Clinical Network Systems Biology: Traversing the Cancer Multiverse
In recent decades, cancer biology and medicine have ushered in a new age of precision medicine through high-throughput approaches that led to the development of novel targeted therapies and immunotherapies for different cancers. The availability of multifaceted high-throughput omics data has reveale...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Isa Mambetsariev [verfasserIn] Jeremy Fricke [verfasserIn] Stephen B. Gruber [verfasserIn] Tingting Tan [verfasserIn] Razmig Babikian [verfasserIn] Pauline Kim [verfasserIn] Priya Vishnubhotla [verfasserIn] Jianjun Chen [verfasserIn] Prakash Kulkarni [verfasserIn] Ravi Salgia [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
In: Journal of Clinical Medicine - MDPI AG, 2013, 12(2023), 13, p 4535 |
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| Übergeordnetes Werk: |
volume:12 ; year:2023 ; number:13, p 4535 |
| Links: |
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| DOI / URN: |
10.3390/jcm12134535 |
|---|
| Katalog-ID: |
DOAJ09400207X |
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10.3390/jcm12134535 doi (DE-627)DOAJ09400207X (DE-599)DOAJ340c0c0322c445c183f6e03e7d892867 DE-627 ger DE-627 rakwb eng Isa Mambetsariev verfasserin aut Clinical Network Systems Biology: Traversing the Cancer Multiverse 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In recent decades, cancer biology and medicine have ushered in a new age of precision medicine through high-throughput approaches that led to the development of novel targeted therapies and immunotherapies for different cancers. The availability of multifaceted high-throughput omics data has revealed that cancer, beyond its genomic heterogeneity, is a complex system of microenvironments, sub-clonal tumor populations, and a variety of other cell types that impinge on the genetic and non-genetic mechanisms underlying the disease. Thus, a systems approach to cancer biology has become instrumental in identifying the key components of tumor initiation, progression, and the eventual emergence of drug resistance. Through the union of clinical medicine and basic sciences, there has been a revolution in the development and approval of cancer therapeutic drug options including tyrosine kinase inhibitors, antibody–drug conjugates, and immunotherapy. This ‘Team Medicine’ approach within the cancer systems biology framework can be further improved upon through the development of high-throughput clinical trial models that utilize machine learning models, rapid sample processing to grow patient tumor cell cultures, test multiple therapeutic options and assign appropriate therapy to individual patients quickly and efficiently. The integration of systems biology into the clinical network would allow for rapid advances in personalized medicine that are often hindered by a lack of drug development and drug testing. team medicine precision medicine cancer systems biology clinical network systems biology Medicine R Jeremy Fricke verfasserin aut Stephen B. Gruber verfasserin aut Tingting Tan verfasserin aut Razmig Babikian verfasserin aut Pauline Kim verfasserin aut Priya Vishnubhotla verfasserin aut Jianjun Chen verfasserin aut Prakash Kulkarni verfasserin aut Ravi Salgia verfasserin aut In Journal of Clinical Medicine MDPI AG, 2013 12(2023), 13, p 4535 (DE-627)718632478 (DE-600)2662592-1 20770383 nnns volume:12 year:2023 number:13, p 4535 https://doi.org/10.3390/jcm12134535 kostenfrei https://doaj.org/article/340c0c0322c445c183f6e03e7d892867 kostenfrei https://www.mdpi.com/2077-0383/12/13/4535 kostenfrei https://doaj.org/toc/2077-0383 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2023 13, p 4535 |
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10.3390/jcm12134535 doi (DE-627)DOAJ09400207X (DE-599)DOAJ340c0c0322c445c183f6e03e7d892867 DE-627 ger DE-627 rakwb eng Isa Mambetsariev verfasserin aut Clinical Network Systems Biology: Traversing the Cancer Multiverse 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In recent decades, cancer biology and medicine have ushered in a new age of precision medicine through high-throughput approaches that led to the development of novel targeted therapies and immunotherapies for different cancers. The availability of multifaceted high-throughput omics data has revealed that cancer, beyond its genomic heterogeneity, is a complex system of microenvironments, sub-clonal tumor populations, and a variety of other cell types that impinge on the genetic and non-genetic mechanisms underlying the disease. Thus, a systems approach to cancer biology has become instrumental in identifying the key components of tumor initiation, progression, and the eventual emergence of drug resistance. Through the union of clinical medicine and basic sciences, there has been a revolution in the development and approval of cancer therapeutic drug options including tyrosine kinase inhibitors, antibody–drug conjugates, and immunotherapy. This ‘Team Medicine’ approach within the cancer systems biology framework can be further improved upon through the development of high-throughput clinical trial models that utilize machine learning models, rapid sample processing to grow patient tumor cell cultures, test multiple therapeutic options and assign appropriate therapy to individual patients quickly and efficiently. The integration of systems biology into the clinical network would allow for rapid advances in personalized medicine that are often hindered by a lack of drug development and drug testing. team medicine precision medicine cancer systems biology clinical network systems biology Medicine R Jeremy Fricke verfasserin aut Stephen B. Gruber verfasserin aut Tingting Tan verfasserin aut Razmig Babikian verfasserin aut Pauline Kim verfasserin aut Priya Vishnubhotla verfasserin aut Jianjun Chen verfasserin aut Prakash Kulkarni verfasserin aut Ravi Salgia verfasserin aut In Journal of Clinical Medicine MDPI AG, 2013 12(2023), 13, p 4535 (DE-627)718632478 (DE-600)2662592-1 20770383 nnns volume:12 year:2023 number:13, p 4535 https://doi.org/10.3390/jcm12134535 kostenfrei https://doaj.org/article/340c0c0322c445c183f6e03e7d892867 kostenfrei https://www.mdpi.com/2077-0383/12/13/4535 kostenfrei https://doaj.org/toc/2077-0383 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2023 13, p 4535 |
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10.3390/jcm12134535 doi (DE-627)DOAJ09400207X (DE-599)DOAJ340c0c0322c445c183f6e03e7d892867 DE-627 ger DE-627 rakwb eng Isa Mambetsariev verfasserin aut Clinical Network Systems Biology: Traversing the Cancer Multiverse 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In recent decades, cancer biology and medicine have ushered in a new age of precision medicine through high-throughput approaches that led to the development of novel targeted therapies and immunotherapies for different cancers. The availability of multifaceted high-throughput omics data has revealed that cancer, beyond its genomic heterogeneity, is a complex system of microenvironments, sub-clonal tumor populations, and a variety of other cell types that impinge on the genetic and non-genetic mechanisms underlying the disease. Thus, a systems approach to cancer biology has become instrumental in identifying the key components of tumor initiation, progression, and the eventual emergence of drug resistance. Through the union of clinical medicine and basic sciences, there has been a revolution in the development and approval of cancer therapeutic drug options including tyrosine kinase inhibitors, antibody–drug conjugates, and immunotherapy. This ‘Team Medicine’ approach within the cancer systems biology framework can be further improved upon through the development of high-throughput clinical trial models that utilize machine learning models, rapid sample processing to grow patient tumor cell cultures, test multiple therapeutic options and assign appropriate therapy to individual patients quickly and efficiently. The integration of systems biology into the clinical network would allow for rapid advances in personalized medicine that are often hindered by a lack of drug development and drug testing. team medicine precision medicine cancer systems biology clinical network systems biology Medicine R Jeremy Fricke verfasserin aut Stephen B. Gruber verfasserin aut Tingting Tan verfasserin aut Razmig Babikian verfasserin aut Pauline Kim verfasserin aut Priya Vishnubhotla verfasserin aut Jianjun Chen verfasserin aut Prakash Kulkarni verfasserin aut Ravi Salgia verfasserin aut In Journal of Clinical Medicine MDPI AG, 2013 12(2023), 13, p 4535 (DE-627)718632478 (DE-600)2662592-1 20770383 nnns volume:12 year:2023 number:13, p 4535 https://doi.org/10.3390/jcm12134535 kostenfrei https://doaj.org/article/340c0c0322c445c183f6e03e7d892867 kostenfrei https://www.mdpi.com/2077-0383/12/13/4535 kostenfrei https://doaj.org/toc/2077-0383 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2023 13, p 4535 |
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10.3390/jcm12134535 doi (DE-627)DOAJ09400207X (DE-599)DOAJ340c0c0322c445c183f6e03e7d892867 DE-627 ger DE-627 rakwb eng Isa Mambetsariev verfasserin aut Clinical Network Systems Biology: Traversing the Cancer Multiverse 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In recent decades, cancer biology and medicine have ushered in a new age of precision medicine through high-throughput approaches that led to the development of novel targeted therapies and immunotherapies for different cancers. The availability of multifaceted high-throughput omics data has revealed that cancer, beyond its genomic heterogeneity, is a complex system of microenvironments, sub-clonal tumor populations, and a variety of other cell types that impinge on the genetic and non-genetic mechanisms underlying the disease. Thus, a systems approach to cancer biology has become instrumental in identifying the key components of tumor initiation, progression, and the eventual emergence of drug resistance. Through the union of clinical medicine and basic sciences, there has been a revolution in the development and approval of cancer therapeutic drug options including tyrosine kinase inhibitors, antibody–drug conjugates, and immunotherapy. This ‘Team Medicine’ approach within the cancer systems biology framework can be further improved upon through the development of high-throughput clinical trial models that utilize machine learning models, rapid sample processing to grow patient tumor cell cultures, test multiple therapeutic options and assign appropriate therapy to individual patients quickly and efficiently. The integration of systems biology into the clinical network would allow for rapid advances in personalized medicine that are often hindered by a lack of drug development and drug testing. team medicine precision medicine cancer systems biology clinical network systems biology Medicine R Jeremy Fricke verfasserin aut Stephen B. Gruber verfasserin aut Tingting Tan verfasserin aut Razmig Babikian verfasserin aut Pauline Kim verfasserin aut Priya Vishnubhotla verfasserin aut Jianjun Chen verfasserin aut Prakash Kulkarni verfasserin aut Ravi Salgia verfasserin aut In Journal of Clinical Medicine MDPI AG, 2013 12(2023), 13, p 4535 (DE-627)718632478 (DE-600)2662592-1 20770383 nnns volume:12 year:2023 number:13, p 4535 https://doi.org/10.3390/jcm12134535 kostenfrei https://doaj.org/article/340c0c0322c445c183f6e03e7d892867 kostenfrei https://www.mdpi.com/2077-0383/12/13/4535 kostenfrei https://doaj.org/toc/2077-0383 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2023 13, p 4535 |
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10.3390/jcm12134535 doi (DE-627)DOAJ09400207X (DE-599)DOAJ340c0c0322c445c183f6e03e7d892867 DE-627 ger DE-627 rakwb eng Isa Mambetsariev verfasserin aut Clinical Network Systems Biology: Traversing the Cancer Multiverse 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In recent decades, cancer biology and medicine have ushered in a new age of precision medicine through high-throughput approaches that led to the development of novel targeted therapies and immunotherapies for different cancers. The availability of multifaceted high-throughput omics data has revealed that cancer, beyond its genomic heterogeneity, is a complex system of microenvironments, sub-clonal tumor populations, and a variety of other cell types that impinge on the genetic and non-genetic mechanisms underlying the disease. Thus, a systems approach to cancer biology has become instrumental in identifying the key components of tumor initiation, progression, and the eventual emergence of drug resistance. Through the union of clinical medicine and basic sciences, there has been a revolution in the development and approval of cancer therapeutic drug options including tyrosine kinase inhibitors, antibody–drug conjugates, and immunotherapy. This ‘Team Medicine’ approach within the cancer systems biology framework can be further improved upon through the development of high-throughput clinical trial models that utilize machine learning models, rapid sample processing to grow patient tumor cell cultures, test multiple therapeutic options and assign appropriate therapy to individual patients quickly and efficiently. The integration of systems biology into the clinical network would allow for rapid advances in personalized medicine that are often hindered by a lack of drug development and drug testing. team medicine precision medicine cancer systems biology clinical network systems biology Medicine R Jeremy Fricke verfasserin aut Stephen B. Gruber verfasserin aut Tingting Tan verfasserin aut Razmig Babikian verfasserin aut Pauline Kim verfasserin aut Priya Vishnubhotla verfasserin aut Jianjun Chen verfasserin aut Prakash Kulkarni verfasserin aut Ravi Salgia verfasserin aut In Journal of Clinical Medicine MDPI AG, 2013 12(2023), 13, p 4535 (DE-627)718632478 (DE-600)2662592-1 20770383 nnns volume:12 year:2023 number:13, p 4535 https://doi.org/10.3390/jcm12134535 kostenfrei https://doaj.org/article/340c0c0322c445c183f6e03e7d892867 kostenfrei https://www.mdpi.com/2077-0383/12/13/4535 kostenfrei https://doaj.org/toc/2077-0383 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2023 13, p 4535 |
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In recent decades, cancer biology and medicine have ushered in a new age of precision medicine through high-throughput approaches that led to the development of novel targeted therapies and immunotherapies for different cancers. The availability of multifaceted high-throughput omics data has revealed that cancer, beyond its genomic heterogeneity, is a complex system of microenvironments, sub-clonal tumor populations, and a variety of other cell types that impinge on the genetic and non-genetic mechanisms underlying the disease. Thus, a systems approach to cancer biology has become instrumental in identifying the key components of tumor initiation, progression, and the eventual emergence of drug resistance. Through the union of clinical medicine and basic sciences, there has been a revolution in the development and approval of cancer therapeutic drug options including tyrosine kinase inhibitors, antibody–drug conjugates, and immunotherapy. This ‘Team Medicine’ approach within the cancer systems biology framework can be further improved upon through the development of high-throughput clinical trial models that utilize machine learning models, rapid sample processing to grow patient tumor cell cultures, test multiple therapeutic options and assign appropriate therapy to individual patients quickly and efficiently. The integration of systems biology into the clinical network would allow for rapid advances in personalized medicine that are often hindered by a lack of drug development and drug testing. |
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In recent decades, cancer biology and medicine have ushered in a new age of precision medicine through high-throughput approaches that led to the development of novel targeted therapies and immunotherapies for different cancers. The availability of multifaceted high-throughput omics data has revealed that cancer, beyond its genomic heterogeneity, is a complex system of microenvironments, sub-clonal tumor populations, and a variety of other cell types that impinge on the genetic and non-genetic mechanisms underlying the disease. Thus, a systems approach to cancer biology has become instrumental in identifying the key components of tumor initiation, progression, and the eventual emergence of drug resistance. Through the union of clinical medicine and basic sciences, there has been a revolution in the development and approval of cancer therapeutic drug options including tyrosine kinase inhibitors, antibody–drug conjugates, and immunotherapy. This ‘Team Medicine’ approach within the cancer systems biology framework can be further improved upon through the development of high-throughput clinical trial models that utilize machine learning models, rapid sample processing to grow patient tumor cell cultures, test multiple therapeutic options and assign appropriate therapy to individual patients quickly and efficiently. The integration of systems biology into the clinical network would allow for rapid advances in personalized medicine that are often hindered by a lack of drug development and drug testing. |
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In recent decades, cancer biology and medicine have ushered in a new age of precision medicine through high-throughput approaches that led to the development of novel targeted therapies and immunotherapies for different cancers. The availability of multifaceted high-throughput omics data has revealed that cancer, beyond its genomic heterogeneity, is a complex system of microenvironments, sub-clonal tumor populations, and a variety of other cell types that impinge on the genetic and non-genetic mechanisms underlying the disease. Thus, a systems approach to cancer biology has become instrumental in identifying the key components of tumor initiation, progression, and the eventual emergence of drug resistance. Through the union of clinical medicine and basic sciences, there has been a revolution in the development and approval of cancer therapeutic drug options including tyrosine kinase inhibitors, antibody–drug conjugates, and immunotherapy. This ‘Team Medicine’ approach within the cancer systems biology framework can be further improved upon through the development of high-throughput clinical trial models that utilize machine learning models, rapid sample processing to grow patient tumor cell cultures, test multiple therapeutic options and assign appropriate therapy to individual patients quickly and efficiently. The integration of systems biology into the clinical network would allow for rapid advances in personalized medicine that are often hindered by a lack of drug development and drug testing. |
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