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

BMC Cancer

Steffen Falgreen¹  Karen Dybkær²  Ken H Young⁴  Zijun Y Xu-Monette⁴  Tarec C El-Galaly³  Maria Bach Laursen¹  Julie S Bødker¹  Malene K Kjeldsen¹  Alexander Schmitz¹  Mette Nyegaard⁵  Hans Erik Johnsen²  Martin Bøgsted² 

[1] Department of Haematology, Research Section, Aalborg University Hospital, Sdr. Skovvej 15, Aalborg, 9000, Denmark

[2] Clinical Cancer Research Center, Aalborg University Hospital, Aalborg, Denmark

[3] Department of Clinical Medicine, Aalborg University, Aalborg, Denmark

[4] Department of Hematopathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

[5] Department of Biomedicine, Aarhus University, Aarhus, Denmark

关键词: Cancer;    Gene expression profiling;    Preclinical model;    Drug resistance;    Drug screen;   

Others  :  1171828
DOI  :  10.1186/s12885-015-1237-6

 received in 2014-09-25, accepted in 2015-03-20,  发布年份 2015

【 摘 要 】

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.

【 授权许可】

   
2015 Falgreen et al.; licensee BioMed Central.

【 图 表 】

Figure 1.

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Figure 3.

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