scholarly journals FORESEE: a tool for the systematic comparison of translational drug response modeling pipelines

2019 ◽  
Vol 35 (19) ◽  
pp. 3846-3848 ◽  
Author(s):  
Lisa-Katrin Turnhoff ◽  
Ali Hadizadeh Esfahani ◽  
Maryam Montazeri ◽  
Nina Kusch ◽  
Andreas Schuppert
Keyword(s):  
Drug Response ◽  
Drug Efficacy ◽  
Response Prediction ◽  
R Package ◽  
Supplementary Information ◽  
Training Algorithms ◽  
Anti Cancer ◽  
Model Training ◽  
Response Modeling

Abstract Summary Translational models that utilize omics data generated in in vitro studies to predict the drug efficacy of anti-cancer compounds in patients are highly distinct, which complicates the benchmarking process for new computational approaches. In reaction to this, we introduce the uniFied translatiOnal dRug rESponsE prEdiction platform FORESEE, an open-source R-package. FORESEE not only provides a uniform data format for public cell line and patient datasets, but also establishes a standardized environment for drug response prediction pipelines, incorporating various state-of-the-art pre-processing methods, model training algorithms and validation techniques. The modular implementation of individual elements of the pipeline facilitates a straightforward development of combinatorial models, which can be used to re-evaluate and improve already existing pipelines as well as to develop new ones. Availability and implementation FORESEE is licensed under GNU General Public License v3.0 and available at https://github.com/JRC-COMBINE/FORESEE and https://doi.org/10.17605/OSF.IO/RF6QK, and provides vignettes for documentation and application both online and in the Supplementary Files 2 and 3. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals FORESEE: a tool for the systematic comparison of translational drug response modeling pipelines

2018 ◽  
Author(s):  
Lisa-Katrin Turnhoff ◽  
Ali Hadizadeh Esfahani ◽  
Maryam Montazeri ◽  
Nina Kusch ◽  
Andreas Schuppert
Keyword(s):  
Drug Response ◽  
Drug Efficacy ◽  
Response Prediction ◽  
R Package ◽  
Supplementary Information ◽  
Supplementary File ◽  
Data Sets ◽  
Training Algorithms ◽  
Model Training

Translational models that utilize omics data generated in in vitro studies to predict the drug efficacy of anti-cancer compounds in patients are highly distinct, which complicates the benchmarking process for new computational approaches. In reaction to this, we introduce the uniFied translatiOnal dRug rESponsE prEdiction platform FORESEE, an open-source R-package. FORESEE not only provides a uniform data format for public cell line and patient data sets, but also establishes a standardized environment for drug response prediction pipelines, incorporating various state-of-the-art preprocessing methods, model training algorithms and validation techniques. The modular implementation of individual elements of the pipeline facilitates a straightforward development of combinatorial models, which can be used to re-evaluate and improve already existing pipelines as well as to develop new ones. Availability and Implementation: FORESEE is licensed under GNU General Public License v3.0 and available at https://github.com/JRC-COMBINE/FORESEE . Supplementary Information: Supplementary Files 1 and 2 provide detailed descriptions of the pipeline and the data preparation process, while Supplementary File 3 presents basic use cases of the package. Contact: [email protected]

scholarly journals FORESEE: a tool for the systematic comparison of translational drug response modeling pipelines

2019 ◽  
Author(s):  
Lisa-Katrin Turnhoff ◽  
Ali Hadizadeh Esfahani ◽  
Maryam Montazeri ◽  
Nina Kusch ◽  
Andreas Schuppert
Keyword(s):  
Drug Response ◽  
Drug Efficacy ◽  
Response Prediction ◽  
R Package ◽  
Supplementary Information ◽  
Supplementary File ◽  
Data Sets ◽  
Training Algorithms ◽  
Model Training

Translational models that utilize omics data generated in in vitro studies to predict the drug efficacy of anti-cancer compounds in patients are highly distinct, which complicates the benchmarking process for new computational approaches. In reaction to this, we introduce the uniFied translatiOnal dRug rESponsE prEdiction platform FORESEE, an open-source R-package. FORESEE not only provides a uniform data format for public cell line and patient data sets, but also establishes a standardized environment for drug response prediction pipelines, incorporating various state-of-the-art preprocessing methods, model training algorithms and validation techniques. The modular implementation of individual elements of the pipeline facilitates a straightforward development of combinatorial models, which can be used to re-evaluate and improve already existing pipelines as well as to develop new ones. Availability and Implementation: FORESEE is licensed under GNU General Public License v3.0 and available at https://github.com/JRC-COMBINE/FORESEE . Supplementary Information: Supplementary Files 1 and 2 provide detailed descriptions of the pipeline and the data preparation process, while Supplementary File 3 presents basic use cases of the package. Contact: [email protected]

scholarly journals FORESEE: a tool for the systematic comparison of translational drug response modeling pipelines

2019 ◽  
Author(s):  
Lisa-Katrin Turnhoff ◽  
Ali Hadizadeh Esfahani ◽  
Maryam Montazeri ◽  
Nina Kusch ◽  
Andreas Schuppert
Keyword(s):  
Drug Response ◽  
Drug Efficacy ◽  
Response Prediction ◽  
R Package ◽  
Supplementary Information ◽  
Supplementary File ◽  
Data Sets ◽  
Training Algorithms ◽  
Model Training

Translational models that utilize omics data generated in in vitro studies to predict the drug efficacy of anti-cancer compounds in patients are highly distinct, which complicates the benchmarking process for new computational approaches. In reaction to this, we introduce the uniFied translatiOnal dRug rESponsE prEdiction platform FORESEE, an open-source R-package. FORESEE not only provides a uniform data format for public cell line and patient data sets, but also establishes a standardized environment for drug response prediction pipelines, incorporating various state-of-the-art preprocessing methods, model training algorithms and validation techniques. The modular implementation of individual elements of the pipeline facilitates a straightforward development of combinatorial models, which can be used to re-evaluate and improve already existing pipelines as well as to develop new ones. Availability and Implementation: FORESEE is licensed under GNU General Public License v3.0 and available at https://github.com/JRC-COMBINE/FORESEE . Supplementary Information: Supplementary Files 1 and 2 provide detailed descriptions of the pipeline and the data preparation process, while Supplementary File 3 presents basic use cases of the package. Contact: [email protected]

scholarly journals MOLI: multi-omics late integration with deep neural networks for drug response prediction

2019 ◽  
Vol 35 (14) ◽  
pp. i501-i509 ◽  
Author(s):  
Hossein Sharifi-Noghabi ◽  
Olga Zolotareva ◽  
Colin C Collins ◽  
Martin Ester
Keyword(s):  
Gene Expression ◽  
Neural Networks ◽  
Prediction Accuracy ◽  
Drug Response ◽  
Deep Neural Networks ◽  
Response Prediction ◽  
Supplementary Information ◽  
Precision Oncology

Abstract Motivation Historically, gene expression has been shown to be the most informative data for drug response prediction. Recent evidence suggests that integrating additional omics can improve the prediction accuracy which raises the question of how to integrate the additional omics. Regardless of the integration strategy, clinical utility and translatability are crucial. Thus, we reasoned a multi-omics approach combined with clinical datasets would improve drug response prediction and clinical relevance. Results We propose MOLI, a multi-omics late integration method based on deep neural networks. MOLI takes somatic mutation, copy number aberration and gene expression data as input, and integrates them for drug response prediction. MOLI uses type-specific encoding sub-networks to learn features for each omics type, concatenates them into one representation and optimizes this representation via a combined cost function consisting of a triplet loss and a binary cross-entropy loss. The former makes the representations of responder samples more similar to each other and different from the non-responders, and the latter makes this representation predictive of the response values. We validate MOLI on in vitro and in vivo datasets for five chemotherapy agents and two targeted therapeutics. Compared to state-of-the-art single-omics and early integration multi-omics methods, MOLI achieves higher prediction accuracy in external validations. Moreover, a significant improvement in MOLI’s performance is observed for targeted drugs when training on a pan-drug input, i.e. using all the drugs with the same target compared to training only on drug-specific inputs. MOLI’s high predictive power suggests it may have utility in precision oncology. Availability and implementation https://github.com/hosseinshn/MOLI. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Facilitating Drug Discovery in Breast Cancer by Virtually Screening Patients Using In Vitro Drug Response Modeling

Cancers ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 885
Author(s):  
Robert F. Gruener ◽  
Alexander Ling ◽  
Ya-Fang Chang ◽  
Gladys Morrison ◽  
Paul Geeleher ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Drug Discovery ◽  
Cell Line ◽  
Drug Response ◽  
Drug Efficacy ◽  
Standard Of Care ◽  
Breast Cancer Patients ◽  
Response Modeling

(1) Background: Drug imputation methods often aim to translate in vitro drug response to in vivo drug efficacy predictions. While commonly used in retrospective analyses, our aim is to investigate the use of drug prediction methods for the generation of novel drug discovery hypotheses. Triple-negative breast cancer (TNBC) is a severe clinical challenge in need of new therapies. (2) Methods: We used an established machine learning approach to build models of drug response based on cell line transcriptome data, which we then applied to patient tumor data to obtain predicted sensitivity scores for hundreds of drugs in over 1000 breast cancer patients. We then examined the relationships between predicted drug response and patient clinical features. (3) Results: Our analysis recapitulated several suspected vulnerabilities in TNBC and identified a number of compounds-of-interest. AZD-1775, a Wee1 inhibitor, was predicted to have preferential activity in TNBC (p < 2.2 × 10−16) and its efficacy was highly associated with TP53 mutations (p = 1.2 × 10−46). We validated these findings using independent cell line screening data and pathway analysis. Additionally, co-administration of AZD-1775 with standard-of-care paclitaxel was able to inhibit tumor growth (p < 0.05) and increase survival (p < 0.01) in a xenograft mouse model of TNBC. (4) Conclusions: Overall, this study provides a framework to turn any cancer transcriptomic dataset into a dataset for drug discovery. Using this framework, one can quickly generate meaningful drug discovery hypotheses for a cancer population of interest.

scholarly journals DeepCDR: a hybrid graph convolutional network for predicting cancer drug response

2020 ◽  
Author(s):  
Qiao Liu ◽  
Zhiqiang Hu ◽  
Rui Jiang ◽  
Mu Zhou
Keyword(s):  
Drug Design ◽  
Cancer Biology ◽  
Drug Response ◽  
Drug Efficacy ◽  
Supplementary Information ◽  
Cancer Drug ◽  
Convolutional Network ◽  
Chemical Structures ◽  
Anti Cancer ◽  
Cancer Types

AbstractMotivationAccurate prediction of cancer drug response (CDR) is challenging due to the uncertainty of drug efficacy and heterogeneity of cancer patients. Strong evidences have implicated the high dependence of CDR on tumor genomic and transcriptomic profiles of individual patients. Precise identification of CDR is crucial in both guiding anti-cancer drug design and understanding cancer biology.ResultsIn this study, we present DeepCDR which integrates multi-omics profiles of cancer cells and explores intrinsic chemical structures of drugs for predicting cancer drug response. Specifically, DeepCDR is a hybrid graph convolutional network consisting of a uniform graph convolutional network (UGCN) and multiple subnetworks. Unlike prior studies modeling hand-crafted features of drugs, DeepCDR automatically learns the latent representation of topological structures among atoms and bonds of drugs. Extensive experiments showed that DeepCDR outperformed state-of-the-art methods in both classification and regression settings under various data settings. We also evaluated the contribution of different types of omics profiles for assessing drug response. Furthermore, we provided an exploratory strategy for identifying potential cancer-associated genes concerning specific cancer types. Our results highlighted the predictive power of DeepCDR and its potential translational value in guiding disease-specific drug design.AvailabilityDeepCDR is freely available at https://github.com/kimmo1019/[email protected]; [email protected] informationSupplementary data are available at Bioinformatics online.

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 Methodological Challenges in Translational Drug Response Modeling in Cancer

2019 ◽  
Author(s):  
Lisa-Katrin Schätzle ◽  
Ali Hadizadeh Esfahani ◽  
Andreas Schuppert
Keyword(s):  
Drug Response ◽  
R Package ◽  
Systematic Investigation ◽  
Computational Aspect ◽  
Ongoing Research ◽  
Model Components ◽  
Response Modeling ◽  
Translational Models

AbstractTranslational models directly relating drug response-specific processes observed in vitro to their in vivo role in cancer patients constitute a crucial part of the development of personalized medication. Unfortunately, ongoing research is often confined by the irreproducibility of the results in other contexts. While the inconsistency of pharmacological data has received great attention recently, the computational aspect of this crisis still deserves closer examination. Notably, studies often focus only on isolated model characteristics instead of examining the overall workflow and the interplay of individual model components. Here, we present a systematic investigation of translational models using the R-package FORESEE. Our findings confirm that with the current exploitation of the available data and the prevailing trend of optimizing methods to only one specific use case, modeling solutions will continue to suffer from non-transferability. Instead, the conduct of developing translational approaches urgently needs to change to retrieve clinical relevance in the future.

scholarly journals DeepCDR: a hybrid graph convolutional network for predicting cancer drug response

2020 ◽  
Vol 36 (Supplement_2) ◽  
pp. i911-i918
Author(s):  
Qiao Liu ◽  
Zhiqiang Hu ◽  
Rui Jiang ◽  
Mu Zhou
Keyword(s):  
Drug Design ◽  
Cancer Biology ◽  
Drug Response ◽  
Drug Efficacy ◽  
Supplementary Information ◽  
Cancer Drug ◽  
Convolutional Network ◽  
Chemical Structures ◽  
Anti Cancer ◽  
Cancer Types

Abstract Motivation Accurate prediction of cancer drug response (CDR) is challenging due to the uncertainty of drug efficacy and heterogeneity of cancer patients. Strong evidences have implicated the high dependence of CDR on tumor genomic and transcriptomic profiles of individual patients. Precise identification of CDR is crucial in both guiding anti-cancer drug design and understanding cancer biology. Results In this study, we present DeepCDR which integrates multi-omics profiles of cancer cells and explores intrinsic chemical structures of drugs for predicting CDR. Specifically, DeepCDR is a hybrid graph convolutional network consisting of a uniform graph convolutional network and multiple subnetworks. Unlike prior studies modeling hand-crafted features of drugs, DeepCDR automatically learns the latent representation of topological structures among atoms and bonds of drugs. Extensive experiments showed that DeepCDR outperformed state-of-the-art methods in both classification and regression settings under various data settings. We also evaluated the contribution of different types of omics profiles for assessing drug response. Furthermore, we provided an exploratory strategy for identifying potential cancer-associated genes concerning specific cancer types. Our results highlighted the predictive power of DeepCDR and its potential translational value in guiding disease-specific drug design. Availability and implementation DeepCDR is freely available at https://github.com/kimmo1019/DeepCDR. Supplementary information Supplementary data are available at Bioinformatics online.

AttDRP: Attention Mechanism-based Model for Anti-Cancer Drug Response Prediction

2021 ◽  
Vol 48 (6) ◽  
pp. 713-722
Author(s):  
Jonghwan Choi ◽  
Sangmin Seo ◽  
Sanghyun Park
Keyword(s):  
Drug Response ◽  
Response Prediction ◽  
Attention Mechanism ◽  
Cancer Drug ◽  
Anti Cancer
Sign in / Sign up

Export Citation Format

Share Document