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Ma Y, Shih CH, Cheng J, Chen HC, Wang LJ, Tan Y, Chiu YC, Chen YC. High-Throughput Empirical and Virtual Screening to Discover Novel Inhibitors of Polyploid Giant Cancer Cells in Breast Cancer. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.09.23.614522. [PMID: 39386568 PMCID: PMC11463688 DOI: 10.1101/2024.09.23.614522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 10/12/2024]
Abstract
Therapy resistance in breast cancer is increasingly attributed to polyploid giant cancer cells (PGCCs), which arise through whole-genome doubling and exhibit heightened resilience to standard treatments. Characterized by enlarged nuclei and increased DNA content, these cells tend to be dormant under therapeutic stress, driving disease relapse. Despite their critical role in resistance, strategies to effectively target PGCCs are limited, largely due to the lack of high-throughput methods for assessing their viability. Traditional assays lack the sensitivity needed to detect PGCC-specific elimination, prompting the development of novel approaches. To address this challenge, we developed a high-throughput single-cell morphological analysis workflow designed to differentiate compounds that selectively inhibit non-PGCCs, PGCCs, or both. Using this method, we screened a library of 2,726 FDA Phase 1-approved drugs, identifying promising anti-PGCC candidates, including proteasome inhibitors, FOXM1, CHK, and macrocyclic lactones. Notably, RNA-Seq analysis of cells treated with the macrocyclic lactone Pyronaridine revealed AXL inhibition as a potential strategy for targeting PGCCs. Although our single-cell morphological analysis pipeline is powerful, empirically testing all existing compounds is impractical and inefficient. To overcome this limitation, we trained a machine learning model to predict anti-PGCC efficacy in silico , integrating chemical fingerprints and compound descriptions from prior publications and databases. The model demonstrated a high correlation with experimental outcomes and predicted efficacious compounds in an expanded library of over 6,000 drugs. Among the top-ranked predictions, we experimentally validated two compounds as potent PGCC inhibitors. These findings underscore the synergistic potential of integrating high-throughput empirical screening with machine learning-based virtual screening to accelerate the discovery of novel therapies, particularly for targeting therapy-resistant PGCCs in breast cancer.
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2
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Qin G, Zhang Y, Tyner JW, Kemp CJ, Shmulevich I. Knowledge graphs facilitate prediction of drug response for acute myeloid leukemia. iScience 2024; 27:110755. [PMID: 39280607 PMCID: PMC11401200 DOI: 10.1016/j.isci.2024.110755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 05/04/2024] [Accepted: 08/14/2024] [Indexed: 09/18/2024] Open
Abstract
Acute myeloid leukemia (AML) is a highly aggressive and heterogeneous disease, underscoring the need for improved therapeutic options and methods to optimally predict responses. With the wealth of available data resources, including clinical features, multiomics analysis, and ex vivo drug screening from AML patients, development of drug response prediction models has become feasible. Knowledge graphs (KGs) embed the relationships between different entities or features, allowing for explanation of a wide breadth of drug sensitivity and resistance mechanisms. We designed AML drug response prediction models guided by KGs. Our models included engineered features, relative gene expression between marker genes for each drug and regulators (e.g., transcription factors). We identified relative gene expression of FGD4-MIR4519, NPC2-GATA2, and BCL2-NFKB2 as predictive features for venetoclax ex vivo drug response. The KG-guided models provided high accuracy in independent test sets, overcame potential platform batch effects, and provided candidate drug sensitivity biomarkers for further validation.
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Affiliation(s)
- Guangrong Qin
- Institute for Systems Biology, Seattle, WA 98109, USA
| | - Yue Zhang
- Institute for Systems Biology, Seattle, WA 98109, USA
| | - Jeffrey W Tyner
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
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3
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Kamble P, Nagar PR, Bhakhar KA, Garg P, Sobhia ME, Naidu S, Bharatam PV. Cancer pharmacoinformatics: Databases and analytical tools. Funct Integr Genomics 2024; 24:166. [PMID: 39294509 DOI: 10.1007/s10142-024-01445-5] [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: 07/29/2024] [Revised: 08/26/2024] [Accepted: 09/03/2024] [Indexed: 09/20/2024]
Abstract
Cancer is a subject of extensive investigation, and the utilization of omics technology has resulted in the generation of substantial volumes of big data in cancer research. Numerous databases are being developed to manage and organize this data effectively. These databases encompass various domains such as genomics, transcriptomics, proteomics, metabolomics, immunology, and drug discovery. The application of computational tools into various core components of pharmaceutical sciences constitutes "Pharmacoinformatics", an emerging paradigm in rational drug discovery. The three major features of pharmacoinformatics include (i) Structure modelling of putative drugs and targets, (ii) Compilation of databases and analysis using statistical approaches, and (iii) Employing artificial intelligence/machine learning algorithms for the discovery of novel therapeutic molecules. The development, updating, and analysis of databases using statistical approaches play a pivotal role in pharmacoinformatics. Multiple software tools are associated with oncoinformatics research. This review catalogs the databases and computational tools related to cancer drug discovery and highlights their potential implications in the pharmacoinformatics of cancer.
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Affiliation(s)
- Pradnya Kamble
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, S.A.S. Nagar, Punjab, India
| | - Prinsa R Nagar
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, S.A.S. Nagar, Punjab, India
| | - Kaushikkumar A Bhakhar
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, S.A.S. Nagar, Punjab, India
| | - Prabha Garg
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, S.A.S. Nagar, Punjab, India
| | - M Elizabeth Sobhia
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, S.A.S. Nagar, Punjab, India
| | - Srivatsava Naidu
- Center of Biomedical Engineering, Indian Institute of Technology Ropar, Rupnagar, Punjab, India
| | - Prasad V Bharatam
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, S.A.S. Nagar, Punjab, India.
- Department of Medicinal Chemistry, National Institute of Pharmaceutical Education and Research, S.A.S. Nagar, Punjab, India.
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4
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Connell W, Garcia K, Goodarzi H, Keiser MJ. Learning chemical sensitivity reveals mechanisms of cellular response. Commun Biol 2024; 7:1149. [PMID: 39278951 PMCID: PMC11402971 DOI: 10.1038/s42003-024-06865-4] [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: 12/11/2023] [Accepted: 09/06/2024] [Indexed: 09/18/2024] Open
Abstract
Chemical probes interrogate disease mechanisms at the molecular level by linking genetic changes to observable traits. However, comprehensive chemical screens in diverse biological models are impractical. To address this challenge, we develop ChemProbe, a model that predicts cellular sensitivity to hundreds of molecular probes and drugs by learning to combine transcriptomes and chemical structures. Using ChemProbe, we infer the chemical sensitivity of cancer cell lines and tumor samples and analyze how the model makes predictions. We retrospectively evaluate drug response predictions for precision breast cancer treatment and prospectively validate chemical sensitivity predictions in new cellular models, including a genetically modified cell line. Our model interpretation analysis identifies transcriptome features reflecting compound targets and protein network modules, identifying genes that drive ferroptosis. ChemProbe is an interpretable in silico screening tool that allows researchers to measure cellular response to diverse compounds, facilitating research into molecular mechanisms of chemical sensitivity.
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Affiliation(s)
- William Connell
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA
- Institute for Neurodegenerative Diseases, University of California, San Francisco, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Kristle Garcia
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
- Department of Urology, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
| | - Hani Goodarzi
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
- Department of Urology, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
| | - Michael J Keiser
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA.
- Institute for Neurodegenerative Diseases, University of California, San Francisco, San Francisco, CA, USA.
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.
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5
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Torrik A, Zarif M. Machine learning assisted sorting of active microswimmers. J Chem Phys 2024; 161:094907. [PMID: 39225539 DOI: 10.1063/5.0216862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2024] [Accepted: 08/19/2024] [Indexed: 09/04/2024] Open
Abstract
Active matter systems, being in a non-equilibrium state, exhibit complex behaviors, such as self-organization, giving rise to emergent phenomena. There are many examples of active particles with biological origins, including bacteria and spermatozoa, or with artificial origins, such as self-propelled swimmers and Janus particles. The ability to manipulate active particles is vital for their effective application, e.g., separating motile spermatozoa from nonmotile and dead ones, to increase fertilization chance. In this study, we proposed a mechanism-an apparatus-to sort and demix active particles based on their motility values (Péclet number). Initially, using Brownian simulations, we demonstrated the feasibility of sorting self-propelled particles. Following this, we employed machine learning methods, supplemented with data from comprehensive simulations that we conducted for this study, to model the complex behavior of active particles. This enabled us to sort them based on their Péclet number. Finally, we evaluated the performance of the developed models and showed their effectiveness in demixing and sorting the active particles. Our findings can find applications in various fields, including physics, biology, and biomedical science, where the sorting and manipulation of active particles play a pivotal role.
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Affiliation(s)
- Abdolhalim Torrik
- Department of Physical and Computational Chemistry, Shahid Beheshti University, Tehran 19839-9411, Iran
| | - Mahdi Zarif
- Department of Physical and Computational Chemistry, Shahid Beheshti University, Tehran 19839-9411, Iran
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Guo J, Chen B, Cao H, Dai Q, Qin L, Zhang J, Zhang Y, Zhang H, Sui Y, Chen T, Yang D, Gong X, Li D. Cross-modal deep learning model for predicting pathologic complete response to neoadjuvant chemotherapy in breast cancer. NPJ Precis Oncol 2024; 8:189. [PMID: 39237596 PMCID: PMC11377584 DOI: 10.1038/s41698-024-00678-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Accepted: 08/26/2024] [Indexed: 09/07/2024] Open
Abstract
Pathological complete response (pCR) serves as a critical measure of the success of neoadjuvant chemotherapy (NAC) in breast cancer, directly influencing subsequent therapeutic decisions. With the continuous advancement of artificial intelligence, methods for early and accurate prediction of pCR are being extensively explored. In this study, we propose a cross-modal multi-pathway automated prediction model that integrates temporal and spatial information. This model fuses digital pathology images from biopsy specimens and multi-temporal ultrasound (US) images to predict pCR status early in NAC. The model demonstrates exceptional predictive efficacy. Our findings lay the foundation for developing personalized treatment paradigms based on individual responses. This approach has the potential to become a critical auxiliary tool for the early prediction of NAC response in breast cancer patients.
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Affiliation(s)
- Jianming Guo
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, 150000, Harbin, China
| | - Baihui Chen
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, 150000, Harbin, China
| | - Hongda Cao
- School of Computer, Beihang University, 100191, Beijing, China
| | - Quan Dai
- Medicine & Laboratory of Translational Research in Ultrasound Theranostics, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, 610041, Chengdu, China
- Department of Ultrasound, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, 610041, Chengdu, China
| | - Ling Qin
- Department of Pathology, Harbin Medical University Cancer Hospital, 150000, Harbin, China
| | - Jinfeng Zhang
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, 150000, Harbin, China
| | - Youxue Zhang
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, 150000, Harbin, China
| | - Huanyu Zhang
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, 150000, Harbin, China
| | - Yuan Sui
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, 150000, Harbin, China
| | - Tianyu Chen
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, 150000, Harbin, China
| | - Dongxu Yang
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, 150000, Harbin, China
| | - Xue Gong
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, 150000, Harbin, China
| | - Dalin Li
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, 150000, Harbin, China.
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7
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Lenhof K, Eckhart L, Rolli LM, Lenhof HP. Trust me if you can: a survey on reliability and interpretability of machine learning approaches for drug sensitivity prediction in cancer. Brief Bioinform 2024; 25:bbae379. [PMID: 39101498 PMCID: PMC11299037 DOI: 10.1093/bib/bbae379] [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: 03/07/2024] [Revised: 07/08/2024] [Accepted: 07/19/2024] [Indexed: 08/06/2024] Open
Abstract
With the ever-increasing number of artificial intelligence (AI) systems, mitigating risks associated with their use has become one of the most urgent scientific and societal issues. To this end, the European Union passed the EU AI Act, proposing solution strategies that can be summarized under the umbrella term trustworthiness. In anti-cancer drug sensitivity prediction, machine learning (ML) methods are developed for application in medical decision support systems, which require an extraordinary level of trustworthiness. This review offers an overview of the ML landscape of methods for anti-cancer drug sensitivity prediction, including a brief introduction to the four major ML realms (supervised, unsupervised, semi-supervised, and reinforcement learning). In particular, we address the question to what extent trustworthiness-related properties, more specifically, interpretability and reliability, have been incorporated into anti-cancer drug sensitivity prediction methods over the previous decade. In total, we analyzed 36 papers with approaches for anti-cancer drug sensitivity prediction. Our results indicate that the need for reliability has hardly been addressed so far. Interpretability, on the other hand, has often been considered for model development. However, the concept is rather used intuitively, lacking clear definitions. Thus, we propose an easily extensible taxonomy for interpretability, unifying all prevalent connotations explicitly or implicitly used within the field.
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Affiliation(s)
- Kerstin Lenhof
- Center for Bioinformatics, Chair for Bioinformatics, Saarland Informatics Campus (E2.1) Saarland University, Campus, D-66123 Saarbrücken, Saarland, Germany
| | - Lea Eckhart
- Center for Bioinformatics, Chair for Bioinformatics, Saarland Informatics Campus (E2.1) Saarland University, Campus, D-66123 Saarbrücken, Saarland, Germany
| | - Lisa-Marie Rolli
- Center for Bioinformatics, Chair for Bioinformatics, Saarland Informatics Campus (E2.1) Saarland University, Campus, D-66123 Saarbrücken, Saarland, Germany
| | - Hans-Peter Lenhof
- Center for Bioinformatics, Chair for Bioinformatics, Saarland Informatics Campus (E2.1) Saarland University, Campus, D-66123 Saarbrücken, Saarland, Germany
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8
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Yeh SJ, Paithankar S, Chen R, Xing J, Sun M, Liu K, Zhou J, Chen B. TransCell: In Silico Characterization of Genomic Landscape and Cellular Responses by Deep Transfer Learning. GENOMICS, PROTEOMICS & BIOINFORMATICS 2024; 22:qzad008. [PMID: 39240541 PMCID: PMC11378636 DOI: 10.1093/gpbjnl/qzad008] [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: 10/27/2022] [Revised: 06/30/2023] [Accepted: 09/20/2023] [Indexed: 09/07/2024]
Abstract
Gene expression profiling of new or modified cell lines becomes routine today; however, obtaining comprehensive molecular characterization and cellular responses for a variety of cell lines, including those derived from underrepresented groups, is not trivial when resources are minimal. Using gene expression to predict other measurements has been actively explored; however, systematic investigation of its predictive power in various measurements has not been well studied. Here, we evaluated commonly used machine learning methods and presented TransCell, a two-step deep transfer learning framework that utilized the knowledge derived from pan-cancer tumor samples to predict molecular features and responses. Among these models, TransCell had the best performance in predicting metabolite, gene effect score (or genetic dependency), and drug sensitivity, and had comparable performance in predicting mutation, copy number variation, and protein expression. Notably, TransCell improved the performance by over 50% in drug sensitivity prediction and achieved a correlation of 0.7 in gene effect score prediction. Furthermore, predicted drug sensitivities revealed potential repurposing candidates for new 100 pediatric cancer cell lines, and predicted gene effect scores reflected BRAF resistance in melanoma cell lines. Together, we investigated the predictive power of gene expression in six molecular measurement types and developed a web portal (http://apps.octad.org/transcell/) that enables the prediction of 352,000 genomic and cellular response features solely from gene expression profiles.
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Affiliation(s)
- Shan-Ju Yeh
- Department of Pediatrics and Human Development, Michigan State University, Grand Rapids, MI 49503, USA
| | - Shreya Paithankar
- Department of Pediatrics and Human Development, Michigan State University, Grand Rapids, MI 49503, USA
| | - Ruoqiao Chen
- Department of Pharmacology and Toxicology, Michigan State University, Grand Rapids, MI 49503, USA
| | - Jing Xing
- Department of Pediatrics and Human Development, Michigan State University, Grand Rapids, MI 49503, USA
| | - Mengying Sun
- Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48824, USA
| | - Ke Liu
- Department of Pediatrics and Human Development, Michigan State University, Grand Rapids, MI 49503, USA
| | - Jiayu Zhou
- Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48824, USA
| | - Bin Chen
- Department of Pediatrics and Human Development, Michigan State University, Grand Rapids, MI 49503, USA
- Department of Pharmacology and Toxicology, Michigan State University, Grand Rapids, MI 49503, USA
- Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48824, USA
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9
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Chen HO, Cui YC, Lin PC, Chiang JH. An Innovative Multi-Omics Model Integrating Latent Alignment and Attention Mechanism for Drug Response Prediction. J Pers Med 2024; 14:694. [PMID: 39063948 PMCID: PMC11277895 DOI: 10.3390/jpm14070694] [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: 05/27/2024] [Revised: 06/18/2024] [Accepted: 06/24/2024] [Indexed: 07/28/2024] Open
Abstract
By using omics, we can now examine all components of biological systems simultaneously. Deep learning-based drug prediction methods have shown promise by integrating cancer-related multi-omics data. However, the complex interaction between genes poses challenges in accurately projecting multi-omics data. In this research, we present a predictive model for drug response that incorporates diverse types of omics data, comprising genetic mutation, copy number variation, methylation, and gene expression data. This study proposes latent alignment for information mismatch in integration, which is achieved through an attention module capturing interactions among diverse types of omics data. The latent alignment and attention modules significantly improve predictions, outperforming the baseline model, with MSE = 1.1333, F1-score = 0.5342, and AUROC = 0.5776. High accuracy was achieved in predicting drug responses for piplartine and tenovin-6, while the accuracy was comparatively lower for mitomycin-C and obatoclax. The latent alignment module exclusively outperforms the baseline model, enhancing the MSE by 0.2375, the F1-score by 4.84%, and the AUROC by 6.1%. Similarly, the attention module only improves these metrics by 0.1899, 2.88%, and 2.84%, respectively. In the interpretability case study, panobinostat exhibited the most effective predicted response, with a value of -4.895. We provide reliable insights for drug selection in personalized medicine by identifying crucial genetic factors influencing drug response.
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Affiliation(s)
- Hui-O Chen
- Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 701, Taiwan
- Institute of Medical Informatics, National Cheng Kung University, Tainan 701, Taiwan
| | - Yuan-Chi Cui
- Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 701, Taiwan
- Institute of Medical Informatics, National Cheng Kung University, Tainan 701, Taiwan
| | - Peng-Chan Lin
- Department of Oncology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 701, Taiwan
- Department of Genomic Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 701, Taiwan
| | - Jung-Hsien Chiang
- Department of Oncology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 701, Taiwan
- Department of Genomic Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 701, Taiwan
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10
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Lawrence PJ, Burns B, Ning X. Enhancing drug and cell line representations via contrastive learning for improved anti-cancer drug prioritization. NPJ Precis Oncol 2024; 8:106. [PMID: 38762647 PMCID: PMC11102516 DOI: 10.1038/s41698-024-00589-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 03/22/2024] [Indexed: 05/20/2024] Open
Abstract
Due to cancer's complex nature and variable response to therapy, precision oncology informed by omics sequence analysis has become the current standard of care. However, the amount of data produced for each patient makes it difficult to quickly identify the best treatment regimen. Moreover, limited data availability has hindered computational methods' abilities to learn patterns associated with effective drug-cell line pairs. In this work, we propose the use of contrastive learning to improve learned drug and cell line representations by preserving relationship structures associated with drug mechanisms of action and cell line cancer types. In addition to achieving enhanced performance relative to a state-of-the-art method, we find that classifiers using our learned representations exhibit a more balanced reliance on drug- and cell line-derived features when making predictions. This facilitates more personalized drug prioritizations that are informed by signals related to drug resistance.
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Affiliation(s)
- Patrick J Lawrence
- Biomedical Informatics Department, The Ohio State University, 1800 Cannon Drive, Lincoln Tower 250, Columbus, OH, 43210, USA
| | - Benjamin Burns
- Computer Science and Engineering Department, The Ohio State University, 2015 Neil Avenue, Columbus, OH, 43210, USA
| | - Xia Ning
- Biomedical Informatics Department, The Ohio State University, 1800 Cannon Drive, Lincoln Tower 250, Columbus, OH, 43210, USA.
- Computer Science and Engineering Department, The Ohio State University, 2015 Neil Avenue, Columbus, OH, 43210, USA.
- Translational Data Analytics Institute, The Ohio State University, 1760 Neil Avenue, Columbus, OH, 43210, USA.
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11
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Pang W, Chen M, Qin Y. Prediction of anticancer drug sensitivity using an interpretable model guided by deep learning. BMC Bioinformatics 2024; 25:182. [PMID: 38724920 PMCID: PMC11080240 DOI: 10.1186/s12859-024-05669-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 01/22/2024] [Indexed: 05/13/2024] Open
Abstract
BACKGROUND The prediction of drug sensitivity plays a crucial role in improving the therapeutic effect of drugs. However, testing the effectiveness of drugs is challenging due to the complex mechanism of drug reactions and the lack of interpretability in most machine learning and deep learning methods. Therefore, it is imperative to establish an interpretable model that receives various cell line and drug feature data to learn drug response mechanisms and achieve stable predictions between available datasets. RESULTS This study proposes a new and interpretable deep learning model, DrugGene, which integrates gene expression, gene mutation, gene copy number variation of cancer cells, and chemical characteristics of anticancer drugs to predict their sensitivity. This model comprises two different branches of neural networks, where the first involves a hierarchical structure of biological subsystems that uses the biological processes of human cells to form a visual neural network (VNN) and an interpretable deep neural network for human cancer cells. DrugGene receives genotype input from the cell line and detects changes in the subsystem states. We also employ a traditional artificial neural network (ANN) to capture the chemical structural features of drugs. DrugGene generates final drug response predictions by combining VNN and ANN and integrating their outputs into a fully connected layer. The experimental results using drug sensitivity data extracted from the Cancer Drug Sensitivity Genome Database and the Cancer Treatment Response Portal v2 reveal that the proposed model is better than existing prediction methods. Therefore, our model achieves higher accuracy, learns the reaction mechanisms between anticancer drugs and cell lines from various features, and interprets the model's predicted results. CONCLUSIONS Our method utilizes biological pathways to construct neural networks, which can use genotypes to monitor changes in the state of network subsystems, thereby interpreting the prediction results in the model and achieving satisfactory prediction accuracy. This will help explore new directions in cancer treatment. More available code resources can be downloaded for free from GitHub ( https://github.com/pangweixiong/DrugGene ).
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Affiliation(s)
- Weixiong Pang
- College of Information Technology, Shanghai Ocean University, Hucheng Ring Road, Shanghai, China
- Key Laboratory of Fisheries Information Ministry of Agriculture, Shanghai, China
| | - Ming Chen
- College of Information Technology, Shanghai Ocean University, Hucheng Ring Road, Shanghai, China
- Key Laboratory of Fisheries Information Ministry of Agriculture, Shanghai, China
| | - Yufang Qin
- College of Information Technology, Shanghai Ocean University, Hucheng Ring Road, Shanghai, China.
- Key Laboratory of Fisheries Information Ministry of Agriculture, Shanghai, China.
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12
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Deng D, Xu X, Cui T, Xu M, Luo K, Zhang H, Wang Q, Song C, Li C, Li G, Shang D. PBAC: A pathway-based attention convolution neural network for predicting clinical drug treatment responses. J Cell Mol Med 2024; 28:e18298. [PMID: 38683133 PMCID: PMC11057419 DOI: 10.1111/jcmm.18298] [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: 12/07/2023] [Revised: 03/05/2024] [Accepted: 03/25/2024] [Indexed: 05/01/2024] Open
Abstract
Precise and personalized drug application is crucial in the clinical treatment of complex diseases. Although neural networks offer a new approach to improving drug strategies, their internal structure is difficult to interpret. Here, we propose PBAC (Pathway-Based Attention Convolution neural network), which integrates a deep learning framework and attention mechanism to address the complex biological pathway information, thereby provide a biology function-based robust drug responsiveness prediction model. PBAC has four layers: gene-pathway layer, attention layer, convolution layer and fully connected layer. PBAC improves the performance of predicting drug responsiveness by focusing on important pathways, helping us understand the mechanism of drug action in diseases. We validated the PBAC model using data from four chemotherapy drugs (Bortezomib, Cisplatin, Docetaxel and Paclitaxel) and 11 immunotherapy datasets. In the majority of datasets, PBAC exhibits superior performance compared to traditional machine learning methods and other research approaches (area under curve = 0.81, the area under the precision-recall curve = 0.73). Using PBAC attention layer output, we identified some pathways as potential core cancer regulators, providing good interpretability for drug treatment prediction. In summary, we presented PBAC, a powerful tool to predict drug responsiveness based on the biology pathway information and explore the potential cancer-driving pathways.
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Affiliation(s)
- Dexun Deng
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical SchoolUniversity of South ChinaHengyangHunanChina
- Hunan Provincial Key Laboratory of Multi‐omics And Artificial Intelligence of Cardiovascular DiseasesUniversity of South ChinaHengyangHunanChina
- School of ComputerUniversity of South ChinaHengyangHunanChina
| | - Xiaoqiang Xu
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical SchoolUniversity of South ChinaHengyangHunanChina
- Hunan Provincial Key Laboratory of Multi‐omics And Artificial Intelligence of Cardiovascular DiseasesUniversity of South ChinaHengyangHunanChina
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical SchoolUniversity of South ChinaHengyangHunanChina
- Department of Cardiology, The First Affiliated Hospital, Hengyang Medical SchoolUniversity of South ChinaHengyangChina
| | - Ting Cui
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical SchoolUniversity of South ChinaHengyangHunanChina
- Hunan Provincial Key Laboratory of Multi‐omics And Artificial Intelligence of Cardiovascular DiseasesUniversity of South ChinaHengyangHunanChina
| | - Mingcong Xu
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical SchoolUniversity of South ChinaHengyangHunanChina
- Hunan Provincial Key Laboratory of Multi‐omics And Artificial Intelligence of Cardiovascular DiseasesUniversity of South ChinaHengyangHunanChina
| | - Kunpeng Luo
- Department of Gastroenterology and HepatologySecond Affiliated Hospital of Harbin Medical UniversityHarbinHeilongjiangChina
| | - Han Zhang
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical SchoolUniversity of South ChinaHengyangHunanChina
- Hunan Provincial Key Laboratory of Multi‐omics And Artificial Intelligence of Cardiovascular DiseasesUniversity of South ChinaHengyangHunanChina
- School of ComputerUniversity of South ChinaHengyangHunanChina
| | - Qiuyu Wang
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical SchoolUniversity of South ChinaHengyangHunanChina
- Hunan Provincial Key Laboratory of Multi‐omics And Artificial Intelligence of Cardiovascular DiseasesUniversity of South ChinaHengyangHunanChina
- School of ComputerUniversity of South ChinaHengyangHunanChina
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical SchoolUniversity of South ChinaHengyangHunanChina
- Department of Cardiology, The First Affiliated Hospital, Hengyang Medical SchoolUniversity of South ChinaHengyangChina
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Hengyang Medical SchoolUniversity of South ChinaHengyangHunanChina
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Hengyang Medical SchoolUniversity of South ChinaHengyangHunanChina
| | - Chao Song
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical SchoolUniversity of South ChinaHengyangHunanChina
- Hunan Provincial Key Laboratory of Multi‐omics And Artificial Intelligence of Cardiovascular DiseasesUniversity of South ChinaHengyangHunanChina
- School of ComputerUniversity of South ChinaHengyangHunanChina
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical SchoolUniversity of South ChinaHengyangHunanChina
- Department of Cardiology, The First Affiliated Hospital, Hengyang Medical SchoolUniversity of South ChinaHengyangChina
| | - Chao Li
- Department of AnesthesiologyThe First Affiliated Hospital of University of South ChinaHengyangPR China
| | - Guohua Li
- Department of Pathophysiology, Key Laboratory for Arteriosclerology of Hunan Province, MOE Key Lab of Rare Pediatric Diseases, Hengyang Medical SchoolInstitute of Cardiovascular Disease, Hunan International Scientific and Technological Cooperation Base of Arteriosclerotic Disease, University of South ChinaHengyangHunanChina
| | - Desi Shang
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical SchoolUniversity of South ChinaHengyangHunanChina
- Hunan Provincial Key Laboratory of Multi‐omics And Artificial Intelligence of Cardiovascular DiseasesUniversity of South ChinaHengyangHunanChina
- School of ComputerUniversity of South ChinaHengyangHunanChina
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical SchoolUniversity of South ChinaHengyangHunanChina
- Department of Cardiology, The First Affiliated Hospital, Hengyang Medical SchoolUniversity of South ChinaHengyangChina
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Hengyang Medical SchoolUniversity of South ChinaHengyangHunanChina
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Hengyang Medical SchoolUniversity of South ChinaHengyangHunanChina
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13
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Hsu YC, Chiu YC, Lu TP, Hsiao TH, Chen Y. Predicting drug response through tumor deconvolution by cancer cell lines. PATTERNS (NEW YORK, N.Y.) 2024; 5:100949. [PMID: 38645769 PMCID: PMC11026976 DOI: 10.1016/j.patter.2024.100949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 02/07/2024] [Accepted: 02/12/2024] [Indexed: 04/23/2024]
Abstract
Large-scale cancer drug sensitivity data have become available for a collection of cancer cell lines, but only limited drug response data from patients are available. Bridging the gap in pharmacogenomics knowledge between in vitro and in vivo datasets remains challenging. In this study, we trained a deep learning model, Scaden-CA, for deconvoluting tumor data into proportions of cancer-type-specific cell lines. Then, we developed a drug response prediction method using the deconvoluted proportions and the drug sensitivity data from cell lines. The Scaden-CA model showed excellent performance in terms of concordance correlation coefficients (>0.9 for model testing) and the correctly deconvoluted rate (>70% across most cancers) for model validation using Cancer Cell Line Encyclopedia (CCLE) bulk RNA data. We applied the model to tumors in The Cancer Genome Atlas (TCGA) dataset and examined associations between predicted cell viability and mutation status or gene expression levels to understand underlying mechanisms of potential value for drug repurposing.
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Affiliation(s)
- Yu-Ching Hsu
- Bioinformatics Program, Taiwan International Graduate Program, National Taiwan University, Taipei 115, Taiwan
- Bioinformatics Program, Institute of Statistical Science, Taiwan International Graduate Program, Academia Sinica, Taipei 115, Taiwan
- Institute of Health Data Analytics and Statistics, Department of Public Health, College of Public Health, National Taiwan University, Taipei 100, Taiwan
- Greehey Children’s Cancer Research Institute, University of Texas Health San Antonio, San Antonio, TX 78229, USA
| | - Yu-Chiao Chiu
- Department of Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15261, USA
- UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA 15232, USA
| | - Tzu-Pin Lu
- Institute of Health Data Analytics and Statistics, Department of Public Health, College of Public Health, National Taiwan University, Taipei 100, Taiwan
| | - Tzu-Hung Hsiao
- Department of Medical Research, Taichung Veterans General Hospital, Taichung 40705, Taiwan
| | - Yidong Chen
- Greehey Children’s Cancer Research Institute, University of Texas Health San Antonio, San Antonio, TX 78229, USA
- Department of Population Health Sciences, University of Texas Health San Antonio, San Antonio, TX 78229, USA
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14
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Abbasi M, Carvalho FG, Ribeiro B, Arrais JP. Predicting drug activity against cancer through genomic profiles and SMILES. Artif Intell Med 2024; 150:102820. [PMID: 38553160 DOI: 10.1016/j.artmed.2024.102820] [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: 12/05/2022] [Revised: 01/09/2024] [Accepted: 02/21/2024] [Indexed: 04/02/2024]
Abstract
Due to the constant increase in cancer rates, the disease has become a leading cause of death worldwide, enhancing the need for its detection and treatment. In the era of personalized medicine, the main goal is to incorporate individual variability in order to choose more precisely which therapy and prevention strategies suit each person. However, predicting the sensitivity of tumors to anticancer treatments remains a challenge. In this work, we propose two deep neural network models to predict the impact of anticancer drugs in tumors through the half-maximal inhibitory concentration (IC50). These models join biological and chemical data to apprehend relevant features of the genetic profile and the drug compounds, respectively. In order to predict the drug response in cancer cell lines, this study employed different DL methods, resorting to Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs). In the first stage, two autoencoders were pre-trained with high-dimensional gene expression and mutation data of tumors. Afterward, this genetic background is transferred to the prediction models that return the IC50 value that portrays the potency of a substance in inhibiting a cancer cell line. When comparing RSEM Expected counts and TPM as methods for displaying gene expression data, RSEM has been shown to perform better in deep models and CNNs model can obtain better insight in these types of data. Moreover, the obtained results reflect the effectiveness of the extracted deep representations in the prediction of the IC50 value that portrays the potency of a substance in inhibiting a tumor, achieving a performance of a mean squared error of 1.06 and surpassing previous state-of-the-art models.
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Affiliation(s)
- Maryam Abbasi
- Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal; Polytechnic Institute of Coimbra, Applied Research Institute, Coimbra, Portugal; Research Centre for Natural Resources Environment and Society (CERNAS), Polytechnic Institute of Coimbra, Coimbra, Portugal.
| | - Filipa G Carvalho
- Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal
| | - Bernardete Ribeiro
- Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal
| | - Joel P Arrais
- Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal
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15
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Unger M, Kather JN. Deep learning in cancer genomics and histopathology. Genome Med 2024; 16:44. [PMID: 38539231 PMCID: PMC10976780 DOI: 10.1186/s13073-024-01315-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 03/13/2024] [Indexed: 07/08/2024] Open
Abstract
Histopathology and genomic profiling are cornerstones of precision oncology and are routinely obtained for patients with cancer. Traditionally, histopathology slides are manually reviewed by highly trained pathologists. Genomic data, on the other hand, is evaluated by engineered computational pipelines. In both applications, the advent of modern artificial intelligence methods, specifically machine learning (ML) and deep learning (DL), have opened up a fundamentally new way of extracting actionable insights from raw data, which could augment and potentially replace some aspects of traditional evaluation workflows. In this review, we summarize current and emerging applications of DL in histopathology and genomics, including basic diagnostic as well as advanced prognostic tasks. Based on a growing body of evidence, we suggest that DL could be the groundwork for a new kind of workflow in oncology and cancer research. However, we also point out that DL models can have biases and other flaws that users in healthcare and research need to know about, and we propose ways to address them.
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Affiliation(s)
- Michaela Unger
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.
| | - Jakob Nikolas Kather
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.
- Department of Medicine I, University Hospital Dresden, Dresden, Germany.
- Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany.
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16
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Shi Y, Li C, Zhang X, Peng C, Sun P, Zhang Q, Wu L, Ding Y, Xie D, Xu Z, Zhu W. D3EGFR: a webserver for deep learning-guided drug sensitivity prediction and drug response information retrieval for EGFR mutation-driven lung cancer. Brief Bioinform 2024; 25:bbae121. [PMID: 38555474 PMCID: PMC10981678 DOI: 10.1093/bib/bbae121] [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: 10/03/2023] [Revised: 02/24/2024] [Accepted: 03/04/2024] [Indexed: 04/02/2024] Open
Abstract
As key oncogenic drivers in non-small-cell lung cancer (NSCLC), various mutations in the epidermal growth factor receptor (EGFR) with variable drug sensitivities have been a major obstacle for precision medicine. To achieve clinical-level drug recommendations, a platform for clinical patient case retrieval and reliable drug sensitivity prediction is highly expected. Therefore, we built a database, D3EGFRdb, with the clinicopathologic characteristics and drug responses of 1339 patients with EGFR mutations via literature mining. On the basis of D3EGFRdb, we developed a deep learning-based prediction model, D3EGFRAI, for drug sensitivity prediction of new EGFR mutation-driven NSCLC. Model validations of D3EGFRAI showed a prediction accuracy of 0.81 and 0.85 for patients from D3EGFRdb and our hospitals, respectively. Furthermore, mutation scanning of the crucial residues inside drug-binding pockets, which may occur in the future, was performed to explore their drug sensitivity changes. D3EGFR is the first platform to achieve clinical-level drug response prediction of all approved small molecule drugs for EGFR mutation-driven lung cancer and is freely accessible at https://www.d3pharma.com/D3EGFR/index.php.
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Affiliation(s)
- Yulong Shi
- State Key Laboratory of Drug Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- School of Pharmacy, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Chongwu Li
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai 200433, China
| | - Xinben Zhang
- State Key Laboratory of Drug Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Cheng Peng
- State Key Laboratory of Drug Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- School of Pharmacy, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Peng Sun
- Key Laboratory of Human Functional Genomics of Jiangsu Province, Department of Biochemistry and Molecular Biology, Nanjing Medical University, Nanjing 211166, China
| | - Qian Zhang
- School of Computer Science and Technology, East China Normal University, Shanghai 200062, China
| | - Leilei Wu
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai 200433, China
| | - Ying Ding
- Department of Pathology, the First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Dong Xie
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai 200433, China
| | - Zhijian Xu
- State Key Laboratory of Drug Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- School of Pharmacy, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Weiliang Zhu
- State Key Laboratory of Drug Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- School of Pharmacy, University of Chinese Academy of Sciences, Beijing 100049, China
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17
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Hajim WI, Zainudin S, Mohd Daud K, Alheeti K. Optimized models and deep learning methods for drug response prediction in cancer treatments: a review. PeerJ Comput Sci 2024; 10:e1903. [PMID: 38660174 PMCID: PMC11042005 DOI: 10.7717/peerj-cs.1903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 01/31/2024] [Indexed: 04/26/2024]
Abstract
Recent advancements in deep learning (DL) have played a crucial role in aiding experts to develop personalized healthcare services, particularly in drug response prediction (DRP) for cancer patients. The DL's techniques contribution to this field is significant, and they have proven indispensable in the medical field. This review aims to analyze the diverse effectiveness of various DL models in making these predictions, drawing on research published from 2017 to 2023. We utilized the VOS-Viewer 1.6.18 software to create a word cloud from the titles and abstracts of the selected studies. This study offers insights into the focus areas within DL models used for drug response. The word cloud revealed a strong link between certain keywords and grouped themes, highlighting terms such as deep learning, machine learning, precision medicine, precision oncology, drug response prediction, and personalized medicine. In order to achieve an advance in DRP using DL, the researchers need to work on enhancing the models' generalizability and interoperability. It is also crucial to develop models that not only accurately represent various architectures but also simplify these architectures, balancing the complexity with the predictive capabilities. In the future, researchers should try to combine methods that make DL models easier to understand; this will make DRP reviews more open and help doctors trust the decisions made by DL models in cancer DRP.
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Affiliation(s)
- Wesam Ibrahim Hajim
- Department of Applied Geology, College of Sciences, Tirkit University, Tikrit, Salah ad Din, Iraq
- Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Selangor, Malaysia
| | - Suhaila Zainudin
- Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Selangor, Malaysia
| | - Kauthar Mohd Daud
- Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Selangor, Malaysia
| | - Khattab Alheeti
- Department of Computer Networking Systems, College of Computer Sciences and Information Technology, University of Anbar, Al Anbar, Ramadi, Iraq
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18
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Wang LJ, Ning M, Nayak T, Kasper MJ, Monga SP, Huang Y, Chen Y, Chiu YC. shinyDeepDR: A user-friendly R Shiny app for predicting anti-cancer drug response using deep learning. PATTERNS (NEW YORK, N.Y.) 2024; 5:100894. [PMID: 38370127 PMCID: PMC10873157 DOI: 10.1016/j.patter.2023.100894] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 11/10/2023] [Accepted: 11/14/2023] [Indexed: 02/20/2024]
Abstract
Advancing precision oncology requires accurate prediction of treatment response and accessible prediction models. To this end, we present shinyDeepDR, a user-friendly implementation of our innovative deep learning model, DeepDR, for predicting anti-cancer drug sensitivity. The web tool makes DeepDR more accessible to researchers without extensive programming experience. Using shinyDeepDR, users can upload mutation and/or gene expression data from a cancer sample (cell line or tumor) and perform two main functions: "Find Drug," which predicts the sample's response to 265 approved and investigational anti-cancer compounds, and "Find Sample," which searches for cell lines in the Cancer Cell Line Encyclopedia (CCLE) and tumors in The Cancer Genome Atlas (TCGA) with genomics profiles similar to those of the query sample to study potential effective treatments. shinyDeepDR provides an interactive interface to interpret prediction results and to investigate individual compounds. In conclusion, shinyDeepDR is an intuitive and free-to-use web tool for in silico anti-cancer drug screening.
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Affiliation(s)
- Li-Ju Wang
- Cancer Therapeutics Program, University of Pittsburgh Medical Center Hillman Cancer Center, Pittsburgh, PA 15232, USA
| | - Michael Ning
- Cancer Therapeutics Program, University of Pittsburgh Medical Center Hillman Cancer Center, Pittsburgh, PA 15232, USA
| | - Tapsya Nayak
- Greehey Children’s Cancer Research Institute, University of Texas Health San Antonio, San Antonio, TX 78229, USA
| | - Michael J. Kasper
- Cancer Therapeutics Program, University of Pittsburgh Medical Center Hillman Cancer Center, Pittsburgh, PA 15232, USA
| | - Satdarshan P. Monga
- Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA
- Pittsburgh Liver Research Center, University of Pittsburgh Medical Center and University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA
- Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA
| | - Yufei Huang
- Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA
- Cancer Virology Program, University of Pittsburgh Medical Center Hillman Cancer Center, Pittsburgh, PA 15232, USA
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA 15261, USA
- Department of Pharmaceutical Sciences, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Yidong Chen
- Greehey Children’s Cancer Research Institute, University of Texas Health San Antonio, San Antonio, TX 78229, USA
- Department of Population Health Sciences, University of Texas Health San Antonio, San Antonio, TX 78229, USA
| | - Yu-Chiao Chiu
- Cancer Therapeutics Program, University of Pittsburgh Medical Center Hillman Cancer Center, Pittsburgh, PA 15232, USA
- Pittsburgh Liver Research Center, University of Pittsburgh Medical Center and University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA
- Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA
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19
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Kim J, Park SH, Lee H. PANCDR: precise medicine prediction using an adversarial network for cancer drug response. Brief Bioinform 2024; 25:bbae088. [PMID: 38487849 PMCID: PMC10940842 DOI: 10.1093/bib/bbae088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 01/09/2024] [Accepted: 02/16/2024] [Indexed: 03/18/2024] Open
Abstract
Pharmacogenomics aims to provide personalized therapy to patients based on their genetic variability. However, accurate prediction of cancer drug response (CDR) is challenging due to genetic heterogeneity. Since clinical data are limited, most studies predicting drug response use preclinical data to train models. However, such models might not be generalizable to external clinical data due to differences between the preclinical and clinical datasets. In this study, a Precision Medicine Prediction using an Adversarial Network for Cancer Drug Response (PANCDR) model is proposed. PANCDR consists of two sub-models, an adversarial model and a CDR prediction model. The adversarial model reduces the gap between the preclinical and clinical datasets, while the CDR prediction model extracts features and predicts responses. PANCDR was trained using both preclinical data and unlabeled clinical data. Subsequently, it was tested on external clinical data, including The Cancer Genome Atlas and brain tumor patients. PANCDR outperformed other machine learning models in predicting external test data. Our results demonstrate the robustness of PANCDR and its potential in precision medicine by recommending patient-specific drug candidates. The PANCDR codes and data are available at https://github.com/DMCB-GIST/PANCDR.
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Affiliation(s)
- Juyeon Kim
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, 61005, Gwangju, South Korea
| | - Sung-Hye Park
- Department of Pathology, Seoul National University Hospital, Seoul National University College of Medicine, 03080, Seoul, South Korea
- Neuroscience Research Institute, Seoul National University College of Medicine, 03080, Seoul, South Korea
| | - Hyunju Lee
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, 61005, Gwangju, South Korea
- Artificial Intelligence Graduate School, Gwangju Institute of Science and Technology, 61005, Gwangju, South Korea
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20
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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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21
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Paraqindes H, Mourksi NEH, Ballesta S, Hedjam J, Bourdelais F, Fenouil T, Picart T, Catez F, Combe T, Ferrari A, Kielbassa J, Thomas E, Tonon L, Viari A, Attignon V, Carrere M, Perrossier J, Giraud S, Vanbelle C, Gabut M, Bergeron D, Scott MS, Castro Vega L, Magne N, Huillard E, Sanson M, Meyronet D, Diaz JJ, Ducray F, Marcel V, Durand S. Isocitrate dehydrogenase wt and IDHmut adult-type diffuse gliomas display distinct alterations in ribosome biogenesis and 2'O-methylation of ribosomal RNA. Neuro Oncol 2023; 25:2191-2206. [PMID: 37531290 PMCID: PMC10708943 DOI: 10.1093/neuonc/noad140] [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: 03/01/2023] [Indexed: 08/04/2023] Open
Abstract
BACKGROUND High-grade adult-type diffuse gliomas (HGGs) constitute a heterogeneous group of aggressive tumors that are mostly incurable. Recent advances highlighting the contribution of ribosomes to cancer development have offered new clinical perspectives. Here, we uncovered that isocitrate dehydrogenase (IDH)wt and IDHmut HGGs display distinct alterations of ribosome biology, in terms of rRNA epitranscriptomics and ribosome biogenesis, which could constitute novel hallmarks that can be exploited for the management of these pathologies. METHODS We analyzed (1) the ribosomal RNA 2'O-ribose methylation (rRNA 2'Ome) using RiboMethSeq and in-house developed bioinformatics tools (https://github.com/RibosomeCRCL/ribomethseq-nfandrRMSAnalyzer) on 3 independent cohorts compiling 71 HGGs (IDHwt n = 30, IDHmut n = 41) and 9 non-neoplastic samples, (2) the expression of ribosome biogenesis factors using medium throughput RT-qPCR as a readout of ribosome biogenesis, and (3) the sensitivity of 5 HGG cell lines to RNA Pol I inhibitors (CX5461, BMH-21). RESULTS Unsupervised analysis demonstrated that HGGs could be distinguished based on their rRNA 2'Ome epitranscriptomic profile, with IDHwt glioblastomas displaying the most significant alterations of rRNA 2'Ome at specific sites. In contrast, IDHmut HGGs are largely characterized by an overexpression of ribosome biogenesis factors compared to non-neoplastic tissues or IDHwt glioblastomas. Finally, IDHmut HGG-derived spheroids display higher cytotoxicity to CX5461 than IDHwt glioblastoma, while all HGG spheroids display a similar cytotoxicity to BMH-21. CONCLUSIONS In HGGs, IDH mutational status is associated with specific alterations of the ribosome biology and with distinct sensitivities to RNA Pol I inhibitors.
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Affiliation(s)
- Hermes Paraqindes
- LabEx Dev2CAN, Institut Convergence Plascan, Centre de Recherche en Cancérologie de Lyon, Inserm U1052, CNRS UMR5286, Université de Lyon, Université Claude Bernard Lyon, Centre Léon Bérard, CEDEX 08, Lyon, France
- Synergie Lyon Cancer, Gilles Thomas Bioinformatics Platform, Centre Léon Bérard, CEDEX 08, Lyon, France
| | - Nour-El-Houda Mourksi
- LabEx Dev2CAN, Institut Convergence Plascan, Centre de Recherche en Cancérologie de Lyon, Inserm U1052, CNRS UMR5286, Université de Lyon, Université Claude Bernard Lyon, Centre Léon Bérard, CEDEX 08, Lyon, France
| | - Samantha Ballesta
- LabEx Dev2CAN, Institut Convergence Plascan, Centre de Recherche en Cancérologie de Lyon, Inserm U1052, CNRS UMR5286, Université de Lyon, Université Claude Bernard Lyon, Centre Léon Bérard, CEDEX 08, Lyon, France
- Plateforme 3D-ONCO, Université de Lyon, Université Claude Bernard Lyon 1, Inserm U1052, CNRS UMR5286, Centre Léon Bérard, Centre de Recherche en Cancérologie de Lyon (CRCL), Lyon, France
| | - Jordan Hedjam
- LabEx Dev2CAN, Institut Convergence Plascan, Centre de Recherche en Cancérologie de Lyon, Inserm U1052, CNRS UMR5286, Université de Lyon, Université Claude Bernard Lyon, Centre Léon Bérard, CEDEX 08, Lyon, France
| | - Fleur Bourdelais
- LabEx Dev2CAN, Institut Convergence Plascan, Centre de Recherche en Cancérologie de Lyon, Inserm U1052, CNRS UMR5286, Université de Lyon, Université Claude Bernard Lyon, Centre Léon Bérard, CEDEX 08, Lyon, France
| | - Tanguy Fenouil
- LabEx Dev2CAN, Institut Convergence Plascan, Centre de Recherche en Cancérologie de Lyon, Inserm U1052, CNRS UMR5286, Université de Lyon, Université Claude Bernard Lyon, Centre Léon Bérard, CEDEX 08, Lyon, France
- Hospices Civils de Lyon, Laboratoire de biologie médicale et d’anatomie pathologique, Lyon, France
| | - Thiébaud Picart
- LabEx Dev2CAN, Institut Convergence Plascan, Centre de Recherche en Cancérologie de Lyon, Inserm U1052, CNRS UMR5286, Université de Lyon, Université Claude Bernard Lyon, Centre Léon Bérard, CEDEX 08, Lyon, France
- Hospices Civils de Lyon, Laboratoire de biologie médicale et d’anatomie pathologique, Lyon, France
| | - Frédéric Catez
- LabEx Dev2CAN, Institut Convergence Plascan, Centre de Recherche en Cancérologie de Lyon, Inserm U1052, CNRS UMR5286, Université de Lyon, Université Claude Bernard Lyon, Centre Léon Bérard, CEDEX 08, Lyon, France
| | - Théo Combe
- LabEx Dev2CAN, Institut Convergence Plascan, Centre de Recherche en Cancérologie de Lyon, Inserm U1052, CNRS UMR5286, Université de Lyon, Université Claude Bernard Lyon, Centre Léon Bérard, CEDEX 08, Lyon, France
- Synergie Lyon Cancer, Gilles Thomas Bioinformatics Platform, Centre Léon Bérard, CEDEX 08, Lyon, France
| | - Anthony Ferrari
- LabEx Dev2CAN, Institut Convergence Plascan, Centre de Recherche en Cancérologie de Lyon, Inserm U1052, CNRS UMR5286, Université de Lyon, Université Claude Bernard Lyon, Centre Léon Bérard, CEDEX 08, Lyon, France
- Synergie Lyon Cancer, Gilles Thomas Bioinformatics Platform, Centre Léon Bérard, CEDEX 08, Lyon, France
| | - Janice Kielbassa
- Synergie Lyon Cancer, Gilles Thomas Bioinformatics Platform, Centre Léon Bérard, CEDEX 08, Lyon, France
| | - Emilie Thomas
- LabEx Dev2CAN, Institut Convergence Plascan, Centre de Recherche en Cancérologie de Lyon, Inserm U1052, CNRS UMR5286, Université de Lyon, Université Claude Bernard Lyon, Centre Léon Bérard, CEDEX 08, Lyon, France
- Synergie Lyon Cancer, Gilles Thomas Bioinformatics Platform, Centre Léon Bérard, CEDEX 08, Lyon, France
| | - Laurie Tonon
- LabEx Dev2CAN, Institut Convergence Plascan, Centre de Recherche en Cancérologie de Lyon, Inserm U1052, CNRS UMR5286, Université de Lyon, Université Claude Bernard Lyon, Centre Léon Bérard, CEDEX 08, Lyon, France
- Synergie Lyon Cancer, Gilles Thomas Bioinformatics Platform, Centre Léon Bérard, CEDEX 08, Lyon, France
| | - Alain Viari
- LabEx Dev2CAN, Institut Convergence Plascan, Centre de Recherche en Cancérologie de Lyon, Inserm U1052, CNRS UMR5286, Université de Lyon, Université Claude Bernard Lyon, Centre Léon Bérard, CEDEX 08, Lyon, France
- Synergie Lyon Cancer, Gilles Thomas Bioinformatics Platform, Centre Léon Bérard, CEDEX 08, Lyon, France
- INRIA Grenoble Rhône-Alpes, Montbonnot-Saint-Martin, France
| | - Valéry Attignon
- LabEx Dev2CAN, Institut Convergence Plascan, Centre de Recherche en Cancérologie de Lyon, Inserm U1052, CNRS UMR5286, Université de Lyon, Université Claude Bernard Lyon, Centre Léon Bérard, CEDEX 08, Lyon, France
- Cancer Genomics Platform, Centre de Recherche en Cancérologie de Lyon, CEDEX 08, Lyon, France
| | - Marjorie Carrere
- Cancer Genomics Platform, Centre de Recherche en Cancérologie de Lyon, CEDEX 08, Lyon, France
| | - Jessie Perrossier
- Cancer Genomics Platform, Centre de Recherche en Cancérologie de Lyon, CEDEX 08, Lyon, France
| | - Stéphane Giraud
- Plateforme 3D-ONCO, Université de Lyon, Université Claude Bernard Lyon 1, Inserm U1052, CNRS UMR5286, Centre Léon Bérard, Centre de Recherche en Cancérologie de Lyon (CRCL), Lyon, France
| | - Christophe Vanbelle
- Plateforme d’Imagerie Cellulaire, Université de Lyon, Université Claude Bernard Lyon 1, Inserm U1052, CNRS UMR5286, Centre Léon Bérard, Centre de Recherche en Cancérologie de Lyon (CRCL), Lyon, France
| | - Mathieu Gabut
- LabEx Dev2CAN, Institut Convergence Plascan, Centre de Recherche en Cancérologie de Lyon, Inserm U1052, CNRS UMR5286, Université de Lyon, Université Claude Bernard Lyon, Centre Léon Bérard, CEDEX 08, Lyon, France
| | - Danny Bergeron
- Département de biochimie et génomique fonctionnelle, Faculté de médecine et des sciences de la santé, Université de Sherbrooke, Sherbrooke, Québec, Canada
| | - Michelle S Scott
- Département de biochimie et génomique fonctionnelle, Faculté de médecine et des sciences de la santé, Université de Sherbrooke, Sherbrooke, Québec, Canada
| | - Luis Castro Vega
- Sorbonne Université, Inserm, CNRS, UMRS1127, Institut du Cerveau, ICM, AP-HP, Hôpitaux Universitaires La Pitié Salpêtrière – Charles Foix, Service de Neurologie 2-Mazarin, Paris, France
| | - Nathalie Magne
- Sorbonne Université, Inserm, CNRS, UMRS1127, Institut du Cerveau, ICM, AP-HP, Hôpitaux Universitaires La Pitié Salpêtrière – Charles Foix, Service de Neurologie 2-Mazarin, Paris, France
| | - Emmanuelle Huillard
- Sorbonne Université, Inserm, CNRS, UMRS1127, Institut du Cerveau, ICM, AP-HP, Hôpitaux Universitaires La Pitié Salpêtrière – Charles Foix, Service de Neurologie 2-Mazarin, Paris, France
| | - Marc Sanson
- Sorbonne Université, Inserm, CNRS, UMRS1127, Institut du Cerveau, ICM, AP-HP, Hôpitaux Universitaires La Pitié Salpêtrière – Charles Foix, Service de Neurologie 2-Mazarin, Paris, France
| | - David Meyronet
- LabEx Dev2CAN, Institut Convergence Plascan, Centre de Recherche en Cancérologie de Lyon, Inserm U1052, CNRS UMR5286, Université de Lyon, Université Claude Bernard Lyon, Centre Léon Bérard, CEDEX 08, Lyon, France
- Hospices Civils de Lyon, Laboratoire de biologie médicale et d’anatomie pathologique, Lyon, France
| | - Jean-Jacques Diaz
- LabEx Dev2CAN, Institut Convergence Plascan, Centre de Recherche en Cancérologie de Lyon, Inserm U1052, CNRS UMR5286, Université de Lyon, Université Claude Bernard Lyon, Centre Léon Bérard, CEDEX 08, Lyon, France
| | - François Ducray
- LabEx Dev2CAN, Institut Convergence Plascan, Centre de Recherche en Cancérologie de Lyon, Inserm U1052, CNRS UMR5286, Université de Lyon, Université Claude Bernard Lyon, Centre Léon Bérard, CEDEX 08, Lyon, France
- Hospices Civils de Lyon, Service de neuro-oncologie, Hôpital Pierre Wertheimer, Lyon, France
| | - Virginie Marcel
- LabEx Dev2CAN, Institut Convergence Plascan, Centre de Recherche en Cancérologie de Lyon, Inserm U1052, CNRS UMR5286, Université de Lyon, Université Claude Bernard Lyon, Centre Léon Bérard, CEDEX 08, Lyon, France
| | - Sébastien Durand
- LabEx Dev2CAN, Institut Convergence Plascan, Centre de Recherche en Cancérologie de Lyon, Inserm U1052, CNRS UMR5286, Université de Lyon, Université Claude Bernard Lyon, Centre Léon Bérard, CEDEX 08, Lyon, France
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22
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Connell W, Garcia K, Goodarzi H, Keiser MJ. Learning chemical sensitivity reveals mechanisms of cellular response. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.26.554851. [PMID: 37693536 PMCID: PMC10491110 DOI: 10.1101/2023.08.26.554851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Chemical probes interrogate disease mechanisms at the molecular level by linking genetic changes to observable traits. However, comprehensive chemical screens in diverse biological models are impractical. To address this challenge, we developed ChemProbe, a model that predicts cellular sensitivity to hundreds of molecular probes and drugs by learning to combine transcriptomes and chemical structures. Using ChemProbe, we inferred the chemical sensitivity of cancer cell lines and tumor samples and analyzed how the model makes predictions. We retrospectively evaluated drug response predictions for precision breast cancer treatment and prospectively validated chemical sensitivity predictions in new cellular models, including a genetically modified cell line. Our model interpretation analysis identified transcriptome features reflecting compound targets and protein network modules, identifying genes that drive ferroptosis. ChemProbe is an interpretable in silico screening tool that allows researchers to measure cellular response to diverse compounds, facilitating research into molecular mechanisms of chemical sensitivity.
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Affiliation(s)
- William Connell
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA
- Institute for Neurodegenerative Diseases, University of California, San Francisco, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Kristle Garcia
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
- Department of Urology, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
| | - Hani Goodarzi
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
- Department of Urology, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
| | - Michael J. Keiser
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA
- Institute for Neurodegenerative Diseases, University of California, San Francisco, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
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23
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Guo S, Ma Y, Li X, Li W, He X, Yuan Z, Hu Y. Identification of stromal cell proportion-related genes in the breast cancer tumor microenvironment using CorDelSFS feature selection: implications for tumor progression and prognosis. Front Genet 2023; 14:1165648. [PMID: 37576555 PMCID: PMC10421750 DOI: 10.3389/fgene.2023.1165648] [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: 02/14/2023] [Accepted: 07/12/2023] [Indexed: 08/15/2023] Open
Abstract
Background: The tumor microenvironment (TME) of breast cancer (BRCA) is a complex and dynamic micro-ecosystem that influences BRCA occurrence, progression, and prognosis through its cellular and molecular components. However, as the tumor progresses, the dynamic changes of stromal and immune cells in TME become unclear. Objective: The aim of this study was to identify differentially co-expressed genes (DCGs) associated with the proportion of stromal cells in TME of BRCA, to explore the patterns of cell proportion changes, and ultimately, their impact on prognosis. Methods: A new heuristic feature selection strategy (CorDelSFS) was combined with differential co-expression analysis to identify TME-key DCGs. The expression pattern and co-expression network of TME-key DCGs were analyzed across different TMEs. A prognostic model was constructed using six TME-key DCGs, and the correlation between the risk score and the proportion of stromal cells and immune cells in TME was evaluated. Results: TME-key DCGs mimicked the dynamic trend of BRCA TME and formed cell type-specific subnetworks. The IG gene-related subnetwork, plasmablast-specific expression, played a vital role in the BRCA TME through its adaptive immune function and tumor progression inhibition. The prognostic model showed that the risk score was significantly correlated with the proportion of stromal cells and immune cells in TME, and low-risk patients had stronger adaptive immune function. IGKV1D-39 was identified as a novel BRCA prognostic marker specifically expressed in plasmablasts and involved in adaptive immune responses. Conclusions: This study explores the role of proportionate-related genes in the tumor microenvironment using a machine learning approach and provides new insights for discovering the key biological processes in tumor progression and clinical prognosis.
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Affiliation(s)
- Sicheng Guo
- Hunan Engineering & Technology Research Centre for Agricultural Big Data Analysis & Decision-Making, Hunan Agricultural University, Changsha, Hunan, China
| | - Yuting Ma
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Xiaokang Li
- Hunan Engineering & Technology Research Centre for Agricultural Big Data Analysis & Decision-Making, Hunan Agricultural University, Changsha, Hunan, China
| | - Wei Li
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Xiaogang He
- Hunan Engineering & Technology Research Centre for Agricultural Big Data Analysis & Decision-Making, Hunan Agricultural University, Changsha, Hunan, China
| | - Zheming Yuan
- Hunan Engineering & Technology Research Centre for Agricultural Big Data Analysis & Decision-Making, Hunan Agricultural University, Changsha, Hunan, China
| | - Yuan Hu
- Hunan Engineering & Technology Research Centre for Agricultural Big Data Analysis & Decision-Making, Hunan Agricultural University, Changsha, Hunan, China
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24
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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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25
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Luo L, Chen X, Huang F. Machine learning revealed ferroptosis features and ferroptosis-related gene-based immune microenvironment in lung adenocarcinoma. Chem Biol Interact 2023; 378:110471. [PMID: 37061114 DOI: 10.1016/j.cbi.2023.110471] [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] [Received: 07/06/2022] [Revised: 03/20/2023] [Accepted: 04/03/2023] [Indexed: 04/17/2023]
Abstract
Ferroptosis has been identified as a novel type of programmed cell death that has a major effect on the development of lung adenocarcinoma. Nevertheless, there has yet to be a clear set of therapeutic targets based on ferroptosis. This study seeks to employ machine learning methods to determine the regulators of ferroptosis in LUAD. 318 LUAD samples were investigated to determine three ferroptosis molecular phenotypes in LUAD, and then Boruta dimensionality reduction combined with principal component analysis was used to measure the ferroptosis regulation score (FRS) of patients. We additionally presented DeepFerr, a deep learning neural network model, which used the transcriptome map of 11 ferroptosis regulators to predict ferroptosis in LUAD. LASSO, SVM-RFE and elastic net were used to dissect the differential ferroptosis regulators, and the eight pivotal ferroptosis regulators have considerable ferroptosis prediction ability. It was established that RRM2 and AURKA are key suppressors of ferroptosis, and the depletion of RRM2 and AURKA caused an increase in ferroptosis in H358 cells. In addition, not only did they act as pro-proliferative factors that hindered immune infiltration in LUAD, but they were also essential for anti-PD1 therapy and chemotherapy. In summary, this research confirms the regulatory role of RRM2 and AURKA in ferroptosis, and could be useful in predicting individualized treatment for patients with LUAD.
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Affiliation(s)
- Lianxiang Luo
- The Marine Biomedical Research Institute, Guangdong Medical University, Zhanjiang, Guangdong, 524023, China; The Marine Biomedical Research Institute of Guangdong Zhanjiang, Zhanjiang, Guangdong, 524023, China.
| | - Xinming Chen
- The First Clinical College, Guangdong Medical University, Zhanjiang, Guangdong, 524023, China
| | - Fangfang Huang
- Graduate School, Guangdong Medical University, Zhanjiang, Guangdong, 524023, China
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26
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Li H, Zou L, Kowah JAH, He D, Liu Z, Ding X, Wen H, Wang L, Yuan M, Liu X. A compact review of progress and prospects of deep learning in drug discovery. J Mol Model 2023; 29:117. [PMID: 36976427 DOI: 10.1007/s00894-023-05492-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Accepted: 02/27/2023] [Indexed: 03/29/2023]
Abstract
BACKGROUND Drug discovery processes, such as new drug development, drug synergy, and drug repurposing, consume significant yearly resources. Computer-aided drug discovery can effectively improve the efficiency of drug discovery. Traditional computer methods such as virtual screening and molecular docking have achieved many gratifying results in drug development. However, with the rapid growth of computer science, data structures have changed considerably; with more extensive and dimensional data and more significant amounts of data, traditional computer methods can no longer be applied well. Deep learning methods are based on deep neural network structures that can handle high-dimensional data very well, so they are used in current drug development. RESULTS This review summarized the applications of deep learning methods in drug discovery, such as drug target discovery, drug de novo design, drug recommendation, drug synergy, and drug response prediction. While applying deep learning methods to drug discovery suffers from a lack of data, transfer learning is an excellent solution to this problem. Furthermore, deep learning methods can extract deeper features and have higher predictive power than other machine learning methods. Deep learning methods have great potential in drug discovery and are expected to facilitate drug discovery development.
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Affiliation(s)
- Huijun Li
- College of Medicine, Guangxi University, Nanning, 530004, China
| | - Lin Zou
- College of Medicine, Guangxi University, Nanning, 530004, China
| | | | - Dongqiong He
- College of Chemistry and Chemical Engineering, Guangxi University, Nanning, 530004, China
| | - Zifan Liu
- College of Medicine, Guangxi University, Nanning, 530004, China
| | - Xuejie Ding
- College of Medicine, Guangxi University, Nanning, 530004, China
| | - Hao Wen
- College of Chemistry and Chemical Engineering, Guangxi University, Nanning, 530004, China
| | - Lisheng Wang
- College of Medicine, Guangxi University, Nanning, 530004, China
| | - Mingqing Yuan
- College of Medicine, Guangxi University, Nanning, 530004, China
| | - Xu Liu
- College of Medicine, Guangxi University, Nanning, 530004, China.
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27
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Bakrania A, Joshi N, Zhao X, Zheng G, Bhat M. Artificial intelligence in liver cancers: Decoding the impact of machine learning models in clinical diagnosis of primary liver cancers and liver cancer metastases. Pharmacol Res 2023; 189:106706. [PMID: 36813095 DOI: 10.1016/j.phrs.2023.106706] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Revised: 02/17/2023] [Accepted: 02/19/2023] [Indexed: 02/22/2023]
Abstract
Liver cancers are the fourth leading cause of cancer-related mortality worldwide. In the past decade, breakthroughs in the field of artificial intelligence (AI) have inspired development of algorithms in the cancer setting. A growing body of recent studies have evaluated machine learning (ML) and deep learning (DL) algorithms for pre-screening, diagnosis and management of liver cancer patients through diagnostic image analysis, biomarker discovery and predicting personalized clinical outcomes. Despite the promise of these early AI tools, there is a significant need to explain the 'black box' of AI and work towards deployment to enable ultimate clinical translatability. Certain emerging fields such as RNA nanomedicine for targeted liver cancer therapy may also benefit from application of AI, specifically in nano-formulation research and development given that they are still largely reliant on lengthy trial-and-error experiments. In this paper, we put forward the current landscape of AI in liver cancers along with the challenges of AI in liver cancer diagnosis and management. Finally, we have discussed the future perspectives of AI application in liver cancer and how a multidisciplinary approach using AI in nanomedicine could accelerate the transition of personalized liver cancer medicine from bench side to the clinic.
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Affiliation(s)
- Anita Bakrania
- Toronto General Hospital Research Institute, Toronto, ON, Canada; Ajmera Transplant Program, University Health Network, Toronto, ON, Canada; Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.
| | | | - Xun Zhao
- Toronto General Hospital Research Institute, Toronto, ON, Canada; Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
| | - Gang Zheng
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada; Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada; Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Mamatha Bhat
- Toronto General Hospital Research Institute, Toronto, ON, Canada; Ajmera Transplant Program, University Health Network, Toronto, ON, Canada; Division of Gastroenterology, Department of Medicine, University Health Network and University of Toronto, Toronto, ON, Canada; Department of Medical Sciences, Toronto, ON, Canada.
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28
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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: 23] [Impact Index Per Article: 23.0] [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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29
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Gangurde R, Jagota V, Khan MS, Sakthi VS, Boppana UM, Osei B, Kishore KH. Developing an Efficient Cancer Detection and Prediction Tool Using Convolution Neural Network Integrated with Neural Pattern Recognition. BIOMED RESEARCH INTERNATIONAL 2023; 2023:6970256. [PMID: 36760472 PMCID: PMC9904903 DOI: 10.1155/2023/6970256] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 09/08/2022] [Accepted: 11/24/2022] [Indexed: 02/03/2023]
Abstract
The application of computational approaches in medical science for diagnosis is made possible by the development in technical advancements connected to computer and biological sciences. The current cancer diagnosis system is becoming outmoded due to the new and rapid growth in cancer cases, and new, effective, and efficient methodologies are now required. Accurate cancer-type prediction is essential for cancer diagnosis and treatment. Understanding, diagnosing, and identifying the various types of cancer can be greatly aided by knowledge of the cancer genes. The Convolution Neural Network (CNN) and neural pattern recognition (NPR) approaches are used in this study paper to detect and predict the type of cancer. Different Convolution Neural Networks (CNNs) have been proposed by various researchers up to this point. Each model concentrated on a certain set of parameters to simulate the expression of genes. We have developed a novel CNN-NPR architecture that predicts cancer type while accounting for the tissue of origin using high-dimensional gene expression inputs. The 5000-person sample of the 1-D CNN integrated with NPR is trained and tested on the gene profile, mapping with various cancer kinds. The proposed model's accuracy of 94% suggests that the suggested combination may be useful for long-term cancer diagnosis and detection. Fewer parameters are required for the suggested model to be efficiently trained before prediction.
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Affiliation(s)
- Roshan Gangurde
- School of Computer Science, MIT World Peace University, Pune, India
| | - Vishal Jagota
- Model Institute of Engineering and Technology, Jammu, J&K, India
| | | | - Viji Siva Sakthi
- Zoology Department and Research Centre, Sarah Tucker College (Autonomous), Affiliated to Manonmaniam Sundaranar University, Tirunelveli, India
| | | | - Bernard Osei
- Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
| | - Kakarla Hari Kishore
- Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, India
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30
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Tumor growth prediction and classification based on the KNN algorithm and discrete-time Markov chains (DTMC). Neural Comput Appl 2023. [DOI: 10.1007/s00521-023-08212-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
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31
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Shen B, Feng F, Li K, Lin P, Ma L, Li H. A systematic assessment of deep learning methods for drug response prediction: from in vitro to clinical applications. Brief Bioinform 2023; 24:6961794. [PMID: 36575826 DOI: 10.1093/bib/bbac605] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 10/30/2022] [Accepted: 12/09/2022] [Indexed: 12/29/2022] Open
Abstract
Drug response prediction is an important problem in personalized cancer therapy. Among various newly developed models, significant improvement in prediction performance has been reported using deep learning methods. However, systematic comparisons of deep learning methods, especially of the transferability from preclinical models to clinical cohorts, are currently lacking. To provide a more rigorous assessment, the performance of six representative deep learning methods for drug response prediction using nine evaluation metrics, including the overall prediction accuracy, predictability of each drug, potential associated factors and transferability to clinical cohorts, in multiple application scenarios was benchmarked. Most methods show promising prediction within cell line datasets, and TGSA, with its lower time cost and better performance, is recommended. Although the performance metrics decrease when applying models trained on cell lines to patients, a certain amount of power to distinguish clinical response on some drugs can be maintained using CRDNN and TGSA. With these assessments, we provide a guidance for researchers to choose appropriate methods, as well as insights into future directions for the development of more effective methods in clinical scenarios.
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Affiliation(s)
- Bihan Shen
- Cancer Systems Biology group at Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Fangyoumin Feng
- Cancer Systems Biology group at Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Kunshi Li
- Cancer Systems Biology group at Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Ping Lin
- Cancer Systems Biology group at Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Liangxiao Ma
- Bio-Med Big Data Center at Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Hong Li
- Cancer Systems Biology group at Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
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Liu XY, Mei XY. Prediction of drug sensitivity based on multi-omics data using deep learning and similarity network fusion approaches. Front Bioeng Biotechnol 2023; 11:1156372. [PMID: 37139048 PMCID: PMC10150883 DOI: 10.3389/fbioe.2023.1156372] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 03/31/2023] [Indexed: 05/05/2023] Open
Abstract
With the rapid development of multi-omics technologies and accumulation of large-scale bio-datasets, many studies have conducted a more comprehensive understanding of human diseases and drug sensitivity from multiple biomolecules, such as DNA, RNA, proteins and metabolites. Using single omics data is difficult to systematically and comprehensively analyze the complex disease pathology and drug pharmacology. The molecularly targeted therapy-based approaches face some challenges, such as insufficient target gene labeling ability, and no clear targets for non-specific chemotherapeutic drugs. Consequently, the integrated analysis of multi-omics data has become a new direction for scientists to explore the mechanism of disease and drug. However, the available drug sensitivity prediction models based on multi-omics data still have problems such as overfitting, lack of interpretability, difficulties in integrating heterogeneous data, and the prediction accuracy needs to be improved. In this paper, we proposed a novel drug sensitivity prediction (NDSP) model based on deep learning and similarity network fusion approaches, which extracts drug targets using an improved sparse principal component analysis (SPCA) method for each omics data, and construct sample similarity networks based on the sparse feature matrices. Furthermore, the fused similarity networks are put into a deep neural network for training, which greatly reduces the data dimensionality and weakens the risk of overfitting problem. We use three omics of data, RNA sequence, copy number aberration and methylation, and select 35 drugs from Genomics of Drug Sensitivity in Cancer (GDSC) for experiments, including Food and Drug Administration (FDA)-approved targeted drugs, FDA-unapproved targeted drugs and non-specific therapies. Compared with some current deep learning methods, our proposed method can extract highly interpretable biological features to achieve highly accurate sensitivity prediction of targeted and non-specific cancer drugs, which is beneficial for the development of precision oncology beyond targeted therapy.
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Affiliation(s)
- Xiao-Ying Liu
- Guangdong Polytechnic of Science and Technology, Zhuhai, China
- *Correspondence: Xiao-Ying Liu,
| | - Xin-Yue Mei
- Institute of Systems Engineering, Macau University of Science and Technology, Taipa, China
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33
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Artificial intelligence in cancer research and precision medicine: Applications, limitations and priorities to drive transformation in the delivery of equitable and unbiased care. Cancer Treat Rev 2023; 112:102498. [PMID: 36527795 DOI: 10.1016/j.ctrv.2022.102498] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 12/03/2022] [Accepted: 12/06/2022] [Indexed: 12/14/2022]
Abstract
Artificial intelligence (AI) has experienced explosive growth in oncology and related specialties in recent years. The improved expertise in data capture, the increased capacity for data aggregation and analytic power, along with decreasing costs of genome sequencing and related biologic "omics", set the foundation and need for novel tools that can meaningfully process these data from multiple sources and of varying types. These advances provide value across biomedical discovery, diagnosis, prognosis, treatment, and prevention, in a multimodal fashion. However, while big data and AI tools have already revolutionized many fields, medicine has partially lagged due to its complexity and multi-dimensionality, leading to technical challenges in developing and validating solutions that generalize to diverse populations. Indeed, inner biases and miseducation of algorithms, in view of their implementation in daily clinical practice, are increasingly relevant concerns; critically, it is possible for AI to mirror the unconscious biases of the humans who generated these algorithms. Therefore, to avoid worsening existing health disparities, it is critical to employ a thoughtful, transparent, and inclusive approach that involves addressing bias in algorithm design and implementation along the cancer care continuum. In this review, a broad landscape of major applications of AI in cancer care is provided, with a focus on cancer research and precision medicine. Major challenges posed by the implementation of AI in the clinical setting will be discussed. Potentially feasible solutions for mitigating bias are provided, in the light of promoting cancer health equity.
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34
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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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35
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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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36
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Pandiyan S, Wang L. A comprehensive review on recent approaches for cancer drug discovery associated with artificial intelligence. Comput Biol Med 2022; 150:106140. [PMID: 36179510 DOI: 10.1016/j.compbiomed.2022.106140] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2022] [Revised: 07/20/2022] [Accepted: 09/18/2022] [Indexed: 11/03/2022]
Abstract
Through the revolutionization of artificial intelligence (AI) technologies in clinical research, significant improvement is observed in diagnosis of cancer. Utilization of these AI technologies, such as machine and deep learning, is imperative for the discovery of novel anticancer drugs and improves existing/ongoing cancer therapeutics. However, building a model for complicated cancers and their types remains a challenge due to lack of effective therapeutics that hinder the establishment of effective computational tools. In this review, we exploit recent approaches and state-of-the-art in implementing AI methods for anticancer drug discovery, and discussed how advances in these applications need to be considered in the current cancer therapeutics. Considering the immense potential of AI, we explore molecular docking and their interactions to recognize metabolic activities that support drug design. Finally, we highlight corresponding strategies in applying machine and deep learning methods to various types of cancer with their pros and cons.
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Affiliation(s)
- Sanjeevi Pandiyan
- Research Center for Intelligent Information Technology, Nantong University, Nantong, China; School of Information Science and Technology, Nantong University, Nantong, China; Nantong Research Institute for Advanced Communication Technologies, Nantong, China
| | - Li Wang
- Research Center for Intelligent Information Technology, Nantong University, Nantong, China; School of Information Science and Technology, Nantong University, Nantong, China; Nantong Research Institute for Advanced Communication Technologies, Nantong, China.
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37
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Li G, Wu X, Ma X. Artificial intelligence in radiotherapy. Semin Cancer Biol 2022; 86:160-171. [PMID: 35998809 DOI: 10.1016/j.semcancer.2022.08.005] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 08/18/2022] [Indexed: 11/19/2022]
Abstract
Radiotherapy is a discipline closely integrated with computer science. Artificial intelligence (AI) has developed rapidly over the past few years. With the explosive growth of medical big data, AI promises to revolutionize the field of radiotherapy through highly automated workflow, enhanced quality assurance, improved regional balances of expert experiences, and individualized treatment guided by multi-omics. In addition to independent researchers, the increasing number of large databases, biobanks, and open challenges significantly facilitated AI studies on radiation oncology. This article reviews the latest research, clinical applications, and challenges of AI in each part of radiotherapy including image processing, contouring, planning, quality assurance, motion management, and outcome prediction. By summarizing cutting-edge findings and challenges, we aim to inspire researchers to explore more future possibilities and accelerate the arrival of AI radiotherapy.
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Affiliation(s)
- Guangqi Li
- Division of Biotherapy, Cancer Center, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China
| | - Xin Wu
- Head & Neck Oncology ward, Division of Radiotherapy Oncology, Cancer Center, West China Hospital, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China
| | - Xuelei Ma
- Division of Biotherapy, Cancer Center, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China.
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38
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Lenhof K, Eckhart L, Gerstner N, Kehl T, Lenhof HP. Simultaneous regression and classification for drug sensitivity prediction using an advanced random forest method. Sci Rep 2022; 12:13458. [PMID: 35931707 PMCID: PMC9356072 DOI: 10.1038/s41598-022-17609-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 07/28/2022] [Indexed: 12/02/2022] Open
Abstract
Machine learning methods trained on cancer cell line panels are intensively studied for the prediction of optimal anti-cancer therapies. While classification approaches distinguish effective from ineffective drugs, regression approaches aim to quantify the degree of drug effectiveness. However, the high specificity of most anti-cancer drugs induces a skewed distribution of drug response values in favor of the more drug-resistant cell lines, negatively affecting the classification performance (class imbalance) and regression performance (regression imbalance) for the sensitive cell lines. Here, we present a novel approach called SimultAneoUs Regression and classificatiON Random Forests (SAURON-RF) based on the idea of performing a joint regression and classification analysis. We demonstrate that SAURON-RF improves the classification and regression performance for the sensitive cell lines at the expense of a moderate loss for the resistant ones. Furthermore, our results show that simultaneous classification and regression can be superior to regression or classification alone.
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Affiliation(s)
- Kerstin Lenhof
- Center for Bioinformatics, Saarland University, Saarland Informatics Campus (E2.1), 66123, Saarbrücken, Saarland, Germany.
| | - Lea Eckhart
- Center for Bioinformatics, Saarland University, Saarland Informatics Campus (E2.1), 66123, Saarbrücken, Saarland, Germany
| | - Nico Gerstner
- Center for Bioinformatics, Saarland University, Saarland Informatics Campus (E2.1), 66123, Saarbrücken, Saarland, Germany
| | - Tim Kehl
- Center for Bioinformatics, Saarland University, Saarland Informatics Campus (E2.1), 66123, Saarbrücken, Saarland, Germany
| | - Hans-Peter Lenhof
- Center for Bioinformatics, Saarland University, Saarland Informatics Campus (E2.1), 66123, Saarbrücken, Saarland, Germany
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39
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Alharbi WS, Rashid M. A review of deep learning applications in human genomics using next-generation sequencing data. Hum Genomics 2022; 16:26. [PMID: 35879805 PMCID: PMC9317091 DOI: 10.1186/s40246-022-00396-x] [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: 11/24/2021] [Accepted: 07/12/2022] [Indexed: 12/02/2022] Open
Abstract
Genomics is advancing towards data-driven science. Through the advent of high-throughput data generating technologies in human genomics, we are overwhelmed with the heap of genomic data. To extract knowledge and pattern out of this genomic data, artificial intelligence especially deep learning methods has been instrumental. In the current review, we address development and application of deep learning methods/models in different subarea of human genomics. We assessed over- and under-charted area of genomics by deep learning techniques. Deep learning algorithms underlying the genomic tools have been discussed briefly in later part of this review. Finally, we discussed briefly about the late application of deep learning tools in genomic. Conclusively, this review is timely for biotechnology or genomic scientists in order to guide them why, when and how to use deep learning methods to analyse human genomic data.
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Affiliation(s)
- Wardah S Alharbi
- Department of AI and Bioinformatics, King Abdullah International Medical Research Center (KAIMRC), King Saud Bin Abdulaziz University for Health Sciences (KSAU-HS), King Abdulaziz Medical City, Ministry of National Guard Health Affairs, P.O. Box 22490, Riyadh, 11426, Saudi Arabia
| | - Mamoon Rashid
- Department of AI and Bioinformatics, King Abdullah International Medical Research Center (KAIMRC), King Saud Bin Abdulaziz University for Health Sciences (KSAU-HS), King Abdulaziz Medical City, Ministry of National Guard Health Affairs, P.O. Box 22490, Riyadh, 11426, Saudi Arabia.
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40
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Chen J, Hao L, Qian X, Lin L, Pan Y, Han X. Machine learning models based on immunological genes to predict the response to neoadjuvant therapy in breast cancer patients. Front Immunol 2022; 13:948601. [PMID: 35935976 PMCID: PMC9352856 DOI: 10.3389/fimmu.2022.948601] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Accepted: 06/29/2022] [Indexed: 12/13/2022] Open
Abstract
Breast cancer (BC) is the most common malignancy worldwide and neoadjuvant therapy (NAT) plays an important role in the treatment of patients with early BC. However, only a subset of BC patients can achieve pathological complete response (pCR) and benefit from NAT. It is therefore necessary to predict the responses to NAT. Although many models to predict the response to NAT based on gene expression determined by the microarray platform have been proposed, their applications in clinical practice are limited due to the data normalization methods during model building and the disadvantages of the microarray platform compared with the RNA-seq platform. In this study, we first reconfirmed the correlation between immune profiles and pCR in an RNA-seq dataset. Then, we employed multiple machine learning algorithms and a model stacking strategy to build an immunological gene based model (Ipredictor model) and an immunological gene and receptor status based model ICpredictor model) in the RNA-seq dataset. The areas under the receiver operator characteristic curves for the Ipredictor model and ICpredictor models were 0.745 and 0.769 in an independent external test set based on the RNA-seq platform, and were 0.716 and 0.752 in another independent external test set based on the microarray platform. Furthermore, we found that the predictive score of the Ipredictor model was correlated with immune microenvironment and genomic aberration markers. These results demonstrated that the models can accurately predict the response to NAT for BC patients and will contribute to individualized therapy.
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Affiliation(s)
- Jian Chen
- Department of Oncology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- Clinical Research Center for Cancer Bioimmunotherapy of Anhui Province, Hefei, China
| | - Li Hao
- Department of Oncology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- Clinical Research Center for Cancer Bioimmunotherapy of Anhui Province, Hefei, China
| | - Xiaojun Qian
- Department of Oncology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- Clinical Research Center for Cancer Bioimmunotherapy of Anhui Province, Hefei, China
| | - Lin Lin
- Department of Oncology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- Clinical Research Center for Cancer Bioimmunotherapy of Anhui Province, Hefei, China
| | - Yueyin Pan
- Department of Oncology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- Clinical Research Center for Cancer Bioimmunotherapy of Anhui Province, Hefei, China
| | - Xinghua Han
- Department of Oncology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- Clinical Research Center for Cancer Bioimmunotherapy of Anhui Province, Hefei, China
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41
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Sharma R, Kannourakis G, Prithviraj P, Ahmed N. Precision Medicine: An Optimal Approach to Patient Care in Renal Cell Carcinoma. Front Med (Lausanne) 2022; 9:766869. [PMID: 35775004 PMCID: PMC9237320 DOI: 10.3389/fmed.2022.766869] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Accepted: 04/11/2022] [Indexed: 12/24/2022] Open
Abstract
Renal cell cancer (RCC) is a heterogeneous tumor that shows both intra- and inter-heterogeneity. Heterogeneity is displayed not only in different patients but also among RCC cells in the same tumor, which makes treatment difficult because of varying degrees of responses generated in RCC heterogeneous tumor cells even with targeted treatment. In that context, precision medicine (PM), in terms of individualized treatment catered for a specific patient or groups of patients, can shift the paradigm of treatment in the clinical management of RCC. Recent progress in the biochemical, molecular, and histological characteristics of RCC has thrown light on many deregulated pathways involved in the pathogenesis of RCC. As PM-based therapies are rapidly evolving and few are already in current clinical practice in oncology, one can expect that PM will expand its way toward the robust treatment of patients with RCC. This article provides a comprehensive background on recent strategies and breakthroughs of PM in oncology and provides an overview of the potential applicability of PM in RCC. The article also highlights the drawbacks of PM and provides a holistic approach that goes beyond the involvement of clinicians and encompasses appropriate legislative and administrative care imparted by the healthcare system and insurance providers. It is anticipated that combined efforts from all sectors involved will make PM accessible to RCC and other patients with cancer, making a tremendous positive leap on individualized treatment strategies. This will subsequently enhance the quality of life of patients.
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Affiliation(s)
- Revati Sharma
- Fiona Elsey Cancer Research Institute, Ballarat Central Technology Central Park, Ballarat Central, VIC, Australia
- School of Science, Psychology and Sport, Federation University, Mt Helen, VIC, Australia
| | - George Kannourakis
- Fiona Elsey Cancer Research Institute, Ballarat Central Technology Central Park, Ballarat Central, VIC, Australia
- School of Science, Psychology and Sport, Federation University, Mt Helen, VIC, Australia
| | - Prashanth Prithviraj
- Fiona Elsey Cancer Research Institute, Ballarat Central Technology Central Park, Ballarat Central, VIC, Australia
- School of Science, Psychology and Sport, Federation University, Mt Helen, VIC, Australia
| | - Nuzhat Ahmed
- Fiona Elsey Cancer Research Institute, Ballarat Central Technology Central Park, Ballarat Central, VIC, Australia
- School of Science, Psychology and Sport, Federation University, Mt Helen, VIC, Australia
- Centre for Reproductive Health, Hudson Institute of Medical Research and Department of Translational Medicine, Monash University, Clayton, VIC, Australia
- Department of Obstetrics and Gynaecology, University of Melbourne, Melbourne, VIC, Australia
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42
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Hostallero DE, Li Y, Emad A. Looking at the BiG Picture: Incorporating bipartite graphs in drug response prediction. Bioinformatics 2022; 38:3609-3620. [PMID: 35674359 DOI: 10.1093/bioinformatics/btac383] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Revised: 04/17/2022] [Accepted: 06/01/2022] [Indexed: 12/15/2022] Open
Abstract
MOTIVATION The increasing number of publicly available databases containing drugs' chemical structures, their response in cell lines, and molecular profiles of the cell lines has garnered attention to the problem of drug response prediction. However, many existing methods do not fully leverage the information that is shared among cell lines and drugs with similar structure. As such, drug similarities in terms of cell line responses and chemical structures could prove to be useful in forming drug representations to improve drug response prediction accuracy. RESULTS We present two deep learning approaches, BiG-DRP and BiG-DRP+, for drug response prediction. Our models take advantage of the drugs' chemical structure and the underlying relationships of drugs and cell lines through a bipartite graph and a heterogenous graph convolutional network that incorporate sensitive and resistant cell line information in forming drug representations. Evaluation of our methods and other state-of-the-art models in different scenarios shows that incorporating this bipartite graph significantly improves the prediction performance. Additionally, genes that contribute significantly to the performance of our models also point to important biological processes and signaling pathways. Analysis of predicted drug response of patients' tumors using our model revealed important associations between mutations and drug sensitivity, illustrating the utility of our model in pharmacogenomics studies. AVAILABILITY AND IMPLEMENTATION An implementation of the algorithms in Python is provided in https://github.com/ddhostallero/BiG-DRP. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- David Earl Hostallero
- Department of Electrical and Computer Engineering, McGill University, Montreal, QC H3A 0E9, Canada
- Mila, Quebec AI Institute, Montreal, QC H2S 3H1, Canada
| | - Yihui Li
- Department of Electrical and Computer Engineering, McGill University, Montreal, QC H3A 0E9, Canada
| | - Amin Emad
- Department of Electrical and Computer Engineering, McGill University, Montreal, QC H3A 0E9, Canada
- Mila, Quebec AI Institute, Montreal, QC H2S 3H1, Canada
- The Rosalind and Morris Goodman Cancer Institute, Montreal, QC H3A 1A3, Canada
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43
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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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44
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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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45
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Costa B, Estrada MF, Barroso MT, Fior R. Zebrafish Patient-Derived Avatars from Digestive Cancers for Anti-cancer Therapy Screening. Curr Protoc 2022; 2:e415. [PMID: 35436037 DOI: 10.1002/cpz1.415] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Patient-derived xenografts (PDXs), also called "avatars," are generated by the implantation of human primary tumor cells or tissues into a host animal. Given the complexity and unique characteristics of each tumor, PDXs are models of choice in cancer research and precision medicine. In this context, the zebrafish PDX model (zPDX or zAvatar) has been recognized as a promising in vivo model to directly challenge patient cells with anti-cancer therapies in a personalized manner. The assay relies on the injection of tumor cells from patients into zebrafish embryos to then test and identify the best available drug combination for a particular patient. Compared to mouse PDXs, zAvatar assays take less time and do not require in vitro or in vivo cell expansion. The present article describes how to generate zAvatars from resected digestive cancer from surgeries and how to then use them for anti-cancer therapy screening. We describe the steps for tumor sample collection and cryopreservation, sample preparation and fluorescent labeling for microinjection into zebrafish embryos, drug administration, and analysis of tumor behavior by single-cell confocal imaging. We provide detailed protocols and helpful tips for performing this assay, and we address the technical challenges associated with the workflow. © 2022 Wiley Periodicals LLC. Basic Protocol 1: Patient tumor sample collection and cryopreservation Basic Protocol 2: Generation of zAvatars and anti-cancer treatment Basic Protocol 3: Whole-mount immunofluorescence Basic Protocol 4: Confocal imaging and analysis.
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Affiliation(s)
- Bruna Costa
- Champalimaud Foundation, Champalimaud Research, Lisbon, Portugal
| | - Marta F Estrada
- Champalimaud Foundation, Champalimaud Research, Lisbon, Portugal
| | | | - Rita Fior
- Champalimaud Foundation, Champalimaud Research, Lisbon, Portugal
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Lee JW, Park YS, Choi JY, Chang WJ, Lee S, Sung JS, Kim B, Lee SB, Lee SY, Choi J, Kim YH. Genetic Characteristics Associated With Drug Resistance in Lung Cancer and Colorectal Cancer Using Whole Exome Sequencing of Cell-Free DNA. Front Oncol 2022; 12:843561. [PMID: 35402275 PMCID: PMC8987589 DOI: 10.3389/fonc.2022.843561] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2021] [Accepted: 03/01/2022] [Indexed: 12/12/2022] Open
Abstract
Circulating cell-free DNA (cfDNA) can be used to characterize tumor genomes through next-generation sequencing (NGS)-based approaches. We aim to identify novel genetic alterations associated with drug resistance in lung cancer and colorectal cancer patients who were treated with EGFR-targeted therapy and cytotoxic chemotherapy through whole exome sequencing (WES) of cfDNA. A cohort of 18 lung cancer patients was treated with EGFR TKI or cytotoxic chemotherapy, and a cohort of 37 colorectal cancer patients was treated with EGFR monoclonal antibody or cytotoxic chemotherapy alone. Serum samples were drawn before and after development of drug resistance, and the genetic mutational profile was analyzed with WES data. For 110 paired cfDNA and matched germline DNA WES samples, mean coverage of 138x (range, 52–208.4x) and 47x (range, 30.5–125.1x) was achieved, respectively. After excluding synonymous variants, mutants identified in more than two patients at the time of acquired resistance were selected. Seven genes in lung cancer and 16 genes in colorectal cancer were found, namely, APC, TP53, KRAS, SMAD4, and EGFR. In addition, the GPR155 I357S mutation in lung cancer and ADAMTS20 S1597P and TTN R7415H mutations in colorectal cancer were frequently detected at the time of acquired resistance, indicating that these mutations have an important function in acquired resistance to chemotherapy. Our data suggest that novel genetic variants associated with drug resistance can be identified using cfDNA WES. Further validation is necessary, but these candidate genes are promising therapeutic targets for overcoming drug resistance in lung cancer and colorectal cancer.
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Affiliation(s)
- Jong Won Lee
- Cancer Research Institute, Korea University College of Medicine, Seoul, South Korea
- Brain Korea 21 Plus Project for Biomedical Science, Korea University College of Medicine, Seoul, South Korea
- Department of Biomedical Sciences, Korea University College of Medicine, Seoul, South Korea
| | - Young Soo Park
- Cancer Research Institute, Korea University College of Medicine, Seoul, South Korea
| | - Jung Yoon Choi
- Division of Hematology–Oncology, Department of Internal Medicine, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, South Korea
| | - Won Jin Chang
- Division of Hematology–Oncology, Department of Internal Medicine, Korea University Anam Hospital, Korea University College of Medicine, Seoul, South Korea
| | - Soohyeon Lee
- Division of Hematology–Oncology, Department of Internal Medicine, Korea University Anam Hospital, Korea University College of Medicine, Seoul, South Korea
| | - Jae Sook Sung
- Cancer Research Institute, Korea University College of Medicine, Seoul, South Korea
| | - Boyeon Kim
- Cancer Research Institute, Korea University College of Medicine, Seoul, South Korea
- Brain Korea 21 Plus Project for Biomedical Science, Korea University College of Medicine, Seoul, South Korea
| | - Saet Byeol Lee
- Cancer Research Institute, Korea University College of Medicine, Seoul, South Korea
- Brain Korea 21 Plus Project for Biomedical Science, Korea University College of Medicine, Seoul, South Korea
| | - Sung Yong Lee
- Division of Pulmonary, Allergy and Critical Care Medicine, Department of Internal Medicine, Korea University Medical Center, Korea University College of Medicine, Seoul, South Korea
| | - Jungmin Choi
- Department of Biomedical Sciences, Korea University College of Medicine, Seoul, South Korea
- Department of Genetics, Yale University School of Medicine, New Haven, CT, United States
| | - Yeul Hong Kim
- Cancer Research Institute, Korea University College of Medicine, Seoul, South Korea
- Brain Korea 21 Plus Project for Biomedical Science, Korea University College of Medicine, Seoul, South Korea
- Division of Hematology–Oncology, Department of Internal Medicine, Korea University Anam Hospital, Korea University College of Medicine, Seoul, South Korea
- *Correspondence: Yeul Hong Kim,
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Abstract
This overview of the molecular pathology of lung cancer includes a review of the most salient molecular alterations of the genome, transcriptome, and the epigenome. The insights provided by the growing use of next-generation sequencing (NGS) in lung cancer will be discussed, and interrelated concepts such as intertumor heterogeneity, intratumor heterogeneity, tumor mutational burden, and the advent of liquid biopsy will be explored. Moreover, this work describes how the evolving field of molecular pathology refines the understanding of different histologic phenotypes of non-small-cell lung cancer (NSCLC) and the underlying biology of small-cell lung cancer. This review will provide an appreciation for how ongoing scientific findings and technologic advances in molecular pathology are crucial for development of biomarkers, therapeutic agents, clinical trials, and ultimately improved patient care.
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Affiliation(s)
- James J Saller
- Departments of Pathology and Thoracic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida 33612, USA
| | - Theresa A Boyle
- Departments of Pathology and Thoracic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida 33612, USA
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Bernstam EV, Shireman PK, Meric‐Bernstam F, N. Zozus M, Jiang X, Brimhall BB, Windham AK, Schmidt S, Visweswaran S, Ye Y, Goodrum H, Ling Y, Barapatre S, Becich MJ. Artificial intelligence in clinical and translational science: Successes, challenges and opportunities. Clin Transl Sci 2022; 15:309-321. [PMID: 34706145 PMCID: PMC8841416 DOI: 10.1111/cts.13175] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 10/01/2021] [Indexed: 01/12/2023] Open
Abstract
Artificial intelligence (AI) is transforming many domains, including finance, agriculture, defense, and biomedicine. In this paper, we focus on the role of AI in clinical and translational research (CTR), including preclinical research (T1), clinical research (T2), clinical implementation (T3), and public (or population) health (T4). Given the rapid evolution of AI in CTR, we present three complementary perspectives: (1) scoping literature review, (2) survey, and (3) analysis of federally funded projects. For each CTR phase, we addressed challenges, successes, failures, and opportunities for AI. We surveyed Clinical and Translational Science Award (CTSA) hubs regarding AI projects at their institutions. Nineteen of 63 CTSA hubs (30%) responded to the survey. The most common funding source (48.5%) was the federal government. The most common translational phase was T2 (clinical research, 40.2%). Clinicians were the intended users in 44.6% of projects and researchers in 32.3% of projects. The most common computational approaches were supervised machine learning (38.6%) and deep learning (34.2%). The number of projects steadily increased from 2012 to 2020. Finally, we analyzed 2604 AI projects at CTSA hubs using the National Institutes of Health Research Portfolio Online Reporting Tools (RePORTER) database for 2011-2019. We mapped available abstracts to medical subject headings and found that nervous system (16.3%) and mental disorders (16.2) were the most common topics addressed. From a computational perspective, big data (32.3%) and deep learning (30.0%) were most common. This work represents a snapshot in time of the role of AI in the CTSA program.
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Affiliation(s)
- Elmer V. Bernstam
- School of Biomedical InformaticsThe University of Texas Health Science Center at HoustonHoustonTexasUSA
- Division of General Internal MedicineDepartment of Internal MedicineMcGovern Medical SchoolThe University of Texas Health Science Center at HoustonHoustonTexasUSA
| | - Paula K. Shireman
- Departments of Surgery and MicrobiologyImmunology & Molecular GeneticsUniversity of Texas Health San AntonioSan AntonioTexasUSA
- University HealthSan AntonioTexasUSA
- South Texas Veterans Health Care SystemSan AntonioTexasUSA
| | - Funda Meric‐Bernstam
- Department of Investigational Cancer TherapeuticsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Meredith N. Zozus
- Division of Clinical Research InformaticsDepartment of Population Health SciencesUniversity of Texas Health San AntonioSan AntonioTexasUSA
| | - Xiaoqian Jiang
- School of Biomedical InformaticsThe University of Texas Health Science Center at HoustonHoustonTexasUSA
| | - Bradley B. Brimhall
- University HealthSan AntonioTexasUSA
- Department of PathologyUniversity of Texas Health San AntonioSan AntonioTexasUSA
| | - Ashley K. Windham
- University HealthSan AntonioTexasUSA
- Department of PathologyUniversity of Texas Health San AntonioSan AntonioTexasUSA
| | - Susanne Schmidt
- Department of Population Health SciencesUniversity of Texas Health San AntonioSan AntonioTexasUSA
| | - Shyam Visweswaran
- Department of Biomedical InformaticsUniversity of Pittsburgh School of MedicinePittsburghPennsylvaniaUSA
| | - Ye Ye
- Department of Biomedical InformaticsUniversity of Pittsburgh School of MedicinePittsburghPennsylvaniaUSA
| | - Heath Goodrum
- School of Biomedical InformaticsThe University of Texas Health Science Center at HoustonHoustonTexasUSA
| | - Yaobin Ling
- School of Biomedical InformaticsThe University of Texas Health Science Center at HoustonHoustonTexasUSA
| | - Seemran Barapatre
- Department of Biomedical InformaticsUniversity of Pittsburgh School of MedicinePittsburghPennsylvaniaUSA
| | - Michael J. Becich
- Department of Biomedical InformaticsUniversity of Pittsburgh School of MedicinePittsburghPennsylvaniaUSA
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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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50
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Ren S, Tao Y, Yu K, Xue Y, Schwartz R, Lu X. De novo Prediction of Cell-Drug Sensitivities Using Deep Learning-based Graph Regularized Matrix Factorization. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2022; 27:278-289. [PMID: 34890156 PMCID: PMC8691529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Application of artificial intelligence (AI) in precision oncology typically involves predicting whether the cancer cells of a patient (previously unseen by AI models) will respond to any of a set of existing anticancer drugs, based on responses of previous training cell samples to those drugs. To expand the repertoire of anticancer drugs, AI has also been used to repurpose drugs that have not been tested in an anticancer setting, i.e., predicting the anticancer effects of a new drug on previously unseen cancer cells de novo. Here, we report a computational model that addresses both of the above tasks in a unified AI framework. Our model, referred to as deep learning-based graph regularized matrix factorization (DeepGRMF), integrates neural networks, graph models, and matrix-factorization techniques to utilize diverse information from drug chemical structures, their impact on cellular signaling systems, and cancer cell cellular states to predict cell response to drugs. DeepGRMF learns embeddings of drugs so that drugs sharing similar structures and mechanisms of action (MOAs) are closely related in the embedding space. Similarly, DeepGRMF also learns representation embeddings of cells such that cells sharing similar cellular states and drug responses are closely related. Evaluation of DeepGRMF and competing models on Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE) datasets show its superiority in prediction performance. Finally, we show that the model is capable of predicting effectiveness of a chemotherapy regimen on patient outcomes for the lung cancer patients in The Cancer Genome Atlas (TCGA) dataset*.
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