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Tarin M, Babaie M, Eshghi H, Matin MM, Saljooghi AS. Elesclomol, a copper-transporting therapeutic agent targeting mitochondria: from discovery to its novel applications. J Transl Med 2023; 21:745. [PMID: 37864163 PMCID: PMC10589935 DOI: 10.1186/s12967-023-04533-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 09/16/2023] [Indexed: 10/22/2023] Open
Abstract
Copper (Cu) is an essential element that is involved in a variety of biochemical processes. Both deficiency and accumulation of Cu are associated with various diseases; and a high amount of accumulated Cu in cells can be fatal. The production of reactive oxygen species (ROS), oxidative stress, and cuproptosis are among the proposed mechanisms of copper toxicity at high concentrations. Elesclomol (ELC) is a mitochondrion-targeting agent discovered for the treatment of solid tumors. In this review, we summarize the synthesis of this drug, its mechanisms of action, and the current status of its applications in the treatment of various diseases such as cancer, tuberculosis, SARS-CoV-2 infection, and other copper-associated disorders. We also provide some detailed information about future directions to improve its clinical performance.
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Affiliation(s)
- Mojtaba Tarin
- Department of Chemistry, Faculty of Science, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Maryam Babaie
- Department of Chemistry, Faculty of Science, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Hossein Eshghi
- Department of Chemistry, Faculty of Science, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Maryam M. Matin
- Department of Biology, Faculty of Science, Ferdowsi University of Mashhad, Mashhad, Iran
- Novel Diagnostics and Therapeutics Research Group, Institute of Biotechnology, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Amir Sh. Saljooghi
- Department of Chemistry, Faculty of Science, Ferdowsi University of Mashhad, Mashhad, Iran
- Novel Diagnostics and Therapeutics Research Group, Institute of Biotechnology, Ferdowsi University of Mashhad, Mashhad, Iran
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Zhang W, Lee AM, Jena S, Huang Y, Ho Y, Tietz KT, Miller CR, Su MC, Mentzer J, Ling AL, Li Y, Dehm SM, Huang RS. Computational drug discovery for castration-resistant prostate cancers through in vitro drug response modeling. Proc Natl Acad Sci U S A 2023; 120:e2218522120. [PMID: 37068243 PMCID: PMC10151558 DOI: 10.1073/pnas.2218522120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 03/17/2023] [Indexed: 04/19/2023] Open
Abstract
Prostate cancer (PC) is the most frequently diagnosed malignancy and a leading cause of cancer deaths in US men. Many PC cases metastasize and develop resistance to systemic hormonal therapy, a stage known as castration-resistant prostate cancer (CRPC). Therefore, there is an urgent need to develop effective therapeutic strategies for CRPC. Traditional drug discovery pipelines require significant time and capital input, which highlights a need for novel methods to evaluate the repositioning potential of existing drugs. Here, we present a computational framework to predict drug sensitivities of clinical CRPC tumors to various existing compounds and identify treatment options with high potential for clinical impact. We applied this method to a CRPC patient cohort and nominated drugs to combat resistance to hormonal therapies including abiraterone and enzalutamide. The utility of this method was demonstrated by nomination of multiple drugs that are currently undergoing clinical trials for CRPC. Additionally, this method identified the tetracycline derivative COL-3, for which we validated higher efficacy in an isogenic cell line model of enzalutamide-resistant vs. enzalutamide-sensitive CRPC. In enzalutamide-resistant CRPC cells, COL-3 displayed higher activity for inhibiting cell growth and migration, and for inducing G1-phase cell cycle arrest and apoptosis. Collectively, these findings demonstrate the utility of a computational framework for independent validation of drugs being tested in CRPC clinical trials, and for nominating drugs with enhanced biological activity in models of enzalutamide-resistant CRPC. The efficiency of this method relative to traditional drug development approaches indicates a high potential for accelerating drug development for CRPC.
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Affiliation(s)
- Weijie Zhang
- Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, MN55455
- The Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN55455
| | - Adam M. Lee
- The Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN55455
| | - Sampreeti Jena
- The Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN55455
| | - Yingbo Huang
- The Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN55455
| | - Yeung Ho
- Department of Laboratory Medicine and Pathology, The University of Minnesota Medical School, Minneapolis, MN55455
| | - Kiel T. Tietz
- Department of Laboratory Medicine and Pathology, The University of Minnesota Medical School, Minneapolis, MN55455
| | - Conor R. Miller
- Department of Laboratory Medicine and Pathology, The University of Minnesota Medical School, Minneapolis, MN55455
| | - Mei-Chi Su
- The Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN55455
| | - Joshua Mentzer
- The Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN55455
| | - Alexander L. Ling
- The Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN55455
| | - Yingming Li
- Department of Laboratory Medicine and Pathology, The University of Minnesota Medical School, Minneapolis, MN55455
| | - Scott M. Dehm
- Department of Laboratory Medicine and Pathology, The University of Minnesota Medical School, Minneapolis, MN55455
| | - R. Stephanie Huang
- Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, MN55455
- The Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN55455
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Padmanabhan R, Kheraldine H, Gupta I, Meskin N, Hamad A, Vranic S, Al Moustafa AE. Quantification of the growth suppression of HER2+ breast cancer colonies under the effect of trastuzumab and PD-1/PD-L1 inhibitor. Front Oncol 2022; 12:977664. [PMID: 36568154 PMCID: PMC9769711 DOI: 10.3389/fonc.2022.977664] [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: 06/24/2022] [Accepted: 10/26/2022] [Indexed: 12/12/2022] Open
Abstract
Introduction Immune checkpoint blockade (ICB)-based therapy is revolutionizing cancer treatment by fostering successful immune surveillance and effector cell responses against various types of cancers. However, patients with HER2+ cancers are yet to benefit from this therapeutic strategy. Precisely, several questions regarding the right combination of drugs, drug modality, and effective dose recommendations pertaining to the use of ICB-based therapy for HER2+ patients remain unanswered. Methods In this study, we use a mathematical modeling-based approach to quantify the growth inhibition of HER2+ breast cancer (BC) cell colonies (ZR75) when treated with anti-HER2; trastuzumab (TZ) and anti-PD-1/PD-L1 (BMS-202) agents. Results and discussion Our data show that a combination therapy of TZ and BMS-202 can significantly reduce the viability of ZR75 cells and trigger several morphological changes. The combination decreased the cell's invasiveness along with altering several key pathways, such as Akt/mTor and ErbB2 compared to monotherapy. In addition, BMS-202 causes dose-dependent growth inhibition of HER2+ BC cell colonies alone, while this effect is significantly improved when used in combination with TZ. Based on the in-vitro monoculture experiments conducted, we argue that BMS-202 can cause tumor growth suppression not only by mediating immune response but also by interfering with the growth signaling pathways of HER2+BC. Nevertheless, further studies are imperative to substantiate this argument and to uncover the potential crosstalk between PD-1/PD-L1 inhibitors and HER2 growth signaling pathways in breast cancer.
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Affiliation(s)
| | - Hadeel Kheraldine
- College of Medicine, Qatar University (QU) Health, Qatar University, Doha, Qatar,Biomedical Research Centre, Qatar University, Doha, Qatar
| | - Ishita Gupta
- College of Medicine, Qatar University (QU) Health, Qatar University, Doha, Qatar,Biomedical Research Centre, Qatar University, Doha, Qatar
| | - Nader Meskin
- Department of Electrical Engineering, Qatar University, Doha, Qatar,*Correspondence: Nader Meskin, ; Ala-Eddin Al Moustafa,
| | - Anas Hamad
- Pharmaceutical Department at Hamad Medical Corporation, Hamad Medical Corporation, Doha, Qatar
| | - Semir Vranic
- College of Medicine, Qatar University (QU) Health, Qatar University, Doha, Qatar
| | - Ala-Eddin Al Moustafa
- College of Medicine, Qatar University (QU) Health, Qatar University, Doha, Qatar,Biomedical Research Centre, Qatar University, Doha, Qatar,*Correspondence: Nader Meskin, ; Ala-Eddin Al Moustafa,
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4
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Zheng Z, Xie W, Chen X, Wang F, Huang L, Li X, Lin Q, Wong KC. Subclass-specific Prognosis and Treatment Efficacy Inference in Head and Neck Squamous Carcinoma. IEEE J Biomed Health Inform 2022; 26:4303-4313. [PMID: 35439152 DOI: 10.1109/jbhi.2022.3168289] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Exploring the prognostic classification and biomarkers in Head and Neck Squamous Carcinoma (HNSC) is of great clinical significance. We hybridized three prominent strategies to comprehensively characterize the molecular features of HNSC. We constructed a 15-gene signature to predict patients death risk with an average AUC of 0.744 for 1-, 3-, and 5-year on TCGA-HNSC training set, and average AUCs of 0.636, 0.584, 0.755 in GSE65858, GSE-112026, CPTAC-HNSCC datasets, respectively. By combined with NMF clustering and consensus clustering of fraction of tumor immune cell infiltration (ICI) in the tumor microenvironment (TME), we captured a more refined biological characteristics of HNSC, and observed a prognosis heterogeneity in high tumor immunity patients. By matching tumor subset-specific expression signatures to drug-induced cell line expression profiles from large-scale pharmacogenomic databases in the OCTAD workspace, we identified a group of HNSC patients featured with poor prognosis and demonstrated that the individuals in this group are likely to receive increased drug sensitivity to reverse differentially expressed disease signature genes. This trend is especially highlighted among those with higher death risk and tumour immunity.
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Giridharan M, Rupani V, Banerjee S. Signaling Pathways and Targeted Therapies for Stem Cells in Prostate Cancer. ACS Pharmacol Transl Sci 2022; 5:193-206. [PMID: 35434534 PMCID: PMC9003388 DOI: 10.1021/acsptsci.2c00019] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Indexed: 12/30/2022]
Abstract
Prostate cancer (PCa) is one of the most frequently occurring cancers among men, and the current statistics show that it is the second leading cause of cancer-related deaths among men. Over the years, research in PCa treatment and therapies has made many advances. Despite these efforts, the standardized therapies such as radiation, chemotherapy, hormonal therapy and surgery are not considered completely effective in treating advanced and metastatic PCa. In most situations, fast-dividing tumor cells are targeted, leaving behind relatively slowly dividing, chemoresistant cells known as cancer stem cells. Therefore, following the seemingly successful treatments, the lingering quiescent cancer stem cells are able to renew themselves, undergo differentiation into mature tumor cells, and sufficiently reinitiate the disease, leading to cancer relapse. Thus, prostate cancer stem cells (PCSCs) have been reported to play a vital role in controlling the dynamics of tumorigenesis, progression, and resistance to therapies in PCa. However, the complete knowledge on the mechanisms regulating the stemness of PCSCs is still unclear. Thus, studying the stemness of PCSCs will allow for the development of more effective cancer therapies due to the durable response, resulting in a reduction in recurrences of cancer. In this Review, we will specifically describe the molecular mechanisms responsible for regulating the stemness of PCSCs. Furthermore, current developments in stem cell-specific therapeutic approaches along with future prospects will also be discussed.
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Affiliation(s)
- Madhuvanthi Giridharan
- School of Biosciences and Technology, Vellore Institute of Technology, Vellore-632104, Tamil Nadu, India
| | - Vasu Rupani
- School of Biosciences and Technology, Vellore Institute of Technology, Vellore-632104, Tamil Nadu, India
| | - Satarupa Banerjee
- School of Biosciences and Technology, Vellore Institute of Technology, Vellore-632104, Tamil Nadu, India
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Ravanmehr V, Blau H, Cappelletti L, Fontana T, Carmody L, Coleman B, George J, Reese J, Joachimiak M, Bocci G, Hansen P, Bult C, Rueter J, Casiraghi E, Valentini G, Mungall C, Oprea TI, Robinson PN. Supervised learning with word embeddings derived from PubMed captures latent knowledge about protein kinases and cancer. NAR Genom Bioinform 2021; 3:lqab113. [PMID: 34888523 PMCID: PMC8652379 DOI: 10.1093/nargab/lqab113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Revised: 10/14/2021] [Accepted: 11/24/2021] [Indexed: 11/17/2022] Open
Abstract
Inhibiting protein kinases (PKs) that cause cancers has been an important topic in cancer therapy for years. So far, almost 8% of >530 PKs have been targeted by FDA-approved medications, and around 150 protein kinase inhibitors (PKIs) have been tested in clinical trials. We present an approach based on natural language processing and machine learning to investigate the relations between PKs and cancers, predicting PKs whose inhibition would be efficacious to treat a certain cancer. Our approach represents PKs and cancers as semantically meaningful 100-dimensional vectors based on word and concept neighborhoods in PubMed abstracts. We use information about phase I-IV trials in ClinicalTrials.gov to construct a training set for random forest classification. Our results with historical data show that associations between PKs and specific cancers can be predicted years in advance with good accuracy. Our tool can be used to predict the relevance of inhibiting PKs for specific cancers and to support the design of well-focused clinical trials to discover novel PKIs for cancer therapy.
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Affiliation(s)
- Vida Ravanmehr
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | - Hannah Blau
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | - Luca Cappelletti
- AnacletoLab, Dipartimento di Informatica, Università degli Studi di Milano, Italy
| | - Tommaso Fontana
- AnacletoLab, Dipartimento di Informatica, Università degli Studi di Milano, Italy
| | - Leigh Carmody
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | - Ben Coleman
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
- University of Connecticut Health Center, Department of Genetics and Genome Sciences, Farmington, CT 06030, USA
| | - Joshy George
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | - Justin Reese
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94710, USA
| | - Marcin Joachimiak
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94710, USA
| | - Giovanni Bocci
- Department of Internal Medicine and UNM Comprehensive Cancer Center, UNM School of, Medicine, Albuquerque, NM 87102, USA
| | - Peter Hansen
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | - Carol Bult
- The Jackson Laboratory for Mammalian Genetics, Bar Harbor, ME 04609, USA
| | - Jens Rueter
- The Jackson Laboratory for Mammalian Genetics, Bar Harbor, ME 04609, USA
| | - Elena Casiraghi
- AnacletoLab, Dipartimento di Informatica, Università degli Studi di Milano, Italy
| | - Giorgio Valentini
- AnacletoLab, Dipartimento di Informatica, Università degli Studi di Milano, Italy
| | - Christopher Mungall
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94710, USA
| | - Tudor I Oprea
- Department of Internal Medicine and UNM Comprehensive Cancer Center, UNM School of, Medicine, Albuquerque, NM 87102, USA
| | - Peter N Robinson
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
- Institute for Systems Genomics, University of Connecticut, Farmington, CT 06032, USA
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7
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Patient-derived xenografts and organoids model therapy response in prostate cancer. Nat Commun 2021; 12:1117. [PMID: 33602919 PMCID: PMC7892572 DOI: 10.1038/s41467-021-21300-6] [Citation(s) in RCA: 73] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Accepted: 01/19/2021] [Indexed: 02/08/2023] Open
Abstract
Therapy resistance and metastatic processes in prostate cancer (PCa) remain undefined, due to lack of experimental models that mimic different disease stages. We describe an androgen-dependent PCa patient-derived xenograft (PDX) model from treatment-naïve, soft tissue metastasis (PNPCa). RNA and whole-exome sequencing of the PDX tissue and organoids confirmed transcriptomic and genomic similarity to primary tumor. PNPCa harbors BRCA2 and CHD1 somatic mutations, shows an SPOP/FOXA1-like transcriptomic signature and microsatellite instability, which occurs in 3% of advanced PCa and has never been modeled in vivo. Comparison of the treatment-naïve PNPCa with additional metastatic PDXs (BM18, LAPC9), in a medium-throughput organoid screen of FDA-approved compounds, revealed differential drug sensitivities. Multikinase inhibitors (ponatinib, sunitinib, sorafenib) were broadly effective on all PDX- and patient-derived organoids from advanced cases with acquired resistance to standard-of-care compounds. This proof-of-principle study may provide a preclinical tool to screen drug responses to standard-of-care and newly identified, repurposed compounds.
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Naeem A, Dakshanamurthy S, Walthieu H, Parasido E, Avantaggiati M, Tricoli L, Kumar D, Lee RJ, Feldman A, Noon MS, Byers S, Rodriguez O, Albanese C. Predicting new drug indications for prostate cancer: The integration of an in silico proteochemometric network pharmacology platform with patient-derived primary prostate cells. Prostate 2020; 80:1233-1243. [PMID: 32761925 PMCID: PMC7540414 DOI: 10.1002/pros.24050] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Accepted: 07/21/2020] [Indexed: 12/13/2022]
Abstract
BACKGROUND Drug repurposing enables the discovery of potential cancer treatments using publically available data from over 4000 published Food and Drug Administration approved and experimental drugs. However, the ability to effectively evaluate the drug's efficacy remains a challenge. Impediments to broad applicability include inaccuracies in many of the computational drug-target algorithms and a lack of clinically relevant biologic modeling systems to validate the computational data for subsequent translation. METHODS We have integrated our computational proteochemometric systems network pharmacology platform, DrugGenEx-Net, with primary, continuous cultures of conditionally reprogrammed (CR) normal and prostate cancer (PCa) cells derived from treatment-naive patients with primary PCa. RESULTS Using the transcriptomic data from two matched pairs of benign and tumor-derived CR cells, we constructed drug networks to describe the biological perturbation associated with each prostate cell subtype at multiple levels of biological action. We prioritized the drugs by analyzing these networks for statistical coincidence with the drug action networks originating from known and predicted drug-protein targets. Prioritized drugs shared between the two patients' PCa cells included carfilzomib (CFZ), bortezomib (BTZ), sulforaphane, and phenethyl isothiocyanate. The effects of these compounds were then tested in the CR cells, in vitro. We observed that the IC50 values of the normal PCa CR cells for CFZ and BTZ were higher than their matched tumor CR cells. Transcriptomic analysis of CFZ-treated CR cells revealed that genes involved in cell proliferation, proteases, and downstream targets of serine proteases were inhibited while KLK7 and KLK8 were induced in the tumor-derived CR cells. CONCLUSIONS Given that the drugs in the database are extremely well-characterized and that the patient-derived cells are easily scalable for high throughput drug screening, this combined in vitro and in silico approach may significantly advance personalized PCa treatment and for other cancer applications.
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Affiliation(s)
- Aisha Naeem
- Department of Oncology, Lombardi Comprehensive Cancer CenterGeorgetown University Medical CenterWashington DC
- Ministry of Public HealthDohaQatar
| | - Sivanesan Dakshanamurthy
- Department of Oncology, Lombardi Comprehensive Cancer CenterGeorgetown University Medical CenterWashington DC
| | - Henry Walthieu
- Department of Oncology, Lombardi Comprehensive Cancer CenterGeorgetown University Medical CenterWashington DC
| | - Erika Parasido
- Department of Oncology, Lombardi Comprehensive Cancer CenterGeorgetown University Medical CenterWashington DC
| | - Maria Avantaggiati
- Department of Oncology, Lombardi Comprehensive Cancer CenterGeorgetown University Medical CenterWashington DC
| | - Lucas Tricoli
- Julius L. Chambers Biomedical/Biotechnology Research InstituteNorth Carolina Central UniversityDurhamNorth Carolina
| | - Deepak Kumar
- Julius L. Chambers Biomedical/Biotechnology Research InstituteNorth Carolina Central UniversityDurhamNorth Carolina
| | - Richard J. Lee
- Department of MedicineMassachusetts General Hospital Cancer CenterBostonMassachusetts
| | - Adam Feldman
- Department of MedicineMassachusetts General Hospital Cancer CenterBostonMassachusetts
| | | | - Stephen Byers
- Department of Oncology, Lombardi Comprehensive Cancer CenterGeorgetown University Medical CenterWashington DC
| | - Olga Rodriguez
- Department of Oncology, Lombardi Comprehensive Cancer CenterGeorgetown University Medical CenterWashington DC
- Center for Translational ImagingGeorgetown University Medical CenterWashington DC
| | - Chris Albanese
- Department of Oncology, Lombardi Comprehensive Cancer CenterGeorgetown University Medical CenterWashington DC
- Center for Translational ImagingGeorgetown University Medical CenterWashington DC
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Lee JY, Gallo RA, Ledon PJ, Tao W, Tse DT, Pelaez D, Wester ST. Integrating Differential Gene Expression Analysis with Perturbagen-Response Signatures May Identify Novel Therapies for Thyroid-Associated Orbitopathy. Transl Vis Sci Technol 2020; 9:39. [PMID: 32908802 PMCID: PMC7453043 DOI: 10.1167/tvst.9.9.39] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Accepted: 07/10/2020] [Indexed: 01/21/2023] Open
Abstract
Purpose To evaluate the efficacy of Library of Integrated Network-based Cellular Signatures (LINCS) perturbagen prediction software to identify small molecules that revert pathologic gene signature and alter disease phenotype in orbital adipose stem cells (OASCs) derived from patients with thyroid-associated orbitopathy (TAO). Methods Differentially expressed genes identified via RNA sequencing were inputted into LINCS L1000 Characteristic Direction Signature Search Engine (L1000CDS2) to predict candidate small molecules to reverse pathologic gene expression. TAO OASC cell lines were treated in vitro with six identified small molecules (Torin-2, PX12, withaferin A, isoliquiritigenin, mitoxantrone, and MLN8054), and expression of key adipogenic and differentially expressed genes was measured with quantitative polymerase chain reaction after 7 days of treatment. OASCs were differentiated into adipocytes, treated for 15 days, and stained with Oil Red O (OD 490 nm) to evaluate adipogenic changes. Results The expression of key differentially expressed genes (IRX1, HOXB2, S100B, and KCNA4) and adipogenic genes (peroxisome proliferator activated receptor-γ, FABP4) was significantly decreased in TAO OASCs after treatment (P < .05). In treated TAO adipocytes (n = 3), all six tested small molecules yielded significant decrease (P < .05) in Oil Red O staining. In treated non-TAO adipocytes (n = 3), only three of the drugs yielded a significant decrease in Oil Red O staining. Conclusions Combining disease expression signatures with LINCS small molecule prediction software can identify promising preclinical drug candidates for TAO. Translational Relevance These findings may offer insight into future potential therapeutic options for TAO and demonstrate a streamlined model to predict drug candidates for other diseases.
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Affiliation(s)
- John Y Lee
- Dr. Nasser Al-Rashid Orbital Vision Research Center, Bascom Palmer Eye Institute, Department of Ophthalmology, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - Ryan A Gallo
- Dr. Nasser Al-Rashid Orbital Vision Research Center, Bascom Palmer Eye Institute, Department of Ophthalmology, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - Paul J Ledon
- Dr. Nasser Al-Rashid Orbital Vision Research Center, Bascom Palmer Eye Institute, Department of Ophthalmology, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - Wensi Tao
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - David T Tse
- Dr. Nasser Al-Rashid Orbital Vision Research Center, Bascom Palmer Eye Institute, Department of Ophthalmology, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - Daniel Pelaez
- Dr. Nasser Al-Rashid Orbital Vision Research Center, Bascom Palmer Eye Institute, Department of Ophthalmology, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - Sara T Wester
- Dr. Nasser Al-Rashid Orbital Vision Research Center, Bascom Palmer Eye Institute, Department of Ophthalmology, University of Miami Miller School of Medicine, Miami, Florida, USA
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