuracy and overlap analyses were performed. We created enlarged signatures by including all probes with significant correlation to at least one of the genes in the original signatures. We gathered a collection of gene sets from four databases (GO, KEGG, Reactome, MSDB). For each signature, we evaluated whether specific gene sets (modules) are overrepresented. We tested the prognosis ability of each of them.
 Results:
 The survival and predictive accuracy analyses gave similar results for each of the 9 signatures. They all added significant information to a multivariate model including standard pathological and clinical criteria. Nevertheless, we showed that none of these signatures were able to identify good and poor prognosis patients when applied to samples with intrinsically poor prognosis features (Positive Lymph Node, Negative Estrogen Receptor, High Grade). Conversely they identified good and poor prognosis patients when applied to samples with intrinsically good prognosis features (Negative LN, Positive ER Low Grade). The overlap analysis showed a low agreement between the signatures. 50% of the samples had almost one discordant classification result out of the 9 classifiers tested. The intersection of the signatures revealed a set of proliferation genes. The signatures were build on 10 different gene ontology modules with prognostic ability.
 Conclusion:
 This study underlines the need of large prospective validation studies of gene expression signatures. Further computational intelligence and system biology studies would be held to determine the best way to use these classifiers in clinical routine. Citation Information: Cancer Res 2009;69(2 Suppl):Abstract nr 2026.
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