ArticleBMC cancer2015
Predicting response to multidrug regimens in cancer patients using cell line experiments and regularised regression models.
Article in BMC cancer, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.
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Who cites it
33 citing papers in PubMed, 1 synthesis or guideline pooled it.
- American Society of Blood and Marrow Transplantation, European Society of Blood and Marrow Transplantation, Blood and Marrow Transplant Clinical Trials Network, and International Myeloma Working Group Consensus Conference on Salvage Hematopoietic Cell Transplantation in Patients with Relapsed Multiple Myeloma.Biology of blood and marrow transplantation : journal of the American Society for Blood and Marrow Transplantation · 2015Guideline
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- Machine learning-driven exploration of drug therapies for triple-negative breast cancer treatment.Frontiers in molecular biosciences · 2023Article
- Hsp90 inhibition sensitizes DLBCL cells to cisplatin.Cancer chemotherapy and pharmacology · 2022Article
- An overview of machine learning methods for monotherapy drug response prediction.Briefings in bioinformatics · 2022Article
- Big Data to Knowledge: Application of Machine Learning to Predictive Modeling of Therapeutic Response in Cancer.Current genomics · 2021Review
- Presentation of Diffuse Large B-Cell Lymphoma with Shoulder Pain: A Case Report.The American journal of case reports · 2021Article
- A bendamustine resistance gene signature in diffuse large B-cell lymphoma and multiple myeloma.Cancer drug resistance (Alhambra, Calif.) · 2021Article
- Prognostic and predictive value of a mRNA signature in peripheral T-cell lymphomas: A mRNA expression analysis.Journal of cellular and molecular medicine · 2021Article
- An integrated prognosis model of pharmacogenomic gene signature and clinical information for diffuse large B-cell lymphoma patients following CHOP-like chemotherapy.Journal of translational medicine · 2020Article
- Article
- Development of a Precision Medicine Workflow in Hematological Cancers, Aalborg University Hospital, Denmark.Cancers · 2020Article
- Artificial Intelligence to Decode Cancer Mechanism: Beyond Patient Stratification for Precision Oncology.Frontiers in pharmacology · 2020Review
- Tissue-guided LASSO for prediction of clinical drug response using preclinical samples.PLoS computational biology · 2020Article
- Drug screening approach combines epigenetic sensitization with immunochemotherapy in cancer.Clinical epigenetics · 2019Article
- MicroRNA-155 controls vincristine sensitivity and predicts superior clinical outcome in diffuse large B-cell lymphoma.Blood advances · 2019Article
- Expression of NOTCH3 exon 16 differentiates Diffuse Large B-cell Lymphoma into molecular subtypes and is associated with prognosis.Scientific reports · 2019Article
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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