An online survival analysis tool to rapidly assess the effect of 22,277 genes on breast cancer prognosis using microarray data ... View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2009-12-18

AUTHORS

Balazs Györffy, Andras Lanczky, Aron C. Eklund, Carsten Denkert, Jan Budczies, Qiyuan Li, Zoltan Szallasi

ABSTRACT

Validating prognostic or predictive candidate genes in appropriately powered breast cancer cohorts are of utmost interest. Our aim was to develop an online tool to draw survival plots, which can be used to assess the relevance of the expression levels of various genes on the clinical outcome both in untreated and treated breast cancer patients. A background database was established using gene expression data and survival information of 1,809 patients downloaded from GEO (Affymetrix HGU133A and HGU133+2 microarrays). The median relapse free survival is 6.43 years, 968/1,231 patients are estrogen-receptor (ER) positive, and 190/1,369 are lymph-node positive. After quality control and normalization only probes present on both Affymetrix platforms were retained (n = 22,277). In order to analyze the prognostic value of a particular gene, the cohorts are divided into two groups according to the median (or upper/lower quartile) expression of the gene. The two groups can be compared in terms of relapse free survival, overall survival, and distant metastasis free survival. A survival curve is displayed, and the hazard ratio with 95% confidence intervals and logrank P value are calculated and displayed. Additionally, three subgroups of patients can be assessed: systematically untreated patients, endocrine-treated ER positive patients, and patients with a distribution of clinical characteristics representative of those seen in general clinical practice in the US. Web address: www.kmplot.com. We used this integrative data analysis tool to confirm the prognostic power of the proliferation-related genes TOP2A and TOP2B, MKI67, CCND2, CCND3, CCNDE2, as well as CDKN1A, and TK2. We also validated the capability of microarrays to determine estrogen receptor status in 1,231 patients. The tool is highly valuable for the preliminary assessment of biomarkers, especially for research groups with limited bioinformatic resources. More... »

PAGES

725-731

References to SciGraph publications

  • 2008-12-05. Meta-analysis of gene expression profiles related to relapse-free survival in 1,079 breast cancer patients in BREAST CANCER RESEARCH AND TREATMENT
  • 2008-09-21. The removal of multiplicative, systematic bias allows integration of breast cancer gene expression datasets – improving meta-analysis and prediction of prognosis in BMC MEDICAL GENOMICS
  • 2008-05-22. Predicting prognosis using molecular profiling in estrogen receptor-positive breast cancer treated with tamoxifen in BMC GENOMICS
  • 2009-07-02. The Gene expression Grade Index: a potential predictor of relapse for endocrine-treated breast cancer patients in the BIG 1–98 trial in BMC MEDICAL GENOMICS
  • 2006-09-01. The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements in NATURE BIOTECHNOLOGY
  • 2009-05-06. Genes that mediate breast cancer metastasis to the brain in NATURE
  • 2001-06. Estrogen receptor analysis in primary breast tumors by ligand-binding assay, immunocytochemical assay, and northern blot: a comparison in BREAST CANCER RESEARCH AND TREATMENT
  • 2008-09-23. The 76-gene signature defines high-risk patients that benefit from adjuvant tamoxifen therapy in BREAST CANCER RESEARCH AND TREATMENT
  • 2007-01-19. Hormone receptor status, tumor characteristics, and prognosis: a prospective cohort of breast cancer patients in BREAST CANCER RESEARCH
  • 2005-10-03. Gene expression profiling spares early breast cancer patients from adjuvant therapy: derived and validated in two population-based cohorts in BREAST CANCER RESEARCH
  • Journal

    TITLE

    Breast Cancer Research and Treatment

    ISSUE

    3

    VOLUME

    123

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s10549-009-0674-9

    DOI

    http://dx.doi.org/10.1007/s10549-009-0674-9

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1019543499

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/20020197


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    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s10549-009-0674-9'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s10549-009-0674-9'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s10549-009-0674-9'


     

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