The Experts below are selected from a list of 186837 Experts worldwide ranked by ideXlab platform
Jing Tang - One of the best experts on this subject based on the ideXlab platform.
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Drug Combination sensitivity scoring facilitates the discovery of synergistic and efficacious Drug Combinations in cancer.
PLOS Computational Biology, 2019Co-Authors: Alina Malyutina, Alberto Pessia, Caroline A. Heckman, Muntasir Mamun Majumder, Wenyu Wang, Jing TangAbstract:High-throughput Drug screening has facilitated the discovery of Drug Combinations in cancer. Many existing studies adopted a full matrix design, aiming for the characterization of Drug pair effects for cancer cells. However, the full matrix design may be suboptimal as it requires a Drug pair to be combined at multiple concentrations in a full factorial manner. Furthermore, many of the computational tools assess only the synergy but not the sensitivity of Drug Combinations, which might lead to false positive discoveries. We proposed a novel cross design to enable a more cost-effective and simultaneous testing of Drug Combination sensitivity and synergy. We developed a Drug Combination sensitivity score (CSS) to determine the sensitivity of a Drug pair, and showed that the CSS is highly reproducible between the replicates and thus supported its usage as a robust metric. We further showed that CSS can be predicted using machine learning approaches which determined the top pharmaco-features to cluster cancer cell lines based on their Drug Combination sensitivity profiles. To assess the degree of Drug interactions using the cross design, we developed an S synergy score based on the difference between the Drug Combination and the single Drug dose-response curves. We showed that the S score is able to detect true synergistic and antagonistic Drug Combinations at an accuracy level comparable to that using the full matrix design. Taken together, we showed that the cross design coupled with the CSS sensitivity and S synergy scoring methods may provide a robust and accurate characterization of both Drug Combination sensitivity and synergy levels, with minimal experimental materials required. Our experimental-computational approach could be utilized as an efficient pipeline for improving the discovery rate in high-throughput Drug Combination screening, particularly for primary patient samples which are difficult to obtain.
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DrugComb - an integrative cancer Drug Combination data portal
2019Co-Authors: Bulat Zagidullin, Alina Malyutina, Alberto Pessia, Wenyu Wang, Jehad Aldahdooh, Shuyu Zheng, Yinyin Wang, Joseph Saad, Jing TangAbstract:ABSTRACT Drug Combination therapy has the potential to enhance efficacy, reduce dose-dependent toxicity and prevent the emergence of Drug resistance. However, discovery of synergistic and effective Drug Combinations has been a laborious and often serendipitous process. In recent years, identification of Combination therapies has been accelerated due to the advances in high-throughput Drug screening, but informatics approaches for systems-level data management and analysis are needed. To contribute toward this goal, we created an open-access data portal (https://Drugcomb.fimm.fi) where the results of Drug Combination screening studies are accumulated, standardized and harmonized. Through the data portal, we provided the web server to analyze and visualize users’ own Drug Combination screening data. The users have an option to upload their data to DrugComb, as part of a crowdsourcing data curation effort. To initiate the data repository, we collected 437,932 Drug Combinations tested on a variety of cancer cell lines. We showed that linear regression approaches, when considering chemical fingerprints as predictors, have the potential to achieve high accuracy of predicting the sensitivity and synergy of Drug Combinations. All the data and informatics tools are freely available in DrugComb to enable a more efficient utilization of data resources for future Drug Combination discovery.
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Drug Combination sensitivity scoring facilitates the discovery of synergistic and efficacious Drug Combinations in cancer
bioRxiv, 2019Co-Authors: Alina Malyutina, Alberto Pessia, Caroline A. Heckman, Muntasir Mamun Majumder, Wenyu Wang, Jing TangAbstract:High-throughput Drug sensitivity screening has been utilized for facilitating the discovery of Drug Combinations in cancer. Many existing studies adopted a dose-response matrix design, aiming for the characterization of Drug Combination sensitivity and synergy. However, there is lack of consensus on the definition of sensitivity and synergy, leading to the use of different mathematical models that do not necessarily agree with each other. We proposed a cross design to enable a more cost-effective testing of sensitivity and synergy for a Drug pair. We developed a Drug Combination sensitivity score (CSS) to summarize the Drug Combination dose-response curves. Using a high-throughput Drug Combination dataset, we showed that the CSS is highly reproducible among the replicates. With machine learning approaches such as Elastic Net, Random Forests and Support Vector Machines, the CSS can also be predicted with high accuracy. Furthermore, we defined a synergy score based on the difference between the Drug Combination and the single Drug dose-response curves. We showed that the CSS-based synergy score is able to detect true synergistic and antagonistic Drug Combinations. The cross Drug Combination design coupled with the CSS scoring facilitated the evaluation of Drug Combination sensitivity and synergy using the same scale, with minimal experimental material that is required. Our approach could be utilized as an efficient pipeline for improving the discovery rate in high-throughput Drug Combination screening. The R scripts for calculating and predicting CSS are available at https://github.com/amalyutina/CSS.
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Drug Combination sensitivity scoring facilitates the discovery of synergistic and efficacious Drug Combinations in cancer
bioRxiv, 2019Co-Authors: Alina Malyutina, Alberto Pessia, Caroline A. Heckman, Muntasir Mamun Majumder, Wenyu Wang, Jing TangAbstract:High-throughput Drug sensitivity screening has been utilized for facilitating the discovery of Drug Combinations in cancer. Many existing studies adopted a dose-response matrix design, aiming for the characterization of Drug Combination sensitivity and synergy. However, there is lack of consensus on the definition of sensitivity and synergy, leading to the use of different mathematical models that do not necessarily agree with each other. We proposed a cross design to enable a more cost-effective testing of sensitivity and synergy for a Drug pair. We developed a Drug Combination sensitivity score (CSS) to summarize the Drug Combination dose-response curves. Using a high-throughput Drug Combination dataset, we showed that the CSS is highly reproducible among the replicates. With machine learning approaches such as Elastic Net, Random Forests and Support Vector Machines, the CSS can also be predicted with high accuracy. Furthermore, we defined a synergy score based on the difference between the Drug Combination and the single Drug dose-response curves. We showed that the CSS-based synergy score is able to detect true synergistic and antagonistic Drug Combinations. The cross Drug Combination design coupled with the CSS scoring facilitated the evaluation of Drug Combination sensitivity and synergy using the same scale, with minimal experimental material that is required. Our approach could be utilized as an efficient pipeline for improving the discovery rate in high-throughput Drug Combination screening. The R scripts for calculating and predicting CSS are available at https://github.com/amalyutina/CSS.
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synergyfinder a web application for analyzing Drug Combination dose response matrix data
Bioinformatics, 2017Co-Authors: Aleksandr Ianevski, Jing Tang, Tero AittokallioAbstract:Summary Rational design of Drug Combinations has become a promising strategy to tackle the Drug sensitivity and resistance problem in cancer treatment. To systematically evaluate the pre-clinical significance of pairwise Drug Combinations, functional screening assays that probe Combination effects in a dose-response matrix assay are commonly used. To facilitate the analysis of such Drug Combination experiments, we implemented a web application that uses key functions of R-package SynergyFinder, and provides not only the flexibility of using multiple synergy scoring models, but also a user-friendly interface for visualizing the Drug Combination landscapes in an interactive manner. Availability and implementation The SynergyFinder web application is freely accessible at https://synergyfinder.fimm.fi ; The R-package and its source-code are freely available at http://bioconductor.org/packages/release/bioc/html/synergyfinder.html . Contact jing.tang@helsinki.fi.
Ting-chao Chou - One of the best experts on this subject based on the ideXlab platform.
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Drug Combination studies and their synergy quantification using the chou talalay method
Cancer Research, 2010Co-Authors: Ting-chao ChouAbstract:This brief perspective article focuses on the most common errors and pitfalls, as well as the do's and don'ts in Drug Combination studies, in terms of experimental design, data acquisition, data interpretation, and computerized simulation. The Chou-Talalay method for Drug Combination is based on the median-effect equation, derived from the mass-action law principle, which is the unified theory that provides the common link between single entity and multiple entities, and first order and higher order dynamics. This general equation encompasses the Michaelis-Menten, Hill, Henderson-Hasselbalch, and Scatchard equations in biochemistry and biophysics. The resulting Combination index (CI) theorem of Chou-Talalay offers quantitative definition for additive effect (CI = 1), synergism (CI 1) in Drug Combinations. This theory also provides algorithms for automated computer simulation for synergism and/or antagonism at any effect and dose level, as shown in the CI plot and isobologram, respectively.
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Preclinical versus clinical Drug Combination studies.
Leukemia & lymphoma, 2008Co-Authors: Ting-chao ChouAbstract:This brief review provides a practical guide for Drug Combination studies and delineates its essence in terms of the mass-action-based theory, experimental design and automated computerised data analysis. The Combination index (CI) method of Chou-Talalay is based on the multiple Drug effect equation derived from the median-effect principle of the mass-action law. It provides quantitative determination for synergism (CI 1), and provides the algorithm for computer software for automated simulation for Drug Combinations. It takes into account both the potency (the Dm value) and the shape of the dose–effect curve (the m value) of each Drug alone and their Combination. The best feature is that it allows for small size experiments. The automated computer simulation reveals whether there is a synergism, determines how much synergism (the CI value) at any effect levels (the Fa–CI plot), or at any dose levels (the isobologram), provides the information regarding ...
Alberto Pessia - One of the best experts on this subject based on the ideXlab platform.
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Drug Combination sensitivity scoring facilitates the discovery of synergistic and efficacious Drug Combinations in cancer.
PLOS Computational Biology, 2019Co-Authors: Alina Malyutina, Alberto Pessia, Caroline A. Heckman, Muntasir Mamun Majumder, Wenyu Wang, Jing TangAbstract:High-throughput Drug screening has facilitated the discovery of Drug Combinations in cancer. Many existing studies adopted a full matrix design, aiming for the characterization of Drug pair effects for cancer cells. However, the full matrix design may be suboptimal as it requires a Drug pair to be combined at multiple concentrations in a full factorial manner. Furthermore, many of the computational tools assess only the synergy but not the sensitivity of Drug Combinations, which might lead to false positive discoveries. We proposed a novel cross design to enable a more cost-effective and simultaneous testing of Drug Combination sensitivity and synergy. We developed a Drug Combination sensitivity score (CSS) to determine the sensitivity of a Drug pair, and showed that the CSS is highly reproducible between the replicates and thus supported its usage as a robust metric. We further showed that CSS can be predicted using machine learning approaches which determined the top pharmaco-features to cluster cancer cell lines based on their Drug Combination sensitivity profiles. To assess the degree of Drug interactions using the cross design, we developed an S synergy score based on the difference between the Drug Combination and the single Drug dose-response curves. We showed that the S score is able to detect true synergistic and antagonistic Drug Combinations at an accuracy level comparable to that using the full matrix design. Taken together, we showed that the cross design coupled with the CSS sensitivity and S synergy scoring methods may provide a robust and accurate characterization of both Drug Combination sensitivity and synergy levels, with minimal experimental materials required. Our experimental-computational approach could be utilized as an efficient pipeline for improving the discovery rate in high-throughput Drug Combination screening, particularly for primary patient samples which are difficult to obtain.
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DrugComb: an integrative cancer Drug Combination data portal.
Nucleic acids research, 2019Co-Authors: Bulat Zagidullin, Alina Malyutina, Wenyu Wang, Jehad Aldahdooh, Shuyu Zheng, Yinyin Wang, Joseph Saad, Mohieddin Jafari, Ziaurrehman Tanoli, Alberto PessiaAbstract:Drug Combination therapy has the potential to enhance efficacy, reduce dose-dependent toxicity and prevent the emergence of Drug resistance. However, discovery of synergistic and effective Drug Combinations has been a laborious and often serendipitous process. In recent years, identification of Combination therapies has been accelerated due to the advances in high-throughput Drug screening, but informatics approaches for systems-level data management and analysis are needed. To contribute toward this goal, we created an open-access data portal called DrugComb (https://Drugcomb.fimm.fi) where the results of Drug Combination screening studies are accumulated, standardized and harmonized. Through the data portal, we provided a web server to analyze and visualize users' own Drug Combination screening data. The users can also effectively participate a crowdsourcing data curation effect by depositing their data at DrugComb. To initiate the data repository, we collected 437 932 Drug Combinations tested on a variety of cancer cell lines. We showed that linear regression approaches, when considering chemical fingerprints as predictors, have the potential to achieve high accuracy of predicting the sensitivity of Drug Combinations. All the data and informatics tools are freely available in DrugComb to enable a more efficient utilization of data resources for future Drug Combination discovery.
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DrugComb - an integrative cancer Drug Combination data portal
2019Co-Authors: Bulat Zagidullin, Alina Malyutina, Alberto Pessia, Wenyu Wang, Jehad Aldahdooh, Shuyu Zheng, Yinyin Wang, Joseph Saad, Jing TangAbstract:ABSTRACT Drug Combination therapy has the potential to enhance efficacy, reduce dose-dependent toxicity and prevent the emergence of Drug resistance. However, discovery of synergistic and effective Drug Combinations has been a laborious and often serendipitous process. In recent years, identification of Combination therapies has been accelerated due to the advances in high-throughput Drug screening, but informatics approaches for systems-level data management and analysis are needed. To contribute toward this goal, we created an open-access data portal (https://Drugcomb.fimm.fi) where the results of Drug Combination screening studies are accumulated, standardized and harmonized. Through the data portal, we provided the web server to analyze and visualize users’ own Drug Combination screening data. The users have an option to upload their data to DrugComb, as part of a crowdsourcing data curation effort. To initiate the data repository, we collected 437,932 Drug Combinations tested on a variety of cancer cell lines. We showed that linear regression approaches, when considering chemical fingerprints as predictors, have the potential to achieve high accuracy of predicting the sensitivity and synergy of Drug Combinations. All the data and informatics tools are freely available in DrugComb to enable a more efficient utilization of data resources for future Drug Combination discovery.
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Drug Combination sensitivity scoring facilitates the discovery of synergistic and efficacious Drug Combinations in cancer
bioRxiv, 2019Co-Authors: Alina Malyutina, Alberto Pessia, Caroline A. Heckman, Muntasir Mamun Majumder, Wenyu Wang, Jing TangAbstract:High-throughput Drug sensitivity screening has been utilized for facilitating the discovery of Drug Combinations in cancer. Many existing studies adopted a dose-response matrix design, aiming for the characterization of Drug Combination sensitivity and synergy. However, there is lack of consensus on the definition of sensitivity and synergy, leading to the use of different mathematical models that do not necessarily agree with each other. We proposed a cross design to enable a more cost-effective testing of sensitivity and synergy for a Drug pair. We developed a Drug Combination sensitivity score (CSS) to summarize the Drug Combination dose-response curves. Using a high-throughput Drug Combination dataset, we showed that the CSS is highly reproducible among the replicates. With machine learning approaches such as Elastic Net, Random Forests and Support Vector Machines, the CSS can also be predicted with high accuracy. Furthermore, we defined a synergy score based on the difference between the Drug Combination and the single Drug dose-response curves. We showed that the CSS-based synergy score is able to detect true synergistic and antagonistic Drug Combinations. The cross Drug Combination design coupled with the CSS scoring facilitated the evaluation of Drug Combination sensitivity and synergy using the same scale, with minimal experimental material that is required. Our approach could be utilized as an efficient pipeline for improving the discovery rate in high-throughput Drug Combination screening. The R scripts for calculating and predicting CSS are available at https://github.com/amalyutina/CSS.
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Drug Combination sensitivity scoring facilitates the discovery of synergistic and efficacious Drug Combinations in cancer
bioRxiv, 2019Co-Authors: Alina Malyutina, Alberto Pessia, Caroline A. Heckman, Muntasir Mamun Majumder, Wenyu Wang, Jing TangAbstract:High-throughput Drug sensitivity screening has been utilized for facilitating the discovery of Drug Combinations in cancer. Many existing studies adopted a dose-response matrix design, aiming for the characterization of Drug Combination sensitivity and synergy. However, there is lack of consensus on the definition of sensitivity and synergy, leading to the use of different mathematical models that do not necessarily agree with each other. We proposed a cross design to enable a more cost-effective testing of sensitivity and synergy for a Drug pair. We developed a Drug Combination sensitivity score (CSS) to summarize the Drug Combination dose-response curves. Using a high-throughput Drug Combination dataset, we showed that the CSS is highly reproducible among the replicates. With machine learning approaches such as Elastic Net, Random Forests and Support Vector Machines, the CSS can also be predicted with high accuracy. Furthermore, we defined a synergy score based on the difference between the Drug Combination and the single Drug dose-response curves. We showed that the CSS-based synergy score is able to detect true synergistic and antagonistic Drug Combinations. The cross Drug Combination design coupled with the CSS scoring facilitated the evaluation of Drug Combination sensitivity and synergy using the same scale, with minimal experimental material that is required. Our approach could be utilized as an efficient pipeline for improving the discovery rate in high-throughput Drug Combination screening. The R scripts for calculating and predicting CSS are available at https://github.com/amalyutina/CSS.
Alina Malyutina - One of the best experts on this subject based on the ideXlab platform.
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Drug Combination sensitivity scoring facilitates the discovery of synergistic and efficacious Drug Combinations in cancer.
PLOS Computational Biology, 2019Co-Authors: Alina Malyutina, Alberto Pessia, Caroline A. Heckman, Muntasir Mamun Majumder, Wenyu Wang, Jing TangAbstract:High-throughput Drug screening has facilitated the discovery of Drug Combinations in cancer. Many existing studies adopted a full matrix design, aiming for the characterization of Drug pair effects for cancer cells. However, the full matrix design may be suboptimal as it requires a Drug pair to be combined at multiple concentrations in a full factorial manner. Furthermore, many of the computational tools assess only the synergy but not the sensitivity of Drug Combinations, which might lead to false positive discoveries. We proposed a novel cross design to enable a more cost-effective and simultaneous testing of Drug Combination sensitivity and synergy. We developed a Drug Combination sensitivity score (CSS) to determine the sensitivity of a Drug pair, and showed that the CSS is highly reproducible between the replicates and thus supported its usage as a robust metric. We further showed that CSS can be predicted using machine learning approaches which determined the top pharmaco-features to cluster cancer cell lines based on their Drug Combination sensitivity profiles. To assess the degree of Drug interactions using the cross design, we developed an S synergy score based on the difference between the Drug Combination and the single Drug dose-response curves. We showed that the S score is able to detect true synergistic and antagonistic Drug Combinations at an accuracy level comparable to that using the full matrix design. Taken together, we showed that the cross design coupled with the CSS sensitivity and S synergy scoring methods may provide a robust and accurate characterization of both Drug Combination sensitivity and synergy levels, with minimal experimental materials required. Our experimental-computational approach could be utilized as an efficient pipeline for improving the discovery rate in high-throughput Drug Combination screening, particularly for primary patient samples which are difficult to obtain.
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DrugComb: an integrative cancer Drug Combination data portal.
Nucleic acids research, 2019Co-Authors: Bulat Zagidullin, Alina Malyutina, Wenyu Wang, Jehad Aldahdooh, Shuyu Zheng, Yinyin Wang, Joseph Saad, Mohieddin Jafari, Ziaurrehman Tanoli, Alberto PessiaAbstract:Drug Combination therapy has the potential to enhance efficacy, reduce dose-dependent toxicity and prevent the emergence of Drug resistance. However, discovery of synergistic and effective Drug Combinations has been a laborious and often serendipitous process. In recent years, identification of Combination therapies has been accelerated due to the advances in high-throughput Drug screening, but informatics approaches for systems-level data management and analysis are needed. To contribute toward this goal, we created an open-access data portal called DrugComb (https://Drugcomb.fimm.fi) where the results of Drug Combination screening studies are accumulated, standardized and harmonized. Through the data portal, we provided a web server to analyze and visualize users' own Drug Combination screening data. The users can also effectively participate a crowdsourcing data curation effect by depositing their data at DrugComb. To initiate the data repository, we collected 437 932 Drug Combinations tested on a variety of cancer cell lines. We showed that linear regression approaches, when considering chemical fingerprints as predictors, have the potential to achieve high accuracy of predicting the sensitivity of Drug Combinations. All the data and informatics tools are freely available in DrugComb to enable a more efficient utilization of data resources for future Drug Combination discovery.
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DrugComb - an integrative cancer Drug Combination data portal
2019Co-Authors: Bulat Zagidullin, Alina Malyutina, Alberto Pessia, Wenyu Wang, Jehad Aldahdooh, Shuyu Zheng, Yinyin Wang, Joseph Saad, Jing TangAbstract:ABSTRACT Drug Combination therapy has the potential to enhance efficacy, reduce dose-dependent toxicity and prevent the emergence of Drug resistance. However, discovery of synergistic and effective Drug Combinations has been a laborious and often serendipitous process. In recent years, identification of Combination therapies has been accelerated due to the advances in high-throughput Drug screening, but informatics approaches for systems-level data management and analysis are needed. To contribute toward this goal, we created an open-access data portal (https://Drugcomb.fimm.fi) where the results of Drug Combination screening studies are accumulated, standardized and harmonized. Through the data portal, we provided the web server to analyze and visualize users’ own Drug Combination screening data. The users have an option to upload their data to DrugComb, as part of a crowdsourcing data curation effort. To initiate the data repository, we collected 437,932 Drug Combinations tested on a variety of cancer cell lines. We showed that linear regression approaches, when considering chemical fingerprints as predictors, have the potential to achieve high accuracy of predicting the sensitivity and synergy of Drug Combinations. All the data and informatics tools are freely available in DrugComb to enable a more efficient utilization of data resources for future Drug Combination discovery.
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Drug Combination sensitivity scoring facilitates the discovery of synergistic and efficacious Drug Combinations in cancer
bioRxiv, 2019Co-Authors: Alina Malyutina, Alberto Pessia, Caroline A. Heckman, Muntasir Mamun Majumder, Wenyu Wang, Jing TangAbstract:High-throughput Drug sensitivity screening has been utilized for facilitating the discovery of Drug Combinations in cancer. Many existing studies adopted a dose-response matrix design, aiming for the characterization of Drug Combination sensitivity and synergy. However, there is lack of consensus on the definition of sensitivity and synergy, leading to the use of different mathematical models that do not necessarily agree with each other. We proposed a cross design to enable a more cost-effective testing of sensitivity and synergy for a Drug pair. We developed a Drug Combination sensitivity score (CSS) to summarize the Drug Combination dose-response curves. Using a high-throughput Drug Combination dataset, we showed that the CSS is highly reproducible among the replicates. With machine learning approaches such as Elastic Net, Random Forests and Support Vector Machines, the CSS can also be predicted with high accuracy. Furthermore, we defined a synergy score based on the difference between the Drug Combination and the single Drug dose-response curves. We showed that the CSS-based synergy score is able to detect true synergistic and antagonistic Drug Combinations. The cross Drug Combination design coupled with the CSS scoring facilitated the evaluation of Drug Combination sensitivity and synergy using the same scale, with minimal experimental material that is required. Our approach could be utilized as an efficient pipeline for improving the discovery rate in high-throughput Drug Combination screening. The R scripts for calculating and predicting CSS are available at https://github.com/amalyutina/CSS.
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Drug Combination sensitivity scoring facilitates the discovery of synergistic and efficacious Drug Combinations in cancer
bioRxiv, 2019Co-Authors: Alina Malyutina, Alberto Pessia, Caroline A. Heckman, Muntasir Mamun Majumder, Wenyu Wang, Jing TangAbstract:High-throughput Drug sensitivity screening has been utilized for facilitating the discovery of Drug Combinations in cancer. Many existing studies adopted a dose-response matrix design, aiming for the characterization of Drug Combination sensitivity and synergy. However, there is lack of consensus on the definition of sensitivity and synergy, leading to the use of different mathematical models that do not necessarily agree with each other. We proposed a cross design to enable a more cost-effective testing of sensitivity and synergy for a Drug pair. We developed a Drug Combination sensitivity score (CSS) to summarize the Drug Combination dose-response curves. Using a high-throughput Drug Combination dataset, we showed that the CSS is highly reproducible among the replicates. With machine learning approaches such as Elastic Net, Random Forests and Support Vector Machines, the CSS can also be predicted with high accuracy. Furthermore, we defined a synergy score based on the difference between the Drug Combination and the single Drug dose-response curves. We showed that the CSS-based synergy score is able to detect true synergistic and antagonistic Drug Combinations. The cross Drug Combination design coupled with the CSS scoring facilitated the evaluation of Drug Combination sensitivity and synergy using the same scale, with minimal experimental material that is required. Our approach could be utilized as an efficient pipeline for improving the discovery rate in high-throughput Drug Combination screening. The R scripts for calculating and predicting CSS are available at https://github.com/amalyutina/CSS.
Wenyu Wang - One of the best experts on this subject based on the ideXlab platform.
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Drug Combination sensitivity scoring facilitates the discovery of synergistic and efficacious Drug Combinations in cancer.
PLOS Computational Biology, 2019Co-Authors: Alina Malyutina, Alberto Pessia, Caroline A. Heckman, Muntasir Mamun Majumder, Wenyu Wang, Jing TangAbstract:High-throughput Drug screening has facilitated the discovery of Drug Combinations in cancer. Many existing studies adopted a full matrix design, aiming for the characterization of Drug pair effects for cancer cells. However, the full matrix design may be suboptimal as it requires a Drug pair to be combined at multiple concentrations in a full factorial manner. Furthermore, many of the computational tools assess only the synergy but not the sensitivity of Drug Combinations, which might lead to false positive discoveries. We proposed a novel cross design to enable a more cost-effective and simultaneous testing of Drug Combination sensitivity and synergy. We developed a Drug Combination sensitivity score (CSS) to determine the sensitivity of a Drug pair, and showed that the CSS is highly reproducible between the replicates and thus supported its usage as a robust metric. We further showed that CSS can be predicted using machine learning approaches which determined the top pharmaco-features to cluster cancer cell lines based on their Drug Combination sensitivity profiles. To assess the degree of Drug interactions using the cross design, we developed an S synergy score based on the difference between the Drug Combination and the single Drug dose-response curves. We showed that the S score is able to detect true synergistic and antagonistic Drug Combinations at an accuracy level comparable to that using the full matrix design. Taken together, we showed that the cross design coupled with the CSS sensitivity and S synergy scoring methods may provide a robust and accurate characterization of both Drug Combination sensitivity and synergy levels, with minimal experimental materials required. Our experimental-computational approach could be utilized as an efficient pipeline for improving the discovery rate in high-throughput Drug Combination screening, particularly for primary patient samples which are difficult to obtain.
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DrugComb: an integrative cancer Drug Combination data portal.
Nucleic acids research, 2019Co-Authors: Bulat Zagidullin, Alina Malyutina, Wenyu Wang, Jehad Aldahdooh, Shuyu Zheng, Yinyin Wang, Joseph Saad, Mohieddin Jafari, Ziaurrehman Tanoli, Alberto PessiaAbstract:Drug Combination therapy has the potential to enhance efficacy, reduce dose-dependent toxicity and prevent the emergence of Drug resistance. However, discovery of synergistic and effective Drug Combinations has been a laborious and often serendipitous process. In recent years, identification of Combination therapies has been accelerated due to the advances in high-throughput Drug screening, but informatics approaches for systems-level data management and analysis are needed. To contribute toward this goal, we created an open-access data portal called DrugComb (https://Drugcomb.fimm.fi) where the results of Drug Combination screening studies are accumulated, standardized and harmonized. Through the data portal, we provided a web server to analyze and visualize users' own Drug Combination screening data. The users can also effectively participate a crowdsourcing data curation effect by depositing their data at DrugComb. To initiate the data repository, we collected 437 932 Drug Combinations tested on a variety of cancer cell lines. We showed that linear regression approaches, when considering chemical fingerprints as predictors, have the potential to achieve high accuracy of predicting the sensitivity of Drug Combinations. All the data and informatics tools are freely available in DrugComb to enable a more efficient utilization of data resources for future Drug Combination discovery.
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DrugComb - an integrative cancer Drug Combination data portal
2019Co-Authors: Bulat Zagidullin, Alina Malyutina, Alberto Pessia, Wenyu Wang, Jehad Aldahdooh, Shuyu Zheng, Yinyin Wang, Joseph Saad, Jing TangAbstract:ABSTRACT Drug Combination therapy has the potential to enhance efficacy, reduce dose-dependent toxicity and prevent the emergence of Drug resistance. However, discovery of synergistic and effective Drug Combinations has been a laborious and often serendipitous process. In recent years, identification of Combination therapies has been accelerated due to the advances in high-throughput Drug screening, but informatics approaches for systems-level data management and analysis are needed. To contribute toward this goal, we created an open-access data portal (https://Drugcomb.fimm.fi) where the results of Drug Combination screening studies are accumulated, standardized and harmonized. Through the data portal, we provided the web server to analyze and visualize users’ own Drug Combination screening data. The users have an option to upload their data to DrugComb, as part of a crowdsourcing data curation effort. To initiate the data repository, we collected 437,932 Drug Combinations tested on a variety of cancer cell lines. We showed that linear regression approaches, when considering chemical fingerprints as predictors, have the potential to achieve high accuracy of predicting the sensitivity and synergy of Drug Combinations. All the data and informatics tools are freely available in DrugComb to enable a more efficient utilization of data resources for future Drug Combination discovery.
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Drug Combination sensitivity scoring facilitates the discovery of synergistic and efficacious Drug Combinations in cancer
bioRxiv, 2019Co-Authors: Alina Malyutina, Alberto Pessia, Caroline A. Heckman, Muntasir Mamun Majumder, Wenyu Wang, Jing TangAbstract:High-throughput Drug sensitivity screening has been utilized for facilitating the discovery of Drug Combinations in cancer. Many existing studies adopted a dose-response matrix design, aiming for the characterization of Drug Combination sensitivity and synergy. However, there is lack of consensus on the definition of sensitivity and synergy, leading to the use of different mathematical models that do not necessarily agree with each other. We proposed a cross design to enable a more cost-effective testing of sensitivity and synergy for a Drug pair. We developed a Drug Combination sensitivity score (CSS) to summarize the Drug Combination dose-response curves. Using a high-throughput Drug Combination dataset, we showed that the CSS is highly reproducible among the replicates. With machine learning approaches such as Elastic Net, Random Forests and Support Vector Machines, the CSS can also be predicted with high accuracy. Furthermore, we defined a synergy score based on the difference between the Drug Combination and the single Drug dose-response curves. We showed that the CSS-based synergy score is able to detect true synergistic and antagonistic Drug Combinations. The cross Drug Combination design coupled with the CSS scoring facilitated the evaluation of Drug Combination sensitivity and synergy using the same scale, with minimal experimental material that is required. Our approach could be utilized as an efficient pipeline for improving the discovery rate in high-throughput Drug Combination screening. The R scripts for calculating and predicting CSS are available at https://github.com/amalyutina/CSS.
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Drug Combination sensitivity scoring facilitates the discovery of synergistic and efficacious Drug Combinations in cancer
bioRxiv, 2019Co-Authors: Alina Malyutina, Alberto Pessia, Caroline A. Heckman, Muntasir Mamun Majumder, Wenyu Wang, Jing TangAbstract:High-throughput Drug sensitivity screening has been utilized for facilitating the discovery of Drug Combinations in cancer. Many existing studies adopted a dose-response matrix design, aiming for the characterization of Drug Combination sensitivity and synergy. However, there is lack of consensus on the definition of sensitivity and synergy, leading to the use of different mathematical models that do not necessarily agree with each other. We proposed a cross design to enable a more cost-effective testing of sensitivity and synergy for a Drug pair. We developed a Drug Combination sensitivity score (CSS) to summarize the Drug Combination dose-response curves. Using a high-throughput Drug Combination dataset, we showed that the CSS is highly reproducible among the replicates. With machine learning approaches such as Elastic Net, Random Forests and Support Vector Machines, the CSS can also be predicted with high accuracy. Furthermore, we defined a synergy score based on the difference between the Drug Combination and the single Drug dose-response curves. We showed that the CSS-based synergy score is able to detect true synergistic and antagonistic Drug Combinations. The cross Drug Combination design coupled with the CSS scoring facilitated the evaluation of Drug Combination sensitivity and synergy using the same scale, with minimal experimental material that is required. Our approach could be utilized as an efficient pipeline for improving the discovery rate in high-throughput Drug Combination screening. The R scripts for calculating and predicting CSS are available at https://github.com/amalyutina/CSS.
