We are analyzing https://link.springer.com/article/10.1007/s10549-009-0674-9.

Title:
An online survival analysis tool to rapidly assess the effect of 22,277 genes on breast cancer prognosis using microarray data of 1,809 patients | Breast Cancer Research and Treatment
Description:
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.
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Keywords {🔍}

breast, cancer, article, google, scholar, pubmed, cas, gene, expression, patients, survival, prognostic, res, analysis, data, research, prognosis, clinical, oncol, genes, microarray, estrogen, clin, status, profiling, privacy, cookies, content, tool, györffy, receptor, access, information, publish, search, lanczky, eklund, zoltan, szallasi, metastasis, markers, adjuvant, early, nodenegative, profiles, treat, bmc, molecular, desmedt, grade,

Topics {✒️}

formalin-fixed paraffin-embedded tissue month download article/chapter gene-expression profiling study gene-expression pro-files node-negative breast cancer gene expression profiling proliferation-related genes top2a breast cancer prognosis early breast cancer survival analysis study relapse free survival relapse-free survival gene expression measurements breast cancer patients gene expression data 76-gene prognostic signature lymph-node positive estrogen receptor analysis primary breast tumors full article pdf privacy choices/manage cookies biological sequence analysis kiaa0101 gene revealed spitznagel el jr draw survival plots estrogen-positive tumors predict distant metastasis author information authors tumor markers population-based validation estrogen receptor discordance article györffy meta-analysis gene expression oestrogen-receptor status hormone receptor status key prognostic impact balazs györffy breast cancer related subjects prognostic model adjuvant population-based cohorts molecular profiling general clinical practice european economic area upper/lower quartile limited bioinformatic resources scarff-bloom-richardson project shows inter fine-needle aspirations

Questions {❓}

Colozza M, Azambuja E, Cardoso F et al (2005) Proliferative markers as prognostic and predictive tools in early breast cancer: where are we now?

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         description: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.
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