Summary

About 10-25% of breast cancer patients achieve a pathologically confirmed complete response after neoadjuvant chemotherapy. Tissue samples of pretreatment core biopsies are a valuable resource for translational research aiming towards predictive biomarkers for selecting patients who are likely to benefit from neoadjuvant therapy. The German Breast Group (GBG) and the AGO-B Group (AGO = Working Group Gynecological Oncology) have extensive experience in conducting neoadjuvant clinical trials. Technologies as immunohistochemistry on tissue microarrays and standardized reverse transcription-polymerase chain reaction (RT-PCR) approaches on formalin-fixed paraffin-embedded samples allow high-throughput investigation of protein and mRNA biomarkers. With these approaches, we could demonstrate that molecular tumor subtypes and immunological infiltrates are valuable and independent predictors of therapy response. New biomarkers such as poly(ADPribose) polymerase (PARP) might be useful for the prediction of response to conventional and new targeted therapies. This review summarizes current research projects focusing on biomarker discovery in the neoadjuvant setting.

Gene expression profiles provide important information about the biology of breast tumors and can be used to develop prognostic tests. However, the implementation of quantitative RNA-based testing in routine molecular pathology has not been accomplished, so far. The EndoPredict assay has recently been described as a quantitative RT-PCR-based multigene expression test to identify a subgroup of hormone–receptor-positive tumors that have an excellent prognosis with endocrine therapy only. To transfer this test from bench to bedside, it is essential to evaluate the test–performance in a multicenter setting in different molecular pathology laboratories. In this study, we have evaluated the EndoPredict (EP) assay in seven different molecular pathology laboratories in Germany, Austria, and Switzerland. A set of ten formalin-fixed paraffin-embedded tumors was tested in the different labs, and the variance and accuracy of the EndoPredict assays were determined using predefined reference values. Extraction of a sufficient amount of RNA and generation of a valid EP score was possible for all 70 study samples (100%). The EP scores measured by the individual participants showed an excellent correlation with the reference values, respectively, as reflected by Pearson correlation coefficients ranging from 0.987 to 0.999. The Pearson correlation coefficient of all values compared to the reference value was 0.994. All laboratories determined EP scores for all samples differing not more than 1.0 score units from the pre-defined references. All samples were assigned to the correct EP risk group, resulting in a sensitivity and specificity of 100%, a concordance of 100%, and a kappa of 1.0. Taken together, the EndoPredict test could be successfully implemented in all seven participating laboratories and is feasible for reliable decentralized assessment of gene expression in luminal breast cancer.