scholarly journals Gene isoforms as expression-based biomarkers predictive of drug response in vitro

2017 ◽  
Author(s):  
Zhaleh Safikhani ◽  
Kelsie L. Thu ◽  
Jennifer Silvester ◽  
Petr Smirnov ◽  
Mathieu Lupien ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Cell ◽  
Large Scale ◽  
Drug Response ◽  
Drug Sensitivity ◽  
Biomarker Discovery ◽  
Kinase Inhibitor ◽  
Tissue Type ◽  
Breast Cancer Cell Lines ◽  
Molecular Features

ABSTRACTBackgroundOne of the main challenges in precision medicine is the identification of molecular features associated to drug response to provide clinicians with tools to select the best therapy for each individual cancer patient. The recent adoption of next-generation sequencing technologies enables accurate profiling of not only gene expression but also alternatively-spliced transcripts in large-scale pharmacogenomic studies. Given that altered mRNA splicing has been shown to be prominent in cancers, linking this feature to drug response will open new avenues of research in biomarker discovery.MethodsTo address the lack of reproducibility of drug sensitivity measurements across studies, we developed a meta-analytical framework combining the pharmacological data generated within the Cancer Cell Line Encyclopedia (CCLE) and the Genomics of Drug Sensitivity in Cancer (GDSC). Predictive models are fitted with CCLE RNA-seq data as predictor variables, controlled for tissue type, and combined GDSC and CCLE drug sensitivity values as dependent variables.ResultsWe first validated the biomarkers identified from GDSC and CCLE using an existing pharmacogenomic dataset of 70 breast cancer cell lines. We further selected four drugs with the most promising biomarkers to test whether their predictive value is robust to change in pharmacological assay. We successfully validated 10 isoform-based biomarkers predictive of drug response in breast cancer, including TGFA-001 for the MEK tyrosine kinase inhibitor (TKI) AZD6244, DUOX-001 for the EGFR inhibitor erlotinib, and CPEB4-001 transcript expression associated with lack of sensitivity to paclitaxel.ConclusionThe results of our meta-analysis of pharmacogenomic data suggest that isoforms represent a rich resource for biomarkers predictive of response to chemo- and targeted therapies. Our study also showed that the validation rate for this type of biomarkers is low (<50%) for most drugs, supporting the requirements for independent datasets to identify reproducible predictors of response to anticancer drugs.

scholarly journals Silencing cyclin-dependent kinase inhibitor 3 inhibits the migration of breast cancer cell lines

2016 ◽  
Vol 14 (2) ◽  
pp. 1523-1530 ◽  
Author(s):  
Miao Deng ◽  
Jianguang Wang ◽  
Yanbin Chen ◽  
Like Zhang ◽  
Gangqiang Xie ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cell Lines ◽  
Cancer Cell ◽  
Breast Cancer Cell ◽  
Kinase Inhibitor ◽  
Cancer Cell Lines ◽  
Breast Cancer Cell Lines ◽  
Cyclin Dependent Kinase ◽  
Cyclin Dependent Kinase Inhibitor

scholarly journals Boolean ErbB network reconstructions and perturbation simulations reveal individual drug response in different breast cancer cell lines

2014 ◽  
Vol 8 (1) ◽  
pp. 75 ◽  
Author(s):  
Silvia der Heyde ◽  
Christian Bender ◽  
Frauke Henjes ◽  
Johanna Sonntag ◽  
Ulrike Korf ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cell Lines ◽  
Cancer Cell ◽  
Breast Cancer Cell ◽  
Drug Response ◽  
Cancer Cell Lines ◽  
Breast Cancer Cell Lines ◽  
Individual Drug ◽  
Individual Drug Response

Clinical drug response prediction from preclinical cancer cell lines by logistic matrix factorization approach

2021 ◽  
Author(s):  
Akram Emdadi ◽  
Changiz Eslahchi
Keyword(s):  
Cell Line ◽  
Cell Lines ◽  
Cancer Cell ◽  
Matrix Factorization ◽  
Drug Response ◽  
Drug Sensitivity ◽  
Cancer Cell Lines ◽  
Tissue Type ◽  
Factorization Approach ◽  
Clinical Drug

Predicting tumor drug response using cancer cell line drug response values for a large number of anti-cancer drugs is a significant challenge in personalized medicine. Predicting patient response to drugs from data obtained from preclinical models is made easier by the availability of different knowledge on cell lines and drugs. This paper proposes the TCLMF method, a predictive model for predicting drug response in tumor samples that was trained on preclinical samples and is based on the logistic matrix factorization approach. The TCLMF model is designed based on gene expression profiles, tissue type information, the chemical structure of drugs and drug sensitivity (IC 50) data from cancer cell lines. We use preclinical data from the Genomics of Drug Sensitivity in Cancer dataset (GDSC) to train the proposed drug response model, which we then use to predict drug sensitivity of samples from the Cancer Genome Atlas (TCGA) dataset. The TCLMF approach focuses on identifying successful features of cell lines and drugs in order to calculate the probability of the tumor samples being sensitive to drugs. The closest cell line neighbours for each tumor sample are calculated using a description of similarity between tumor samples and cell lines in this study. The drug response for a new tumor is then calculated by averaging the low-rank features obtained from its neighboring cell lines. We compare the results of the TCLMF model with the results of the previously proposed methods using two databases and two approaches to test the model’s performance. In the first approach, 12 drugs with enough known clinical drug response, considered in previous methods, are studied. For 7 drugs out of 12, the TCLMF can significantly distinguish between patients that are resistance to these drugs and the patients that are sensitive to them. These approaches are converted to classification models using a threshold in the second approach, and the results are compared. The results demonstrate that the TCLMF method provides accurate predictions across the results of the other algorithms. Finally, we accurately classify tumor tissue type using the latent vectors obtained from TCLMF’s logistic matrix factorization process. These findings demonstrate that the TCLMF approach produces effective latent vectors for tumor samples. The source code of the TCLMF method is available in https://github.com/emdadi/TCLMF.

scholarly journals MicroRNAs as possible indicators of drug sensitivity in breast cancer cell lines

PLoS ONE ◽  
2019 ◽  
Vol 14 (5) ◽  
pp. e0216400 ◽  
Author(s):  
Katharina Uhr ◽  
Wendy J. C. Prager-van der Smissen ◽  
Anouk A. J. Heine ◽  
Bahar Ozturk ◽  
Marijn T. M. van Jaarsveld ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cell Lines ◽  
Cancer Cell ◽  
Breast Cancer Cell ◽  
Drug Sensitivity ◽  
Cancer Cell Lines ◽  
Breast Cancer Cell Lines
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