1
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Amani MS, Peymani M. Investigating the impact of SMAD2 and SMAD4 downregulation in colorectal cancer and their correlation with immune markers, prognosis, and drug resistance and sensitivity. Mol Biol Rep 2024; 51:831. [PMID: 39037563 DOI: 10.1007/s11033-024-09697-x] [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: 04/11/2024] [Accepted: 06/03/2024] [Indexed: 07/23/2024]
Abstract
BACKGROUND While many genes linked to colorectal cancer (CRC) contribute to cancer development, a thorough investigation is needed to explore crucial hub genes yet to be fully studied. A pivotal pathway in CRC is transforming growth factor-beta (TGF-β). This study aimed to assess SMAD2 and SMAD4 gene expression from this pathway. METHODS AND RESULTS Counted data from the Cancer Genome Atlas (TCGA) were examined, comparing 483 tumor and 41 normal samples. Using clinical data, genes impacting overall survival (OS) were evaluated. GSE39582 was employed to confirmed the levels of genes in CRC compared to the normal samples. Additionally, employing unhealthy samples and the RT-qPCR means our outcomes was validated. Finally, PharmacoGx information were utilized to connect the levels of potential genes to drug tolerance and susceptibility. Our findings showed SMAD2 and SMAD4 levels in TGF-β signaling were more significant than other pathway genes. Our findings indicated that the protein levels of these genes were lower in malignant tissues than in healthy tissues. Results revealed a significant correlation between low levels of SMAD2 and unfavorable OS in CRC individuals. RT-qPCR results demonstrated decreased expressions of both SMAD2 and SMAD4 in cancer tissues compared to elevated levels in adjacent normal samples. Our results showed significant association between selected genes and immune cell infiltration markers such as CD8+, and B-cells. Our results indicated a potential association among the levels of SMAD2 and SMAD4 genes and tolerance and susceptibility to Nilotinib and Panobinostat drugs. CONCLUSION Reduced expression of SMAD2 and SMAD4 may be pivotal in CRC progression, impacting downstream genes unrelated to patient OS. These findings suggest a potential role for SMAD2 and SMAD4 as predictive markers for drug response in CRC patients.
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Affiliation(s)
- Melika Saadat Amani
- Department of Biology, Faculty of Basic Sciences, Shahrekord Branch, Islamic Azad University, Shahrekord, Iran
| | - Maryam Peymani
- Department of Biology, Faculty of Basic Sciences, Shahrekord Branch, Islamic Azad University, Shahrekord, Iran.
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2
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Meimetis N, Lauffenburger DA, Nilsson A. Inference of drug off-target effects on cellular signaling using interactome-based deep learning. iScience 2024; 27:109509. [PMID: 38591003 PMCID: PMC11000001 DOI: 10.1016/j.isci.2024.109509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 02/04/2024] [Accepted: 03/13/2024] [Indexed: 04/10/2024] Open
Abstract
Many diseases emerge from dysregulated cellular signaling, and drugs are often designed to target specific signaling proteins. Off-target effects are, however, common and may ultimately result in failed clinical trials. Here we develop a computer model of the cell's transcriptional response to drugs for improved understanding of their mechanisms of action. The model is based on ensembles of artificial neural networks and simultaneously infers drug-target interactions and their downstream effects on intracellular signaling. With this, it predicts transcription factors' activities, while recovering known drug-target interactions and inferring many new ones, which we validate with an independent dataset. As a case study, we analyze the effects of the drug Lestaurtinib on downstream signaling. Alongside its intended target, FLT3, the model predicts an inhibition of CDK2 that enhances the downregulation of the cell cycle-critical transcription factor FOXM1. Our approach can therefore enhance our understanding of drug signaling for therapeutic design.
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Affiliation(s)
- Nikolaos Meimetis
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Douglas A. Lauffenburger
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Avlant Nilsson
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of Cell and Molecular Biology, SciLifeLab, Karolinska Institutet, Stockholm, Sweden
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, SE 41296, Sweden
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3
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Godinez WJ, Trifonov V, Fang B, Kuzu G, Pei L, Guiguemde WA, Martin EJ, King FJ, Jenkins JL, Skewes-Cox P. Compound Activity Prediction with Dose-Dependent Transcriptomic Profiles and Deep Learning. J Chem Inf Model 2024; 64:2695-2704. [PMID: 38293736 DOI: 10.1021/acs.jcim.3c01855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
Predicting compound activity in assays is a long-standing challenge in drug discovery. Computational models based on compound-induced gene expression signatures from a single profiling assay have shown promise toward predicting compound activity in other, seemingly unrelated, assays. Applications of such models include predicting mechanisms-of-action (MoA) for phenotypic hits, identifying off-target activities, and identifying polypharmacologies. Here, we introduce transcriptomics-to-activity transformer (TAT) models that leverage gene expression profiles observed over compound treatment at multiple concentrations to predict the compound activity in other biochemical or cellular assays. We built TAT models based on gene expression data from a RASL-seq assay to predict the activity of 2692 compounds in 262 dose-response assays. We obtained useful models for 51% of the assays, as determined through a realistic held-out set. Prospectively, we experimentally validated the activity predictions of a TAT model in a malaria inhibition assay. With a 63% hit rate, TAT successfully identified several submicromolar malaria inhibitors. Our results thus demonstrate the potential of transcriptomic responses over compound concentration and the TAT modeling framework as a cost-efficient way to identify the bioactivities of promising compounds across many assays.
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Affiliation(s)
- William J Godinez
- Novartis Institutes for BioMedical Research, Emeryville, California 94608, United States
| | - Vladimir Trifonov
- Novartis Institutes for BioMedical Research, San Diego, California 92121, United States
| | - Bin Fang
- Novartis Institutes for BioMedical Research, San Diego, California 92121, United States
| | - Guray Kuzu
- Novartis Institutes for BioMedical Research, San Diego, California 92121, United States
| | - Luying Pei
- Novartis Institutes for BioMedical Research, Emeryville, California 94608, United States
| | - W Armand Guiguemde
- Novartis Institutes for BioMedical Research, Emeryville, California 94608, United States
| | - Eric J Martin
- Novartis Institutes for BioMedical Research, Emeryville, California 94608, United States
| | - Frederick J King
- Novartis Institutes for BioMedical Research, San Diego, California 92121, United States
| | - Jeremy L Jenkins
- Novartis Institutes for BioMedical Research, Cambridge, Massachusetts 02139, United States
| | - Peter Skewes-Cox
- Novartis Institutes for BioMedical Research, Emeryville, California 94608, United States
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4
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Golozar M, Motlagh AV, Mahdevar M, Peymani M, InanlooRahatloo K, Ghaedi K. TBX15 and SDHB expression changes in colorectal cancer serve as potential prognostic biomarkers. Exp Mol Pathol 2024; 136:104890. [PMID: 38378070 DOI: 10.1016/j.yexmp.2024.104890] [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: 02/14/2023] [Revised: 01/07/2024] [Accepted: 02/16/2024] [Indexed: 02/22/2024]
Abstract
Alterations in the expression of certain genes could be associated with both patient mortality rates and drug resistance. This study aimed to identify genes in colorectal cancer (CRC) that potentially serve as hub genes influencing patient survival rates. RNA-Seq data were downloaded from the cancer genome atlas database, and differential expression analysis was performed between tumors and healthy controls. Through the utilization of univariate and multivariate Cox regression analyses, in combination with the MCODE clustering module, the genes whose expression changes were related to survival rate and the hub genes related to them were identified. The mortality risk model was computed using the hub genes. CRC samples and the RT-qPCR method were utilized to confirm the outcomes. PharmacoGx data were employed to link the expression of potential genes to medication resistance and sensitivity. The results revealed the discovery of seven hub genes, which emerged as independent prognostic markers. These included HOXC6, HOXC13, HOXC8, and TBX15, which were associated with poor prognosis and overexpression, as well as SDHB, COX5A, and UQCRC1, linked to favorable prognosis and downregulation. Applying the risk model developed with the mentioned genes revealed a markedly higher incidence of deceased patients in the high-risk group compared to the low-risk group. RT-qPCR results indicated a decrease in SDHB expression and an elevation in TBX15 levels in cancer samples relative to adjacent healthy tissue. Also, PharmacoGx data indicated that the expression level of SDHB was correlated with drug sensitivity to Crizotinib and Dovitinib. Our findings highlight the potential association between alterations in the expression of genes such as HOXC6, HOXC13, HOXC8, TBX15, SDHB, COX5A, and UQCRC1 and increased mortality rates in CRC patients. As revealed by the PPI network, these genes exhibited the most connections with other genes linked to survival.
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Affiliation(s)
- Melika Golozar
- Kish International Campus, School of Biology, College of Science, University of Tehran, Tehran, Iran
| | - Ali Valipour Motlagh
- Department of Animal Biotechnology, Cell Science Research Center, Royan Institute for Biotechnology, ACECR, Isfahan 8165131378, Iran
| | - Mohammad Mahdevar
- Genius Gene, Genetics and Biotechnology Company, Tehran, Iran; Department of Genetics and Molecular Biology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Maryam Peymani
- Department of Biology, Faculty of Basic Sciences, Shahrekord Branch, Islamic Azad University, Shahrekord, Iran
| | - Kolsoum InanlooRahatloo
- Kish International Campus, School of Biology, College of Science, University of Tehran, Tehran, Iran
| | - Kamran Ghaedi
- Department of Cell and Molecular Biology and Microbiology, Faculty of Biological Science and Technology, University of Isfahan, Isfahan, Iran.
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5
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Wu G, Zaker A, Ebrahimi A, Tripathi S, Mer AS. Text-mining-based feature selection for anticancer drug response prediction. BIOINFORMATICS ADVANCES 2024; 4:vbae047. [PMID: 38606185 PMCID: PMC11009020 DOI: 10.1093/bioadv/vbae047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 03/09/2024] [Accepted: 03/22/2024] [Indexed: 04/13/2024]
Abstract
Motivation Predicting anticancer treatment response from baseline genomic data is a critical obstacle in personalized medicine. Machine learning methods are commonly used for predicting drug response from gene expression data. In the process of constructing these machine learning models, one of the most significant challenges is identifying appropriate features among a massive number of genes. Results In this study, we utilize features (genes) extracted using the text-mining of scientific literatures. Using two independent cancer pharmacogenomic datasets, we demonstrate that text-mining-based features outperform traditional feature selection techniques in machine learning tasks. In addition, our analysis reveals that text-mining feature-based machine learning models trained on in vitro data also perform well when predicting the response of in vivo cancer models. Our results demonstrate that text-mining-based feature selection is an easy to implement approach that is suitable for building machine learning models for anticancer drug response prediction. Availability and implementation https://github.com/merlab/text_features.
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Affiliation(s)
- Grace Wu
- Division of Engineering Science, University of Toronto, Toronto, M5S2E4, Canada
| | - Arvin Zaker
- Department of Biochemistry, Microbiology & Immunology, University of Ottawa, Ottawa, K1H8M5, Canada
- Ottawa Institute of Systems Biology, University of Ottawa, Ottawa, K1H8M5, Canada
| | - Amirhosein Ebrahimi
- Department of Biochemistry, Microbiology & Immunology, University of Ottawa, Ottawa, K1H8M5, Canada
| | - Shivanshi Tripathi
- Department of Biochemistry, Microbiology & Immunology, University of Ottawa, Ottawa, K1H8M5, Canada
- Ottawa Institute of Systems Biology, University of Ottawa, Ottawa, K1H8M5, Canada
| | - Arvind Singh Mer
- Department of Biochemistry, Microbiology & Immunology, University of Ottawa, Ottawa, K1H8M5, Canada
- Ottawa Institute of Systems Biology, University of Ottawa, Ottawa, K1H8M5, Canada
- School of Electrical Engineering & Computer Science, University of Ottawa, Ottawa, K1N6N5, Canada
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6
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Arribas AJ, Napoli S, Cascione L, Barnabei L, Sartori G, Cannas E, Gaudio E, Tarantelli C, Mensah AA, Spriano F, Zucchetto A, Rossi FM, Rinaldi A, Castro de Moura M, Jovic S, Bordone Pittau R, Stathis A, Stussi G, Gattei V, Brown JR, Esteller M, Zucca E, Rossi D, Bertoni F. ERBB4-Mediated Signaling Is a Mediator of Resistance to PI3K and BTK Inhibitors in B-cell Lymphoid Neoplasms. Mol Cancer Ther 2024; 23:368-380. [PMID: 38052765 DOI: 10.1158/1535-7163.mct-23-0068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 08/28/2023] [Accepted: 10/11/2023] [Indexed: 12/07/2023]
Abstract
BTK and PI3K inhibitors are among the drugs approved for the treatment of patients with lymphoid neoplasms. Although active, their ability to lead to long-lasting complete remission is rather limited, especially in the lymphoma setting. This indicates that tumor cells often develop resistance to the drugs. We started from a marginal zone lymphoma cell line, Karpas-1718, kept under prolonged exposure to the PI3Kδ inhibitor idelalisib until acquisition of resistance, or with no drug. Cells underwent transcriptome, miRNA and methylation profiling, whole-exome sequencing, and pharmacologic screening, which led to the identification of the overexpression of ERBB4 and its ligands HBEGF and NRG2 in the resistant cells. Cellular and genetic experiments demonstrated the involvement of this axis in blocking the antitumor activity of various BTK/PI3K inhibitors, currently used in the clinical setting. Addition of recombinant HBEGF induced resistance to BTK/PI3K inhibitors in parental cells and in additional lymphoma models. Combination with the ERBB inhibitor lapatinib was beneficial in resistant cells and in other lymphoma models already expressing the identified resistance factors. An epigenetic reprogramming sustained the expression of the resistance-related factors, and pretreatment with demethylating agents or EZH2 inhibitors overcame the resistance. Resistance factors were also shown to be expressed in clinical specimens. In conclusion, we showed that the overexpression of ERBB4 and its ligands represents a novel mechanism of resistance for lymphoma cells to bypass the antitumor activity of BTK and PI3K inhibitors and that targeted pharmacologic interventions can restore sensitivity to the small molecules.
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Affiliation(s)
- Alberto J Arribas
- Institute of Oncology Research, Faculty of Biomedical Sciences, USI, Bellinzona, Switzerland
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Sara Napoli
- Institute of Oncology Research, Faculty of Biomedical Sciences, USI, Bellinzona, Switzerland
| | - Luciano Cascione
- Institute of Oncology Research, Faculty of Biomedical Sciences, USI, Bellinzona, Switzerland
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Laura Barnabei
- Institute of Oncology Research, Faculty of Biomedical Sciences, USI, Bellinzona, Switzerland
| | - Giulio Sartori
- Institute of Oncology Research, Faculty of Biomedical Sciences, USI, Bellinzona, Switzerland
| | - Eleonora Cannas
- Institute of Oncology Research, Faculty of Biomedical Sciences, USI, Bellinzona, Switzerland
| | - Eugenio Gaudio
- Institute of Oncology Research, Faculty of Biomedical Sciences, USI, Bellinzona, Switzerland
| | - Chiara Tarantelli
- Institute of Oncology Research, Faculty of Biomedical Sciences, USI, Bellinzona, Switzerland
| | - Afua A Mensah
- Institute of Oncology Research, Faculty of Biomedical Sciences, USI, Bellinzona, Switzerland
| | - Filippo Spriano
- Institute of Oncology Research, Faculty of Biomedical Sciences, USI, Bellinzona, Switzerland
| | | | | | - Andrea Rinaldi
- Institute of Oncology Research, Faculty of Biomedical Sciences, USI, Bellinzona, Switzerland
| | - Manuel Castro de Moura
- Josep Carreras Leukaemia Research Institute (IJC), Badalona, Barcelona, Catalonia, Spain
| | - Sandra Jovic
- Institute for Research in Biomedicine, Università della Svizzera italiana, Bellinzona, Switzerland
| | | | - Anastasios Stathis
- Oncology Institute of Southern Switzerland, Bellinzona, Switzerland
- Faculty of Biomedical Sciences, USI, Bellinzona, Switzerland
| | - Georg Stussi
- Oncology Institute of Southern Switzerland, Bellinzona, Switzerland
| | - Valter Gattei
- Centro di Riferimento Oncologico di Aviano - CRO, Aviano, Italy
| | - Jennifer R Brown
- Chronic Lymphocytic Leukemia Center, Division of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, Massachusetts
| | - Manel Esteller
- Josep Carreras Leukaemia Research Institute (IJC), Badalona, Barcelona, Catalonia, Spain
- Centro de Investigacion Biomedica en Red Cancer (CIBERONC), Madrid, Spain
- Institucio Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Catalonia, Spain
- Physiological Sciences Department, School of Medicine and Health Sciences, University of Barcelona (UB), Barcelona, Catalonia, Spain
| | - Emanuele Zucca
- Institute of Oncology Research, Faculty of Biomedical Sciences, USI, Bellinzona, Switzerland
- Oncology Institute of Southern Switzerland, Bellinzona, Switzerland
| | - Davide Rossi
- Institute of Oncology Research, Faculty of Biomedical Sciences, USI, Bellinzona, Switzerland
- Oncology Institute of Southern Switzerland, Bellinzona, Switzerland
| | - Francesco Bertoni
- Institute of Oncology Research, Faculty of Biomedical Sciences, USI, Bellinzona, Switzerland
- Oncology Institute of Southern Switzerland, Bellinzona, Switzerland
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7
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Yakavets I, Ayachit M, Kheiri S, Chen Z, Rakhshani F, McWhirter S, Young EWK, Walker GC, Kumacheva E. A Microfluidic Platform for Evaluating the Internalization of Liposome Drug Carriers in Tumor Spheroids. ACS APPLIED MATERIALS & INTERFACES 2024; 16:9690-9701. [PMID: 38357740 DOI: 10.1021/acsami.3c16330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/16/2024]
Abstract
The development of in vitro models recapitulating nanoparticle transport under physiological flow conditions is of great importance for predicting the efficacy of nanoparticle drug carriers. Liposomes are extensively used for drug delivery owing to their biocompatibility and biodegradability and the ability to carry both hydrophilic and hydrophobic compounds. Here, we used a library of liposomes with various dimensions and a microfluidic platform comprising a large array of uniformly sized breast cancer spheroids to explore size-dependent liposome internalization and retention in the spheroids under close-to-physiological interstitial conditions. Such a platform showed promising applications in the preclinical screening of small molecule drugs; however, the capability to deliver nanoparticles in the spheroid interior under close-to-physiological flow conditions was not explored. For the liposomes with diameters in the range of 45-200 nm, we show experimentally and by simulations that in comparison with liposome delivery solely by diffusion, flow significantly enhances liposome internalization in the microgels and mitigates the size-dependent spheroid penetration by the liposomes. The utility of the microfluidic platform was validated by evaluating the efficacy of clinically approved doxorubicin-loaded liposomes (Doxil), which exhibited superior retention in the spheroids under flow conditions, in comparison with free doxorubicin. This MF platform can serve as an in vitro model for screening the efficacy of drugs encapsulated in liposomes and find applications for screening other types of nanoparticle carriers for vaccine delivery, diagnostics, and skincare.
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Affiliation(s)
- Ilya Yakavets
- Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, Ontario M5S 3H6, Canada
| | - Monica Ayachit
- Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, Ontario M5S 3H6, Canada
- Department of Chemistry, Queen's University, Kingston, Ontario K7L 3N6, Canada
| | - Sina Kheiri
- Department of Mechanical & Industrial Engineering, University of Toronto, 5 King's College Road, Toronto, Ontario M5S 3G8, Canada
| | - Zhengkun Chen
- Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, Ontario M5S 3H6, Canada
| | - Faeze Rakhshani
- Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, Ontario M5S 3H6, Canada
| | - Samantha McWhirter
- Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, Ontario M5S 3H6, Canada
| | - Edmond W K Young
- Department of Mechanical & Industrial Engineering, University of Toronto, 5 King's College Road, Toronto, Ontario M5S 3G8, Canada
- Institute of Biomedical Engineering, University of Toronto, 164 College Street, Toronto, Ontario M5S 3G9, Canada
| | - Gilbert C Walker
- Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, Ontario M5S 3H6, Canada
| | - Eugenia Kumacheva
- Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, Ontario M5S 3H6, Canada
- Institute of Biomedical Engineering, University of Toronto, 164 College Street, Toronto, Ontario M5S 3G9, Canada
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, 200 College Street, Toronto, Ontario M5S 3E5, Canada
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8
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Dai J, Zheng S, Falco MM, Bao J, Eriksson J, Pikkusaari S, Forstén S, Jiang J, Wang W, Gao L, Perez-Villatoro F, Dufva O, Saeed K, Wang Y, Amiryousefi A, Färkkilä A, Mustjoki S, Kauppi L, Tang J, Vähärautio A. Tracing back primed resistance in cancer via sister cells. Nat Commun 2024; 15:1158. [PMID: 38326354 PMCID: PMC10850087 DOI: 10.1038/s41467-024-45478-7] [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: 04/25/2023] [Accepted: 01/24/2024] [Indexed: 02/09/2024] Open
Abstract
Exploring non-genetic evolution of cell states during cancer treatments has become attainable by recent advances in lineage-tracing methods. However, transcriptional changes that drive cells into resistant fates may be subtle, necessitating high resolution analysis. Here, we present ReSisTrace that uses shared transcriptomic features of sister cells to predict the states priming treatment resistance. Applying ReSisTrace in ovarian cancer cells perturbed with olaparib, carboplatin or natural killer (NK) cells reveals pre-resistant phenotypes defined by proteostatic and mRNA surveillance features, reflecting traits enriched in the upcoming subclonal selection. Furthermore, we show that DNA repair deficiency renders cells susceptible to both DNA damaging agents and NK killing in a context-dependent manner. Finally, we leverage the obtained pre-resistance profiles to predict and validate small molecules driving cells to sensitive states prior to treatment. In summary, ReSisTrace resolves pre-existing transcriptional features of treatment vulnerability, facilitating both molecular patient stratification and discovery of synergistic pre-sensitizing therapies.
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Affiliation(s)
- Jun Dai
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Shuyu Zheng
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Matías M Falco
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Jie Bao
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Johanna Eriksson
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Sanna Pikkusaari
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Sofia Forstén
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Jing Jiang
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Wenyu Wang
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Luping Gao
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Fernando Perez-Villatoro
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- iCAN Digital Precision Cancer Medicine, Helsinki, Finland
| | - Olli Dufva
- Research Program in Translational Immunology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Khalid Saeed
- Research Program in Translational Immunology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Yinyin Wang
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Ali Amiryousefi
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Anniina Färkkilä
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- iCAN Digital Precision Cancer Medicine, Helsinki, Finland
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Sciences, University of Helsinki, Helsinki, Finland
- Department of Obstetrics and Gynecology, and Clinical Trial Unit, Comprehensive Cancer Centre, Helsinki University Hospital, Helsinki, Finland
| | - Satu Mustjoki
- Research Program in Translational Immunology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Liisa Kauppi
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Jing Tang
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
| | - Anna Vähärautio
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
- Foundation for the Finnish Cancer Institute, Helsinki, Finland.
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9
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Takamatsu S, Murakami K, Matsumura N. Homologous Recombination Deficiency Unrelated to Platinum and PARP Inhibitor Response in Cell Line Libraries. Sci Data 2024; 11:171. [PMID: 38321018 PMCID: PMC10847511 DOI: 10.1038/s41597-024-03018-4] [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: 10/16/2023] [Accepted: 01/29/2024] [Indexed: 02/08/2024] Open
Abstract
While large publicly available cancer cell line databases are invaluable for preclinical drug discovery and biomarker development, the association between homologous recombination deficiency (HRD) and drug sensitivity in these resources remains unclear. In this study, we comprehensively analyzed molecular profiles and drug screening data from the Cancer Cell Line Encyclopedia. Unexpectedly, gene alterations in BRCA1/2 or homologous recombination-related genes, HRD scores, or mutational signature 3 were not positively correlated with sensitivity to platinum agents or PARP inhibitors. Rather, higher HRD scores and mutational signature 3 were significantly associated with resistance to these agents in multiple assays. These findings were consistent when analyzing exclusively breast and ovarian cancer cell lines and when using data from the COSMIC Cell Line Project. Collectively, the existing data from established cancer cell lines do not reflect the expected association between HRD status and drug response to platinum agents and PARP inhibitors in clinical tumors. This discrepancy may extend to other tumor characteristics, highlighting the importance of recognizing potential limitations in cell line data for researchers.
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Affiliation(s)
- Shiro Takamatsu
- Department of Gynecology and Obstetrics, Kyoto University Graduate School of Medicine, Kyoto, Kyoto, Japan
- Department of Genomic Medicine, the University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Kosuke Murakami
- Department of Obstetrics and Gynecology, Kindai University Faculty of Medicine, Osaka-Sayama, Osaka, Japan
- The Bloomberg~Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University, Baltimore, Maryland, USA
| | - Noriomi Matsumura
- Department of Obstetrics and Gynecology, Kindai University Faculty of Medicine, Osaka-Sayama, Osaka, Japan.
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10
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Wang Y, Ma X, Xu E, Huang Z, Yang C, Zhu K, Dong Y, Zhang C. Identifying squalene epoxidase as a metabolic vulnerability in high-risk osteosarcoma using an artificial intelligence-derived prognostic index. Clin Transl Med 2024; 14:e1586. [PMID: 38372422 PMCID: PMC10875711 DOI: 10.1002/ctm2.1586] [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: 11/10/2023] [Revised: 01/24/2024] [Accepted: 01/31/2024] [Indexed: 02/20/2024] Open
Abstract
BACKGROUND Osteosarcoma (OSA) presents a clinical challenge and has a low 5-year survival rate. Currently, the lack of advanced stratification models makes personalized therapy difficult. This study aims to identify novel biomarkers to stratify high-risk OSA patients and guide treatment. METHODS We combined 10 machine-learning algorithms into 101 combinations, from which the optimal model was established for predicting overall survival based on transcriptomic profiles for 254 samples. Alterations in transcriptomic, genomic and epigenomic landscapes were assessed to elucidate mechanisms driving poor prognosis. Single-cell RNA sequencing (scRNA-seq) unveiled genes overexpressed in OSA cells as potential therapeutic targets, one of which was validated via tissue staining, knockdown and pharmacological inhibition. We characterized changes in multiple phenotypes, including proliferation, colony formation, migration, invasion, apoptosis, chemosensitivity and in vivo tumourigenicity. RNA-seq and Western blotting elucidated the impact of squalene epoxidase (SQLE) suppression on signalling pathways. RESULTS The artificial intelligence-derived prognostic index (AIDPI), generated by our model, was an independent prognostic biomarker, outperforming clinicopathological factors and previously published signatures. Incorporating the AIDPI with clinical factors into a nomogram improved predictive accuracy. For user convenience, both the model and nomogram are accessible online. Patients in the high-AIDPI group exhibited chemoresistance, coupled with overexpression of MYC and SQLE, increased mTORC1 signalling, disrupted PI3K-Akt signalling, and diminished immune infiltration. ScRNA-seq revealed high expression of MYC and SQLE in OSA cells. Elevated SQLE expression correlated with chemoresistance and worse outcomes in OSA patients. Therapeutically, silencing SQLE suppressed OSA malignancy and enhanced chemosensitivity, mediated by cholesterol depletion and suppression of the FAK/PI3K/Akt/mTOR pathway. Furthermore, the SQLE-specific inhibitor FR194738 demonstrated anti-OSA effects in vivo and exhibited synergistic effects with chemotherapeutic agents. CONCLUSIONS AIDPI is a robust biomarker for identifying the high-risk subset of OSA patients. The SQLE protein emerges as a metabolic vulnerability in these patients, providing a target with translational potential.
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Affiliation(s)
- Yongjie Wang
- Department of Orthopaedic SurgeryShanghai Tenth People's Hospital, School of Medicine, Tongji UniversityShanghaiP. R. China
- Institute of Bone Tumor Affiliated to Tongji University School of MedicineShanghaiP. R. China
- Proteomics and Cancer Cell Signaling Group, German Cancer Research Center (DKFZ)HeidelbergGermany
| | - Xiaolong Ma
- Department of Orthopaedic SurgeryShanghai Tenth People's Hospital, School of Medicine, Tongji UniversityShanghaiP. R. China
- Institute of Bone Tumor Affiliated to Tongji University School of MedicineShanghaiP. R. China
| | - Enjie Xu
- Department of Orthopaedic SurgeryShanghai Tenth People's Hospital, School of Medicine, Tongji UniversityShanghaiP. R. China
- Institute of Bone Tumor Affiliated to Tongji University School of MedicineShanghaiP. R. China
| | - Zhen Huang
- Department of Orthopaedic SurgeryShanghai Tenth People's Hospital, School of Medicine, Tongji UniversityShanghaiP. R. China
- Institute of Bone Tumor Affiliated to Tongji University School of MedicineShanghaiP. R. China
| | - Chen Yang
- State Key Laboratory of Oncogenes and Related GenesShanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of MedicineShanghaiP. R. China
| | - Kunpeng Zhu
- Department of Orthopaedic SurgeryShanghai Tenth People's Hospital, School of Medicine, Tongji UniversityShanghaiP. R. China
- Institute of Bone Tumor Affiliated to Tongji University School of MedicineShanghaiP. R. China
| | - Yang Dong
- Department of OrthopaedicsShanghai Jiao Tong University Affiliated Sixth People's HospitalShanghai Jiao Tong UniversityShanghaiP. R. China
| | - Chunlin Zhang
- Department of Orthopaedic SurgeryShanghai Tenth People's Hospital, School of Medicine, Tongji UniversityShanghaiP. R. China
- Institute of Bone Tumor Affiliated to Tongji University School of MedicineShanghaiP. R. China
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Baek B, Jang E, Park S, Park SH, Williams DR, Jung DW, Lee H. Integrated drug response prediction models pinpoint repurposed drugs with effectiveness against rhabdomyosarcoma. PLoS One 2024; 19:e0295629. [PMID: 38277404 PMCID: PMC10817174 DOI: 10.1371/journal.pone.0295629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 11/24/2023] [Indexed: 01/28/2024] Open
Abstract
Targeted therapies for inhibiting the growth of cancer cells or inducing apoptosis are urgently needed for effective rhabdomyosarcoma (RMS) treatment. However, identifying cancer-targeting compounds with few side effects, among the many potential compounds, is expensive and time-consuming. A computational approach to reduce the number of potential candidate drugs can facilitate the discovery of attractive lead compounds. To address this and obtain reliable predictions of novel cell-line-specific drugs, we apply prediction models that have the potential to improve drug discovery approaches for RMS treatment. The results of two prediction models were ensemble and validated via in vitro experiments. The computational models were trained using data extracted from the Genomics of Drug Sensitivity in Cancer database and tested on two RMS cell lines to select potential RMS drug candidates. Among 235 candidate drugs, 22 were selected following the result of the computational approach, and three candidate drugs were identified (NSC207895, vorinostat, and belinostat) that showed selective effectiveness in RMS cell lines in vitro via the induction of apoptosis. Our in vitro experiments have demonstrated that our proposed methods can effectively identify and repurpose drugs for treating RMS.
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Affiliation(s)
- Bin Baek
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea
| | - Eunmi Jang
- School of Life Sciences, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea
| | - Sejin Park
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea
| | - Sung-Hye Park
- Department of Pathology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea
- Institute of Neuroscience, Seoul National University Hospital, Seoul, Republic of Korea
| | - Darren Reece Williams
- School of Life Sciences, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea
| | - Da-Woon Jung
- School of Life Sciences, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea
| | - Hyunju Lee
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea
- Artificial Intelligence Graduate School, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea
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12
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Yang Y, Teng H, Zhang Y, Wang F, Tang L, Zhang C, Hu Z, Chen Y, Ge Y, Wang Z, Yu Y. A glycosylation-related gene signature predicts prognosis, immune microenvironment infiltration, and drug sensitivity in glioma. Front Pharmacol 2024; 14:1259051. [PMID: 38293671 PMCID: PMC10824914 DOI: 10.3389/fphar.2023.1259051] [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: 07/15/2023] [Accepted: 12/11/2023] [Indexed: 02/01/2024] Open
Abstract
Glioma represents the most common primary cancer of the central nervous system in adults. Glycosylation is a prevalent post-translational modification that occurs in eukaryotic cells, leading to a wide array of modifications on proteins. We obtained the clinical information, bulk RNA-seq data, and single-cell RNA sequencing (scRNA-seq) from The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), Gene Expression Omnibus (GEO), and Repository of Molecular Brain Neoplasia Data (Rembrandt) databases. RNA sequencing data for normal brain tissues were accessed from the Genotype-Tissue Expression (GTEx) database. Then, the glycosylation genes that were differentially expressed were identified and further subjected to variable selection using a least absolute shrinkage and selection operator (LASSO)-regularized Cox model. We further conducted enrichment analysis, qPCR, nomogram, and single-cell transcriptome to detect the glycosylation signature. Drug sensitivity analysis was also conducted. A five-gene glycosylation signature (CHPF2, PYGL, GALNT13, EXT2, and COLGALT2) classified patients into low- or high-risk groups. Survival analysis, qPCR, ROC curves, and stratified analysis revealed worse outcomes in the high-risk group. Furthermore, GSEA and immune infiltration analysis indicated that the glycosylation signature has the potential to predict the immune response in glioma. In addition, four drugs (crizotinib, lapatinib, nilotinib, and topotecan) showed different responses between the two risk groups. Glioma cells had been classified into seven lines based on single-cell expression profiles. The five-gene glycosylation signature can accurately predict the prognosis of glioma and may offer additional guidance for immunotherapy.
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Affiliation(s)
- Yanbo Yang
- China-Japan Friendship Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Haiying Teng
- Department of Neurosurgery and Brain and Nerve Research Laboratory, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
- Suzhou Medical College of Soochow University, Suzhou, Jiangsu, China
| | - Yulian Zhang
- Department of Neurosurgery, China-Japan Friendship Hospital, Beijing, China
| | - Fei Wang
- Department of Neurosurgery and Brain and Nerve Research Laboratory, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
- Suzhou Medical College of Soochow University, Suzhou, Jiangsu, China
| | - Liyan Tang
- Department of Neurosurgery and Brain and Nerve Research Laboratory, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Chuanpeng Zhang
- Department of Neurosurgery, China-Japan Friendship Hospital, Beijing, China
- Department of Neurosurgery, Peking University China-Japan Friendship School of Clinical Medicine, Beijing, China
| | - Ziyi Hu
- Department of Neurosurgery and Brain and Nerve Research Laboratory, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
- Suzhou Medical College of Soochow University, Suzhou, Jiangsu, China
| | - Yuxuan Chen
- Department of Neurosurgery and Brain and Nerve Research Laboratory, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
- Suzhou Medical College of Soochow University, Suzhou, Jiangsu, China
| | - Yi Ge
- The Affiliated Changzhou Second People’s Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Zhong Wang
- Department of Neurosurgery and Brain and Nerve Research Laboratory, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Yanbing Yu
- China-Japan Friendship Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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13
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Singh S, Kaushik AC, Gupta H, Jhinjharia D, Sahi S. Identification of Prognostic Markers and Potential Therapeutic Targets using Gene Expression Profiling and Simulation Studies in Pancreatic Cancer. Curr Comput Aided Drug Des 2024; 20:955-973. [PMID: 37711100 DOI: 10.2174/1573409920666230914100826] [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: 04/28/2023] [Revised: 07/07/2023] [Accepted: 08/08/2023] [Indexed: 09/16/2023]
Abstract
BACKGROUND Pancreatic ductal adenocarcinoma (PDAC) has a 5-year relative survival rate of less than 10% making it one of the most fatal cancers. A lack of early measures of prognosis, challenges in molecular targeted therapy, ineffective adjuvant chemotherapy, and strong resistance to chemotherapy cumulatively make pancreatic cancer challenging to manage. OBJECTIVE The present study aims to enhance understanding of the disease mechanism and its progression by identifying prognostic biomarkers, potential drug targets, and candidate drugs that can be used for therapy in pancreatic cancer. METHODS Gene expression profiles from the GEO database were analyzed to identify reliable prognostic markers and potential drug targets. The disease's molecular mechanism and biological pathways were studied by investigating gene ontologies, KEGG pathways, and survival analysis to understand the strong prognostic power of key DEGs. FDA-approved anti-cancer drugs were screened through cell line databases, and docking studies were performed to identify drugs with high affinity for ARNTL2 and PIK3C2A. Molecular dynamic simulations of drug targets ARNTL2 and PIK3C2A in their native state and complex with nilotinib were carried out for 100 ns to validate their therapeutic potential in PDAC. RESULTS Differentially expressed genes that are crucial regulators, including SUN1, PSMG3, PIK3C2A, SCRN1, and TRIAP1, were identified. Nilotinib as a candidate drug was screened using sensitivity analysis on CCLE and GDSC pancreatic cancer cell lines. Molecular dynamics simulations revealed the underlying mechanism of the binding of nilotinib with ARNTL2 and PIK3C2A and the dynamic perturbations. It validated nilotinib as a promising drug for pancreatic cancer. CONCLUSION This study accounts for prognostic markers, drug targets, and repurposed anti-cancer drugs to highlight their usefulness for translational research on developing novel therapies. Our results revealed potential and prospective clinical applications in drug targets ARNTL2, EGFR, and PI3KC2A for pancreatic cancer therapy.
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Affiliation(s)
- Samvedna Singh
- School of Biotechnology, Gautam Buddha University, Greater Noida, India
| | | | - Himanshi Gupta
- School of Biotechnology, Gautam Buddha University, Greater Noida, India
| | - Divya Jhinjharia
- School of Biotechnology, Gautam Buddha University, Greater Noida, India
| | - Shakti Sahi
- School of Biotechnology, Gautam Buddha University, Greater Noida, India
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14
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Zimmerman JAO, Fang M, Pufall MA. PI3Kδ Inhibition Potentiates Glucocorticoids in B-lymphoblastic Leukemia by Decreasing Receptor Phosphorylation and Enhancing Gene Regulation. Cancers (Basel) 2023; 16:143. [PMID: 38201570 PMCID: PMC10778422 DOI: 10.3390/cancers16010143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 12/14/2023] [Accepted: 12/22/2023] [Indexed: 01/12/2024] Open
Abstract
Glucocorticoids are the cornerstone of B-lymphoblastic leukemia (B-ALL) therapy. Because response to glucocorticoids alone predicts overall outcomes for B-ALL, enhancing glucocorticoid potency should improve treatment. We previously showed that inhibition of the lymphoid-restricted PI3Kδ with idelalisib enhances glucocorticoid activity in B-ALL cells. Here, we show that idelalisib enhances glucocorticoid potency in 90% of primary B-ALL specimens and is most pronounced at sub-saturating doses of glucocorticoids near the EC50. Potentiation is associated with enhanced regulation of all glucocorticoid-regulated genes, including genes that drive B-ALL cell death. Idelalisib reduces phosphorylation of the glucocorticoid receptor (GR) at PI3Kδ/MAPK1 (ERK2) targets S203 and S226. Ablation of these phospho-acceptor sites enhances sensitivity to glucocorticoids with ablation of S226 in particular reducing synergy. We also show that phosphorylation of S226 reduces the affinity of GR for DNA in vitro. We propose that PI3Kδ inhibition improves glucocorticoid efficacy in B-ALL in part by decreasing GR phosphorylation, increasing DNA binding affinity, and enhancing downstream gene regulation. This mechanism and the response of patient specimens suggest that idelalisib will benefit most patients with B-ALL, but particularly patients with less responsive, including high-risk, disease. This combination is also promising for the development of less toxic glucocorticoid-sparing therapies.
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Affiliation(s)
- Jessica A. O. Zimmerman
- Division of Pediatric Hematology/Oncology, Stead Family Department of Pediatrics, Carver College of Medicine, University of Iowa, Iowa City, IA 52242, USA;
- Holden Comprehensive Cancer Center, University of Iowa, Iowa City, IA 52242, USA;
| | - Mimi Fang
- Holden Comprehensive Cancer Center, University of Iowa, Iowa City, IA 52242, USA;
- Department of Biochemistry and Molecular Biology, Carver College of Medicine, University of Iowa, Iowa City, IA 52242, USA
| | - Miles A. Pufall
- Holden Comprehensive Cancer Center, University of Iowa, Iowa City, IA 52242, USA;
- Department of Biochemistry and Molecular Biology, Carver College of Medicine, University of Iowa, Iowa City, IA 52242, USA
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15
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Wolf Á, Romeder-Finger S, Széll K, Galambos P. WITHDRAWN: Towards robotic laboratory automation Plug & play: Survey and concept proposal on teaching-free robot integration with the LAPP digital twin. SLAS DISCOVERY : ADVANCING LIFE SCIENCES R & D 2023; 29:132. [PMID: 38101573 DOI: 10.1016/j.slasd.2023.12.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 12/19/2022] [Accepted: 01/10/2023] [Indexed: 12/17/2023]
Affiliation(s)
- Ádám Wolf
- Takeda Manufacturing Austria AG, Industriestraße 67, Wien, A-1221, Austria; Doctoral School of Applied Informatics and Applied Mathematics, Óbuda University, Baxalta Innovations GmbH, a Takeda Company, Austria.
| | | | - Károly Széll
- Alba Regia Technical Faculty, Óbuda University, H-8000, Székesfehérvár, Hungary
| | - Péter Galambos
- Antal Bejczy Center for Intelligent Robotics, Óbuda University, Hungary
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16
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Bayer FP, Gander M, Kuster B, The M. CurveCurator: a recalibrated F-statistic to assess, classify, and explore significance of dose-response curves. Nat Commun 2023; 14:7902. [PMID: 38036588 PMCID: PMC10689459 DOI: 10.1038/s41467-023-43696-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 11/16/2023] [Indexed: 12/02/2023] Open
Abstract
Dose-response curves are key metrics in pharmacology and biology to assess phenotypic or molecular actions of bioactive compounds in a quantitative fashion. Yet, it is often unclear whether or not a measured response significantly differs from a curve without regulation, particularly in high-throughput applications or unstable assays. Treating potency and effect size estimates from random and true curves with the same level of confidence can lead to incorrect hypotheses and issues in training machine learning models. Here, we present CurveCurator, an open-source software that provides reliable dose-response characteristics by computing p-values and false discovery rates based on a recalibrated F-statistic and a target-decoy procedure that considers dataset-specific effect size distributions. The application of CurveCurator to three large-scale datasets enables a systematic drug mode of action analysis and demonstrates its scalable utility across several application areas, facilitated by a performant, interactive dashboard for fast data exploration.
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Affiliation(s)
- Florian P Bayer
- Proteomics and Bioanalytics, School of Life Sciences, Technical University of Munich, 85354, Freising, Germany
| | - Manuel Gander
- Proteomics and Bioanalytics, School of Life Sciences, Technical University of Munich, 85354, Freising, Germany
| | - Bernhard Kuster
- Proteomics and Bioanalytics, School of Life Sciences, Technical University of Munich, 85354, Freising, Germany
- German Cancer Consortium (DKTK), Partner Site Munich, 80336, Munich, Germany
| | - Matthew The
- Proteomics and Bioanalytics, School of Life Sciences, Technical University of Munich, 85354, Freising, Germany.
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17
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Arribas AJ, Gaudio E, Napoli S, Yvon Herbaux CJ, Tarantelli C, Bordone RP, Cascione L, Munz N, Aresu L, Sgrignani J, Rinaldi A, Kwee I, Rossi D, Cavalli A, Zucca E, Stussi G, Stathis A, Sloss C, Davids MS, Bertoni F. PI3Kδ activation, IL6 over-expression, and CD37 loss cause resistance to the targeting of CD37-positive lymphomas with the antibody-drug conjugate naratuximab emtansine. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.14.566994. [PMID: 38014209 PMCID: PMC10680772 DOI: 10.1101/2023.11.14.566994] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Purpose The transmembrane protein CD37 is expressed almost exclusively in lymphoid tissues, with the highest abundance in mature B cells. CD37-directed antibody- and, more recently, cellular-based approaches have shown preclinical and promising early clinical activity. Naratuximab emtansine (Debio 1562, IMGN529) is an antibodydrug conjugate (ADC) that incorporates an anti-CD37 monoclonal antibody conjugated to the maytansinoid DM1 as payload. Naratuximab emtansine has shown activity as a single agent and in combination with the anti-CD20 monoclonal antibody rituximab in B cell lymphoma patients. Experimental Design We assessed the activity of naratuximab emtansine using in vitro models of lymphomas, correlated its activity with CD37 expression levels, characterized two resistance mechanisms to the ADC, and identified combination partners providing synergy. Results The anti-tumor activity of naratuximab emtansine was tested in 54 lymphoma cell lines alongside its free payload. The median IC 50 of naratuximab emtansine was 780 pM, and the activity, primarily cytotoxic, was more potent in B than in T cell lymphoma cell lines. In the subgroup of cell lines derived from B cell lymphoma, there was some correlation between sensitivity to DM1 and sensitivity to naratuximab emtansine (r=0.28, P = 0.06). After prolonged exposure to the ADC, one diffuse large B cell lymphoma (DLBCL) cell line developed resistance to the ADC due to the biallelic loss of the CD37 gene. After CD37 loss, we also observed upregulation of IL6 (IL-6) and other transcripts from MYD88/IL6-signaling. Recombinant IL6 led to resistance to naratuximab emtansine, while the anti-IL6 antibody tocilizumab improved the cytotoxic activity of the ADC in CD37-positive cells. In a second model, resistance was sustained by an activating mutation in the PIK3CD gene, associated with increased sensitivity to PI3K δ inhibition and a switch from functional dependence on the anti-apoptotic protein MCL1 to reliance on BCL2. The addition of idelalisib or venetoclax to naratuximab emtansine overcame resistance to the ADC in the resistant derivative while also improving the cytotoxic activity of the ADC in the parental cells. Conclusions Targeting B cell lymphoma with the CD37 targeting ADC naratuximab emtansine showed vigorous anti-tumor activity as a single agent, which was also observed in models bearing genetic lesions associated with inferior outcomes, such as MYC translocations and TP53 inactivation or resistance to R-CHOP. Resistance DLBCL models identified active combinations of naratuximab emtansine with drugs targeting IL6, PI3K δ , and BCL2. Despite notable progress in recent decades, we still face challenges in achieving a cure for a substantial number of lymphoma patients (1,2). A pertinent example is diffuse large B cell lymphoma (DLBCL), the most prevalent type of lymphoma (3). More than half of DLBCL patients can achieve remission, but around 40% of them experience refractory disease or relapse following an initial positive response (3). Regrettably, the prognosis for many of these cases remains unsatisfactory despite introducing the most recent antibody-based or cellular therapies (3,4), underscoring the importance of innovating new therapeutic strategies and gaining insights into the mechanisms of therapy resistance. CD37 is a transmembrane glycoprotein belonging to the tetraspanin family, primarily expressed on the surface of immune cells, principally in mature B cells but also, at lower levels, in T cells, macrophages/monocytes, granulocytes and dendritic cells (5) (6-8). CD37 plays a crucial role in various immune functions, including B cell activation, proliferation, and signaling, although its precise role still needs to be fully elucidated. CD37 interacts with multiple molecules, including SYK, LYN, CD19, CD22, PI3K δ , PI3K γ , and different integrins, among others (6-8). In mice, the lack of CD37 is paired with reduced T cell-dependent antibody-secreting cells and memory B cells, apparently due to the loss of CD37-mediated clustering of α 4 β 1 integrins (VLA-4) on germinal center B cells and decreased downstream activation of PI3K/AKT signaling and cell survival (5). Reflecting the expression pattern observed in normal lymphocytes, CD37 exhibits elevated expression in all mature B-cell lymphoid neoplasms, including most lymphoma subtypes, and absence in early progenitor cells or terminally differentiated plasma cells (6,8-14). In DLBCL, CD37 expression has been reported between 40% and 90% of cases across multiple studies performed using different antibodies (10,14-16). CD37-directed antibody- and, more recently, cellular-based approaches have shown preclinical (7,10-14,17-23) and early promising clinical activity (24-32). Among the CD37-targeting agents, naratuximab emtansine (Debio 1562, IMGN529) is an antibody-drug conjugate (ADC) that incorporates the anti-CD37 humanized IgG1 monoclonal antibody K7153A conjugated to the maytansinoid DM1, as payload, via the thioether linker, N-succinimidyl-4-(N-maleimidomethyl)cyclohexane-1-carboxylate (SMCC) (10). Based on the initial in vitro and in vivo evidence of anti-tumor activity in lymphoma and chronic lymphocytic leukemia (CLL) (7,10), naratuximab emtansine entered the clinical evaluation as a single agent. The phase 1 study exploring naratuximab emtansine enrolled 39 patients with relapsed/refractory B cell lymphoma (27). The overall response rate (ORR) was 13% across all patients and 22% in DLBCL patients, including the only observed complete remission (CR) (27). In preliminary results of a phase 2 trial exploring the combination of naratuximab emtansine with the anti-CD20 monoclonal antibody rituximab (18), based on positive preclinical data (18), the ORR was 45% in 76 patients with DLBCL with 24 CRs (32%), 57% in 14 patients with follicular lymphoma (five CR), 50% in four MCL patients (2 CR) (31). Here, we studied the pattern of activity of naratuximab emtansine across a large panel of cell lines derived from DLBCL and other lymphoma subtypes and characterized two resistance mechanisms to the ADC.
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18
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Yang Y, Wang F, Teng H, Zhang C, Zhang Y, Chen P, Li Q, Kan X, Chen Z, Wang Z, Yu Y. Integrative analysis of multi-omics data reveals a pseudouridine-related lncRNA signature for prediction of glioma prognosis and chemoradiotherapy sensitivity. Comput Biol Med 2023; 166:107428. [PMID: 37748218 DOI: 10.1016/j.compbiomed.2023.107428] [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: 05/06/2023] [Revised: 07/12/2023] [Accepted: 08/28/2023] [Indexed: 09/27/2023]
Abstract
BACKGROUND Glioblastoma is the most common type of glioma with a high incidence and poor prognosis, and effective medical treatment remains challenging. Pseudouridine (Ψ) is the first post-transcriptional modification discovered and one of the most abundant modifications to RNA. However, the prognostic value of Ψ-related lncRNAs (ΨrLs) for glioma patients has never been systematically evaluated. This study aims to construct a risk model based on ΨrLs signature and to validate the predictive efficiency of the model. METHOD Transcriptomic data, genomic data, and relevant clinical data of glioma patients were extracted from the Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). ΨrLs with significant correlation with Ψ-related genes were identified, and univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression, and multivariate Cox regression were used to further select biomarkers and construct a ΨrLs signature risk model. Then, the expression of lncRNAs of ΨrLs signature in multiple glioma cell lines was detected by qPCR. Further, ROC analysis, stratification analysis, correlation analysis, survival analysis, nomogram, enrichment analysis, immune infiltration analysis, chemoradiotherapy sensitivity analysis, somatic mutation, and recurrent copy number variation (CNV) analysis were used to validate the predictive efficiency of ΨrLs signature in TCGA and CGGA datasets. RESULTS A four-lncRNA ΨrLs signature (DNAJC27-AS1, GDNF-AS1, ZBTB20-AS4, and DNMBP-AS1) risk model was constructed. By ROC analysis, stratified analysis, correlation analysis, survival analysis, and nomogram, the signature showed satisfactory predictive efficiency. Functional enrichment analysis revealed the differences in immune-related biological processes between high- and low-risk groups. Immune infiltration analysis showed that the high-risk group had lower tumor purity and higher stromal, immune and ESTIMATE scores. Mitoxantrone was identified as effective drug for low-risk group of glioma patients. Key genes in glioma development, including IDH1, EGFR, PTEN, etc., were differentially mutated between risk groups. The main recurrent CNVs in low-risk groups were 19q13.42 deletion and 7q34 amplification; 10q23.31 deletion and 12q14.1 in the high-risk group. CONCLUSIONS Our study identified a four-lncRNA ΨrLs signature that effectively predicts the prognosis of glioma patients and may serve as a diagnostic tool. Risk scores of glioma patients generated by the signature is associated with immune-related biological processes and chemoradiotherapy sensitivity. These findings may inform the development of more targeted and effective therapies for glioma patients.
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Affiliation(s)
- Yanbo Yang
- China-Japan Friendship Hospital (Institute of Clinical Medical Sciences), Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100000, China.
| | - Fei Wang
- Department of Neurosurgery, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, 215400, China; Suzhou Medical College of Soochow University, Suzhou, Jiangsu Province, 215127, China.
| | - Haiying Teng
- Department of Neurosurgery, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, 215400, China.
| | - Chuanpeng Zhang
- Department of Neurosurgery, Peking University China-Japan Friendship School of Clinical Medicine, Beijing, China.
| | - Yulian Zhang
- Department of Neurosurgery, China-Japan Friendship Hospital, Beijing, China.
| | - Pengyu Chen
- China-Japan Friendship Hospital (Institute of Clinical Medical Sciences), Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100000, China.
| | - Quan Li
- Department of Neurosurgery, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, 215400, China; Suzhou Medical College of Soochow University, Suzhou, Jiangsu Province, 215127, China.
| | - Xiuji Kan
- Department of Neurosurgery, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, 215400, China; Suzhou Medical College of Soochow University, Suzhou, Jiangsu Province, 215127, China.
| | - Zhouqing Chen
- Department of Neurosurgery, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, 215400, China.
| | - Zhong Wang
- Department of Neurosurgery, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, 215400, China.
| | - Yanbing Yu
- China-Japan Friendship Hospital (Institute of Clinical Medical Sciences), Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100000, China.
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19
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Buckbinder L, St. Jean DJ, Tieu T, Ladd B, Hilbert B, Wang W, Alltucker JT, Manimala S, Kryukov GV, Brooijmans N, Dowdell G, Jonsson P, Huff M, Guzman-Perez A, Jackson EL, Goncalves MD, Stuart DD. STX-478, a Mutant-Selective, Allosteric PI3Kα Inhibitor Spares Metabolic Dysfunction and Improves Therapeutic Response in PI3Kα-Mutant Xenografts. Cancer Discov 2023; 13:2432-2447. [PMID: 37623743 PMCID: PMC10618743 DOI: 10.1158/2159-8290.cd-23-0396] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 07/24/2023] [Accepted: 08/23/2023] [Indexed: 08/26/2023]
Abstract
Phosphoinositide 3-kinase α (PIK3CA) is one of the most mutated genes across cancers, especially breast, gynecologic, and head and neck squamous cell carcinoma tumors. Mutations occur throughout the gene, but hotspot mutations in the helical and kinase domains predominate. The therapeutic benefit of isoform-selective PI3Kα inhibition was established with alpelisib, which displays equipotent activity against the wild-type and mutant enzyme. Inhibition of wild-type PI3Kα is associated with severe hyperglycemia and rash, which limits alpelisib use and suggests that selectively targeting mutant PI3Kα could reduce toxicity and improve efficacy. Here we describe STX-478, an allosteric PI3Kα inhibitor that selectively targets prevalent PI3Kα helical- and kinase-domain mutant tumors. STX-478 demonstrated robust efficacy in human tumor xenografts without causing the metabolic dysfunction observed with alpelisib. Combining STX-478 with fulvestrant and/or cyclin-dependent kinase 4/6 inhibitors was well tolerated and provided robust and durable tumor regression in ER+HER2- xenograft tumor models. SIGNIFICANCE These preclinical data demonstrate that the mutant-selective, allosteric PI3Kα inhibitor STX-478 provides robust efficacy while avoiding the metabolic dysfunction associated with the nonselective inhibitor alpelisib. Our results support the ongoing clinical evaluation of STX-478 in PI3Kα-mutated cancers, which is expected to expand the therapeutic window and mitigate counterregulatory insulin release. See related commentary by Kearney and Vasan, p. 2313. This article is featured in Selected Articles from This Issue, p. 2293.
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Affiliation(s)
| | - David J. St. Jean
- Research and Development, Scorpion Therapeutics, Boston, Massachusetts
| | - Trang Tieu
- Research and Development, Scorpion Therapeutics, Boston, Massachusetts
| | - Brendon Ladd
- Research and Development, Scorpion Therapeutics, Boston, Massachusetts
| | - Brendan Hilbert
- Research and Development, Scorpion Therapeutics, Boston, Massachusetts
| | - Weixue Wang
- Research and Development, Scorpion Therapeutics, Boston, Massachusetts
| | | | - Samantha Manimala
- Research and Development, Scorpion Therapeutics, Boston, Massachusetts
| | | | | | - Gregory Dowdell
- Research and Development, Scorpion Therapeutics, Boston, Massachusetts
| | - Philip Jonsson
- Research and Development, Scorpion Therapeutics, Boston, Massachusetts
| | - Michael Huff
- Research and Development, Scorpion Therapeutics, Boston, Massachusetts
| | | | - Erica L. Jackson
- Department of Biology, Scorpion Therapeutics, South San Francisco, California
| | - Marcus D. Goncalves
- Division of Endocrinology, Department of Medicine, Weill Cornell Medicine, New York, New York
| | - Darrin D. Stuart
- Research and Development, Scorpion Therapeutics, Boston, Massachusetts
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20
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Ling AL, Zhang W, Lee A, Xia Y, Su MC, Gruener RF, Jena S, Huang Y, Pareek S, Shan Y, Huang RS. Simplicity: web-based visualization and analysis of high-throughput cancer cell line screens. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.08.556619. [PMID: 37745579 PMCID: PMC10515753 DOI: 10.1101/2023.09.08.556619] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
High-throughput drug screens are a powerful tool for cancer drug development. However, the results of such screens are often made available only as raw data, which is intractable for researchers without informatic skills, or as highly processed summary statistics, which can lack essential information for translating screening results into clinically meaningful discoveries. To improve the usability of these datasets, we developed Simplicity, a robust and user-friendly web interface for visualizing, exploring, and summarizing raw and processed data from high-throughput drug screens. Importantly, Simplicity allows for easy recalculation of summary statistics at user-defined drug concentrations. This allows Simplicity's outputs to be used with methods that rely on statistics being calculated at clinically relevant doses. Simplicity can be freely accessed at https://oncotherapyinformatics.org/simplicity/.
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Affiliation(s)
- Alexander L. Ling
- Harvey Cushing Neuro-oncology Laboratories, Department of Neurosurgery, Hale Building for Transformative Medicine, 4th and 8th floor, Brigham and Women’s Hospital; 60 Fenwood Road, Boston, MA 02116
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Weijie Zhang
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Adam Lee
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Yunong Xia
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Mei-Chi Su
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Robert F. Gruener
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Sampreeti Jena
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Yingbo Huang
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Siddhika Pareek
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Yuting Shan
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
| | - R. Stephanie Huang
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
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21
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Sun J, Si S, Ru J, Wang X. DeepdlncUD: Predicting regulation types of small molecule inhibitors on modulating lncRNA expression by deep learning. Comput Biol Med 2023; 163:107226. [PMID: 37450966 DOI: 10.1016/j.compbiomed.2023.107226] [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: 03/17/2023] [Revised: 05/31/2023] [Accepted: 07/01/2023] [Indexed: 07/18/2023]
Abstract
Targeting lncRNAs by small molecules (SM-lncR) to alter their expression levels has emerged as an important therapeutic modality for disease treatment. To date, no computational tools have been dedicated to predicting small molecule-mediated upregulation or downregulation of lncRNA expression. Here, we introduce DeepdlncUD, which integrates predictions of nine deep learning algorithms together, to infer the regulation types of small molecules on modulating lncRNA expression. Through systematic optimization on a training set of 771 upregulation and 739 downregulation SM-lncR pairs, each encoding 1369 sequence, representational, and physiochemical features, this method outperforms a recently released program, DeepsmirUD, by achieving 0.674 in AUC (area under the receiver operating characteristic curve), 0.722 in AUCPR (area under the precision-recall curve), 0.681 in F1-score, and 0.516 in Jaccard Index on a test set of 222 SM-lncR pairs. By extracting 125 upregulation and 46 downregulation SM-lncR pairs that involve disease-associated lncRNAs, DeepdlncUD is shown to gain an accuracy of 0.700 in the pathological context. Using connectivity scores, around half of the small molecules are correctly estimated as drugs to treat lncRNA-regulated diseases. This tool can be run at a fast speed to assist the discovery of potential small molecule drugs of lncRNA targets on a large scale. DeepdlncUD is publicly available at https://github.com/2003100127/deepdlncud.
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Affiliation(s)
- Jianfeng Sun
- Botnar Research Centre, University of Oxford, Headington, Oxford, OX3 7LD, UK.
| | - Shuyue Si
- School of Mathematics and Physics, Xi'an Jiaotong-liverpool University, Renai, Suzhou, 215028, China
| | - Jinlong Ru
- Chair of Prevention of Microbial Diseases, School of Life Sciences Weihenstephan, Technical University of Munich, 85354, Freising, Germany
| | - Xia Wang
- College of Animal Science and Technology, Northwest A&F University, Yangling, 712100, China.
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22
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Wang C, Zhang M, Zhao J, Li B, Xiao X, Zhang Y. The prediction of drug sensitivity by multi-omics fusion reveals the heterogeneity of drug response in pan-cancer. Comput Biol Med 2023; 163:107220. [PMID: 37406589 DOI: 10.1016/j.compbiomed.2023.107220] [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: 05/15/2023] [Revised: 06/14/2023] [Accepted: 06/30/2023] [Indexed: 07/07/2023]
Abstract
Cancer drug response prediction based on genomic information plays a crucial role in modern pharmacogenomics, enabling individualized therapy. Given the expensive and complexity of biological experiments, computational methods serve as effective tools in predicting cancer drug sensitivity. In this study, we proposed a novel method called Multi-Omics Integrated Collective Variational Autoencoders (MOICVAE), which leverages integrated omics knowledge, including genomic and transcriptomic data, to fill in missing cancer-drug associations and enhance drug sensitivity prediction. Our method employs an encoder-decoder network to learn latent feature representations from cell lines. These learned feature vectors are then fed into a collective variational autoencoder network to train an association matrix. We evaluated MOICVAE on the GDSC and CCLE benchmark datasets using 10-fold cross-validation and achieved impressive AUCs of 0.856 and 0.808, respectively, outperforming state-of-the-art methods. Furthermore, on the TCGA dataset, consisting of 25 drugs across 7 cancer types, MOICVAE exhibited an average AUC of 0.91 in predicting drug sensitivity. Additionally, significant differences were observed in survival, tumor inflammatory assessment, and tumor microenvironment between the predicted drug-sensitive and drug-resistant groups. These results are consistent with predictions made on the METABRIC dataset. Moreover, we discovered that fusing omics data based on mRNA and CNV (copy number variations) yielded superior results in drug sensitivity prediction. MOICVAE not only achieved higher accuracy in drug sensitivity prediction but also provided additional value for combining immunotherapy with chemotherapy, offering patients with more precise treatment options. The code and dataset for MOICVAE are freely available at https://github.com/wanggnoc/MOICVAE.
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Affiliation(s)
- Cong Wang
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150001, China
| | - Mengyan Zhang
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150001, China
| | - Jiyun Zhao
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150001, China
| | - Bin Li
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150001, China
| | - Xingjun Xiao
- Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, 246 Xuefu Road, Harbin, 150086, People's Republic of China.
| | - Yan Zhang
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150001, China; College of Pathology, Qiqihar Medical University, Qiqihar, 161042, China.
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23
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Sada Del Real K, Rubio A. Discovering the mechanism of action of drugs with a sparse explainable network. EBioMedicine 2023; 95:104767. [PMID: 37633093 PMCID: PMC10474372 DOI: 10.1016/j.ebiom.2023.104767] [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: 02/17/2023] [Revised: 07/31/2023] [Accepted: 08/08/2023] [Indexed: 08/28/2023] Open
Abstract
BACKGROUND Although Deep Neural Networks (DDNs) have been successful in predicting the efficacy of cancer drugs, the lack of explainability in their decision-making process is a significant challenge. Previous research proposed mimicking the Gene Ontology structure to allow for interpretation of each neuron in the network. However, these previous approaches require huge amount of GPU resources and hinder its extension to genome-wide models. METHODS We developed SparseGO, a sparse and interpretable neural network, for predicting drug response in cancer cell lines and their Mechanism of Action (MoA). To ensure model generalization, we trained it on multiple datasets and evaluated its performance using three cross-validation schemes. Its efficiency allows it to be used with gene expression. In addition, SparseGO integrates an eXplainable Artificial Intelligence (XAI) technique, DeepLIFT, with Support Vector Machines to computationally discover the MoA of drugs. FINDINGS SparseGO's sparse implementation significantly reduced GPU memory usage and training speed compared to other methods, allowing it to process gene expression instead of mutations as input data. SparseGO using expression improved the accuracy and enabled its use on drug repositioning. Furthermore, gene expression allows the prediction of MoA using 265 drugs to train it. It was validated on understudied drugs such as parbendazole and PD153035. INTERPRETATION SparseGO is an effective XAI method for predicting, but more importantly, understanding drug response. FUNDING The Accelerator Award Programme funded by Cancer Research UK [C355/A26819], Fundación Científica de la AECC and Fondazione AIRC, Project PIBA_2020_1_0055 funded by the Basque Government and the Synlethal Project (RETOS Investigacion, Spanish Government).
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Affiliation(s)
- Katyna Sada Del Real
- Departamento de Ingeniería Biomédica y Ciencias, TECNUN, Universidad de Navarra, San Sebastián 20018, Spain
| | - Angel Rubio
- Departamento de Ingeniería Biomédica y Ciencias, TECNUN, Universidad de Navarra, San Sebastián 20018, Spain; Instituto de Ciencia de Datos e Inteligencia Artificial (DATAI), Universidad de Navarra, Pamplona 31080, Spain.
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24
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Kim J, Kim Y, Lee B. Identification of Long Non-Coding RNA Profiles and Potential Therapeutic Agents for Fibrolamellar Carcinoma Based on RNA-Sequencing Data. Genes (Basel) 2023; 14:1709. [PMID: 37761849 PMCID: PMC10530820 DOI: 10.3390/genes14091709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 08/21/2023] [Accepted: 08/26/2023] [Indexed: 09/29/2023] Open
Abstract
BACKGROUND Fibrolamellar carcinoma (FLC) is a rare type of liver cancer that primarily affects adolescents and young adults without prior liver disease or viral infections. Patients with FLC generally have non-specific symptoms, are often diagnosed at a later stage, and experience a higher frequency of metastases compared to patients with other liver cancers. A fusion transcript of DNAJB1 and PRKACA, which can lead to increased activity of PKA and cellular proliferation, has been identified in all FLC patients, but the exact mechanism through which FLC develops remains unclear. In this study, we investigated common lncRNA profiles in various FLC samples using bioinformatics analyses. METHODS We analyzed differentially expressed (DE) lncRNAs from three RNA sequencing datasets. Using lncRNAs and DE mRNAs, we predicted potential lncRNA target genes and performed Gene Ontology (GO) and KEGG analyses with the DE lncRNA target genes. Moreover, we screened for small-molecule compounds that could act as therapeutic targets for FLC. RESULTS We identified 308 DE lncRNAs from the RNA sequencing datasets. In addition, we performed a trans-target prediction analysis and identified 454 co-expressed pairs in FLC. The GO analysis showed that the lncRNA-related up-regulated mRNAs were enriched in the regulation of protein kinase C signaling and cAMP catabolic processes, while lncRNA-related down-regulated mRNAs were enriched in steroid, retinol, cholesterol, and xenobiotic metabolic processes. The analysis of small-molecule compounds for FLC treatment identified vitexin, chlorthalidone, triamterene, and amiloride, among other compounds. CONCLUSIONS We identified potential therapeutic targets for FLC, including lncRNA target genes as well as small-molecule compounds that could potentially be used as treatments. Our findings could contribute to furthering our understanding of FLC and providing potential avenues for diagnosis and treatment.
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Affiliation(s)
- Janghyun Kim
- Department of Oral Pathology, School of Dentistry, Chonnam National University, Gwangju 61186, Republic of Korea (Y.K.)
| | - Young Kim
- Department of Oral Pathology, School of Dentistry, Chonnam National University, Gwangju 61186, Republic of Korea (Y.K.)
| | - Bora Lee
- Department of Biochemistry, Chonnam National University Medical School, Hwasun 58128, Republic of Korea
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25
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Arora R, Cao C, Kumar M, Sinha S, Chanda A, McNeil R, Samuel D, Arora RK, Matthews TW, Chandarana S, Hart R, Dort JC, Biernaskie J, Neri P, Hyrcza MD, Bose P. Spatial transcriptomics reveals distinct and conserved tumor core and edge architectures that predict survival and targeted therapy response. Nat Commun 2023; 14:5029. [PMID: 37596273 PMCID: PMC10439131 DOI: 10.1038/s41467-023-40271-4] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 07/19/2023] [Indexed: 08/20/2023] Open
Abstract
The spatial organization of the tumor microenvironment has a profound impact on biology and therapy response. Here, we perform an integrative single-cell and spatial transcriptomic analysis on HPV-negative oral squamous cell carcinoma (OSCC) to comprehensively characterize malignant cells in tumor core (TC) and leading edge (LE) transcriptional architectures. We show that the TC and LE are characterized by unique transcriptional profiles, neighboring cellular compositions, and ligand-receptor interactions. We demonstrate that the gene expression profile associated with the LE is conserved across different cancers while the TC is tissue specific, highlighting common mechanisms underlying tumor progression and invasion. Additionally, we find our LE gene signature is associated with worse clinical outcomes while TC gene signature is associated with improved prognosis across multiple cancer types. Finally, using an in silico modeling approach, we describe spatially-regulated patterns of cell development in OSCC that are predictably associated with drug response. Our work provides pan-cancer insights into TC and LE biology and interactive spatial atlases ( http://www.pboselab.ca/spatial_OSCC/ ; http://www.pboselab.ca/dynamo_OSCC/ ) that can be foundational for developing novel targeted therapies.
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Affiliation(s)
- Rohit Arora
- Department of Biochemistry & Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Christian Cao
- Department of Biochemistry & Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Mehul Kumar
- Department of Biochemistry & Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Arnie Charbonneau Cancer Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Sarthak Sinha
- Department of Comparative Biology and Experimental Medicine, Faculty of Veterinary Medicine, University of Calgary, Calgary, AB, Canada
| | - Ayan Chanda
- Department of Biochemistry & Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Arnie Charbonneau Cancer Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Reid McNeil
- Department of Biochemistry & Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Arnie Charbonneau Cancer Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Divya Samuel
- Department of Biochemistry & Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Arnie Charbonneau Cancer Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Rahul K Arora
- Center for Health Informatics, University of Calgary, Calgary, AB, Canada
- Institute of Biomedical Engineering, University of Oxford, Oxford, United Kingdom
| | - T Wayne Matthews
- Ohlson Research Initiative, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Section of Otolaryngology Head & Neck Surgery, Department of Surgery, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Shamir Chandarana
- Ohlson Research Initiative, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Section of Otolaryngology Head & Neck Surgery, Department of Surgery, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Robert Hart
- Ohlson Research Initiative, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Section of Otolaryngology Head & Neck Surgery, Department of Surgery, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Joseph C Dort
- Arnie Charbonneau Cancer Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Ohlson Research Initiative, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Section of Otolaryngology Head & Neck Surgery, Department of Surgery, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Jeff Biernaskie
- Department of Comparative Biology and Experimental Medicine, Faculty of Veterinary Medicine, University of Calgary, Calgary, AB, Canada
- Alberta Children's Hospital Research Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Department of Surgery, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Paola Neri
- Arnie Charbonneau Cancer Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Division of Hematology, Department of Oncology, University of Calgary, Calgary, AB, Canada
| | - Martin D Hyrcza
- Arnie Charbonneau Cancer Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Department of Pathology and Laboratory Medicine, University of Calgary, Calgary, AB, Canada
| | - Pinaki Bose
- Department of Biochemistry & Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
- Arnie Charbonneau Cancer Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
- Institute of Biomedical Engineering, University of Oxford, Oxford, United Kingdom.
- Department of Oncology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
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26
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Zhan Y, Guo J, Philip Chen CL, Meng XB. iBT-Net: an incremental broad transformer network for cancer drug response prediction. Brief Bioinform 2023:bbad256. [PMID: 37429577 DOI: 10.1093/bib/bbad256] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 05/30/2023] [Accepted: 06/15/2023] [Indexed: 07/12/2023] Open
Abstract
In modern precision medicine, it is an important research topic to predict cancer drug response. Due to incomplete chemical structures and complex gene features, however, it is an ongoing work to design efficient data-driven methods for predicting drug response. Moreover, since the clinical data cannot be easily obtained all at once, the data-driven methods may require relearning when new data are available, resulting in increased time consumption and cost. To address these issues, an incremental broad Transformer network (iBT-Net) is proposed for cancer drug response prediction. Different from the gene expression features learning from cancer cell lines, structural features are further extracted from drugs by Transformer. Broad learning system is then designed to integrate the learned gene features and structural features of drugs to predict the response. With the capability of incremental learning, the proposed method can further use new data to improve its prediction performance without retraining totally. Experiments and comparison studies demonstrate the effectiveness and superiority of iBT-Net under different experimental configurations and continuous data learning.
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Affiliation(s)
- Yongkang Zhan
- School of Computer Science & Engineering,South China University of Technology, 510006, China
| | - Jifeng Guo
- School of Computer Science & Engineering,South China University of Technology, 510006, China
| | - C L Philip Chen
- School of Computer Science & Engineering,South China University of Technology, 510006, China
- Brain and Affective Cognitive Research Center, Pazhou Lab, 510335, China
| | - Xian-Bing Meng
- School of Electromechanical Engineering, Guangdong University of Technology, 510006, China
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27
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Bakhsh MR, Rouhi L, Ghaedi K, Hashemi M, Peymani M, Samarghandian S. Therapeutic effects of guanidine hydrochloride on breast cancer through targeting KCNG1 gene. Biomed Pharmacother 2023; 164:114982. [PMID: 37311278 DOI: 10.1016/j.biopha.2023.114982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 05/28/2023] [Accepted: 06/01/2023] [Indexed: 06/15/2023] Open
Abstract
BACKGROUND Triple-negative breast cancer (TNBC) is one of the subtypes of breast cancer (BC) that is associated with poor survival rates and failure to respond to hormonal and targeted therapies. OBJECTIVE The aim of this study was to identify a specific gene at the expression level for TNBC and targeting of this type of breast cancer based on it. Using TCGA database, genes that are particularly high expression in TNBC subtypes compared to other BC subtypes (in terms of receptor status) and normal samples were identified and their sensitivity and specificity were evaluated. Using PharmacoGX and Drug Bank data, drug sensitivity and drug-appropriate genes were identified, respectively. The effects of the identified drug on triple-negative cell lines (MDA-MB-468) were evaluated in comparison with the cell line of other subtypes (MCF7) by apoptosis and MTS tests. RESULTS Data analyzes showed that the expression level of KCNG1 gene in the TNBC subgroup was significantly higher compared to other BC subtypes from the KCN gene family and ROC results showed that this gene had highest sensitivity and specificity in TNBC subtype. The results of drug resistance and sensitivity showed that an increase in the expression level of KCNG1 was associated with sensitivity to Cisplatin and Oxaliplatin. Moreover, Drug Bank results showed that Guanidine hydrochloride (GuHCl) was a suitable inhibitor for KCNG1. In vitro results showed that the expression level of KCNG1 was higher in MDA-MB-468 compared to MCF7. In addition, the rate of apoptosis in response to GuHCl treatment in MDA-MB-468 cell line as TNBC cell model was higher than MCF7 in the same concentration. CONCLUSION This study revealed that GuHCl could be a suitable treatment for TNBC subtype by targeting of KCNG1.
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Affiliation(s)
- Mehdi Roshanian Bakhsh
- Department of Biology, Faculty of Basic Sciences, Shahrekord Branch, Islamic Azad University, Shahrekord, the Islamic Republic of Iran
| | - Leila Rouhi
- Department of Biology, Faculty of Basic Sciences, Shahrekord Branch, Islamic Azad University, Shahrekord, the Islamic Republic of Iran
| | - Kamran Ghaedi
- Department of Biology, Faculty of Basic Sciences, Shahrekord Branch, Islamic Azad University, Shahrekord, the Islamic Republic of Iran; Department of Cell and Molecular Biology and Microbiology, Faculty of Biological Science and Technology, University of Isfahan, Isfahan, the Islamic Republic of Iran
| | - Mehrdad Hashemi
- Department of Genetics, Faculty of Advanced Science and Technology, Tehran Medical Sciences, Islamic Azad University, Tehran, the Islamic Republic of Iran; Farhikhtegan Medical Convergence sciences Research Center, Farhikhtegan Hospital Tehran Medical sciences, Islamic Azad University, Tehran, the Islamic Republic of Iran.
| | - Maryam Peymani
- Department of Biology, Faculty of Basic Sciences, Shahrekord Branch, Islamic Azad University, Shahrekord, the Islamic Republic of Iran.
| | - Saeed Samarghandian
- Healthy Ageing Research Centre, Neyshabur University of Medical Sciences, Neyshabur, the Islamic Republic of Iran.
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28
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Das T, Bhattarai K, Rajaganapathy S, Wang L, Cerhan JR, Zong N. Leveraging multi-source to resolve inconsistency across pharmacogenomic datasets in drug sensitivity prediction. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.05.25.23290546. [PMID: 37333219 PMCID: PMC10274988 DOI: 10.1101/2023.05.25.23290546] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Pharmacogenomics datasets have been generated for various purposes, such as investigating different biomarkers. However, when studying the same cell line with the same drugs, differences in drug responses exist between studies. These variations arise from factors such as inter-tumoral heterogeneity, experimental standardization, and the complexity of cell subtypes. Consequently, drug response prediction suffers from limited generalizability. To address these challenges, we propose a computational model based on Federated Learning (FL) for drug response prediction. By leveraging three pharmacogenomics datasets (CCLE, GDSC2, and gCSI), we evaluate the performance of our model across diverse cell line-based databases. Our results demonstrate superior predictive performance compared to baseline methods and traditional FL approaches through various experimental tests. This study underscores the potential of employing FL to leverage multiple data sources, enabling the development of generalized models that account for inconsistencies among pharmacogenomics datasets. By addressing the limitations of low generalizability, our approach contributes to advancing drug response prediction in precision oncology.
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Affiliation(s)
- Trisha Das
- University of Illinois Urbana-Champaign, Champaign, Illinois, United States
| | | | - Sivaraman Rajaganapathy
- Department of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, MN, USA
| | - Liewei Wang
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN
| | - James R. Cerhan
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Nansu Zong
- Department of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, MN, USA
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29
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Gonzalez RD, Small GW, Green AJ, Akhtari FS, Motsinger-Reif AA, Quintanilha JCF, Havener TM, Reif DM, McLeod HL, Wiltshire T. MKX-AS1 Gene Expression Associated with Variation in Drug Response to Oxaliplatin and Clinical Outcomes in Colorectal Cancer Patients. Pharmaceuticals (Basel) 2023; 16:ph16050757. [PMID: 37242540 DOI: 10.3390/ph16050757] [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: 03/15/2023] [Revised: 05/11/2023] [Accepted: 05/16/2023] [Indexed: 05/28/2023] Open
Abstract
Oxaliplatin (OXAL) is a commonly used chemotherapy for treating colorectal cancer (CRC). A recent genome wide association study (GWAS) showed that a genetic variant (rs11006706) in the lncRNA gene MKX-AS1 and partnered sense gene MKX could impact the response of genetically varied cell lines to OXAL treatment. This study found that the expression levels of MKX-AS1 and MKX in lymphocytes (LCLs) and CRC cell lines differed between the rs11006706 genotypes, indicating that this gene pair could play a role in OXAL response. Further analysis of patient survival data from the Cancer Genome Atlas (TCGA) and other sources showed that patients with high MKX-AS1 expression status had significantly worse overall survival (HR = 3.2; 95%CI = (1.17-9); p = 0.024) compared to cases with low MKX-AS1 expression status. Alternatively, high MKX expression status had significantly better overall survival (HR = 0.22; 95%CI = (0.07-0.7); p = 0.01) compared to cases with low MKX expression status. These results suggest an association between MKX-AS1 and MKX expression status that could be useful as a prognostic marker of response to OXAL and potential patient outcomes in CRC.
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Affiliation(s)
- Ricardo D Gonzalez
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Center for Pharmacogenomics and Individualized Therapy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - George W Small
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Center for Pharmacogenomics and Individualized Therapy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Adrian J Green
- Department of Biological Sciences, North Carolina State University, Raleigh, NC 27606, USA
- Bioinformatics Research Center, North Carolina State University, Raleigh, NC 27606, USA
| | - Farida S Akhtari
- Biostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Research Triangle Park, NC 27709, USA
| | - Alison A Motsinger-Reif
- Biostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Research Triangle Park, NC 27709, USA
| | | | - Tammy M Havener
- Structural Genomics Consortium and Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - David M Reif
- Predictive Toxicology Branch, Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, NC 27709, USA
| | - Howard L McLeod
- Center for Precision Medicine and Functional Genomics, Utah Tech University, St. George, UT 84770, USA
| | - Tim Wiltshire
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Center for Pharmacogenomics and Individualized Therapy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
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30
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Sun J, Xu M, Ru J, James-Bott A, Xiong D, Wang X, Cribbs AP. Small molecule-mediated targeting of microRNAs for drug discovery: Experiments, computational techniques, and disease implications. Eur J Med Chem 2023; 257:115500. [PMID: 37262996 DOI: 10.1016/j.ejmech.2023.115500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 05/05/2023] [Accepted: 05/15/2023] [Indexed: 06/03/2023]
Abstract
Small molecules have been providing medical breakthroughs for human diseases for more than a century. Recently, identifying small molecule inhibitors that target microRNAs (miRNAs) has gained importance, despite the challenges posed by labour-intensive screening experiments and the significant efforts required for medicinal chemistry optimization. Numerous experimentally-verified cases have demonstrated the potential of miRNA-targeted small molecule inhibitors for disease treatment. This new approach is grounded in their posttranscriptional regulation of the expression of disease-associated genes. Reversing dysregulated gene expression using this mechanism may help control dysfunctional pathways. Furthermore, the ongoing improvement of algorithms has allowed for the integration of computational strategies built on top of laboratory-based data, facilitating a more precise and rational design and discovery of lead compounds. To complement the use of extensive pharmacogenomics data in prioritising potential drugs, our previous work introduced a computational approach based on only molecular sequences. Moreover, various computational tools for predicting molecular interactions in biological networks using similarity-based inference techniques have been accumulated in established studies. However, there are a limited number of comprehensive reviews covering both computational and experimental drug discovery processes. In this review, we outline a cohesive overview of both biological and computational applications in miRNA-targeted drug discovery, along with their disease implications and clinical significance. Finally, utilizing drug-target interaction (DTIs) data from DrugBank, we showcase the effectiveness of deep learning for obtaining the physicochemical characterization of DTIs.
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Affiliation(s)
- Jianfeng Sun
- Botnar Research Centre, Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, UK.
| | - Miaoer Xu
- Department of Biology, Emory University, Atlanta, GA, 30322, USA
| | - Jinlong Ru
- Chair of Prevention of Microbial Diseases, School of Life Sciences Weihenstephan, Technical University of Munich, Freising, 85354, Germany
| | - Anna James-Bott
- Botnar Research Centre, Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, UK
| | - Dapeng Xiong
- Department of Computational Biology, Cornell University, Ithaca, NY, 14853, USA; Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, 14853, USA
| | - Xia Wang
- College of Animal Science and Technology, Northwest A&F University, Yangling, 712100, China.
| | - Adam P Cribbs
- Botnar Research Centre, Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, UK.
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31
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Gonzalez RD, Small GW, Green AJ, Akhtari FS, Havener TM, Quintanilha JCF, Cipriani AB, Reif DM, McLeod HL, Motsinger-Reif AA, Wiltshire T. RYK Gene Expression Associated with Drug Response Variation of Temozolomide and Clinical Outcomes in Glioma Patients. Pharmaceuticals (Basel) 2023; 16:ph16050726. [PMID: 37242509 DOI: 10.3390/ph16050726] [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: 03/28/2023] [Revised: 04/25/2023] [Accepted: 05/08/2023] [Indexed: 05/28/2023] Open
Abstract
Temozolomide (TMZ) chemotherapy is an important tool in the treatment of glioma brain tumors. However, variable patient response and chemo-resistance remain exceptionally challenging. Our previous genome-wide association study (GWAS) identified a suggestively significant association of SNP rs4470517 in the RYK (receptor-like kinase) gene with TMZ drug response. Functional validation of RYK using lymphocytes and glioma cell lines resulted in gene expression analysis indicating differences in expression status between genotypes of the cell lines and TMZ dose response. We conducted univariate and multivariate Cox regression analyses using publicly available TCGA and GEO datasets to investigate the impact of RYK gene expression status on glioma patient overall (OS) and progression-free survival (PFS). Our results indicated that in IDH mutant gliomas, RYK expression and tumor grade were significant predictors of survival. In IDH wildtype glioblastomas (GBM), MGMT status was the only significant predictor. Despite this result, we revealed a potential benefit of RYK expression in IDH wildtype GBM patients. We found that a combination of RYK expression and MGMT status could serve as an additional biomarker for improved survival. Overall, our findings suggest that RYK expression may serve as an important prognostic or predictor of TMZ response and survival for glioma patients.
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Affiliation(s)
- Ricardo D Gonzalez
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Center for Pharmacogenomics and Individualized Therapy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - George W Small
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Center for Pharmacogenomics and Individualized Therapy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Adrian J Green
- Department of Biological Sciences, North Carolina State University, Raleigh, NC 27606, USA
- Bioinformatics Research Center, North Carolina State University, Raleigh, NC 27606, USA
| | - Farida S Akhtari
- Biostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Research Triangle Park, NC 27709, USA
| | - Tammy M Havener
- Structural Genomics Consortium and Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | | | - Amber B Cipriani
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - David M Reif
- Predictive Toxicology Branch, Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, Durham, NC 27709, USA
| | - Howard L McLeod
- Center for Precision Medicine and Functional Genomics, Utah Tech University, St. George, UT 84770, USA
| | - Alison A Motsinger-Reif
- Biostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Research Triangle Park, NC 27709, USA
| | - Tim Wiltshire
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Center for Pharmacogenomics and Individualized Therapy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
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32
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Singh AV, Chandrasekar V, Paudel N, Laux P, Luch A, Gemmati D, Tissato V, Prabhu KS, Uddin S, Dakua SP. Integrative toxicogenomics: Advancing precision medicine and toxicology through artificial intelligence and OMICs technology. Biomed Pharmacother 2023; 163:114784. [PMID: 37121152 DOI: 10.1016/j.biopha.2023.114784] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 04/15/2023] [Accepted: 04/24/2023] [Indexed: 05/02/2023] Open
Abstract
More information about a person's genetic makeup, drug response, multi-omics response, and genomic response is now available leading to a gradual shift towards personalized treatment. Additionally, the promotion of non-animal testing has fueled the computational toxicogenomics as a pivotal part of the next-gen risk assessment paradigm. Artificial Intelligence (AI) has the potential to provid new ways analyzing the patient data and making predictions about treatment outcomes or toxicity. As personalized medicine and toxicogenomics involve huge data processing, AI can expedite this process by providing powerful data processing, analysis, and interpretation algorithms. AI can process and integrate a multitude of data including genome data, patient records, clinical data and identify patterns to derive predictive models anticipating clinical outcomes and assessing the risk of any personalized medicine approaches. In this article, we have studied the current trends and future perspectives in personalized medicine & toxicology, the role of toxicogenomics in connecting the two fields, and the impact of AI on personalized medicine & toxicology. In this work, we also study the key challenges and limitations in personalized medicine, toxicogenomics, and AI in order to fully realize their potential.
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Affiliation(s)
- Ajay Vikram Singh
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), 10589 Berlin, Germany
| | | | - Namuna Paudel
- Department of Chemistry, Amrit Campus, Institute of Science and Technology, Tribhuvan University, Lainchaur, Kathmandu 44600 Nepal
| | - Peter Laux
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), 10589 Berlin, Germany
| | - Andreas Luch
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), 10589 Berlin, Germany
| | - Donato Gemmati
- Department of Translational Medicine, University of Ferrara, 44121 Ferrara, Italy; Centre Hemostasis & Thrombosis, University of Ferrara, 44121 Ferrara, Italy; Centre for Gender Medicine, University of Ferrara, 44121 Ferrara, Italy
| | - Veronica Tissato
- Department of Translational Medicine, University of Ferrara, 44121 Ferrara, Italy; Centre Hemostasis & Thrombosis, University of Ferrara, 44121 Ferrara, Italy; Centre for Gender Medicine, University of Ferrara, 44121 Ferrara, Italy
| | - Kirti S Prabhu
- Translational Research Institute, Academic Health System, Hamad Medical Corporation, Doha, Qatar
| | - Shahab Uddin
- Translational Research Institute, Academic Health System, Hamad Medical Corporation, Doha, Qatar
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33
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He YQ, Luo LT, Wang TM, Xue WQ, Yang DW, Li DH, Diao H, Xiao RW, Deng CM, Zhang WL, Liao Y, Wu YX, Wang QL, Zhou T, Li XZ, Zheng XH, Zhang PF, Zhang SD, Hu YZ, Sun Y, Jia WH. Clinical and genome-wide association analysis of chemoradiation-induced hearing loss in nasopharyngeal carcinoma. Hum Genet 2023; 142:759-772. [PMID: 37062025 PMCID: PMC10182145 DOI: 10.1007/s00439-023-02554-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Accepted: 04/07/2023] [Indexed: 04/17/2023]
Abstract
Chemoradiation-induced hearing loss (CRIHL) is one of the most devasting side effects for nasopharyngeal carcinoma (NPC) patients, which seriously affects survivors' long-term quality of life. However, few studies have comprehensively characterized the risk factors for CRIHL. In this study, we found that age at diagnosis, tumor stage, and concurrent cisplatin dose were positively associated with chemoradiation-induced hearing loss. We performed a genome-wide association study (GWAS) in 777 NPC patients and identified rs1050851 (within the exon 2 of NFKBIA), a variant with a high deleteriousness score, to be significantly associated with hearing loss risk (HR = 5.46, 95% CI 2.93-10.18, P = 9.51 × 10-08). The risk genotype of rs1050851 was associated with higher NFKBIA expression, which was correlated with lower cellular tolerance to cisplatin. According to permutation-based enrichment analysis, the variants mapping to 149 hereditary deafness genes were significantly enriched among GWAS top signals, which indicated the genetic similarity between hereditary deafness and CRIHL. Pathway analysis suggested that synaptic signaling was involved in the development of CRIHL. Additionally, the risk score integrating genetic and clinical factors can predict the risk of hearing loss with a relatively good performance in the test set. Collectively, this study shed new light on the etiology of chemoradiation-induced hearing loss, which facilitates high-risk individuals' identification for personalized prevention and treatment.
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Affiliation(s)
- Yong-Qiao He
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, 510060, People's Republic of China
| | - Lu-Ting Luo
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, 510060, People's Republic of China
- School of Public Health, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Tong-Min Wang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, 510060, People's Republic of China
| | - Wen-Qiong Xue
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, 510060, People's Republic of China
| | - Da-Wei Yang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, 510060, People's Republic of China
- School of Public Health, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Dan-Hua Li
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, 510060, People's Republic of China
| | - Hua Diao
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, 510060, People's Republic of China
- School of Public Health, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Ruo-Wen Xiao
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, 510060, People's Republic of China
| | - Chang-Mi Deng
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, 510060, People's Republic of China
| | - Wen-Li Zhang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, 510060, People's Republic of China
| | - Ying Liao
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, 510060, People's Republic of China
| | - Yan-Xia Wu
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, 510060, People's Republic of China
| | - Qiao-Ling Wang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, 510060, People's Republic of China
- School of Public Health, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Ting Zhou
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, 510060, People's Republic of China
- Biobank of Sun Yat‑sen University Cancer Center, Guangzhou, People's Republic of China
| | - Xi-Zhao Li
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, 510060, People's Republic of China
- Biobank of Sun Yat‑sen University Cancer Center, Guangzhou, People's Republic of China
| | - Xiao-Hui Zheng
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, 510060, People's Republic of China
- Biobank of Sun Yat‑sen University Cancer Center, Guangzhou, People's Republic of China
| | - Pei-Fen Zhang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, 510060, People's Republic of China
- Biobank of Sun Yat‑sen University Cancer Center, Guangzhou, People's Republic of China
| | - Shao-Dan Zhang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, 510060, People's Republic of China
- Biobank of Sun Yat‑sen University Cancer Center, Guangzhou, People's Republic of China
| | - Ye-Zhu Hu
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, 510060, People's Republic of China
- Biobank of Sun Yat‑sen University Cancer Center, Guangzhou, People's Republic of China
| | - Ying Sun
- Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China
| | - Wei-Hua Jia
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, 510060, People's Republic of China.
- School of Public Health, Sun Yat-sen University, Guangzhou, People's Republic of China.
- Biobank of Sun Yat‑sen University Cancer Center, Guangzhou, People's Republic of China.
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Mehmood A, Nawab S, Jin Y, Hassan H, Kaushik AC, Wei DQ. Ranking Breast Cancer Drugs and Biomarkers Identification Using Machine Learning and Pharmacogenomics. ACS Pharmacol Transl Sci 2023; 6:399-409. [PMID: 36926455 PMCID: PMC10012252 DOI: 10.1021/acsptsci.2c00212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Indexed: 02/26/2023]
Abstract
Breast cancer is one of the major causes of death in women worldwide. It is a diverse illness with substantial intersubject heterogeneity, even among individuals with the same type of tumor, and customized therapy has become increasingly important in this sector. Because of the clinical and physical variability of different kinds of breast cancers, multiple staging and classification systems have been developed. As a result, these tumors exhibit a wide range of gene expression and prognostic indicators. To date, no comprehensive investigation of model training procedures on information from numerous cell line screenings has been conducted together with radiation data. We used human breast cancer cell lines and drug sensitivity information from Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) databases to scan for potential drugs using cell line data. The results are further validated through three machine learning approaches: Elastic Net, LASSO, and Ridge. Next, we selected top-ranked biomarkers based on their role in breast cancer and tested them further for their resistance to radiation using the data from the Cleveland database. We have identified six drugs named Palbociclib, Panobinostat, PD-0325901, PLX4720, Selumetinib, and Tanespimycin that significantly perform on breast cancer cell lines. Also, five biomarkers named TNFSF15, DCAF6, KDM6A, PHETA2, and IFNGR1 are sensitive to all six shortlisted drugs and show sensitivity to the radiations. The proposed biomarkers and drug sensitivity analysis are helpful in translational cancer studies and provide valuable insights for clinical trial design.
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Affiliation(s)
- Aamir Mehmood
- Department
of Bioinformatics and Biological Statistics, School of Life Sciences
and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, P.R. China
| | - Sadia Nawab
- State
Key Laboratory of Microbial Metabolism and School of Life Sciences
and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, P.R. China
| | - Yifan Jin
- Department
of Bioinformatics and Biological Statistics, School of Life Sciences
and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, P.R. China
| | - Hesham Hassan
- Department
of Pathology, College of Medicine, King
Khalid University, Abha 61421, Saudi Arabia
- Department
of Pathology, Faculty of Medicine, Assiut
University, Assiut 71515, Egypt
| | - Aman Chandra Kaushik
- Department
of Bioinformatics and Biological Statistics, School of Life Sciences
and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, P.R. China
| | - Dong-Qing Wei
- State
Key Laboratory of Microbial Metabolism, Shanghai-Islamabad-Belgrade
Joint Innovation Center on Antibacterial Resistances, Joint International
Research Laboratory of Metabolic & Developmental Sciences and
School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200030, P.R. China
- Zhongjing
Research and Industrialization Institute of Chinese Medicine, Zhongguancun Scientific Park, Meixi, Nanyang, Henan 473006, P.R. China
- Peng
Cheng National Laboratory, Vanke Cloud City Phase I Building 8, Xili Street, Nanshan District, Shenzhen, Guangdong 518055, P.R. China
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35
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Feodoroff M, Mikkonen P, Arjama M, Murumägi A, Kallioniemi O, Potdar S, Turunen L, Pietiäinen V. Protocol for 3D drug sensitivity and resistance testing of patient-derived cancer cells in 384-well plates. SLAS DISCOVERY : ADVANCING LIFE SCIENCES R & D 2023; 28:36-41. [PMID: 36464160 DOI: 10.1016/j.slasd.2022.11.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 11/01/2022] [Accepted: 11/15/2022] [Indexed: 12/03/2022]
Abstract
Establishment of drug testing of patient-derived cancer cells (PDCs) in physiologically relevant 3-dimensional (3D) culture is central for drug discovery and cancer research, as well as for functional precision medicine. Here, we describe the detailed protocol allowing the 3D drug testing of PDCs - or any type of cells of interest - in Matrigel in 384-well plate format using automation. We also provide an alternative protocol, which does not require supporting matrices. The cancer tissue is obtained directly from clinics (after surgery or biopsy) and processed into single cell suspension. Systematic drug sensitivity and resistance testing (DSRT) is carried out on the PDCs directly after cancer cell isolation from tissue or on cells expanded for a few passages. In the 3D-DSRT assay, the PDCs are plated in 384-well plates in Matrigel, grown as spheroids, and treated with compounds of interest for 72 h. The cell viability is directly measured using a luminescence-based assay. Alternatively, prior to the cell viability measurement, drug-treated cells can be directly subjected to automated high-content bright field imaging or stained for fluorescence (live) cell microscopy for further image analysis. This is followed by the quality control and data analysis. The 3D-DSRT can be performed within a 1-3-week timeframe of the clinical sampling of cancer tissue, depending on the amount of the obtained tissue, growth rate of cancer cells, and the number of drugs being tested. The 3D-DSRT method can be flexibly modified, e.g., to be carried out with or without supporting matrices with U-bottom 384-well plates when appropriate for the PDCs or other cell models used.
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Affiliation(s)
- Michaela Feodoroff
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute for Life Sciences (HiLIFE), University of Helsinki, Helsinki, Finland; Laboratory of Immunovirotherapy, Drug Research Program, Division of Pharmaceutical Biosciences, Faculty of Pharmacy, University of Helsinki, Helsinki, Finland; Translational Immunology Research Program (TRIMM), University of Helsinki, Helsinki, Finland; iCAN Digital Precision Cancer Medicine Flagship, University of Helsinki, Helsinki, Finland
| | - Piia Mikkonen
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute for Life Sciences (HiLIFE), University of Helsinki, Helsinki, Finland; UPM-Kymmene Corporation, UPM Biomedicals, Helsinki, Finland
| | - Mariliina Arjama
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute for Life Sciences (HiLIFE), University of Helsinki, Helsinki, Finland
| | - Astrid Murumägi
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute for Life Sciences (HiLIFE), University of Helsinki, Helsinki, Finland
| | - Olli Kallioniemi
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute for Life Sciences (HiLIFE), University of Helsinki, Helsinki, Finland; iCAN Digital Precision Cancer Medicine Flagship, University of Helsinki, Helsinki, Finland; Science for Life Laboratory (SciLifeLab), Department of Oncology and Pathology, Karolinska Institutet, Solna, Sweden
| | - Swapnil Potdar
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute for Life Sciences (HiLIFE), University of Helsinki, Helsinki, Finland
| | - Laura Turunen
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute for Life Sciences (HiLIFE), University of Helsinki, Helsinki, Finland
| | - Vilja Pietiäinen
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute for Life Sciences (HiLIFE), University of Helsinki, Helsinki, Finland; iCAN Digital Precision Cancer Medicine Flagship, University of Helsinki, Helsinki, Finland.
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36
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He H, Duo H, Hao Y, Zhang X, Zhou X, Zeng Y, Li Y, Li B. Computational drug repurposing by exploiting large-scale gene expression data: Strategy, methods and applications. Comput Biol Med 2023; 155:106671. [PMID: 36805225 DOI: 10.1016/j.compbiomed.2023.106671] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 02/05/2023] [Accepted: 02/10/2023] [Indexed: 02/18/2023]
Abstract
De novo drug development is an extremely complex, time-consuming and costly task. Urgent needs for therapies of various diseases have greatly accelerated searches for more effective drug development methods. Luckily, drug repurposing provides a new and effective perspective on disease treatment. Rapidly increased large-scale transcriptome data paints a detailed prospect of gene expression during disease onset and thus has received wide attention in the field of computational drug repurposing. However, how to efficiently mine transcriptome data and identify new indications for old drugs remains a critical challenge. This review discussed the irreplaceable role of transcriptome data in computational drug repurposing and summarized some representative databases, tools and strategies. More importantly, it proposed a practical guideline through establishing the correspondence between three gene expression data types and five strategies, which would facilitate researchers to adopt appropriate strategies to deeply mine large-scale transcriptome data and discover more effective therapies.
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Affiliation(s)
- Hao He
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China; State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, 200032, PR China
| | - Hongrui Duo
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Youjin Hao
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Xiaoxi Zhang
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Xinyi Zhou
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Yujie Zeng
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Yinghong Li
- The Key Laboratory on Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, 400065, PR China
| | - Bo Li
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China.
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Chapola H, de Bastiani MA, Duarte MM, Freitas MB, Schuster JS, de Vargas DM, Klamt F. A comparative study of COVID-19 transcriptional signatures between clinical samples and preclinical cell models in the search for disease master regulators and drug repositioning candidates. Virus Res 2023; 326:199053. [PMID: 36709793 PMCID: PMC9877318 DOI: 10.1016/j.virusres.2023.199053] [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: 10/12/2022] [Revised: 12/29/2022] [Accepted: 01/24/2023] [Indexed: 01/27/2023]
Abstract
Coronavirus disease 2019 (COVID-19) is an acute viral disease with millions of cases worldwide. Although the number of daily new cases and deaths has been dropping, there is still a need for therapeutic alternatives to deal with severe cases. A promising strategy to prospect new therapeutic candidates is to investigate the regulatory mechanisms involved in COVID-19 progression using integrated transcriptomics approaches. In this work, we aimed to identify COVID-19 Master Regulators (MRs) using a series of publicly available gene expression datasets of lung tissue from patients which developed the severe form of the disease. We were able to identify a set of six potential COVID-19 MRs related to its severe form, namely TAL1, TEAD4, EPAS1, ATOH8, ERG, and ARNTL2. In addition, using the Connectivity Map drug repositioning approach, we identified 52 different drugs which could be used to revert the disease signature, thus being candidates for the design of novel clinical treatments. Furthermore, we compared the identified signature and drugs with the ones obtained from the analysis of nasopharyngeal swab samples from infected patients and preclinical cell models. This comparison showed significant similarities between them, although also revealing some limitations on the overlap between clinical and preclinical data in COVID-19, highlighting the need for careful selection of the best model for each disease stage.
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Affiliation(s)
- Henrique Chapola
- Laboratory of Cellular Biochemistry, Biochemistry Department, Institute of Health Sciences (ICBS), Federal University of Rio Grande do Sul (UFRGS), Porto Alegre, RS 90035-003, Brazil
| | - Marco Antônio de Bastiani
- Laboratory of Cellular Biochemistry, Biochemistry Department, Institute of Health Sciences (ICBS), Federal University of Rio Grande do Sul (UFRGS), Porto Alegre, RS 90035-003, Brazil; Zimmer Lab, Biochemistry Department, Institute of Health Sciences (ICBS), Federal University of Rio Grande do Sul (UFRGS), Porto Alegre, RS 90035-003, Brazil
| | - Marcelo Mendes Duarte
- Laboratory of Cellular Biochemistry, Biochemistry Department, Institute of Health Sciences (ICBS), Federal University of Rio Grande do Sul (UFRGS), Porto Alegre, RS 90035-003, Brazil
| | - Matheus Becker Freitas
- Estacio College of Rio Grande do Sul (ESTACIO FARGS), Porto Alegre, RS 90020-060, Brazil
| | | | - Daiani Machado de Vargas
- Laboratory of Cellular Biochemistry, Biochemistry Department, Institute of Health Sciences (ICBS), Federal University of Rio Grande do Sul (UFRGS), Porto Alegre, RS 90035-003, Brazil.
| | - Fábio Klamt
- Laboratory of Cellular Biochemistry, Biochemistry Department, Institute of Health Sciences (ICBS), Federal University of Rio Grande do Sul (UFRGS), Porto Alegre, RS 90035-003, Brazil; Zimmer Lab, Biochemistry Department, Institute of Health Sciences (ICBS), Federal University of Rio Grande do Sul (UFRGS), Porto Alegre, RS 90035-003, Brazil; National Institutes of Science & Technology, Translational Medicine (INCT-TM), Porto Alegre, RS 90035-903, Brazil; IMMUNESHARE - MCTI Trial (CNPq/MCTI #137541939766794), Brazil
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38
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Samarin J, Fabrowski P, Kurilov R, Nuskova H, Hummel-Eisenbeiss J, Pink H, Li N, Weru V, Alborzinia H, Yildiz U, Grob L, Taubert M, Czech M, Morgen M, Brandstädter C, Becker K, Mao L, Jayavelu AK, Goncalves A, Uhrig U, Seiler J, Lyu Y, Diederichs S, Klingmüller U, Muckenthaler M, Kopp-Schneider A, Teleman A, Miller AK, Gunkel N. Low level of antioxidant capacity biomarkers but not target overexpression predicts vulnerability to ROS-inducing drugs. Redox Biol 2023; 62:102639. [PMID: 36958250 PMCID: PMC10053401 DOI: 10.1016/j.redox.2023.102639] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 02/13/2023] [Accepted: 02/14/2023] [Indexed: 02/25/2023] Open
Abstract
Despite a strong rationale for why cancer cells are susceptible to redox-targeting drugs, such drugs often face tumor resistance or dose-limiting toxicity in preclinical and clinical studies. An important reason is the lack of specific biomarkers to better select susceptible cancer entities and stratify patients. Using a large panel of lung cancer cell lines, we identified a set of "antioxidant-capacity" biomarkers (ACB), which were tightly repressed, partly by STAT3 and STAT5A/B in sensitive cells, rendering them susceptible to multiple redox-targeting and ferroptosis-inducing drugs. Contrary to expectation, constitutively low ACB expression was not associated with an increased steady state level of reactive oxygen species (ROS) but a high level of nitric oxide, which is required to sustain high replication rates. Using ACBs, we identified cancer entities with a high percentage of patients with favorable ACB expression pattern, making it likely that more responders to ROS-inducing drugs could be stratified for clinical trials.
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Affiliation(s)
- Jana Samarin
- Cancer Drug Development, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Piotr Fabrowski
- Cancer Drug Development, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Roman Kurilov
- Applied Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Hana Nuskova
- Cancer Drug Development, German Cancer Research Center (DKFZ), Heidelberg, Germany; Division of Signal Transduction in Cancer and Metabolism, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | - Hannelore Pink
- Cancer Drug Development, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Nan Li
- Somatic Evolution and Early Detection, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Vivienn Weru
- Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Hamed Alborzinia
- Heidelberg Institute for Stem Cell Technology and Experimental Medicine, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Umut Yildiz
- Heidelberg Institute for Stem Cell Technology and Experimental Medicine, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Laura Grob
- Cancer Drug Development, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Minerva Taubert
- Cancer Drug Development, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Marie Czech
- Cancer Drug Development, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Michael Morgen
- Cancer Drug Development, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Christina Brandstädter
- Biochemistry and Molecular Biology, Interdisciplinary Research Center, Justus Liebig University, Giessen, Germany
| | - Katja Becker
- Biochemistry and Molecular Biology, Interdisciplinary Research Center, Justus Liebig University, Giessen, Germany
| | - Lianghao Mao
- Proteomics and Cancer Cell Signaling Group, CCU Pediatric Leukemia, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Ashok Kumar Jayavelu
- Proteomics and Cancer Cell Signaling Group, CCU Pediatric Leukemia, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Angela Goncalves
- Somatic Evolution and Early Detection, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Ulrike Uhrig
- Chemical Biology Core Facility, EMBL, Heidelberg, Germany
| | - Jeanette Seiler
- Division of RNA Biology & Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Yanhong Lyu
- Division of RNA Biology & Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany; Division of Cancer Research, Department of Thoracic Surgery, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, German Cancer Consortium (DKTK) - Partner Site Freiburg, Freiburg, Germany
| | - Sven Diederichs
- Division of RNA Biology & Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany; Division of Cancer Research, Department of Thoracic Surgery, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, German Cancer Consortium (DKTK) - Partner Site Freiburg, Freiburg, Germany
| | - Ursula Klingmüller
- Division of Systems Biology of Signal Transduction, German Cancer Research Center (DKFZ), Heidelberg, Germany; Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Heidelberg, Germany
| | - Martina Muckenthaler
- Department of Pediatric Hematology, Oncology and Immunology, University of Heidelberg, Heidelberg, Germany
| | | | - Aurelio Teleman
- Division of Signal Transduction in Cancer and Metabolism, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Aubry K Miller
- Cancer Drug Development, German Cancer Research Center (DKFZ), Heidelberg, Germany; German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Nikolas Gunkel
- Cancer Drug Development, German Cancer Research Center (DKFZ), Heidelberg, Germany; German Cancer Consortium (DKTK), Heidelberg, Germany.
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Abbas-Aghababazadeh F, Xu W, Haibe-Kains B. The impact of violating the independence assumption in meta-analysis on biomarker discovery. Front Genet 2023; 13:1027345. [PMID: 36726714 PMCID: PMC9885264 DOI: 10.3389/fgene.2022.1027345] [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: 08/24/2022] [Accepted: 11/25/2022] [Indexed: 01/06/2023] Open
Abstract
With rapid advancements in high-throughput sequencing technologies, massive amounts of "-omics" data are now available in almost every biomedical field. Due to variance in biological models and analytic methods, findings from clinical and biological studies are often not generalizable when tested in independent cohorts. Meta-analysis, a set of statistical tools to integrate independent studies addressing similar research questions, has been proposed to improve the accuracy and robustness of new biological insights. However, it is common practice among biomarker discovery studies using preclinical pharmacogenomic data to borrow molecular profiles of cancer cell lines from one study to another, creating dependence across studies. The impact of violating the independence assumption in meta-analyses is largely unknown. In this study, we review and compare different meta-analyses to estimate variations across studies along with biomarker discoveries using preclinical pharmacogenomics data. We further evaluate the performance of conventional meta-analysis where the dependence of the effects was ignored via simulation studies. Results show that, as the number of non-independent effects increased, relative mean squared error and lower coverage probability increased. Additionally, we also assess potential bias in the estimation of effects for established meta-analysis approaches when data are duplicated and the assumption of independence is violated. Using pharmacogenomics biomarker discovery, we find that treating dependent studies as independent can substantially increase the bias of meta-analyses. Importantly, we show that violating the independence assumption decreases the generalizability of the biomarker discovery process and increases false positive results, a key challenge in precision oncology.
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Affiliation(s)
| | - Wei Xu
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada,Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Benjamin Haibe-Kains
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada,Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada,Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada,Ontario Institute for Cancer Research, Toronto, ON, Canada,Department of Computer Science, University of Toronto, Toronto, ON, Canada,*Correspondence: Benjamin Haibe-Kains,
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40
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Jacoberger-Foissac C, Cousineau I, Bareche Y, Allard D, Chrobak P, Allard B, Pommey S, Messaoudi N, McNicoll Y, Soucy G, Koseoglu S, Masia R, Lake AC, Seo H, Eeles CB, Rohatgi N, Robson SC, Turcotte S, Haibe-Kains B, Stagg J. CD73 Inhibits cGAS-STING and Cooperates with CD39 to Promote Pancreatic Cancer. Cancer Immunol Res 2023; 11:56-71. [PMID: 36409930 PMCID: PMC9812927 DOI: 10.1158/2326-6066.cir-22-0260] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 09/07/2022] [Accepted: 11/18/2022] [Indexed: 11/22/2022]
Abstract
The ectonucleotidases CD39 and CD73 catalyze extracellular ATP to immunosuppressive adenosine, and as such, represent potential cancer targets. We investigated biological impacts of CD39 and CD73 in pancreatic ductal adenocarcinoma (PDAC) by studying clinical samples and experimental mouse tumors. Stromal CD39 and tumoral CD73 expression significantly associated with worse survival in human PDAC samples and abolished the favorable prognostic impact associated with the presence of tumor-infiltrating CD8+ T cells. In mouse transplanted KPC tumors, both CD39 and CD73 on myeloid cells, as well as CD73 on tumor cells, promoted polarization of infiltrating myeloid cells towards an M2-like phenotype, which enhanced tumor growth. CD39 on tumor-specific CD8+ T cells and pancreatic stellate cells also suppressed IFNγ production by T cells. Although therapeutic inhibition of CD39 or CD73 alone significantly delayed tumor growth in vivo, targeting of both ectonucleotidases exhibited markedly superior antitumor activity. CD73 expression on human and mouse PDAC tumor cells also protected against DNA damage induced by gemcitabine and irradiation. Accordingly, large-scale pharmacogenomic analyses of human PDAC cell lines revealed significant associations between CD73 expression and gemcitabine chemoresistance. Strikingly, increased DNA damage in CD73-deficient tumor cells associated with activation of the cGAS-STING pathway. Moreover, cGAS expression in mouse KPC tumor cells was required for antitumor activity of the CD73 inhibitor AB680 in vivo. Our study, thus, illuminates molecular mechanisms whereby CD73 and CD39 seemingly cooperate to promote PDAC progression.
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Affiliation(s)
- Célia Jacoberger-Foissac
- Faculty of Pharmacy, University of Montreal., Cancer Axis, Centre de Recherche Du Centre Hospitalier de l’Université de Montréal, Montreal, Quebec, Canada., Institut du Cancer de Montréal
| | - Isabelle Cousineau
- Cancer Axis, Centre de Recherche Du Centre Hospitalier de l’Université de Montréal, Montreal, Quebec, Canada., Institut du Cancer de Montréal
| | - Yacine Bareche
- Faculty of Pharmacy, University of Montreal., Cancer Axis, Centre de Recherche Du Centre Hospitalier de l’Université de Montréal, Montreal, Quebec, Canada., Institut du Cancer de Montréal
| | - David Allard
- Faculty of Pharmacy, University of Montreal., Cancer Axis, Centre de Recherche Du Centre Hospitalier de l’Université de Montréal, Montreal, Quebec, Canada., Institut du Cancer de Montréal
| | - Pavel Chrobak
- Cancer Axis, Centre de Recherche Du Centre Hospitalier de l’Université de Montréal, Montreal, Quebec, Canada., Institut du Cancer de Montréal
| | - Bertrand Allard
- Cancer Axis, Centre de Recherche Du Centre Hospitalier de l’Université de Montréal, Montreal, Quebec, Canada., Institut du Cancer de Montréal
| | - Sandra Pommey
- Cancer Axis, Centre de Recherche Du Centre Hospitalier de l’Université de Montréal, Montreal, Quebec, Canada., Institut du Cancer de Montréal
| | - Nouredin Messaoudi
- Department of Surgery, University of Antwerp, Antwerp, Belgium., Department of Surgery, Vrije Universiteit Brussel, Universitair Ziekenhuis Brussel and Europe Hospitals, Brussels, Belgium
| | - Yannic McNicoll
- Surgery Department, Hôpital Jean-Talon, CIUSSS NIM, Montreal, Quebec, Canada
| | - Geneviève Soucy
- Pathology Service, Centre Hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
| | | | - Ricard Masia
- Surface Oncology, Inc. Cambridge, Massachusetts, USA
| | | | - Heewon Seo
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Christopher B. Eeles
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Neha Rohatgi
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Simon C. Robson
- Center for Inflammation Research, Gastroenterology, Departments of Medicine and Anesthesia, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Simon Turcotte
- Cancer Axis, Centre de Recherche Du Centre Hospitalier de l’Université de Montréal, Montreal, Quebec, Canada., Institut du Cancer de Montréal., Hepatopancreatobiliary Surgery & Liver Transplantation Service, Centre Hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
| | - Benjamin Haibe-Kains
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada., Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada, Department of Computer Science, University of Toronto, Toronto, Ontario, Canada, Ontario Institute for Cancer Research, Toronto, Ontario, Canada, Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada
| | - John Stagg
- Faculty of Pharmacy, University of Montreal., Cancer Axis, Centre de Recherche Du Centre Hospitalier de l’Université de Montréal, Montreal, Quebec, Canada., Institut du Cancer de Montréal.,Correspondence: 900 St-Denis Street, Montréal, QC, Canada, H2X 0A9; ; Tel: 514-890-8000 ex:25170
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41
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Arribas AJ, Napoli S, Cascione L, Barnabei L, Sartori G, Cannas E, Gaudio E, Tarantelli C, Mensah AA, Spriano F, Zucchetto A, Rossi FM, Rinaldi A, de Moura MC, Jovic S, Pittau RB, Stathis A, Stussi G, Gattei V, Brown JR, Esteller M, Zucca E, Rossi D, Bertoni F. ERBB4-mediated signaling is a mediator of resistance to BTK and PI3K inhibitors in B cell lymphoid neoplasms. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.01.522017. [PMID: 36711490 PMCID: PMC9881865 DOI: 10.1101/2023.01.01.522017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
BTK and PI3K inhibitors are among the drugs approved for the treatment of patients with lymphoid neoplasms. Although active, their ability to lead as single agents to long-lasting complete remission is rather limited especially in the lymphoma setting. This indicates that tumor cells often develop resistance to the drugs. Here, we show that the overexpression of ERBB4 and its ligands represents a modality for B cell neoplastic cells to bypass the anti-tumor activity of BTK and PI3K inhibitors and that targeted pharmacological interventions can restore sensitivity to the small molecules. We started from a marginal zone lymphoma (MZL) cell line, Karpas-1718, kept under prolonged exposure to the PI3Kδ inhibitor idelalisib until acquisition of resistance, or with no drug. Cells underwent transcriptome, miRNA and methylation profiling, whole exome sequencing, and pharmacological screening which led to the identification of the overexpression of ERBB4 and its ligands HBEGF and NRG2 in the resistant cells. Cellular and genetic experiments demonstrated the involvement of this axis in blocking the anti-tumor activity of various BTK and PI3K inhibitors, currently used in the clinical setting. Addition of recombinant HBEGF induced resistance to BTK and PI3K inhibitors in parental cells but also in additional lymphoma models. Combination with the ERBB inhibitor lapatinib was beneficial in resistant cells and in other lymphoma models already expressing the identified resistance factors. Multi-omics analysis underlined that an epigenetic reprogramming affected the expression of the resistance-related factors, and pretreatment with demethylating agents or EZH2 inhibitors overcame the resistance. Resistance factors were shown to be expressed in clinical samples, further extending the findings of the study. In conclusions, we identified a novel ERBB4-driven mechanism of resistance to BTK and PI3K inhibitors and treatments that appear to overcome it. Key points A mechanism of secondary resistance to the PI3Kδ and BTK inhibitors in B cell neoplasms driven by secreted factors.Resistance can be reverted by targeting ERBB signaling.
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42
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Gendoo DMA. Overview of Bioinformatics Software and Databases for Metabolic Engineering. Methods Mol Biol 2023; 2553:265-274. [PMID: 36227548 DOI: 10.1007/978-1-0716-2617-7_13] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
The explosion of the "omics" era has introduced a growing number of sets and tools that facilitate molecular interrogation of the metabolome. These include various bioinformatics and pharmacogenomics resources that can be utilized independently or collectively to facilitate metabolic engineering across disease, clinical oncology, and understanding of molecular changes across larger systems. This review provides starting points for accessing publicly available data and computational tools that support assessment of metabolic profiles and metabolic regulation, providing both a depth-and-breadth approach toward understanding the metabolome. We focus in particular on pathway databases and tools, which provide in-depth analysis of metabolic pathways, which is at the heart of metabolic engineering.
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Affiliation(s)
- Deena M A Gendoo
- Centre for Computational Biology, Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom.
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom.
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43
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Utilization of Cancer Cell Line Screening to Elucidate the Anticancer Activity and Biological Pathways Related to the Ruthenium-Based Therapeutic BOLD-100. Cancers (Basel) 2022; 15:cancers15010028. [PMID: 36612025 PMCID: PMC9817855 DOI: 10.3390/cancers15010028] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 11/30/2022] [Accepted: 12/16/2022] [Indexed: 12/24/2022] Open
Abstract
BOLD-100 (sodium trans-[tetrachlorobis(1H indazole)ruthenate(III)]) is a ruthenium-based anticancer compound currently in clinical development. The identification of cancer types that show increased sensitivity towards BOLD-100 can lead to improved developmental strategies. Sensitivity profiling can also identify mechanisms of action that are pertinent for the bioactivity of complex therapeutics. Sensitivity to BOLD-100 was measured in a 319-cancer-cell line panel spanning 24 tissues. BOLD-100's sensitivity profile showed variation across the tissue lineages, including increased response in esophageal, bladder, and hematologic cancers. Multiple cancers, including esophageal, bile duct and colon cancer, had higher relative response to BOLD-100 than to cisplatin. Response to BOLD-100 showed only moderate correlation to anticancer compounds in the Genomics of Drug Sensitivity in Cancer (GDSC) database, as well as no clear theme in bioactivity of correlated hits, suggesting that BOLD-100 may have a differentiated therapeutic profile. The genomic modalities of cancer cell lines were modeled against the BOLD-100 sensitivity profile, which revealed that genes related to ribosomal processes were associated with sensitivity to BOLD-100. Machine learning modeling of the sensitivity profile to BOLD-100 and gene expression data provided moderative predictive value. These findings provide further mechanistic understanding around BOLD-100 and support its development for additional cancer types.
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44
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Kameda-Smith MM, Zhu H, Luo EC, Suk Y, Xella A, Yee B, Chokshi C, Xing S, Tan F, Fox RG, Adile AA, Bakhshinyan D, Brown K, Gwynne WD, Subapanditha M, Miletic P, Picard D, Burns I, Moffat J, Paruch K, Fleming A, Hope K, Provias JP, Remke M, Lu Y, Reya T, Venugopal C, Reimand J, Wechsler-Reya RJ, Yeo GW, Singh SK. Characterization of an RNA binding protein interactome reveals a context-specific post-transcriptional landscape of MYC-amplified medulloblastoma. Nat Commun 2022; 13:7506. [PMID: 36473869 PMCID: PMC9726987 DOI: 10.1038/s41467-022-35118-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Accepted: 11/18/2022] [Indexed: 12/12/2022] Open
Abstract
Pediatric medulloblastoma (MB) is the most common solid malignant brain neoplasm, with Group 3 (G3) MB representing the most aggressive subgroup. MYC amplification is an independent poor prognostic factor in G3 MB, however, therapeutic targeting of the MYC pathway remains limited and alternative therapies for G3 MB are urgently needed. Here we show that the RNA-binding protein, Musashi-1 (MSI1) is an essential mediator of G3 MB in both MYC-overexpressing mouse models and patient-derived xenografts. MSI1 inhibition abrogates tumor initiation and significantly prolongs survival in both models. We identify binding targets of MSI1 in normal neural and G3 MB stem cells and then cross referenced these data with unbiased large-scale screens at the transcriptomic, translatomic and proteomic levels to systematically dissect its functional role. Comparative integrative multi-omic analyses of these large datasets reveal cancer-selective MSI1-bound targets sharing multiple MYC associated pathways, providing a valuable resource for context-specific therapeutic targeting of G3 MB.
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Affiliation(s)
- Michelle M. Kameda-Smith
- grid.25073.330000 0004 1936 8227Centre for Discovery in Cancer Research (CDCR), McMaster University, Hamilton, ON Canada ,grid.25073.330000 0004 1936 8227Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, ON Canada ,grid.25073.330000 0004 1936 8227Surgery, Faculty of Health Sciences, McMaster University, Hamilton, ON Canada
| | - Helen Zhu
- grid.419890.d0000 0004 0626 690XComputational Biology Program, Ontario Institute for Cancer Research, Toronto, Canada ,grid.17063.330000 0001 2157 2938Department of Medical Biophysics, University of Toronto, Toronto, Canada ,grid.231844.80000 0004 0474 0428University Health Network, Toronto, ON Canada ,grid.494618.6Vector Institute Toronto, Toronto, ON Canada
| | - En-Ching Luo
- grid.266100.30000 0001 2107 4242Department of Cellular and Molecular Medicine, University of California at San Diego, La Jolla, CA USA ,grid.266100.30000 0001 2107 4242Stem Cell Program, University of California San Diego, La Jolla, CA USA ,grid.468218.10000 0004 5913 3393Sanford Consortium for Regenerative Medicine, La Jolla, CA USA
| | - Yujin Suk
- grid.25073.330000 0004 1936 8227Centre for Discovery in Cancer Research (CDCR), McMaster University, Hamilton, ON Canada ,grid.25073.330000 0004 1936 8227Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, ON Canada ,grid.25073.330000 0004 1936 8227Michael G DeGroote School of Medicine, McMaster University, Hamilton, Canada
| | - Agata Xella
- grid.479509.60000 0001 0163 8573Tumor Initiation and Maintenance Program, National Cancer Institute-Designated Cancer Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA USA
| | - Brian Yee
- grid.266100.30000 0001 2107 4242Department of Cellular and Molecular Medicine, University of California at San Diego, La Jolla, CA USA ,grid.266100.30000 0001 2107 4242Stem Cell Program, University of California San Diego, La Jolla, CA USA ,grid.468218.10000 0004 5913 3393Sanford Consortium for Regenerative Medicine, La Jolla, CA USA
| | - Chirayu Chokshi
- grid.25073.330000 0004 1936 8227Centre for Discovery in Cancer Research (CDCR), McMaster University, Hamilton, ON Canada ,grid.25073.330000 0004 1936 8227Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, ON Canada
| | - Sansi Xing
- grid.25073.330000 0004 1936 8227Centre for Discovery in Cancer Research (CDCR), McMaster University, Hamilton, ON Canada ,grid.25073.330000 0004 1936 8227Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, ON Canada
| | - Frederick Tan
- grid.266100.30000 0001 2107 4242Department of Cellular and Molecular Medicine, University of California at San Diego, La Jolla, CA USA ,grid.266100.30000 0001 2107 4242Stem Cell Program, University of California San Diego, La Jolla, CA USA ,grid.468218.10000 0004 5913 3393Sanford Consortium for Regenerative Medicine, La Jolla, CA USA
| | - Raymond G. Fox
- grid.266100.30000 0001 2107 4242Departments of Pharmacology and Medicine, University of California San Diego School of Medicine, Sanford Consortium for Regenerative Medicine, La Jolla, CA USA
| | - Ashley A. Adile
- grid.25073.330000 0004 1936 8227Centre for Discovery in Cancer Research (CDCR), McMaster University, Hamilton, ON Canada ,grid.25073.330000 0004 1936 8227Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, ON Canada
| | - David Bakhshinyan
- grid.25073.330000 0004 1936 8227Centre for Discovery in Cancer Research (CDCR), McMaster University, Hamilton, ON Canada ,grid.25073.330000 0004 1936 8227Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, ON Canada
| | - Kevin Brown
- grid.17063.330000 0001 2157 2938Donnelly Centre, Department of Molecular Genetics, University of Toronto, Toronto, Canada
| | - William D. Gwynne
- grid.25073.330000 0004 1936 8227Centre for Discovery in Cancer Research (CDCR), McMaster University, Hamilton, ON Canada ,grid.25073.330000 0004 1936 8227Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, ON Canada
| | - Minomi Subapanditha
- grid.25073.330000 0004 1936 8227Centre for Discovery in Cancer Research (CDCR), McMaster University, Hamilton, ON Canada
| | - Petar Miletic
- grid.25073.330000 0004 1936 8227Centre for Discovery in Cancer Research (CDCR), McMaster University, Hamilton, ON Canada ,grid.25073.330000 0004 1936 8227Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, ON Canada
| | - Daniel Picard
- grid.14778.3d0000 0000 8922 7789Department of Pediatric Oncology, Hematology, and Clinical Immunology, Medical Faculty, University Hospital Düsseldorf, Düsseldorf, Germany
| | - Ian Burns
- grid.25073.330000 0004 1936 8227Michael G DeGroote School of Medicine, McMaster University, Hamilton, Canada
| | - Jason Moffat
- grid.17063.330000 0001 2157 2938Donnelly Centre, Department of Molecular Genetics, University of Toronto, Toronto, Canada
| | - Kamil Paruch
- grid.10267.320000 0001 2194 0956Department of Chemistry, CZ Openscreen, Faculty of Science, Masaryk University, Kamenice 5, 625 00 Brno, Czech Republic ,grid.483343.bInternational Clinical Research Center, St. Anne’s University Hospital in Brno, 602 00 Brno, Czech Republic
| | - Adam Fleming
- grid.25073.330000 0004 1936 8227McMaster University, Departments of Pediatrics, Hematology and Oncology Division, Hamilton, Canada
| | - Kristin Hope
- grid.25073.330000 0004 1936 8227Centre for Discovery in Cancer Research (CDCR), McMaster University, Hamilton, ON Canada ,grid.25073.330000 0004 1936 8227Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, ON Canada
| | - John P. Provias
- grid.25073.330000 0004 1936 8227McMaster University, Departments of Neuropathology, Hamilton, Canada
| | - Marc Remke
- grid.14778.3d0000 0000 8922 7789Department of Pediatric Oncology, Hematology, and Clinical Immunology, Medical Faculty, University Hospital Düsseldorf, Düsseldorf, Germany
| | - Yu Lu
- grid.25073.330000 0004 1936 8227Centre for Discovery in Cancer Research (CDCR), McMaster University, Hamilton, ON Canada ,grid.25073.330000 0004 1936 8227Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, ON Canada
| | - Tannishtha Reya
- grid.266100.30000 0001 2107 4242Departments of Pharmacology and Medicine, University of California San Diego School of Medicine, Sanford Consortium for Regenerative Medicine, La Jolla, CA USA ,grid.239585.00000 0001 2285 2675Present Address: Herbert Irving Comprehensive Cancer Center, Department of Physiology and Cellular Biophysics, Columbia University Medical Center, New York, NY USA
| | - Chitra Venugopal
- grid.25073.330000 0004 1936 8227Centre for Discovery in Cancer Research (CDCR), McMaster University, Hamilton, ON Canada ,grid.25073.330000 0004 1936 8227Surgery, Faculty of Health Sciences, McMaster University, Hamilton, ON Canada
| | - Jüri Reimand
- grid.419890.d0000 0004 0626 690XComputational Biology Program, Ontario Institute for Cancer Research, Toronto, Canada ,grid.17063.330000 0001 2157 2938Department of Medical Biophysics, University of Toronto, Toronto, Canada ,grid.17063.330000 0001 2157 2938Department of Molecular Genetics, University of Toronto, Toronto, Canada
| | - Robert J. Wechsler-Reya
- grid.479509.60000 0001 0163 8573Tumor Initiation and Maintenance Program, National Cancer Institute-Designated Cancer Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA USA ,grid.239585.00000 0001 2285 2675Present Address: Herbert Irving Comprehensive Cancer Center, Department of Physiology and Cellular Biophysics, Columbia University Medical Center, New York, NY USA
| | - Gene W. Yeo
- grid.266100.30000 0001 2107 4242Department of Cellular and Molecular Medicine, University of California at San Diego, La Jolla, CA USA ,grid.266100.30000 0001 2107 4242Stem Cell Program, University of California San Diego, La Jolla, CA USA ,grid.468218.10000 0004 5913 3393Sanford Consortium for Regenerative Medicine, La Jolla, CA USA
| | - Sheila K. Singh
- grid.25073.330000 0004 1936 8227Centre for Discovery in Cancer Research (CDCR), McMaster University, Hamilton, ON Canada ,grid.25073.330000 0004 1936 8227Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, ON Canada ,grid.25073.330000 0004 1936 8227Surgery, Faculty of Health Sciences, McMaster University, Hamilton, ON Canada ,grid.25073.330000 0004 1936 8227McMaster University, Department of Pediatrics, Hamilton, Canada
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45
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Chen H, Chew G, Devapragash N, Loh JZ, Huang KY, Guo J, Liu S, Tan ELS, Chen S, Tee NGZ, Mia MM, Singh MK, Zhang A, Behmoaras J, Petretto E. The E3 ubiquitin ligase WWP2 regulates pro-fibrogenic monocyte infiltration and activity in heart fibrosis. Nat Commun 2022; 13:7375. [PMID: 36450710 PMCID: PMC9712659 DOI: 10.1038/s41467-022-34971-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 11/11/2022] [Indexed: 12/03/2022] Open
Abstract
Non-ischemic cardiomyopathy (NICM) can cause left ventricular dysfunction through interstitial fibrosis, which corresponds to the failure of cardiac tissue remodeling. Recent evidence implicates monocytes/macrophages in the etiopathology of cardiac fibrosis, but giving their heterogeneity and the antagonizing roles of macrophage subtypes in fibrosis, targeting these cells has been challenging. Here we focus on WWP2, an E3 ubiquitin ligase that acts as a positive genetic regulator of human and murine cardiac fibrosis, and show that myeloid specific deletion of WWP2 reduces cardiac fibrosis in hypertension-induced NICM. By using single cell RNA sequencing analysis of immune cells in the same model, we establish the functional heterogeneity of macrophages and define an early pro-fibrogenic phase of NICM that is driven by Ccl5-expressing Ly6chigh monocytes. Among cardiac macrophage subtypes, WWP2 dysfunction primarily affects Ly6chigh monocytes via modulating Ccl5, and consequentially macrophage infiltration and activation, which contributes to reduced myofibroblast trans-differentiation. WWP2 interacts with transcription factor IRF7, promoting its non-degradative mono-ubiquitination, nuclear translocation and transcriptional activity, leading to upregulation of Ccl5 at transcriptional level. We identify a pro-fibrogenic macrophage subtype in non-ischemic cardiomyopathy, and demonstrate that WWP2 is a key regulator of IRF7-mediated Ccl5/Ly6chigh monocyte axis in heart fibrosis.
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Affiliation(s)
- Huimei Chen
- grid.428397.30000 0004 0385 0924Programme in Cardiovascular and Metabolic Disorders, Duke-NUS Medical School, 8 College Road, 169857 Singapore, Singapore ,grid.254147.10000 0000 9776 7793Institute for Big Data and Artificial Intelligence in Medicine, School of Science, China Pharmaceutical University, Nanjing, 210009 China
| | - Gabriel Chew
- grid.428397.30000 0004 0385 0924Programme in Cardiovascular and Metabolic Disorders, Duke-NUS Medical School, 8 College Road, 169857 Singapore, Singapore
| | - Nithya Devapragash
- grid.428397.30000 0004 0385 0924Programme in Cardiovascular and Metabolic Disorders, Duke-NUS Medical School, 8 College Road, 169857 Singapore, Singapore
| | - Jui Zhi Loh
- grid.428397.30000 0004 0385 0924Programme in Cardiovascular and Metabolic Disorders, Duke-NUS Medical School, 8 College Road, 169857 Singapore, Singapore
| | - Kevin Y. Huang
- grid.428397.30000 0004 0385 0924Programme in Cardiovascular and Metabolic Disorders, Duke-NUS Medical School, 8 College Road, 169857 Singapore, Singapore
| | - Jing Guo
- grid.428397.30000 0004 0385 0924Programme in Cardiovascular and Metabolic Disorders, Duke-NUS Medical School, 8 College Road, 169857 Singapore, Singapore
| | - Shiyang Liu
- grid.428397.30000 0004 0385 0924Programme in Cardiovascular and Metabolic Disorders, Duke-NUS Medical School, 8 College Road, 169857 Singapore, Singapore
| | - Elisabeth Li Sa Tan
- grid.428397.30000 0004 0385 0924Programme in Cardiovascular and Metabolic Disorders, Duke-NUS Medical School, 8 College Road, 169857 Singapore, Singapore
| | - Shuang Chen
- grid.254147.10000 0000 9776 7793Institute for Big Data and Artificial Intelligence in Medicine, School of Science, China Pharmaceutical University, Nanjing, 210009 China ,grid.452511.6Department of Nephrology, Children’s Hospital of Nanjing Medical University, Nanjing, 210008 China
| | - Nicole Gui Zhen Tee
- grid.419385.20000 0004 0620 9905National Heart Centre Singapore, Singapore, 169609 Singapore
| | - Masum M. Mia
- grid.428397.30000 0004 0385 0924Programme in Cardiovascular and Metabolic Disorders, Duke-NUS Medical School, 8 College Road, 169857 Singapore, Singapore
| | - Manvendra K. Singh
- grid.428397.30000 0004 0385 0924Programme in Cardiovascular and Metabolic Disorders, Duke-NUS Medical School, 8 College Road, 169857 Singapore, Singapore
| | - Aihua Zhang
- grid.452511.6Department of Nephrology, Children’s Hospital of Nanjing Medical University, Nanjing, 210008 China
| | - Jacques Behmoaras
- grid.428397.30000 0004 0385 0924Programme in Cardiovascular and Metabolic Disorders, Duke-NUS Medical School, 8 College Road, 169857 Singapore, Singapore ,grid.413629.b0000 0001 0705 4923Centre for Inflammatory Disease, Imperial College London, Hammersmith Hospital, London, W12 0NN UK
| | - Enrico Petretto
- grid.428397.30000 0004 0385 0924Programme in Cardiovascular and Metabolic Disorders, Duke-NUS Medical School, 8 College Road, 169857 Singapore, Singapore ,grid.254147.10000 0000 9776 7793Institute for Big Data and Artificial Intelligence in Medicine, School of Science, China Pharmaceutical University, Nanjing, 210009 China
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46
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Multi-Omics Alleviates the Limitations of Panel Sequencing for Cancer Drug Response Prediction. Cancers (Basel) 2022; 14:cancers14225604. [PMID: 36428696 PMCID: PMC9688044 DOI: 10.3390/cancers14225604] [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: 10/19/2022] [Revised: 11/10/2022] [Accepted: 11/12/2022] [Indexed: 11/17/2022] Open
Abstract
Comprehensive genomic profiling using cancer gene panels has been shown to improve treatment options for a variety of cancer types. However, genomic aberrations detected via such gene panels do not necessarily serve as strong predictors of drug sensitivity. In this study, using pharmacogenomics datasets of cell lines, patient-derived xenografts, and ex vivo treated fresh tumor specimens, we demonstrate that utilizing the transcriptome on top of gene panel features substantially improves drug response prediction performance in cancer.
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47
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van Leeuwen JE, Ba-Alawi W, Branchard E, Cruickshank J, Schormann W, Longo J, Silvester J, Gross PL, Andrews DW, Cescon DW, Haibe-Kains B, Penn LZ, Gendoo DMA. Computational pharmacogenomic screen identifies drugs that potentiate the anti-breast cancer activity of statins. Nat Commun 2022; 13:6323. [PMID: 36280687 PMCID: PMC9592602 DOI: 10.1038/s41467-022-33144-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 09/02/2022] [Indexed: 12/25/2022] Open
Abstract
Statins, a family of FDA-approved cholesterol-lowering drugs that inhibit the rate-limiting enzyme of the mevalonate metabolic pathway, have demonstrated anticancer activity. Evidence shows that dipyridamole potentiates statin-induced cancer cell death by blocking a restorative feedback loop triggered by statin treatment. Leveraging this knowledge, we develop an integrative pharmacogenomics pipeline to identify compounds similar to dipyridamole at the level of drug structure, cell sensitivity and molecular perturbation. To overcome the complex polypharmacology of dipyridamole, we focus our pharmacogenomics pipeline on mevalonate pathway genes, which we name mevalonate drug-network fusion (MVA-DNF). We validate top-ranked compounds, nelfinavir and honokiol, and identify that low expression of the canonical epithelial cell marker, E-cadherin, is associated with statin-compound synergy. Analysis of remaining prioritized hits led to the validation of additional compounds, clotrimazole and vemurafenib. Thus, our computational pharmacogenomic approach identifies actionable compounds with pathway-specific activities.
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Affiliation(s)
- Jenna E. van Leeuwen
- grid.17063.330000 0001 2157 2938Department of Medical Biophysics, University of Toronto, 101 College Street, Toronto, ON M5G 1L7 Canada ,grid.231844.80000 0004 0474 0428Princess Margaret Cancer Centre, University Health Network, 101 College Street, Toronto, ON M5G 1L7 Canada
| | - Wail Ba-Alawi
- grid.17063.330000 0001 2157 2938Department of Medical Biophysics, University of Toronto, 101 College Street, Toronto, ON M5G 1L7 Canada ,grid.231844.80000 0004 0474 0428Princess Margaret Cancer Centre, University Health Network, 101 College Street, Toronto, ON M5G 1L7 Canada
| | - Emily Branchard
- grid.231844.80000 0004 0474 0428Princess Margaret Cancer Centre, University Health Network, 101 College Street, Toronto, ON M5G 1L7 Canada
| | - Jennifer Cruickshank
- grid.231844.80000 0004 0474 0428Princess Margaret Cancer Centre, University Health Network, 101 College Street, Toronto, ON M5G 1L7 Canada
| | - Wiebke Schormann
- grid.17063.330000 0001 2157 2938Biological Sciences, Sunnybrook Research Institute, University of Toronto, Toronto, ON M4N 3M5 Canada
| | - Joseph Longo
- grid.17063.330000 0001 2157 2938Department of Medical Biophysics, University of Toronto, 101 College Street, Toronto, ON M5G 1L7 Canada ,grid.231844.80000 0004 0474 0428Princess Margaret Cancer Centre, University Health Network, 101 College Street, Toronto, ON M5G 1L7 Canada
| | - Jennifer Silvester
- grid.231844.80000 0004 0474 0428Princess Margaret Cancer Centre, University Health Network, 101 College Street, Toronto, ON M5G 1L7 Canada
| | - Peter L. Gross
- grid.25073.330000 0004 1936 8227Department of Medicine, McMaster University, 1280 Main St W, Hamilton, ON L8S 4L8 Canada
| | - David W. Andrews
- grid.17063.330000 0001 2157 2938Department of Medical Biophysics, University of Toronto, 101 College Street, Toronto, ON M5G 1L7 Canada ,grid.17063.330000 0001 2157 2938Biological Sciences, Sunnybrook Research Institute, University of Toronto, Toronto, ON M4N 3M5 Canada
| | - David W. Cescon
- grid.231844.80000 0004 0474 0428Princess Margaret Cancer Centre, University Health Network, 101 College Street, Toronto, ON M5G 1L7 Canada ,grid.17063.330000 0001 2157 2938Division of Medical Oncology and Hematology, Department of Medicine, University of Toronto, 27 King’s College Circle, Toronto, ON M5S 1A1 Canada
| | - Benjamin Haibe-Kains
- grid.17063.330000 0001 2157 2938Department of Medical Biophysics, University of Toronto, 101 College Street, Toronto, ON M5G 1L7 Canada ,grid.231844.80000 0004 0474 0428Princess Margaret Cancer Centre, University Health Network, 101 College Street, Toronto, ON M5G 1L7 Canada ,grid.17063.330000 0001 2157 2938Department of Computer Science, University of Toronto, 10 King’s College Road, Toronto, ON M5S 3G4 Canada ,grid.419890.d0000 0004 0626 690XOntario Institute of Cancer Research, 661 University Avenue, Suite 510, Toronto, ON M5G 0A3 Canada
| | - Linda Z. Penn
- grid.17063.330000 0001 2157 2938Department of Medical Biophysics, University of Toronto, 101 College Street, Toronto, ON M5G 1L7 Canada ,grid.231844.80000 0004 0474 0428Princess Margaret Cancer Centre, University Health Network, 101 College Street, Toronto, ON M5G 1L7 Canada
| | - Deena M. A. Gendoo
- grid.6572.60000 0004 1936 7486Centre for Computational Biology, Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, Birmingham, B15 2TT UK ,grid.6572.60000 0004 1936 7486Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, Birmingham, B15 2TT UK
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48
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Precision oncology using ex vivo technology: a step towards individualised cancer care? Expert Rev Mol Med 2022; 24:e39. [PMID: 36184897 PMCID: PMC9884776 DOI: 10.1017/erm.2022.32] [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] [Indexed: 01/11/2023]
Abstract
Despite advances in cancer genomics and the increased use of genomic medicine, metastatic cancer is still mostly an incurable and fatal disease. With diminishing returns from traditional drug discovery strategies, and high clinical failure rates, more emphasis is being placed on alternative drug discovery platforms, such as ex vivo approaches. Ex vivo approaches aim to embed biological relevance and inter-patient variability at an earlier stage of drug discovery, and to offer more precise treatment stratification for patients. However, these techniques also have a high potential to offer personalised therapies to patients, complementing and enhancing genomic medicine. Although an array of approaches are available to researchers, only a minority of techniques have made it through to direct patient treatment within robust clinical trials. Within this review, we discuss the current challenges to ex vivo approaches within clinical practice and summarise the contemporary literature which has directed patient treatment. Finally, we map out how ex vivo approaches could transition from a small-scale, predominantly research based technology to a robust and validated predictive tool. In future, these pre-clinical approaches may be integrated into clinical cancer pathways to assist in the personalisation of therapy choices and to hopefully improve patient experiences and outcomes.
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49
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Gene expression based inference of cancer drug sensitivity. Nat Commun 2022; 13:5680. [PMID: 36167836 PMCID: PMC9515171 DOI: 10.1038/s41467-022-33291-z] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 09/12/2022] [Indexed: 11/09/2022] Open
Abstract
Inter and intra-tumoral heterogeneity are major stumbling blocks in the treatment of cancer and are responsible for imparting differential drug responses in cancer patients. Recently, the availability of high-throughput screening datasets has paved the way for machine learning based personalized therapy recommendations using the molecular profiles of cancer specimens. In this study, we introduce Precily, a predictive modeling approach to infer treatment response in cancers using gene expression data. In this context, we demonstrate the benefits of considering pathway activity estimates in tandem with drug descriptors as features. We apply Precily on single-cell and bulk RNA sequencing data associated with hundreds of cancer cell lines. We then assess the predictability of treatment outcomes using our in-house prostate cancer cell line and xenografts datasets exposed to differential treatment conditions. Further, we demonstrate the applicability of our approach on patient drug response data from The Cancer Genome Atlas and an independent clinical study describing the treatment journey of three melanoma patients. Our findings highlight the importance of chemo-transcriptomics approaches in cancer treatment selection. Predicting treatment response in cancer remains a highly complex task. Here, the authors develop Precily, a deep neural network framework to predict treatment response in cancer by considering gene expression, pathway activity estimates and drug features, and test this method in multiple datasets and preclinical models.
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50
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Dastjerdi S, Haghparast A, Amroabadi JM, Dolatabadi NF, Mirzaei S, Zamani A, Hashemi M, Mahdevar M, Ghaedi K. Elevated CDK5R1 expression associated with poor prognosis, proliferation, and drug resistance in colorectal and breast malignancies: CDK5R1 as an oncogene in cancers. Chem Biol Interact 2022; 368:110190. [PMID: 36162454 DOI: 10.1016/j.cbi.2022.110190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Revised: 09/15/2022] [Accepted: 09/20/2022] [Indexed: 11/03/2022]
Abstract
BACKGROUND Studies have shown that the CDK5R1 gene could have a part in some types of cancer. This study sought to examine the relationship between CDK5R1 expression and prognosis and medication resistance in 13 commonly occurring cancers. METHOD The cancer genome atlas data and clinical data were utilized to assess the role of CDK5R1 in malignancies. The expression data of 13 cancers were also integrated and used for the co-expression network. The relationship between CDK5R1 expression and drug resistance and sensitivity was evaluated using pharmacogenomics data. The colorectal cancer (CRC) and breast cancer (BC) were used to confirm the results through the RT-qPCR method. RESULTS With the exception of gastric cancer, all common malignancies showed an increase in CDK5R1 expression. Also, outcomes of sensitivity and specificity showed that CDK5R1 level could be a really good potential biomarker. Additionally, CDK5R1 expression was higher in CRC and BC samples compared to adjacent normal, according to RT-qPCR data. In six types of tumors and combined data, a poor prognosis was associated with increased CDK5R1 expression. The CDK5R1-associated genes were connected to the primary oncogenic pathways in cancer cells, according to the co-expression network. Also, CDK5R1 level was significantly linked to the resistance and sensitivity of several chemotherapy drugs and caused the highest resistance to cyclophosphamide. CONCLUSION CDK5R1 expression is upregulated in 12 prevalent cancers and can play an oncogenic role. Also, this gene's expression could be used as a biomarker to predict patient survival and medication resistance.
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Affiliation(s)
- Shaghayegh Dastjerdi
- Department of Cellular and Molecular Sciences, Faculty of Advanced Sciences and Technology, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | | | | | | | - Sepideh Mirzaei
- Department of Biology, Faculty of Science, Islamic Azad University, Science and Research Branch, Tehran, Iran
| | - Atefeh Zamani
- Gene Raz Bu Ali, Genetic and Biotechnology Academy, Isfahan, Iran
| | - Mehrdad Hashemi
- Department of Genetics, Faculty of Advanced Science and Technology, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran; Farhikhtegan Medical Convergence Sciences Research Center, Farhikhtegan Hospital Tehran Medical Sciences, Islamic Azad University, Tehran, Iran.
| | | | - Kamran Ghaedi
- Department of Cell and Molecular Biology and Microbiology, Faculty of Biological Science and Technology, University of Isfahan, Isfahan, Iran.
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