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Hu X, Zhang P, Zhang J, Deng L. DeepFusionCDR: Employing Multi-Omics Integration and Molecule-Specific Transformers for Enhanced Prediction of Cancer Drug Responses. IEEE J Biomed Health Inform 2024; 28:6248-6258. [PMID: 38935469 DOI: 10.1109/jbhi.2024.3417014] [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: 06/29/2024]
Abstract
Deep learning approaches have demonstrated remarkable potential in predicting cancer drug responses (CDRs), using cell line and drug features. However, existing methods predominantly rely on single-omics data of cell lines, potentially overlooking the complex biological mechanisms governing cell line responses. This paper introduces DeepFusionCDR, a novel approach employing unsupervised contrastive learning to amalgamate multi-omics features, including mutation, transcriptome, methylome, and copy number variation data, from cell lines. Furthermore, we incorporate molecular SMILES-specific transformers to derive drug features from their chemical structures. The unified multi-omics and drug signatures are combined, and a multi-layer perceptron (MLP) is applied to predict IC50 values for cell line-drug pairs. Moreover, this MLP can discern whether a cell line is resistant or sensitive to a particular drug. We assessed DeepFusionCDR's performance on the GDSC dataset and juxtaposed it against cutting-edge methods, demonstrating its superior performance in regression and classification tasks. We also conducted ablation studies and case analyses to exhibit the effectiveness and versatility of our proposed approach. Our results underscore the potential of DeepFusionCDR to enhance CDR predictions by harnessing the power of multi-omics fusion and molecular-specific transformers. The prediction of DeepFusionCDR on TCGA patient data and case study highlight the practical application scenarios of DeepFusionCDR in real-world environments.
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2
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Xu M, Zhu Z, Zhao Y, He K, Huang Q, Zhao Y. RedCDR: Dual Relation Distillation for Cancer Drug Response Prediction. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2024; 21:1468-1479. [PMID: 38776197 DOI: 10.1109/tcbb.2024.3404262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2024]
Abstract
Based on multi-omics data and drug information, predicting the response of cancer cell lines to drugs is a crucial area of research in modern oncology, as it can promote the development of personalized treatments. Despite the promising performance achieved by existing models, most of them overlook the variations among different omics and lack effective integration of multi-omics data. Moreover, the explicit modeling of cell line/drug attribute and cell line-drug association has not been thoroughly investigated in existing approaches. To address these issues, we propose RedCDR, a dual relation distillation model for cancer drug response (CDR) prediction. Specifically, a parallel dual-branch architecture is designed to enable both the independent learning and interactive fusion feasible for cell line/drug attribute and cell line-drug association information. To facilitate the adaptive interacting integration of multi-omics data, the proposed multi-omics encoder introduces the multiple similarity relations between cell lines and takes the importance of different omics data into account. To accomplish knowledge transfer from the two independent attribute and association branches to their fusion, a dual relation distillation mechanism consisting of representation distillation and prediction distillation is presented. Experiments conducted on the GDSC and CCLE datasets show that RedCDR outperforms previous state-of-the-art approaches in CDR prediction.
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Yang X, Tang X, Li C, Han H. Singular value thresholding two-stage matrix completion for drug sensitivity discovery. Comput Biol Chem 2024; 110:108071. [PMID: 38718497 DOI: 10.1016/j.compbiolchem.2024.108071] [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: 09/30/2023] [Revised: 04/06/2024] [Accepted: 04/11/2024] [Indexed: 05/27/2024]
Abstract
Incomplete data presents significant challenges in drug sensitivity analysis, especially in critical areas like oncology, where precision is paramount. Our study introduces an innovative imputation method designed specifically for low-rank matrices, addressing the crucial challenge of data completion in anticancer drug sensitivity testing. Our method unfolds in two main stages: Initially, the singular value thresholding algorithm is employed for preliminary matrix completion, establishing a solid foundation for subsequent steps. Then, the matrix rows are segmented into distinct blocks based on hierarchical clustering of correlation coefficients, applying singular value thresholding to the largest block, which has been proved to possess the largest entropy. This is followed by a refined data restoration process, where the reconstructed largest block is integrated into the initial matrix completion to achieve the final matrix completion. Compared to other methods, our approach not only improves the accuracy of data restoration but also ensures the integrity and reliability of the imputed values, establishing it as a robust tool for future drug sensitivity analysis.
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Affiliation(s)
- Xuemei Yang
- School of Mathematics and Statistics, Xianyang Normal University, Xianyang, 712000, China.
| | - Xiaoduan Tang
- School of Mathematics and Statistics, Hainan Normal University, Haikou 571158, China.
| | - Chun Li
- College of Elementary Education, Hainan Normal University, Haikou 571158, China; Key Laboratory of Data Science and Intelligence Education of Ministry of Education, Hainan Normal University, Haikou 571158, China.
| | - Henry Han
- The Laboratory of Data Science and Artificial Intelligence Innovation, Department of Computer Science, School of Engineering and Computer Science, Baylor University, Waco, TX 76798 USA.
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Rashid MM, Selvarajoo K. Advancing drug-response prediction using multi-modal and -omics machine learning integration (MOMLIN): a case study on breast cancer clinical data. Brief Bioinform 2024; 25:bbae300. [PMID: 38904542 PMCID: PMC11190965 DOI: 10.1093/bib/bbae300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Revised: 05/30/2024] [Accepted: 06/11/2024] [Indexed: 06/22/2024] Open
Abstract
The inherent heterogeneity of cancer contributes to highly variable responses to any anticancer treatments. This underscores the need to first identify precise biomarkers through complex multi-omics datasets that are now available. Although much research has focused on this aspect, identifying biomarkers associated with distinct drug responders still remains a major challenge. Here, we develop MOMLIN, a multi-modal and -omics machine learning integration framework, to enhance drug-response prediction. MOMLIN jointly utilizes sparse correlation algorithms and class-specific feature selection algorithms, which identifies multi-modal and -omics-associated interpretable components. MOMLIN was applied to 147 patients' breast cancer datasets (clinical, mutation, gene expression, tumor microenvironment cells and molecular pathways) to analyze drug-response class predictions for non-responders and variable responders. Notably, MOMLIN achieves an average AUC of 0.989, which is at least 10% greater when compared with current state-of-the-art (data integration analysis for biomarker discovery using latent components, multi-omics factor analysis, sparse canonical correlation analysis). Moreover, MOMLIN not only detects known individual biomarkers such as genes at mutation/expression level, most importantly, it correlates multi-modal and -omics network biomarkers for each response class. For example, an interaction between ER-negative-HMCN1-COL5A1 mutations-FBXO2-CSF3R expression-CD8 emerge as a multimodal biomarker for responders, potentially affecting antimicrobial peptides and FLT3 signaling pathways. In contrast, for resistance cases, a distinct combination of lymph node-TP53 mutation-PON3-ENSG00000261116 lncRNA expression-HLA-E-T-cell exclusions emerged as multimodal biomarkers, possibly impacting neurotransmitter release cycle pathway. MOMLIN, therefore, is expected advance precision medicine, such as to detect context-specific multi-omics network biomarkers and better predict drug-response classifications.
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Affiliation(s)
- Md Mamunur Rashid
- Biomolecular Sequence to Function Division, BII, (ASTAR), Singapore 138671, Republic of Singapore
| | - Kumar Selvarajoo
- Biomolecular Sequence to Function Division, BII, (ASTAR), Singapore 138671, Republic of Singapore
- Synthetic Biology Translational Research Program, Yong Loo Lin School of Medicine, NUS, Singapore 117456, Republic of Singapore
- School of Biological Sciences, Nanyang Technological University (NTU), Singapore 639798, Republic of Singapore
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5
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Wu G, Zaker A, Ebrahimi A, Tripathi S, Mer AS. Text-mining-based feature selection for anticancer drug response prediction. BIOINFORMATICS ADVANCES 2024; 4:vbae047. [PMID: 38606185 PMCID: PMC11009020 DOI: 10.1093/bioadv/vbae047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 03/09/2024] [Accepted: 03/22/2024] [Indexed: 04/13/2024]
Abstract
Motivation Predicting anticancer treatment response from baseline genomic data is a critical obstacle in personalized medicine. Machine learning methods are commonly used for predicting drug response from gene expression data. In the process of constructing these machine learning models, one of the most significant challenges is identifying appropriate features among a massive number of genes. Results In this study, we utilize features (genes) extracted using the text-mining of scientific literatures. Using two independent cancer pharmacogenomic datasets, we demonstrate that text-mining-based features outperform traditional feature selection techniques in machine learning tasks. In addition, our analysis reveals that text-mining feature-based machine learning models trained on in vitro data also perform well when predicting the response of in vivo cancer models. Our results demonstrate that text-mining-based feature selection is an easy to implement approach that is suitable for building machine learning models for anticancer drug response prediction. Availability and implementation https://github.com/merlab/text_features.
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Affiliation(s)
- Grace Wu
- Division of Engineering Science, University of Toronto, Toronto, M5S2E4, Canada
| | - Arvin Zaker
- Department of Biochemistry, Microbiology & Immunology, University of Ottawa, Ottawa, K1H8M5, Canada
- Ottawa Institute of Systems Biology, University of Ottawa, Ottawa, K1H8M5, Canada
| | - Amirhosein Ebrahimi
- Department of Biochemistry, Microbiology & Immunology, University of Ottawa, Ottawa, K1H8M5, Canada
| | - Shivanshi Tripathi
- Department of Biochemistry, Microbiology & Immunology, University of Ottawa, Ottawa, K1H8M5, Canada
- Ottawa Institute of Systems Biology, University of Ottawa, Ottawa, K1H8M5, Canada
| | - Arvind Singh Mer
- Department of Biochemistry, Microbiology & Immunology, University of Ottawa, Ottawa, K1H8M5, Canada
- Ottawa Institute of Systems Biology, University of Ottawa, Ottawa, K1H8M5, Canada
- School of Electrical Engineering & Computer Science, University of Ottawa, Ottawa, K1N6N5, Canada
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6
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Lao C, Zheng P, Chen H, Liu Q, An F, Li Z. DeepAEG: a model for predicting cancer drug response based on data enhancement and edge-collaborative update strategies. BMC Bioinformatics 2024; 25:105. [PMID: 38461284 PMCID: PMC10925015 DOI: 10.1186/s12859-024-05723-8] [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: 12/25/2023] [Accepted: 02/27/2024] [Indexed: 03/11/2024] Open
Abstract
MOTIVATION The prediction of cancer drug response is a challenging subject in modern personalized cancer therapy due to the uncertainty of drug efficacy and the heterogeneity of patients. It has been shown that the characteristics of the drug itself and the genomic characteristics of the patient can greatly influence the results of cancer drug response. Therefore, accurate, efficient, and comprehensive methods for drug feature extraction and genomics integration are crucial to improve the prediction accuracy. RESULTS Accurate prediction of cancer drug response is vital for guiding the design of anticancer drugs. In this study, we propose an end-to-end deep learning model named DeepAEG which is based on a complete-graph update mode to predict IC50. Specifically, we integrate an edge update mechanism on the basis of a hybrid graph convolutional network to comprehensively learn the potential high-dimensional representation of topological structures in drugs, including atomic characteristics and chemical bond information. Additionally, we present a novel approach for enhancing simplified molecular input line entry specification data by employing sequence recombination to eliminate the defect of single sequence representation of drug molecules. Our extensive experiments show that DeepAEG outperforms other existing methods across multiple evaluation parameters in multiple test sets. Furthermore, we identify several potential anticancer agents, including bortezomib, which has proven to be an effective clinical treatment option. Our results highlight the potential value of DeepAEG in guiding the design of specific cancer treatment regimens.
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Affiliation(s)
- Chuanqi Lao
- Research Center for Graph Computing, Zhejiang Lab, Yuhang, Hangzhou, 311121, Zhejiang, China
| | - Pengfei Zheng
- Research Center for Graph Computing, Zhejiang Lab, Yuhang, Hangzhou, 311121, Zhejiang, China
| | - Hongyang Chen
- Research Center for Graph Computing, Zhejiang Lab, Yuhang, Hangzhou, 311121, Zhejiang, China.
| | - Qiao Liu
- Department of Statistics, Stanford University, Stanford, Palo Alto, CA, 94305, USA
| | - Feng An
- Research Center for Graph Computing, Zhejiang Lab, Yuhang, Hangzhou, 311121, Zhejiang, China
| | - Zhao Li
- Research Center for Graph Computing, Zhejiang Lab, Yuhang, Hangzhou, 311121, Zhejiang, China
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7
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Dawood M, Vu QD, Young LS, Branson K, Jones L, Rajpoot N, Minhas FUAA. Cancer drug sensitivity prediction from routine histology images. NPJ Precis Oncol 2024; 8:5. [PMID: 38184744 PMCID: PMC10771481 DOI: 10.1038/s41698-023-00491-9] [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: 08/11/2023] [Accepted: 12/08/2023] [Indexed: 01/08/2024] Open
Abstract
Drug sensitivity prediction models can aid in personalising cancer therapy, biomarker discovery, and drug design. Such models require survival data from randomised controlled trials which can be time consuming and expensive. In this proof-of-concept study, we demonstrate for the first time that deep learning can link histological patterns in whole slide images (WSIs) of Haematoxylin & Eosin (H&E) stained breast cancer sections with drug sensitivities inferred from cell lines. We employ patient-wise drug sensitivities imputed from gene expression-based mapping of drug effects on cancer cell lines to train a deep learning model that predicts patients' sensitivity to multiple drugs from WSIs. We show that it is possible to use routine WSIs to predict the drug sensitivity profile of a cancer patient for a number of approved and experimental drugs. We also show that the proposed approach can identify cellular and histological patterns associated with drug sensitivity profiles of cancer patients.
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Affiliation(s)
- Muhammad Dawood
- Tissue Image Analytics Centre, University of Warwick, Coventry, UK.
| | - Quoc Dang Vu
- Tissue Image Analytics Centre, University of Warwick, Coventry, UK
| | - Lawrence S Young
- Warwick Medical School, University of Warwick, Coventry, UK
- Cancer Research Centre, University of Warwick, Coventry, UK
| | - Kim Branson
- Artificial Intelligence & Machine Learning, GlaxoSmithKline, San Francisco, CA, USA
| | - Louise Jones
- Barts Cancer Institute, Queen Mary University of London, London, UK
| | - Nasir Rajpoot
- Tissue Image Analytics Centre, University of Warwick, Coventry, UK
- Cancer Research Centre, University of Warwick, Coventry, UK
- The Alan Turing Institute, London, UK
| | - Fayyaz Ul Amir Afsar Minhas
- Tissue Image Analytics Centre, University of Warwick, Coventry, UK
- Cancer Research Centre, University of Warwick, Coventry, UK
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8
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Wang Y, Yu X, Gu Y, Li W, Zhu K, Chen L, Tang Y, Liu G. XGraphCDS: An explainable deep learning model for predicting drug sensitivity from gene pathways and chemical structures. Comput Biol Med 2024; 168:107746. [PMID: 38039896 DOI: 10.1016/j.compbiomed.2023.107746] [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: 08/12/2023] [Revised: 10/29/2023] [Accepted: 11/20/2023] [Indexed: 12/03/2023]
Abstract
Cancer is a highly complex disease characterized by genetic and phenotypic heterogeneity among individuals. In the era of precision medicine, understanding the genetic basis of these individual differences is crucial for developing new drugs and achieving personalized treatment. Despite the increasing abundance of cancer genomics data, predicting the relationship between cancer samples and drug sensitivity remains challenging. In this study, we developed an explainable graph neural network framework for predicting cancer drug sensitivity (XGraphCDS) based on comparative learning by integrating cancer gene expression information and drug chemical structure knowledge. Specifically, XGraphCDS consists of a unified heterogeneous network and multiple sub-networks, with molecular graphs representing drugs and gene enrichment scores representing cell lines. Experimental results showed that XGraphCDS consistently outperformed most state-of-the-art baselines (R2 = 0.863, AUC = 0.858). We also constructed a separate in vivo prediction model by using transfer learning strategies with in vitro experimental data and achieved good predictive power (AUC = 0.808). Simultaneously, our framework is interpretable, providing insights into resistance mechanisms alongside accurate predictions. The excellent performance of XGraphCDS highlights its immense potential in aiding the development of selective anti-tumor drugs and personalized dosing strategies in the field of precision medicine.
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Affiliation(s)
- Yimeng Wang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China
| | - Xinxin Yu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China
| | - Yaxin Gu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China
| | - Weihua Li
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China
| | - Keyun Zhu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China
| | - Long Chen
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China
| | - Yun Tang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China.
| | - Guixia Liu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China.
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Iqbal J, Cortés Jaimes DC, Makineni P, Subramani S, Hemaida S, Thugu TR, Butt AN, Sikto JT, Kaur P, Lak MA, Augustine M, Shahzad R, Arain M. Reimagining Healthcare: Unleashing the Power of Artificial Intelligence in Medicine. Cureus 2023; 15:e44658. [PMID: 37799217 PMCID: PMC10549955 DOI: 10.7759/cureus.44658] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/04/2023] [Indexed: 10/07/2023] Open
Abstract
Artificial intelligence (AI) has opened new medical avenues and revolutionized diagnostic and therapeutic practices, allowing healthcare providers to overcome significant challenges associated with cost, disease management, accessibility, and treatment optimization. Prominent AI technologies such as machine learning (ML) and deep learning (DL) have immensely influenced diagnostics, patient monitoring, novel pharmaceutical discoveries, drug development, and telemedicine. Significant innovations and improvements in disease identification and early intervention have been made using AI-generated algorithms for clinical decision support systems and disease prediction models. AI has remarkably impacted clinical drug trials by amplifying research into drug efficacy, adverse events, and candidate molecular design. AI's precision and analysis regarding patients' genetic, environmental, and lifestyle factors have led to individualized treatment strategies. During the COVID-19 pandemic, AI-assisted telemedicine set a precedent for remote healthcare delivery and patient follow-up. Moreover, AI-generated applications and wearable devices have allowed ambulatory monitoring of vital signs. However, apart from being immensely transformative, AI's contribution to healthcare is subject to ethical and regulatory concerns. AI-backed data protection and algorithm transparency should be strictly adherent to ethical principles. Vigorous governance frameworks should be in place before incorporating AI in mental health interventions through AI-operated chatbots, medical education enhancements, and virtual reality-based training. The role of AI in medical decision-making has certain limitations, necessitating the importance of hands-on experience. Therefore, reaching an optimal balance between AI's capabilities and ethical considerations to ensure impartial and neutral performance in healthcare applications is crucial. This narrative review focuses on AI's impact on healthcare and the importance of ethical and balanced incorporation to make use of its full potential.
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Affiliation(s)
| | - Diana Carolina Cortés Jaimes
- Epidemiology, Universidad Autónoma de Bucaramanga, Bucaramanga, COL
- Medicine, Pontificia Universidad Javeriana, Bogotá, COL
| | - Pallavi Makineni
- Medicine, All India Institute of Medical Sciences, Bhubaneswar, Bhubaneswar, IND
| | - Sachin Subramani
- Medicine and Surgery, Employees' State Insurance Corporation (ESIC) Medical College, Gulbarga, IND
| | - Sarah Hemaida
- Internal Medicine, Istanbul Okan University, Istanbul, TUR
| | - Thanmai Reddy Thugu
- Internal Medicine, Sri Padmavathi Medical College for Women, Sri Venkateswara Institute of Medical Sciences (SVIMS), Tirupati, IND
| | - Amna Naveed Butt
- Medicine/Internal Medicine, Allama Iqbal Medical College, Lahore, PAK
| | | | - Pareena Kaur
- Medicine, Punjab Institute of Medical Sciences, Jalandhar, IND
| | | | | | - Roheen Shahzad
- Medicine, Combined Military Hospital (CMH) Lahore Medical College and Institute of Dentistry, Lahore, PAK
| | - Mustafa Arain
- Internal Medicine, Civil Hospital Karachi, Karachi, PAK
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10
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Kaushik AC, Zhao Z. Machine learning-driven exploration of drug therapies for triple-negative breast cancer treatment. Front Mol Biosci 2023; 10:1215204. [PMID: 37602329 PMCID: PMC10436744 DOI: 10.3389/fmolb.2023.1215204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Accepted: 07/21/2023] [Indexed: 08/22/2023] Open
Abstract
Breast cancer is the second leading cause of cancer death in women among all cancer types. It is highly heterogeneous in nature, which means that the tumors have different morphologies and there is heterogeneity even among people who have the same type of tumor. Several staging and classifying systems have been developed due to the variability of different types of breast cancer. Due to high heterogeneity, personalized treatment has become a new strategy. Out of all breast cancer subtypes, triple-negative breast cancer (TNBC) comprises ∼10%-15%. TNBC refers to the subtype of breast cancer where cells do not express estrogen receptors, progesterone receptors, or human epidermal growth factor receptors (ERs, PRs, and HERs). Tumors in TNBC have a diverse set of genetic markers and prognostic indicators. We scanned the Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) databases for potential drugs using human breast cancer cell lines and drug sensitivity data. Three different machine-learning approaches were used to evaluate the prediction of six effective drugs against the TNBC cell lines. The top biomarkers were then shortlisted on the basis of their involvement in breast cancer and further subjected to testing for radion resistance using data from the Cleveland database. It was observed that Panobinostat, PLX4720, Lapatinib, Nilotinib, Selumetinib, and Tanespimycin were six effective drugs against the TNBC cell lines. We could identify potential derivates that may be used against approved drugs. Only one biomarker (SETD7) was sensitive to all six drugs on the shortlist, while two others (SRARP and YIPF5) were sensitive to both radiation and drugs. Furthermore, we did not find any radioresistance markers for the TNBC. The proposed biomarkers and drug sensitivity analysis will provide potential candidates for future clinical investigation.
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Affiliation(s)
- Aman Chandra Kaushik
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Zhongming Zhao
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, United States
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, United States
- MD Anderson Cancer Center, UTHealth Graduate School of Biomedical Sciences, Houston, TX, United States
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11
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Park A, Lee Y, Nam S. A performance evaluation of drug response prediction models for individual drugs. Sci Rep 2023; 13:11911. [PMID: 37488424 PMCID: PMC10366128 DOI: 10.1038/s41598-023-39179-2] [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: 01/18/2023] [Accepted: 07/20/2023] [Indexed: 07/26/2023] Open
Abstract
Drug response prediction is important to establish personalized medicine for cancer therapy. Model construction for predicting drug response (i.e., cell viability half-maximal inhibitory concentration [IC50]) of an individual drug by inputting pharmacogenomics in disease models remains critical. Machine learning (ML) has been predominantly applied for prediction, despite the advent of deep learning (DL). Moreover, whether DL or traditional ML models are superior for predicting cell viability IC50s has to be established. Herein, we constructed ML and DL drug response prediction models for 24 individual drugs and compared the performance of the models by employing gene expression and mutation profiles of cancer cell lines as input. We observed no significant difference in drug response prediction performance between DL and ML models for 24 drugs [root mean squared error (RMSE) ranging from 0.284 to 3.563 for DL and from 0.274 to 2.697 for ML; R2 ranging from -7.405 to 0.331 for DL and from -8.113 to 0.470 for ML]. Among the 24 individual drugs, the ridge model of panobinostat exhibited the best performance (R2 0.470 and RMSE 0.623). Thus, we selected the ridge model of panobinostat for further application of explainable artificial intelligence (XAI). Using XAI, we further identified important genomic features for panobinostat response prediction in the ridge model, suggesting the genomic features of 22 genes. Based on our findings, results for an individual drug employing both DL and ML models were comparable. Our study confirms the applicability of drug response prediction models for individual drugs.
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Affiliation(s)
- Aron Park
- Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology (GAIHST), Gachon University, Incheon, 21999, Republic of Korea
| | - Yeeun Lee
- Department of Genome Medicine and Science, AI Convergence Center for Medical Science, Gachon University Gil Medical Center, Gachon University College of Medicine, Incheon, 21565, Republic of Korea
| | - Seungyoon Nam
- Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology (GAIHST), Gachon University, Incheon, 21999, Republic of Korea.
- Department of Genome Medicine and Science, AI Convergence Center for Medical Science, Gachon University Gil Medical Center, Gachon University College of Medicine, Incheon, 21565, Republic of Korea.
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12
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Zhan Y, Guo J, Philip Chen CL, Meng XB. iBT-Net: an incremental broad transformer network for cancer drug response prediction. Brief Bioinform 2023:bbad256. [PMID: 37429577 DOI: 10.1093/bib/bbad256] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 05/30/2023] [Accepted: 06/15/2023] [Indexed: 07/12/2023] Open
Abstract
In modern precision medicine, it is an important research topic to predict cancer drug response. Due to incomplete chemical structures and complex gene features, however, it is an ongoing work to design efficient data-driven methods for predicting drug response. Moreover, since the clinical data cannot be easily obtained all at once, the data-driven methods may require relearning when new data are available, resulting in increased time consumption and cost. To address these issues, an incremental broad Transformer network (iBT-Net) is proposed for cancer drug response prediction. Different from the gene expression features learning from cancer cell lines, structural features are further extracted from drugs by Transformer. Broad learning system is then designed to integrate the learned gene features and structural features of drugs to predict the response. With the capability of incremental learning, the proposed method can further use new data to improve its prediction performance without retraining totally. Experiments and comparison studies demonstrate the effectiveness and superiority of iBT-Net under different experimental configurations and continuous data learning.
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Affiliation(s)
- Yongkang Zhan
- School of Computer Science & Engineering,South China University of Technology, 510006, China
| | - Jifeng Guo
- School of Computer Science & Engineering,South China University of Technology, 510006, China
| | - C L Philip Chen
- School of Computer Science & Engineering,South China University of Technology, 510006, China
- Brain and Affective Cognitive Research Center, Pazhou Lab, 510335, China
| | - Xian-Bing Meng
- School of Electromechanical Engineering, Guangdong University of Technology, 510006, China
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13
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Hostallero DE, Wei L, Wang L, Cairns J, Emad A. Preclinical-to-clinical Anti-cancer Drug Response Prediction and Biomarker Identification Using TINDL. GENOMICS, PROTEOMICS & BIOINFORMATICS 2023; 21:535-550. [PMID: 36775056 PMCID: PMC10787192 DOI: 10.1016/j.gpb.2023.01.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 11/28/2022] [Accepted: 01/31/2023] [Indexed: 02/12/2023]
Abstract
Prediction of the response of cancer patients to different treatments and identification of biomarkers of drug response are two major goals of individualized medicine. Here, we developed a deep learning framework called TINDL, completely trained on preclinical cancer cell lines (CCLs), to predict the response of cancer patients to different treatments. TINDL utilizes a tissue-informed normalization to account for the tissue type and cancer type of the tumors and to reduce the statistical discrepancies between CCLs and patient tumors. Moreover, by making the deep learning black box interpretable, this model identifies a small set of genes whose expression levels are predictive of drug response in the trained model, enabling identification of biomarkers of drug response. Using data from two large databases of CCLs and cancer tumors, we showed that this model can distinguish between sensitive and resistant tumors for 10 (out of 14) drugs, outperforming various other machine learning models. In addition, our small interfering RNA (siRNA) knockdown experiments on 10 genes identified by this model for one of the drugs (tamoxifen) confirmed that tamoxifen sensitivity is substantially influenced by all of these genes in MCF7 cells, and seven of these genes in T47D cells. Furthermore, genes implicated for multiple drugs pointed to shared mechanism of action among drugs and suggested several important signaling pathways. In summary, this study provides a powerful deep learning framework for prediction of drug response and identification of biomarkers of drug response in cancer. The code can be accessed at https://github.com/ddhostallero/tindl.
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Affiliation(s)
- David Earl Hostallero
- Department of Electrical and Computer Engineering, McGill University, Montreal, QC H3A, Canada; Mila - Quebec Artificial Intelligence Institute, Montreal, QC H2S, Canada
| | - Lixuan Wei
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA
| | - Liewei Wang
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA
| | - Junmei Cairns
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA.
| | - Amin Emad
- Department of Electrical and Computer Engineering, McGill University, Montreal, QC H3A, Canada; Mila - Quebec Artificial Intelligence Institute, Montreal, QC H2S, Canada; The Rosalind and Morris Goodman Cancer Institute, McGill University, Montreal, QC H3A, Canada.
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14
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Mehmood A, Nawab S, Jin Y, Hassan H, Kaushik AC, Wei DQ. Ranking Breast Cancer Drugs and Biomarkers Identification Using Machine Learning and Pharmacogenomics. ACS Pharmacol Transl Sci 2023; 6:399-409. [PMID: 36926455 PMCID: PMC10012252 DOI: 10.1021/acsptsci.2c00212] [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: 11/03/2022] [Indexed: 02/26/2023]
Abstract
Breast cancer is one of the major causes of death in women worldwide. It is a diverse illness with substantial intersubject heterogeneity, even among individuals with the same type of tumor, and customized therapy has become increasingly important in this sector. Because of the clinical and physical variability of different kinds of breast cancers, multiple staging and classification systems have been developed. As a result, these tumors exhibit a wide range of gene expression and prognostic indicators. To date, no comprehensive investigation of model training procedures on information from numerous cell line screenings has been conducted together with radiation data. We used human breast cancer cell lines and drug sensitivity information from Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) databases to scan for potential drugs using cell line data. The results are further validated through three machine learning approaches: Elastic Net, LASSO, and Ridge. Next, we selected top-ranked biomarkers based on their role in breast cancer and tested them further for their resistance to radiation using the data from the Cleveland database. We have identified six drugs named Palbociclib, Panobinostat, PD-0325901, PLX4720, Selumetinib, and Tanespimycin that significantly perform on breast cancer cell lines. Also, five biomarkers named TNFSF15, DCAF6, KDM6A, PHETA2, and IFNGR1 are sensitive to all six shortlisted drugs and show sensitivity to the radiations. The proposed biomarkers and drug sensitivity analysis are helpful in translational cancer studies and provide valuable insights for clinical trial design.
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Affiliation(s)
- Aamir Mehmood
- Department
of Bioinformatics and Biological Statistics, School of Life Sciences
and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, P.R. China
| | - Sadia Nawab
- State
Key Laboratory of Microbial Metabolism and School of Life Sciences
and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, P.R. China
| | - Yifan Jin
- Department
of Bioinformatics and Biological Statistics, School of Life Sciences
and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, P.R. China
| | - Hesham Hassan
- Department
of Pathology, College of Medicine, King
Khalid University, Abha 61421, Saudi Arabia
- Department
of Pathology, Faculty of Medicine, Assiut
University, Assiut 71515, Egypt
| | - Aman Chandra Kaushik
- Department
of Bioinformatics and Biological Statistics, School of Life Sciences
and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, P.R. China
| | - Dong-Qing Wei
- State
Key Laboratory of Microbial Metabolism, Shanghai-Islamabad-Belgrade
Joint Innovation Center on Antibacterial Resistances, Joint International
Research Laboratory of Metabolic & Developmental Sciences and
School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200030, P.R. China
- Zhongjing
Research and Industrialization Institute of Chinese Medicine, Zhongguancun Scientific Park, Meixi, Nanyang, Henan 473006, P.R. China
- Peng
Cheng National Laboratory, Vanke Cloud City Phase I Building 8, Xili Street, Nanshan District, Shenzhen, Guangdong 518055, P.R. China
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15
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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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16
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Wang H, Dai C, Wen Y, Wang X, Liu W, He S, Bo X, Peng S. GADRP: graph convolutional networks and autoencoders for cancer drug response prediction. Brief Bioinform 2023; 24:6865039. [PMID: 36460622 DOI: 10.1093/bib/bbac501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 10/19/2022] [Accepted: 10/22/2022] [Indexed: 12/04/2022] Open
Abstract
Drug response prediction in cancer cell lines is of great significance in personalized medicine. In this study, we propose GADRP, a cancer drug response prediction model based on graph convolutional networks (GCNs) and autoencoders (AEs). We first use a stacked deep AE to extract low-dimensional representations from cell line features, and then construct a sparse drug cell line pair (DCP) network incorporating drug, cell line, and DCP similarity information. Later, initial residual and layer attention-based GCN (ILGCN) that can alleviate over-smoothing problem is utilized to learn DCP features. And finally, fully connected network is employed to make prediction. Benchmarking results demonstrate that GADRP can significantly improve prediction performance on all metrics compared with baselines on five datasets. Particularly, experiments of predictions of unknown DCP responses, drug-cancer tissue associations, and drug-pathway associations illustrate the predictive power of GADRP. All results highlight the effectiveness of GADRP in predicting drug responses, and its potential value in guiding anti-cancer drug selection.
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Affiliation(s)
- Hong Wang
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China
| | - Chong Dai
- College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China.,Department of Bioinformatics, Beijing Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Yuqi Wen
- Department of Bioinformatics, Beijing Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Xiaoqi Wang
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China
| | - Wenjuan Liu
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China
| | - Song He
- Department of Bioinformatics, Beijing Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Xiaochen Bo
- Department of Bioinformatics, Beijing Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Shaoliang Peng
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China.,The State Key Laboratory of Chemo/Biosensing and Chemometrics, Hunan University, Changsha 410082, China
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17
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DasGupta R, Yap A, Yaqing EY, Chia S. Evolution of precision oncology-guided treatment paradigms. WIREs Mech Dis 2023; 15:e1585. [PMID: 36168283 DOI: 10.1002/wsbm.1585] [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: 03/07/2022] [Revised: 06/30/2022] [Accepted: 07/11/2022] [Indexed: 01/31/2023]
Abstract
Cancer treatment is gradually evolving from the classical use of nonspecific cytotoxic drugs targeting generic mechanisms of cell growth and proliferation. Instead, new "patient-specific treatment paradigms" that are based on an individual patient's tumor-specific molecular features are emerging, and these include "druggable" genomic alterations such as oncogenic driver mutations, downstream activities of cancer-signaling pathways, and the expression of specific genes involved in tumorigenesis and cancer progression. This evolving landscape of making evidence-based treatment decisions forms the foundation of precision oncology, which aims to deliver "the right drug, to the right patient and at the right time". The long-term vision for this approach is to maximize the treatment efficacy while minimizing exposure to ineffective therapy and reducing co-morbidity-related side effects. Successful clinical translation and implementation of this vision have the potential to revolutionize treatment paradigms from predominantly reactive, to more evidence-based, proactive and predictive care. In this article, we review the past and current approaches in precision oncology, and describe their remarkable power and limitations. We also speculate on the evolution of newly emerging methodologies of the future that can be used to address some of the key challenges associated with the existing paradigms. This article is categorized under: Cancer > Genetics/Genomics/Epigenetics Cancer > Molecular and Cellular Physiology Cancer > Computational Models.
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Affiliation(s)
- Ramanuj DasGupta
- Laboratory of Precision Oncology and Cancer Evolution, Genome Institute of Singapore, A*STAR, Singapore, Singapore.,Cancer Science Institute, National University of Singapore, Singapore, Singapore
| | - Aixin Yap
- Laboratory of Precision Oncology and Cancer Evolution, Genome Institute of Singapore, A*STAR, Singapore, Singapore
| | - Elena Yong Yaqing
- Laboratory of Precision Oncology and Cancer Evolution, Genome Institute of Singapore, A*STAR, Singapore, Singapore
| | - Shumei Chia
- Laboratory of Precision Oncology and Cancer Evolution, Genome Institute of Singapore, A*STAR, Singapore, Singapore
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18
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Utilization of Cancer Cell Line Screening to Elucidate the Anticancer Activity and Biological Pathways Related to the Ruthenium-Based Therapeutic BOLD-100. Cancers (Basel) 2022; 15:cancers15010028. [PMID: 36612025 PMCID: PMC9817855 DOI: 10.3390/cancers15010028] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 11/30/2022] [Accepted: 12/16/2022] [Indexed: 12/24/2022] Open
Abstract
BOLD-100 (sodium trans-[tetrachlorobis(1H indazole)ruthenate(III)]) is a ruthenium-based anticancer compound currently in clinical development. The identification of cancer types that show increased sensitivity towards BOLD-100 can lead to improved developmental strategies. Sensitivity profiling can also identify mechanisms of action that are pertinent for the bioactivity of complex therapeutics. Sensitivity to BOLD-100 was measured in a 319-cancer-cell line panel spanning 24 tissues. BOLD-100's sensitivity profile showed variation across the tissue lineages, including increased response in esophageal, bladder, and hematologic cancers. Multiple cancers, including esophageal, bile duct and colon cancer, had higher relative response to BOLD-100 than to cisplatin. Response to BOLD-100 showed only moderate correlation to anticancer compounds in the Genomics of Drug Sensitivity in Cancer (GDSC) database, as well as no clear theme in bioactivity of correlated hits, suggesting that BOLD-100 may have a differentiated therapeutic profile. The genomic modalities of cancer cell lines were modeled against the BOLD-100 sensitivity profile, which revealed that genes related to ribosomal processes were associated with sensitivity to BOLD-100. Machine learning modeling of the sensitivity profile to BOLD-100 and gene expression data provided moderative predictive value. These findings provide further mechanistic understanding around BOLD-100 and support its development for additional cancer types.
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19
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Shandhi MMH, Dunn JP. AI in medicine: Where are we now and where are we going? Cell Rep Med 2022; 3:100861. [PMID: 36543109 PMCID: PMC9798019 DOI: 10.1016/j.xcrm.2022.100861] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 11/14/2022] [Accepted: 11/17/2022] [Indexed: 12/24/2022]
Abstract
Advancements in AI enable personalizing healthcare, for example by investigating disease origins at the genetic or molecular level, understanding intraindividual drug effects, and fusing multi-modal personal physiological, behavioral, laboratory, and clinical data to uncover new aspects of pathophysiology. Future efforts should address equity, fairness, explainability, and generalizability of AI models.
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Affiliation(s)
| | - Jessilyn P. Dunn
- Department of Biomedical Engineering, Duke University, Durham, NC, USA,Department of Biostatistics & Bioinformatics, Duke University, Durham, NC, USA,Duke Clinical Research Institute, Durham, NC, USA,Corresponding author
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20
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Xie M, Lei X, Zhong J, Ouyang J, Li G. Drug response prediction using graph representation learning and Laplacian feature selection. BMC Bioinformatics 2022; 23:532. [PMID: 36494630 PMCID: PMC9733001 DOI: 10.1186/s12859-022-05080-4] [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: 11/06/2022] [Accepted: 11/22/2022] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Knowing the responses of a patient to drugs is essential to make personalized medicine practical. Since the current clinical drug response experiments are time-consuming and expensive, utilizing human genomic information and drug molecular characteristics to predict drug responses is of urgent importance. Although a variety of computational drug response prediction methods have been proposed, their effectiveness is still not satisfying. RESULTS In this study, we propose a method called LGRDRP (Learning Graph Representation for Drug Response Prediction) to predict cell line-drug responses. At first, LGRDRP constructs a heterogeneous network integrating multiple kinds of information: cell line miRNA expression profiles, drug chemical structure similarity, gene-gene interaction, cell line-gene interaction and known cell line-drug responses. Then, for each cell line, learning graph representation and Laplacian feature selection are combined to obtain network topology features related to the cell line. The learning graph representation method learns network topology structure features, and the Laplacian feature selection method further selects out some most important ones from them. Finally, LGRDRP trains an SVM model to predict drug responses based on the selected features of the known cell line-drug responses. Our five-fold cross-validation results show that LGRDRP is significantly superior to the art-of-the-state methods in the measures of the average area under the receiver operating characteristics curve, the average area under the precision-recall curve and the recall rate of top-k predicted sensitive cell lines. CONCLUSIONS Our results demonstrated that the usage of multiple types of information about cell lines and drugs, the learning graph representation method, and the Laplacian feature selection is useful to the improvement of performance in predicting drug responses. We believe that such an approach would be easily extended to similar problems such as miRNA-disease relationship inference.
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Affiliation(s)
- Minzhu Xie
- grid.411427.50000 0001 0089 3695College of Information Science and Engineering, Hunan Normal University, Changsha, China ,grid.411427.50000 0001 0089 3695Key Laboratory of Computing and Stochastic Mathematics (LCSM) (Ministry of Education), School of Mathematics and Statistics, Hunan Normal University, Changsha, China
| | - Xiaowen Lei
- grid.411427.50000 0001 0089 3695College of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Jianchen Zhong
- grid.411427.50000 0001 0089 3695College of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Jianxing Ouyang
- grid.411427.50000 0001 0089 3695College of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Guijing Li
- grid.411427.50000 0001 0089 3695College of Information Science and Engineering, Hunan Normal University, Changsha, China
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21
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Lee M, Kim PJ, Joe H, Kim HG. Gene-centric multi-omics integration with convolutional encoders for cancer drug response prediction. Comput Biol Med 2022; 151:106192. [PMID: 36327883 DOI: 10.1016/j.compbiomed.2022.106192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 08/26/2022] [Accepted: 10/08/2022] [Indexed: 12/27/2022]
Abstract
MOTIVATION Tumor heterogeneity, including genetic and transcriptomic characteristics, can reduce the efficacy of anticancer pharmacological therapy, resulting in clinical variability in patient response to therapeutic medications. Multi-omics integration can allow in silico models to provide an additional perspective on a biological system. METHODS In this study, we propose a gene-centric multi-channel (GCMC) architecture to integrate multi-omics for predicting cancer drug response. GCMC transformed multi-omics profiles into a three-dimensional tensor with an additional dimension for omics types. GCMC's convolutional encoders captures multi-omics profiles for each gene and yields gene-centric features to predict drug responses. RESULTS We evaluated GCMC on various datasets, including The Cancer Genome Atlas (TCGA) patients, patient-derived xenografts (PDX) mice models, and the Genomics of Drug Sensitivity in Cancer (GDSC) cell line datasets. GCMC achieved better performance than baseline models, including single-omics models, in more than 75% of 265 drugs from GDSC cell line datasets. Furthermore, as for the clinical applicability of GCMC, it achieved the best performance on TCGA and PDX datasets in terms of both AUPR and AUC. We also analyzed models' capability of integrating multi-omics profiles by measuring the contribution ratio of omics types. GCMC can incorporate multi-omics profiles in various manners to enhance performance for each drug type. These results suggested that GCMC can improve performance and feature extraction capability by integrating multi-omics profiles in a gene-centric manner.
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Affiliation(s)
- Munhwan Lee
- Biomedical Knowledge Engineering Lab., Seoul National University, 1 Gwanak-ro, Seoul, 08826, Republic of Korea.
| | - Pil-Jong Kim
- Biomedical Knowledge Engineering Lab., Seoul National University, 1 Gwanak-ro, Seoul, 08826, Republic of Korea.
| | - Hyunwhan Joe
- Biomedical Knowledge Engineering Lab., Seoul National University, 1 Gwanak-ro, Seoul, 08826, Republic of Korea.
| | - Hong-Gee Kim
- Biomedical Knowledge Engineering Lab., Seoul National University, 1 Gwanak-ro, Seoul, 08826, Republic of Korea.
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22
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Wang C, Lye X, Kaalia R, Kumar P, Rajapakse JC. Deep learning and multi-omics approach to predict drug responses in cancer. BMC Bioinformatics 2022; 22:632. [PMID: 36443676 PMCID: PMC9703655 DOI: 10.1186/s12859-022-04964-9] [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: 09/21/2022] [Accepted: 09/25/2022] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Cancers are genetically heterogeneous, so anticancer drugs show varying degrees of effectiveness on patients due to their differing genetic profiles. Knowing patient's responses to numerous cancer drugs are needed for personalized treatment for cancer. By using molecular profiles of cancer cell lines available from Cancer Cell Line Encyclopedia (CCLE) and anticancer drug responses available in the Genomics of Drug Sensitivity in Cancer (GDSC), we will build computational models to predict anticancer drug responses from molecular features. RESULTS We propose a novel deep neural network model that integrates multi-omics data available as gene expressions, copy number variations, gene mutations, reverse phase protein array expressions, and metabolomics expressions, in order to predict cellular responses to known anti-cancer drugs. We employ a novel graph embedding layer that incorporates interactome data as prior information for prediction. Moreover, we propose a novel attention layer that effectively combines different omics features, taking their interactions into account. The network outperformed feedforward neural networks and reported 0.90 for [Formula: see text] values for prediction of drug responses from cancer cell lines data available in CCLE and GDSC. CONCLUSION The outstanding results of our experiments demonstrate that the proposed method is capable of capturing the interactions of genes and proteins, and integrating multi-omics features effectively. Furthermore, both the results of ablation studies and the investigations of the attention layer imply that gene mutation has a greater influence on the prediction of drug responses than other omics data types. Therefore, we conclude that our approach can not only predict the anti-cancer drug response precisely but also provides insights into reaction mechanisms of cancer cell lines and drugs as well.
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Affiliation(s)
- Conghao Wang
- grid.59025.3b0000 0001 2224 0361School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798 Singapore
| | - Xintong Lye
- grid.59025.3b0000 0001 2224 0361School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798 Singapore
| | - Rama Kaalia
- grid.59025.3b0000 0001 2224 0361School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798 Singapore
| | - Parvin Kumar
- grid.59025.3b0000 0001 2224 0361School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798 Singapore
| | - Jagath C. Rajapakse
- grid.59025.3b0000 0001 2224 0361School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798 Singapore
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23
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Samal BR, Loers JU, Vermeirssen V, De Preter K. Opportunities and challenges in interpretable deep learning for drug sensitivity prediction of cancer cells. FRONTIERS IN BIOINFORMATICS 2022; 2:1036963. [PMID: 36466148 PMCID: PMC9714662 DOI: 10.3389/fbinf.2022.1036963] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 11/03/2022] [Indexed: 01/02/2024] Open
Abstract
In precision oncology, therapy stratification is done based on the patients' tumor molecular profile. Modeling and prediction of the drug response for a given tumor molecular type will further improve therapeutic decision-making for cancer patients. Indeed, deep learning methods hold great potential for drug sensitivity prediction, but a major problem is that these models are black box algorithms and do not clarify the mechanisms of action. This puts a limitation on their clinical implementation. To address this concern, many recent studies attempt to overcome these issues by developing interpretable deep learning methods that facilitate the understanding of the logic behind the drug response prediction. In this review, we discuss strengths and limitations of recent approaches, and suggest future directions that could guide further improvement of interpretable deep learning in drug sensitivity prediction in cancer research.
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Affiliation(s)
- Bikash Ranjan Samal
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
- Center for Medical Genetics Ghent (CMGG), Ghent University, Ghent, Belgium
- Cancer Research Institute Ghent (CRIG), Ghent, Belgium
| | - Jens Uwe Loers
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
- Center for Medical Genetics Ghent (CMGG), Ghent University, Ghent, Belgium
- Cancer Research Institute Ghent (CRIG), Ghent, Belgium
- Department of Biomedical Molecular Biology, Ghent University, Ghent, Belgium
| | - Vanessa Vermeirssen
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
- Center for Medical Genetics Ghent (CMGG), Ghent University, Ghent, Belgium
- Cancer Research Institute Ghent (CRIG), Ghent, Belgium
- Department of Biomedical Molecular Biology, Ghent University, Ghent, Belgium
| | - Katleen De Preter
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
- Center for Medical Genetics Ghent (CMGG), Ghent University, Ghent, Belgium
- Cancer Research Institute Ghent (CRIG), Ghent, Belgium
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24
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Shin J, Piao Y, Bang D, Kim S, Jo K. DRPreter: Interpretable Anticancer Drug Response Prediction Using Knowledge-Guided Graph Neural Networks and Transformer. Int J Mol Sci 2022; 23:13919. [PMID: 36430395 PMCID: PMC9699175 DOI: 10.3390/ijms232213919] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 10/27/2022] [Accepted: 11/08/2022] [Indexed: 11/16/2022] Open
Abstract
Some of the recent studies on drug sensitivity prediction have applied graph neural networks to leverage prior knowledge on the drug structure or gene network, and other studies have focused on the interpretability of the model to delineate the mechanism governing the drug response. However, it is crucial to make a prediction model that is both knowledge-guided and interpretable, so that the prediction accuracy is improved and practical use of the model can be enhanced. We propose an interpretable model called DRPreter (drug response predictor and interpreter) that predicts the anticancer drug response. DRPreter learns cell line and drug information with graph neural networks; the cell-line graph is further divided into multiple subgraphs with domain knowledge on biological pathways. A type-aware transformer in DRPreter helps detect relationships between pathways and a drug, highlighting important pathways that are involved in the drug response. Extensive experiments on the GDSC (Genomics of Drug Sensitivity and Cancer) dataset demonstrate that the proposed method outperforms state-of-the-art graph-based models for drug response prediction. In addition, DRPreter detected putative key genes and pathways for specific drug-cell-line pairs with supporting evidence in the literature, implying that our model can help interpret the mechanism of action of the drug.
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Affiliation(s)
- Jihye Shin
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea
| | - Yinhua Piao
- Department of Computer Science and Engineering, Institute of Engineering Research, Seoul National University, Seoul 08826, Korea
| | - Dongmin Bang
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea
- AIGENDRUG Co., Ltd., Seoul 08826, Korea
| | - Sun Kim
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea
- Department of Computer Science and Engineering, Institute of Engineering Research, Seoul National University, Seoul 08826, Korea
- Interdisciplinary Program in Artificial Intelligence, Seoul National University, Seoul 08826, Korea
- MOGAM Institute for Biomedical Research, Yongin-si 16924, Korea
| | - Kyuri Jo
- Department of Computer Engineering, Chungbuk National University, Cheongju 28644, Korea
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25
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Zaman A, Bivona TG. Quantitative Framework for Bench-to-Bedside Cancer Research. Cancers (Basel) 2022; 14:5254. [PMID: 36358671 PMCID: PMC9658824 DOI: 10.3390/cancers14215254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 10/20/2022] [Accepted: 10/24/2022] [Indexed: 11/29/2022] Open
Abstract
Bioscience is an interdisciplinary venture. Driven by a quantum shift in the volume of high throughput data and in ready availability of data-intensive technologies, mathematical and quantitative approaches have become increasingly common in bioscience. For instance, a recent shift towards a quantitative description of cells and phenotypes, which is supplanting conventional qualitative descriptions, has generated immense promise and opportunities in the field of bench-to-bedside cancer OMICS, chemical biology and pharmacology. Nevertheless, like any burgeoning field, there remains a lack of shared and standardized framework for quantitative cancer research. Here, in the context of cancer, we present a basic framework and guidelines for bench-to-bedside quantitative research and therapy. We outline some of the basic concepts and their parallel use cases for chemical-protein interactions. Along with several recommendations for assay setup and conditions, we also catalog applications of these quantitative techniques in some of the most widespread discovery pipeline and analytical methods in the field. We believe adherence to these guidelines will improve experimental design, reduce variabilities and standardize quantitative datasets.
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Affiliation(s)
- Aubhishek Zaman
- Department of Medicine, University of California, San Francisco, CA 94158, USA
- UCSF Helen Diller Comprehensive Cancer Center, University of California, San Francisco, CA 94158, USA
| | - Trever G. Bivona
- Department of Medicine, University of California, San Francisco, CA 94158, USA
- UCSF Helen Diller Comprehensive Cancer Center, University of California, San Francisco, CA 94158, USA
- Chan-Zuckerberg Biohub, San Francisco, CA 94158, USA
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26
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Praveen S, Tyagi N, Singh B, Karetla GR, Thalor MA, Joshi K, Tsegaye M. PSO-Based Evolutionary Approach to Optimize Head and Neck Biomedical Image to Detect Mesothelioma Cancer. BIOMED RESEARCH INTERNATIONAL 2022; 2022:3618197. [PMID: 36033562 PMCID: PMC9410819 DOI: 10.1155/2022/3618197] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 06/30/2022] [Accepted: 07/21/2022] [Indexed: 11/17/2022]
Abstract
Mesothelioma is a form of cancer that is aggressive and fatal. It is a thin layer of tissue that covers the majority of the patient's internal organs. The treatments are available; however, a cure is not attainable for the majority of patients. So, a lot of research is being done on detection of mesothelioma cancer using various different approaches; but this paper focuses on optimization techniques for optimizing the biomedical images to detect the cancer. With the restricted number of samples in the medical field, a Relief-PSO head and mesothelioma neck cancer pathological image feature selection approach is proposed. The approach reduces multilevel dimensionality. To begin, the relief technique picks different feature weights depending on the relationship between features and categories. Second, the hybrid binary particle swarm optimization (HBPSO) is suggested to automatically determine the optimum feature subset for candidate feature subsets. The technique outperforms seven other feature selection algorithms in terms of morphological feature screening, dimensionality reduction, and classification performance.
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Affiliation(s)
| | - Neha Tyagi
- Department of IT, G.L Bajaj Institute of Technology & Management, Greater Noida, India
| | - Bhagwant Singh
- Informatics Cluster, School of Computer Science, University of Petroleum and Energy Studies (UPES) Dehradun, Uttrakhand, 248007, India
| | - Girija Rani Karetla
- School of Computer, Data and Mathematical Sciences, Western Sydney University, Sydney, Australia
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27
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Sotudian S, Paschalidis IC. Machine Learning for Pharmacogenomics and Personalized Medicine: A Ranking Model for Drug Sensitivity Prediction. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:2324-2333. [PMID: 34043512 PMCID: PMC9642333 DOI: 10.1109/tcbb.2021.3084562] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
It is infeasible to test many different chemotherapy drugs on actual patients in large clinical trials, which motivates computational methods with the ability to learn and exploit associations between drug effectiveness and patient characteristics. This work proposes a machine learning approach to infer robust predictors of drug responses from patient genomic information. Rather than predicting the exact drug response on a given cell line, we introduce an elastic-net regression methodology to compare a drug-cell line pair against an alternative pair. Using predicted pairwise comparisons we rank the effectiveness of different drugs on the same cell line. A total of 173 cell lines and 100 drug responses were used in various settings for training and testing the proposed models. By comparing our approach against twelve baseline methods, we demonstrate that it outperforms the state-of-the-art methods in the literature. In contrast to most other methods, the algorithm is able to maintain its high performance even when we use a large number of drugs and few cell lines.
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28
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Zhu EY, Dupuy AJ. Machine learning approach informs biology of cancer drug response. BMC Bioinformatics 2022; 23:184. [PMID: 35581546 PMCID: PMC9112473 DOI: 10.1186/s12859-022-04720-z] [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: 11/15/2021] [Accepted: 05/03/2022] [Indexed: 12/12/2022] Open
Abstract
Background The mechanism of action for most cancer drugs is not clear. Large-scale pharmacogenomic cancer cell line datasets offer a rich resource to obtain this knowledge. Here, we present an analysis strategy for revealing biological pathways that contribute to drug response using publicly available pharmacogenomic cancer cell line datasets. Methods We present a custom machine-learning based approach for identifying biological pathways involved in cancer drug response. We test the utility of our approach with a pan-cancer analysis of ML210, an inhibitor of GPX4, and a melanoma-focused analysis of inhibitors of BRAFV600. We apply our approach to reveal determinants of drug resistance to microtubule inhibitors. Results Our method implicated lipid metabolism and Rac1/cytoskeleton signaling in the context of ML210 and BRAF inhibitor response, respectively. These findings are consistent with current knowledge of how these drugs work. For microtubule inhibitors, our approach implicated Notch and Akt signaling as pathways that associated with response. Conclusions Our results demonstrate the utility of combining informed feature selection and machine learning algorithms in understanding cancer drug response. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04720-z.
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Affiliation(s)
- Eliot Y Zhu
- Department of Anatomy and Cell Biology, The University of Iowa, Iowa City, IA, USA.,Holden Comprehensive Cancer Center, The University of Iowa, Iowa City, IA, USA.,Cancer Biology Graduate Program, The University of Iowa, Iowa City, IA, USA.,The Medical Scientist Training Program, The University of Iowa, Iowa City, IA, USA
| | - Adam J Dupuy
- Department of Anatomy and Cell Biology, The University of Iowa, Iowa City, IA, USA. .,Holden Comprehensive Cancer Center, The University of Iowa, Iowa City, IA, USA.
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29
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Park A, Joo M, Kim K, Son WJ, Lim G, Lee J, Kim JH, Lee DH, Nam S. A comprehensive evaluation of regression-based drug responsiveness prediction models, using cell viability inhibitory concentrations (IC50 values). Bioinformatics 2022; 38:2810-2817. [PMID: 35561188 DOI: 10.1093/bioinformatics/btac177] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Revised: 03/06/2022] [Accepted: 03/22/2022] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Predicting drug response is critical for precision medicine. Diverse methods have predicted drug responsiveness, as measured by the half-maximal drug inhibitory concentration (IC50), in cultured cells. Although IC50s are continuous, traditional prediction models have dealt mainly with binary classification of responsiveness. However, since there are few regression-based IC50 predictions, comprehensive evaluations of regression-based IC50 prediction models, including machine learning (ML) and deep learning (DL), for diverse data types and dataset sizes, have not been addressed. RESULTS Here, we constructed 11 input data settings, including multi-omics settings, with varying dataset sizes, then evaluated the performance of regression-based ML and DL models to predict IC50s. DL models considered two convolutional neural network architectures: CDRScan and residual neural network (ResNet). ResNet was introduced in regression-based DL models for predicting drug response for the first time. As a result, DL models performed better than ML models in all the settings. Also, ResNet performed better than or comparable to CDRScan and ML models in all settings. AVAILABILITY AND IMPLEMENTATION The data underlying this article are available in GitHub at https://github.com/labnams/IC50evaluation. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Aron Park
- Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology (GAIHST), Gachon University, Incheon 21999, Korea
| | - Minjae Joo
- Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology (GAIHST), Gachon University, Incheon 21999, Korea
| | | | - Won-Joon Son
- Samsung Advanced Institute of Technology, Samsung Electronics, Suwon, Gyeonggi-do 16678, Korea
| | - GyuTae Lim
- Genome Editing Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon 34141, Korea
| | - Jinhyuk Lee
- Genome Editing Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon 34141, Korea
- Department of Bioinformatics, University of Sciences and Technology, Daejeon 34113, Korea
| | - Jung Ho Kim
- Department of Internal Medicine, Gachon University Gil Medical Center, Gachon University School of Medicine, Incheon 21565, Korea
| | - Dae Ho Lee
- Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology (GAIHST), Gachon University, Incheon 21999, Korea
- Department of Internal Medicine, Gachon University Gil Medical Center, Gachon University School of Medicine, Incheon 21565, Korea
| | - Seungyoon Nam
- Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology (GAIHST), Gachon University, Incheon 21999, Korea
- AI Convergence Center for Medical Science, Department of Genome Medicine and Science, Gachon University Gil Medical Center, Gachon University College of Medicine, Incheon 21565, Korea
- Department of Life Sciences, Gachon University, Seongnam, Gyeonggi-do 13120, Korea
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30
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Marastoni S, Madariaga A, Pesic A, Nair SN, Li ZJ, Shalev Z, Ketela T, Colombo I, Mandilaras V, Cabanero M, Bruce JP, Li X, Garg S, Wang L, Chen EX, Gill S, Dhani NC, Zhang W, Pintilie M, Bowering V, Koritzinsky M, Rottapel R, Wouters BG, Oza AM, Joshua AM, Lheureux S. Repurposing Itraconazole and Hydroxychloroquine to Target Lysosomal Homeostasis in Epithelial Ovarian Cancer. CANCER RESEARCH COMMUNICATIONS 2022; 2:293-306. [PMID: 36875717 PMCID: PMC9981200 DOI: 10.1158/2767-9764.crc-22-0037] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 03/13/2022] [Accepted: 04/22/2022] [Indexed: 11/16/2022]
Abstract
Drug repurposing is an attractive option for oncology drug development. Itraconazole is an antifungal ergosterol synthesis inhibitor that has pleiotropic actions including cholesterol antagonism, inhibition of Hedgehog and mTOR pathways. We tested a panel of 28 epithelial ovarian cancer (EOC) cell lines with itraconazole to define its spectrum of activity. To identify synthetic lethality in combination with itraconazole, a whole-genome drop-out genome-scale clustered regularly interspaced short palindromic repeats sensitivity screen in two cell lines (TOV1946 and OVCAR5) was performed. On this basis, we conducted a phase I dose-escalation study assessing the combination of itraconazole and hydroxychloroquine in patients with platinum refractory EOC (NCT03081702). We identified a wide spectrum of sensitivity to itraconazole across the EOC cell lines. Pathway analysis showed significant involvement of lysosomal compartments, the trans-golgi network and late endosomes/lysosomes; similar pathways are phenocopied by the autophagy inhibitor, chloroquine. We then demonstrated that the combination of itraconazole and chloroquine displayed Bliss defined synergy in EOC cancer cell lines. Furthermore, there was an association of cytotoxic synergy with the ability to induce functional lysosome dysfunction, by chloroquine. Within the clinical trial, 11 patients received at least one cycle of itraconazole and hydroxychloroquine. Treatment was safe and feasible with the recommended phase II dose of 300 and 600 mg twice daily, respectively. No objective responses were detected. Pharmacodynamic measurements on serial biopsies demonstrated limited pharmacodynamic impact. In vitro, itraconazole and chloroquine have synergistic activity and exert a potent antitumor effect by affecting lysosomal function. The drug combination had no clinical antitumor activity in dose escalation. Significance The combination of the antifungal drug itraconazole with antimalarial drug hydroxychloroquine leads to a cytotoxic lysosomal dysfunction, supporting the rational for further research on lysosomal targeting in ovarian cancer.
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Affiliation(s)
- Stefano Marastoni
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Ainhoa Madariaga
- Division of Medical Oncology & Hematology, Princess Margaret Cancer Centre, Toronto, Ontario, Canada.,Autonomous University of Barcelona, Barcelona, Spain
| | - Aleksandra Pesic
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Sree Narayanan Nair
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Zhu Juan Li
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Zvi Shalev
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Troy Ketela
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Ilaria Colombo
- Division of Medical Oncology & Hematology, Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Victoria Mandilaras
- Division of Medical Oncology & Hematology, Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Michael Cabanero
- Department of Pathology, Toronto General Hospital, Toronto, Ontario, Canada
| | - Jeff P Bruce
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Xuan Li
- Division of Medical Oncology & Hematology, Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Swati Garg
- Division of Medical Oncology & Hematology, Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Lisa Wang
- Division of Medical Oncology & Hematology, Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Eric X Chen
- Division of Medical Oncology & Hematology, Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Sarbjot Gill
- Division of Medical Oncology & Hematology, Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Neesha C Dhani
- Division of Medical Oncology & Hematology, Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Wenjiang Zhang
- Division of Medical Oncology & Hematology, Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Melania Pintilie
- Department of Biostatistics, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Valerie Bowering
- Division of Medical Oncology & Hematology, Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Marianne Koritzinsky
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.,Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada
| | - Robert Rottapel
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Bradly G Wouters
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada
| | - Amit M Oza
- Division of Medical Oncology & Hematology, Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Anthony M Joshua
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.,Kinghorn Cancer Centre, Department of Medical Oncology, St Vincents Hospital, Sydney, Australia.,Garvan Institute of Medical Research, Sydney, Australia
| | - Stephanie Lheureux
- Division of Medical Oncology & Hematology, Princess Margaret Cancer Centre, Toronto, Ontario, Canada
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31
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Automatic identification of drug sensitivity of cancer cell with novel regression-based ensemble convolution neural network model. Soft comput 2022. [DOI: 10.1007/s00500-022-07098-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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32
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Effectiveness of Artificial Intelligence for Personalized Medicine in Neoplasms: A Systematic Review. BIOMED RESEARCH INTERNATIONAL 2022; 2022:7842566. [PMID: 35434134 PMCID: PMC9010213 DOI: 10.1155/2022/7842566] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 01/29/2022] [Accepted: 03/06/2022] [Indexed: 02/07/2023]
Abstract
Purpose Artificial intelligence (AI) techniques are used in precision medicine to explore novel genotypes and phenotypes data. The main aims of precision medicine include early diagnosis, screening, and personalized treatment regime for a patient based on genetic-oriented features and characteristics. The main objective of this study was to review AI techniques and their effectiveness in neoplasm precision medicine. Materials and Methods A comprehensive search was performed in Medline (through PubMed), Scopus, ISI Web of Science, IEEE Xplore, Embase, and Cochrane databases from inception to December 29, 2021, in order to identify the studies that used AI methods for cancer precision medicine and evaluate outcomes of the models. Results Sixty-three studies were included in this systematic review. The main AI approaches in 17 papers (26.9%) were linear and nonlinear categories (random forest or decision trees), and in 21 citations, rule-based systems and deep learning models were used. Notably, 62% of the articles were done in the United States and China. R package was the most frequent software, and breast and lung cancer were the most selected neoplasms in the papers. Out of 63 papers, in 34 articles, genomic data like gene expression, somatic mutation data, phenotype data, and proteomics with drug-response which is functional data was used as input in AI methods; in 16 papers' (25.3%) drug response, functional data was utilized in personalization of treatment. The maximum values of the assessment indicators such as accuracy, sensitivity, specificity, precision, recall, and area under the curve (AUC) in included studies were 0.99, 1.00, 0.96, 0.98, 0.99, and 0.9929, respectively. Conclusion The findings showed that in many cases, the use of artificial intelligence methods had effective application in personalized medicine.
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34
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Pouryahya M, Oh JH, Mathews JC, Belkhatir Z, Moosmüller C, Deasy JO, Tannenbaum AR. Pan-Cancer Prediction of Cell-Line Drug Sensitivity Using Network-Based Methods. Int J Mol Sci 2022; 23:ijms23031074. [PMID: 35163005 PMCID: PMC8835038 DOI: 10.3390/ijms23031074] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 01/15/2022] [Accepted: 01/17/2022] [Indexed: 01/02/2023] Open
Abstract
The development of reliable predictive models for individual cancer cell lines to identify an optimal cancer drug is a crucial step to accelerate personalized medicine, but vast differences in cancer cell lines and drug characteristics make it quite challenging to develop predictive models that result in high predictive power and explain the similarity of cell lines or drugs. Our study proposes a novel network-based methodology that breaks the problem into smaller, more interpretable problems to improve the predictive power of anti-cancer drug responses in cell lines. For the drug-sensitivity study, we used the GDSC database for 915 cell lines and 200 drugs. The theory of optimal mass transport was first used to separately cluster cell lines and drugs, using gene-expression profiles and extensive cheminformatic drug features, represented in a form of data networks. To predict cell-line specific drug responses, random forest regression modeling was separately performed for each cell-line drug cluster pair. Post-modeling biological analysis was further performed to identify potential biological correlates associated with drug responses. The network-based clustering method resulted in 30 distinct cell-line drug cluster pairs. Predictive modeling on each cell-line-drug cluster outperformed alternative computational methods in predicting drug responses. We found that among the four drugs top-ranked with respect to prediction performance, three targeted the PI3K/mTOR signaling pathway. Predictive modeling on clustered subsets of cell lines and drugs improved the prediction accuracy of cell-line specific drug responses. Post-modeling analysis identified plausible biological processes associated with drug responses.
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Affiliation(s)
- Maryam Pouryahya
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (M.P.); (J.C.M.); (J.O.D.)
| | - Jung Hun Oh
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (M.P.); (J.C.M.); (J.O.D.)
- Correspondence:
| | - James C. Mathews
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (M.P.); (J.C.M.); (J.O.D.)
| | - Zehor Belkhatir
- School of Engineering and Sustainable Development, De Montfort University, Leicester LE1 9BH, UK;
| | - Caroline Moosmüller
- Department of Mathematics, University of California at San Diego, La Jolla, CA 92093, USA;
| | - Joseph O. Deasy
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (M.P.); (J.C.M.); (J.O.D.)
| | - Allen R. Tannenbaum
- Departments of Computer Science and Applied Mathematics & Statistics, Stony Brook University, Stony Brook, NY 11794, USA;
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35
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Firoozbakht F, Yousefi B, Schwikowski B. An overview of machine learning methods for monotherapy drug response prediction. Brief Bioinform 2022; 23:bbab408. [PMID: 34619752 PMCID: PMC8769705 DOI: 10.1093/bib/bbab408] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 08/25/2021] [Accepted: 09/06/2021] [Indexed: 12/11/2022] Open
Abstract
For an increasing number of preclinical samples, both detailed molecular profiles and their responses to various drugs are becoming available. Efforts to understand, and predict, drug responses in a data-driven manner have led to a proliferation of machine learning (ML) methods, with the longer term ambition of predicting clinical drug responses. Here, we provide a uniquely wide and deep systematic review of the rapidly evolving literature on monotherapy drug response prediction, with a systematic characterization and classification that comprises more than 70 ML methods in 13 subclasses, their input and output data types, modes of evaluation, and code and software availability. ML experts are provided with a fundamental understanding of the biological problem, and how ML methods are configured for it. Biologists and biomedical researchers are introduced to the basic principles of applicable ML methods, and their application to the problem of drug response prediction. We also provide systematic overviews of commonly used data sources used for training and evaluation methods.
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Affiliation(s)
- Farzaneh Firoozbakht
- Systems Biology Group, Department of Computational Biology, Institut Pasteur, Paris, France
| | - Behnam Yousefi
- Systems Biology Group, Department of Computational Biology, Institut Pasteur, Paris, France
- Sorbonne Université, École Doctorale Complexite du Vivant, Paris, France
| | - Benno Schwikowski
- Systems Biology Group, Department of Computational Biology, Institut Pasteur, Paris, France
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36
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Zhu Y, Ouyang Z, Chen W, Feng R, Chen DZ, Cao J, Wu J. TGSA: protein-protein association-based twin graph neural networks for drug response prediction with similarity augmentation. Bioinformatics 2022; 38:461-468. [PMID: 34559177 DOI: 10.1093/bioinformatics/btab650] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 08/16/2021] [Accepted: 09/24/2021] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Drug response prediction (DRP) plays an important role in precision medicine (e.g. for cancer analysis and treatment). Recent advances in deep learning algorithms make it possible to predict drug responses accurately based on genetic profiles. However, existing methods ignore the potential relationships among genes. In addition, similarity among cell lines/drugs was rarely considered explicitly. RESULTS We propose a novel DRP framework, called TGSA, to make better use of prior domain knowledge. TGSA consists of Twin Graph neural networks for Drug Response Prediction (TGDRP) and a Similarity Augmentation (SA) module to fuse fine-grained and coarse-grained information. Specifically, TGDRP abstracts cell lines as graphs based on STRING protein-protein association networks and uses Graph Neural Networks (GNNs) for representation learning. SA views DRP as an edge regression problem on a heterogeneous graph and utilizes GNNs to smooth the representations of similar cell lines/drugs. Besides, we introduce an auxiliary pre-training strategy to remedy the identified limitations of scarce data and poor out-of-distribution generalization. Extensive experiments on the GDSC2 dataset demonstrate that our TGSA consistently outperforms all the state-of-the-art baselines under various experimental settings. We further evaluate the effectiveness and contributions of each component of TGSA via ablation experiments. The promising performance of TGSA shows enormous potential for clinical applications in precision medicine. AVAILABILITY AND IMPLEMENTATION The source code is available at https://github.com/violet-sto/TGSA. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yiheng Zhu
- College of Computer Science and Technology, Zhejiang University, Hangzhou 310000, China
| | - Zhenqiu Ouyang
- Polytechnic Institute, Zhejiang University, Hangzhou 310000, China
| | - Wenbo Chen
- Polytechnic Institute, Zhejiang University, Hangzhou 310000, China
| | - Ruiwei Feng
- College of Computer Science and Technology, Zhejiang University, Hangzhou 310000, China
| | - Danny Z Chen
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA
| | - Ji Cao
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310000, China
| | - Jian Wu
- Department of Ophthalmology of the Second Affiliated Hospital School of Medicine, and School of Public Health, Zhejiang University, Hangzhou 310000, China
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37
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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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38
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Pepe G, Carrino C, Parca L, Helmer-Citterich M. Dissecting the Genome for Drug Response Prediction. Methods Mol Biol 2022; 2449:187-196. [PMID: 35507263 DOI: 10.1007/978-1-0716-2095-3_7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The prediction of the cancer cell lines sensitivity to a specific treatment is one of the current challenges in precision medicine. With omics and pharmacogenomics data being available for over 1000 cancer cell lines, several machine learning and deep learning algorithms have been proposed for drug sensitivity prediction. However, deciding which omics data to use and which computational methods can efficiently incorporate data from different sources is the challenge which several research groups are working on. In this review, we summarize recent advances in the representative computational methods that have been developed in the last 2 years on three public datasets: COSMIC, CCLE, NCI-60. These methods aim to improve the prediction of the cancer cell lines sensitivity to a given treatment by incorporating drug's chemical information in the input or using a priori feature selection. Finally, we discuss the latest published method which aims to improve the prediction of clinical drug response of real patients starting from cancer cell line molecular profiles.
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Affiliation(s)
- Gerardo Pepe
- Department of Biology, Centro di Bioinformatica Molecolare, University of Rome "Tor Vergata", Rome, Italy
| | - Chiara Carrino
- Department of Biology, Centro di Bioinformatica Molecolare, University of Rome "Tor Vergata", Rome, Italy
| | - Luca Parca
- Italian Space Agency, Via del Politecnico snc, Rome, Italy
| | - Manuela Helmer-Citterich
- Department of Biology, Centro di Bioinformatica Molecolare, University of Rome "Tor Vergata", Rome, Italy.
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39
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Prabahar A. Integration of Transcriptomics Data and Metabolomic Data Using Biomedical Literature Mining and Pathway Analysis. Methods Mol Biol 2022; 2496:301-316. [PMID: 35713871 DOI: 10.1007/978-1-0716-2305-3_16] [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] [Indexed: 06/15/2023]
Abstract
Recent progress in omics technologies such as transcriptomics and metabolomics offers an unprecedented opportunity to understand the disease mechanisms and determines the associated biomedical entities using biomedical literature mining. Tremendous data available in the biomedical literature helps in addressing complex biomedical problems. Advancements in genomics and transcriptomics helps in decoding the genetic information obtained from various high throughput techniques for its use in personalized medicine and therapeutics. Integration of data from biomedical literature and data from large-scale genomic studies aids in the determination of the etiology of a disease and drug targets. This chapter addresses the perspectives of transcriptomics and metabolomics in biomedical literature mining and gives an overview of state-of-the-art techniques in this field.
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Affiliation(s)
- Archana Prabahar
- R&D Division, Eriks-Precision Components India Pvt Ltd, Mohali, Punjab, India.
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40
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Panja S, Rahem S, Chu CJ, Mitrofanova A. Big Data to Knowledge: Application of Machine Learning to Predictive Modeling of Therapeutic Response in Cancer. Curr Genomics 2021; 22:244-266. [PMID: 35273457 PMCID: PMC8822229 DOI: 10.2174/1389202921999201224110101] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Revised: 09/16/2020] [Accepted: 09/30/2020] [Indexed: 11/22/2022] Open
Abstract
Background In recent years, the availability of high throughput technologies, establishment of large molecular patient data repositories, and advancement in computing power and storage have allowed elucidation of complex mechanisms implicated in therapeutic response in cancer patients. The breadth and depth of such data, alongside experimental noise and missing values, requires a sophisticated human-machine interaction that would allow effective learning from complex data and accurate forecasting of future outcomes, ideally embedded in the core of machine learning design. Objective In this review, we will discuss machine learning techniques utilized for modeling of treatment response in cancer, including Random Forests, support vector machines, neural networks, and linear and logistic regression. We will overview their mathematical foundations and discuss their limitations and alternative approaches in light of their application to therapeutic response modeling in cancer. Conclusion We hypothesize that the increase in the number of patient profiles and potential temporal monitoring of patient data will define even more complex techniques, such as deep learning and causal analysis, as central players in therapeutic response modeling.
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Affiliation(s)
| | | | | | - Antonina Mitrofanova
- Address correspondence to this author at the Department of Health Informatics, Rutgers School of Health Professions, Rutgers Biomedical and Health Sciences, Newark, NJ 07107, USA; E-mail:
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41
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Wang S, Li J, Wang Y. WMMDCA: Prediction of Drug Responses by Weight-Based Modular Mapping in Cancer Cell Lines. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:2733-2740. [PMID: 32142453 DOI: 10.1109/tcbb.2020.2976997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Due to the high consumption of cost and time for experimental verification in clinical trials, drug response prediction by computational models have become important challenges. The existing drug response data in diverse cell lines enable prediction of potential sensitive associations. Here, we propose a weight-based modular mapping method, named as WMMDCA, to predict drug-cell line associations. The method fully considers the effects of drugs' chemical structural feature, and adds modular information into the network projection. Leave-one-out cross-validation was used to evaluate the predictive ability of WMMDCA, which showed the best performance among several state-of-the-art methods in not only the whole dataset but also the major tissue types of cell lines. Literature support of highly ranked potential associations was found manually, demonstrating the effectiveness of WMMDCA on drug response prediction.
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42
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Liu X, Song C, Huang F, Fu H, Xiao W, Zhang W. GraphCDR: a graph neural network method with contrastive learning for cancer drug response prediction. Brief Bioinform 2021; 23:6415314. [PMID: 34727569 DOI: 10.1093/bib/bbab457] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Revised: 09/25/2021] [Accepted: 10/07/2021] [Indexed: 12/29/2022] Open
Abstract
Predicting the response of a cancer cell line to a therapeutic drug is an important topic in modern oncology that can help personalized treatment for cancers. Although numerous machine learning methods have been developed for cancer drug response (CDR) prediction, integrating diverse information about cancer cell lines, drugs and their known responses still remains a great challenge. In this paper, we propose a graph neural network method with contrastive learning for CDR prediction. GraphCDR constructs a graph neural network based on multi-omics profiles of cancer cell lines, the chemical structure of drugs and known cancer cell line-drug responses for CDR prediction, while a contrastive learning task is presented as a regularizer within a multi-task learning paradigm to enhance the generalization ability. In the computational experiments, GraphCDR outperforms state-of-the-art methods under different experimental configurations, and the ablation study reveals the key components of GraphCDR: biological features, known cancer cell line-drug responses and contrastive learning are important for the high-accuracy CDR prediction. The experimental analyses imply the predictive power of GraphCDR and its potential value in guiding anti-cancer drug selection.
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Affiliation(s)
- Xuan Liu
- College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China
| | - Congzhi Song
- College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China
| | - Feng Huang
- College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China
| | - Haitao Fu
- College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China
| | - Wenjie Xiao
- Information School, University of Washington, Washington, 98105, USA
| | - Wen Zhang
- College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China
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43
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Miranda SP, Baião FA, Fleck JL, Piccolo SR. Predicting drug sensitivity of cancer cells based on DNA methylation levels. PLoS One 2021; 16:e0238757. [PMID: 34506489 PMCID: PMC8432830 DOI: 10.1371/journal.pone.0238757] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Accepted: 06/28/2021] [Indexed: 01/22/2023] Open
Abstract
Cancer cell lines, which are cell cultures derived from tumor samples, represent one of the least expensive and most studied preclinical models for drug development. Accurately predicting drug responses for a given cell line based on molecular features may help to optimize drug-development pipelines and explain mechanisms behind treatment responses. In this study, we focus on DNA methylation profiles as one type of molecular feature that is known to drive tumorigenesis and modulate treatment responses. Using genome-wide, DNA methylation profiles from 987 cell lines in the Genomics of Drug Sensitivity in Cancer database, we used machine-learning algorithms to evaluate the potential to predict cytotoxic responses for eight anti-cancer drugs. We compared the performance of five classification algorithms and four regression algorithms representing diverse methodologies, including tree-, probability-, kernel-, ensemble-, and distance-based approaches. We artificially subsampled the data to varying degrees, aiming to understand whether training based on relatively extreme outcomes would yield improved performance. When using classification or regression algorithms to predict discrete or continuous responses, respectively, we consistently observed excellent predictive performance when the training and test sets consisted of cell-line data. Classification algorithms performed best when we trained the models using cell lines with relatively extreme drug-response values, attaining area-under-the-receiver-operating-characteristic-curve values as high as 0.97. The regression algorithms performed best when we trained the models using the full range of drug-response values, although this depended on the performance metrics we used. Finally, we used patient data from The Cancer Genome Atlas to evaluate the feasibility of classifying clinical responses for human tumors based on models derived from cell lines. Generally, the algorithms were unable to identify patterns that predicted patient responses reliably; however, predictions by the Random Forests algorithm were significantly correlated with Temozolomide responses for low-grade gliomas.
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Affiliation(s)
- Sofia P. Miranda
- Department of Industrial Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, Rio de Janeiro, Brazil
| | - Fernanda A. Baião
- Department of Industrial Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, Rio de Janeiro, Brazil
| | - Julia L. Fleck
- Mines Saint-Etienne, Univ Clermont Auvergne, CNRS, UMR 6158 LIMOS, Centre CIS, Saint-Etienne, France
| | - Stephen R. Piccolo
- Department of Biology, Brigham Young University, Provo, Utah, United States of America
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44
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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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45
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He B, Hou F, Ren C, Bing P, Xiao X. A Review of Current In Silico Methods for Repositioning Drugs and Chemical Compounds. Front Oncol 2021; 11:711225. [PMID: 34367996 PMCID: PMC8340770 DOI: 10.3389/fonc.2021.711225] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 07/07/2021] [Indexed: 12/23/2022] Open
Abstract
Drug repositioning is a new way of applying the existing therapeutics to new disease indications. Due to the exorbitant cost and high failure rate in developing new drugs, the continued use of existing drugs for treatment, especially anti-tumor drugs, has become a widespread practice. With the assistance of high-throughput sequencing techniques, many efficient methods have been proposed and applied in drug repositioning and individualized tumor treatment. Current computational methods for repositioning drugs and chemical compounds can be divided into four categories: (i) feature-based methods, (ii) matrix decomposition-based methods, (iii) network-based methods, and (iv) reverse transcriptome-based methods. In this article, we comprehensively review the widely used methods in the above four categories. Finally, we summarize the advantages and disadvantages of these methods and indicate future directions for more sensitive computational drug repositioning methods and individualized tumor treatment, which are critical for further experimental validation.
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Affiliation(s)
- Binsheng He
- Academician Workstation, Changsha Medical University, Changsha, China
| | - Fangxing Hou
- Queen Mary School, Nanchang University, Jiangxi, China
| | - Changjing Ren
- School of Science, Dalian Maritime University, Dalian, China.,Genies Beijing Co., Ltd., Beijing, China
| | - Pingping Bing
- Academician Workstation, Changsha Medical University, Changsha, China
| | - Xiangzuo Xiao
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Jiangxi, China
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46
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Huang S, Hu P, Lakowski TM. Predicting breast cancer drug response using a multiple-layer cell line drug response network model. BMC Cancer 2021; 21:648. [PMID: 34059012 PMCID: PMC8166022 DOI: 10.1186/s12885-021-08359-6] [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: 07/19/2020] [Accepted: 05/13/2021] [Indexed: 01/04/2023] Open
Abstract
Background Predicting patient drug response based on a patient’s molecular profile is one of the key goals of precision medicine in breast cancer (BC). Multiple drug response prediction models have been developed to address this problem. However, most of them were developed to make sensitivity predictions for multiple single drugs within cell lines from various cancer types instead of a single cancer type, do not take into account drug properties, and have not been validated in cancer patient-derived data. Among the multi-omics data, gene expression profiles have been shown to be the most informative data for drug response prediction. However, these models were often developed with individual genes. Therefore, this study aimed to develop a drug response prediction model for BC using multiple data types from both cell lines and drugs. Methods We first collected the baseline gene expression profiles of 49 BC cell lines along with IC50 values for 220 drugs tested in these cell lines from Genomics of Drug Sensitivity in Cancer (GDSC). Using these data, we developed a multiple-layer cell line-drug response network (ML-CDN2) by integrating a one-layer cell line similarity network based on the pathway activity profiles and a three-layer drug similarity network based on the drug structures, targets, and pan-cancer IC50 profiles. We further used ML-CDN2 to predict the drug response for new BC cell lines or patient-derived samples. Results ML-CDN2 demonstrated a good predictive performance, with the Pearson correlation coefficient between the observed and predicted IC50 values for all GDSC cell line-drug pairs of 0.873. Also, ML-CDN2 showed a good performance when used to predict drug response in new BC cell lines from the Cancer Cell Line Encyclopedia (CCLE), with a Pearson correlation coefficient of 0.718. Moreover, we found that the cell line-derived ML-CDN2 model could be applied to predict drug response in the BC patient-derived samples from The Cancer Genome Atlas (TCGA). Conclusions The ML-CDN2 model was built to predict BC drug response using comprehensive information from both cell lines and drugs. Compared with existing methods, it has the potential to predict the drug response for BC patient-derived samples. Supplementary Information The online version contains supplementary material available at 10.1186/s12885-021-08359-6.
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Affiliation(s)
- Shujun Huang
- College of Pharmacy, University of Manitoba, Apotex Centre, 750 McDermot Avenue, Winnipeg, Manitoba, R3E 0T5, Canada
| | - Pingzhao Hu
- Department of Biochemistry and Medical Genetics, University of Manitoba, Room 308 - Basic Medical Sciences Building, 745 Bannatyne Avenue, Winnipeg, Manitoba, R3E 0J9, Canada. .,Cancer Care Manitoba Research Institute, 675 McDermot Avenue, Winnipeg, Manitoba, R3E 0V9, Canada.
| | - Ted M Lakowski
- College of Pharmacy, University of Manitoba, Apotex Centre, 750 McDermot Avenue, Winnipeg, Manitoba, R3E 0T5, Canada.
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47
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Park S, Soh J, Lee H. Super.FELT: supervised feature extraction learning using triplet loss for drug response prediction with multi-omics data. BMC Bioinformatics 2021; 22:269. [PMID: 34034645 PMCID: PMC8152321 DOI: 10.1186/s12859-021-04146-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 04/22/2021] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Predicting the drug response of a patient is important for precision oncology. In recent studies, multi-omics data have been used to improve the prediction accuracy of drug response. Although multi-omics data are good resources for drug response prediction, the large dimension of data tends to hinder performance improvement. In this study, we aimed to develop a new method, which can effectively reduce the large dimension of data, based on the supervised deep learning model for predicting drug response. RESULTS We proposed a novel method called Supervised Feature Extraction Learning using Triplet loss (Super.FELT) for drug response prediction. Super.FELT consists of three stages, namely, feature selection, feature encoding using a supervised method, and binary classification of drug response (sensitive or resistant). We used multi-omics data including mutation, copy number aberration, and gene expression, and these were obtained from cell lines [Genomics of Drug Sensitivity in Cancer (GDSC), Cancer Cell Line Encyclopedia (CCLE), and Cancer Therapeutics Response Portal (CTRP)], patient-derived tumor xenografts (PDX), and The Cancer Genome Atlas (TCGA). GDSC was used for training and cross-validation tests, and CCLE, CTRP, PDX, and TCGA were used for external validation. We performed ablation studies for the three stages and verified that the use of multi-omics data guarantees better performance of drug response prediction. Our results verified that Super.FELT outperformed the other methods at external validation on PDX and TCGA and was good at cross-validation on GDSC and external validation on CCLE and CTRP. In addition, through our experiments, we confirmed that using multi-omics data is useful for external non-cell line data. CONCLUSION By separating the three stages, Super.FELT achieved better performance than the other methods. Through our results, we found that it is important to train encoders and a classifier independently, especially for external test on PDX and TCGA. Moreover, although gene expression is the most powerful data on cell line data, multi-omics promises better performance for external validation on non-cell line data than gene expression data. Source codes of Super.FELT are available at https://github.com/DMCB-GIST/Super.FELT .
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Affiliation(s)
- Sejin Park
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, South Korea
| | - Jihee Soh
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, South Korea
| | - Hyunju Lee
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, South Korea.
- Graduate School of Artificial Intelligence, Gwangju Institute of Science and Technology, Gwangju, South Korea.
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48
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Ahmadi Moughari F, Eslahchi C. A computational method for drug sensitivity prediction of cancer cell lines based on various molecular information. PLoS One 2021; 16:e0250620. [PMID: 33914775 PMCID: PMC8084246 DOI: 10.1371/journal.pone.0250620] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Accepted: 04/09/2021] [Indexed: 12/29/2022] Open
Abstract
Determining sensitive drugs for a patient is one of the most critical problems in precision medicine. Using genomic profiles of the tumor and drug information can help in tailoring the most efficient treatment for a patient. In this paper, we proposed a classification machine learning approach that predicts the sensitive/resistant drugs for a cell line. It can be performed by using both drug and cell line similarities, one of the cell line or drug similarities, or even not using any similarity information. This paper investigates the influence of using previously defined as well as two newly introduced similarities on predicting anti-cancer drug sensitivity. The proposed method uses max concentration thresholds for assigning drug responses to class labels. Its performance was evaluated using stratified five-fold cross-validation on cell line-drug pairs in two datasets. Assessing the predictive powers of the proposed model and three sets of methods, including state-of-the-art classification methods, state-of-the-art regression methods, and off-the-shelf classification machine learning approaches shows that the proposed method outperforms other methods. Moreover, The efficiency of the model is evaluated in tissue-specific conditions. Besides, the novel sensitive associations predicted by this model were verified by several supportive evidence in the literature and reliable database. Therefore, the proposed model can efficiently be used in predicting anti-cancer drug sensitivity. Material and implementation are available at https://github.com/fahmadimoughari/CDSML.
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Affiliation(s)
- Fatemeh Ahmadi Moughari
- 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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49
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Malik V, Kalakoti Y, Sundar D. Deep learning assisted multi-omics integration for survival and drug-response prediction in breast cancer. BMC Genomics 2021; 22:214. [PMID: 33761889 PMCID: PMC7992339 DOI: 10.1186/s12864-021-07524-2] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2020] [Accepted: 03/09/2021] [Indexed: 12/16/2022] Open
Abstract
Background Survival and drug response are two highly emphasized clinical outcomes in cancer research that directs the prognosis of a cancer patient. Here, we have proposed a late multi omics integrative framework that robustly quantifies survival and drug response for breast cancer patients with a focus on the relative predictive ability of available omics datatypes. Neighborhood component analysis (NCA), a supervised feature selection algorithm selected relevant features from multi-omics datasets retrieved from The Cancer Genome Atlas (TCGA) and Genomics of Drug Sensitivity in Cancer (GDSC) databases. A Neural network framework, fed with NCA selected features, was used to develop survival and drug response prediction models for breast cancer patients. The drug response framework used regression and unsupervised clustering (K-means) to segregate samples into responders and non-responders based on their predicted IC50 values (Z-score). Results The survival prediction framework was highly effective in categorizing patients into risk subtypes with an accuracy of 94%. Compared to single-omics and early integration approaches, our drug response prediction models performed significantly better and were able to predict IC50 values (Z-score) with a mean square error (MSE) of 1.154 and an overall regression value of 0.92, showing a linear relationship between predicted and actual IC50 values. Conclusion The proposed omics integration strategy provides an effective way of extracting critical information from diverse omics data types enabling estimation of prognostic indicators. Such integrative models with high predictive power would have a significant impact and utility in precision oncology. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-021-07524-2.
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Affiliation(s)
- Vidhi Malik
- DAILAB, Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology (IIT) Delhi, New Delhi, India
| | - Yogesh Kalakoti
- DAILAB, Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology (IIT) Delhi, New Delhi, India
| | - Durai Sundar
- DAILAB, Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology (IIT) Delhi, New Delhi, India.
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50
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Liu Q, Hu Z, Jiang R, Zhou M. DeepCDR: a hybrid graph convolutional network for predicting cancer drug response. Bioinformatics 2021; 36:i911-i918. [PMID: 33381841 DOI: 10.1093/bioinformatics/btaa822] [Citation(s) in RCA: 84] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
MOTIVATION Accurate prediction of cancer drug response (CDR) is challenging due to the uncertainty of drug efficacy and heterogeneity of cancer patients. Strong evidences have implicated the high dependence of CDR on tumor genomic and transcriptomic profiles of individual patients. Precise identification of CDR is crucial in both guiding anti-cancer drug design and understanding cancer biology. RESULTS In this study, we present DeepCDR which integrates multi-omics profiles of cancer cells and explores intrinsic chemical structures of drugs for predicting CDR. Specifically, DeepCDR is a hybrid graph convolutional network consisting of a uniform graph convolutional network and multiple subnetworks. Unlike prior studies modeling hand-crafted features of drugs, DeepCDR automatically learns the latent representation of topological structures among atoms and bonds of drugs. Extensive experiments showed that DeepCDR outperformed state-of-the-art methods in both classification and regression settings under various data settings. We also evaluated the contribution of different types of omics profiles for assessing drug response. Furthermore, we provided an exploratory strategy for identifying potential cancer-associated genes concerning specific cancer types. Our results highlighted the predictive power of DeepCDR and its potential translational value in guiding disease-specific drug design. AVAILABILITY AND IMPLEMENTATION DeepCDR is freely available at https://github.com/kimmo1019/DeepCDR. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Qiao Liu
- Ministry of Education Key Laboratory of Bioinformatics, Research Department of Bioinformatics, Beijing National Research Center, Information Science and Technology, Center for Synthetic and Systems Biology.,Department of Automation, Tsinghua University, Beijing 100084, China
| | - Zhiqiang Hu
- Department of Automation, Tsinghua University, Beijing 100084, China.,SenseTime Research, Shanghai 200233, China
| | - Rui Jiang
- Ministry of Education Key Laboratory of Bioinformatics, Research Department of Bioinformatics, Beijing National Research Center, Information Science and Technology, Center for Synthetic and Systems Biology.,Department of Automation, Tsinghua University, Beijing 100084, China
| | - Mu Zhou
- SenseBrain Research, San Jose, CA 95131, USA
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