1
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Dey V, Ning X. Improving Anticancer Drug Selection and Prioritization via Neural Learning to Rank. J Chem Inf Model 2024; 64:4071-4088. [PMID: 38740382 PMCID: PMC11134508 DOI: 10.1021/acs.jcim.3c01060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 03/27/2024] [Accepted: 04/16/2024] [Indexed: 05/16/2024]
Abstract
Personalized cancer treatment requires a thorough understanding of complex interactions between drugs and cancer cell lines in varying genetic and molecular contexts. To address this, high-throughput screening has been used to generate large-scale drug response data, facilitating data-driven computational models. Such models can capture complex drug-cell line interactions across various contexts in a fully data-driven manner. However, accurately prioritizing the most effective drugs for each cell line still remains a significant challenge. To address this, we developed multiple neural ranking approaches that leverage large-scale drug response data across multiple cell lines from diverse cancer types. Unlike existing approaches that primarily utilize regression and classification techniques for drug response prediction, we formulated the objective of drug selection and prioritization as a drug ranking problem. In this work, we proposed multiple pairwise and listwise neural ranking methods that learn latent representations of drugs and cell lines and then use those representations to score drugs in each cell line via a learnable scoring function. Specifically, we developed neural pairwise and listwise ranking methods, Pair-PushC and List-One on top of the existing methods, pLETORg and ListNet, respectively. Additionally, we proposed a novel listwise ranking method, List-All, that focuses on all the effective drugs instead of the top effective drug, unlike List-One. We also provide an exhaustive empirical evaluation with state-of-the-art regression and ranking baselines on large-scale data sets across multiple experimental settings. Our results demonstrate that our proposed ranking methods mostly outperform the best baselines with significant improvements of as much as 25.6% in terms of selecting truly effective drugs within the top 20 predicted drugs (i.e., hit@20) across 50% test cell lines. Furthermore, our analyses suggest that the learned latent spaces from our proposed methods demonstrate informative clustering structures and capture relevant underlying biological features. Moreover, our comprehensive evaluation provides a thorough and objective comparison of the performance of different methods (including our proposed ones).
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Affiliation(s)
- Vishal Dey
- Department
of Computer Science and Engineering, The
Ohio State University, Columbus, Ohio 43210, United States
| | - Xia Ning
- Department
of Computer Science and Engineering, The
Ohio State University, Columbus, Ohio 43210, United States
- Biomedical
Informatics, The Ohio State University, Columbus, Ohio 43210, United States
- Translational
Data Analytics Institute, The Ohio State
University, Columbus, Ohio 43210, United States
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2
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Pang Y, Chen Y, Lin M, Zhang Y, Zhang J, Wang L. MMSyn: A New Multimodal Deep Learning Framework for Enhanced Prediction of Synergistic Drug Combinations. J Chem Inf Model 2024; 64:3689-3705. [PMID: 38676916 DOI: 10.1021/acs.jcim.4c00165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/29/2024]
Abstract
Combination therapy is a promising strategy for the successful treatment of cancer. The large number of possible combinations, however, mean that it is laborious and expensive to screen for synergistic drug combinations in vitro. Nevertheless, because of the availability of high-throughput screening data and advances in computational techniques, deep learning (DL) can be a useful tool for the prediction of synergistic drug combinations. In this study, we proposed a multimodal DL framework, MMSyn, for the prediction of synergistic drug combinations. First, features embedded in the drug molecules were extracted: structure, fingerprint, and string encoding. Then, gene expression data, DNA copy number, and pathway activity were used to describe cancer cell lines. Finally, these processed features were integrated using an attention mechanism and an interaction module and then input into a multilayer perceptron to predict drug synergy. Experimental results showed that our method outperformed five state-of-the-art DL methods and three traditional machine learning models for drug combination prediction. We verified that MMSyn achieved superior performance in stratified cross-validation settings using both the drug combination and cell line data. Moreover, we performed a set of ablation experiments to illustrate the effectiveness of each component and the efficacy of our model. In addition, our visual representation and case studies further confirmed the effectiveness of our model. All results showed that MMSyn can be used as a powerful tool for the prediction of synergistic drug combinations.
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Affiliation(s)
- Yu Pang
- Joint International Research Laboratory of Synthetic Biology and Medicine, Ministry of Education, Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
| | - Yihao Chen
- Joint International Research Laboratory of Synthetic Biology and Medicine, Ministry of Education, Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
| | - Mujie Lin
- Joint International Research Laboratory of Synthetic Biology and Medicine, Ministry of Education, Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
| | - Yanhong Zhang
- Joint International Research Laboratory of Synthetic Biology and Medicine, Ministry of Education, Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
| | - Jiquan Zhang
- Guizhou Provincial Engineering Technology Research Center for Chemical Drug R&D, College of Pharmacy, Guizhou Medical University, Guiyang 550025, P. R. China
| | - Ling Wang
- Joint International Research Laboratory of Synthetic Biology and Medicine, Ministry of Education, Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
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3
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Hajim WI, Zainudin S, Mohd Daud K, Alheeti K. Optimized models and deep learning methods for drug response prediction in cancer treatments: a review. PeerJ Comput Sci 2024; 10:e1903. [PMID: 38660174 PMCID: PMC11042005 DOI: 10.7717/peerj-cs.1903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 01/31/2024] [Indexed: 04/26/2024]
Abstract
Recent advancements in deep learning (DL) have played a crucial role in aiding experts to develop personalized healthcare services, particularly in drug response prediction (DRP) for cancer patients. The DL's techniques contribution to this field is significant, and they have proven indispensable in the medical field. This review aims to analyze the diverse effectiveness of various DL models in making these predictions, drawing on research published from 2017 to 2023. We utilized the VOS-Viewer 1.6.18 software to create a word cloud from the titles and abstracts of the selected studies. This study offers insights into the focus areas within DL models used for drug response. The word cloud revealed a strong link between certain keywords and grouped themes, highlighting terms such as deep learning, machine learning, precision medicine, precision oncology, drug response prediction, and personalized medicine. In order to achieve an advance in DRP using DL, the researchers need to work on enhancing the models' generalizability and interoperability. It is also crucial to develop models that not only accurately represent various architectures but also simplify these architectures, balancing the complexity with the predictive capabilities. In the future, researchers should try to combine methods that make DL models easier to understand; this will make DRP reviews more open and help doctors trust the decisions made by DL models in cancer DRP.
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Affiliation(s)
- Wesam Ibrahim Hajim
- Department of Applied Geology, College of Sciences, Tirkit University, Tikrit, Salah ad Din, Iraq
- Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Selangor, Malaysia
| | - Suhaila Zainudin
- Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Selangor, Malaysia
| | - Kauthar Mohd Daud
- Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Selangor, Malaysia
| | - Khattab Alheeti
- Department of Computer Networking Systems, College of Computer Sciences and Information Technology, University of Anbar, Al Anbar, Ramadi, Iraq
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4
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Liyaqat T, Ahmad T, Saxena C. TeM-DTBA: time-efficient drug target binding affinity prediction using multiple modalities with Lasso feature selection. J Comput Aided Mol Des 2023; 37:573-584. [PMID: 37777631 DOI: 10.1007/s10822-023-00533-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 09/07/2023] [Indexed: 10/02/2023]
Abstract
Drug discovery, especially virtual screening and drug repositioning, can be accelerated through deeper understanding and prediction of Drug Target Interactions (DTIs). The advancement of deep learning as well as the time and financial costs associated with conventional wet-lab experiments have made computational methods for DTI prediction more popular. However, the majority of these computational methods handle the DTI problem as a binary classification task, ignoring the quantitative binding affinity that determines the drug efficacy to their target proteins. Moreover, computational space as well as execution time of the model is often ignored over accuracy. To address these challenges, we introduce a novel method, called Time-efficient Multimodal Drug Target Binding Affinity (TeM-DTBA), which predicts the binding affinity between drugs and targets by fusing different modalities based on compound structures and target sequences. We employ the Lasso feature selection method, which lowers the dimensionality of feature vectors and speeds up the proposed model training time by more than 50%. The results from two benchmark datasets demonstrate that our method outperforms state-of-the-art methods in terms of performance. The mean squared errors of 18.8% and 23.19%, achieved on the KIBA and Davis datasets, respectively, suggest that our method is more accurate in predicting drug-target binding affinity.
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Affiliation(s)
- Tanya Liyaqat
- Department of Computer Engineering, Jamia Millia Islamia, New Delhi, India.
| | - Tanvir Ahmad
- Department of Computer Engineering, Jamia Millia Islamia, New Delhi, India
| | - Chandni Saxena
- The Chinese University of Hong Kong, Sha Tin, SAR, China
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5
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Bao X, Sun J, Yi M, Qiu J, Chen X, Shuai SC, Zhao Q. MPFFPSDC: A multi-pooling feature fusion model for predicting synergistic drug combinations. Methods 2023:S1046-2023(23)00098-1. [PMID: 37321525 DOI: 10.1016/j.ymeth.2023.06.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 06/11/2023] [Accepted: 06/12/2023] [Indexed: 06/17/2023] Open
Abstract
Drug combination therapies are common practice in the treatment of cancer, but not all combinations result in synergy. As traditional screening approaches are restricted in their ability to uncover synergistic drug combinations, computer-aided medicine is becoming a increasingly prevalent in this field. In this work, a predictive model of potential interactions between drugs named MPFFPSDC is presented, which can maintain the symmetry of drug inputs and eliminate inconsistencies in predictive results caused by different drug inputting sequences or positions. The experimental results show that MPFFPSDC outperforms comparative models in major performance indicators and exhibits better generalization for independent data. Furthermore, the case study demonstrates that our model can capture molecular substructures that contribute to the synergistic effect of two drugs. These results indicate that MPFFPSDC not only offers strong predictive performance, but also has good model interpretability that may provide new insights for the study of drug interaction mechanisms and the development of new drugs.
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Affiliation(s)
- Xin Bao
- School of Automation and Electrical Engineering, Linyi University, Linyi 276000, China
| | - Jianqiang Sun
- School of Automation and Electrical Engineering, Linyi University, Linyi 276000, China.
| | - Ming Yi
- School of Mathematics and Physics, China University of Geosciences, Wuhan 430000, China
| | - Jianlong Qiu
- School of Automation and Electrical Engineering, Linyi University, Linyi 276000, China
| | - Xiangyong Chen
- School of Automation and Electrical Engineering, Linyi University, Linyi 276000, China
| | - Stella C Shuai
- Biological Science, Northwestern University, Evanston, IL 60208, USA
| | - Qi Zhao
- School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan 114051, China.
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6
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Liu H, Fan Z, Lin J, Yang Y, Ran T, Chen H. The recent progress of deep-learning-based in silico prediction of drug combination. Drug Discov Today 2023:103625. [PMID: 37236526 DOI: 10.1016/j.drudis.2023.103625] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 04/24/2023] [Accepted: 05/17/2023] [Indexed: 05/28/2023]
Abstract
Drug combination therapy has become a common strategy for the treatment of complex diseases. There is an urgent need for computational methods to efficiently identify appropriate drug combinations owing to the high cost of experimental screening. In recent years, deep learning has been widely used in the field of drug discovery. Here, we provide a comprehensive review on deep-learning-based drug combination prediction algorithms from multiple aspects. Current studies highlight the flexibility of this technology in integrating multimodal data and the ability to achieve state-of-art performance; it is expected that deep-learning-based prediction of drug combinations should play an important part in future drug discovery.
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Affiliation(s)
- Haoyang Liu
- Department of Drug and Vaccine Research, Guangzhou Laboratory, Guangzhou 513000, China; College of Life Sciences, Nankai University, Tianjin 300071, China
| | - Zhiguang Fan
- Department of Drug and Vaccine Research, Guangzhou Laboratory, Guangzhou 513000, China; School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510000, China
| | - Jie Lin
- Department of Drug and Vaccine Research, Guangzhou Laboratory, Guangzhou 513000, China
| | - Yuedong Yang
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510000, China.
| | - Ting Ran
- Department of Drug and Vaccine Research, Guangzhou Laboratory, Guangzhou 513000, China.
| | - Hongming Chen
- Department of Drug and Vaccine Research, Guangzhou Laboratory, Guangzhou 513000, China.
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7
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Rønneberg L, Kirk PDW, Zucknick M. Dose-response prediction for in-vitro drug combination datasets: a probabilistic approach. BMC Bioinformatics 2023; 24:161. [PMID: 37085771 PMCID: PMC10120211 DOI: 10.1186/s12859-023-05256-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 03/28/2023] [Indexed: 04/23/2023] Open
Abstract
In this paper we propose PIICM, a probabilistic framework for dose-response prediction in high-throughput drug combination datasets. PIICM utilizes a permutation invariant version of the intrinsic co-regionalization model for multi-output Gaussian process regression, to predict dose-response surfaces in untested drug combination experiments. Coupled with an observation model that incorporates experimental uncertainty, PIICM is able to learn from noisily observed cell-viability measurements in settings where the underlying dose-response experiments are of varying quality, utilize different experimental designs, and the resulting training dataset is sparsely observed. We show that the model can accurately predict dose-response in held out experiments, and the resulting function captures relevant features indicating synergistic interaction between drugs.
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Affiliation(s)
- Leiv Rønneberg
- Oslo Centre for Biostatistics and Epidemiology, University of Oslo, Oslo, Norway
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Paul D W Kirk
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- Cambridge Institute of Therapeutic Immunology and Infectious Disease, University of Cambridge, Cambridge, UK
- Ovarian Cancer Programme, Cancer Research UK Cambridge Centre, Cambridge, UK
| | - Manuela Zucknick
- Oslo Centre for Biostatistics and Epidemiology, University of Oslo, Oslo, Norway.
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8
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Torkamannia A, Omidi Y, Ferdousi R. SYNDEEP: a deep learning approach for the prediction of cancer drugs synergy. Sci Rep 2023; 13:6184. [PMID: 37061563 PMCID: PMC10105711 DOI: 10.1038/s41598-023-33271-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Accepted: 04/11/2023] [Indexed: 04/17/2023] Open
Abstract
Drug combinations can be the prime strategy for increasing the initial treatment options in cancer therapy. However, identifying the combinations through experimental approaches is very laborious and costly. Notably, in vitro and/or in vivo examination of all the possible combinations might not be plausible. This study presented a novel computational approach to predicting synergistic drug combinations. Specifically, the deep neural network-based binary classification was utilized to develop the model. Various physicochemical, genomic, protein-protein interaction and protein-metabolite interaction information were used to predict the synergy effects of the combinations of different drugs. The performance of the constructed model was compared with shallow neural network (SNN), k-nearest neighbors (KNN), random forest (RF), support vector machines (SVMs), and gradient boosting classifiers (GBC). Based on our findings, the proposed deep neural network model was found to be capable of predicting synergistic drug combinations with high accuracy. The prediction accuracy and AUC metrics for this model were 92.21% and 97.32% in tenfold cross-validation. According to the results, the integration of different types of physicochemical and genomics features leads to more accurate prediction of synergy in cancer drugs.
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Affiliation(s)
- Anna Torkamannia
- Department of Health Information Technology, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, 51656/65811, Iran
| | - Yadollah Omidi
- Department of Pharmaceutical Sciences, College of Pharmacy, Nova Southeastern University, Fort Lauderdale, FL, 33328, USA
| | - Reza Ferdousi
- Department of Health Information Technology, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, 51656/65811, Iran.
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9
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Howell R, Davies J, Clarke MA, Appios A, Mesquita I, Jayal Y, Ringham-Terry B, Boned Del Rio I, Fisher J, Bennett CL. Localized immune surveillance of primary melanoma in the skin deciphered through executable modeling. SCIENCE ADVANCES 2023; 9:eadd1992. [PMID: 37043573 PMCID: PMC10096595 DOI: 10.1126/sciadv.add1992] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 03/10/2023] [Indexed: 06/19/2023]
Abstract
While skin is a site of active immune surveillance, primary melanomas often escape detection. Here, we have developed an in silico model to determine the local cross-talk between melanomas and Langerhans cells (LCs), the primary antigen-presenting cells at the site of melanoma development. The model predicts that melanomas fail to activate LC migration to lymph nodes until tumors reach a critical size, which is determined by a positive TNF-α feedback loop within melanomas, in line with our observations of murine tumors. In silico drug screening, supported by subsequent experimental testing, shows that treatment of primary tumors with MAPK pathway inhibitors may further prevent LC migration. In addition, our in silico model predicts treatment combinations that bypass LC dysfunction. In conclusion, our combined approach of in silico and in vivo studies suggests a molecular mechanism that explains how early melanomas develop under the radar of immune surveillance by LC.
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Affiliation(s)
| | | | - Matthew A. Clarke
- UCL Cancer Institute, University College London, 72 Huntley Street, London WC1E 6DD, UK
| | - Anna Appios
- UCL Cancer Institute, University College London, 72 Huntley Street, London WC1E 6DD, UK
| | - Inês Mesquita
- UCL Cancer Institute, University College London, 72 Huntley Street, London WC1E 6DD, UK
| | - Yashoda Jayal
- UCL Cancer Institute, University College London, 72 Huntley Street, London WC1E 6DD, UK
| | - Ben Ringham-Terry
- UCL Cancer Institute, University College London, 72 Huntley Street, London WC1E 6DD, UK
| | - Isabel Boned Del Rio
- UCL Cancer Institute, University College London, 72 Huntley Street, London WC1E 6DD, UK
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10
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Zhang H, Wang Z, Nan Y, Zagidullin B, Yi D, Tang J, Guan Y. Harmonizing across datasets to improve the transferability of drug combination prediction. Commun Biol 2023; 6:397. [PMID: 37041243 PMCID: PMC10090076 DOI: 10.1038/s42003-023-04783-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 03/30/2023] [Indexed: 04/13/2023] Open
Abstract
Combination treatment has multiple advantages over traditional monotherapy in clinics, thus becoming a target of interest for many high-throughput screening (HTS) studies, which enables the development of machine learning models predicting the response of new drug combinations. However, most existing models have been tested only within a single study, and these models cannot generalize across different datasets due to significantly variable experimental settings. Here, we thoroughly assessed the transferability issue of single-study-derived models on new datasets. More importantly, we propose a method to overcome the experimental variability by harmonizing dose-response curves of different studies. Our method improves the prediction performance of machine learning models by 184% and 1367% compared to the baseline models in intra-study and inter-study predictions, respectively, and shows consistent improvement in multiple cross-validation settings. Our study addresses the crucial question of the transferability in drug combination predictions, which is fundamental for such models to be extrapolated to new drug combination discovery and clinical applications that are de facto different datasets.
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Affiliation(s)
- Hanrui Zhang
- Department of Computational Medicine and Bioinformatics, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Ziyan Wang
- Department of Electrical Engineering and Computer Science (EECS) - CSE Division, University of Michigan, Ann Arbor, MI, USA
| | - Yiyang Nan
- Department of Computational Medicine and Bioinformatics, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Bulat Zagidullin
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Daiyao Yi
- Department of Computational Medicine and Bioinformatics, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Jing Tang
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
| | - Yuanfang Guan
- Department of Computational Medicine and Bioinformatics, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA.
- Department of Internal medicine, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA.
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11
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Baptista D, Ferreira PG, Rocha M. A systematic evaluation of deep learning methods for the prediction of drug synergy in cancer. PLoS Comput Biol 2023; 19:e1010200. [PMID: 36952569 PMCID: PMC10072473 DOI: 10.1371/journal.pcbi.1010200] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 04/04/2023] [Accepted: 02/08/2023] [Indexed: 03/25/2023] Open
Abstract
One of the main obstacles to the successful treatment of cancer is the phenomenon of drug resistance. A common strategy to overcome resistance is the use of combination therapies. However, the space of possibilities is huge and efficient search strategies are required. Machine Learning (ML) can be a useful tool for the discovery of novel, clinically relevant anti-cancer drug combinations. In particular, deep learning (DL) has become a popular choice for modeling drug combination effects. Here, we set out to examine the impact of different methodological choices on the performance of multimodal DL-based drug synergy prediction methods, including the use of different input data types, preprocessing steps and model architectures. Focusing on the NCI ALMANAC dataset, we found that feature selection based on prior biological knowledge has a positive impact-limiting gene expression data to cancer or drug response-specific genes improved performance. Drug features appeared to be more predictive of drug response, with a 41% increase in coefficient of determination (R2) and 26% increase in Spearman correlation relative to a baseline model that used only cell line and drug identifiers. Molecular fingerprint-based drug representations performed slightly better than learned representations-ECFP4 fingerprints increased R2 by 5.3% and Spearman correlation by 2.8% w.r.t the best learned representations. In general, fully connected feature-encoding subnetworks outperformed other architectures. DL outperformed other ML methods by more than 35% (R2) and 14% (Spearman). Additionally, an ensemble combining the top DL and ML models improved performance by about 6.5% (R2) and 4% (Spearman). Using a state-of-the-art interpretability method, we showed that DL models can learn to associate drug and cell line features with drug response in a biologically meaningful way. The strategies explored in this study will help to improve the development of computational methods for the rational design of effective drug combinations for cancer therapy.
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Affiliation(s)
- Delora Baptista
- CEB - Centre of Biological Engineering, University of Minho, Braga, Portugal
- LABBELS - Associate Laboratory, Braga, Guimarães, Portugal
| | - Pedro G Ferreira
- Department of Computer Science, Faculty of Sciences, University of Porto, Porto, Portugal
- INESC TEC, Porto, Portugal
- Ipatimup - Institute of Molecular Pathology and Immunology of the University of Porto, Porto, Portugal
- i3s - Instituto de Investigação e Inovação em Saúde da Universidade do Porto, Porto, Portugal
| | - Miguel Rocha
- CEB - Centre of Biological Engineering, University of Minho, Braga, Portugal
- LABBELS - Associate Laboratory, Braga, Guimarães, Portugal
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12
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Partin A, Brettin TS, Zhu Y, Narykov O, Clyde A, Overbeek J, Stevens RL. Deep learning methods for drug response prediction in cancer: Predominant and emerging trends. Front Med (Lausanne) 2023; 10:1086097. [PMID: 36873878 PMCID: PMC9975164 DOI: 10.3389/fmed.2023.1086097] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 01/23/2023] [Indexed: 02/17/2023] Open
Abstract
Cancer claims millions of lives yearly worldwide. While many therapies have been made available in recent years, by in large cancer remains unsolved. Exploiting computational predictive models to study and treat cancer holds great promise in improving drug development and personalized design of treatment plans, ultimately suppressing tumors, alleviating suffering, and prolonging lives of patients. A wave of recent papers demonstrates promising results in predicting cancer response to drug treatments while utilizing deep learning methods. These papers investigate diverse data representations, neural network architectures, learning methodologies, and evaluations schemes. However, deciphering promising predominant and emerging trends is difficult due to the variety of explored methods and lack of standardized framework for comparing drug response prediction models. To obtain a comprehensive landscape of deep learning methods, we conducted an extensive search and analysis of deep learning models that predict the response to single drug treatments. A total of 61 deep learning-based models have been curated, and summary plots were generated. Based on the analysis, observable patterns and prevalence of methods have been revealed. This review allows to better understand the current state of the field and identify major challenges and promising solution paths.
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Affiliation(s)
- Alexander Partin
- Division of Data Science and Learning, Argonne National Laboratory, Lemont, IL, United States
| | - Thomas S. Brettin
- Division of Data Science and Learning, Argonne National Laboratory, Lemont, IL, United States
| | - Yitan Zhu
- Division of Data Science and Learning, Argonne National Laboratory, Lemont, IL, United States
| | - Oleksandr Narykov
- Division of Data Science and Learning, Argonne National Laboratory, Lemont, IL, United States
| | - Austin Clyde
- Division of Data Science and Learning, Argonne National Laboratory, Lemont, IL, United States
| | - Jamie Overbeek
- Division of Data Science and Learning, Argonne National Laboratory, Lemont, IL, United States
| | - Rick L. Stevens
- Division of Data Science and Learning, Argonne National Laboratory, Lemont, IL, United States
- Department of Computer Science, The University of Chicago, Chicago, IL, United States
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13
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He Y, Zhang X, Zhang S, Zhang Y, Xie B, Huang M, Zhang J, Shen L, Long W, Liu Q. Prognostic RNA-editing signature predicts immune functions and therapy responses in gliomas. Front Genet 2023; 14:1120354. [PMID: 36845382 PMCID: PMC9945230 DOI: 10.3389/fgene.2023.1120354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 01/30/2023] [Indexed: 02/11/2023] Open
Abstract
Background: RNA-editing refers to post-transcriptional transcript alterations that lead to the formation of protein isoforms and the progression of various tumors. However, little is known about its roles in gliomas. Aim: The aim of this study is to identify prognosis-related RNA-editing sites (PREs) in glioma, and to explore their specific effects on glioma and potential mechanisms of action. Methods: Glioma genomic and clinical data were obtained from TCGA database and SYNAPSE platform. The PREs was identified with regression analyses and the corresponding prognostic model was evaluated with survival analysis and receiver operating characteristic curve. Functional enrichment of differentially expressed genes between risk groups was performed to explore action mechanisms. The CIBERSORT, ssGSEA, gene set variation analysis, and ESTIMATE algorithms were employed to assess the association between PREs risk score and variations of tumor microenvironment, immune cell infiltration, immune checkpoints, and immune responses. The maftools and pRRophetic packages were used to evaluate tumor mutation burden and predict drug sensitivity. Results: A total of thirty-five RNA-editing sites were identified as prognosis-related in glioma. Functional enrichment implied variation of immune-related pathways between groups. Notably, glioma samples with higher PREs risk score exhibited higher immune score, lower tumor purity, increased infiltration of macrophage and regulatory T cells, suppressed NK cell activation, elevated immune function score, upregulated immune checkpoint gene expression, and higher tumor mutation burden, all of which implied worse response to immune therapy. Finally, high-risk glioma samples are more sensitive to Z-LLNle-CHO and temozolomide, while the low-risk ones respond better to Lisitinib. Conclusion: We identified a PREs signature of thirty-five RNA editing sites and calculated their corresponding risk coefficients. Higher total signature risk score indicates worse prognosis and worse immune response and lower sensitivity to immune therapy. The novel PREs signature could help risk stratification, immunotherapy response prediction, individualized treatment strategy-making for glioma patients, and development of novel therapeutic approaches.
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Affiliation(s)
- Yi He
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Xingshu Zhang
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Sen Zhang
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Yi Zhang
- Department of Neurosurgery, People’s Hospital of Dengzhou, Dengzhou, Henan, China
| | - Bo Xie
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Meng Huang
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China,Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China,Guangdong Cardiovascular Institute, Guangzhou, China
| | - Junjie Zhang
- Department of Thoracic Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Lili Shen
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Wenyong Long
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China,*Correspondence: Wenyong Long, ; Qing Liu,
| | - Qing Liu
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China,*Correspondence: Wenyong Long, ; Qing Liu,
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14
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Hosseini SR, Zhou X. CCSynergy: an integrative deep-learning framework enabling context-aware prediction of anti-cancer drug synergy. Brief Bioinform 2023; 24:bbac588. [PMID: 36562722 PMCID: PMC9851301 DOI: 10.1093/bib/bbac588] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 11/21/2022] [Accepted: 11/30/2022] [Indexed: 12/24/2022] Open
Abstract
Combination therapy is a promising strategy for confronting the complexity of cancer. However, experimental exploration of the vast space of potential drug combinations is costly and unfeasible. Therefore, computational methods for predicting drug synergy are much needed for narrowing down this space, especially when examining new cellular contexts. Here, we thus introduce CCSynergy, a flexible, context aware and integrative deep-learning framework that we have established to unleash the potential of the Chemical Checker extended drug bioactivity profiles for the purpose of drug synergy prediction. We have shown that CCSynergy enables predictions of superior accuracy, remarkable robustness and improved context generalizability as compared to the state-of-the-art methods in the field. Having established the potential of CCSynergy for generating experimentally validated predictions, we next exhaustively explored the untested drug combination space. This resulted in a compendium of potentially synergistic drug combinations on hundreds of cancer cell lines, which can guide future experimental screens.
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Affiliation(s)
- Sayed-Rzgar Hosseini
- School of Biomedical Informatics, University of Texas Health Science Center (UTHealth), Houston, TX, USA
| | - Xiaobo Zhou
- School of Biomedical Informatics, University of Texas Health Science Center (UTHealth), Houston, TX, USA
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15
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Bang D, Gu J, Park J, Jeong D, Koo B, Yi J, Shin J, Jung I, Kim S, Lee S. A Survey on Computational Methods for Investigation on ncRNA-Disease Association through the Mode of Action Perspective. Int J Mol Sci 2022; 23:ijms231911498. [PMID: 36232792 PMCID: PMC9570358 DOI: 10.3390/ijms231911498] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 09/18/2022] [Accepted: 09/26/2022] [Indexed: 02/01/2023] Open
Abstract
Molecular and sequencing technologies have been successfully used in decoding biological mechanisms of various diseases. As revealed by many novel discoveries, the role of non-coding RNAs (ncRNAs) in understanding disease mechanisms is becoming increasingly important. Since ncRNAs primarily act as regulators of transcription, associating ncRNAs with diseases involves multiple inference steps. Leveraging the fast-accumulating high-throughput screening results, a number of computational models predicting ncRNA-disease associations have been developed. These tools suggest novel disease-related biomarkers or therapeutic targetable ncRNAs, contributing to the realization of precision medicine. In this survey, we first introduce the biological roles of different ncRNAs and summarize the databases containing ncRNA-disease associations. Then, we suggest a new trend in recent computational prediction of ncRNA-disease association, which is the mode of action (MoA) network perspective. This perspective includes integrating ncRNAs with mRNA, pathway and phenotype information. In the next section, we describe computational methodologies widely used in this research domain. Existing computational studies are then summarized in terms of their coverage of the MoA network. Lastly, we discuss the potential applications and future roles of the MoA network in terms of integrating biological mechanisms for ncRNA-disease associations.
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Affiliation(s)
- Dongmin Bang
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea
| | - Jeonghyeon Gu
- Interdisciplinary Program in Artificial Intelligence, Seoul National University, Seoul 08826, Korea
| | - Joonhyeong Park
- Department of Computer Science and Engineering, Seoul National University, Seoul 08826, Korea
| | - Dabin Jeong
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea
| | - Bonil Koo
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea
| | - Jungseob Yi
- Interdisciplinary Program in Artificial Intelligence, Seoul National University, Seoul 08826, Korea
| | - Jihye Shin
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea
| | - Inuk Jung
- Department of Computer Science and Engineering, Kyungpook National University, Daegu 41566, Korea
| | - Sun Kim
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea
- Interdisciplinary Program in Artificial Intelligence, Seoul National University, Seoul 08826, Korea
- Department of Computer Science and Engineering, Seoul National University, Seoul 08826, Korea
- MOGAM Institute for Biomedical Research, Yongin-si 16924, Korea
| | - Sunho Lee
- AIGENDRUG Co., Ltd., Seoul 08826, Korea
- Correspondence:
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16
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Preto AJ, Matos-Filipe P, Mourão J, Moreira IS. SYNPRED: prediction of drug combination effects in cancer using different synergy metrics and ensemble learning. Gigascience 2022; 11:6717722. [PMID: 36155782 PMCID: PMC9511701 DOI: 10.1093/gigascience/giac087] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Revised: 06/14/2022] [Accepted: 08/18/2022] [Indexed: 11/26/2022] Open
Abstract
Background In cancer research, high-throughput screening technologies produce large amounts of multiomics data from different populations and cell types. However, analysis of such data encounters difficulties due to disease heterogeneity, further exacerbated by human biological complexity and genomic variability. The specific profile of cancer as a disease (or, more realistically, a set of diseases) urges the development of approaches that maximize the effect while minimizing the dosage of drugs. Now is the time to redefine the approach to drug discovery, bringing an artificial intelligence (AI)–powered informational view that integrates the relevant scientific fields and explores new territories. Results Here, we show SYNPRED, an interdisciplinary approach that leverages specifically designed ensembles of AI algorithms, as well as links omics and biophysical traits to predict anticancer drug synergy. It uses 5 reference models (Bliss, Highest Single Agent, Loewe, Zero Interaction Potency, and Combination Sensitivity Score), which, coupled with AI algorithms, allowed us to attain the ones with the best predictive performance and pinpoint the most appropriate reference model for synergy prediction, often overlooked in similar studies. By using an independent test set, SYNPRED exhibits state-of-the-art performance metrics either in the classification (accuracy, 0.85; precision, 0.91; recall, 0.90; area under the receiver operating characteristic, 0.80; and F1-score, 0.91) or in the regression models, mainly when using the Combination Sensitivity Score synergy reference model (root mean square error, 11.07; mean squared error, 122.61; Pearson, 0.86; mean absolute error, 7.43; Spearman, 0.87). Moreover, data interpretability was achieved by deploying the most current and robust feature importance approaches. A simple web-based application was constructed, allowing easy access by nonexpert researchers. Conclusions The performance of SYNPRED rivals that of the existing methods that tackle the same problem, yielding unbiased results trained with one of the most comprehensive datasets available (NCI ALMANAC). The leveraging of different reference models allowed deeper insights into which of them can be more appropriately used for synergy prediction. The Combination Sensitivity Score clearly stood out with improved performance among the full scope of surveyed approaches and synergy reference models. Furthermore, SYNPRED takes a particular focus on data interpretability, which has been in the spotlight lately when using the most advanced AI techniques.
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Affiliation(s)
- António J Preto
- Center for Neuroscience and Cell Biology, University of Coimbra, 3004-504 Coimbra, Portugal.,PhD Programme in Experimental Biology and Biomedicine, Institute for Interdisciplinary Research (IIIUC), University of Coimbra, Casa Costa Alemão, 3030-789 Coimbra, Portugal
| | - Pedro Matos-Filipe
- Center for Neuroscience and Cell Biology, University of Coimbra, 3004-504 Coimbra, Portugal
| | - Joana Mourão
- CNC-Center for Neuroscience and Cell Biology, CIBB-Center for Innovative Biomedicine and Biotechnology, 3004-504 Coimbra, Portugal
| | - Irina S Moreira
- Department of Life Sciences, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal.,CNC-Center for Neuroscience and Cell Biology, CIBB-Center for Innovative Biomedicine and Biotechnology, 3004-504 Coimbra, Portugal
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17
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Kuru HI, Tastan O, Cicek AE. MatchMaker: A Deep Learning Framework for Drug Synergy Prediction. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:2334-2344. [PMID: 34086576 DOI: 10.1109/tcbb.2021.3086702] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Drug combination therapies have been a viable strategy for the treatment of complex diseases such as cancer due to increased efficacy and reduced side effects. However, experimentally validating all possible combinations for synergistic interaction even with high-throughout screens is intractable due to vast combinatorial search space. Computational techniques can reduce the number of combinations to be evaluated experimentally by prioritizing promising candidates. We present MatchMaker that predicts drug synergy scores using drug chemical structure information and gene expression profiles of cell lines in a deep learning framework. For the first time, our model utilizes the largest known drug combination dataset to date, DrugComb. We compare the performance of MatchMaker with the state-of-the-art models and observe up to ∼ 15% correlation and ∼ 33% mean squared error (MSE) improvements over the next best method. We investigate the cell types and drug pairs that are relatively harder to predict and present novel candidate pairs. MatchMaker is built and available at https://github.com/tastanlab/matchmaker.
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18
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DCE-DForest: A Deep Forest Model for the Prediction of Anticancer Drug Combination Effects. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:8693746. [PMID: 35720022 PMCID: PMC9203182 DOI: 10.1155/2022/8693746] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Revised: 05/13/2022] [Accepted: 05/23/2022] [Indexed: 11/18/2022]
Abstract
Drug combinations have recently been studied intensively due to their critical role in cancer treatment. Computational prediction of drug synergy has become a popular alternative strategy to experimental methods for anticancer drug synergy predictions. In this paper, a deep learning model called DCE-DForest is proposed to predict the synergistic effect of drug combinations. To sufficiently extract drug information, the paper leverages BERT (Bidirectional Encoder Representations from Transformers) to encode the drug and the deep forest to model the nonlinear relationship between the drugs and cell lines. The experimental results on the synergy datasets demonstrate that the proposed method consistently shows superior performance over the other machine learning models.
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19
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Yan J, Hu Z, Li ZW, Sun S, Guo WF. Network Control Models With Personalized Genomics Data for Understanding Tumor Heterogeneity in Cancer. Front Oncol 2022; 12:891676. [PMID: 35712516 PMCID: PMC9195174 DOI: 10.3389/fonc.2022.891676] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 04/12/2022] [Indexed: 11/25/2022] Open
Abstract
Due to rapid development of high-throughput sequencing and biotechnology, it has brought new opportunities and challenges in developing efficient computational methods for exploring personalized genomics data of cancer patients. Because of the high-dimension and small sample size characteristics of these personalized genomics data, it is difficult for excavating effective information by using traditional statistical methods. In the past few years, network control methods have been proposed to solve networked system with high-dimension and small sample size. Researchers have made progress in the design and optimization of network control principles. However, there are few studies comprehensively surveying network control methods to analyze the biomolecular network data of individual patients. To address this problem, here we comprehensively surveyed complex network control methods on personalized omics data for understanding tumor heterogeneity in precision medicine of individual patients with cancer.
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Affiliation(s)
- Jipeng Yan
- Department of Nephrology, Xijing Hospital, The Fourth Military Medical University, Xi’an, China
| | - Zhuo Hu
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, China
| | - Zong-Wei Li
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, China
| | - Shiren Sun
- Department of Nephrology, Xijing Hospital, The Fourth Military Medical University, Xi’an, China
- *Correspondence: Wei-Feng Guo, ; Shiren Sun,
| | - Wei-Feng Guo
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, China
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
- *Correspondence: Wei-Feng Guo, ; Shiren Sun,
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20
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Park H, Yamaguchi R, Imoto S, Miyano S. Xprediction: Explainable EGFR-TKIs response prediction based on drug sensitivity specific gene networks. PLoS One 2022; 17:e0261630. [PMID: 35584089 PMCID: PMC9116684 DOI: 10.1371/journal.pone.0261630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2021] [Accepted: 12/06/2021] [Indexed: 12/03/2022] Open
Abstract
In recent years, drug sensitivity prediction has garnered a great deal of attention due to the growing interest in precision medicine. Several computational methods have been developed for drug sensitivity prediction and the identification of related markers. However, most previous studies have ignored genetic interaction, although complex diseases (e.g., cancer) involve many genes intricately connected in a molecular network rather than the abnormality of a single gene. To effectively predict drug sensitivity and understand its mechanism, we propose a novel strategy for explainable drug sensitivity prediction based on sample-specific gene regulatory networks, designated Xprediction. Our strategy first estimates sample-specific gene regulatory networks that enable us to identify the molecular interplay underlying varying clinical characteristics of cell lines. We then, predict drug sensitivity based on the estimated sample-specific gene regulatory networks. The predictive models are based on machine learning approaches, i.e., random forest, kernel support vector machine, and deep neural network. Although the machine learning models provide remarkable results for prediction and classification, we cannot understand how the models reach their decisions. In other words, the methods suffer from the black box problem and thus, we cannot identify crucial molecular interactions that involve drug sensitivity-related mechanisms. To address this issue, we propose a method that describes the importance of each molecular interaction for the drug sensitivity prediction result. The proposed method enables us to identify crucial gene-gene interactions and thereby, interpret the prediction results based on the identified markers. To evaluate our strategy, we applied Xprediction to EGFR-TKIs prediction based on drug sensitivity specific gene regulatory networks and identified important molecular interactions for EGFR-TKIs prediction. Our strategy effectively performed drug sensitivity prediction compared with prediction based on the expression levels of genes. We also verified through literature, the EGFR-TKIs-related mechanisms of a majority of the identified markers. We expect our strategy to be a useful tool for predicting tasks and uncovering complex mechanisms related to pharmacological profiles, such as mechanisms of acquired drug resistance or sensitivity of cancer cells.
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Affiliation(s)
- Heewon Park
- M&D Data Science Center, Tokyo Medical and Dental University, Bunkyo-ku, Tokyo, Japan
- * E-mail:
| | - Rui Yamaguchi
- Division of Cancer Systems Biology, Aichi Cancer Center Research Institute, Chikusa-ku, Nagoya, Aichi, Japan
- Division of Cancer Informatics, Nagoya University Graduate School of Medicine, Showa-ku, Nagoya, Aichi, Japan
- Human Genome Center, The Institute of Medical Science, The University of Tokyo, Minato-ku, Tokyo, Japan
| | - Seiya Imoto
- Human Genome Center, The Institute of Medical Science, The University of Tokyo, Minato-ku, Tokyo, Japan
| | - Satoru Miyano
- M&D Data Science Center, Tokyo Medical and Dental University, Bunkyo-ku, Tokyo, Japan
- Human Genome Center, The Institute of Medical Science, The University of Tokyo, Minato-ku, Tokyo, Japan
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21
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Shim Y, Lee M, Kim PJ, Kim HG. A novel approach to predicting the synergy of anti-cancer drug combinations using document-based feature extraction. BMC Bioinformatics 2022; 23:163. [PMID: 35513784 PMCID: PMC9069794 DOI: 10.1186/s12859-022-04698-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 04/26/2022] [Indexed: 11/10/2022] Open
Abstract
Background To reduce drug side effects and enhance their therapeutic effect compared with single drugs, drug combination research, combining two or more drugs, is highly important. Conducting in-vivo and in-vitro experiments on a vast number of drug combinations incurs astronomical time and cost. To reduce the number of combinations, researchers classify whether drug combinations are synergistic through in-silico methods. Since unstructured data, such as biomedical documents, include experimental types, methods, and results, it can be beneficial extracting features from documents to predict anti-cancer drug combination synergy. However, few studies predict anti-cancer drug combination synergy using document-extracted features. Results We present a novel approach for anti-cancer drug combination synergy prediction using document-based feature extraction. Our approach is divided into two steps. First, we extracted documents containing validated anti-cancer drug combinations and cell lines. Drug and cell line synonyms in the extracted documents were converted into representative words, and the documents were preprocessed by tokenization, lemmatization, and stopword removal. Second, the drug and cell line features were extracted from the preprocessed documents, and training data were constructed by feature concatenation. A prediction model based on deep and machine learning was created using the training data. The use of our features yielded higher results compared to the majority of published studies. Conclusions Using our prediction model, researchers can save time and cost on new anti-cancer drug combination discoveries. Additionally, since our feature extraction method does not require structuring of unstructured data, new data can be immediately applied without any data scalability issues.
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Affiliation(s)
- Yongsun Shim
- Biomedical Knowledge Engineering, Seoul National University, Seoul, Republic of Korea
| | - Munhwan Lee
- Biomedical Knowledge Engineering, Seoul National University, Seoul, Republic of Korea
| | - Pil-Jong Kim
- School of Dentistry and Dental Research Institute, Seoul National University, Seoul, Republic of Korea
| | - Hong-Gee Kim
- Biomedical Knowledge Engineering, Seoul National University, Seoul, Republic of Korea. .,School of Dentistry and Dental Research Institute, Seoul National University, Seoul, Republic of Korea.
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22
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Torkamannia A, Omidi Y, Ferdousi R. A review of machine learning approaches for drug synergy prediction in cancer. Brief Bioinform 2022; 23:6552269. [PMID: 35323854 DOI: 10.1093/bib/bbac075] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 01/19/2022] [Accepted: 02/14/2022] [Indexed: 02/06/2023] Open
Abstract
Combinational pharmacotherapy with the synergistic/additive effect is a powerful treatment strategy for complex diseases such as malignancies. Identifying synergistic combinations with various compounds and structures requires testing a large number of compound combinations. However, in practice, examining different compounds by in vivo and in vitro approaches is costly, infeasible and challenging. In the last decades, significant success has been achieved by expanding computational methods in different pharmacological and bioinformatics domains. As promising tools, computational approaches such as machine learning algorithms (MLAs) are used for prioritizing combinational pharmacotherapies. This review aims to provide the models developed to predict synergistic drug combinations in cancer by MLAs with various information, including gene expression, protein-protein interactions, metabolite interactions, pathways and pharmaceutical information such as chemical structure, molecular descriptor and drug-target interactions.
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Affiliation(s)
- Anna Torkamannia
- Department of Health Information Technology, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Yadollah Omidi
- Department of Pharmaceutical Sciences, College of Pharmacy, Nova Southeastern University, Fort Lauderdale, Florida, United States
| | - Reza Ferdousi
- Department of Health Information Technology, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran
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23
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Soh J, Park S, Lee H. HIDTI: integration of heterogeneous information to predict drug-target interactions. Sci Rep 2022; 12:3793. [PMID: 35260608 PMCID: PMC8904809 DOI: 10.1038/s41598-022-07608-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 02/16/2022] [Indexed: 11/21/2022] Open
Abstract
Identification of drug-target interactions (DTIs) plays a crucial role in drug development. Traditional laboratory-based DTI discovery is generally costly and time-consuming. Therefore, computational approaches have been developed to predict interactions between drug candidates and disease-causing proteins. We designed a novel method, termed heterogeneous information integration for DTI prediction (HIDTI), based on the concept of predicting vectors for all of unknown/unavailable heterogeneous drug- and protein-related information. We applied a residual network in HIDTI to extract features of such heterogeneous information for predicting DTIs, and tested the model using drug-based ten-fold cross-validation to examine the prediction performance for unseen drugs. As a result, HIDTI outperformed existing models using heterogeneous information, and was demonstrating that our method predicted heterogeneous information on unseen data better than other models. In conclusion, our study suggests that HIDTI has the potential to advance the field of drug development by accurately predicting the targets of new drugs.
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Affiliation(s)
- Jihee Soh
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, 61005, South Korea
| | - Sejin Park
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, 61005, South Korea
| | - Hyunju Lee
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, 61005, South Korea.
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24
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Xia F, Allen J, Balaprakash P, Brettin T, Garcia-Cardona C, Clyde A, Cohn J, Doroshow J, Duan X, Dubinkina V, Evrard Y, Fan YJ, Gans J, He S, Lu P, Maslov S, Partin A, Shukla M, Stahlberg E, Wozniak JM, Yoo H, Zaki G, Zhu Y, Stevens R. A cross-study analysis of drug response prediction in cancer cell lines. Brief Bioinform 2022; 23:bbab356. [PMID: 34524425 PMCID: PMC8769697 DOI: 10.1093/bib/bbab356] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 07/26/2021] [Accepted: 08/11/2021] [Indexed: 11/28/2022] Open
Abstract
To enable personalized cancer treatment, machine learning models have been developed to predict drug response as a function of tumor and drug features. However, most algorithm development efforts have relied on cross-validation within a single study to assess model accuracy. While an essential first step, cross-validation within a biological data set typically provides an overly optimistic estimate of the prediction performance on independent test sets. To provide a more rigorous assessment of model generalizability between different studies, we use machine learning to analyze five publicly available cell line-based data sets: National Cancer Institute 60, ancer Therapeutics Response Portal (CTRP), Genomics of Drug Sensitivity in Cancer, Cancer Cell Line Encyclopedia and Genentech Cell Line Screening Initiative (gCSI). Based on observed experimental variability across studies, we explore estimates of prediction upper bounds. We report performance results of a variety of machine learning models, with a multitasking deep neural network achieving the best cross-study generalizability. By multiple measures, models trained on CTRP yield the most accurate predictions on the remaining testing data, and gCSI is the most predictable among the cell line data sets included in this study. With these experiments and further simulations on partial data, two lessons emerge: (1) differences in viability assays can limit model generalizability across studies and (2) drug diversity, more than tumor diversity, is crucial for raising model generalizability in preclinical screening.
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Affiliation(s)
| | | | | | | | | | - Austin Clyde
- Argonne National Laboratory
- University of Chicago
| | | | | | | | | | | | - Ya Ju Fan
- Lawrence Livermore National Laboratory
| | | | | | - Pinyi Lu
- Frederick National Laboratory for Cancer Research
| | | | | | | | | | | | | | - George Zaki
- Frederick National Laboratory for Cancer Research
| | | | - Rick Stevens
- Argonne National Laboratory
- University of Chicago
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25
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AIM in Genomic Basis of Medicine: Applications. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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26
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Artificial Intelligence for Precision Oncology. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1361:249-268. [DOI: 10.1007/978-3-030-91836-1_14] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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27
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Ye Z, Chen F, Zeng J, Gao J, Zhang MQ. ScaffComb: A Phenotype-Based Framework for Drug Combination Virtual Screening in Large-Scale Chemical Datasets. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2021; 8:e2102092. [PMID: 34723439 PMCID: PMC8693048 DOI: 10.1002/advs.202102092] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 07/29/2021] [Indexed: 06/13/2023]
Abstract
Combinational therapy is used for a long time in cancer treatment to overcome drug resistance related to monotherapy. Increased pharmacological data and the rapid development of deep learning methods have enabled the construction of models to predict and screen drug pairs. However, the size of drug libraries is restricted to hundreds to thousands of compounds. The ScaffComb framework, which aims to bridge the gaps in the virtual screening of drug combinations in large-scale databases, is proposed here. Inspired by phenotype-based drug design, ScaffComb integrates phenotypic information into molecular scaffolds, which can be used to screen the drug library and identify potent drug combinations. First, ScaffComb is validated using the US food and drug administration dataset and known drug combinations are successfully reidentified. Then, ScaffComb is applied to screen the ZINC and ChEMBL databases, which yield novel drug combinations and reveal an ability to discover new synergistic mechanisms. To our knowledge, ScaffComb is the first method to use phenotype-based virtual screening of drug combinations in large-scale chemical datasets.
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Affiliation(s)
- Zhaofeng Ye
- MOE Key Laboratory of BioinformaticsBioinformatics DivisionCenter for Synthetic and Systems BiologyBNRistDepartment of AutomationTsinghua UniversityBeijing100084China
- School of MedicineTsinghua UniversityBeijing100084China
| | - Fengling Chen
- Center for Stem Cell Biology and Regenerative MedicineMOE Key Laboratory of BioinformaticsTsinghua UniversityBeijing100084China
- Tsinghua‐Peking Center for Life SciencesBeijing100084China
| | - Jiangyang Zeng
- MOE Key Laboratory of BioinformaticsBioinformatics DivisionCenter for Synthetic and Systems BiologyBNRistDepartment of AutomationTsinghua UniversityBeijing100084China
- Institute for Interdisciplinary Information SciencesTsinghua UniversityBeijing100084China
| | - Juntao Gao
- MOE Key Laboratory of BioinformaticsBioinformatics DivisionCenter for Synthetic and Systems BiologyBNRistDepartment of AutomationTsinghua UniversityBeijing100084China
| | - Michael Q. Zhang
- MOE Key Laboratory of BioinformaticsBioinformatics DivisionCenter for Synthetic and Systems BiologyBNRistDepartment of AutomationTsinghua UniversityBeijing100084China
- School of MedicineTsinghua UniversityBeijing100084China
- Department of Biological SciencesCenter for Systems BiologyThe University of Texas at DallasRichardsonTX75080‐3021USA
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28
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Newnes HV, Armitage JD, Audsley KM, Bosco A, Waithman J. Directing the Future Breakthroughs in Immunotherapy: The Importance of a Holistic Approach to the Tumour Microenvironment. Cancers (Basel) 2021; 13:cancers13235911. [PMID: 34885021 PMCID: PMC8656826 DOI: 10.3390/cancers13235911] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 11/22/2021] [Accepted: 11/23/2021] [Indexed: 12/24/2022] Open
Abstract
Simple Summary Immunotherapies have changed the way we treat cancer and, while some patients have benefitted greatly, there are still those that do not respond to therapy. Understanding why some patients respond to therapy and others do not is critical in developing new immunotherapeutic strategies. The increasing awareness of the importance of investigating the tumour in its entirety, including the surrounding tissue and role of various immune cells is helping to differentiate responders and non-responders. In addition, the resolution gained by the development of sophisticated bioinformatic technologies allows for a deeper understanding of the complex roles of individual cells in the tumour. This advancement will be critical for the development of novel therapies to treat cancer. Abstract Immunotherapy has revolutionised the treatment of cancers by exploiting the immune system to eliminate tumour cells. Despite the impressive response in a proportion of patients, clinical benefit has been limited thus far. A significant focus to date has been the identification of specific markers associated with response to immunotherapy. Unfortunately, the heterogeneity between patients and cancer types means identifying markers of response to therapy is inherently complex. There is a growing appreciation for the role of the tumour microenvironment (TME) in directing response to immunotherapy. The TME is highly heterogeneous and contains immune, stromal, vascular and tumour cells that all communicate and interact with one another to form solid tumours. This review analyses major cell populations present within the TME with a focus on their diverse and often contradictory roles in cancer and how this informs our understanding of immunotherapy. Furthermore, we discuss the role of integrated omics in providing a comprehensive view of the TME and demonstrate the potential of leveraging multi-omics to decipher the underlying mechanisms of anti-tumour immunity for the development of novel immunotherapeutic strategies.
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29
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Fan K, Cheng L, Li L. Artificial intelligence and machine learning methods in predicting anti-cancer drug combination effects. Brief Bioinform 2021; 22:bbab271. [PMID: 34347041 PMCID: PMC8574962 DOI: 10.1093/bib/bbab271] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 06/04/2021] [Accepted: 06/25/2021] [Indexed: 12/27/2022] Open
Abstract
Drug combinations have exhibited promising therapeutic effects in treating cancer patients with less toxicity and adverse side effects. However, it is infeasible to experimentally screen the enormous search space of all possible drug combinations. Therefore, developing computational models to efficiently and accurately identify potential anti-cancer synergistic drug combinations has attracted a lot of attention from the scientific community. Hypothesis-driven explicit mathematical methods or network pharmacology models have been popular in the last decade and have been comprehensively reviewed in previous surveys. With the surge of artificial intelligence and greater availability of large-scale datasets, machine learning especially deep learning methods are gaining popularity in the field of computational models for anti-cancer drug synergy prediction. Machine learning-based methods can be derived without strong assumptions about underlying mechanisms and have achieved state-of-the-art prediction performances, promoting much greater growth of the field. Here, we present a structured overview of available large-scale databases and machine learning especially deep learning methods in computational predictive models for anti-cancer drug synergy prediction. We provide a unified framework for machine learning models and detail existing model architectures as well as their contributions and limitations, shedding light into the future design of computational models. Besides, unbiased experiments are conducted to provide in-depth comparisons between reviewed papers in terms of their prediction performance.
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Affiliation(s)
- Kunjie Fan
- Department of Biomedical Informatics of The Ohio State University, 43202 Columbus, OH, USA
| | - Lijun Cheng
- Department of Biomedical Informatics of The Ohio State University, 43202 Columbus, OH, USA
| | - Lang Li
- Department of Biomedical Informatics of The Ohio State University, 43202 Columbus, OH, USA
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30
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Huang K, Xiao C, Glass LM, Critchlow CW, Gibson G, Sun J. Machine learning applications for therapeutic tasks with genomics data. PATTERNS (NEW YORK, N.Y.) 2021; 2:100328. [PMID: 34693370 PMCID: PMC8515011 DOI: 10.1016/j.patter.2021.100328] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Thanks to the increasing availability of genomics and other biomedical data, many machine learning algorithms have been proposed for a wide range of therapeutic discovery and development tasks. In this survey, we review the literature on machine learning applications for genomics through the lens of therapeutic development. We investigate the interplay among genomics, compounds, proteins, electronic health records, cellular images, and clinical texts. We identify 22 machine learning in genomics applications that span the whole therapeutics pipeline, from discovering novel targets, personalizing medicine, developing gene-editing tools, all the way to facilitating clinical trials and post-market studies. We also pinpoint seven key challenges in this field with potentials for expansion and impact. This survey examines recent research at the intersection of machine learning, genomics, and therapeutic development.
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Affiliation(s)
- Kexin Huang
- Department of Computer Science, Stanford University, Stanford, CA 94305, USA
| | - Cao Xiao
- Amplitude, San Francisco, CA 94105, USA
| | - Lucas M. Glass
- Analytics Center of Excellence, IQVIA, Cambridge, MA 02139, USA
| | | | - Greg Gibson
- Center for Integrative Genomics, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Jimeng Sun
- Computer Science Department and Carle's Illinois College of Medicine, University of Illinois at Urbana-Champaign, Urbana, IL 61820, USA
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31
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Challenges Faced by Clinicians in the Personalized Treatment Planning: A Literature Review and the First Results of the Russian National Cancer Program. Crit Care Res Pract 2021; 2021:6649771. [PMID: 34603796 PMCID: PMC8483928 DOI: 10.1155/2021/6649771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 09/15/2021] [Indexed: 11/25/2022] Open
Abstract
Advances in cancer molecular profiling have enabled the development of more effective approaches to the diagnosis and personalized treatment of tumors. However, treatment planning has become more labor intensive, requiring hours or even days of clinician effort to optimize an individual patient case in a trial-and-error manner. Lessons learned from the world cancer programs provide insights into ways to develop approaches for the treatment strategy definition which can be introduced into clinical practice. This article highlights the variety of breakthroughs in patients' cancer treatment and some challenges that this field faces now in Russia. In this report, we consider the key characteristics for planning an optimal clinical treatment regimen and which should be included in the algorithm of clinical decision support systems. We discuss the perspectives of implementing artificial intelligence-based systems in cancer treatment planning in Russia.
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32
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Deep learning identifies synergistic drug combinations for treating COVID-19. Proc Natl Acad Sci U S A 2021; 118:2105070118. [PMID: 34526388 PMCID: PMC8488647 DOI: 10.1073/pnas.2105070118] [Citation(s) in RCA: 53] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/21/2021] [Indexed: 11/18/2022] Open
Abstract
Effective treatments for COVID-19 are urgently needed. However, discovering single-agent therapies with activity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been challenging. Combination therapies play an important role in antiviral therapies, due to their improved efficacy and reduced toxicity. Recent approaches have applied deep learning to identify synergistic drug combinations for diseases with vast preexisting datasets, but these are not applicable to new diseases with limited combination data, such as COVID-19. Given that drug synergy often occurs through inhibition of discrete biological targets, here we propose a neural network architecture that jointly learns drug-target interaction and drug-drug synergy. The model consists of two parts: a drug-target interaction module and a target-disease association module. This design enables the model to utilize drug-target interaction data and single-agent antiviral activity data, in addition to available drug-drug combination datasets, which may be small in nature. By incorporating additional biological information, our model performs significantly better in synergy prediction accuracy than previous methods with limited drug combination training data. We empirically validated our model predictions and discovered two drug combinations, remdesivir and reserpine as well as remdesivir and IQ-1S, which display strong antiviral SARS-CoV-2 synergy in vitro. Our approach, which was applied here to address the urgent threat of COVID-19, can be readily extended to other diseases for which a dearth of chemical-chemical combination data exists.
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33
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An X, Chen X, Yi D, Li H, Guan Y. Representation of molecules for drug response prediction. Brief Bioinform 2021; 23:6375515. [PMID: 34571534 DOI: 10.1093/bib/bbab393] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 08/28/2021] [Accepted: 08/30/2021] [Indexed: 12/18/2022] Open
Abstract
The rapid development of machine learning and deep learning algorithms in the recent decade has spurred an outburst of their applications in many research fields. In the chemistry domain, machine learning has been widely used to aid in drug screening, drug toxicity prediction, quantitative structure-activity relationship prediction, anti-cancer synergy score prediction, etc. This review is dedicated to the application of machine learning in drug response prediction. Specifically, we focus on molecular representations, which is a crucial element to the success of drug response prediction and other chemistry-related prediction tasks. We introduce three types of commonly used molecular representation methods, together with their implementation and application examples. This review will serve as a brief introduction of the broad field of molecular representations.
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Affiliation(s)
- Xin An
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Xi Chen
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Daiyao Yi
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Hongyang Li
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Yuanfang Guan
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
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34
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Piyawajanusorn C, Nguyen LC, Ghislat G, Ballester PJ. A gentle introduction to understanding preclinical data for cancer pharmaco-omic modeling. Brief Bioinform 2021; 22:6343527. [PMID: 34368843 DOI: 10.1093/bib/bbab312] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 06/25/2021] [Accepted: 07/20/2021] [Indexed: 12/16/2022] Open
Abstract
A central goal of precision oncology is to administer an optimal drug treatment to each cancer patient. A common preclinical approach to tackle this problem has been to characterize the tumors of patients at the molecular and drug response levels, and employ the resulting datasets for predictive in silico modeling (mostly using machine learning). Understanding how and why the different variants of these datasets are generated is an important component of this process. This review focuses on providing such introduction aimed at scientists with little previous exposure to this research area.
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Affiliation(s)
- Chayanit Piyawajanusorn
- Cancer Research Center of Marseille, INSERM U1068, F-13009 Marseille, France.,Institut Paoli-Calmettes, F-13009 Marseille, France.,Aix-Marseille Université, F-13284 Marseille, France.,CNRS UMR7258, F-13009 Marseille, France.,Faculty of Medicine and Public Health, HRH Princess Chulabhorn College of Medical Science, Chulabhorn Royal Academy, Bangkok, Thailand
| | - Linh C Nguyen
- Cancer Research Center of Marseille, INSERM U1068, F-13009 Marseille, France.,Institut Paoli-Calmettes, F-13009 Marseille, France.,Aix-Marseille Université, F-13284 Marseille, France.,CNRS UMR7258, F-13009 Marseille, France.,Department of Life Sciences, University of Science and Technology of Hanoi, Vietnam Academy of Science and Technology, Hanoi, Vietnam
| | - Ghita Ghislat
- U1104, CNRS UMR7280, Centre d'Immunologie de Marseille-Luminy, Inserm, Marseille, France
| | - Pedro J Ballester
- Cancer Research Center of Marseille, INSERM U1068, F-13009 Marseille, France.,Institut Paoli-Calmettes, F-13009 Marseille, France.,Aix-Marseille Université, F-13284 Marseille, France.,CNRS UMR7258, F-13009 Marseille, France
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35
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Koras K, Kizling E, Juraeva D, Staub E, Szczurek E. Interpretable deep recommender system model for prediction of kinase inhibitor efficacy across cancer cell lines. Sci Rep 2021; 11:15993. [PMID: 34362938 PMCID: PMC8346627 DOI: 10.1038/s41598-021-94564-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Accepted: 07/06/2021] [Indexed: 01/02/2023] Open
Abstract
Computational models for drug sensitivity prediction have the potential to significantly improve personalized cancer medicine. Drug sensitivity assays, combined with profiling of cancer cell lines and drugs become increasingly available for training such models. Multiple methods were proposed for predicting drug sensitivity from cancer cell line features, some in a multi-task fashion. So far, no such model leveraged drug inhibition profiles. Importantly, multi-task models require a tailored approach to model interpretability. In this work, we develop DEERS, a neural network recommender system for kinase inhibitor sensitivity prediction. The model utilizes molecular features of the cancer cell lines and kinase inhibition profiles of the drugs. DEERS incorporates two autoencoders to project cell line and drug features into 10-dimensional hidden representations and a feed-forward neural network to combine them into response prediction. We propose a novel interpretability approach, which in addition to the set of modeled features considers also the genes and processes outside of this set. Our approach outperforms simpler matrix factorization models, achieving R [Formula: see text] 0.82 correlation between true and predicted response for the unseen cell lines. The interpretability analysis identifies 67 biological processes that drive the cell line sensitivity to particular compounds. Detailed case studies are shown for PHA-793887, XMD14-99 and Dabrafenib.
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Affiliation(s)
- Krzysztof Koras
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Warsaw, Poland
| | - Ewa Kizling
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Warsaw, Poland
| | - Dilafruz Juraeva
- Oncology Bioinformatics, Translational Medicine, Merck Healthcare KGaA, Darmstadt, Germany
| | - Eike Staub
- Oncology Bioinformatics, Translational Medicine, Merck Healthcare KGaA, Darmstadt, Germany
| | - Ewa Szczurek
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Warsaw, Poland.
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36
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Bazgir O, Ghosh S, Pal R. Investigation of REFINED CNN ensemble learning for anti-cancer drug sensitivity prediction. Bioinformatics 2021; 37:i42-i50. [PMID: 34252971 PMCID: PMC8275339 DOI: 10.1093/bioinformatics/btab336] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Motivation Anti-cancer drug sensitivity prediction using deep learning models for individual cell line is a significant challenge in personalized medicine. Recently developed REFINED (REpresentation of Features as Images with NEighborhood Dependencies) CNN (Convolutional Neural Network)-based models have shown promising results in improving drug sensitivity prediction. The primary idea behind REFINED-CNN is representing high dimensional vectors as compact images with spatial correlations that can benefit from CNN architectures. However, the mapping from a high dimensional vector to a compact 2D image depends on the a priori choice of the distance metric and projection scheme with limited empirical procedures guiding these choices. Results In this article, we consider an ensemble of REFINED-CNN built under different choices of distance metrics and/or projection schemes that can improve upon a single projection based REFINED-CNN model. Results, illustrated using NCI60 and NCI-ALMANAC databases, demonstrate that the ensemble approaches can provide significant improvement in prediction performance as compared to individual models. We also develop the theoretical framework for combining different distance metrics to arrive at a single 2D mapping. Results demonstrated that distance-averaged REFINED-CNN produced comparable performance as obtained from stacking REFINED-CNN ensemble but with significantly lower computational cost. Availability and implementation The source code, scripts, and data used in the paper have been deposited in GitHub (https://github.com/omidbazgirTTU/IntegratedREFINED). Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Omid Bazgir
- Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX 79409, USA
| | - Souparno Ghosh
- Department of Mathematics and Statistics, University of Nebraska-Lincoln, Lincoln, NE 68583, USA
| | - Ranadip Pal
- Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX 79409, USA
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37
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Rafique R, Islam SR, Kazi JU. Machine learning in the prediction of cancer therapy. Comput Struct Biotechnol J 2021; 19:4003-4017. [PMID: 34377366 PMCID: PMC8321893 DOI: 10.1016/j.csbj.2021.07.003] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Revised: 07/06/2021] [Accepted: 07/07/2021] [Indexed: 12/15/2022] Open
Abstract
Resistance to therapy remains a major cause of cancer treatment failures, resulting in many cancer-related deaths. Resistance can occur at any time during the treatment, even at the beginning. The current treatment plan is dependent mainly on cancer subtypes and the presence of genetic mutations. Evidently, the presence of a genetic mutation does not always predict the therapeutic response and can vary for different cancer subtypes. Therefore, there is an unmet need for predictive models to match a cancer patient with a specific drug or drug combination. Recent advancements in predictive models using artificial intelligence have shown great promise in preclinical settings. However, despite massive improvements in computational power, building clinically useable models remains challenging due to a lack of clinically meaningful pharmacogenomic data. In this review, we provide an overview of recent advancements in therapeutic response prediction using machine learning, which is the most widely used branch of artificial intelligence. We describe the basics of machine learning algorithms, illustrate their use, and highlight the current challenges in therapy response prediction for clinical practice.
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Affiliation(s)
| | - S.M. Riazul Islam
- Department of Computer Science and Engineering, Sejong University, Seoul, South Korea
| | - Julhash U. Kazi
- Division of Translational Cancer Research, Department of Laboratory Medicine, Lund University, Lund, Sweden
- Lund Stem Cell Center, Department of Laboratory Medicine, Lund University, Lund, Sweden
- Corresponding author at: Division of Translational Cancer Research, Department of Laboratory Medicine, Lund University, Medicon village Building 404:C3, Scheelevägen 8, 22363 Lund, Sweden.
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38
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Meybodi FY, Eslahchi C. Predicting Anti-Cancer Drug Response by Finding Optimal Subset of Drugs. Bioinformatics 2021; 37:4509-4516. [PMID: 34170297 DOI: 10.1093/bioinformatics/btab466] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 05/26/2021] [Accepted: 06/22/2021] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION One of the most difficult challenges in precision medicine is determining the best treatment strategy for each patient based on personal information. Since drug response prediction in vitro is extremely expensive, time-consuming, and virtually impossible, and because there are so many cell lines and drug data, computational methods are needed. RESULTS MinDrug is a method for predicting anti-cancer drug response which try to identify the best subset of drugs that are the most similar to other drugs. MinDrug predicts the anti-cancer drug response on a new cell line using information from drugs in this subset and their connections to other drugs. MinDrug employs a heuristic star algorithm to identify an optimal subset of drugs and a regression technique known as Elastic-Net approaches to predict anti-cancer drug response in a new cell line. To test MinDrug, we use both statistical and biological methods to assess the selected drugs. MinDrug is also compared to four state-of-the-art approaches using various k-fold cross-validations on two large public datasets: GDSC and CCLE. MinDrug outperforms the other approaches in terms of precision, robustness, and speed. Furthermore, we compare the evaluation results of all the approaches with an external dataset with a statistical distribution that is not exactly the same as the training data. The results show that MinDrug continues to outperform the other approaches. AVAILABILITY MinDrug's source code can be found at https://github.com/yassaee/MinDrug. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Fatemeh Yassaee Meybodi
- Department of Computer and Data Sciences, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran, Iran
| | - Changiz Eslahchi
- Department of Computer and Data Sciences, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran, Iran.,School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
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39
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Boniolo F, Dorigatti E, Ohnmacht AJ, Saur D, Schubert B, Menden MP. Artificial intelligence in early drug discovery enabling precision medicine. Expert Opin Drug Discov 2021; 16:991-1007. [PMID: 34075855 DOI: 10.1080/17460441.2021.1918096] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Introduction: Precision medicine is the concept of treating diseases based on environmental factors, lifestyles, and molecular profiles of patients. This approach has been found to increase success rates of clinical trials and accelerate drug approvals. However, current precision medicine applications in early drug discovery use only a handful of molecular biomarkers to make decisions, whilst clinics gear up to capture the full molecular landscape of patients in the near future. This deep multi-omics characterization demands new analysis strategies to identify appropriate treatment regimens, which we envision will be pioneered by artificial intelligence.Areas covered: In this review, the authors discuss the current state of drug discovery in precision medicine and present our vision of how artificial intelligence will impact biomarker discovery and drug design.Expert opinion: Precision medicine is expected to revolutionize modern medicine; however, its traditional form is focusing on a few biomarkers, thus not equipped to leverage the full power of molecular landscapes. For learning how the development of drugs can be tailored to the heterogeneity of patients across their molecular profiles, artificial intelligence algorithms are the next frontier in precision medicine and will enable a fully personalized approach in drug design, and thus ultimately impacting clinical practice.
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Affiliation(s)
- Fabio Boniolo
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Centre for Environmental Health, Munich, Germany.,School of Medicine, Chair of Translational Cancer Research and Institute for Experimental Cancer Therapy, Klinikum Rechts Der Isar, Technische Universität München, Munich, Germany
| | - Emilio Dorigatti
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Centre for Environmental Health, Munich, Germany.,Statistical Learning and Data Science, Department of Statistics, Ludwig Maximilian Universität München, Munich, Germany
| | - Alexander J Ohnmacht
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Centre for Environmental Health, Munich, Germany.,Department of Biology, Ludwig-Maximilians University Munich, Martinsried, Germany
| | - Dieter Saur
- School of Medicine, Chair of Translational Cancer Research and Institute for Experimental Cancer Therapy, Klinikum Rechts Der Isar, Technische Universität München, Munich, Germany
| | - Benjamin Schubert
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Centre for Environmental Health, Munich, Germany.,Department of Mathematics, Technical University of Munich, Garching, Germany
| | - Michael P Menden
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Centre for Environmental Health, Munich, Germany.,Department of Biology, Ludwig-Maximilians University Munich, Martinsried, Germany.,German Centre for Diabetes Research (DZD e.V.), Neuherberg, Germany
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40
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Tan X, Yu Y, Duan K, Zhang J, Sun P, Sun H. Current Advances and Limitations of Deep Learning in Anticancer Drug Sensitivity Prediction. Curr Top Med Chem 2021; 20:1858-1867. [PMID: 32648840 DOI: 10.2174/1568026620666200710101307] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Revised: 04/02/2020] [Accepted: 04/14/2020] [Indexed: 02/06/2023]
Abstract
Anticancer drug screening can accelerate drug discovery to save the lives of cancer patients, but cancer heterogeneity makes this screening challenging. The prediction of anticancer drug sensitivity is useful for anticancer drug development and the identification of biomarkers of drug sensitivity. Deep learning, as a branch of machine learning, is an important aspect of in silico research. Its outstanding computational performance means that it has been used for many biomedical purposes, such as medical image interpretation, biological sequence analysis, and drug discovery. Several studies have predicted anticancer drug sensitivity based on deep learning algorithms. The field of deep learning has made progress regarding model performance and multi-omics data integration. However, deep learning is limited by the number of studies performed and data sources available, so it is not perfect as a pre-clinical approach for use in the anticancer drug screening process. Improving the performance of deep learning models is a pressing issue for researchers. In this review, we introduce the research of anticancer drug sensitivity prediction and the use of deep learning in this research area. To provide a reference for future research, we also review some common data sources and machine learning methods. Lastly, we discuss the advantages and disadvantages of deep learning, as well as the limitations and future perspectives regarding this approach.
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Affiliation(s)
- Xian Tan
- School of Information Science and Technology, Northeast Normal University, Changchun 130117, China
| | - Yang Yu
- School of Information Science and Technology, Northeast Normal University, Changchun 130117, China
| | - Kaiwen Duan
- School of Information Science and Technology, Northeast Normal University, Changchun 130117, China
| | - Jingbo Zhang
- School of Information Science and Technology, Northeast Normal University, Changchun 130117, China
| | - Pingping Sun
- School of Information Science and Technology, Northeast Normal University, Changchun 130117, China
| | - Hui Sun
- College of Humanities and Sciences of Northeast Normal University, Changchun 130117, China
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Partin A, Brettin T, Evrard YA, Zhu Y, Yoo H, Xia F, Jiang S, Clyde A, Shukla M, Fonstein M, Doroshow JH, Stevens RL. Learning curves for drug response prediction in cancer cell lines. BMC Bioinformatics 2021; 22:252. [PMID: 34001007 PMCID: PMC8130157 DOI: 10.1186/s12859-021-04163-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2020] [Accepted: 05/04/2021] [Indexed: 11/10/2022] Open
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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Affiliation(s)
- Alexander Partin
- Division of Data Science and Learning, Argonne National Laboratory, Lemont, IL, USA. .,University of Chicago Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL, USA.
| | - Thomas Brettin
- University of Chicago Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL, USA.,Computing, Environment and Life Sciences, Argonne National Laboratory, Lemont, IL, USA
| | - Yvonne A Evrard
- Frederick National Laboratory for Cancer Research, Leidos Biomedical Research Inc., Frederick, MD, USA
| | - Yitan Zhu
- Division of Data Science and Learning, Argonne National Laboratory, Lemont, IL, USA.,University of Chicago Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL, USA
| | - Hyunseung Yoo
- Division of Data Science and Learning, Argonne National Laboratory, Lemont, IL, USA.,University of Chicago Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL, USA
| | - Fangfang Xia
- Division of Data Science and Learning, Argonne National Laboratory, Lemont, IL, USA.,University of Chicago Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL, USA
| | - Songhao Jiang
- Department of Computer Science, University of Chicago, Chicago, IL, USA
| | - Austin Clyde
- Division of Data Science and Learning, Argonne National Laboratory, Lemont, IL, USA.,Department of Computer Science, University of Chicago, Chicago, IL, USA
| | - Maulik Shukla
- Division of Data Science and Learning, Argonne National Laboratory, Lemont, IL, USA.,University of Chicago Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL, USA
| | - Michael Fonstein
- Biosciences Division, Argonne National Laboratory, Lemont, IL, USA
| | - James H Doroshow
- Division of Cancer Therapeutics and Diagnosis, National Cancer Institute, Bethesda, MD, USA
| | - Rick L Stevens
- Computing, Environment and Life Sciences, Argonne National Laboratory, Lemont, IL, USA.,Department of Computer Science, University of Chicago, Chicago, IL, USA
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42
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Kamada M, Okuno Y. AIM in Genomic Basis of Medicine: Applications. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_264-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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43
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Ahmed KT, Park S, Jiang Q, Yeu Y, Hwang T, Zhang W. Network-based drug sensitivity prediction. BMC Med Genomics 2020; 13:193. [PMID: 33371891 PMCID: PMC7771088 DOI: 10.1186/s12920-020-00829-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 11/17/2020] [Indexed: 12/15/2022] Open
Abstract
Background Drug sensitivity prediction and drug responsive biomarker selection on high-throughput genomic data is a critical step in drug discovery. Many computational methods have been developed to serve this purpose including several deep neural network models. However, the modular relations among genomic features have been largely ignored in these methods. To overcome this limitation, the role of the gene co-expression network on drug sensitivity prediction is investigated in this study. Methods In this paper, we first introduce a network-based method to identify representative features for drug response prediction by using the gene co-expression network. Then, two graph-based neural network models are proposed and both models integrate gene network information directly into neural network for outcome prediction. Next, we present a large-scale comparative study among the proposed network-based methods, canonical prediction algorithms (i.e., Elastic Net, Random Forest, Partial Least Squares Regression, and Support Vector Regression), and deep neural network models for drug sensitivity prediction. All the source code and processed datasets in this study are available at https://github.com/compbiolabucf/drug-sensitivity-prediction. Results In the comparison of different feature selection methods and prediction methods on a non-small cell lung cancer (NSCLC) cell line RNA-seq gene expression dataset with 50 different drug treatments, we found that (1) the network-based feature selection method improves the prediction performance compared to Pearson correlation coefficients; (2) Random Forest outperforms all the other canonical prediction algorithms and deep neural network models; (3) the proposed graph-based neural network models show better prediction performance compared to deep neural network model; (4) the prediction performance is drug dependent and it may relate to the drug’s mechanism of action. Conclusions Network-based feature selection method and prediction models improve the performance of the drug response prediction. The relations between the genomic features are more robust and stable compared to the correlation between each individual genomic feature and the drug response in high dimension and low sample size genomic datasets.
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Affiliation(s)
- Khandakar Tanvir Ahmed
- Department of Computer Science, University of Central Florida, 4000 Central Florida Blvd, Orlando, FL, 32816, USA
| | - Sunho Park
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, 9211 Euclid Ave, Cleveland, OH, 44106, USA
| | - Qibing Jiang
- Department of Computer Science, University of Central Florida, 4000 Central Florida Blvd, Orlando, FL, 32816, USA
| | - Yunku Yeu
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, 9211 Euclid Ave, Cleveland, OH, 44106, USA
| | - TaeHyun Hwang
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, 9211 Euclid Ave, Cleveland, OH, 44106, USA
| | - Wei Zhang
- Department of Computer Science, University of Central Florida, 4000 Central Florida Blvd, Orlando, FL, 32816, USA.
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44
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Djawad YA, Kiely J, Luxton R. Classification of the mechanism of toxicity as applied to human cell line ECV304. Comput Methods Biomech Biomed Engin 2020; 24:933-944. [PMID: 33356573 DOI: 10.1080/10255842.2020.1861255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
The objective of this study was to identify the pattern of cytotoxicity testing of the human cell line ECV304 using three techniques of an ensemble learning algorithm (bagging, boosting and stacking). The study of cell morphology of ECV304 cell line was conducted using impedimetric measurement. Three types of toxins were applied to the ECV304 cell line namely 1 mM hydrogen peroxide (H2O2), 5% dimethyl sulfoxide and 10 μg Saponin. The measurement was conducted using electrodes and lock-in amplifier to detect impedance changes during cytotoxicity testing within a frequency range 200 and 830 kHz. The results were analysed, processed and extracted using detrended fluctuation analysis to obtain characteristics and features of the cells when exposed to the each of the toxins. Three ensemble algorithms applied showed slightly different results on the performance for classifying the data set from the feature extraction that was performed. However, the results show that the cell reaction to the toxins could be classified.
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Affiliation(s)
- Yasser Abd Djawad
- Department of Electronics, Universitas Negeri Makassar, Makassar, Indonesia
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Hackman GL, Collins M, Lu X, Lodi A, DiGiovanni J, Tiziani S. Predicting and Quantifying Antagonistic Effects of Natural Compounds Given with Chemotherapeutic Agents: Applications for High-Throughput Screening. Cancers (Basel) 2020; 12:cancers12123714. [PMID: 33322034 PMCID: PMC7763027 DOI: 10.3390/cancers12123714] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Revised: 12/04/2020] [Accepted: 12/09/2020] [Indexed: 01/12/2023] Open
Abstract
Natural products have been used for centuries to treat various human ailments. In recent decades, multi-drug combinations that utilize natural products to synergistically enhance the therapeutic effects of cancer drugs have been identified and have shown success in improving treatment outcomes. While drug synergy research is a burgeoning field, there are disagreements on the definitions and mathematical parameters that prevent the standardization and proper usage of the terms synergy, antagonism, and additivity. This contributes to the relatively small amount of data on the antagonistic effects of natural products on cancer drugs that can diminish their therapeutic efficacy and prevent cancer regression. The ability of natural products to potentially degrade or reverse the molecular activity of cancer therapeutics represents an important but highly under-emphasized area of research that is often overlooked in both pre-clinical and clinical studies. This review aims to evaluate the body of work surrounding the antagonistic interactions between natural products and cancer therapeutics and highlight applications for high-throughput screening (HTS) and deep learning techniques for the identification of natural products that antagonize cancer drug efficacy.
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Affiliation(s)
- G. Lavender Hackman
- Department of Nutritional Sciences, College of Natural Sciences, The University of Texas at Austin, Austin, TX 78712, USA; (G.L.H.); (M.C.); (X.L.); (A.L.)
- Department of Pediatrics, Dell Medical School, The University of Texas at Austin, Austin, TX 78723, USA;
| | - Meghan Collins
- Department of Nutritional Sciences, College of Natural Sciences, The University of Texas at Austin, Austin, TX 78712, USA; (G.L.H.); (M.C.); (X.L.); (A.L.)
- Department of Pediatrics, Dell Medical School, The University of Texas at Austin, Austin, TX 78723, USA;
| | - Xiyuan Lu
- Department of Nutritional Sciences, College of Natural Sciences, The University of Texas at Austin, Austin, TX 78712, USA; (G.L.H.); (M.C.); (X.L.); (A.L.)
- Department of Pediatrics, Dell Medical School, The University of Texas at Austin, Austin, TX 78723, USA;
| | - Alessia Lodi
- Department of Nutritional Sciences, College of Natural Sciences, The University of Texas at Austin, Austin, TX 78712, USA; (G.L.H.); (M.C.); (X.L.); (A.L.)
- Department of Pediatrics, Dell Medical School, The University of Texas at Austin, Austin, TX 78723, USA;
| | - John DiGiovanni
- Department of Pediatrics, Dell Medical School, The University of Texas at Austin, Austin, TX 78723, USA;
- Division of Pharmacology and Toxicology, College of Pharmacy, The University of Texas at Austin, Austin, TX 78712, USA
| | - Stefano Tiziani
- Department of Nutritional Sciences, College of Natural Sciences, The University of Texas at Austin, Austin, TX 78712, USA; (G.L.H.); (M.C.); (X.L.); (A.L.)
- Department of Pediatrics, Dell Medical School, The University of Texas at Austin, Austin, TX 78723, USA;
- Department of Oncology, Dell Medical School, LiveSTRONG Cancer Institutes, The University of Texas at Austin, Austin, TX 78723, USA
- Correspondence: ; Tel.: +1-512-495-4706
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Chiu YC, Chen HIH, Gorthi A, Mostavi M, Zheng S, Huang Y, Chen Y. Deep learning of pharmacogenomics resources: moving towards precision oncology. Brief Bioinform 2020; 21:2066-2083. [PMID: 31813953 PMCID: PMC7711267 DOI: 10.1093/bib/bbz144] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2019] [Revised: 08/22/2019] [Accepted: 10/18/2019] [Indexed: 12/13/2022] Open
Abstract
The recent accumulation of cancer genomic data provides an opportunity to understand how a tumor's genomic characteristics can affect its responses to drugs. This field, called pharmacogenomics, is a key area in the development of precision oncology. Deep learning (DL) methodology has emerged as a powerful technique to characterize and learn from rapidly accumulating pharmacogenomics data. We introduce the fundamentals and typical model architectures of DL. We review the use of DL in classification of cancers and cancer subtypes (diagnosis and treatment stratification of patients), prediction of drug response and drug synergy for individual tumors (treatment prioritization for a patient), drug repositioning and discovery and the study of mechanism/mode of action of treatments. For each topic, we summarize current genomics and pharmacogenomics data resources such as pan-cancer genomics data for cancer cell lines (CCLs) and tumors, and systematic pharmacologic screens of CCLs. By revisiting the published literature, including our in-house analyses, we demonstrate the unprecedented capability of DL enabled by rapid accumulation of data resources to decipher complex drug response patterns, thus potentially improving cancer medicine. Overall, this review provides an in-depth summary of state-of-the-art DL methods and up-to-date pharmacogenomics resources and future opportunities and challenges to realize the goal of precision oncology.
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Affiliation(s)
- Yu-Chiao Chiu
- Greehey Children’s Cancer Research Institute, University of Texas Health San Antonio, San Antonio, TX 78229, USA
| | - Hung-I Harry Chen
- Greehey Children’s Cancer Research Institute, University of Texas Health San Antonio, San Antonio, TX 78229, USA
- Department of Electrical and Computer Engineering, the University of Texas at San Antonio, San Antonio, TX 78249, USA
| | - Aparna Gorthi
- Greehey Children’s Cancer Research Institute, University of Texas Health San Antonio, San Antonio, TX 78229, USA
| | - Milad Mostavi
- Greehey Children’s Cancer Research Institute, University of Texas Health San Antonio, San Antonio, TX 78229, USA
- Department of Electrical and Computer Engineering, the University of Texas at San Antonio, San Antonio, TX 78249, USA
| | - Siyuan Zheng
- Greehey Children’s Cancer Research Institute, University of Texas Health San Antonio, San Antonio, TX 78229, USA
- Department of Population Health Sciences, University of Texas Health San Antonio, San Antonio, TX 78229, USA
| | - Yufei Huang
- Department of Electrical and Computer Engineering, the University of Texas at San Antonio, San Antonio, TX 78249, USA
- Department of Population Health Sciences, University of Texas Health San Antonio, San Antonio, TX 78229, USA
| | - Yidong Chen
- Greehey Children’s Cancer Research Institute, University of Texas Health San Antonio, San Antonio, TX 78229, USA
- Department of Population Health Sciences, University of Texas Health San Antonio, San Antonio, TX 78229, USA
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Zhu Y, Brettin T, Evrard YA, Partin A, Xia F, Shukla M, Yoo H, Doroshow JH, Stevens RL. Ensemble transfer learning for the prediction of anti-cancer drug response. Sci Rep 2020; 10:18040. [PMID: 33093487 PMCID: PMC7581765 DOI: 10.1038/s41598-020-74921-0] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Accepted: 10/08/2020] [Indexed: 12/13/2022] Open
Abstract
Transfer learning, which transfers patterns learned on a source dataset to a related target dataset for constructing prediction models, has been shown effective in many applications. In this paper, we investigate whether transfer learning can be used to improve the performance of anti-cancer drug response prediction models. Previous transfer learning studies for drug response prediction focused on building models to predict the response of tumor cells to a specific drug treatment. We target the more challenging task of building general prediction models that can make predictions for both new tumor cells and new drugs. Uniquely, we investigate the power of transfer learning for three drug response prediction applications including drug repurposing, precision oncology, and new drug development, through different data partition schemes in cross-validation. We extend the classic transfer learning framework through ensemble and demonstrate its general utility with three representative prediction algorithms including a gradient boosting model and two deep neural networks. The ensemble transfer learning framework is tested on benchmark in vitro drug screening datasets. The results demonstrate that our framework broadly improves the prediction performance in all three drug response prediction applications with all three prediction algorithms.
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Affiliation(s)
- Yitan Zhu
- Computing, Environment and Life Sciences, Argonne National Laboratory, Lemont, IL, 60439, USA.
| | - Thomas Brettin
- Computing, Environment and Life Sciences, Argonne National Laboratory, Lemont, IL, 60439, USA
| | - Yvonne A Evrard
- Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc., Frederick, MD, 21702, USA
| | - Alexander Partin
- Computing, Environment and Life Sciences, Argonne National Laboratory, Lemont, IL, 60439, USA
| | - Fangfang Xia
- Computing, Environment and Life Sciences, Argonne National Laboratory, Lemont, IL, 60439, USA
| | - Maulik Shukla
- Computing, Environment and Life Sciences, Argonne National Laboratory, Lemont, IL, 60439, USA
| | - Hyunseung Yoo
- Computing, Environment and Life Sciences, Argonne National Laboratory, Lemont, IL, 60439, USA
| | - James H Doroshow
- Developmental Therapeutics Branch, National Cancer Institute, Bethesda, MD, 20892, USA
| | - Rick L Stevens
- Computing, Environment and Life Sciences, Argonne National Laboratory, Lemont, IL, 60439, USA
- Department of Computer Science, The University of Chicago, Chicago, IL, 60637, USA
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Li Z, Lam YW, Liu Q, Lau AYK, Yu Au-Yeung H, Chan RHM. Machine Learning-Driven Drug Discovery: Prediction of Structure-Cytotoxicity Correlation Leads to Identification of Potential Anti-Leukemia Compounds. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:5464-5467. [PMID: 33019216 DOI: 10.1109/embc44109.2020.9175850] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
In vitro cytotoxicity screening is a crucial step of anticancer drug discovery. The application of deep learning methodology is gaining increasing attentions in processing drug screening data and studying anticancer mechanisms of chemical compounds. In this work, we explored the utilization of convolutional neural network in modeling the anticancer efficacy of small molecules. In particular, we presented a VGG19 model trained on 2D structural formulae to predict the growth-inhibitory effects of compounds against leukemia cell line CCRF-CEM, without any use of chemical descriptors. The model achieved a normalized RMSE of 15.76% on predicting growth inhibition and a Pearson Correlation Coefficient of 0.72 between predicted and experimental data, demonstrating a strong predictive power in this task. Furthermore, we implemented the Layer-wise Relevance Propagation technique to interpret the network and visualize the chemical groups predicted by the model that contribute to toxicity with human-readable representations.Clinical relevance-This work predicts the cytotoxicity of chemical compounds against human leukemic lymphoblast CCRF-CEM cell lines on a continuous scale, which only requires 2D images of the structural formulae of the compounds as inputs. Knowledge in the structure-toxicity relationship of small molecules will potentially increase the hit rate of primary drug screening assays.
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Ter-Levonian AS, Koshechkin KA. Review of Machine Learning Technologies and Neural Networks in Drug Synergy Combination pharmacological research. RESEARCH RESULTS IN PHARMACOLOGY 2020. [DOI: 10.3897/rrpharmacology.6.49591] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Abstract
Introduction: Nowadays an increase in the amount of information creates the need to replace and update data processing technologies. One of the tasks of clinical pharmacology is to create the right combination of drugs for the treatment of a particular disease. It takes months and even years to create a treatment regimen. Using machine learning (in silico) allows predicting how to get the right combination of drugs and skip the experimental steps in a study that take a lot of time and financial expenses. Gradual preparation is needed for the Deep Learning of Drug Synergy, starting from creating a base of drugs, their characteristics and ways of interacting.
Aim: Our review aims to draw attention to the prospect of the introduction of Deep Learning technology to predict possible combinations of drugs for the treatment of various diseases.
Materials and methods: Literary review of articles based on the PUBMED project and related bibliographic resources over the past 5 years (2015–2019).
Results and discussion: In the analyzed articles, Machine or Deep Learning completed the assigned tasks. It was able to determine the most appropriate combinations for the treatment of certain diseases, select the necessary regimen and doses. In addition, using this technology, new combinations have been identified that may be further involved in preclinical studies.
Conclusions: From the analysis of the articles, we obtained evidence of the positive effects of Deep Learning to select “key” combinations for further stages of preclinical research.
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50
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Enhanced Co-Expression Extrapolation (COXEN) Gene Selection Method for Building Anti-Cancer Drug Response Prediction Models. Genes (Basel) 2020; 11:genes11091070. [PMID: 32933072 PMCID: PMC7565427 DOI: 10.3390/genes11091070] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Revised: 08/27/2020] [Accepted: 09/09/2020] [Indexed: 01/06/2023] Open
Abstract
The co-expression extrapolation (COXEN) method has been successfully used in multiple studies to select genes for predicting the response of tumor cells to a specific drug treatment. Here, we enhance the COXEN method to select genes that are predictive of the efficacies of multiple drugs for building general drug response prediction models that are not specific to a particular drug. The enhanced COXEN method first ranks the genes according to their prediction power for each individual drug and then takes a union of top predictive genes of all the drugs, among which the algorithm further selects genes whose co-expression patterns are well preserved between cancer cases for building prediction models. We apply the proposed method on benchmark in vitro drug screening datasets and compare the performance of prediction models built based on the genes selected by the enhanced COXEN method to that of models built on genes selected by the original COXEN method and randomly picked genes. Models built with the enhanced COXEN method always present a statistically significantly improved prediction performance (adjusted p-value ≤ 0.05). Our results demonstrate the enhanced COXEN method can dramatically increase the power of gene expression data for predicting drug response.
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