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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...

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Autores principales: Mambetsariev, Isa, Fricke, Jeremy, Gruber, Stephen B., Tan, Tingting, Babikian, Razmig, Kim, Pauline, Vishnubhotla, Priya, Chen, Jianjun, Kulkarni, Prakash, Salgia, Ravi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10342467/
https://www.ncbi.nlm.nih.gov/pubmed/37445570
http://dx.doi.org/10.3390/jcm12134535
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author Mambetsariev, Isa
Fricke, Jeremy
Gruber, Stephen B.
Tan, Tingting
Babikian, Razmig
Kim, Pauline
Vishnubhotla, Priya
Chen, Jianjun
Kulkarni, Prakash
Salgia, Ravi
author_facet Mambetsariev, Isa
Fricke, Jeremy
Gruber, Stephen B.
Tan, Tingting
Babikian, Razmig
Kim, Pauline
Vishnubhotla, Priya
Chen, Jianjun
Kulkarni, Prakash
Salgia, Ravi
author_sort Mambetsariev, Isa
collection PubMed
description 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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spelling pubmed-103424672023-07-14 Clinical Network Systems Biology: Traversing the Cancer Multiverse Mambetsariev, Isa Fricke, Jeremy Gruber, Stephen B. Tan, Tingting Babikian, Razmig Kim, Pauline Vishnubhotla, Priya Chen, Jianjun Kulkarni, Prakash Salgia, Ravi J Clin Med Review 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. MDPI 2023-07-07 /pmc/articles/PMC10342467/ /pubmed/37445570 http://dx.doi.org/10.3390/jcm12134535 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Mambetsariev, Isa
Fricke, Jeremy
Gruber, Stephen B.
Tan, Tingting
Babikian, Razmig
Kim, Pauline
Vishnubhotla, Priya
Chen, Jianjun
Kulkarni, Prakash
Salgia, Ravi
Clinical Network Systems Biology: Traversing the Cancer Multiverse
title Clinical Network Systems Biology: Traversing the Cancer Multiverse
title_full Clinical Network Systems Biology: Traversing the Cancer Multiverse
title_fullStr Clinical Network Systems Biology: Traversing the Cancer Multiverse
title_full_unstemmed Clinical Network Systems Biology: Traversing the Cancer Multiverse
title_short Clinical Network Systems Biology: Traversing the Cancer Multiverse
title_sort clinical network systems biology: traversing the cancer multiverse
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10342467/
https://www.ncbi.nlm.nih.gov/pubmed/37445570
http://dx.doi.org/10.3390/jcm12134535
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