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 Anticancer Drug Response Prediction in Cell Lines Using Weighted Graph Regularized Matrix Factorization

2019 ◽  
Vol 17 ◽  
pp. 164-174 ◽  
Author(s):  
Na-Na Guan ◽  
Yan Zhao ◽  
Chun-Chun Wang ◽  
Jian-Qiang Li ◽  
Xing Chen ◽  
...  
Keyword(s):  
Cell Lines ◽  
Anticancer Drug ◽  
Matrix Factorization ◽  
Drug Response ◽  
Weighted Graph ◽  
Response Prediction ◽  
Anticancer Drug Response

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 Author Correction: ADRML: anticancer drug response prediction using manifold learning

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

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

scholarly journals Genomic data integration by WON-PARAFAC identifies interpretable factors for predicting drug-sensitivity in vivo

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Yongsoo Kim ◽  
Tycho Bismeijer ◽  
Wilbert Zwart ◽  
Lodewyk F. A. Wessels ◽  
Daniel J. Vis
Keyword(s):  
Data Integration ◽  
Cell Line ◽  
Cell Lines ◽  
Cancer Biology ◽  
Drug Response ◽  
Genomic Data ◽  
Response Prediction ◽  
Molecular Data ◽  
Cancer Type

Abstract Integrative analyses that summarize and link molecular data to treatment sensitivity are crucial to capture the biological complexity which is essential to further precision medicine. We introduce Weighted Orthogonal Nonnegative parallel factor analysis (WON-PARAFAC), a data integration method that identifies sparse and interpretable factors. WON-PARAFAC summarizes the GDSC1000 cell line compendium in 130 factors. We interpret the factors based on their association with recurrent molecular alterations, pathway enrichment, cancer type, and drug-response. Crucially, the cell line derived factors capture the majority of the relevant biological variation in Patient-Derived Xenograft (PDX) models, strongly suggesting our factors capture invariant and generalizable aspects of cancer biology. Furthermore, drug response in cell lines is better and more consistently translated to PDXs using factor-based predictors as compared to raw feature-based predictors. WON-PARAFAC efficiently summarizes and integrates multiway high-dimensional genomic data and enhances translatability of drug response prediction from cell lines to patient-derived xenografts.

scholarly journals Application of network diffusion approaches to drug screenings: A perspective on multilayered networks derived from cell lines and drugs

2017 ◽  
Author(s):  
Vigneshwari Subramanian ◽  
Bence Szalai ◽  
Luis Tobalina ◽  
Julio Saez-Rodriguez
Keyword(s):  
Cell Line ◽  
Cell Lines ◽  
Protein Interactions ◽  
Drug Response ◽  
Prediction Models ◽  
Response Prediction ◽  
Single Type ◽  
Disease Genes ◽  
Cell Line Cell ◽  
Network Diffusion

Network diffusion approaches are frequently used for identifying the relevant disease genes and for prioritizing the genes for drug sensitivity predictions. Majority of these studies rely on networks representing a single type of information. However, using multiplex heterogeneous networks (networks with multiple interconnected layers) is much more informative and helps to understand the global topology. We built a multi-layered network that incorporates information on protein-protein interactions, drug-drug similarities, cell line-cell line similarities and co-expressed genes. We applied Random Walk with Restart algorithm to investigate the interactions between drugs, targets and cancer cell lines. Results of ANOVA models show that these prioritized genes are among the most significant ones that relate to drug response. Moreover, the predictive power of the drug response prediction models built using the gene expression data of only the top ranked genes is similar to the models built using all the available genes. Taken together, the results confirm that the multiplex heterogeneous network-based approach is efficient in identifying the most significant genes associated with drug response.

scholarly journals Application of network diffusion approaches to drug screenings: A perspective on multilayered networks derived from cell lines and drugs

2017 ◽  
Author(s):  
Vigneshwari Subramanian ◽  
Bence Szalai ◽  
Luis Tobalina ◽  
Julio Saez-Rodriguez
Keyword(s):  
Cell Line ◽  
Cell Lines ◽  
Protein Interactions ◽  
Drug Response ◽  
Prediction Models ◽  
Response Prediction ◽  
Single Type ◽  
Disease Genes ◽  
Cell Line Cell ◽  
Network Diffusion

Network diffusion approaches are frequently used for identifying the relevant disease genes and for prioritizing the genes for drug sensitivity predictions. Majority of these studies rely on networks representing a single type of information. However, using multiplex heterogeneous networks (networks with multiple interconnected layers) is much more informative and helps to understand the global topology. We built a multi-layered network that incorporates information on protein-protein interactions, drug-drug similarities, cell line-cell line similarities and co-expressed genes. We applied Random Walk with Restart algorithm to investigate the interactions between drugs, targets and cancer cell lines. Results of ANOVA models show that these prioritized genes are among the most significant ones that relate to drug response. Moreover, the predictive power of the drug response prediction models built using the gene expression data of only the top ranked genes is similar to the models built using all the available genes. Taken together, the results confirm that the multiplex heterogeneous network-based approach is efficient in identifying the most significant genes associated with drug response.

Meta-GDBP: a high-level stacked regression model to improve anticancer drug response prediction

2019 ◽  
Vol 21 (3) ◽  
pp. 996-1005 ◽  
Author(s):  
Ran Su ◽  
Xinyi Liu ◽  
Guobao Xiao ◽  
Leyi Wei
Keyword(s):  
Anticancer Drug ◽  
Drug Response ◽  
Chemical Properties ◽  
Cancer Cell Line ◽  
Response Prediction ◽  
Optimization Strategy ◽  
Response Sets ◽  
High Level ◽  
Anticancer Drug Response ◽  
Biological Context

Abstract Anticancer drug response prediction plays an important role in personalized medicine. In particular, precisely predicting drug response in specific cancer types and patients is still a challenge problem. Here we propose Meta-GDBP, a novel anticancer drug-response model, which involves two levels. At the first level of Meta-GDBP, we build four optimized base models (BMs) using genetic information, chemical properties and biological context with an ensemble optimization strategy, while at the second level, we construct a weighted model to integrate the four BMs. Notably, the weights of the models are learned upstream, thus the parameter cost is significantly reduced compared to previous methods. We evaluate the Meta-GDBP on Genomics of Drug Sensitivity in Cancer (GDSC) and the Cancer Cell Line Encyclopedia (CCLE) data sets. Benchmarking results demonstrate that compared to other methods, the Meta-GDBP achieves a much higher correlation between the predicted and the observed responses for almost all the drugs. Moreover, we apply the Meta-GDBP to predict the GDSC-missing drug response and use the CCLE-known data to validate the performance. The results show quite a similar tendency between these two response sets. Particularly, we here for the first time introduce a biological context-based frequency matrix (BCFM) to associate the biological context with the drug response. It is encouraging that the proposed BCFM is biologically meaningful and consistent with the reported biological mechanism, further demonstrating its efficacy for predicting drug response. The R implementation for the proposed Meta-GDBP is available at https://github.com/RanSuLab/Meta-GDBP.

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