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BMC Cancer
Published in: BMC Cancer 1/2015

Open Access 01-12-2015 | Research article

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

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

Published in: BMC Cancer | Issue 1/2015

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Abstract

Background

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.

Methods

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.

Results

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.

Conclusions

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.
Appendix
Available only for authorised users
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Metadata
Title
Predicting response to multidrug regimens in cancer patients using cell line experiments and regularised regression models
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
Publication date
01-12-2015
Publisher
BioMed Central
Published in
BMC Cancer / Issue 1/2015
Electronic ISSN: 1471-2407
DOI
https://doi.org/10.1186/s12885-015-1237-6

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