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BMC Cancer. 2015 Apr 8;15:235. doi: 10.1186/s12885-015-1237-6.

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

Author information

1
Department of Haematology, Research Section, Aalborg University Hospital, Sdr. Skovvej 15, 9000, Aalborg, Denmark. sfl@rn.dk.
2
Department of Haematology, Research Section, Aalborg University Hospital, Sdr. Skovvej 15, 9000, Aalborg, Denmark. k.dybkaer@rn.dk.
3
Department of Clinical Medicine, Aalborg University, Aalborg, Denmark. k.dybkaer@rn.dk.
4
Clinical Cancer Research Center, Aalborg University Hospital, Aalborg, Denmark. k.dybkaer@rn.dk.
5
Department of Hematopathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. khyoung@mdanderson.org.
6
Department of Hematopathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. zxu3@mdanderson.org.
7
Department of Haematology, Research Section, Aalborg University Hospital, Sdr. Skovvej 15, 9000, Aalborg, Denmark. tarec.galaly@gmail.com.
8
Department of Clinical Medicine, Aalborg University, Aalborg, Denmark. tarec.galaly@gmail.com.
9
Department of Haematology, Research Section, Aalborg University Hospital, Sdr. Skovvej 15, 9000, Aalborg, Denmark. mabala@rn.dk.
10
Department of Haematology, Research Section, Aalborg University Hospital, Sdr. Skovvej 15, 9000, Aalborg, Denmark. j.boedker@rn.dk.
11
Department of Haematology, Research Section, Aalborg University Hospital, Sdr. Skovvej 15, 9000, Aalborg, Denmark. makrp@rn.dk.
12
Department of Haematology, Research Section, Aalborg University Hospital, Sdr. Skovvej 15, 9000, Aalborg, Denmark. alex.schmitz@rn.dk.
13
Department of Haematology, Research Section, Aalborg University Hospital, Sdr. Skovvej 15, 9000, Aalborg, Denmark. nyegaard@hum-gen.au.dk.
14
Department of Biomedicine, Aarhus University, Aarhus, Denmark. nyegaard@hum-gen.au.dk.
15
Department of Haematology, Research Section, Aalborg University Hospital, Sdr. Skovvej 15, 9000, Aalborg, Denmark. haej@rn.dk.
16
Department of Clinical Medicine, Aalborg University, Aalborg, Denmark. haej@rn.dk.
17
Clinical Cancer Research Center, Aalborg University Hospital, Aalborg, Denmark. haej@rn.dk.
18
Department of Haematology, Research Section, Aalborg University Hospital, Sdr. Skovvej 15, 9000, Aalborg, Denmark. martin.boegsted@rn.dk.
19
Department of Clinical Medicine, Aalborg University, Aalborg, Denmark. martin.boegsted@rn.dk.
20
Clinical Cancer Research Center, Aalborg University Hospital, Aalborg, Denmark. martin.boegsted@rn.dk.

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.

PMID:
25881228
PMCID:
PMC4396063
DOI:
10.1186/s12885-015-1237-6
[Indexed for MEDLINE]
Free PMC Article

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