RecurrenceOnline: an online analysis tool to determine breast cancer recurrence and hormone receptor status using microarray data

TitleRecurrenceOnline: an online analysis tool to determine breast cancer recurrence and hormone receptor status using microarray data
Publication TypeJournal Article
Year of Publication2012
AuthorsGyorffy B., Benke Z., Lanczky A., Balazs B., Szallasi Z., Timar J., Schafer R.
JournalBreast Cancer Res Treat
Date PublishedApr
ISBN Number1573-7217 (Electronic)0167-6806 (Linking)
Accession Number21773767
KeywordsAlgorithms, Area Under Curve, Breast Neoplasms/*genetics/metabolism/mortality, Computer Simulation, Disease-Free Survival, Female, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Kaplan-Meier Estimate, Models, Genetic, Models, Statistical, Neoplasm Recurrence, Local/*genetics, Oligonucleotide Array Sequence Analysis, Online Systems, Prognosis, Proportional Hazards Models, Receptor, erbB-2/*genetics/metabolism, Receptors, Estrogen/*genetics/metabolism, Risk Factors, ROC Curve, User-Computer Interface

In the last decades, several gene expression-based 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 . 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.