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Castilho RM, Castilho LS, Palomares BH, Squarize CH. Determinants of Chromatin Organization in Aging and Cancer-Emerging Opportunities for Epigenetic Therapies and AI Technology. Genes (Basel) 2024; 15:710. [PMID: 38927646 PMCID: PMC11202709 DOI: 10.3390/genes15060710] [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: 03/31/2024] [Revised: 05/21/2024] [Accepted: 05/26/2024] [Indexed: 06/28/2024] Open
Abstract
This review article critically examines the pivotal role of chromatin organization in gene regulation, cellular differentiation, disease progression and aging. It explores the dynamic between the euchromatin and heterochromatin, coded by a complex array of histone modifications that orchestrate essential cellular processes. We discuss the pathological impacts of chromatin state misregulation, particularly in cancer and accelerated aging conditions such as progeroid syndromes, and highlight the innovative role of epigenetic therapies and artificial intelligence (AI) in comprehending and harnessing the histone code toward personalized medicine. In the context of aging, this review explores the use of AI and advanced machine learning (ML) algorithms to parse vast biological datasets, leading to the development of predictive models for epigenetic modifications and providing a framework for understanding complex regulatory mechanisms, such as those governing cell identity genes. It supports innovative platforms like CEFCIG for high-accuracy predictions and tools like GridGO for tailored ChIP-Seq analysis, which are vital for deciphering the epigenetic landscape. The review also casts a vision on the prospects of AI and ML in oncology, particularly in the personalization of cancer therapy, including early diagnostics and treatment optimization for diseases like head and neck and colorectal cancers by harnessing computational methods, AI advancements and integrated clinical data for a transformative impact on healthcare outcomes.
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Affiliation(s)
- Rogerio M. Castilho
- Laboratory of Epithelial Biology, Department of Periodontics and Oral Medicine, School of Dentistry, University of Michigan, Ann Arbor, MI 48109-1078, USA; (L.S.C.); (C.H.S.)
- Rogel Cancer Center, University of Michigan, Ann Arbor, MI 48109-1078, USA
| | - Leonard S. Castilho
- Laboratory of Epithelial Biology, Department of Periodontics and Oral Medicine, School of Dentistry, University of Michigan, Ann Arbor, MI 48109-1078, USA; (L.S.C.); (C.H.S.)
| | - Bruna H. Palomares
- Oral Diagnosis Department, Piracicaba School of Dentistry, State University of Campinas, Piracicaba 13414-903, Sao Paulo, Brazil;
| | - Cristiane H. Squarize
- Laboratory of Epithelial Biology, Department of Periodontics and Oral Medicine, School of Dentistry, University of Michigan, Ann Arbor, MI 48109-1078, USA; (L.S.C.); (C.H.S.)
- Rogel Cancer Center, University of Michigan, Ann Arbor, MI 48109-1078, USA
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Li P, Jiang Z, Liu T, Liu X, Qiao H, Yao X. Improving drug response prediction via integrating gene relationships with deep learning. Brief Bioinform 2024; 25:bbae153. [PMID: 38600666 PMCID: PMC11006795 DOI: 10.1093/bib/bbae153] [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: 12/26/2023] [Revised: 03/05/2024] [Accepted: 03/18/2024] [Indexed: 04/12/2024] Open
Abstract
Predicting the drug response of cancer cell lines is crucial for advancing personalized cancer treatment, yet remains challenging due to tumor heterogeneity and individual diversity. In this study, we present a deep learning-based framework named Deep neural network Integrating Prior Knowledge (DIPK) (DIPK), which adopts self-supervised techniques to integrate multiple valuable information, including gene interaction relationships, gene expression profiles and molecular topologies, to enhance prediction accuracy and robustness. We demonstrated the superior performance of DIPK compared to existing methods on both known and novel cells and drugs, underscoring the importance of gene interaction relationships in drug response prediction. In addition, DIPK extends its applicability to single-cell RNA sequencing data, showcasing its capability for single-cell-level response prediction and cell identification. Further, we assess the applicability of DIPK on clinical data. DIPK accurately predicted a higher response to paclitaxel in the pathological complete response (pCR) group compared to the residual disease group, affirming the better response of the pCR group to the chemotherapy compound. We believe that the integration of DIPK into clinical decision-making processes has the potential to enhance individualized treatment strategies for cancer patients.
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Affiliation(s)
- Pengyong Li
- School of Computer Science and Technology,Xidian University, 710126 Xi’an, Shaanxi, China
- State Key Laboratory of Quality Research in Chinese Medicine, Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, 519020 Macau, China
| | - Zhengxiang Jiang
- School of Electronic Engineering, Xidian University, 710126 Xi’an, Shaanxi, China
| | - Tianxiao Liu
- School of Computer Science and Technology,Xidian University, 710126 Xi’an, Shaanxi, China
| | - Xinyu Liu
- Beijing Laboratory of Biomedical Materials, Department of Geriatric Dentistry, Peking University School and Hospital of Stomatology, 100081 Beijing, China
| | - Hui Qiao
- Department of Oncology, Tai’an Municipal Hospital, 271021 Tai’an, Shandong, China
| | - Xiaojun Yao
- Centre for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, 999078 Macao, China
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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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Boumendil L, Fontaine M, Lévy V, Pacchiardi K, Itzykson R, Biard L. Drug combinations screening using a Bayesian ranking approach based on dose-response models. Biom J 2024; 66:e2200332. [PMID: 37984849 DOI: 10.1002/bimj.202200332] [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: 11/30/2022] [Revised: 05/05/2023] [Accepted: 06/15/2023] [Indexed: 11/22/2023]
Abstract
Drug combinations have been of increasing interest in recent years for the treatment of complex diseases such as cancer, as they could reduce the risk of drug resistance. Moreover, in oncology, combining drugs may allow tackling tumor heterogeneity. Identifying potent combinations can be an arduous task since exploring the full dose-response matrix of candidate combinations over a large number of drugs is costly and sometimes unfeasible, as the quantity of available biological material is limited and may vary across patients. Our objective was to develop a rank-based screening approach for drug combinations in the setting of limited biological resources. A hierarchical Bayesian 4-parameter log-logistic (4PLL) model was used to estimate dose-response curves of dose-candidate combinations based on a parsimonious experimental design. We computed various activity ranking metrics, such as the area under the dose-response curve and Bliss synergy score, and we used the posterior distributions of ranks and the surface under the cumulative ranking curve to obtain a comprehensive final ranking of combinations. Based on simulations, our proposed method achieved good operating characteristics to identifying the most promising treatments in various scenarios with limited sample sizes and interpatient variability. We illustrate the proposed approach on real data from a combination screening experiment in acute myeloid leukemia.
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Affiliation(s)
- Luana Boumendil
- Université Paris Cité, INSERM U1153, Team ECSTRRA, Paris, France
| | - Morgane Fontaine
- Université Paris Cité, Génomes, biologie cellulaire et thérapeutique U944, INSERM, CNRS, Paris, France
| | - Vincent Lévy
- Université Paris Cité, INSERM U1153, Team ECSTRRA, Paris, France
- Sorbonne Paris Nord, Unité de Recherche Clinique, Hôpital Avicenne, Assistance Publique-Hôpitaux de Paris, Bobigny, France
| | - Kim Pacchiardi
- Université Paris Cité, Génomes, biologie cellulaire et thérapeutique U944, INSERM, CNRS, Paris, France
- Laboratoire d'Hématologie, Hôpital Saint-Louis, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Raphaël Itzykson
- Université Paris Cité, Génomes, biologie cellulaire et thérapeutique U944, INSERM, CNRS, Paris, France
- Service Hématologie Adultes, Hôpital Saint-Louis, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Lucie Biard
- Université Paris Cité, INSERM U1153, Team ECSTRRA, Paris, France
- Service de Biostatistique et Information Médicale, Hôpital Saint-Louis, Assistance Publique-Hôpitaux de Paris, Paris, France
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Zhang H, Ek CH, Rattray M, Milo M. SynBa: improved estimation of drug combination synergies with uncertainty quantification. Bioinformatics 2023; 39:i121-i130. [PMID: 37387161 DOI: 10.1093/bioinformatics/btad240] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/01/2023] Open
Abstract
MOTIVATION There exists a range of different quantification frameworks to estimate the synergistic effect of drug combinations. The diversity and disagreement in estimates make it challenging to determine which combinations from a large drug screening should be proceeded with. Furthermore, the lack of accurate uncertainty quantification for those estimates precludes the choice of optimal drug combinations based on the most favourable synergistic effect. RESULTS In this work, we propose SynBa, a flexible Bayesian approach to estimate the uncertainty of the synergistic efficacy and potency of drug combinations, so that actionable decisions can be derived from the model outputs. The actionability is enabled by incorporating the Hill equation into SynBa, so that the parameters representing the potency and the efficacy can be preserved. Existing knowledge may be conveniently inserted due to the flexibility of the prior, as shown by the empirical Beta prior defined for the normalized maximal inhibition. Through experiments on large combination screenings and comparison against benchmark methods, we show that SynBa provides improved accuracy of dose-response predictions and better-calibrated uncertainty estimation for the parameters and the predictions. AVAILABILITY AND IMPLEMENTATION The code for SynBa is available at https://github.com/HaotingZhang1/SynBa. The datasets are publicly available (DOI of DREAM: 10.7303/syn4231880; DOI of the NCI-ALMANAC subset: 10.5281/zenodo.4135059).
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Affiliation(s)
- Haoting Zhang
- Department of Computer Science and Technology, University of Cambridge, Cambridge CB3 0FD, United Kingdom
- Health Data Research UK, London NW1 2BE, United Kingdom
| | - Carl Henrik Ek
- Department of Computer Science and Technology, University of Cambridge, Cambridge CB3 0FD, United Kingdom
| | - Magnus Rattray
- Division of Informatics, Imaging and Data Sciences, University of Manchester, Manchester M13 9PL, United Kingdom
- Alan Turing Institute, London NW1 2DB, United Kingdom
| | - Marta Milo
- Oncology Data Science, Oncology R&D AstraZeneca, Cambridge CB2 8PA, United Kingdom
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Rønneberg L, Cremaschi A, Hanes R, Enserink JM, Zucknick M. bayesynergy: flexible Bayesian modelling of synergistic interaction effects in in vitro drug combination experiments. Brief Bioinform 2021; 22:bbab251. [PMID: 34308471 PMCID: PMC8575029 DOI: 10.1093/bib/bbab251] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Revised: 05/26/2021] [Accepted: 06/14/2021] [Indexed: 11/24/2022] Open
Abstract
The effect of cancer therapies is often tested pre-clinically via in vitro experiments, where the post-treatment viability of the cancer cell population is measured through assays estimating the number of viable cells. In this way, large libraries of compounds can be tested, comparing the efficacy of each treatment. Drug interaction studies focus on the quantification of the additional effect encountered when two drugs are combined, as opposed to using the treatments separately. In the bayesynergy R package, we implement a probabilistic approach for the description of the drug combination experiment, where the observed dose response curve is modelled as a sum of the expected response under a zero-interaction model and an additional interaction effect (synergistic or antagonistic). Although the model formulation makes use of the Bliss independence assumption, we note that the posterior estimates of the dose-response surface can also be used to extract synergy scores based on other reference models, which we illustrate for the Highest Single Agent model. The interaction is modelled in a flexible manner, using a Gaussian process formulation. Since the proposed approach is based on a statistical model, it allows the natural inclusion of replicates, handles missing data and uneven concentration grids, and provides uncertainty quantification around the results. The model is implemented in the open-source Stan programming language providing a computationally efficient sampler, a fast approximation of the posterior through variational inference, and features parallel processing for working with large drug combination screens.
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Affiliation(s)
- Leiv Rønneberg
- Oslo Centre for Biostatistics and Epidemiology (OCBE), University of Oslo, Norway
| | - Andrea Cremaschi
- Singapore Institute for Clinical Sciences (SICS), A*STAR, Singapore
| | - Robert Hanes
- Department of Molecular Cell Biology, Institute for Cancer Research, The Norwegian Radium Hospital, Montebello, Oslo 0379, Norway
- Centre for Cancer Cell Reprogramming, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Jorrit M Enserink
- Department of Molecular Cell Biology, Institute for Cancer Research, The Norwegian Radium Hospital, Montebello, Oslo 0379, Norway
- Centre for Cancer Cell Reprogramming, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
- Department of Biosciences, Faculty of Mathematics and Natural Sciences, University of Oslo, PO Box 1066 Blindern, Oslo 0316, Norway
| | - Manuela Zucknick
- Oslo Centre for Biostatistics and Epidemiology (OCBE), University of Oslo, Norway
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Tansey W, Veitch V, Zhang H, Rabadan R, Blei DM. The Holdout Randomization Test for Feature Selection in Black Box Models. J Comput Graph Stat 2021. [DOI: 10.1080/10618600.2021.1923520] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Wesley Tansey
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Victor Veitch
- Department of Statistics, Columbia University, New York, NY
| | - Haoran Zhang
- Department of Computer Science, University of Texas at Austin, TX
| | - Raul Rabadan
- Department of Systems Biology, Columbia University Medical Center, New York, NY
| | - David M. Blei
- Departments of Computer Science and Statistics, Columbia University, New York, NY
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Koukouli E, Wang D, Dondelinger F, Park J. A regularized functional regression model enabling transcriptome-wide dosage-dependent association study of cancer drug response. PLoS Comput Biol 2021; 17:e1008066. [PMID: 33493149 PMCID: PMC7920352 DOI: 10.1371/journal.pcbi.1008066] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Revised: 03/01/2021] [Accepted: 12/17/2020] [Indexed: 11/18/2022] Open
Abstract
Cancer treatments can be highly toxic and frequently only a subset of the patient population will benefit from a given treatment. Tumour genetic makeup plays an important role in cancer drug sensitivity. We suspect that gene expression markers could be used as a decision aid for treatment selection or dosage tuning. Using in vitro cancer cell line dose-response and gene expression data from the Genomics of Drug Sensitivity in Cancer (GDSC) project, we build a dose-varying regression model. Unlike existing approaches, this allows us to estimate dosage-dependent associations with gene expression. We include the transcriptomic profiles as dose-invariant covariates into the regression model and assume that their effect varies smoothly over the dosage levels. A two-stage variable selection algorithm (variable screening followed by penalized regression) is used to identify genetic factors that are associated with drug response over the varying dosages. We evaluate the effectiveness of our method using simulation studies focusing on the choice of tuning parameters and cross-validation for predictive accuracy assessment. We further apply the model to data from five BRAF targeted compounds applied to different cancer cell lines under different dosage levels. We highlight the dosage-dependent dynamics of the associations between the selected genes and drug response, and we perform pathway enrichment analysis to show that the selected genes play an important role in pathways related to tumorigenesis and DNA damage response.
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Affiliation(s)
- Evanthia Koukouli
- Department of Mathematics and Statistics, Fylde College, Lancaster University, Bailrigg, Lancaster, UK
| | - Dennis Wang
- Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield, UK
- Department of Computer Science, University of Sheffield, Sheffield, UK
| | - Frank Dondelinger
- Centre for Health Informatics and Statistics, Lancaster Medical School, Lancaster University, Bailrigg, Lancaster, UK
| | - Juhyun Park
- Department of Mathematics and Statistics, Fylde College, Lancaster University, Bailrigg, Lancaster, UK
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