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Lu Q, Yu A, Pu J, Chen D, Zhong Y, Bai D, Yang L. Post-stroke cognitive impairment: exploring molecular mechanisms and omics biomarkers for early identification and intervention. Front Mol Neurosci 2024; 17:1375973. [PMID: 38845616 PMCID: PMC11153683 DOI: 10.3389/fnmol.2024.1375973] [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: 01/24/2024] [Accepted: 05/08/2024] [Indexed: 06/09/2024] Open
Abstract
Post-stroke cognitive impairment (PSCI) is a major stroke consequence that has a severe impact on patients' quality of life and survival rate. For this reason, it is especially crucial to identify and intervene early in high-risk groups during the acute phase of stroke. Currently, there are no reliable and efficient techniques for the early diagnosis, appropriate evaluation, or prognostication of PSCI. Instead, plenty of biomarkers in stroke patients have progressively been linked to cognitive impairment in recent years. High-throughput omics techniques that generate large amounts of data and process it to a high quality have been used to screen and identify biomarkers of PSCI in order to investigate the molecular mechanisms of the disease. These techniques include metabolomics, which explores dynamic changes in the organism, gut microbiomics, which studies host-microbe interactions, genomics, which elucidates deeper disease mechanisms, transcriptomics and proteomics, which describe gene expression and regulation. We looked through electronic databases like PubMed, the Cochrane Library, Embase, Web of Science, and common databases for each omics to find biomarkers that might be connected to the pathophysiology of PSCI. As all, we found 34 studies: 14 in the field of metabolomics, 5 in the field of gut microbiomics, 5 in the field of genomics, 4 in the field of transcriptomics, and 7 in the field of proteomics. We discovered that neuroinflammation, oxidative stress, and atherosclerosis may be the primary causes of PSCI development, and that metabolomics may play a role in the molecular mechanisms of PSCI. In this study, we summarized the existing issues across omics technologies and discuss the latest discoveries of PSCI biomarkers in the context of omics, with the goal of investigating the molecular causes of post-stroke cognitive impairment. We also discuss the potential therapeutic utility of omics platforms for PSCI mechanisms, diagnosis, and intervention in order to promote the area's advancement towards precision PSCI treatment.
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Affiliation(s)
- Qiuyi Lu
- Department of Rehabilitation, The First Affiliated Hospital of Chongqing Medical University, Chonging, China
| | - Anqi Yu
- Department of Rehabilitation, The First Affiliated Hospital of Chongqing Medical University, Chonging, China
| | - Juncai Pu
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chonging, China
| | - Dawei Chen
- Department of Rehabilitation, The First Affiliated Hospital of Chongqing Medical University, Chonging, China
| | - Yujie Zhong
- Department of Rehabilitation, The First Affiliated Hospital of Chongqing Medical University, Chonging, China
| | - Dingqun Bai
- Department of Rehabilitation, The First Affiliated Hospital of Chongqing Medical University, Chonging, China
| | - Lining Yang
- Department of Rehabilitation, The First Affiliated Hospital of Chongqing Medical University, Chonging, China
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2
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Schmid K, Sehring J, Németh A, Harter PN, Weber KJ, Vengadeswaran A, Storf H, Seidemann C, Karki K, Fischer P, Dohmen H, Selignow C, von Deimling A, Grau S, Schröder U, Plate KH, Stein M, Uhl E, Acker T, Amsel D. DistSNE: Distributed computing and online visualization of DNA methylation-based central nervous system tumor classification. Brain Pathol 2024; 34:e13228. [PMID: 38012085 PMCID: PMC11007060 DOI: 10.1111/bpa.13228] [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: 10/29/2023] [Accepted: 11/10/2023] [Indexed: 11/29/2023] Open
Abstract
The current state-of-the-art analysis of central nervous system (CNS) tumors through DNA methylation profiling relies on the tumor classifier developed by Capper and colleagues, which centrally harnesses DNA methylation data provided by users. Here, we present a distributed-computing-based approach for CNS tumor classification that achieves a comparable performance to centralized systems while safeguarding privacy. We utilize the t-distributed neighborhood embedding (t-SNE) model for dimensionality reduction and visualization of tumor classification results in two-dimensional graphs in a distributed approach across multiple sites (DistSNE). DistSNE provides an intuitive web interface (https://gin-tsne.med.uni-giessen.de) for user-friendly local data management and federated methylome-based tumor classification calculations for multiple collaborators in a DataSHIELD environment. The freely accessible web interface supports convenient data upload, result review, and summary report generation. Importantly, increasing sample size as achieved through distributed access to additional datasets allows DistSNE to improve cluster analysis and enhance predictive power. Collectively, DistSNE enables a simple and fast classification of CNS tumors using large-scale methylation data from distributed sources, while maintaining the privacy and allowing easy and flexible network expansion to other institutes. This approach holds great potential for advancing human brain tumor classification and fostering collaborative precision medicine in neuro-oncology.
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Affiliation(s)
- Kai Schmid
- Institute of Neuropathology, Justus‐Liebig University GiessenGiessenGermany
| | - Jannik Sehring
- Institute of Neuropathology, Justus‐Liebig University GiessenGiessenGermany
| | - Attila Németh
- Institute of Neuropathology, Justus‐Liebig University GiessenGiessenGermany
| | - Patrick N. Harter
- Neurological Institute (Edinger Institute)University Hospital FrankfurtFrankfurtGermany
- Present address:
Center for Neuropathology and Prion ResearchUniversity Hospital of MunichMunichGermany
| | - Katharina J. Weber
- Neurological Institute (Edinger Institute)University Hospital FrankfurtFrankfurtGermany
- German Cancer Consortium (DKTK)HeidelbergGermany
- German Cancer Research Center (DKFZ)HeidelbergGermany
- Frankfurt Cancer Institute (FCI)FrankfurtGermany
- University Cancer Center (UCT) FrankfurtFrankfurtGermany
| | - Abishaa Vengadeswaran
- Medical Informatics Group (MIG), Goethe University FrankfurtUniversity Hospital FrankfurtFrankfurt am MainGermany
| | - Holger Storf
- Medical Informatics Group (MIG), Goethe University FrankfurtUniversity Hospital FrankfurtFrankfurt am MainGermany
| | | | - Kapil Karki
- DIZ MarburgPhillips University MarburgMarburgGermany
| | - Patrick Fischer
- Institute for Medical InformaticsJustus‐Liebig UniversityGiessenGermany
- Department of Neuropathology, German Cancer Research Center (DKFZ)Universitätsklinikum Heidelberg, and CCU NeuropathologyHeidelbergGermany
| | - Hildegard Dohmen
- Institute of Neuropathology, Justus‐Liebig University GiessenGiessenGermany
| | - Carmen Selignow
- Institute of Neuropathology, Justus‐Liebig University GiessenGiessenGermany
| | | | - Stefan Grau
- Department of NeurosurgeryHospital FuldaFuldaGermany
| | - Uwe Schröder
- Department of NeurosurgeryMVZ Frankfurt/OderFrankfurtGermany
| | - Karl H. Plate
- Neurological Institute (Edinger Institute)University Hospital FrankfurtFrankfurtGermany
| | - Marco Stein
- Department of NeurosurgeryUniversity Hospital Giessen und Marburg Location GiessenGiessenGermany
| | - Eberhard Uhl
- Department of NeurosurgeryUniversity Hospital Giessen und Marburg Location GiessenGiessenGermany
| | - Till Acker
- Institute of Neuropathology, Justus‐Liebig University GiessenGiessenGermany
| | - Daniel Amsel
- Institute of Neuropathology, Justus‐Liebig University GiessenGiessenGermany
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3
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Fu Y, Wang C, Wu Z, Zhang X, Liu Y, Wang X, Liu F, Chen Y, Zhang Y, Zhao H, Wang Q. Discovery of the potential biomarkers for early diagnosis of endometrial cancer via integrating metabolomics and transcriptomics. Comput Biol Med 2024; 173:108327. [PMID: 38552279 DOI: 10.1016/j.compbiomed.2024.108327] [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: 02/06/2024] [Revised: 03/07/2024] [Accepted: 03/17/2024] [Indexed: 04/17/2024]
Abstract
Endometrial cancer (EC) is one of the most common malignant tumors in women, and the increasing incidence and mortality pose a serious threat to the public health. Early diagnosis of EC could prolong the survival period and optimize the survivorship, greatly alleviating patients' suffering and social medical pressure. In this study, we collected urine and serum samples from the recruited patients, analyzed the samples using LC-MS approach, and identified the differential metabolites through metabolomic analysis. Then, the differentially expressed genes were identified through the systematic transcriptomic analysis of EC-related dataset from Gene Expression Omnibus (GEO), followed by network profiling of metabolic-reaction-enzyme-gene. In this experiment, a total of 83 differential metabolites and 19 hub genes were discovered, of which 10 different metabolites and 3 hub genes were further evaluated as more potential biomarkers based on network analysis. According to the KEGG enrichment analysis, the potential biomarkers and gene-encoded proteins were found to be involved in the arginine and proline metabolism, histidine metabolism, and pyrimidine metabolism, which was of significance for the early diagnosis of EC. In particular, the combination of metabolites (histamine, 1-methylhistamine, and methylimidazole acetaldehyde) as well as the combination of RRM2, TYMS and TK1 exerted more accurate discrimination abilities between EC and healthy groups, providing more criteria for the early diagnosis of EC.
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Affiliation(s)
- Yan Fu
- School of Pharmacy, Hebei Medical University, Shijiazhuang, 050017, China; Core Facilities and Centers, Hebei Medical University, Shijiazhuang, 050017, China
| | - Chengzhao Wang
- College of Basic Medicine, Hebei Medical University, Shijiazhuang, 050017, China
| | - Zhimin Wu
- School of Pharmacy, Hebei Medical University, Shijiazhuang, 050017, China
| | - Xiaoguang Zhang
- Core Facilities and Centers, Hebei Medical University, Shijiazhuang, 050017, China; College of Basic Medicine, Hebei Medical University, Shijiazhuang, 050017, China
| | - Yan Liu
- School of Pharmacy, Hebei Medical University, Shijiazhuang, 050017, China
| | - Xu Wang
- School of Pharmacy, Hebei Medical University, Shijiazhuang, 050017, China
| | - Fangfang Liu
- School of Pharmacy, Hebei Medical University, Shijiazhuang, 050017, China
| | - Yujuan Chen
- School of Pharmacy, Hebei Medical University, Shijiazhuang, 050017, China
| | - Yang Zhang
- School of Pharmacy, Hebei Medical University, Shijiazhuang, 050017, China.
| | - Huanhuan Zhao
- Department of Obstetrics and Gynecology, The Fourth Hospital of Hebei Medical University, 050011, China.
| | - Qiao Wang
- School of Pharmacy, Hebei Medical University, Shijiazhuang, 050017, China.
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4
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Sinha S, Vegesna R, Mukherjee S, Kammula AV, Dhruba SR, Wu W, Kerr DL, Nair NU, Jones MG, Yosef N, Stroganov OV, Grishagin I, Aldape KD, Blakely CM, Jiang P, Thomas CJ, Benes CH, Bivona TG, Schäffer AA, Ruppin E. PERCEPTION predicts patient response and resistance to treatment using single-cell transcriptomics of their tumors. NATURE CANCER 2024:10.1038/s43018-024-00756-7. [PMID: 38637658 DOI: 10.1038/s43018-024-00756-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 03/08/2024] [Indexed: 04/20/2024]
Abstract
Tailoring optimal treatment for individual cancer patients remains a significant challenge. To address this issue, we developed PERCEPTION (PERsonalized Single-Cell Expression-Based Planning for Treatments In ONcology), a precision oncology computational pipeline. Our approach uses publicly available matched bulk and single-cell (sc) expression profiles from large-scale cell-line drug screens. These profiles help build treatment response models based on patients' sc-tumor transcriptomics. PERCEPTION demonstrates success in predicting responses to targeted therapies in cultured and patient-tumor-derived primary cells, as well as in two clinical trials for multiple myeloma and breast cancer. It also captures the resistance development in patients with lung cancer treated with tyrosine kinase inhibitors. PERCEPTION outperforms published state-of-the-art sc-based and bulk-based predictors in all clinical cohorts. PERCEPTION is accessible at https://github.com/ruppinlab/PERCEPTION . Our work, showcasing patient stratification using sc-expression profiles of their tumors, will encourage the adoption of sc-omics profiling in clinical settings, enhancing precision oncology tools based on sc-omics.
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Affiliation(s)
- Sanju Sinha
- Cancer Data Science Laboratory, National Cancer Institute, Bethesda, MD, USA.
- NCI-Designated Cancer Center, Sanford Burnham Prebys Medical Discovery Institute, San Diego, CA, USA.
| | - Rahulsimham Vegesna
- Cancer Data Science Laboratory, National Cancer Institute, Bethesda, MD, USA
| | - Sumit Mukherjee
- Cancer Data Science Laboratory, National Cancer Institute, Bethesda, MD, USA
| | - Ashwin V Kammula
- Cancer Data Science Laboratory, National Cancer Institute, Bethesda, MD, USA
- University of Maryland, College Park, MD, USA
| | | | - Wei Wu
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - D Lucas Kerr
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Nishanth Ulhas Nair
- Cancer Data Science Laboratory, National Cancer Institute, Bethesda, MD, USA
| | - Matthew G Jones
- Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA
- Department of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA, USA
- Integrative Program in Quantitative Biology, University of California, San Francisco, San Francisco, CA, USA
- Whitehead Institute, Cambridge, MA, USA
| | - Nir Yosef
- Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA
- Department of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA, USA
| | | | - Ivan Grishagin
- Rancho BioSciences, San Diego, CA, USA
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, MD, USA
| | - Kenneth D Aldape
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Collin M Blakely
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
| | - Peng Jiang
- Cancer Data Science Laboratory, National Cancer Institute, Bethesda, MD, USA
| | - Craig J Thomas
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, MD, USA
- Lymphoid Malignancies Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Cyril H Benes
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Trever G Bivona
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA
- Chan Zuckerberg Biohub Investigator, San Francisco, CA, USA
| | | | - Eytan Ruppin
- Cancer Data Science Laboratory, National Cancer Institute, Bethesda, MD, USA.
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5
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Pereira CA, Reis-de-Oliveira G, Pierone BC, Martins-de-Souza D, Kaster MP. Depicting the molecular features of suicidal behavior: a review from an "omics" perspective. Psychiatry Res 2024; 332:115682. [PMID: 38198856 DOI: 10.1016/j.psychres.2023.115682] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Revised: 12/05/2023] [Accepted: 12/18/2023] [Indexed: 01/12/2024]
Abstract
Background Suicide is one of the leading global causes of death. Behavior patterns from suicide ideation to completion are complex, involving multiple risk factors. Advances in technologies and large-scale bioinformatic tools are changing how we approach biomedical problems. The "omics" field may provide new knowledge about suicidal behavior to improve identification of relevant biological pathways associated with suicidal behavior. Methods We reviewed transcriptomic, proteomic, and metabolomic studies conducted in blood and post-mortem brains from individuals who experienced suicide or suicidal behavior. Omics data were combined using systems biology in silico, aiming at identifying major biological mechanisms and key molecules associated with suicide. Results Post-mortem samples of suicide completers indicate major dysregulations in pathways associated with glial cells (astrocytes and microglia), neurotransmission (GABAergic and glutamatergic systems), neuroplasticity and cell survivor, immune responses and energy homeostasis. In the periphery, studies found alterations in molecules involved in immune responses, polyamines, lipid transport, energy homeostasis, and amino and nucleic acid metabolism. Limitations We included only exploratory, non-hypothesis-driven studies; most studies only included one brain region and whole tissue analysis, and focused on suicide completers who were white males with almost none confounding factors. Conclusions We can highlight the importance of synaptic function, especially the balance between the inhibitory and excitatory synapses, and mechanisms associated with neuroplasticity, common pathways associated with psychiatric disorders. However, some of the pathways highlighted in this review, such as transcriptional factors associated with RNA splicing, formation of cortical connections, and gliogenesis, point to mechanisms that still need to be explored.
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Affiliation(s)
- Caibe Alves Pereira
- Laboratory of Translational Neurosciences, Department of Biochemistry, Federal University of Santa Catarina (UFSC), Florianopolis, Santa Catarina, Brazil
| | - Guilherme Reis-de-Oliveira
- Laboratory of Neuroproteomics, Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas (UNICAMP), Campinas, SP, Brazil
| | - Bruna Caroline Pierone
- Laboratory of Translational Neurosciences, Department of Biochemistry, Federal University of Santa Catarina (UFSC), Florianopolis, Santa Catarina, Brazil
| | - Daniel Martins-de-Souza
- Laboratory of Neuroproteomics, Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas (UNICAMP), Campinas, SP, Brazil; Instituto Nacional de Biomarcadores Em Neuropsiquiatria (INBION) Conselho Nacional de Desenvolvimento Científico E Tecnológico, São Paulo, Brazil; Experimental Medicine Research Cluster (EMRC), University of Campinas, Campinas, SP, Brazil; D'Or Institute for Research and Education (IDOR), São Paulo, Brazil; INCT in Modelling Human Complex Diseases with 3D Platforms (Model3D), São Paulo, Brazil.
| | - Manuella Pinto Kaster
- Laboratory of Translational Neurosciences, Department of Biochemistry, Federal University of Santa Catarina (UFSC), Florianopolis, Santa Catarina, Brazil.
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Mejia-Garcia A, Bonilla DA, Ramirez CM, Escobar-Díaz FA, Combita AL, Forero DA, Orozco C. Genes and Pathways Involved in the Progression of Malignant Pleural Mesothelioma: A Meta-analysis of Genome-Wide Expression Studies. Biochem Genet 2024; 62:352-370. [PMID: 37347449 DOI: 10.1007/s10528-023-10426-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 06/07/2023] [Indexed: 06/23/2023]
Abstract
Malignant pleural mesothelioma (MPM) is a rare and aggressive neoplasm of the pleural tissue that lines the lungs and is mainly associated with long latency from asbestos exposure. This tumor has no effective therapeutic opportunities nowadays and has a very low five-year survival rate. In this sense, identifying molecular events that trigger the development and progression of this tumor is highly important to establish new and potentially effective treatments. We conducted a meta-analysis of genome-wide expression studies publicly available at the Gene Expression Omnibus (GEO) and ArrayExpress databases. The differentially expressed genes (DEGs) were identified, and we performed functional enrichment analysis and protein-protein interaction networks (PPINs) to gain insight into the biological mechanisms underlying these genes. Additionally, we constructed survival prediction models for selected DEGs and predicted the minimum drug inhibition concentration of anticancer drugs for MPM. In total, 115 MPM tumor transcriptomes and 26 pleural tissue controls were analyzed. We identified 1046 upregulated DEGs in the MPM samples. Cellular signaling categories in tumor samples were associated with the TNF, PI3K-Akt, and AMPK pathways. The inflammatory response, regulation of cell migration, and regulation of angiogenesis were overrepresented biological processes. Expression of SOX17 and TACC1 were associated with reduced survival rates. This meta-analysis identified a list of DEGs in MPM tumors, cancer-related signaling pathways, and biological processes that were overrepresented in MPM samples. Some therapeutic targets to treat MPM are suggested, and the prognostic potential of key genes is shown.
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Affiliation(s)
- Alejandro Mejia-Garcia
- Molecular Genetics Research Group (GENMOL), Universidad de Antioquia, Medellín, Colombia
| | - Diego A Bonilla
- Research Division, Dynamical Business & Science Society - DBSS International SAS, Bogotá, Colombia
- Research Group in Physical Activity, Sports and Health Sciences (GICAFS), Universidad de Córdoba, Montería, Colombia
- Sport Genomics Research Group, Department of Genetics, Physical Anthropology and Animal Physiology, Faculty of Science and Technology, University of the Basque Country (UPV/EHU), 48940, Leioa, Spain
| | - Claudia M Ramirez
- Health and Sport Sciences Research Group, School of Health and Sport Sciences, Fundación Universitaria del Área Andina, Bogotá, Colombia
| | - Fabio A Escobar-Díaz
- Public Health and Epidemiology Research Group, School of Health and Sport Sciences, Fundación Universitaria del Área Andina, Bogotá, Colombia
| | - Alba Lucia Combita
- Cancer Biology Research Group, Instituto Nacional de Cancerología, Bogotá, Colombia
- Department of Microbiology, School of Medicine, Universidad Nacional de Colombia, Bogotá, Colombia
| | - Diego A Forero
- Health and Sport Sciences Research Group, School of Health and Sport Sciences, Fundación Universitaria del Área Andina, Bogotá, Colombia
- Professional Program in Respiratory Therapy, School of Health and Sport Sciences, Fundación Universitaria del Área Andina, Bogotá, Colombia
| | - Carlos Orozco
- Health and Sport Sciences Research Group, School of Health and Sport Sciences, Fundación Universitaria del Área Andina, Bogotá, Colombia.
- Professional Program in Surgical Instrumentation, Professional Program in Optometry and Technical Program in Radiology and Diagnostic Imaging, School of Health and Sport Sciences, Fundación Universitaria del Área Andina, Bogotá, Colombia.
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7
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Song H, Guo R, Sun X, Kou Y, Ma X, Chen Y, Song L, Wu Y. Integrated metabolomics and transcriptomics revealed the anti-constipation mechanisms of xylooligosaccharides from corn cobs. Food Funct 2024; 15:894-905. [PMID: 38168976 DOI: 10.1039/d3fo04366e] [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: 01/05/2024]
Abstract
Xylooligosaccharides (XOSs) have recently garnered interest for their potential as an anti-constipation agent. In this study, we investigated the effects of XOSs derived from corn cobs on constipation in mice through a comprehensive analysis of both the metabolome and transcriptome. Our multi-omics approach revealed that XOSs primarily modulated butanoate metabolism and steroid hormone biosynthesis pathways, as well as key signaling pathways such as PPAR and NF-kappa B. Notably, we observed a decrease in inflammatory biomarker expression and an elevation of butyric acid metabolite levels with XOSs treatment. A deeper analysis of gene expression and metabolite alterations highlighted significant changes in genes encoding critical enzymes and metabolites involved in these pathways. Overall, these findings underscore the considerable potential of XOSs derived from corn cobs as a dietary supplement for effectively alleviating constipation.
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Affiliation(s)
- Hong Song
- Department of Food Science and Engineering, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China.
| | - Rui Guo
- Department of Food Science and Engineering, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China.
| | - Xianbao Sun
- Department of Food Science and Engineering, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China.
| | - Yuxing Kou
- Department of Food Science and Engineering, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China.
| | - Xuan Ma
- Department of Food Science and Engineering, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China.
| | - Yinan Chen
- Department of Food Science and Engineering, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China.
| | - Lihua Song
- Department of Food Science and Engineering, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China.
| | - Yan Wu
- Department of Food Science and Engineering, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China.
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8
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Liu Y, Altreuter J, Bodapati S, Cristea S, Wong CJ, Wu CJ, Michor F. Predicting patient outcomes after treatment with immune checkpoint blockade: A review of biomarkers derived from diverse data modalities. CELL GENOMICS 2024; 4:100444. [PMID: 38190106 PMCID: PMC10794784 DOI: 10.1016/j.xgen.2023.100444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 07/12/2023] [Accepted: 10/24/2023] [Indexed: 01/09/2024]
Abstract
Immune checkpoint blockade (ICB) therapy targeting cytotoxic T-lymphocyte-associated protein 4, programmed death 1, and programmed death ligand 1 has shown durable remission and clinical success across different cancer types. However, patient outcomes vary among disease indications. Studies have identified prognostic biomarkers associated with immunotherapy response and patient outcomes derived from diverse data types, including next-generation bulk and single-cell DNA, RNA, T cell and B cell receptor sequencing data, liquid biopsies, and clinical imaging. Owing to inter- and intra-tumor heterogeneity and the immune system's complexity, these biomarkers have diverse efficacy in clinical trials of ICB. Here, we review the genetic and genomic signatures and image features of ICB studies for pan-cancer applications and specific indications. We discuss the advantages and disadvantages of computational approaches for predicting immunotherapy effectiveness and patient outcomes. We also elucidate the challenges of immunotherapy prognostication and the discovery of novel immunotherapy targets.
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Affiliation(s)
- Yang Liu
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02115, USA
| | - Jennifer Altreuter
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02115, USA
| | - Sudheshna Bodapati
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02115, USA
| | - Simona Cristea
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02115, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Cheryl J Wong
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02115, USA; Department of Biomedical Informatics, Harvard Medical School, Boston, MA 20115, USA
| | - Catherine J Wu
- Harvard Medical School, Boston, MA 02115, USA; The Eli and Edythe Broad Institute of MIT and Harvard, Cambridge, MA 02139, USA; Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02115, USA
| | - Franziska Michor
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02115, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Department of Biomedical Informatics, Harvard Medical School, Boston, MA 20115, USA; The Eli and Edythe Broad Institute of MIT and Harvard, Cambridge, MA 02139, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA; Center for Cancer Evolution, Dana-Farber Cancer Institute, Boston, MA 02138, USA; The Ludwig Center at Harvard, Boston, MA 02115, USA.
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9
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Zhang C, Xu J, Tang R, Yang J, Wang W, Yu X, Shi S. Novel research and future prospects of artificial intelligence in cancer diagnosis and treatment. J Hematol Oncol 2023; 16:114. [PMID: 38012673 PMCID: PMC10680201 DOI: 10.1186/s13045-023-01514-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Accepted: 11/20/2023] [Indexed: 11/29/2023] Open
Abstract
Research into the potential benefits of artificial intelligence for comprehending the intricate biology of cancer has grown as a result of the widespread use of deep learning and machine learning in the healthcare sector and the availability of highly specialized cancer datasets. Here, we review new artificial intelligence approaches and how they are being used in oncology. We describe how artificial intelligence might be used in the detection, prognosis, and administration of cancer treatments and introduce the use of the latest large language models such as ChatGPT in oncology clinics. We highlight artificial intelligence applications for omics data types, and we offer perspectives on how the various data types might be combined to create decision-support tools. We also evaluate the present constraints and challenges to applying artificial intelligence in precision oncology. Finally, we discuss how current challenges may be surmounted to make artificial intelligence useful in clinical settings in the future.
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Affiliation(s)
- Chaoyi Zhang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Jin Xu
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Rong Tang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Jianhui Yang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Wei Wang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Xianjun Yu
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China.
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China.
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China.
| | - Si Shi
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China.
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China.
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China.
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10
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Jou J, Kato S, Miyashita H, Thangathurai K, Pabla S, DePietro P, Nesline MK, Conroy JM, Rubin E, Eskander RN, Kurzrock R. Cancer-Immunity Marker RNA Expression Levels across Gynecologic Cancers: Implications for Immunotherapy. Mol Cancer Ther 2023; 22:1352-1362. [PMID: 37619986 DOI: 10.1158/1535-7163.mct-23-0270] [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: 05/04/2023] [Revised: 06/25/2023] [Accepted: 08/22/2023] [Indexed: 08/26/2023]
Abstract
Our objective was to characterize cancer-immunity marker expression in gynecologic cancers and compare immune landscapes between gynecologic tumor subtypes and with nongynecologic solid tumors. RNA expression levels of 51 cancer-immunity markers were analyzed in patients with gynecologic cancers versus nongynecologic cancers, and normalized to a reference population of 735 control cancers, ranked from 0 to 100, and categorized as low (0-24), moderate (25-74), or high (75-100) percentile rank. Of the 72 patients studied, 43 (60%) had ovarian, 24 (33%) uterine, and 5 (7%) cervical cancer. No two immune profiles were identical according to expression rank (0-100) or rank level (low, moderate, or high). Patients with cervical cancer had significantly higher expression level ranks of immune activating, proinflammatory, tumor-infiltrating lymphocyte markers, and checkpoints than patients with uterine or ovarian cancer (P < 0.001 for all comparisons). However, there were no significant differences in immune marker expression between uterine and ovarian cancers. Tumors with PD-L1 tumor proportional score (TPS) ≥1% versus 0% had significantly higher expression levels of proinflammatory markers (58 vs. 49%, P = 0.0004). Compared to patients with nongynecologic cancers, more patients with gynecologic cancers express high levels of IDO-1 (44 vs. 13%, P < 0.001), LAG3 (35 vs. 21%, P = 0.008), and IL10 (31 vs. 15%, P = 0.002.) Patients with gynecologic cancers have complex and heterogeneous immune landscapes that are distinct from patient to patient and from other solid tumors. High levels of IDO1 and LAG3 suggest that clinical trials with IDO1 inhibitors or LAG3 inhibitors, respectively, may be warranted in gynecologic cancers.
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Affiliation(s)
- Jessica Jou
- Division of Gynecologic Oncology, Oregon Health and Sciences University, Knight Cancer Institute, Portland, Oregon
| | - Shumei Kato
- Division of Hematology & Oncology and Center for Personalized Cancer Therapy, University of California San Diego, Moores Cancer Center, La Jolla, California
| | - Hirotaka Miyashita
- Department of Hematology & Oncology, Dartmouth Cancer Center, Lebanon, New Hampshire
| | | | | | - Paul DePietro
- OmniSeq, Inc. (a Labcorp subsidiary), Buffalo, New York
| | | | | | - Eitan Rubin
- The Shraga Segal Department for Microbiology, Immunology and Genetics, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Ramez N Eskander
- Division of Gynecologic Oncology, University of California San Diego, Moores Cancer Center, La Jolla, California
| | - Razelle Kurzrock
- WIN Consortium and Medical College of Wisconsin Cancer Center, Milwaukee, Wisconsin
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11
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Marshall M, Ivanovich J, Schmitt M, Helvie A, Langsford L, Casterline J, Ferguson M. Pediatric precision oncology: "better three hours too soon than a minute too late". Front Oncol 2023; 13:1279953. [PMID: 38023209 PMCID: PMC10643134 DOI: 10.3389/fonc.2023.1279953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2023] [Accepted: 10/12/2023] [Indexed: 12/01/2023] Open
Abstract
Precision oncology is defined as the selection of an effective treatment for a cancer patient based upon genomic profiling of the patient's tumor to identify targetable alterations. The application of precision oncology toward pediatric cancer patients has moved forward more slowly than with adults but is gaining momentum. Clinical and pharmaceutical advances developed over the past decade for adult cancer indications have begun to move into pediatric oncology, expanding treatment options for young high-risk and refractory patients. As a result, the FDA has approved 23 targeted drugs for pediatric cancer indications, moving targeted drugs into the standard of care. Our precision oncology program is in a medium sized children's hospital, lacking internal sequencing capabilities and bioinformatics. We have developed methods, medical and business partnerships to provide state-of-the-art tumor characterization and targeted treatment options for our patients. We present here a streamlined and practical protocol designed to enable any oncologist to implement precision oncology options for their patients.
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Affiliation(s)
- Mark Marshall
- Division of Hematology/Oncology, Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Jennifer Ivanovich
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Morgan Schmitt
- Division of Hematology/Oncology, Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Amy Helvie
- The Medical Affairs Company, Kennesaw, GA, United States
| | - Lisa Langsford
- Division of Hematology/Oncology, Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Jennifer Casterline
- Division of Hematology/Oncology, Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Michael Ferguson
- Division of Hematology/Oncology, Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, United States
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12
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Huang Y, Deng S, Jiang Q, Shi J. LncRNA RARA-AS1 could serve as a novel prognostic biomarker in pan-cancer and promote proliferation and migration in glioblastoma. Sci Rep 2023; 13:17376. [PMID: 37833349 PMCID: PMC10575974 DOI: 10.1038/s41598-023-44677-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 10/11/2023] [Indexed: 10/15/2023] Open
Abstract
Long non-coding RNAs (lncRNAs) have emerged as crucial regulators of cancer progression and are potential biomarkers for diagnosis and treatment. This study investigates the role of RARA Antisense RNA 1 (RARA-AS1) in cancer and its implications for diagnosis and treatment. Various bioinformatics tools were conducted to analyze the expression patterns, immune-related functions, methylation, and gene expression correlations of RARA-AS1, mainly including the comparisons of different subgroups and correlation analyses between RARA-AS1 expression and other factors. Furthermore, we used short hairpin RNA to perform knockdown experiments, investigating the effects of RARA-AS1 on cell proliferation, invasion, and migration in glioblastoma. Our results revealed that RARA-AS1 has distinct expression patterns in different cancers and exhibits notable correlation with prognosis. Additionally, RARA-AS1 is highly correlated with certain immune checkpoints and mismatch repair genes, indicating its potential role in immune infiltration and related immunotherapy. Further analysis identified potential effective drugs for RARA-AS1 and demonstrated its potential RNA binding protein (RBP) mechanism in glioblastoma. Besides, a series of functional experiments indicated inhibiting RARA-AS1 could decrease cell proliferation, invasion, and migration of glioblastoma cell lines. Finally, RARA-AS1 could act as an independent prognostic factor for glioblastoma patients and may serve as a promising therapeutic target. All in all, Our study provides a comprehensive understanding of the functions and implications of RARA-AS1 in pan-cancer, highlighting it as a promising biomarker for survival. It is also an independent risk factor affecting prognosis in glioblastoma and an important factor affecting proliferation and migration in glioblastoma, setting the stage for further mechanistic investigations.
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Affiliation(s)
- Yue Huang
- Department of Neurosurgery, Affiliated Hospital of Nantong University, Medical School of Nantong University, No. 20 West Temple Road, Nantong, 226001, Jiangsu, China
| | - Song Deng
- Department of Neurosurgery, Affiliated Hospital of Nantong University, Medical School of Nantong University, No. 20 West Temple Road, Nantong, 226001, Jiangsu, China
| | - Qiaoji Jiang
- Department of Neurosurgery, Affiliated Yancheng Clinical College of Xuzhou Medical University, Yancheng, 224000, Jiangsu, China
| | - Jinlong Shi
- Department of Neurosurgery, Affiliated Hospital of Nantong University, Medical School of Nantong University, No. 20 West Temple Road, Nantong, 226001, Jiangsu, China.
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13
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Bazyari MJ, Saadat Z, Firouzjaei AA, Aghaee-Bakhtiari SH. Deciphering colorectal cancer progression features and prognostic signature by single-cell RNA sequencing pseudotime trajectory analysis. Biochem Biophys Rep 2023; 35:101491. [PMID: 37601456 PMCID: PMC10439350 DOI: 10.1016/j.bbrep.2023.101491] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 05/16/2023] [Accepted: 05/22/2023] [Indexed: 08/22/2023] Open
Abstract
Colorectal cancer is the third most common cancer and second cancer with the highest mortality rate in the world. Progression, which leads to metastasis, is one of the biggest challenges in cancer treatment, and despite improvement in screening and treatment techniques, 5 years of survival of colorectal cancer patients drop from 91% in stage I to 12% in stage IV. Single-cell RNA sequencing is one of the most powerful tools to study complex diseases such as cancer, and despite its recent emergence, it's rapidly growing. In contrast to bulk RNA sequencing, which averages out expression of thousands of cells, single-cell RNA sequencing can capture intra-tumor heterogeneity. Moreover, cellular dynamic events like progression can be studied by pseudotime trajectory analysis of single-cell RNA sequencing data. Herein we used Samsung Medical Center (SMC) colorectal cancer single-cell RNA sequencing dataset to find important tumor epithelial cells subtypes. Subsequently, we've found important genes with a dynamic pattern along cancer progression by using pseudo-time trajectory analysis. Also, we found TGFB1 and IL1B as effective ligands and several transcription factors which may regulate the expression of pseudo-time related genes. In the end, we've constructed a LASSO cox regression using 20 psudotime genes, which can predict 3-year survival of colorectal cancer patients with AUC >0.7.
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Affiliation(s)
- Mohammad Javad Bazyari
- Department of Medical Biotechnology and Nanotechnology, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Zakie Saadat
- Department of Medical Biotechnology and Nanotechnology, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Ali Ahmadizad Firouzjaei
- Department of Medical Biotechnology, School of Advanced Technologies in Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Seyed Hamid Aghaee-Bakhtiari
- Department of Medical Biotechnology and Nanotechnology, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
- Bioinformatics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
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14
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Strupp C, Corvaro M, Cohen SM, Corton JC, Ogawa K, Richert L, Jacobs MN. Increased Cell Proliferation as a Key Event in Chemical Carcinogenesis: Application in an Integrated Approach for the Testing and Assessment of Non-Genotoxic Carcinogenesis. Int J Mol Sci 2023; 24:13246. [PMID: 37686053 PMCID: PMC10488128 DOI: 10.3390/ijms241713246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 08/17/2023] [Accepted: 08/24/2023] [Indexed: 09/10/2023] Open
Abstract
In contrast to genotoxic carcinogens, there are currently no internationally agreed upon regulatory tools for identifying non-genotoxic carcinogens of human relevance. The rodent cancer bioassay is only used in certain regulatory sectors and is criticized for its limited predictive power for human cancer risk. Cancer is due to genetic errors occurring in single cells. The risk of cancer is higher when there is an increase in the number of errors per replication (genotoxic agents) or in the number of replications (cell proliferation-inducing agents). The default regulatory approach for genotoxic agents whereby no threshold is set is reasonably conservative. However, non-genotoxic carcinogens cannot be regulated in the same way since increased cell proliferation has a clear threshold. An integrated approach for the testing and assessment (IATA) of non-genotoxic carcinogens is under development at the OECD, considering learnings from the regulatory assessment of data-rich substances such as agrochemicals. The aim is to achieve an endorsed IATA that predicts human cancer better than the rodent cancer bioassay, using methodologies that equally or better protect human health and are superior from the view of animal welfare/efficiency. This paper describes the technical opportunities available to assess cell proliferation as the central gateway of an IATA for non-genotoxic carcinogenicity.
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Affiliation(s)
| | | | - Samuel M. Cohen
- Department of Pathology and Microbiology and Buffett Cancer Center, University of Nebraska Medical Center, Omaha, NE 68198, USA
| | - J. Christopher Corton
- Center for Computational Toxicology and Exposure, United States Environmental Protection Agency (US EPA), Research Triangle Park, NC 27711, USA;
| | - Kumiko Ogawa
- Division of Pathology, National Institute of Health Sciences, Kawasaki 210-9501, Japan
| | | | - Miriam N. Jacobs
- United Kingdom Health Security Agency (UK HSA), Radiation, Chemicals and Environmental Hazards, Harwell Innovation Campus, Dicot OX11 0RQ, UK
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15
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Tsakiroglou M, Evans A, Pirmohamed M. Leveraging transcriptomics for precision diagnosis: Lessons learned from cancer and sepsis. Front Genet 2023; 14:1100352. [PMID: 36968610 PMCID: PMC10036914 DOI: 10.3389/fgene.2023.1100352] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 02/20/2023] [Indexed: 03/12/2023] Open
Abstract
Diagnostics require precision and predictive ability to be clinically useful. Integration of multi-omic with clinical data is crucial to our understanding of disease pathogenesis and diagnosis. However, interpretation of overwhelming amounts of information at the individual level requires sophisticated computational tools for extraction of clinically meaningful outputs. Moreover, evolution of technical and analytical methods often outpaces standardisation strategies. RNA is the most dynamic component of all -omics technologies carrying an abundance of regulatory information that is least harnessed for use in clinical diagnostics. Gene expression-based tests capture genetic and non-genetic heterogeneity and have been implemented in certain diseases. For example patients with early breast cancer are spared toxic unnecessary treatments with scores based on the expression of a set of genes (e.g., Oncotype DX). The ability of transcriptomics to portray the transcriptional status at a moment in time has also been used in diagnosis of dynamic diseases such as sepsis. Gene expression profiles identify endotypes in sepsis patients with prognostic value and a potential to discriminate between viral and bacterial infection. The application of transcriptomics for patient stratification in clinical environments and clinical trials thus holds promise. In this review, we discuss the current clinical application in the fields of cancer and infection. We use these paradigms to highlight the impediments in identifying useful diagnostic and prognostic biomarkers and propose approaches to overcome them and aid efforts towards clinical implementation.
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Affiliation(s)
- Maria Tsakiroglou
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United Kingdom
- *Correspondence: Maria Tsakiroglou,
| | - Anthony Evans
- Computational Biology Facility, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United Kingdom
| | - Munir Pirmohamed
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United Kingdom
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16
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The challenging quest of neuroimaging: From clinical to molecular-based subtyping of Parkinson disease and atypical parkinsonisms. HANDBOOK OF CLINICAL NEUROLOGY 2023; 192:231-258. [PMID: 36796945 DOI: 10.1016/b978-0-323-85538-9.00004-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
Abstract
The current framework of Parkinson disease (PD) focuses on phenotypic classification despite its considerable heterogeneity. We argue that this method of classification has restricted therapeutic advances and therefore limited our ability to develop disease-modifying interventions in PD. Advances in neuroimaging have identified several molecular mechanisms relevant to PD, variation within and between clinical phenotypes, and potential compensatory mechanisms with disease progression. Magnetic resonance imaging (MRI) techniques can detect microstructural changes, disruptions in neural pathways, and metabolic and blood flow alterations. Positron emission tomography (PET) and single-photon emission computed tomography (SPECT) imaging have informed the neurotransmitter, metabolic, and inflammatory dysfunctions that could potentially distinguish disease phenotypes and predict response to therapy and clinical outcomes. However, rapid advancements in imaging techniques make it challenging to assess the significance of newer studies in the context of new theoretical frameworks. As such, there needs to not only be a standardization of practice criteria in molecular imaging but also a rethinking of target approaches. In order to harness precision medicine, a coordinated shift is needed toward divergent rather than convergent diagnostic approaches that account for interindividual differences rather than similarities within an affected population, and focus on predictive patterns rather than already lost neural activity.
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17
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Otálora-Otálora BA, López-Kleine L, Rojas A. Lung Cancer Gene Regulatory Network of Transcription Factors Related to the Hallmarks of Cancer. Curr Issues Mol Biol 2023; 45:434-464. [PMID: 36661515 PMCID: PMC9857713 DOI: 10.3390/cimb45010029] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 12/13/2022] [Accepted: 12/15/2022] [Indexed: 01/06/2023] Open
Abstract
The transcriptomic analysis of microarray and RNA-Seq datasets followed our own bioinformatic pipeline to identify a transcriptional regulatory network of lung cancer. Twenty-six transcription factors are dysregulated and co-expressed in most of the lung cancer and pulmonary arterial hypertension datasets, which makes them the most frequently dysregulated transcription factors. Co-expression, gene regulatory, coregulatory, and transcriptional regulatory networks, along with fibration symmetries, were constructed to identify common connection patterns, alignments, main regulators, and target genes in order to analyze transcription factor complex formation, as well as its synchronized co-expression patterns in every type of lung cancer. The regulatory function of the most frequently dysregulated transcription factors over lung cancer deregulated genes was validated with ChEA3 enrichment analysis. A Kaplan-Meier plotter analysis linked the dysregulation of the top transcription factors with lung cancer patients' survival. Our results indicate that lung cancer has unique and common deregulated genes and transcription factors with pulmonary arterial hypertension, co-expressed and regulated in a coordinated and cooperative manner by the transcriptional regulatory network that might be associated with critical biological processes and signaling pathways related to the acquisition of the hallmarks of cancer, making them potentially relevant tumor biomarkers for lung cancer early diagnosis and targets for the development of personalized therapies against lung cancer.
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Affiliation(s)
- Beatriz Andrea Otálora-Otálora
- Grupo de Investigación INPAC, Unidad de Investigación, Fundación Universitaria Sanitas, Bogotá 110131, Colombia
- Facultad de Medicina, Universidad Nacional de Colombia, Bogotá 11001, Colombia
| | - Liliana López-Kleine
- Departamento de Estadística, Universidad Nacional de Colombia, Bogotá 11001, Colombia
- Correspondence: (L.L.-K.); (A.R.)
| | - Adriana Rojas
- Facultad de Medicina, Instituto de Genética Humana, Pontificia Universidad Javeriana, Bogotá 110211, Colombia
- Correspondence: (L.L.-K.); (A.R.)
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18
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Rosenquist R, Fröhling S, Stamatopoulos K. Precision Medicine in Cancer - A Paradigm Shift. Semin Cancer Biol 2022; 84:1-2. [PMID: 35597437 DOI: 10.1016/j.semcancer.2022.05.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Richard Rosenquist
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden; Clinical Genetics, Karolinska University Laboratory, Karolinska University Hospital, Solna, Sweden.
| | - Stefan Fröhling
- Department of Translational Medical Oncology, National Center for Tumor Diseases (NCT) Heidelberg, German Cancer Research Center (DKFZ), Heidelberg, Germany; German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Kostas Stamatopoulos
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden; Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thessaloniki, Greece
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19
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Rincón-Riveros A, Rodríguez JA, Villegas VE, López-Kleine L. Identification of Two Exosomal miRNAs in Circulating Blood of Cancer Patients by Using Integrative Transcriptome and Network Analysis. Noncoding RNA 2022; 8:33. [PMID: 35645340 PMCID: PMC9149928 DOI: 10.3390/ncrna8030033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 04/20/2022] [Accepted: 04/22/2022] [Indexed: 11/16/2022] Open
Abstract
Exosomes carry molecules of great biological and clinical interest, such as miRNAs. The contents of exosomes vary between healthy controls and cancer patients. Therefore, miRNAs and other molecules transported in exosomes are considered a potential source of diagnostic and prognostic biomarkers in cancer. Many miRNAs have been detected in recent years. Consequently, a substantial amount of miRNA-related data comparing patients and healthy individuals is available, which contributes to a better understanding of the initiation, development, malignancy, and metastasis of cancer using non-invasive sampling procedures. However, a re-analysis of available ncRNA data is rare. This study used available data about miRNAs in exosomes comparing healthy individuals and cancer patients to identify possible global changes related to the presence of cancer. A robust transcriptomic analysis identified two common miRNAs (miR-495-3p and miR-543) deregulated in five cancer datasets. They had already been implicated in different cancers but not reported in exosomes circulating in blood. The study also examined their target genes and the implications of these genes for functional processes.
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Affiliation(s)
- Andrés Rincón-Riveros
- Bioinformatics and Systems Biology Group, Universidad Nacional de Colombia, Bogotá 111221, Colombia
| | | | - Victoria E Villegas
- Centro de Investigaciones en Microbiología y Biotecnología-UR (CIMBIUR), Facultad de Ciencias Naturales, Universidad del Rosario, Bogotá 111221, Colombia
| | - Liliana López-Kleine
- Department of Statistics, Faculty of Science, Universidad Nacional de Colombia, Bogotá 111221, Colombia
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20
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Molecular Profiles of Advanced Urological Cancers in the PERMED-01 Precision Medicine Clinical Trial. Cancers (Basel) 2022; 14:cancers14092275. [PMID: 35565404 PMCID: PMC9100924 DOI: 10.3390/cancers14092275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Revised: 04/29/2022] [Accepted: 04/30/2022] [Indexed: 12/02/2022] Open
Abstract
Simple Summary The goal of precision medicine is to deliver therapy matched to a relevant actionable genetic alteration (AGA) identified in the tumor. Few data are available regarding precision medicine in advanced urological cancers (AUC), the prognosis of which remains unfavorable. Sixty-four patients with refractory AUC were enrolled in the PERMED-01 clinical trial and underwent a tumor biopsy that was then profiled using sophisticated molecular analyses. The results were discussed in real-time during a weekly molecular tumor board meeting, and patients with a relevant AGA became candidates for an eventual matched therapy. A complete molecular profile was obtained in 77% of cases and an AGA was identified in 59%. Nineteen percent of patients received a matched therapy on progression, of which 42% showed a clinical benefit. The objective response, disease control rates, and the 6-year overall survival were higher in the “matched therapy group” than in the “non-matched therapy group”. Abstract Introduction. The prognosis of advanced urological cancers (AUC) remains unfavorable, and few data are available regarding precision medicine. Methods: the PERMED-01 prospective clinical trial assessed the impact of molecular profiling in adults with refractory advanced solid cancer, in terms of number of patients with tumor actionable genetic alterations (AGA), feasibility, description of molecular alterations, treatment, and clinical outcome. We present here those results in the 64 patients enrolled with AUC. DNA extracted from a new tumor biopsy was profiled in real-time (targeted NGS, whole-genome array-comparative genomic hybridization), and the results were discussed during a weekly molecular tumor board meeting. Results: a complete molecular profile was obtained in 49 patients (77%). Thirty-eight (59%) had at least one AGA. Twelve (19%) received a matched therapy on progression, of which 42% had a PFS2/PFS1 ratio ≥ 1.3 versus 5% in the “non-matched therapy group” (n = 25). The objective response and disease control rates were higher in the “matched therapy group” (33% and 58%, respectively) than in the “non-matched therapy group” (13% and 22%), as was the 6-month OS (75% vs. 42%). Conclusion: the profiling of a newly biopsied tumor sample identified AGA in 59% of patients with AUC, led to “matched therapy” in 19%, and provided clinical benefit in 8%.
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21
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Bironzo P, Primo L, Novello S, Righi L, Candeloro S, Manganaro L, Bussolino F, Pirri F, Scagliotti GV. Clinical-molecular prospective cohort study in Non-Small Cell Lung Cancer (PROMOLE study): a comprehensive approach to identify new predictive markers of pharmacological response. Clin Lung Cancer 2022; 23:e347-e352. [DOI: 10.1016/j.cllc.2022.05.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 04/11/2022] [Accepted: 05/08/2022] [Indexed: 11/03/2022]
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22
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Gudkov A, Shirokorad V, Kashintsev K, Sokov D, Nikitin D, Anisenko A, Borisov N, Sekacheva M, Gaifullin N, Garazha A, Suntsova M, Koroleva E, Buzdin A, Sorokin M. Gene Expression-Based Signature Can Predict Sorafenib Response in Kidney Cancer. Front Mol Biosci 2022; 9:753318. [PMID: 35359606 PMCID: PMC8963850 DOI: 10.3389/fmolb.2022.753318] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Accepted: 01/28/2022] [Indexed: 01/07/2023] Open
Abstract
Sorafenib is a tyrosine kinase inhibitory drug with multiple molecular specificities that is approved for clinical use in second-line treatments of metastatic and advanced renal cell carcinomas (RCCs). However, only 10–40% of RCC patients respond on sorafenib-containing therapies, and personalization of its prescription may help in finding an adequate balance of clinical efficiency, cost-effectiveness, and side effects. We investigated whether expression levels of known molecular targets of sorafenib in RCC can serve as prognostic biomarker of treatment response. We used Illumina microarrays to profile RNA expression in pre-treatment formalin-fixed paraffin-embedded (FFPE) samples of 22 metastatic or advanced RCC cases with known responses on next-line sorafenib monotherapy. Among them, nine patients showed partial response (PR), three patients—stable disease (SD), and 10 patients—progressive disease (PD) according to Response Evaluation Criteria In Solid Tumors (RECIST) criteria. We then classified PR + SD patients as “responders” and PD patients as “poor responders”. We found that gene signature including eight sorafenib target genes was congruent with the drug response characteristics and enabled high-quality separation of the responders and poor responders [area under a receiver operating characteristic curve (AUC) 0.89]. We validated these findings on another set of 13 experimental annotated FFPE RCC samples (for 2 PR, 1 SD, and 10 PD patients) that were profiled by RNA sequencing and observed AUC 0.97 for 8-gene signature as the response classifier. We further validated these results in a series of qRT-PCR experiments on the third experimental set of 12 annotated RCC biosamples (for 4 PR, 3 SD, and 5 PD patients), where 8-gene signature showed AUC 0.83.
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Affiliation(s)
- Alexander Gudkov
- I. M. Sechenov First Moscow State Medical University, Moscow, Russia
| | | | | | - Dmitriy Sokov
- Moscow City Clinical Oncological Dispensary №. 1, Moscow, Russia
| | | | | | | | - Marina Sekacheva
- World-Class Research Center “Digital Biodesign and Personalized Healthcare”, Sechenov First Moscow State Medical University, Moscow, Russia
| | - Nurshat Gaifullin
- Department of Pathology, Faculty of Medicine, Lomonosov Moscow State University, Moscow, Russia
| | | | - Maria Suntsova
- World-Class Research Center “Digital Biodesign and Personalized Healthcare”, Sechenov First Moscow State Medical University, Moscow, Russia
| | - Elena Koroleva
- Moscow Institute of Physics and Technology, Moscow, Russia
| | - Anton Buzdin
- Moscow Institute of Physics and Technology, Moscow, Russia
- World-Class Research Center “Digital Biodesign and Personalized Healthcare”, Sechenov First Moscow State Medical University, Moscow, Russia
- OmicsWay Corp, Walnut, CA, United States
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia
| | - Maksim Sorokin
- I. M. Sechenov First Moscow State Medical University, Moscow, Russia
- Moscow Institute of Physics and Technology, Moscow, Russia
- OmicsWay Corp, Walnut, CA, United States
- European Organization for Research and Treatment of Cancer (EORTC), Biostatistics and Bioinformatics Subgroup, Brussels, Belgium
- *Correspondence: Maksim Sorokin,
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23
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Multi-omics strategies for personalized and predictive medicine: past, current, and future translational opportunities. Emerg Top Life Sci 2022; 6:215-225. [PMID: 35234253 DOI: 10.1042/etls20210244] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 02/13/2022] [Accepted: 02/21/2022] [Indexed: 12/12/2022]
Abstract
Precision medicine is driven by the paradigm shift of empowering clinicians to predict the most appropriate course of action for patients with complex diseases and improve routine medical and public health practice. It promotes integrating collective and individualized clinical data with patient specific multi-omics data to develop therapeutic strategies, and knowledgebase for predictive and personalized medicine in diverse populations. This study is based on the hypothesis that understanding patient's metabolomics and genetic make-up in conjunction with clinical data will significantly lead to determining predisposition, diagnostic, prognostic and predictive biomarkers and optimal paths providing personalized care for diverse and targeted chronic, acute, and infectious diseases. This study briefs emerging significant, and recently reported multi-omics and translational approaches aimed to facilitate implementation of precision medicine. Furthermore, it discusses current grand challenges, and the future need of Findable, Accessible, Intelligent, and Reproducible (FAIR) approach to accelerate diagnostic and preventive care delivery strategies beyond traditional symptom-driven, disease-causal medical practice.
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24
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Li Y, Sheng Y, Liu J, Xu G, Yu W, Cui Q, Lu X, Du P, An L. Hair-growth promoting effect and anti-inflammatory mechanism of Ginkgo biloba polysaccharides. Carbohydr Polym 2022; 278:118811. [PMID: 34973721 DOI: 10.1016/j.carbpol.2021.118811] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 10/20/2021] [Accepted: 10/22/2021] [Indexed: 12/31/2022]
Abstract
The aim of this study was to optimize the separation and purification technology of water-soluble Ginkgo biloba leaves polysaccharides (WGBP), analyze its composition characteristics, observe its hair-growth promoting effect in alopecia areata mice, clarify the polysaccharide fraction with bioactive activities, and explore its anti-inflammation mechanism. We isolated acidic polysaccharides (WGBP-A2) and purified a RG-I type polysaccharide (WGBP-A2b) with a molecular weight of 44 kDa. Results showed that WGBP-A2 could significantly increase the contents of VEGF and HGF in the skin tissue of alopecia areata mice, decrease the contents of Inflammatory factors in the serum. On a cellular level, the expressions of p-p65 and p-IκBα, TNF-α and IL-1β in HUVECs treated with WGBP-A2b were down-regulated. The bioinformatic analysis showed that the inflammation signaling pathway was significantly changed. Its specific mechanism may be related to its regulating the expression of p-p65 p-IκBα, TNF-α and IL-1β proteins in the inflammation signaling pathway.
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Affiliation(s)
- Yingna Li
- College of Pharmacy, Beihua University, Jilin 132000, China; Research Center of Traditional Chinese Medicine, The Affiliated Hospital to Changchun University of Chinese Medicine, Changchun 130000, China
| | - Yu Sheng
- College of Pharmacy, Beihua University, Jilin 132000, China
| | - Jiuyue Liu
- College of Pharmacy, Beihua University, Jilin 132000, China
| | - Guangyu Xu
- College of Pharmacy, Beihua University, Jilin 132000, China
| | - Wanwen Yu
- College of Forestry, Nanjing Forestry University, 210037, China
| | - Qingwen Cui
- College of Pharmacy, Beihua University, Jilin 132000, China
| | - Xuechun Lu
- College of Pharmacy, Beihua University, Jilin 132000, China
| | - Peige Du
- College of Pharmacy, Beihua University, Jilin 132000, China.
| | - Liping An
- College of Pharmacy, Beihua University, Jilin 132000, China.
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25
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Arjmand B, Hamidpour SK, Tayanloo-Beik A, Goodarzi P, Aghayan HR, Adibi H, Larijani B. Machine Learning: A New Prospect in Multi-Omics Data Analysis of Cancer. Front Genet 2022; 13:824451. [PMID: 35154283 PMCID: PMC8829119 DOI: 10.3389/fgene.2022.824451] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Accepted: 01/10/2022] [Indexed: 12/11/2022] Open
Abstract
Cancer is defined as a large group of diseases that is associated with abnormal cell growth, uncontrollable cell division, and may tend to impinge on other tissues of the body by different mechanisms through metastasis. What makes cancer so important is that the cancer incidence rate is growing worldwide which can have major health, economic, and even social impacts on both patients and the governments. Thereby, the early cancer prognosis, diagnosis, and treatment can play a crucial role at the front line of combating cancer. The onset and progression of cancer can occur under the influence of complicated mechanisms and some alterations in the level of genome, proteome, transcriptome, metabolome etc. Consequently, the advent of omics science and its broad research branches (such as genomics, proteomics, transcriptomics, metabolomics, and so forth) as revolutionary biological approaches have opened new doors to the comprehensive perception of the cancer landscape. Due to the complexities of the formation and development of cancer, the study of mechanisms underlying cancer has gone beyond just one field of the omics arena. Therefore, making a connection between the resultant data from different branches of omics science and examining them in a multi-omics field can pave the way for facilitating the discovery of novel prognostic, diagnostic, and therapeutic approaches. As the volume and complexity of data from the omics studies in cancer are increasing dramatically, the use of leading-edge technologies such as machine learning can have a promising role in the assessments of cancer research resultant data. Machine learning is categorized as a subset of artificial intelligence which aims to data parsing, classification, and data pattern identification by applying statistical methods and algorithms. This acquired knowledge subsequently allows computers to learn and improve accurate predictions through experiences from data processing. In this context, the application of machine learning, as a novel computational technology offers new opportunities for achieving in-depth knowledge of cancer by analysis of resultant data from multi-omics studies. Therefore, it can be concluded that the use of artificial intelligence technologies such as machine learning can have revolutionary roles in the fight against cancer.
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Affiliation(s)
- Babak Arjmand
- Cell Therapy and Regenerative Medicine Research Center, Endocrinology and Metabolism Molecular-Cellular Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
- *Correspondence: Babak Arjmand, ; Bagher Larijani,
| | - Shayesteh Kokabi Hamidpour
- Cell Therapy and Regenerative Medicine Research Center, Endocrinology and Metabolism Molecular-Cellular Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Akram Tayanloo-Beik
- Cell Therapy and Regenerative Medicine Research Center, Endocrinology and Metabolism Molecular-Cellular Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Parisa Goodarzi
- Cell Therapy and Regenerative Medicine Research Center, Endocrinology and Metabolism Molecular-Cellular Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Hamid Reza Aghayan
- Cell Therapy and Regenerative Medicine Research Center, Endocrinology and Metabolism Molecular-Cellular Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Hossein Adibi
- Diabetes Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Bagher Larijani
- Endocrinology and Metabolism Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
- *Correspondence: Babak Arjmand, ; Bagher Larijani,
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26
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Rocca A, Kholodenko BN. Can Systems Biology Advance Clinical Precision Oncology? Cancers (Basel) 2021; 13:6312. [PMID: 34944932 PMCID: PMC8699328 DOI: 10.3390/cancers13246312] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Accepted: 12/10/2021] [Indexed: 12/13/2022] Open
Abstract
Precision oncology is perceived as a way forward to treat individual cancer patients. However, knowing particular cancer mutations is not enough for optimal therapeutic treatment, because cancer genotype-phenotype relationships are nonlinear and dynamic. Systems biology studies the biological processes at the systems' level, using an array of techniques, ranging from statistical methods to network reconstruction and analysis, to mathematical modeling. Its goal is to reconstruct the complex and often counterintuitive dynamic behavior of biological systems and quantitatively predict their responses to environmental perturbations. In this paper, we review the impact of systems biology on precision oncology. We show examples of how the analysis of signal transduction networks allows to dissect resistance to targeted therapies and inform the choice of combinations of targeted drugs based on tumor molecular alterations. Patient-specific biomarkers based on dynamical models of signaling networks can have a greater prognostic value than conventional biomarkers. These examples support systems biology models as valuable tools to advance clinical and translational oncological research.
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Affiliation(s)
- Andrea Rocca
- Hygiene and Public Health, Local Health Unit of Romagna, 47121 Forlì, Italy
| | - Boris N. Kholodenko
- Systems Biology Ireland, School of Medicine, University College Dublin, Belfield, D04 V1W8 Dublin, Ireland
- Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, D04 V1W8 Dublin, Ireland
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT 06520, USA
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27
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Raevskiy M, Sorokin M, Vladimirova U, Suntsova M, Efimov V, Garazha A, Drobyshev A, Moisseev A, Rumiantsev P, Li X, Buzdin A. EGFR Pathway-Based Gene Signatures of Druggable Gene Mutations in Melanoma, Breast, Lung, and Thyroid Cancers. BIOCHEMISTRY. BIOKHIMIIA 2021; 86:1477-1488. [PMID: 34906047 DOI: 10.1134/s0006297921110110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 10/01/2021] [Accepted: 10/01/2021] [Indexed: 06/14/2023]
Abstract
EGFR, BRAF, PIK3CA, and KRAS genes play major roles in EGFR pathway, and accommodate activating mutations that predict response to many targeted therapeutics. However, connections between these mutations and EGFR pathway expression patterns remain unexplored. Here, we investigated transcriptomic associations with these activating mutations in three ways. First, we compared expressions of these genes in the mutant and wild type tumors, respectively, using RNA sequencing profiles from The Cancer Genome Atlas project database (n = 3660). Second, mutations were associated with the activation level of EGFR pathway. Third, they were associated with the gene signatures of differentially expressed genes from these pathways between the mutant and wild type tumors. We found that the upregulated EGFR pathway was linked with mutations in the BRAF (thyroid cancer, melanoma) and PIK3CA (breast cancer) genes. Gene signatures were associated with BRAF (thyroid cancer, melanoma), EGFR (squamous cell lung cancer), KRAS (colorectal cancer), and PIK3CA (breast cancer) mutations. However, only for the BRAF gene signature in the thyroid cancer we observed strong biomarker diagnostic capacity with AUC > 0.7 (0.809). Next, we validated this signature on the independent literature-based dataset (n = 127, fresh-frozen tissue samples, AUC 0.912), and on the experimental dataset (n = 42, formalin fixed, paraffin embedded tissue samples, AUC 0.822). Our results suggest that the RNA sequencing profiles can be used for robust identification of the replacement of Valine at position 600 with Glutamic acid in the BRAF gene in the papillary subtype of thyroid cancer, and evidence that the specific gene expression levels could provide information about the driver carcinogenic mutations.
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Affiliation(s)
- Mikhail Raevskiy
- Omicsway Corp., Walnut, CA 91789, USA.
- Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, 141701, Russia
| | - Maxim Sorokin
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, 117997, Russia.
- Oncobox Ltd., Moscow, 121205, Russia
- Sechenov First Moscow State Medical University, Moscow, 119991, Russia
| | - Uliana Vladimirova
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, 117997, Russia.
| | - Maria Suntsova
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, 117997, Russia.
- Sechenov First Moscow State Medical University, Moscow, 119991, Russia
| | - Victor Efimov
- Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, 141701, Russia.
| | | | - Alexei Drobyshev
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, 117997, Russia.
- Sechenov First Moscow State Medical University, Moscow, 119991, Russia
| | - Aleksey Moisseev
- Sechenov First Moscow State Medical University, Moscow, 119991, Russia.
| | | | - Xinmin Li
- Department of Pathology and Laboratory Medicine, University of California Los Angeles, Los Angeles, CA, 90095 USA.
| | - Anton Buzdin
- Omicsway Corp., Walnut, CA 91789, USA.
- Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, 141701, Russia
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, 117997, Russia
- Sechenov First Moscow State Medical University, Moscow, 119991, Russia
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28
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Mock A, Plath M, Moratin J, Tapken MJ, Jäger D, Krauss J, Fröhling S, Hess J, Zaoui K. EGFR and PI3K Pathway Activities Might Guide Drug Repurposing in HPV-Negative Head and Neck Cancers. Front Oncol 2021; 11:678966. [PMID: 34178665 PMCID: PMC8226088 DOI: 10.3389/fonc.2021.678966] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Accepted: 05/13/2021] [Indexed: 12/18/2022] Open
Abstract
While genetic alterations in Epidermal growth factor receptor (EGFR) and PI3K are common in head and neck squamous cell carcinomas (HNSCC), their impact on oncogenic signaling and cancer drug sensitivities remains elusive. To determine their consequences on the transcriptional network, pathway activities of EGFR, PI3K, and 12 additional oncogenic pathways were inferred in 498 HNSCC samples of The Cancer Genome Atlas using PROGENy. More than half of HPV-negative HNSCC showed a pathway activation in EGFR or PI3K. An amplification in EGFR and a mutation in PI3KCA resulted in a significantly higher activity of the respective pathway (p = 0.017 and p = 0.007). Interestingly, both pathway activations could only be explained by genetic alterations in less than 25% of cases indicating additional molecular events involved in the downstream signaling. Suitable in vitro pathway models could be identified in a published drug screen of 45 HPV-negative HNSCC cell lines. An active EGFR pathway was predictive for the response to the PI3K inhibitor buparlisib (p = 6.36E-03) and an inactive EGFR and PI3K pathway was associated with efficacy of the B-cell lymphoma (BCL) inhibitor navitoclax (p = 9.26E-03). In addition, an inactive PI3K pathway correlated with a response to multiple Histone deacetylase inhibitor (HDAC) inhibitors. These findings require validation in preclinical models and clinical studies.
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Affiliation(s)
- Andreas Mock
- Department of Medical Oncology, National Center for Tumor Diseases (NCT) Heidelberg, Heidelberg University Hospital, Heidelberg, Germany.,Division of Translational Medical Oncology, NCT Heidelberg, German Cancer Center (DKFZ), Heidelberg, Germany
| | - Michaela Plath
- Department of Otorhinolaryngology, Head and Neck Surgery, Heidelberg University Hospital, Heidelberg, Germany
| | - Julius Moratin
- Department of Oral and Cranio-Maxillofacial Surgery, Heidelberg University Hospital, Heidelberg, Germany
| | - Maria Johanna Tapken
- Department of Medical Oncology, National Center for Tumor Diseases (NCT) Heidelberg, Heidelberg University Hospital, Heidelberg, Germany
| | - Dirk Jäger
- Department of Medical Oncology, National Center for Tumor Diseases (NCT) Heidelberg, Heidelberg University Hospital, Heidelberg, Germany
| | - Jürgen Krauss
- Department of Medical Oncology, National Center for Tumor Diseases (NCT) Heidelberg, Heidelberg University Hospital, Heidelberg, Germany
| | - Stefan Fröhling
- Division of Translational Medical Oncology, NCT Heidelberg, German Cancer Center (DKFZ), Heidelberg, Germany
| | - Jochen Hess
- Department of Otorhinolaryngology, Head and Neck Surgery, Heidelberg University Hospital, Heidelberg, Germany.,Molecular Mechanisms of Head and Neck Tumors, DKFZ, Heidelberg, Germany
| | - Karim Zaoui
- Department of Otorhinolaryngology, Head and Neck Surgery, Heidelberg University Hospital, Heidelberg, Germany
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29
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Xiang Y, Zou X, Shi H, Xu X, Wu C, Zhong W, Wang J, Zhou W, Zeng X, He M, Wang Y, Huang L, Wang X. Elastic Net Models Based on DNA Copy Number Variations Predicts Clinical Features, Expression Signatures, and Mutations in Lung Adenocarcinoma. Front Genet 2021; 12:668040. [PMID: 34135942 PMCID: PMC8202527 DOI: 10.3389/fgene.2021.668040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Accepted: 04/26/2021] [Indexed: 11/30/2022] Open
Abstract
In the precision medicine of lung adenocarcinoma, the identification and prediction of tumor phenotypes for specific biomolecular events are still not studied in depth. Various earlier researches sheds light on the close correlation between genetic expression signatures and DNA copy number variations (CNVs), for which analysis of CNVs provides valuable information about molecular and phenotypic changes in tumorigenesis. In this study, we propose a comprehensive analysis combining genome-wide association analysis and an Elastic Net Regression predictive model, focus on predicting the levels of many gene expression signatures in lung adenocarcinoma, based upon DNA copy number features alone. Additionally, we predicted many other key phenotypes, including clinical features (pathological stage), gene mutations, and protein expressions. These Elastic Net prediction methods can also be applied to other gene sets, thereby facilitating their use as biomarkers in monitoring therapy.
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Affiliation(s)
- Yi Xiang
- Department of Oncology, The First Affiliated Hospital, Gannan Medical University, Ganzhou, China
| | - Xiaohuan Zou
- Department of Critical Care Medicine, The First Affiliated Hospital, Gannan Medical University, Ganzhou, China
| | - Huaqiu Shi
- Department of Oncology, The First Affiliated Hospital, Gannan Medical University, Ganzhou, China
| | - Xueming Xu
- Department of Oncology, The First Affiliated Hospital, Gannan Medical University, Ganzhou, China
| | - Caixia Wu
- First Clinical Medical College, Gannan Medical University, Ganzhou, China
| | - Wenjuan Zhong
- Department of Oncology, The First Affiliated Hospital, Gannan Medical University, Ganzhou, China
| | - Jinfeng Wang
- Department of Oncology, The First Affiliated Hospital, Gannan Medical University, Ganzhou, China
| | - Wenting Zhou
- Department of Oncology, The First Affiliated Hospital, Gannan Medical University, Ganzhou, China
| | - Xiaoli Zeng
- Department of Oncology, The First Affiliated Hospital, Gannan Medical University, Ganzhou, China
| | - Miao He
- Department of Oncology, The First Affiliated Hospital, Gannan Medical University, Ganzhou, China
| | - Ying Wang
- First Clinical Medical College, Gannan Medical University, Ganzhou, China
| | - Li Huang
- Department of Oncology, The First Affiliated Hospital, Gannan Medical University, Ganzhou, China
| | - Xiangcai Wang
- Department of Oncology, The First Affiliated Hospital, Gannan Medical University, Ganzhou, China
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30
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Bertucci F, Gonçalves A, Guille A, Adelaïde J, Garnier S, Carbuccia N, Billon E, Finetti P, Sfumato P, Monneur A, Pécheux C, Khran M, Brunelle S, Mescam L, Thomassin-Piana J, Poizat F, Charafe-Jauffret E, Turrini O, Lambaudie E, Provansal M, Extra JM, Madroszyk A, Gilabert M, Sabatier R, Vicier C, Mamessier E, Chabannon C, Pakradouni J, Viens P, André F, Gravis G, Popovici C, Birnbaum D, Chaffanet M. Prospective high-throughput genome profiling of advanced cancers: results of the PERMED-01 clinical trial. Genome Med 2021; 13:87. [PMID: 34006291 PMCID: PMC8132379 DOI: 10.1186/s13073-021-00897-9] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Accepted: 04/27/2021] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND The benefit of precision medicine based on relatively limited gene sets and often-archived samples remains unproven. PERMED-01 (NCT02342158) was a prospective monocentric clinical trial assessing, in adults with advanced solid cancer, the feasibility and impact of extensive molecular profiling applied to newly biopsied tumor sample and based on targeted NGS (t-NGS) of the largest gene panel to date and whole-genome array-comparative genomic hybridization (aCGH) with assessment of single-gene alterations and clinically relevant genomic scores. METHODS Eligible patients with refractory cancer had one tumor lesion accessible to biopsy. Extracted tumor DNA was profiled by t-NGS and aCGH. We assessed alterations of 802 "candidate cancer" genes and global genomic scores, such as homologous recombination deficiency (HRD) score and tumor mutational burden. The primary endpoint was the number of patients with actionable genetic alterations (AGAs). Secondary endpoints herein reported included a description of patients with AGA who received a "matched therapy" and their clinical outcome, and a comparison of AGA identification with t-NGS and aCGH versus whole-exome sequencing (WES). RESULTS Between November 2014 and September 2019, we enrolled 550 patients heavily pretreated. An exploitable complete molecular profile was obtained in 441/550 patients (80%). At least one AGA, defined in real time by our molecular tumor board, was found in 393/550 patients (71%, two-sided 90%CI 68-75%). Only 94/550 patients (17%, 95%CI 14-21) received an "AGA-matched therapy" on progression. The most frequent AGAs leading to "matched therapy" included PIK3CA mutations, KRAS mutations/amplifications, PTEN deletions/mutations, ERBB2 amplifications/mutations, and BRCA1/2 mutations. Such "matched therapy" improved by at least 1.3-fold the progression-free survival on matched therapy (PFS2) compared to PFS on prior therapy (PFS1) in 36% of cases, representing 6% of the enrolled patients. Within patients with AGA treated on progression, the use of "matched therapy" was the sole variable associated with an improved PFS2/PFS1 ratio. Objective responses were observed in 19% of patients treated with "matched therapy," and 6-month overall survival (OS) was 62% (95%CI 52-73). In a subset of 112 metastatic breast cancers, WES did not provide benefit in term of AGA identification when compared with t-NGS/aCGH. CONCLUSIONS Extensive molecular profiling of a newly biopsied tumor sample identified AGA in most of cases, leading to delivery of a "matched therapy" in 17% of screened patients, of which 36% derived clinical benefit. WES did not seem to improve these results. TRIAL REGISTRATION ID-RCB identifier: 2014-A00966-41; ClinicalTrials.gov identifier: NCT02342158 .
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Affiliation(s)
- François Bertucci
- Laboratory of Predictive Oncology, Department of Medical Oncology, Centre de Recherche en Cancérologie de Marseille (CRCM), Institut Paoli-Calmettes, INSERM UMR1068, CNRS UMR725, Aix-Marseille University, 232 Boulevard Sainte-Marguerite, 13009, Marseille, France.
- Department of Medical Oncology, Institut Paoli-Calmettes, Marseille, France.
| | - Anthony Gonçalves
- Laboratory of Predictive Oncology, Department of Medical Oncology, Centre de Recherche en Cancérologie de Marseille (CRCM), Institut Paoli-Calmettes, INSERM UMR1068, CNRS UMR725, Aix-Marseille University, 232 Boulevard Sainte-Marguerite, 13009, Marseille, France
- Department of Medical Oncology, Institut Paoli-Calmettes, Marseille, France
| | - Arnaud Guille
- Laboratory of Predictive Oncology, Department of Medical Oncology, Centre de Recherche en Cancérologie de Marseille (CRCM), Institut Paoli-Calmettes, INSERM UMR1068, CNRS UMR725, Aix-Marseille University, 232 Boulevard Sainte-Marguerite, 13009, Marseille, France
| | - José Adelaïde
- Laboratory of Predictive Oncology, Department of Medical Oncology, Centre de Recherche en Cancérologie de Marseille (CRCM), Institut Paoli-Calmettes, INSERM UMR1068, CNRS UMR725, Aix-Marseille University, 232 Boulevard Sainte-Marguerite, 13009, Marseille, France
| | - Séverine Garnier
- Laboratory of Predictive Oncology, Department of Medical Oncology, Centre de Recherche en Cancérologie de Marseille (CRCM), Institut Paoli-Calmettes, INSERM UMR1068, CNRS UMR725, Aix-Marseille University, 232 Boulevard Sainte-Marguerite, 13009, Marseille, France
| | - Nadine Carbuccia
- Laboratory of Predictive Oncology, Department of Medical Oncology, Centre de Recherche en Cancérologie de Marseille (CRCM), Institut Paoli-Calmettes, INSERM UMR1068, CNRS UMR725, Aix-Marseille University, 232 Boulevard Sainte-Marguerite, 13009, Marseille, France
| | - Emilien Billon
- Department of Medical Oncology, Institut Paoli-Calmettes, Marseille, France
| | - Pascal Finetti
- Laboratory of Predictive Oncology, Department of Medical Oncology, Centre de Recherche en Cancérologie de Marseille (CRCM), Institut Paoli-Calmettes, INSERM UMR1068, CNRS UMR725, Aix-Marseille University, 232 Boulevard Sainte-Marguerite, 13009, Marseille, France
| | - Patrick Sfumato
- Biostatistics Unit, Institut Paoli-Calmettes, Marseille, France
| | - Audrey Monneur
- Department of Medical Oncology, Institut Paoli-Calmettes, Marseille, France
| | - Christophe Pécheux
- Department of Medical genetics, Hôpital Timone Enfants, AP-HM, Marseille, France
| | - Martin Khran
- Department of Medical genetics, Hôpital Timone Enfants, AP-HM, Marseille, France
- Aix-Marseille University, Inserm, U1251-MMG, Marseille Medical Genetics, Marseille, France
| | - Serge Brunelle
- Department of Imaging, Institut Paoli-Calmettes, Marseille, France
| | - Lenaïg Mescam
- Department of Biopathology, Institut Paoli-Calmettes, Marseille, France
| | | | - Flora Poizat
- Department of Biopathology, Institut Paoli-Calmettes, Marseille, France
| | | | - Olivier Turrini
- Department of Surgical Oncology, Institut Paoli-Calmettes, Marseille, France
| | - Eric Lambaudie
- Department of Surgical Oncology, Institut Paoli-Calmettes, Marseille, France
| | - Magali Provansal
- Department of Medical Oncology, Institut Paoli-Calmettes, Marseille, France
| | - Jean-Marc Extra
- Department of Medical Oncology, Institut Paoli-Calmettes, Marseille, France
| | - Anne Madroszyk
- Department of Medical Oncology, Institut Paoli-Calmettes, Marseille, France
| | - Marine Gilabert
- Department of Medical Oncology, Institut Paoli-Calmettes, Marseille, France
| | - Renaud Sabatier
- Department of Medical Oncology, Institut Paoli-Calmettes, Marseille, France
| | - Cécile Vicier
- Department of Medical Oncology, Institut Paoli-Calmettes, Marseille, France
| | - Emilie Mamessier
- Laboratory of Predictive Oncology, Department of Medical Oncology, Centre de Recherche en Cancérologie de Marseille (CRCM), Institut Paoli-Calmettes, INSERM UMR1068, CNRS UMR725, Aix-Marseille University, 232 Boulevard Sainte-Marguerite, 13009, Marseille, France
| | - Christian Chabannon
- Biobank, Department of Hematology, Institut Paoli-Calmettes, Marseille, France
| | - Jihane Pakradouni
- Department of Clinical Research and Innovation, Institut Paoli-Calmettes, Marseille, France
| | - Patrice Viens
- Department of Medical Oncology, Institut Paoli-Calmettes, Marseille, France
| | - Fabrice André
- Department of Medical Oncology, Gustave Roussy Cancer Campus, UMR981 Inserm, Villejuif, France
- Paris Sud University, Orsay, France
| | - Gwenaelle Gravis
- Department of Medical Oncology, Institut Paoli-Calmettes, Marseille, France
| | - Cornel Popovici
- Department of Oncogenetics, Institut Paoli-Calmettes, Marseille, France
| | - Daniel Birnbaum
- Laboratory of Predictive Oncology, Department of Medical Oncology, Centre de Recherche en Cancérologie de Marseille (CRCM), Institut Paoli-Calmettes, INSERM UMR1068, CNRS UMR725, Aix-Marseille University, 232 Boulevard Sainte-Marguerite, 13009, Marseille, France
| | - Max Chaffanet
- Laboratory of Predictive Oncology, Department of Medical Oncology, Centre de Recherche en Cancérologie de Marseille (CRCM), Institut Paoli-Calmettes, INSERM UMR1068, CNRS UMR725, Aix-Marseille University, 232 Boulevard Sainte-Marguerite, 13009, Marseille, France
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