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• W2913362606 abstract "Abstract Motivation Several gene expression-based risk scores and subtype classifiers for breast cancer were developed to distinguish high- and low-risk patients. Evaluating the performance of these classifiers helps to decide which classifiers should be used in clinical practice for personal therapeutic recommendations. So far, studies that compared multiple classifiers in large independent patient cohorts mostly used microarray measurements. qPCR-based classifiers were not included in the comparison or had to be adapted to the different experimental platforms. Results We used a prospective study of 726 early breast cancer patients from seven certified German breast cancer centers. Patients were treated according to national guidelines and the expressions of 94 selected genes were measured by the mid-throughput qPCR platform Fluidigm. Clinical and pathological data including outcome over five years is available. Using these data, we could compare the performance of six classifiers (scmgene and research versions of PAM50, ROR-S, recurrence score, EndoPredict and GGI). Similar to other studies, we found a similar or even higher concordance between most of the classifiers and most were also able to differentiate high- and low-risk patients. The classifiers that were originally developed for microarray data still performed similarly using the Fluidigm data. Therefore, Fluidigm can be used to measure the gene expressions needed by several classifiers for a large cohort with little effort. In addition, we provide an interactive report of the results, which enables a transparent, in-depth comparison of classifiers and their prediction of individual patients. Availability and implementation https://services.bio.ifi.lmu.de/pia/. Supplementary information Supplementary data are available at Bioinformatics online." @default.
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• W2913362606 date "2019-02-13" @default.
• W2913362606 modified "2023-09-24" @default.
• W2913362606 title "Comparison of six breast cancer classifiers using qPCR" @default.
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• W2913362606 doi "https://doi.org/10.1093/bioinformatics/btz103" @default.
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