scholarly journals Iterative sure independent ranking and screening for drug response prediction

2020 ◽  
Vol 20 (S8) ◽  
Author(s):  
Biao An ◽  
Qianwen Zhang ◽  
Yun Fang ◽  
Ming Chen ◽  
Yufang Qin
Keyword(s):  
Drug Response ◽  
Cross Validation ◽  
Marginal Utility ◽  
Cancer Cell Line ◽  
Response Prediction ◽  
Omics Data ◽  
Copy Number Alterations ◽  
Utility Measure ◽  
Personalized Cancer Therapy ◽  
Personalized Cancer

Abstract Background Prediction of drug response based on multi-omics data is a crucial task in the research of personalized cancer therapy. Results We proposed an iterative sure independent ranking and screening (ISIRS) scheme to select drug response-associated features and applied it to the Cancer Cell Line Encyclopedia (CCLE) dataset. For each drug in CCLE, we incorporated multi-omics data including copy number alterations, mutation and gene expression and selected up to 50 features using ISIRS. Then a linear regression model based on the selected features was exploited to predict the drug response. Cross validation test shows that our prediction accuracies are higher than existing methods for most drugs. Conclusions Our study indicates that the features selected by the marginal utility measure, which measures the conditional probability of drug responses given the feature, are helpful for drug response prediction.

Drug response prediction system for personalized cancer therapy

2016 ◽  
Vol 69 ◽  
pp. S81-S82
Author(s):  
R. Kurilov ◽  
D. Juraeva ◽  
D. Weese ◽  
T. Klein ◽  
M. Kapushesky ◽  
...  
Keyword(s):  
Cancer Therapy ◽  
Drug Response ◽  
Response Prediction ◽  
Prediction System ◽  
Personalized Cancer Therapy ◽  
Personalized Cancer

scholarly journals Super.FELT: supervised feature extraction learning using triplet loss for drug response prediction with multi-omics data

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Sejin Park ◽  
Jihee Soh ◽  
Hyunju Lee
Keyword(s):  
Gene Expression ◽  
Cell Line ◽  
Drug Response ◽  
Cross Validation ◽  
External Validation ◽  
Response Prediction ◽  
Omics Data ◽  
Three Stages ◽  
Triplet Loss ◽  
Supervised Feature Extraction

Abstract Background Predicting the drug response of a patient is important for precision oncology. In recent studies, multi-omics data have been used to improve the prediction accuracy of drug response. Although multi-omics data are good resources for drug response prediction, the large dimension of data tends to hinder performance improvement. In this study, we aimed to develop a new method, which can effectively reduce the large dimension of data, based on the supervised deep learning model for predicting drug response. Results We proposed a novel method called Supervised Feature Extraction Learning using Triplet loss (Super.FELT) for drug response prediction. Super.FELT consists of three stages, namely, feature selection, feature encoding using a supervised method, and binary classification of drug response (sensitive or resistant). We used multi-omics data including mutation, copy number aberration, and gene expression, and these were obtained from cell lines [Genomics of Drug Sensitivity in Cancer (GDSC), Cancer Cell Line Encyclopedia (CCLE), and Cancer Therapeutics Response Portal (CTRP)], patient-derived tumor xenografts (PDX), and The Cancer Genome Atlas (TCGA). GDSC was used for training and cross-validation tests, and CCLE, CTRP, PDX, and TCGA were used for external validation. We performed ablation studies for the three stages and verified that the use of multi-omics data guarantees better performance of drug response prediction. Our results verified that Super.FELT outperformed the other methods at external validation on PDX and TCGA and was good at cross-validation on GDSC and external validation on CCLE and CTRP. In addition, through our experiments, we confirmed that using multi-omics data is useful for external non-cell line data. Conclusion By separating the three stages, Super.FELT achieved better performance than the other methods. Through our results, we found that it is important to train encoders and a classifier independently, especially for external test on PDX and TCGA. Moreover, although gene expression is the most powerful data on cell line data, multi-omics promises better performance for external validation on non-cell line data than gene expression data. Source codes of Super.FELT are available at https://github.com/DMCB-GIST/Super.FELT.

scholarly journals Drug Response Prediction of Liver Cancer Cell Line Using Deep Learning

2022 ◽  
Vol 70 (2) ◽  
pp. 2743-2760
Author(s):  
Mehdi Hassan ◽  
Safdar Ali ◽  
Muhammad Sanaullah ◽  
Khuram Shahzad ◽  
Sadaf Mushtaq ◽  
...  
Keyword(s):  
Deep Learning ◽  
Liver Cancer ◽  
Cell Line ◽  
Cancer Cell ◽  
Drug Response ◽  
Cancer Cell Line ◽  
Response Prediction ◽  
Liver Cancer Cell ◽  
Liver Cancer Cell Line

scholarly journals Graph Transformer for drug response prediction

2021 ◽  
Author(s):  
Tuan Thanh Nguyen ◽  
Thang Chu
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Drug Response ◽  
State Of The Art ◽  
Response Prediction ◽  
Graph Representation ◽  
Omics Data ◽  
Information Redundancy ◽  
Fully Connected ◽  
Deep Learning Model

Previous models have shown that learning drug features from their graph representation is more efficient than learning from their strings or numeric representations. Furthermore, integrating multi-omics data of cell lines increases the performance of drug response prediction. However, these models showed drawbacks in extracting drug features from graph representation and incorporating redundancy information from multi-omics data. This paper proposes a deep learning model, GraTransDRP, to better drug representation and reduce information redundancy. First, the Graph transformer was utilized to extract the drug representation more efficiently. Next, Convolutional neural networks were used to learn the mutation, meth, and transcriptomics features. However, the dimension of transcriptomics features is up to 17737. Therefore, KernelPCA was applied to transcriptomics features to reduce the dimension and transform them into a dense presentation before putting them through the CNN model. Finally, drug and omics features were combined to predict a response value by a fully connected network. Experimental results show that our model outperforms some state-of-the-art methods, including GraphDRP, GraOmicDRP.

scholarly journals A survey and systematic assessment of computational methods for drug response prediction

2020 ◽  
Author(s):  
Jinyu Chen ◽  
Louxin Zhang
Keyword(s):  
Drug Response ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Response Prediction ◽  
Future Research ◽  
Omics Data ◽  
Learning Approaches ◽  
Systematic Assessment ◽  
The Past ◽  
Public Datasets

Abstract Drug response prediction arises from both basic and clinical research of personalized therapy, as well as drug discovery for cancers. With gene expression profiles and other omics data being available for over 1000 cancer cell lines and tissues, different machine learning approaches have been applied to drug response prediction. These methods appear in a body of literature and have been evaluated on different datasets with only one or two accuracy metrics. We systematically assess 17 representative methods for drug response prediction, which have been developed in the past 5 years, on four large public datasets in nine metrics. This study provides insights and lessons for future research into drug response prediction.

scholarly journals Personalized cancer therapy prioritization based on driver alteration co-occurrence patterns

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Lidia Mateo ◽  
Miquel Duran-Frigola ◽  
Albert Gris-Oliver ◽  
Marta Palafox ◽  
Maurizio Scaltriti ◽  
...  
Keyword(s):  
Survival Data ◽  
Large Scale ◽  
Drug Response ◽  
Progression Free Survival ◽  
Driver Gene ◽  
Precision Oncology ◽  
Response Predictors ◽  
Personalized Cancer Therapy ◽  
Personalized Cancer ◽  
Occurrence Patterns

Abstract Identification of actionable genomic vulnerabilities is key to precision oncology. Utilizing a large-scale drug screening in patient-derived xenografts, we uncover driver gene alteration connections, derive driver co-occurrence (DCO) networks, and relate these to drug sensitivity. Our collection of 53 drug-response predictors attains an average balanced accuracy of 58% in a cross-validation setting, rising to 66% for a subset of high-confidence predictions. We experimentally validated 12 out of 14 predictions in mice and adapted our strategy to obtain drug-response models from patients’ progression-free survival data. Our strategy reveals links between oncogenic alterations, increasing the clinical impact of genomic profiling.

scholarly journals iTOP: Inferring the Topology of Omics Data

2018 ◽  
Author(s):  
Nanne Aben ◽  
Johan A. Westerhuis ◽  
Yipeng Song ◽  
Henk A.L. Kiers ◽  
Magali Michaut ◽  
...  
Keyword(s):  
Gene Expression ◽  
Binary Data ◽  
Drug Response ◽  
Response Prediction ◽  
R Package ◽  
Supplementary Information ◽  
Omics Data ◽  
Reconstruction Algorithms ◽  
Phenotypic Data ◽  
Rv Coefficient

AbstractMotivationIn biology, we are often faced with multiple datasets recorded on the same set of objects, such as multi-omics and phenotypic data of the same tumors. These datasets are typically not independent from each other. For example, methylation may influence gene expression, which may, in turn, influence drug response. Such relationships can strongly affect analyses performed on the data, as we have previously shown for the identification of biomarkers of drug response. Therefore, it is important to be able to chart the relationships between datasets.ResultsWe present iTOP, a methodology to infera topology of relationships between datasets. We base this methodology on the RV coefficient, a measure of matrix correlation, which can be used to determine how much information is shared between two datasets. We extended the RV coefficient for partial matrix correlations, which allows the use of graph reconstruction algorithms, such as the PC algorithm, to infer the topologies. In addition, since multi-omics data often contain binary data (e.g. mutations), we also extended the RV coefficient for binary data. Applying iTOP to pharmacogenomics data, we found that gene expression acts as a mediator between most other datasets and drug response: only proteomics clearly shares information with drug response that is not present in gene expression. Based on this result, we used TANDEM, a method for drug response prediction, to identify which variables predictive of drug response were distinct to either gene expression or proteomics.AvailabilityAn implementation of our methodology is available in the R package iTOP on CRAN. Additionally, an R Markdown document with code to reproduce all figures is provided as Supplementary [email protected] and [email protected] informationSupplementary data are available at Bioinformatics online.

scholarly journals Personalized Cancer Therapy Prioritization Based on Driver Alteration Co-occurrence Patterns

2019 ◽  
Author(s):  
Lidia Mateo ◽  
Miquel Duran-Frigola ◽  
Albert Gris-Oliver ◽  
Marta Palafox ◽  
Maurizio Scaltriti ◽  
...  
Keyword(s):  
Survival Data ◽  
Large Scale ◽  
Drug Response ◽  
De Novo ◽  
Progression Free Survival ◽  
Driver Gene ◽  
Precision Oncology ◽  
Response Predictors ◽  
Personalized Cancer Therapy ◽  
Personalized Cancer

AbstractIdentification of actionable genomic vulnerabilities is the cornerstone of precision oncology. Based on a large-scale drug screening in patient derived-xenografts, we uncover connections between driver gene alterations, derive Driver Co-Occurrence (DCO) networks, and relate these to drug sensitivity. Our collection of 53 drug response predictors attained an average balanced accuracy of 58% in a cross-validation setting, which rose to a 66% for the subset of high-confidence predictions. Morevover, we experimentally validated 12 out of 14 de novo predictions in mice. Finally, we adapted our strategy to obtain drug-response models from patients’ progression free survival data. By revealing unexpected links between oncogenic alterations, our strategy can increase the clinical impact of genomic profiling.

Sign in / Sign up

Export Citation Format

Share Document