In the last decades, several gene expressionbased predictors of clinical behavior were developed for breast cancer. A common feature of these is the use of multiple genes to predict hormone receptor status and the probability of tumor recurrence, survival or response to chemotherapy. We developed an online analysis tool to compute ER and HER2 status, Oncotype DX 21-gene recurrence score and an independent recurrence risk classification using gene expression data obtained by interrogation of Affymetrix microarray profiles. We implemented rigorous quality control algorithms to promptly exclude any biases related to sample processing, hybridization and scanning. After uploading the raw microarray data, the system performs the complete evaluation automatically and provides a report summarizing the results. The system is accessible online at http://www.recurrenceonline.com. We validated the system using data from 2,472 publicly available microarrays. The validation of the prediction of the 21-gene recurrence score was significant in lymph node negative patients (Cox-Mantel, P = 5.6E-16, HR = 0.4, CI = 0.32-0.5). A correct classification was obtained for 88.5% of ER-and 90.5% of ER ? tumors (n = 1,894). The prediction of recurrence risk in all patients by using the mean of the independent six strongest genes (P \ 1E-16, HR = 2.9, CI = 2.5-3.3), of the four strongest genes in lymph node negative ER positive patients (P \ 1E-16, HR = 2.8, CI = 2.2-3.5) and of the three genes in lymph node positive patients (P = 3.2E-9, HR = 2.5, CI = 1.8-3.4) was highly significant. In summary, we integrated available knowledge in one platform to validate currently used predictors and to provide a global tool for the online determination of different prognostic parameters simultaneously using genome-wide microarrays.
Background: RecurrenceOnline.com uses Affymetrix microarrays to compute the recurrence score and to measure estrogen receptor and HER2-receptor status for breast cancer patients. Our aim was to assess the efficacy of measuring HER2 status as well as the accuracy of survival prediction using an independent set of breast cancer gene expression profiles. Methods: In GEO, we mined new datasets published after the release of Recurrence Online containing detailed clinical characteristics of the profiled cancer patients. The raw microarray data were downloaded and each array was analyzed by running the algorithm available at www.recurrenceonline.com. Then, the subgroups were compared using Kaplan-Meier analysis. For HER2 status determination, the immunohistochemistry (IHC)-based HER2 results were correlated to the microarray results. The cut-off value for HER2 mRNA positivity was set to 4,800. The accuracy was assessed using chi-square tests. Results: Altogether 1,638 new breast cancer samples were analyzed in 9 GEO datasets (these include GSE16446, GSE17907, GSE19615, GSE25066, GSE20711, GSE26971, GSE31448, GSE31519, and GSE20685). The average relapse-free survival among these patients was 4.51 years, 68% of patients were ER positive and 672 were lymph node positive. RecurrenceOnline was capable to classify all patients into good and bad prognosis groups with high accuracy in all patients (p = 2.04E−09), and with p = 1.6E−03 after excluding lymph node positive samples. HER2 IHC data were published for 1,177 patients, with 12% of the patients being positive. HER2 status was correctly assessed for 93% (p < 7E−20) of the patients. Discussion. We verified the classification accuracy of RecurrenceOnline using independent datasets. Our results further support the feasibility of using genome-wide microarrays for patient classification. Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr P2-10-24.
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