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Singh DP, Kaushik B. CTDN (Convolutional Temporal Based Deep- Neural Network): An Improvised Stacked Hybrid Computational Approach for Anticancer Drug Response Prediction. Comput Biol Chem 2023; 105:107868. [PMID: 37257399 DOI: 10.1016/j.compbiolchem.2023.107868] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 03/31/2023] [Accepted: 04/04/2023] [Indexed: 06/02/2023]
Abstract
The characterization of drug - metabolizing enzymes is a significant problem for customized therapy. It is important to choose the right drugs for cancer victims, and the ability to forecast how those drugs will react is usually based on the available information, genetic sequence, and structural properties. To the finest of our knowledge, this is the first study to evaluate optimization algorithms for selection of features and pharmacogenetics categorization using classification methods based on a successful evolutionary algorithm using datasets from the Cancer Cell Line Encyclopaedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC). The study proposes the uses of Firefly and Grey Wolf Optimization techniques for feature extraction, while comparing the traditional Machine Learning (ML), ensemble ML and Stacking Algorithm with the proposed Convolutional Temporal Deep Neural Network or CTDN. With the potential to increase efficiency from the suggested intelligible classifier model for a suggestive chemotherapeutic drugs response prediction, our study is important in particular for selecting an acceptable feature selection method. The comparison analysis demonstrates that the proposed model not only surpasses the prior state-of-the-art methods, but also uses Grey Wolf and Fire Fly Optimization to lessen multicollinearity and overfitting.
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Affiliation(s)
- Davinder Paul Singh
- School of Computer Science and Engineering, Shri Mata Vaishno Devi University, Katra 182320, Jammu and Kashmir, India.
| | - Baijnath Kaushik
- School of Computer Science and Engineering, Shri Mata Vaishno Devi University, Katra 182320, Jammu and Kashmir, India
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2
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Singh DP, Kaushik B. A systematic literature review for the prediction of anticancer drug response using various machine-learning and deep-learning techniques. Chem Biol Drug Des 2023; 101:175-194. [PMID: 36303299 DOI: 10.1111/cbdd.14164] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 10/13/2022] [Accepted: 10/24/2022] [Indexed: 12/24/2022]
Abstract
Computational methods have gained prominence in healthcare research. The accessibility of healthcare data has greatly incited academicians and researchers to develop executions that help in prognosis of cancer drug response. Among various computational methods, machine-learning (ML) and deep-learning (DL) methods provide the most consistent and effectual approaches to handle the serious aftermaths of the deadly disease and drug administered to the patients. Hence, this systematic literature review has reviewed researches that have investigated drug discovery and prognosis of anticancer drug response using ML and DL algorithms. Fot this purpose, PRISMA guidelines have been followed to choose research papers from Google Scholar, PubMed, and Sciencedirect websites. A total count of 105 papers that align with the context of this review were chosen. Further, the review also presents accuracy of the existing ML and DL methods in the prediction of anticancer drug response. It has been found from the review that, amidst the availability of various studies, there are certain challenges associated with each method. Thus, future researchers can consider these limitations and challenges to develop a prominent anticancer drug response prediction method, and it would be greatly beneficial to the medical professionals in administering non-invasive treatment to the patients.
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Affiliation(s)
- Davinder Paul Singh
- School of Computer Science and Engineering, Shri Mata Vaishno Devi University, Katra, Jammu and Kashmir, India
| | - Baijnath Kaushik
- School of Computer Science and Engineering, Shri Mata Vaishno Devi University, Katra, Jammu and Kashmir, India
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3
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Cheng X, Dai C, Wen Y, Wang X, Bo X, He S, Peng S. NeRD: a multichannel neural network to predict cellular response of drugs by integrating multidimensional data. BMC Med 2022; 20:368. [PMID: 36244991 PMCID: PMC9575288 DOI: 10.1186/s12916-022-02549-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/02/2022] [Accepted: 09/01/2022] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND Considering the heterogeneity of tumors, it is a key issue in precision medicine to predict the drug response of each individual. The accumulation of various types of drug informatics and multi-omics data facilitates the development of efficient models for drug response prediction. However, the selection of high-quality data sources and the design of suitable methods remain a challenge. METHODS In this paper, we design NeRD, a multidimensional data integration model based on the PRISM drug response database, to predict the cellular response of drugs. Four feature extractors, including drug structure extractor (DSE), molecular fingerprint extractor (MFE), miRNA expression extractor (mEE), and copy number extractor (CNE), are designed for different types and dimensions of data. A fully connected network is used to fuse all features and make predictions. RESULTS Experimental results demonstrate the effective integration of the global and local structural features of drugs, as well as the features of cell lines from different omics data. For all metrics tested on the PRISM database, NeRD surpassed previous approaches. We also verified that NeRD has strong reliability in the prediction results of new samples. Moreover, unlike other algorithms, when the amount of training data was reduced, NeRD maintained stable performance. CONCLUSIONS NeRD's feature fusion provides a new idea for drug response prediction, which is of great significance for precise cancer treatment.
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Affiliation(s)
- Xiaoxiao Cheng
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
| | - Chong Dai
- College of Life Science and Technology, Beijing University of Chemical Technology, Beijing, China.,Department of Biotechnology, Beijing Institute of Health Service and Transfusion Medicine, Beijing, China
| | - Yuqi Wen
- Department of Biotechnology, Beijing Institute of Health Service and Transfusion Medicine, Beijing, China
| | - Xiaoqi Wang
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
| | - Xiaochen Bo
- Department of Biotechnology, Beijing Institute of Health Service and Transfusion Medicine, Beijing, China.
| | - Song He
- Department of Biotechnology, Beijing Institute of Health Service and Transfusion Medicine, Beijing, China.
| | - Shaoliang Peng
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China. .,The State Key Laboratory of Chemo/Biosensing and Chemometrics, Hunan University, Changsha, China.
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4
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Berlow NE. Probabilistic Boolean Modeling of Pre-clinical Tumor Models for Biomarker Identification in Cancer Drug Development. Curr Protoc 2021; 1:e269. [PMID: 34661991 DOI: 10.1002/cpz1.269] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
As high-throughput sequencing experiments become more widely used in pre-clinical and clinical settings, pharmacogenetic and pharmacogenomic biomarker development plays an increasingly important role in oncology drug development pipelines and programs. Consequently, computer-based learning approaches have entered into use at multiple stages in pre-clinical and clinical pipelines. However, few approaches are available to identify interpretable and implementable biomarkers of response early in the drug development process when only small pre-clinical data packages are available. To address the need for early-stage biomarker development using pre-clinical tumor models, we have adapted the previously published Probabilistic Target Inhibitor Map (PTIM) platform to the challenge of biomarker hypothesis development, and denoted this approach the Probabilistic Target Map-Biomarker (PTM-Biomarker). In this article, we detail the history and design philosophy of PTM-Biomarker, and present two case studies using the biomarker discovery tool to illustrate its utility in guiding cancer drug development. © 2021 Wiley Periodicals LLC.
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Nguyen LC, Naulaerts S, Bruna A, Ghislat G, Ballester PJ. Predicting Cancer Drug Response In Vivo by Learning an Optimal Feature Selection of Tumour Molecular Profiles. Biomedicines 2021; 9:biomedicines9101319. [PMID: 34680436 PMCID: PMC8533095 DOI: 10.3390/biomedicines9101319] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 09/22/2021] [Accepted: 09/23/2021] [Indexed: 12/17/2022] Open
Abstract
(1) Background: Inter-tumour heterogeneity is one of cancer’s most fundamental features. Patient stratification based on drug response prediction is hence needed for effective anti-cancer therapy. However, single-gene markers of response are rare and/or may fail to achieve a significant impact in the clinic. Machine Learning (ML) is emerging as a particularly promising complementary approach to precision oncology. (2) Methods: Here we leverage comprehensive Patient-Derived Xenograft (PDX) pharmacogenomic data sets with dimensionality-reducing ML algorithms with this purpose. (3) Results: Combining multiple gene alterations via ML leads to better discrimination between sensitive and resistant PDXs in 19 of the 26 analysed cases. Highly predictive ML models employing concise gene lists were found for three cases: paclitaxel (breast cancer), binimetinib (breast cancer) and cetuximab (colorectal cancer). Interestingly, each of these multi-gene ML models identifies some treatment-responsive PDXs not harbouring the best actionable mutation for that case. Thus, ML multi-gene predictors generally have much fewer false negatives than the corresponding single-gene marker. (4) Conclusions: As PDXs often recapitulate clinical outcomes, these results suggest that many more patients could benefit from precision oncology if ML algorithms were also applied to existing clinical pharmacogenomics data, especially those algorithms generating classifiers combining data-selected gene alterations.
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Affiliation(s)
- Linh C. Nguyen
- Cancer Research Center of Marseille, INSERM U1068, F-13009 Marseille, France;
- Institut Paoli-Calmettes, F-13009 Marseille, France
- Aix-Marseille Université UM105, F-13009 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 100803, Vietnam
| | - Stefan Naulaerts
- Ludwig Institute for Cancer Research, 1200 Brussels, Belgium;
- Duve Institute, UCLouvain, 1200 Brussels, Belgium
| | | | - Ghita Ghislat
- Centre d’Immunologie de Marseille-Luminy, INSERM U1104, CNRS UMR7280, F-13009 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 UM105, F-13009 Marseille, France
- CNRS UMR7258, F-13009 Marseille, France
- Correspondence: ; Tel.: + 33-(0)4-8697-7201
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Wang H, Xi J, Wang M, Li A. Dual-Layer Strengthened Collaborative Topic Regression Modeling for Predicting Drug Sensitivity. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 17:587-598. [PMID: 30106738 DOI: 10.1109/tcbb.2018.2864739] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
An effective way to facilitate the development of modern oncology precision medicine is the systematical analysis of the known drug sensitivities that have emerged in recent years. Meanwhile, the screening of drug response in cancer cell lines provides an estimable genomic and pharmacological data towards high accuracy prediction. Existing works primarily utilize genomic or functional genomic features to classify or regress the drug response. Here in this work, by the migration and extension of the conventional merchandise recommendation methods, we introduce an innovation model on accurate drug sensitivity prediction by using dual-layer strengthened collaborative topic regression (DS-CTR), which incorporates not only the graphic model to jointly learn drugs and cell lines feature from pharmacogenomics data but also drug and cell line similarity network model to strengthen the correlation of the prediction results. Using Genomics of Drug Sensitivity in Cancer project (GDSC) as benchmark datasets, the 5-fold cross-validation experiment demonstrates that DS-CTR model significantly improves drug response prediction performance compared with four categories of state-of-the-art algorithms as for both Receiver Operator Curve (ROC) and the Area Under Receiver Operator Curve (AUC). By uncovering the unknown cell-drug associations with advanced literature evidences, our novel model DS-CTR is validated and supported. The model also provides the possibility to make the discovery of new anti-cancer therapeutics in the preclinical trials cheaper and faster.
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Computational Cancer Cell Models to Guide Precision Breast Cancer Medicine. Genes (Basel) 2020; 11:genes11030263. [PMID: 32121160 PMCID: PMC7140855 DOI: 10.3390/genes11030263] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2019] [Revised: 02/20/2020] [Accepted: 02/25/2020] [Indexed: 12/22/2022] Open
Abstract
Background: Large-scale screening of drug sensitivity on cancer cell models can mimic in vivo cellular behavior providing wider scope for biological research on cancer. Since the therapeutic effect of a single drug or drug combination depends on the individual patient’s genome characteristics and cancer cells integration reaction, the identification of an effective agent in an in vitro model by using large number of cancer cell models is a promising approach for the development of targeted treatments. Precision cancer medicine is to select the most appropriate treatment or treatments for an individual patient. However, it still lacks the tools to bridge the gap between conventional in vitro cancer cell models and clinical patient response to inhibitors. Methods: An optimal two-layer decision system model is developed to identify the cancer cells that most closely resemble an individual tumor for optimum therapeutic interventions in precision cancer medicine. Accordingly, an optimal grid parameters selection is designed to seek the highest accordance for treatment selection to the patient’s preference for drug response and in vitro cancer cell drug screening. The optimal two-layer decision system model overcomes the challenge of heterology data comparison between the tumor and the cancer cells, as well as between the continual variation of drug responses in vitro and the discrete ones in clinical practice. We simulated the model accuracy using 681 cancer cells’ mRNA and associated 481 drug screenings and validated our results on 315 breast cancer patients drug selection across seven drugs (docetaxel, doxorubicin, fluorouracil, paclitaxel, tamoxifen, cyclophosphamide, lapitinib). Results: Comparing with the real response of a drug in clinical patients, the novel model obtained an overall average accordance over 90.8% across the seven drugs. At the same time, the optimal cancer cells and the associated optimal therapeutic efficacy of cancer drugs are recommended. The novel optimal two-layer decision system model was used on 1097 patients with breast cancer in guiding precision medicine for a recommendation of their optimal cancer cells (30 cancer cells) and associated efficacy of certain cancer drugs. Our model can detect the most similar cancer cells for each individual patient. Conclusion: A successful clinical translation model (optimal two-layer decision system model) was developed to bridge in-vitro basic science to clinical practice in a therapeutic intervention application for the first time. The novel tool kills two birds with one stone. It can help basic science to seek optimal cancer cell models for an individual tumor, while prioritizing clinical drugs’ recommendations in practice. Tool associated platform website: We extended the breast cancer research to 32 more types of cancers across 45 therapy predictions.
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Han Y, Wang C, Dong Q, Chen T, Yang F, Liu Y, Chen B, Zhao Z, Qi L, Zhao W, Liang H, Guo Z, Gu Y. Genetic Interaction-Based Biomarkers Identification for Drug Resistance and Sensitivity in Cancer Cells. MOLECULAR THERAPY. NUCLEIC ACIDS 2019; 17:688-700. [PMID: 31400611 PMCID: PMC6700431 DOI: 10.1016/j.omtn.2019.07.003] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Revised: 06/21/2019] [Accepted: 07/06/2019] [Indexed: 01/08/2023]
Abstract
Cancer cells generally harbor hundreds of alterations in the cancer genomes and act as crucial factors in the development and progression of cancer. Gene alterations in the cancer genome form genetic interactions, which affect the response of patients to drugs. We developed an algorithm that mines copy number alteration and whole-exome mutation profiles from The Cancer Genome Atlas (TCGA), as well as functional screen data generated to identify potential genetic interactions for specific cancer types. As a result, 4,529 synthetic viability (SV) interactions and 10,637 synthetic lethality (SL) interactions were detected. The pharmacogenomic datasets revealed that SV interactions induced drug resistance in cancer cells and that SL interactions mediated drug sensitivity in cancer cells. Deletions of HDAC1 and DVL1, both of which participate in the Notch signaling pathway, had an SV effect in cancer cells, and deletion of DVL1 induced resistance to HDAC1 inhibitors in cancer cells. In addition, patients with low expression of both HDAC1 and DVL1 had poor prognosis. Finally, by integrating current reported genetic interactions from other studies, the Cancer Genetic Interaction database (CGIdb) (http://www.medsysbio.org/CGIdb) was constructed, providing a convenient retrieval for genetic interactions in cancer.
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Affiliation(s)
- Yue Han
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China
| | - Chengyu Wang
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China
| | - Qi Dong
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China
| | - Tingting Chen
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China
| | - Fan Yang
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China
| | - Yaoyao Liu
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China
| | - Bo Chen
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China
| | - Zhangxiang Zhao
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China
| | - Lishuang Qi
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China
| | - Wenyuan Zhao
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China
| | - Haihai Liang
- Department of Pharmacology, College of Pharmacy, Harbin Medical University, Harbin, China
| | - Zheng Guo
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China; Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Department of Bioinformatics, Fujian Medical University, Fuzhou, China; Fujian Key Laboratory of Tumor Microbiology, Fujian Medical University, Fuzhou, China.
| | - Yunyan Gu
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China.
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Probabilistic modeling of personalized drug combinations from integrated chemical screen and molecular data in sarcoma. BMC Cancer 2019; 19:593. [PMID: 31208434 PMCID: PMC6580486 DOI: 10.1186/s12885-019-5681-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2018] [Accepted: 05/07/2019] [Indexed: 12/30/2022] Open
Abstract
Background Cancer patients with advanced disease routinely exhaust available clinical regimens and lack actionable genomic medicine results, leaving a large patient population without effective treatments options when their disease inevitably progresses. To address the unmet clinical need for evidence-based therapy assignment when standard clinical approaches have failed, we have developed a probabilistic computational modeling approach which integrates molecular sequencing data with functional assay data to develop patient-specific combination cancer treatments. Methods Tissue taken from a murine model of alveolar rhabdomyosarcoma was used to perform single agent drug screening and DNA/RNA sequencing experiments; results integrated via our computational modeling approach identified a synergistic personalized two-drug combination. Cells derived from the primary murine tumor were allografted into mouse models and used to validate the personalized two-drug combination. Computational modeling of single agent drug screening and RNA sequencing of multiple heterogenous sites from a single patient’s epithelioid sarcoma identified a personalized two-drug combination effective across all tumor regions. The heterogeneity-consensus combination was validated in a xenograft model derived from the patient’s primary tumor. Cell cultures derived from human and canine undifferentiated pleomorphic sarcoma were assayed by drug screen; computational modeling identified a resistance-abrogating two-drug combination common to both cell cultures. This combination was validated in vitro via a cell regrowth assay. Results Our computational modeling approach addresses three major challenges in personalized cancer therapy: synergistic drug combination predictions (validated in vitro and in vivo in a genetically engineered murine cancer model), identification of unifying therapeutic targets to overcome intra-tumor heterogeneity (validated in vivo in a human cancer xenograft), and mitigation of cancer cell resistance and rewiring mechanisms (validated in vitro in a human and canine cancer model). Conclusions These proof-of-concept studies support the use of an integrative functional approach to personalized combination therapy prediction for the population of high-risk cancer patients lacking viable clinical options and without actionable DNA sequencing-based therapy. Electronic supplementary material The online version of this article (10.1186/s12885-019-5681-6) contains supplementary material, which is available to authorized users.
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Matlock K, Berlow N, Keller C, Pal R. Combination therapy design for maximizing sensitivity and minimizing toxicity. BMC Bioinformatics 2017; 18:116. [PMID: 28361667 PMCID: PMC5374708 DOI: 10.1186/s12859-017-1523-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
Background Design of personalized targeted therapies involve modeling of patient sensitivity to various drugs and drug combinations. Majority of studies evaluate the sensitivity of tumor cells to targeted drugs without modeling the effect of the drugs on normal cells. In this article, we consider the individual modeling of drug responses to tumor and normal cells and utilize them to design targeted combination therapies that maximize sensitivity over tumor cells and minimize toxicity over normal cells. Results The problem is formulated as maximizing sensitivity over tumor cell models while maintaining sensitivity below a threshold over normal cell models. We utilize the constrained structure of tumor proliferation models to design an accelerated lexicographic search algorithm for generating the optimal solution. For comparison purposes, we also designed two suboptimal search algorithms based on evolutionary algorithms and hill-climbing based techniques. Results over synthetic models and models generated from Genomics of Drug Sensitivity in Cancer database shows the ability of the proposed algorithms to arrive at optimal or close to optimal solutions in significantly lower number of steps as compared to exhaustive search. We also present the theoretical analysis of the expected number of comparisons required for the proposed Lexicographic search that compare favorably with the observed number of computations. Conclusions The proposed algorithms provide a framework for design of combination therapy that tackles tumor heterogeneity while satisfying toxicity constraints.
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Affiliation(s)
- Kevin Matlock
- Department of Electrical and Computer Engineering, Texas Tech University, 1012 Boston Ave, Lubbock, 79409, TX, USA
| | - Noah Berlow
- Children's Cancer Therapy Development Institute, Portland, 97005, OR, USA
| | - Charles Keller
- Children's Cancer Therapy Development Institute, Portland, 97005, OR, USA
| | - Ranadip Pal
- Department of Electrical and Computer Engineering, Texas Tech University, 1012 Boston Ave, Lubbock, 79409, TX, USA.
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12
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Tan AC, Ryall KA, Huang PH. Expanding the computational toolbox for interrogating cancer kinomes. Pharmacogenomics 2015; 17:95-7. [PMID: 26666839 DOI: 10.2217/pgs.15.154] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Affiliation(s)
- Aik Choon Tan
- Translational Bioinformatics & Cancer Systems Biology Laboratory, Department of Medicine, Division of Medical Oncology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Karen A Ryall
- Translational Bioinformatics & Cancer Systems Biology Laboratory, Department of Medicine, Division of Medical Oncology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Paul H Huang
- Protein Networks Team, Division of Cancer Biology, The Institute of Cancer Research, London, SW3 6JB, UK
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Modelling of compound combination effects and applications to efficacy and toxicity: state-of-the-art, challenges and perspectives. Drug Discov Today 2015; 21:225-38. [PMID: 26360051 DOI: 10.1016/j.drudis.2015.09.003] [Citation(s) in RCA: 105] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2015] [Revised: 07/30/2015] [Accepted: 09/01/2015] [Indexed: 01/18/2023]
Abstract
The development of treatments involving combinations of drugs is a promising approach towards combating complex or multifactorial disorders. However, the large number of compound combinations that can be generated, even from small compound collections, means that exhaustive experimental testing is infeasible. The ability to predict the behaviour of compound combinations in biological systems, whittling down the number of combinations to be tested, is therefore crucial. Here, we review the current state-of-the-art in the field of compound combination modelling, with the aim to support the development of approaches that, as we hope, will finally lead to an integration of chemical with systems-level biological information for predicting the effect of chemical mixtures.
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Huang Y, Chen Y, Qian X. Selected Articles from the 2012 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS 2012). IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2014; 11:981-983. [PMID: 26605382 DOI: 10.1109/tcbb.2014.2353218] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
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