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Personalized logical models to investigate cancer response to BRAF treatments in melanomas and colorectal cancers

The study of response to cancer treatments has benefited greatly from the contribution of different omics data but their interpretation is sometimes difficult. Some mathematical models based on prior biological knowledge of signaling pathways facilitate this interpretation but often require fitting...

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Autores principales: Béal, Jonas, Pantolini, Lorenzo, Noël, Vincent, Barillot, Emmanuel, Calzone, Laurence
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7872233/
https://www.ncbi.nlm.nih.gov/pubmed/33507915
http://dx.doi.org/10.1371/journal.pcbi.1007900
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author Béal, Jonas
Pantolini, Lorenzo
Noël, Vincent
Barillot, Emmanuel
Calzone, Laurence
author_facet Béal, Jonas
Pantolini, Lorenzo
Noël, Vincent
Barillot, Emmanuel
Calzone, Laurence
author_sort Béal, Jonas
collection PubMed
description The study of response to cancer treatments has benefited greatly from the contribution of different omics data but their interpretation is sometimes difficult. Some mathematical models based on prior biological knowledge of signaling pathways facilitate this interpretation but often require fitting of their parameters using perturbation data. We propose a more qualitative mechanistic approach, based on logical formalism and on the sole mapping and interpretation of omics data, and able to recover differences in sensitivity to gene inhibition without model training. This approach is showcased by the study of BRAF inhibition in patients with melanomas and colorectal cancers who experience significant differences in sensitivity despite similar omics profiles. We first gather information from literature and build a logical model summarizing the regulatory network of the mitogen-activated protein kinase (MAPK) pathway surrounding BRAF, with factors involved in the BRAF inhibition resistance mechanisms. The relevance of this model is verified by automatically assessing that it qualitatively reproduces response or resistance behaviors identified in the literature. Data from over 100 melanoma and colorectal cancer cell lines are then used to validate the model’s ability to explain differences in sensitivity. This generic model is transformed into personalized cell line-specific logical models by integrating the omics information of the cell lines as constraints of the model. The use of mutations alone allows personalized models to correlate significantly with experimental sensitivities to BRAF inhibition, both from drug and CRISPR targeting, and even better with the joint use of mutations and RNA, supporting multi-omics mechanistic models. A comparison of these untrained models with learning approaches highlights similarities in interpretation and complementarity depending on the size of the datasets. This parsimonious pipeline, which can easily be extended to other biological questions, makes it possible to explore the mechanistic causes of the response to treatment, on an individualized basis.
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spelling pubmed-78722332021-02-19 Personalized logical models to investigate cancer response to BRAF treatments in melanomas and colorectal cancers Béal, Jonas Pantolini, Lorenzo Noël, Vincent Barillot, Emmanuel Calzone, Laurence PLoS Comput Biol Research Article The study of response to cancer treatments has benefited greatly from the contribution of different omics data but their interpretation is sometimes difficult. Some mathematical models based on prior biological knowledge of signaling pathways facilitate this interpretation but often require fitting of their parameters using perturbation data. We propose a more qualitative mechanistic approach, based on logical formalism and on the sole mapping and interpretation of omics data, and able to recover differences in sensitivity to gene inhibition without model training. This approach is showcased by the study of BRAF inhibition in patients with melanomas and colorectal cancers who experience significant differences in sensitivity despite similar omics profiles. We first gather information from literature and build a logical model summarizing the regulatory network of the mitogen-activated protein kinase (MAPK) pathway surrounding BRAF, with factors involved in the BRAF inhibition resistance mechanisms. The relevance of this model is verified by automatically assessing that it qualitatively reproduces response or resistance behaviors identified in the literature. Data from over 100 melanoma and colorectal cancer cell lines are then used to validate the model’s ability to explain differences in sensitivity. This generic model is transformed into personalized cell line-specific logical models by integrating the omics information of the cell lines as constraints of the model. The use of mutations alone allows personalized models to correlate significantly with experimental sensitivities to BRAF inhibition, both from drug and CRISPR targeting, and even better with the joint use of mutations and RNA, supporting multi-omics mechanistic models. A comparison of these untrained models with learning approaches highlights similarities in interpretation and complementarity depending on the size of the datasets. This parsimonious pipeline, which can easily be extended to other biological questions, makes it possible to explore the mechanistic causes of the response to treatment, on an individualized basis. Public Library of Science 2021-01-28 /pmc/articles/PMC7872233/ /pubmed/33507915 http://dx.doi.org/10.1371/journal.pcbi.1007900 Text en © 2021 Béal et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Béal, Jonas
Pantolini, Lorenzo
Noël, Vincent
Barillot, Emmanuel
Calzone, Laurence
Personalized logical models to investigate cancer response to BRAF treatments in melanomas and colorectal cancers
title Personalized logical models to investigate cancer response to BRAF treatments in melanomas and colorectal cancers
title_full Personalized logical models to investigate cancer response to BRAF treatments in melanomas and colorectal cancers
title_fullStr Personalized logical models to investigate cancer response to BRAF treatments in melanomas and colorectal cancers
title_full_unstemmed Personalized logical models to investigate cancer response to BRAF treatments in melanomas and colorectal cancers
title_short Personalized logical models to investigate cancer response to BRAF treatments in melanomas and colorectal cancers
title_sort personalized logical models to investigate cancer response to braf treatments in melanomas and colorectal cancers
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7872233/
https://www.ncbi.nlm.nih.gov/pubmed/33507915
http://dx.doi.org/10.1371/journal.pcbi.1007900
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