Abstract
BackgroundPatients 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.MethodsFirst, 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.ResultsBoth 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.ConclusionsThe 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12885-015-1237-6) contains supplementary material, which is available to authorized users.
Highlights
Patients suffering from cancer are often treated with a range of chemotherapeutic agents, but the treatment efficacy varies greatly between patients
In Falgreen et al [19] we recently published a method for analysing dose response experiments that accounts for well-known issues such as varying cell line growth kinetics and variation in seeding concentration. By combining this approach with a panel of human B-cell cancer cell lines (HBCCL), the specific aims of this study were to 1) ensure that resistance gene signatures (REGS) developed using carefully selected cell lines analysed to the requirements of Falgreen et al [19] can be of similar, or even superior, prognostic value as those developed using a large-scale study, 2) generate REGS classifiers and predictors for resistance towards the potent chemotherapeutic agents in R-CHOP, 3) combine them into REGSs for CHO, and 4) compare the performance of REGS classifiers and predictors in clinical data
For C the Area under the curve above 0 (AUC0) values for the diffuse large B-cell lymphoma (DLBCL) cell lines ranged from 165 to 346 (339, 348) with SU-DHL-5 and DB as the most sensitive and resistant, respectively
Summary
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. Patients suffering from cancer are usually treated with a range of chemotherapeutic agents, but the treatment efficacy varies greatly between patients. Studies generating REGS can either be performed by analysis of clinical data generated in vivo followed by a prognosis based reverse-translational approach, or by analysis of laboratory data generated in vitro followed by a predictive drug screen approach.
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