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

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Abstract

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 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.

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Acknowledgements

BG was supported by the Alexander von Humboldt Stiftung.

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Authors and Affiliations

  1. Laboratory of Functional Genomics, Charité, Charitéplatz 1, 10117, Berlin, Germany

    Balázs Győrffy & Reinhold Schäfer

  2. Research Laboratory for Pediatrics and Nephrology, Hungarian Academy of Sciences, Budapest, Hungary

    Balázs Győrffy, Zsombor Benke, András Lánczky & Bálint Balázs

  3. Pázmány Péter University, Faculty of Informatic Technology, Budapest, Hungary

    Zsombor Benke, András Lánczky & Bálint Balázs

  4. Children’s Hospital Informatics Program, Harvard Medical School, Boston, USA

    Zoltán Szállási

  5. 2nd Department of Pathology, Semmelweis University, Budapest, Hungary

    József Timár

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  1. Balázs Győrffy
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  2. Zsombor Benke
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  3. András Lánczky
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  4. Bálint Balázs
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  6. József Timár
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  7. Reinhold Schäfer
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Correspondence to Balázs Győrffy.

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Győrffy, B., Benke, Z., Lánczky, A. et al. RecurrenceOnline: an online analysis tool to determine breast cancer recurrence and hormone receptor status using microarray data. Breast Cancer Res Treat 132, 1025–1034 (2012). https://doi.org/10.1007/s10549-011-1676-y

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  • DOI: https://doi.org/10.1007/s10549-011-1676-y

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