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Predicting response to multidrug regimens in cancer patients using cell line experiments and regularised regression models

Overview of attention for article published in BMC Cancer, April 2015
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Title
Predicting response to multidrug regimens in cancer patients using cell line experiments and regularised regression models
Published in
BMC Cancer, April 2015
DOI 10.1186/s12885-015-1237-6
Pubmed ID
Authors

Steffen Falgreen, Karen Dybkær, Ken H Young, Zijun Y Xu-Monette, Tarec C El-Galaly, Maria Bach Laursen, Julie S Bødker, Malene K Kjeldsen, Alexander Schmitz, Mette Nyegaard, Hans Erik Johnsen, Martin Bøgsted

Abstract

Patients suffering from cancer are often treated with a range of chemotherapeutic agents, but the treatment efficacy varies greatly between patients. Based on recent popularisation of regularised regression models the goal of this study was to establish workflows for pharmacogenomic predictors of response to standard multidrug regimens using baseline gene expression data and origin specific cell lines. The proposed workflows are tested on diffuse large B-cell lymphoma treated with R-CHOP first-line therapy. First, B-cell cancer cell lines were tested successively for resistance towards the chemotherapeutic components of R-CHOP: cyclophosphamide (C), doxorubicin (H), and vincristine (O). Second, baseline gene expression data were obtained for each cell line before treatment. Third, regularised multivariate regression models with cross-validated tuning parameters were used to generate classifier and predictor based resistance gene signatures (REGS) for the combination and individual chemotherapeutic drugs C, H, and O. Fourth, each developed REGS was used to assign resistance levels to individual patients in three clinical cohorts. Both classifier and predictor based REGS, for the combination CHO, were of prognostic value. For patients classified as resistant towards CHO the risk of progression was 2.33 (95% CI: 1.6, 3.3) times greater than for those classified as sensitive. Similarly, an increase in the predicted CHO resistance index of 10 was related to a 22% (9%, 36%) increased risk of progression. Furthermore, the REGS classifier performed significantly better than the REGS predictor. The regularised multivariate regression models provide a flexible workflow for drug resistance studies with promising potential. However, the gene expressions defining the REGSs should be functionally validated and correlated to known biomarkers to improve understanding of molecular mechanisms of drug resistance.

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Mendeley readers

The data shown below were compiled from readership statistics for 98 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Denmark 2 2%
India 1 1%
Hong Kong 1 1%
Unknown 94 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 22 22%
Researcher 16 16%
Student > Master 16 16%
Student > Bachelor 12 12%
Other 9 9%
Other 9 9%
Unknown 14 14%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 18 18%
Agricultural and Biological Sciences 18 18%
Medicine and Dentistry 15 15%
Computer Science 9 9%
Engineering 7 7%
Other 14 14%
Unknown 17 17%