scholarly journals Abstract 214: Genetic and transcriptional instability alters cell line drug response

2018 ◽  
Author(s):  
Uri Ben-David ◽  
Ben Siranosian ◽  
Gavin Ha ◽  
Helen Tang ◽  
Nicholas J. Lyons ◽  
...  
Keyword(s):  
Cell Line ◽  
Drug Response ◽  
Line Drug

Abstract 828: gDR suite: an integrative solution for handling cell line drug response data

2020 ◽  
Author(s):  
Marc Hafner ◽  
Arkadiusz Gladki ◽  
Jane Li ◽  
Eva Lin ◽  
Aaron Lun ◽  
...  
Keyword(s):  
Cell Line ◽  
Drug Response ◽  
Response Data ◽  
Line Drug

scholarly journals Predicting breast cancer drug response using a multiple-layer cell line drug response network model

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Shujun Huang ◽  
Pingzhao Hu ◽  
Ted M. Lakowski
Keyword(s):  
Cell Line ◽  
Cell Lines ◽  
Drug Response ◽  
Expression Profiles ◽  
Pearson Correlation ◽  
Gene Expression Profiles ◽  
Response Prediction ◽  
Layer Cell ◽  
Response Network ◽  
Line Drug

Abstract Background Predicting 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. Methods We 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. Results ML-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). Conclusions The 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.

scholarly journals Genetic and transcriptional evolution alters cancer cell line drug response

Nature ◽  
2018 ◽  
Vol 560 (7718) ◽  
pp. 325-330 ◽  
Author(s):  
Uri Ben-David ◽  
Benjamin Siranosian ◽  
Gavin Ha ◽  
Helen Tang ◽  
Yaara Oren ◽  
...  
Keyword(s):  
Cell Line ◽  
Cancer Cell ◽  
Drug Response ◽  
Cancer Cell Line ◽  
Line Drug

scholarly journals ADRML: anticancer drug response prediction using manifold learning

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Fatemeh Ahmadi Moughari ◽  
Changiz Eslahchi
Keyword(s):  
Cell Line ◽  
Cell Lines ◽  
Anticancer Drug ◽  
Manifold Learning ◽  
Drug Response ◽  
Drug Information ◽  
Response Prediction ◽  
Drug Mechanism ◽  
Line Drug ◽  
Anticancer Drug Response

AbstractOne of the prominent challenges in precision medicine is to select the most appropriate treatment strategy for each patient based on the personalized information. The availability of massive data about drugs and cell lines facilitates the possibility of proposing efficient computational models for predicting anticancer drug response. In this study, we propose ADRML, a model for Anticancer Drug Response Prediction using Manifold Learning to systematically integrate the cell line information with the drug information to make accurate predictions about drug therapeutic. The proposed model maps the drug response matrix into the lower-rank spaces that lead to obtaining new perspectives about cell lines and drugs. The drug response for a new cell line-drug pair is computed using the low-rank features. The evaluation of ADRML performance on various types of cell lines and drug information, in addition to the comparisons with previously proposed methods, shows that ADRML provides accurate and robust predictions. Further investigations about the association between drug response and pathway activity scores reveal that the predicted drug responses can shed light on the underlying drug mechanism. Also, the case studies suggest that the predictions of ADRML about novel cell line-drug pairs are validated by reliable pieces of evidence from the literature. Consequently, the evaluations verify that ADRML can be used in accurately predicting and imputing the anticancer drug response.

scholarly journals Learning curves for drug response prediction in cancer cell lines

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Alexander Partin ◽  
Thomas Brettin ◽  
Yvonne A. Evrard ◽  
Yitan Zhu ◽  
Hyunseung Yoo ◽  
...  
Keyword(s):  
Cell Line ◽  
Power Law ◽  
Drug Response ◽  
Drug Sensitivity ◽  
Prediction Models ◽  
Prediction Performance ◽  
Learning Curves ◽  
Superior Performance ◽  
Data Scaling ◽  
Line Drug

Abstract Background Motivated by the size and availability of cell line drug sensitivity data, researchers have been developing machine learning (ML) models for predicting drug response to advance cancer treatment. As drug sensitivity studies continue generating drug response data, a common question is whether the generalization performance of existing prediction models can be further improved with more training data. Methods We utilize empirical learning curves for evaluating and comparing the data scaling properties of two neural networks (NNs) and two gradient boosting decision tree (GBDT) models trained on four cell line drug screening datasets. The learning curves are accurately fitted to a power law model, providing a framework for assessing the data scaling behavior of these models. Results The curves demonstrate that no single model dominates in terms of prediction performance across all datasets and training sizes, thus suggesting that the actual shape of these curves depends on the unique pair of an ML model and a dataset. The multi-input NN (mNN), in which gene expressions of cancer cells and molecular drug descriptors are input into separate subnetworks, outperforms a single-input NN (sNN), where the cell and drug features are concatenated for the input layer. In contrast, a GBDT with hyperparameter tuning exhibits superior performance as compared with both NNs at the lower range of training set sizes for two of the tested datasets, whereas the mNN consistently performs better at the higher range of training sizes. Moreover, the trajectory of the curves suggests that increasing the sample size is expected to further improve prediction scores of both NNs. These observations demonstrate the benefit of using learning curves to evaluate prediction models, providing a broader perspective on the overall data scaling characteristics. Conclusions A fitted power law learning curve provides a forward-looking metric for analyzing prediction performance and can serve as a co-design tool to guide experimental biologists and computational scientists in the design of future experiments in prospective research studies.

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