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Farooq A, Hassan M, Loya A, Asghar K. Community Outreach and Engagement in Cancer Research Through a Biobank Clinic at Shaukat Khanum Memorial Cancer Hospital and Research Centre, Pakistan. Cureus 2024; 16:e55179. [PMID: 38558595 PMCID: PMC10980601 DOI: 10.7759/cureus.55179] [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] [Accepted: 02/26/2024] [Indexed: 04/04/2024] Open
Abstract
INTRODUCTION Cancer's increasing prevalence across the globe emphasizes the urgency for continued research, prevention, and accessible healthcare to mitigate its impact on individuals and communities. While there have been significant advances made towards controlling cancer morbidity and mortality in recent decades, Pakistan continues to experience a markedly elevated burden of the disease. With this study, we aim to raise awareness about biobank research within the cancer patient community, fostering participation and collaboration to advance the fight against cancer through vital research contributions. METHODS In October 2022, we initiated the biobank clinic at Shaukat Khanum Memorial Cancer Hospital and Research Centre (SKMCH&RC). Here, patients underwent screening and received invitations to voluntarily participate in biobank research. During these interactions, we engaged patients in discussions about the significance of biobank research, addressed their concerns, and encouraged their participation in advancing our research endeavors. Two-sample independent t-tests were performed to compare the mean number of participants in pre-clinic and post-clinic cohorts. RESULTS This research involved a total of 958 participants, with 312 participants enrolled before the clinic and 646 participants enrolled after the clinic. We have observed a noticeable increase in the participation of cancer patients in our research endeavors since the inception of the biobank clinic (p-value<0.001). Over an 11-month time frame, we scheduled appointments for 759 patients, and out of those, 656 patients availed themselves to visit the clinic. Impressively, we achieved the enrollment of 646 patients into the clinic, reflecting an exceptional consent rate of 98.47% for their active involvement in our research initiatives. This underscores our commitment to conducting comprehensive discussions and providing thorough explanations regarding the ethical and procedural aspects of our research. CONCLUSION Biobank clinic plays a pivotal role in raising cancer awareness and fostering research participation, especially in regions with limited healthcare infrastructure and lower literacy rates. It emerges as a community-engagement model that aligns research with local needs, ensuring its relevance and benefit to the population.
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Affiliation(s)
- Asim Farooq
- Basic Sciences Research, Shaukat Khanum Memorial Cancer Hospital and Research Centre, Lahore, PAK
| | - Muhammad Hassan
- Basic Sciences Research, Shaukat Khanum Memorial Cancer Hospital and Research Centre, Lahore, PAK
| | - Asif Loya
- Pathology, Shaukat Khanum Memorial Cancer Hospital and Research Centre, Lahore, PAK
| | - Kashif Asghar
- Basic Sciences Research, Shaukat Khanum Memorial Cancer Hospital and Research Centre, Lahore, PAK
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Qiu X, Lu R, He Q, Chen S, Huang C, Lin D. Metabolic signatures and potential biomarkers for the diagnosis and treatment of colon cancer cachexia. Acta Biochim Biophys Sin (Shanghai) 2023; 55:1913-1924. [PMID: 37705348 DOI: 10.3724/abbs.2023151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/15/2023] Open
Abstract
Cancer cachexia (CAC) is a debilitating condition that often arises from noncachexia cancer (NCAC), with distinct metabolic characteristics and medical treatments. However, the metabolic changes and underlying molecular mechanisms during cachexia progression remain poorly understood. Understanding the progression of CAC is crucial for developing diagnostic approaches to distinguish between CAC and NCAC stages, facilitating appropriate treatment for cancer patients. In this study, we establish a mouse model of colon CAC and categorize the mice into three groups: CAC, NCAC and normal control (NOR). By performing nuclear magnetic resonance (NMR)-based metabolomic profiling on mouse sera, we elucidate the metabolic properties of these groups. Our findings unveil significant differences in the metabolic profiles among the CAC, NCAC and NOR groups, highlighting significant impairments in energy metabolism and amino acid metabolism during cachexia progression. Additionally, we observe the elevated serum levels of lysine and acetate during the transition from the NCAC to CAC stages. Using multivariate ROC analysis, we identify lysine and acetate as potential biomarkers for distinguishing between CAC and NCAC stages. These biomarkers hold promise for the diagnosis of CAC from noncachexia cancer. Our study provides novel insights into the metabolic mechanisms underlying cachexia progression and offers valuable avenues for the diagnosis and treatment of CAC in clinical settings.
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Affiliation(s)
- Xu Qiu
- Key Laboratory for Chemical Biology of Fujian Province, MOE Key Laboratory of Spectrochemical Analysis and Instrumentation, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Ruohan Lu
- Key Laboratory for Chemical Biology of Fujian Province, MOE Key Laboratory of Spectrochemical Analysis and Instrumentation, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Qiqing He
- Key Laboratory for Chemical Biology of Fujian Province, MOE Key Laboratory of Spectrochemical Analysis and Instrumentation, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Shu Chen
- Key Laboratory for Chemical Biology of Fujian Province, MOE Key Laboratory of Spectrochemical Analysis and Instrumentation, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Caihua Huang
- Research and Communication Center of Exercise and Health, Xiamen University of Technology, Xiamen 361005, China
| | - Donghai Lin
- Key Laboratory for Chemical Biology of Fujian Province, MOE Key Laboratory of Spectrochemical Analysis and Instrumentation, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
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Vodicska B, Déri J, Tihanyi D, Várkondi E, Kispéter E, Dóczi R, Lakatos D, Dirner A, Vidermann M, Filotás P, Szalkai-Dénes R, Szegedi I, Bartyik K, Gábor KM, Simon R, Hauser P, Péter G, Kiss C, Garami M, Peták I. Real-world performance analysis of a novel computational method in the precision oncology of pediatric tumors. World J Pediatr 2023; 19:992-1008. [PMID: 36914906 PMCID: PMC10497647 DOI: 10.1007/s12519-023-00700-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Accepted: 01/31/2023] [Indexed: 03/16/2023]
Abstract
BACKGROUND The utility of routine extensive molecular profiling of pediatric tumors is a matter of debate due to the high number of genetic alterations of unknown significance or low evidence and the lack of standardized and personalized decision support methods. Digital drug assignment (DDA) is a novel computational method to prioritize treatment options by aggregating numerous evidence-based associations between multiple drivers, targets, and targeted agents. DDA has been validated to improve personalized treatment decisions based on the outcome data of adult patients treated in the SHIVA01 clinical trial. The aim of this study was to evaluate the utility of DDA in pediatric oncology. METHODS Between 2017 and 2020, 103 high-risk pediatric cancer patients (< 21 years) were involved in our precision oncology program, and samples from 100 patients were eligible for further analysis. Tissue or blood samples were analyzed by whole-exome (WES) or targeted panel sequencing and other molecular diagnostic modalities and processed by a software system using the DDA algorithm for therapeutic decision support. Finally, a molecular tumor board (MTB) evaluated the results to provide therapy recommendations. RESULTS Of the 100 cases with comprehensive molecular diagnostic data, 88 yielded WES and 12 panel sequencing results. DDA identified matching off-label targeted treatment options (actionability) in 72/100 cases (72%), while 57/100 (57%) showed potential drug resistance. Actionability reached 88% (29/33) by 2020 due to the continuous updates of the evidence database. MTB approved the clinical use of a DDA-top-listed treatment in 56 of 72 actionable cases (78%). The approved therapies had significantly higher aggregated evidence levels (AELs) than dismissed therapies. Filtering of WES results for targeted panels missed important mutations affecting therapy selection. CONCLUSIONS DDA is a promising approach to overcome challenges associated with the interpretation of extensive molecular profiling in the routine care of high-risk pediatric cancers. Knowledgebase updates enable automatic interpretation of a continuously expanding gene set, a "virtual" panel, filtered out from genome-wide analysis to always maximize the performance of precision treatment planning.
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Affiliation(s)
- Barbara Vodicska
- Oncompass Medicine Hungary Kft, Retek Str. 34, Budapest, 1024, Hungary
| | - Júlia Déri
- Oncompass Medicine Hungary Kft, Retek Str. 34, Budapest, 1024, Hungary
| | - Dóra Tihanyi
- Oncompass Medicine Hungary Kft, Retek Str. 34, Budapest, 1024, Hungary
| | - Edit Várkondi
- Oncompass Medicine Hungary Kft, Retek Str. 34, Budapest, 1024, Hungary
| | - Enikő Kispéter
- Oncompass Medicine Hungary Kft, Retek Str. 34, Budapest, 1024, Hungary
| | - Róbert Dóczi
- Oncompass Medicine Hungary Kft, Retek Str. 34, Budapest, 1024, Hungary
| | - Dóra Lakatos
- Oncompass Medicine Hungary Kft, Retek Str. 34, Budapest, 1024, Hungary
| | - Anna Dirner
- Oncompass Medicine Hungary Kft, Retek Str. 34, Budapest, 1024, Hungary
| | - Mátyás Vidermann
- Oncompass Medicine Hungary Kft, Retek Str. 34, Budapest, 1024, Hungary
| | - Péter Filotás
- Oncompass Medicine Hungary Kft, Retek Str. 34, Budapest, 1024, Hungary
| | | | - István Szegedi
- Division of Pediatric Hematology-Oncology, Department of Pediatrics, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
| | - Katalin Bartyik
- Department of Pediatrics, Albert Szent-Györgyi Medical School, University of Szeged, Szeged, Hungary
| | - Krisztina Míta Gábor
- Department of Pediatrics, Albert Szent-Györgyi Medical School, University of Szeged, Szeged, Hungary
| | - Réka Simon
- Onco-Hematology Department, Velkey László Paediatric Health Centre, Miskolc, Hungary
| | - Péter Hauser
- Department of Paediatrics, Semmelweis University, Budapest, Hungary
| | - György Péter
- Onco-Hematology Department, Heim Pál Children's Hospital, Budapest, Hungary
| | - Csongor Kiss
- Division of Pediatric Hematology-Oncology, Department of Pediatrics, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
| | - Miklós Garami
- Department of Paediatrics, Semmelweis University, Budapest, Hungary
| | - István Peták
- Oncompass Medicine Hungary Kft, Retek Str. 34, Budapest, 1024, Hungary.
- Department of Pharmacology and Pharmacotherapy, Semmelweis University, Budapest, Hungary.
- Department of Pharmaceutical Sciences, University of Illinois at Chicago, Chicago, USA.
- Genomate Health, Cambridge, MA, USA.
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Frost H, Graham DM, Carter L, O'Regan P, Landers D, Freitas A. Patient attrition in Molecular Tumour Boards: a systematic review. Br J Cancer 2022; 127:1557-1564. [PMID: 35941175 PMCID: PMC9553981 DOI: 10.1038/s41416-022-01922-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 06/22/2022] [Accepted: 07/13/2022] [Indexed: 11/08/2022] Open
Abstract
BACKGROUND Molecular Tumour Boards (MTBs) were created with the purpose of supporting clinical decision-making within precision medicine. Though in use globally, reporting on these meetings often focuses on the small percentages of patients that receive treatment via this process and are less likely to report on, and assess, patients who do not receive treatment. METHODS A literature review was performed to understand patient attrition within MTBs and barriers to patients receiving treatment. A total of 51 papers were reviewed spanning a 6-year period from 11 different countries. RESULTS In total, 20% of patients received treatment through the MTB process. Of those that did not receive treatment, the main reasons were no mutations identified (27%), no actionable mutations (22%) and clinical deterioration (15%). However, data were often incomplete due to inconsistent reporting of MTBs with only 55% reporting on patients having no mutations, 55% reporting on the presence of actionable mutations with no treatment options and 59% reporting on clinical deterioration. DISCUSSION As patient attrition in MTBs is an issue which is very rarely alluded to in reporting, more transparent reporting is needed to understand barriers to treatment and integration of new technologies is required to process increasing omic and treatment data.
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Affiliation(s)
- Hannah Frost
- Digital Experimental Cancer Medicine Team, Cancer Research UK Manchester Institute Cancer Biomarker Centre, Manchester, UK.
- Department of Computer Science, University of Manchester, Manchester, UK.
| | - Donna M Graham
- Digital Experimental Cancer Medicine Team, Cancer Research UK Manchester Institute Cancer Biomarker Centre, Manchester, UK
- Experimental Cancer Medicine Team, The Christie NHS Foundation Trust, Manchester, UK
- Division of Cancer Sciences, The University of Manchester, Manchester, UK
| | - Louise Carter
- Experimental Cancer Medicine Team, The Christie NHS Foundation Trust, Manchester, UK
- Division of Cancer Sciences, The University of Manchester, Manchester, UK
| | - Paul O'Regan
- Digital Experimental Cancer Medicine Team, Cancer Research UK Manchester Institute Cancer Biomarker Centre, Manchester, UK
| | - Dónal Landers
- Digital Experimental Cancer Medicine Team, Cancer Research UK Manchester Institute Cancer Biomarker Centre, Manchester, UK
| | - André Freitas
- Digital Experimental Cancer Medicine Team, Cancer Research UK Manchester Institute Cancer Biomarker Centre, Manchester, UK
- Department of Computer Science, University of Manchester, Manchester, UK
- Idiap Research Institute, Martigny, Switzerland
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Fedorov II, Lineva VI, Tarasova IA, Gorshkov MV. Mass Spectrometry-Based Chemical Proteomics for Drug Target Discoveries. BIOCHEMISTRY. BIOKHIMIIA 2022; 87:983-994. [PMID: 36180990 DOI: 10.1134/s0006297922090103] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 07/04/2022] [Accepted: 07/06/2022] [Indexed: 06/16/2023]
Abstract
Chemical proteomics, emerging rapidly in recent years, has become a main approach to identifying interactions between the small molecules and proteins in the cells on a proteome scale and mapping the signaling and/or metabolic pathways activated and regulated by these interactions. The methods of chemical proteomics allow not only identifying proteins targeted by drugs, characterizing their toxicity and discovering possible off-target proteins, but also elucidation of the fundamental mechanisms of cell functioning under conditions of drug exposure or due to the changes in physiological state of the organism itself. Solving these problems is essential for both basic research in biology and clinical practice, including approaches to early diagnosis of various forms of serious diseases or prediction of the effectiveness of therapeutic treatment. At the same time, recent developments in high-resolution mass spectrometry have provided the technology for searching the drug targets across the whole cell proteomes. This review provides a concise description of the main objectives and problems of mass spectrometry-based chemical proteomics, the methods and approaches to their solution, and examples of implementation of these methods in biomedical research.
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Affiliation(s)
- Ivan I Fedorov
- V. L. Talrose Institute for Energy Problems of Chemical Physics, N. N. Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences, Moscow, 119334, Russia
- Moscow Institute of Physics and Technology (National University), Dolgoprudny, Moscow Region, 141700, Russia
| | - Victoria I Lineva
- Moscow Institute of Physics and Technology (National University), Dolgoprudny, Moscow Region, 141700, Russia
| | - Irina A Tarasova
- V. L. Talrose Institute for Energy Problems of Chemical Physics, N. N. Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences, Moscow, 119334, Russia
| | - Mikhail V Gorshkov
- V. L. Talrose Institute for Energy Problems of Chemical Physics, N. N. Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences, Moscow, 119334, Russia.
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The landscape of receptor-mediated precision cancer combination therapy via a single-cell perspective. Nat Commun 2022; 13:1613. [PMID: 35338126 PMCID: PMC8956718 DOI: 10.1038/s41467-022-29154-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Accepted: 02/22/2022] [Indexed: 02/08/2023] Open
Abstract
Mining a large cohort of single-cell transcriptomics data, here we employ combinatorial optimization techniques to chart the landscape of optimal combination therapies in cancer. We assume that each individual therapy can target any one of 1269 genes encoding cell surface receptors, which may be targets of CAR-T, conjugated antibodies or coated nanoparticle therapies. We find that in most cancer types, personalized combinations composed of at most four targets are then sufficient for killing at least 80% of tumor cells while sparing at least 90% of nontumor cells in the tumor microenvironment. However, as more stringent and selective killing is required, the number of targets needed rises rapidly. Emerging individual targets include PTPRZ1 for brain and head and neck cancers and EGFR in multiple tumor types. In sum, this study provides a computational estimate of the identity and number of targets needed in combination to target cancers selectively and precisely. Intra-tumor heterogeneity is often associated with resistance to targeted therapy, requiring the design of combinatorial therapies. Here, based on tumor single-cell transcriptomic datasets, the authors develop a computational approach to identify optimal combinatorial treatments targeting membrane receptors for cancer therapy.
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Xing F, Liu YC, Huang S, Lyu X, Su SM, Chan UI, Wu PC, Yan Y, Ai N, Li J, Zhao M, Rajendran BK, Liu J, Shao F, Sun H, Choi TK, Zhu W, Luo G, Liu S, Xu DL, Chan KL, Zhao Q, Miao K, Luo KQ, Ge W, Xu X, Wang G, Liu TM, Deng CX. Accelerating precision anti-cancer therapy by time-lapse and label-free 3D tumor slice culture platform. Theranostics 2021; 11:9415-9430. [PMID: 34646378 PMCID: PMC8490519 DOI: 10.7150/thno.59533] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Accepted: 06/29/2021] [Indexed: 11/30/2022] Open
Abstract
The feasibility of personalized medicine for cancer treatment is largely hampered by costly, labor-intensive and time-consuming models for drug discovery. Herein, establishing new pre-clinical models to tackle these issues for personalized medicine is urgently demanded. Methods: We established a three-dimensional tumor slice culture (3D-TSC) platform incorporating label-free techniques for time-course experiments to predict anti-cancer drug efficacy and validated the 3D-TSC model by multiphoton fluorescence microscopy, RNA sequence analysis, histochemical and histological analysis. Results: Using time-lapse imaging of the apoptotic reporter sensor C3 (C3), we performed cell-based high-throughput drug screening and shortlisted high-efficacy drugs to screen murine and human 3D-TSCs, which validate effective candidates within 7 days of surgery. Histological and RNA sequence analyses demonstrated that 3D-TSCs accurately preserved immune components of the original tumor, which enables the successful achievement of immune checkpoint blockade assays with antibodies against PD-1 and/or PD-L1. Label-free multiphoton fluorescence imaging revealed that 3D-TSCs exhibit lipofuscin autofluorescence features in the time-course monitoring of drug response and efficacy. Conclusion: This technology accelerates precision anti-cancer therapy by providing a cheap, fast, and easy platform for anti-cancer drug discovery.
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Raza M, Kumar N, Nair U, Luthra G, Bhattacharyya U, Jayasundar S, Jayasundar R, Sehrawat S. Current updates on precision therapy for breast cancer associated brain metastasis: Emphasis on combination therapy. Mol Cell Biochem 2021; 476:3271-3284. [PMID: 33886058 DOI: 10.1007/s11010-021-04149-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Accepted: 04/01/2021] [Indexed: 12/12/2022]
Abstract
Cancer therapies have undergone a tremendous progress over the past decade. Precision medicine provides a more tailored approach, making the combination of existing therapies more precise. Different types of cancers are characterized by unique biomarkers that are targeted using various genomic approaches by clinicians and companies worldwide to achieve efficient treatment with minimal side effects. Precision medicine has two broad approaches namely stratified and personalized medicine. The driver mutations could vary within a subtype while the same driver mutations could be found across different subtypes. Precision medicine has recently gained a lot of importance for breast cancer therapy. Various kinds of mutations like hotspot mutations, gene alterations, gene amplification mutations are targeted to design a more specific therapy. Apart from these known gene mutations there are various unknown mutations. Thus, tumor heterogeneity can pose a challenge to precision medicine. For breast cancer, one of the most successful models developed in case of precision medicine is the anti-HER2 therapies as HER2 was considered to have the worst prognosis being highly malignant. But now due to the advent of HER2 receptor targeted therapies, it has a good prognosis. Moreover, precision medicine helps in identifying if the drug molecules being used for the treatment of one kind of cancer can be beneficial in the treatment of another kind of cancer as well, considering the signaling pathways and machinery is similar in most of the cancers. This reduces the time for new drug development and is economically more feasible. Precision medicine will prove to be very advantageous in case of brain metastasis.
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Affiliation(s)
- Masoom Raza
- Precision NeuroOncology & NeuroVascular Disease Modeling Group, Department of Life Sciences, School of Natural Sciences, Shiv Nadar University, Delhi NCR, India
| | - Naveen Kumar
- Precision NeuroOncology & NeuroVascular Disease Modeling Group, Department of Life Sciences, School of Natural Sciences, Shiv Nadar University, Delhi NCR, India
| | - Uttara Nair
- Department of Women's and Reproductive Health, Oxford Fertility, Oxford Business Park North, University of Oxford, Oxford, OX4 2HW, UK
| | - Gehna Luthra
- Precision NeuroOncology & NeuroVascular Disease Modeling Group, Department of Life Sciences, School of Natural Sciences, Shiv Nadar University, Delhi NCR, India
| | - Ushosi Bhattacharyya
- Precision NeuroOncology & NeuroVascular Disease Modeling Group, Department of Life Sciences, School of Natural Sciences, Shiv Nadar University, Delhi NCR, India
| | - Smruthi Jayasundar
- Precision NeuroOncology & NeuroVascular Disease Modeling Group, Department of Life Sciences, School of Natural Sciences, Shiv Nadar University, Delhi NCR, India
| | - Rama Jayasundar
- Department of Nuclear Magnetic Resonance & MRI, All India Institute of Medical Sciences, New Delhi, 110029, India
| | - Seema Sehrawat
- Precision NeuroOncology & NeuroVascular Disease Modeling Group, Department of Life Sciences, School of Natural Sciences, Shiv Nadar University, Delhi NCR, India.
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Rostam Niakan Kalhori S, Tanhapour M, Gholamzadeh M. Enhanced childhood diseases treatment using computational models: Systematic review of intelligent experiments heading to precision medicine. J Biomed Inform 2021; 115:103687. [PMID: 33497811 DOI: 10.1016/j.jbi.2021.103687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 12/05/2020] [Accepted: 01/18/2021] [Indexed: 10/22/2022]
Abstract
INTRODUCTION Precision or personalized Medicine (PM) is used for the prevention and treatment of diseases by considering a huge amount of information about individuals variables. Due to high volume of information, AI-based computational models are required. A large set of studies conducted to examine the PM approach to improve childhood clinical outcomes. Thus, the main goal of this study was to review the application of health information technology and especially artificial intelligence (AI) methods for the treatment of childhood disease using PM. METHODS PubMed, Scopus, Web of Science, and EMBASE databases were searched up to December 18, 2019. Articles that focused on informatics applications for childhood disease PM included in this study. Included papers were classified for qualitative analysis and interpreting results. The results were analyzed using Microsoft Excel 2019. RESULTS From 341 citations, 62 papers met our inclusion criteria. The number of published papers that used AI methods to apply for PM in childhood diseases increased from 2010 to 2019. Our results showed that most applied methods were related to machine learning discipline. In terms of clinical scope, the largest number of clinical articles are devoted to oncology. Besides, the analysis showed that genomics was the most PM approach used regarding childhood disease. CONCLUSION This systematic review examined papers that used AI methods for applying PM approaches in childhood diseases from medical informatics perspectives. Thus, it provided new insight to researchers who are interested in knowing research needs in this field.
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Affiliation(s)
- Sharareh Rostam Niakan Kalhori
- Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
| | - Mozhgan Tanhapour
- Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
| | - Marsa Gholamzadeh
- Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran.
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Pot M, Brehme M, El-Heliebi A, Gschmeidler B, Hofer P, Kroneis T, Schirmer M, Schumann S, Prainsack B. Personalized medicine in Austria: expectations and limitations. Per Med 2020; 17:423-428. [PMID: 33026295 DOI: 10.2217/pme-2020-0061] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Affiliation(s)
- Mirjam Pot
- Department of Political Science, University of Vienna, Vienna 1010, Austria
| | | | - Amin El-Heliebi
- Medical University of Graz, Gottfried Schatz Research Center, Division of Cell Biology, Histology and Embryology, Graz 8036, Austria.,Center for Biomarker Research in Medicine, Graz 8010, Austria
| | | | - Philipp Hofer
- Medical University of Vienna, Department of Pathology, Vienna 1090, Austria
| | - Thomas Kroneis
- Medical University of Graz, Gottfried Schatz Research Center, Division of Cell Biology, Histology and Embryology, Graz 8036, Austria.,Center for Biomarker Research in Medicine, Graz 8010, Austria
| | - Michael Schirmer
- Department of Internal Medicine, Medical University of Innsbruck, Clinic II, Innsbruck 6020, Austria
| | - Simone Schumann
- Open Science - Life Sciences in Dialogue, Vienna 1030, Austria
| | - Barbara Prainsack
- Department of Political Science, University of Vienna, Vienna 1010, Austria.,Department of Global Health & Social Medicine, King's College London, Strand, London WC2R 2LS, United Kingdom
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Mao S, Pang Y, Liu T, Shao Y, He J, Yang H, Mao Y, Sun W. Bioprinting of in vitro tumor models for personalized cancer treatment: a review. Biofabrication 2020; 12:042001. [PMID: 32470967 DOI: 10.1088/1758-5090/ab97c0] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Studying biological characteristics of tumors and evaluating the treatment effects require appropriate in vitro tumor models. However, the occurrence, progression, and migration of tumors involve spatiotemporal changes, cell-microenvironment and cell-cell interactions, and signal transmission in cells, which makes the construction of in vitro tumor models extremely challenging. In the past few years, advances in biomaterials and tissue engineering methods, especially development of the bioprinting technology, have paved the way for innovative platform technologies for in vitro cancer research. Bioprinting can accurately control the distribution of cells, active molecules, and biomaterials. Furthermore, this technology recapitulates the key characteristics of the tumor microenvironment and constructs in vitro tumor models with bionic structures and physiological systems. These models can be used as robust platforms to study tumor initiation, interaction with the microenvironment, angiogenesis, motility and invasion, as well as intra- and extravasation. Bioprinted tumor models can also be used for high-throughput drug screening and validation and provide the possibility for personalized cancer treatment research. This review describes the basic characteristics of the tumor and its microenvironment and focuses on the importance and relevance of bioprinting technology in the construction of tumor models. Research progress in the bioprinting of monocellular, multicellular, and personalized tumor models is discussed, and comprehensive application of bioprinting in preclinical drug screening and innovative therapy is reviewed. Finally, we offer our perspective on the shortcomings of the existing models and explore new technologies to outline the direction of future development and application prospects of next-generation tumor models.
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Affiliation(s)
- Shuangshuang Mao
- Department of Mechanical Engineering, Biomanufacturing Center, Tsinghua University, Beijing, People's Republic of China. Biomanufacturing and Rapid Forming Technology Key Laboratory of Beijing, Beijing, People's Republic of China. 'Biomanufacturing and Engineering Living Systems' 111 -Innovation International Talents Base, Beijing, People's Republic of China
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12
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Lavin DP, Tiwari VK. Unresolved Complexity in the Gene Regulatory Network Underlying EMT. Front Oncol 2020; 10:554. [PMID: 32477926 PMCID: PMC7235173 DOI: 10.3389/fonc.2020.00554] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2020] [Accepted: 03/27/2020] [Indexed: 12/14/2022] Open
Abstract
Epithelial to mesenchymal transition (EMT) is the process whereby a polarized epithelial cell ceases to maintain cell-cell contacts, loses expression of characteristic epithelial cell markers, and acquires mesenchymal cell markers and properties such as motility, contractile ability, and invasiveness. A complex process that occurs during development and many disease states, EMT involves a plethora of transcription factors (TFs) and signaling pathways. Whilst great advances have been made in both our understanding of the progressive cell-fate changes during EMT and the gene regulatory networks that drive this process, there are still gaps in our knowledge. Epigenetic modifications are dynamic, chromatin modifying enzymes are vast and varied, transcription factors are pleiotropic, and signaling pathways are multifaceted and rarely act alone. Therefore, it is of great importance that we decipher and understand each intricate step of the process and how these players at different levels crosstalk with each other to successfully orchestrate EMT. A delicate balance and fine-tuned cooperation of gene regulatory mechanisms is required for EMT to occur successfully, and until we resolve the unknowns in this network, we cannot hope to develop effective therapies against diseases that involve aberrant EMT such as cancer. In this review, we focus on data that challenge these unknown entities underlying EMT, starting with EMT stimuli followed by intracellular signaling through to epigenetic mechanisms and chromatin remodeling.
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Affiliation(s)
| | - Vijay K. Tiwari
- The Wellcome-Wolfson Institute for Experimental Medicine, School of Medicine, Dentistry and Biomedical Science, Queen's University Belfast, Belfast, United Kingdom
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13
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Bitzer M, Ostermann L, Horger M, Biskup S, Schulze M, Ruhm K, Hilke F, Öner Ö, Nikolaou K, Schroeder C, Riess O, Fend F, Zips D, Hinterleitner M, Zender L, Tabatabai G, Beha J, Malek NP. Next-Generation Sequencing of Advanced GI Tumors Reveals Individual Treatment Options. JCO Precis Oncol 2020; 4:PO.19.00359. [PMID: 32923905 PMCID: PMC7446530 DOI: 10.1200/po.19.00359] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/31/2020] [Indexed: 12/17/2022] Open
Abstract
PURPOSE Precision oncology connects highly complex diagnostic procedures with patient histories to identify individualized treatment options in interdisciplinary molecular tumor boards (MTBs). Detailed data on MTB-guided treatments and outcome with a focus on advanced GI cancers have not been reported yet. PATIENTS AND METHODS Next-generation sequencing of tumor and normal tissue pairs was performed between April 2016 and February 2018. After identification of relevant molecular alterations, available clinical studies or in-label, off-label, or matched experimental treatment options were recommended. Follow-up data and a response assessment that was based on radiologic imaging were recorded. RESULTS Ninety-six patients were presented to the MTB of Tuebingen University Hospital. Sixteen (17%) showed "pathogenic" or "likely pathogenic" germline variants. Recommendations on the basis of molecular alterations or tumor mutational burden were given for 41 patients (43%). Twenty-five received the suggested drug, and 20 were evaluable for best response assessment. Three patients (15%) reached a partial response (PR), and 6 (30%), stable disease (SD), whereas 11 (55%) had tumor progression (progressive disease). Median progression-free survival (PFS) for all treated and evaluable patients was 2.8 months (range, 1.0-9.0 months), and median overall survival (OS) of all treated patients was 5.2 months (range, 0.1 months to not reached). Patients with SD for ≥ 3 months or PR compared with progressive disease showed both a statistically significant longer median PFS (7.8 months [95% CI, 4.2 to 11.4 months] v 2.2 months [95% CI, 1.5 to 2.8 months], P < .0001) and median OS (18.0 months [95% CI, 10.4 to 25.6 months] v 3.8 months [95% CI, 2.3 to 5.4 months], P < .0001). CONCLUSION Next-generation sequencing diagnostics of advanced GI cancers identified a substantial number of pathogenic or likely pathogenic germline variants and unique individual treatment options. Patients with PR or SD in the course of MTB-recommended treatments seemed to benefit with respect to PFS and OS.
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Affiliation(s)
- Michael Bitzer
- Department of Internal Medicine I, Eberhard-Karls University, Tuebingen, Germany
- Cluster of Excellence, Image Guided and Functionally Instructed Tumor Therapies, Eberhard-Karls University, Tuebingen, Germany
- Center for Personalized Medicine, Eberhard-Karls University, Tuebingen, Germany
| | - Leonie Ostermann
- Department of Internal Medicine I, Eberhard-Karls University, Tuebingen, Germany
| | - Marius Horger
- Department of Diagnostic and Interventional Radiology, Eberhard-Karls University, Tuebingen, Germany
| | - Saskia Biskup
- CeGaT GmbH and Praxis für Humangenetik, Tuebingen, Germany
| | - Martin Schulze
- CeGaT GmbH and Praxis für Humangenetik, Tuebingen, Germany
| | - Kristina Ruhm
- Center for Personalized Medicine, Eberhard-Karls University, Tuebingen, Germany
| | - Franz Hilke
- Institute of Medical Genetics and Applied Genomics, Eberhard-Karls University, Tuebingen, Germany
| | - Öznur Öner
- Center for Personalized Medicine, Eberhard-Karls University, Tuebingen, Germany
| | - Konstantin Nikolaou
- Cluster of Excellence, Image Guided and Functionally Instructed Tumor Therapies, Eberhard-Karls University, Tuebingen, Germany
- Department of Diagnostic and Interventional Radiology, Eberhard-Karls University, Tuebingen, Germany
| | - Christopher Schroeder
- Institute of Medical Genetics and Applied Genomics, Eberhard-Karls University, Tuebingen, Germany
| | - Olaf Riess
- Institute of Medical Genetics and Applied Genomics, Eberhard-Karls University, Tuebingen, Germany
| | - Falko Fend
- Institute of Pathology and Neuropathology, Eberhard-Karls University, Tuebingen, Germany
- German Cancer Research Consortium, German Cancer Research Center, Heidelberg, Germany
| | - Daniel Zips
- German Cancer Research Consortium, German Cancer Research Center, Heidelberg, Germany
- Department of Radiation Oncology, Eberhard-Karls University, Tuebingen, Germany
| | | | - Lars Zender
- Cluster of Excellence, Image Guided and Functionally Instructed Tumor Therapies, Eberhard-Karls University, Tuebingen, Germany
- German Cancer Research Consortium, German Cancer Research Center, Heidelberg, Germany
- Department of Internal Medicine VIII, Eberhard-Karls University, Tuebingen, Germany
| | - Ghazaleh Tabatabai
- Cluster of Excellence, Image Guided and Functionally Instructed Tumor Therapies, Eberhard-Karls University, Tuebingen, Germany
- German Cancer Research Consortium, German Cancer Research Center, Heidelberg, Germany
- Interdisciplinary Division of Neuro-Oncology, Eberhard-Karls University, Tuebingen, Germany
| | - Janina Beha
- Center for Personalized Medicine, Eberhard-Karls University, Tuebingen, Germany
| | - Nisar P. Malek
- Department of Internal Medicine I, Eberhard-Karls University, Tuebingen, Germany
- Cluster of Excellence, Image Guided and Functionally Instructed Tumor Therapies, Eberhard-Karls University, Tuebingen, Germany
- Center for Personalized Medicine, Eberhard-Karls University, Tuebingen, Germany
- German Cancer Research Consortium, German Cancer Research Center, Heidelberg, Germany
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14
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Lamping M, Benary M, Leyvraz S, Messerschmidt C, Blanc E, Kessler T, Schütte M, Lenze D, Jöhrens K, Burock S, Klinghammer K, Ochsenreither S, Sers C, Schäfer R, Tinhofer I, Beule D, Klauschen F, Yaspo ML, Keilholz U, Rieke DT. Support of a molecular tumour board by an evidence-based decision management system for precision oncology. Eur J Cancer 2020; 127:41-51. [PMID: 31982633 DOI: 10.1016/j.ejca.2019.12.017] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Revised: 12/06/2019] [Accepted: 12/10/2019] [Indexed: 12/19/2022]
Abstract
BACKGROUND Reliable and reproducible interpretation of molecular aberrations constitutes a bottleneck of precision medicine. Evidence-based decision management systems may improve rational therapy recommendations. To cope with an increasing amount of complex molecular data in the clinical care of patients with cancer, we established a workflow for the interpretation of molecular analyses. METHODS A specialized physician screened results from molecular analyses for potential biomarkers, irrespective of the diagnostic modality. Best available evidence was retrieved and categorized through establishment of an in-house database and interrogation of publicly available databases. Annotated biomarkers were ranked using predefined evidence levels and subsequently discussed at a molecular tumour board (MTB), which generated treatment recommendations. Subsequent translation into patient treatment and clinical outcomes were followed up. RESULTS One hundred patients were discussed in the MTB between January 2016 and May 2017. Molecular data were obtained for 70 of 100 patients (50 whole exome/RNA sequencing, 18 panel sequencing, 2 immunohistochemistry (IHC)/microsatellite instability analysis). The MTB generated a median of two treatment recommendations each for 63 patients. Thirty-nine patients were treated: 6 partial responses and 12 stable diseases were achieved as best responses. Genetic counselling for germline events was recommended for seven patients. CONCLUSION The development of an evidence-based workflow allowed for the clinical interpretation of complex molecular data and facilitated the translation of personalized treatment strategies into routine clinical care. The high number of treatment recommendations in patients with comprehensive genomic data and promising responses in patients treated with combination therapy warrant larger clinical studies.
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Affiliation(s)
- Mario Lamping
- Charité Comprehensive Cancer Center, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Charitéplatz 1, 10117, Berlin, Germany.
| | - Manuela Benary
- Department of Pathology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Charitéplatz 1, 10117, Berlin, Germany; IRI Life Sciences, Humboldt-Universität zu Berlin, Philippstraße 13, 10115, Berlin, Germany
| | - Serge Leyvraz
- Charité Comprehensive Cancer Center, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Charitéplatz 1, 10117, Berlin, Germany
| | - Clemens Messerschmidt
- Core Unit Bioinformatics, Berlin Institute of Health (BIH), Anna-Louisa-Karsch-Straße 2, 10178, Berlin, Germany
| | - Eric Blanc
- Core Unit Bioinformatics, Berlin Institute of Health (BIH), Anna-Louisa-Karsch-Straße 2, 10178, Berlin, Germany
| | - Thomas Kessler
- Alacris Theranostics GmbH, Max Planck Straße 3, 12489, Berlin, Germany
| | - Moritz Schütte
- Alacris Theranostics GmbH, Max Planck Straße 3, 12489, Berlin, Germany
| | - Dido Lenze
- Department of Pathology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Charitéplatz 1, 10117, Berlin, Germany
| | - Korinna Jöhrens
- Department of Pathology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Charitéplatz 1, 10117, Berlin, Germany
| | - Susen Burock
- Charité Comprehensive Cancer Center, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Charitéplatz 1, 10117, Berlin, Germany
| | - Konrad Klinghammer
- Department of Hematology and Oncology, Campus Benjamin Franklin, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Hindenburgdamm 30, 12203 Berlin, Germany
| | - Sebastian Ochsenreither
- Charité Comprehensive Cancer Center, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Charitéplatz 1, 10117, Berlin, Germany; Department of Hematology and Oncology, Campus Benjamin Franklin, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Hindenburgdamm 30, 12203 Berlin, Germany
| | - Christine Sers
- Department of Pathology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Charitéplatz 1, 10117, Berlin, Germany
| | - Reinhold Schäfer
- Charité Comprehensive Cancer Center, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Charitéplatz 1, 10117, Berlin, Germany; German Cancer Consortium (DKTK) and German Cancer Research Centre (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany
| | - Ingeborg Tinhofer
- German Cancer Consortium (DKTK) and German Cancer Research Centre (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany; Department of Radiooncology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Charitéplatz 1, 10117, Berlin, Germany
| | - Dieter Beule
- Core Unit Bioinformatics, Berlin Institute of Health (BIH), Anna-Louisa-Karsch-Straße 2, 10178, Berlin, Germany
| | - Frederick Klauschen
- Department of Pathology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Charitéplatz 1, 10117, Berlin, Germany
| | - Marie-Laure Yaspo
- Alacris Theranostics GmbH, Max Planck Straße 3, 12489, Berlin, Germany; Max Planck Institute for Molecular Genetics, Otto Warburg Laboratory Gene Regulation and Systems Biology of Cancer, Ihnestraße 63, 14195, Berlin, Germany
| | - Ulrich Keilholz
- Charité Comprehensive Cancer Center, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Charitéplatz 1, 10117, Berlin, Germany; German Cancer Consortium (DKTK) and German Cancer Research Centre (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany
| | - Damian T Rieke
- Charité Comprehensive Cancer Center, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Charitéplatz 1, 10117, Berlin, Germany; Department of Hematology and Oncology, Campus Benjamin Franklin, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Hindenburgdamm 30, 12203 Berlin, Germany; Berlin Institute of Health (BIH), Anna-Louisa-Karsch-Straße 2, 10178, Berlin, Germany.
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15
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Gheyouche E, Launay R, Lethiec J, Labeeuw A, Roze C, Amossé A, Téletchéa S. DockNmine, a Web Portal to Assemble and Analyse Virtual and Experimental Interaction Data. Int J Mol Sci 2019; 20:E5062. [PMID: 31614716 PMCID: PMC6829441 DOI: 10.3390/ijms20205062] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Revised: 10/03/2019] [Accepted: 10/07/2019] [Indexed: 12/22/2022] Open
Abstract
Scientists have to perform multiple experiments producing qualitative and quantitative data to determine if a compound is able to bind to a given target. Due to the large diversity of the potential ligand chemical space, the possibility of experimentally exploring a lot of compounds on a target rapidly becomes out of reach. Scientists therefore need to use virtual screening methods to determine the putative binding mode of ligands on a protein and then post-process the raw docking experiments with a dedicated scoring function in relation with experimental data. Two of the major difficulties for comparing docking predictions with experiments mostly come from the lack of transferability of experimental data and the lack of standardisation in molecule names. Although large portals like PubChem or ChEMBL are available for general purpose, there is no service allowing a formal expert annotation of both experimental data and docking studies. To address these issues, researchers build their own collection of data in flat files, often in spreadsheets, with limited possibilities of extensive annotations or standardisation of ligand descriptions allowing cross-database retrieval. We have conceived the dockNmine platform to provide a service allowing an expert and authenticated annotation of ligands and targets. First, this portal allows a scientist to incorporate controlled information in the database using reference identifiers for the protein (Uniprot ID) and the ligand (SMILES description), the data and the publication associated to it. Second, it allows the incorporation of docking experiments using forms that automatically parse useful parameters and results. Last, the web interface provides a lot of pre-computed outputs to assess the degree of correlations between docking experiments and experimental data.
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Affiliation(s)
- Ennys Gheyouche
- UFIP, Université de Nantes, UMR CNRS 6286, 2 rue de la Houssinière, 44322 Nantes, France.
| | - Romain Launay
- UFIP, Université de Nantes, UMR CNRS 6286, 2 rue de la Houssinière, 44322 Nantes, France.
| | - Jean Lethiec
- UFIP, Université de Nantes, UMR CNRS 6286, 2 rue de la Houssinière, 44322 Nantes, France.
| | - Antoine Labeeuw
- UFIP, Université de Nantes, UMR CNRS 6286, 2 rue de la Houssinière, 44322 Nantes, France.
| | - Caroline Roze
- UFIP, Université de Nantes, UMR CNRS 6286, 2 rue de la Houssinière, 44322 Nantes, France.
| | - Alan Amossé
- UFIP, Université de Nantes, UMR CNRS 6286, 2 rue de la Houssinière, 44322 Nantes, France.
| | - Stéphane Téletchéa
- UFIP, Université de Nantes, UMR CNRS 6286, 2 rue de la Houssinière, 44322 Nantes, France.
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16
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Heyer EE, Deveson IW, Wooi D, Selinger CI, Lyons RJ, Hayes VM, O'Toole SA, Ballinger ML, Gill D, Thomas DM, Mercer TR, Blackburn J. Diagnosis of fusion genes using targeted RNA sequencing. Nat Commun 2019; 10:1388. [PMID: 30918253 PMCID: PMC6437215 DOI: 10.1038/s41467-019-09374-9] [Citation(s) in RCA: 103] [Impact Index Per Article: 20.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2018] [Accepted: 02/22/2019] [Indexed: 01/05/2023] Open
Abstract
Fusion genes are a major cause of cancer. Their rapid and accurate diagnosis can inform clinical action, but current molecular diagnostic assays are restricted in resolution and throughput. Here, we show that targeted RNA sequencing (RNAseq) can overcome these limitations. First, we establish that fusion gene detection with targeted RNAseq is both sensitive and quantitative by optimising laboratory and bioinformatic variables using spike-in standards and cell lines. Next, we analyse a clinical patient cohort and improve the overall fusion gene diagnostic rate from 63% with conventional approaches to 76% with targeted RNAseq while demonstrating high concordance for patient samples with previous diagnoses. Finally, we show that targeted RNAseq offers additional advantages by simultaneously measuring gene expression levels and profiling the immune-receptor repertoire. We anticipate that targeted RNAseq will improve clinical fusion gene detection, and its increasing use will provide a deeper understanding of fusion gene biology. Rapid and accurate detection of fusion genes is important in cancer diagnostics. Here, the authors demonstrate that targeted RNA sequencing provides fast, sensitive and quantitative gene fusion detection and overcomes the limitations of approaches currently in clinical use.
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Affiliation(s)
- Erin E Heyer
- Genomics and Epigenetics Division, Garvan Institute of Medical Research, Sydney, 2010, NSW, Australia
| | - Ira W Deveson
- Genomics and Epigenetics Division, Garvan Institute of Medical Research, Sydney, 2010, NSW, Australia.,St. Vincent's Clinical School, UNSW Australia, Sydney, 2031, NSW, Australia
| | - Danson Wooi
- Genomics and Epigenetics Division, Garvan Institute of Medical Research, Sydney, 2010, NSW, Australia.,St. Vincent's Clinical School, UNSW Australia, Sydney, 2031, NSW, Australia
| | - Christina I Selinger
- Tissue Pathology and Diagnostic Oncology, Royal Prince Alfred Hospital, Sydney, 2050, NSW, Australia
| | - Ruth J Lyons
- Genomics and Epigenetics Division, Garvan Institute of Medical Research, Sydney, 2010, NSW, Australia
| | - Vanessa M Hayes
- Genomics and Epigenetics Division, Garvan Institute of Medical Research, Sydney, 2010, NSW, Australia.,St. Vincent's Clinical School, UNSW Australia, Sydney, 2031, NSW, Australia.,Faculty of Health Sciences, University of Limpopo, Turfloop Campus, Mankweng, 0727, South Africa.,School of Health Systems and Public Health, University of Pretoria, Pretoria, 0002, South Africa.,Central Clinical School, University of Sydney, Sydney, 2006, NSW, Australia
| | - Sandra A O'Toole
- St. Vincent's Clinical School, UNSW Australia, Sydney, 2031, NSW, Australia.,Tissue Pathology and Diagnostic Oncology, Royal Prince Alfred Hospital, Sydney, 2050, NSW, Australia.,Central Clinical School, University of Sydney, Sydney, 2006, NSW, Australia.,The Kinghorn Cancer Centre and Cancer Division, Garvan Institute of Medical Research, Sydney, 2010, NSW, Australia.,Australian Clinical Labs, Sydney, 2010, NSW, Australia
| | - Mandy L Ballinger
- The Kinghorn Cancer Centre and Cancer Division, Garvan Institute of Medical Research, Sydney, 2010, NSW, Australia
| | - Devinder Gill
- Department of Haematology, Princess Alexandra Hospital, Brisbane, 4102, QLD, Australia
| | - David M Thomas
- The Kinghorn Cancer Centre and Cancer Division, Garvan Institute of Medical Research, Sydney, 2010, NSW, Australia
| | - Tim R Mercer
- Genomics and Epigenetics Division, Garvan Institute of Medical Research, Sydney, 2010, NSW, Australia. .,St. Vincent's Clinical School, UNSW Australia, Sydney, 2031, NSW, Australia. .,Altius Institute for Biomedical Sciences, Seattle, 98121, WA, USA.
| | - James Blackburn
- Genomics and Epigenetics Division, Garvan Institute of Medical Research, Sydney, 2010, NSW, Australia. .,St. Vincent's Clinical School, UNSW Australia, Sydney, 2031, NSW, Australia.
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17
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Sánchez-Sánchez I, Astudillo A, Fernández-Vega I. [Study of the evolution in the activity of a Department of Pathology from a third level hospital in the last decade (2007-2016)]. REVISTA ESPAÑOLA DE PATOLOGÍA : PUBLICACIÓN OFICIAL DE LA SOCIEDAD ESPAÑOLA DE ANATOMÍA PATOLÓGICA Y DE LA SOCIEDAD ESPAÑOLA DE CITOLOGÍA 2018; 51:141-146. [PMID: 30012306 DOI: 10.1016/j.patol.2017.11.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2017] [Revised: 10/29/2017] [Accepted: 11/08/2017] [Indexed: 10/18/2022]
Abstract
OBJECTIVE To study the evolution of variables of interest in a department of pathology from a third level hospital during the last decade and to evaluate the impact on these of the hospital relocation in 2014. MATERIAL AND METHOD Retrospective observational study in which the recorded samples (biopsies, cytology specimens, FNA, autopsies, intraoperative) as well as the complementary techniques (IHC, Histochemistry, IF and FISH) and portfolio of services were analyzed during the years 2007-2016 inclusive. For the statistical analysis, the five-year periods 2007-2011 and 2012-2016 were compared. RESULTS The following variables were statistically significant: cytology (34055.8±1994.0 vs 26590.4±2938.3, p=0.002), autopsies (156.2±27.3 vs 122.0±14.78, p=0.039), immunohistochemistry (17855.4±3424.2 vs 28559.2±4734.7, p=0.003), histochemistry (11117.8±2300.9 vs 6225.0±1330.5, p=0.003) and immunofluorescence (610.2±185.3 vs. 1205.4±154.0, p=0.001). Statistical correlations of interest among variables have been identified. In 2014, it was observed that the variables of greater specific weight (biopsies, cytology, IHQ and histochemistry) in the work load of the Department showed an average decrease of 12.5%. A generalized increase in the panel of available samples has been identified, the largest increase being seen in the number of antibodies (78.7%), histochemistry (38.7%) and FISH (400%). CONCLUSION Relevant variations in work volume, as well as the service portfolio, have been identified, especially in the techniques aimed at improving diagnostic accuracy (IHQ and FI), and a significant decrease in the number of cytology specimens, autopsies and histochemistry. In the year 2014 a decrease of more than 12% in the main variables of the study was observed.
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Affiliation(s)
| | - Aurora Astudillo
- Facultad de Medicina, Universidad de Oviedo, España; Departamento de Anatomía Patológica, Hospital Universitario Central de Asturias, Oviedo, España
| | - Iván Fernández-Vega
- Facultad de Medicina, Universidad de Oviedo, España; Departamento de Anatomía Patológica, Hospital Universitario Central de Asturias, Oviedo, España.
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18
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Abstract
The first human genome project, completed in 2003, uncovered the genetic building blocks of humankind. Painstakingly cataloguing the basic constituents of our DNA ('genome sequencing') took ten years, over three billion dollars and was a multinational collaboration. Since then, our ability to sequence genomes has been finessed so much that by 2018 it is possible to explore the 20,000 or so human genes for under £1000, in a matter of days. Such testing offers clues to our past, present and future health, as well as information about how we respond to medications so that truly 'personalised medicine' is now moving closer to a reality. The impact of such a 'genomic era' is likely to have some level of impact on an increasingly large number of us, even if we are not directly using healthcare services ourselves. We explore how advancements in genetics are likely to be experienced by people, as patients, consumers and citizens; and urge policy makers to take stock of the pervasive nature of the technology as well as the human response to it.
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Affiliation(s)
- Jonathan Roberts
- Society and Ethics Research Group, Connecting Science, Cambridge, CB10 1SA, UK
| | - Anna Middleton
- Society and Ethics Research Group, Connecting Science, Cambridge, CB10 1SA, UK
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19
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Roberts J, Middleton A. Genetics in the 21st Century: Implications for patients, consumers and citizens. F1000Res 2017; 6:2020. [PMID: 29259772 PMCID: PMC5721930 DOI: 10.12688/f1000research.12850.1] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/23/2017] [Indexed: 11/13/2023] Open
Abstract
The first human genome project, completed in 2003, uncovered the genetic building blocks of humankind. Painstakingly cataloguing the basic constituents of our DNA ('genome sequencing') took ten years, over three billion dollars and was a multinational collaboration. Since then, our ability to sequence genomes has been finessed so much that by 2017 it is possible to explore the 20,000 or so human genes for under £1000, in a matter of days. Such testing offers clues to our past, present and future health, as well as information about how we respond to medications so that truly 'personalised medicine' is now a reality. The impact of such a 'genomic era' is likely to have some level of impact on all of us, even if we are not directly using healthcare services ourselves. We explore how advancements in genetics are likely to be experienced by people, as patients, consumers and citizens; and urge policy makers to take stock of the pervasive nature of the technology as well as the human response to it.
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Affiliation(s)
- Jonathan Roberts
- Society and Ethics Research Group, Connecting Science, Cambridge, CB10 1SA, UK
| | - Anna Middleton
- Society and Ethics Research Group, Connecting Science, Cambridge, CB10 1SA, UK
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20
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Silvestris N, Ciliberto G, De Paoli P, Apolone G, Lavitrano ML, Pierotti MA, Stanta G. Liquid dynamic medicine and N-of-1 clinical trials: a change of perspective in oncology research. JOURNAL OF EXPERIMENTAL & CLINICAL CANCER RESEARCH : CR 2017; 36:128. [PMID: 28903768 PMCID: PMC5598055 DOI: 10.1186/s13046-017-0598-x] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/11/2017] [Accepted: 09/07/2017] [Indexed: 12/21/2022]
Abstract
The increasing use of genomics to define the pattern of actionable mutations and to test and validate new therapies for individual cancer patients, and the growing application of liquid biopsy to dynamically track tumor evolution and to adapt molecularly targeted therapy according to the emergence of tumor clonal variants is shaping modern medical oncology., In order to better describe this new therapeutic paradigm we propose the term "Liquid dynamic medicine" in the place of "Personalized or Precision medicine". Clinical validation of the "Liquid dynamic medicine" approach is best captured by N-of-1 trials where each patient acts as tester and control of truly personalized therapies.
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Affiliation(s)
- Nicola Silvestris
- Medical Oncology Unit and Scientific Directorate, Cancer Institute "Giovanni Paolo II", Viale Orazio Flacco, 65, 70124, Bari, Italy.
| | - Gennaro Ciliberto
- Scientific Directorate, IRCCS National Cancer Institute "Regina Elena", Rome, Italy
| | - Paolo De Paoli
- Scientific Directorate, IRCCS "Centro di Riferimento Oncologico", Aviano, Italy
| | - Giovanni Apolone
- Scientific Directorate, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Maria Luisa Lavitrano
- BBMRI.it and Department of Medicine and Surgery University Milano-Bicocca, Milan, Italy
| | - Marco A Pierotti
- Senior Group Leader Foundation Institute FIRC Molecular Oncology (IFOM) Milan, Milan, Italy
| | - Giorgio Stanta
- Department of Medical Sciences of the University of Trieste, Trieste, Italy
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21
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Gaebler M, Silvestri A, Haybaeck J, Reichardt P, Lowery CD, Stancato LF, Zybarth G, Regenbrecht CRA. Three-Dimensional Patient-Derived In Vitro Sarcoma Models: Promising Tools for Improving Clinical Tumor Management. Front Oncol 2017; 7:203. [PMID: 28955656 PMCID: PMC5601986 DOI: 10.3389/fonc.2017.00203] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2017] [Accepted: 08/21/2017] [Indexed: 12/12/2022] Open
Abstract
Over the past decade, the development of new targeted therapeutics directed against specific molecular pathways involved in tumor cell proliferation and survival has allowed an essential improvement in carcinoma treatment. Unfortunately, the scenario is different for sarcomas, a group of malignant neoplasms originating from mesenchymal cells, for which the main therapeutic approach still consists in the combination of surgery, chemotherapy, and radiation therapy. The lack of innovative approaches in sarcoma treatment stems from the high degree of heterogeneity of this tumor type, with more that 70 different histopathological subtypes, and the limited knowledge of the molecular drivers of tumor development and progression. Currently, molecular therapies are available mainly for the treatment of gastrointestinal stromal tumor, a soft-tissue malignancy characterized by an activating mutation of the tyrosine kinase KIT. Since the first application of this approach, a strong effort has been made to understand sarcoma molecular alterations that can be potential targets for therapy. The low incidence combined with the high level of histopathological heterogeneity makes the development of clinical trials for sarcomas very challenging. For this reason, preclinical studies are needed to better understand tumor biology with the aim to develop new targeted therapeutics. Currently, these studies are mainly based on in vitro testing, since cell lines, and in particular patient-derived models, represent a reliable and easy to handle tool for investigation. In the present review, we summarize the most important models currently available in the field, focusing in particular on the three-dimensional spheroid/organoid model. This innovative approach for studying tumor biology better represents tissue architecture and cell–cell as well as cell–microenvironment crosstalk, which are fundamental steps for tumor cell proliferation and survival.
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Affiliation(s)
- Manuela Gaebler
- HELIOS Klinikum Berlin-Buch GmbH, Department of Interdisciplinary Oncology, Berlin, Germany
| | | | - Johannes Haybaeck
- Medical Faculty, Department of Pathology, Otto-von-Guericke University Magdeburg, Magdeburg, Germany.,Institute of Pathology, Medical University Graz, Graz, Austria
| | - Peter Reichardt
- HELIOS Klinikum Berlin-Buch GmbH, Department of Interdisciplinary Oncology, Berlin, Germany
| | - Caitlin D Lowery
- Eli Lilly and Company, Oncology Translational Research, Lilly Corporate Center, Indianapolis, IN, United States
| | - Louis F Stancato
- Eli Lilly and Company, Oncology Translational Research, Lilly Corporate Center, Indianapolis, IN, United States
| | - Gabriele Zybarth
- cpo - Cellular Phenomics & Oncology Berlin-Buch GmbH, Berlin, Germany
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