Dataset Information

Predicting Anticancer Drug Responses Using a Dual-Layer Integrated Cell Line-Drug Network Model.

ABSTRACT: The ability to predict the response of a cancer patient to a therapeutic agent is a major goal in modern oncology that should ultimately lead to personalized treatment. Existing approaches to predicting drug sensitivity rely primarily on profiling of cancer cell line panels that have been treated with different drugs and selecting genomic or functional genomic features to regress or classify the drug response. Here, we propose a dual-layer integrated cell line-drug network model, which uses both cell line similarity network (CSN) data and drug similarity network (DSN) data to predict the drug response of a given cell line using a weighted model. Using the Cancer Cell Line Encyclopedia (CCLE) and Cancer Genome Project (CGP) studies as benchmark datasets, our single-layer model with CSN or DSN and only a single parameter achieved a prediction performance comparable to the previously generated elastic net model. When using the dual-layer model integrating both CSN and DSN, our predicted response reached a 0.6 Pearson correlation coefficient with observed responses for most drugs, which is significantly better than the previous results using the elastic net model. We have also applied the dual-layer cell line-drug integrated network model to fill in the missing drug response values in the CGP dataset. Even though the dual-layer integrated cell line-drug network model does not specifically model mutation information, it correctly predicted that BRAF mutant cell lines would be more sensitive than BRAF wild-type cell lines to three MEK1/2 inhibitors tested.

SUBMITTER: Zhang N

PROVIDER: S-EPMC4587957 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Publications

Predicting Anticancer Drug Responses Using a Dual-Layer Integrated Cell Line-Drug Network Model.

Zhang Naiqian N Wang Haiyun H Fang Yun Y Wang Jun J Zheng Xiaoqi X Liu X Shirley XS

PLoS computational biology 20150929 9

The ability to predict the response of a cancer patient to a therapeutic agent is a major goal in modern oncology that should ultimately lead to personalized treatment. Existing approaches to predicting drug sensitivity rely primarily on profiling of cancer cell line panels that have been treated with different drugs and selecting genomic or functional genomic features to regress or classify the drug response. Here, we propose a dual-layer integrated cell line-drug network model, which uses both ...[more]

PMID: 26418249

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Project description:BackgroundPredicting patient drug response based on a patient's molecular profile is one of the key goals of precision medicine in breast cancer (BC). Multiple drug response prediction models have been developed to address this problem. However, most of them were developed to make sensitivity predictions for multiple single drugs within cell lines from various cancer types instead of a single cancer type, do not take into account drug properties, and have not been validated in cancer patient-derived data. Among the multi-omics data, gene expression profiles have been shown to be the most informative data for drug response prediction. However, these models were often developed with individual genes. Therefore, this study aimed to develop a drug response prediction model for BC using multiple data types from both cell lines and drugs.MethodsWe first collected the baseline gene expression profiles of 49 BC cell lines along with IC50 values for 220 drugs tested in these cell lines from Genomics of Drug Sensitivity in Cancer (GDSC). Using these data, we developed a multiple-layer cell line-drug response network (ML-CDN2) by integrating a one-layer cell line similarity network based on the pathway activity profiles and a three-layer drug similarity network based on the drug structures, targets, and pan-cancer IC50 profiles. We further used ML-CDN2 to predict the drug response for new BC cell lines or patient-derived samples.ResultsML-CDN2 demonstrated a good predictive performance, with the Pearson correlation coefficient between the observed and predicted IC50 values for all GDSC cell line-drug pairs of 0.873. Also, ML-CDN2 showed a good performance when used to predict drug response in new BC cell lines from the Cancer Cell Line Encyclopedia (CCLE), with a Pearson correlation coefficient of 0.718. Moreover, we found that the cell line-derived ML-CDN2 model could be applied to predict drug response in the BC patient-derived samples from The Cancer Genome Atlas (TCGA).ConclusionsThe ML-CDN2 model was built to predict BC drug response using comprehensive information from both cell lines and drugs. Compared with existing methods, it has the potential to predict the drug response for BC patient-derived samples.

Project description:The use of anticancer drugs in chemotherapy is increasing, leading to growing environmental concentrations of imatinib mesylate (IMA), cisplatinum (CDDP), and etoposide (ETP) in aquatic systems. Previous studies have shown that these anticancer drugs cause DNA damage in the crustacean Daphnia magna at low, environmentally relevant concentrations. To explore the mechanism of action of these compounds and the downstream effects of DNA damage on D. magna growth and development at a sensitive life stage, we exposed neonates to low level concentrations equivalent to those that elicit DNA damage (IMA: 2000 ng/L, ETP: 300 ng/L, CDDP: 10 ng/L) and performed transcriptomic analysis using an RNA-seq approach. RNA sequencing generated 14 million reads per sample, which were aligned to the D. magna genome and assembled, producing approximately 23,000 transcripts per sample. Over 90% of the transcripts showed homology to proteins in GenBank, revealing a high quality transcriptome assembly, although functional annotation was much lower. RT-qPCR was used to identify robust biomarkers and confirmed the downregulation of angiotensin converting enzyme (ACE) involved in neuropeptide regulation across all three anticancer drugs and the down-regulation of DNA topoisomerase II by ETP. RNA-seq analysis also allowed for an in depth exploration of the differential splicing of transcripts revealing that regulation of different gene isoforms predicts potential impacts on translation and protein expression, providing a more meaningful assessment of transcriptomic data. Enrichment analysis and investigation of affected biological processes suggested that the DNA damage caused by ETP and IMA influences cell cycle regulation and GPCR signaling. This dysregulation is likely responsible for effects to neurological system processes and development, and overall growth and development. Our transcriptomic approach provided insight into the mechanisms that respond to DNA damage caused by anticancer drug exposure and generated novel hypotheses on how these chemicals may impact the growth and survival of this ecologically important zooplankton species.

Dataset Information

Predicting Anticancer Drug Responses Using a Dual-Layer Integrated Cell Line-Drug Network Model.

Publications

Predicting Anticancer Drug Responses Using a Dual-Layer Integrated Cell Line-Drug Network Model.

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