Patient-Specific Modeling of Diffuse Large B-Cell Lymphoma

Personalized medicine aims to tailor treatment to patients based on their individual genetic or molecular background. Especially in diseases with a large molecular heterogeneity, such as diffuse large B-cell lymphoma (DLBCL), personalized medicine has the potential to improve outcome and/or to reduc...

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Autores principales:	Kirsten Thobe, Fabian Konrath, Björn Chapuy, Jana Wolf
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	cancer patient-specific treatment personalized medicine logical modeling DLBCL signaling networks Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/fa11e8ee04db491da78735748c189709
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spelling	oai:doaj.org-article:fa11e8ee04db491da78735748c1897092021-11-25T16:50:20ZPatient-Specific Modeling of Diffuse Large B-Cell Lymphoma10.3390/biomedicines91116552227-9059https://doaj.org/article/fa11e8ee04db491da78735748c1897092021-11-01T00:00:00Zhttps://www.mdpi.com/2227-9059/9/11/1655https://doaj.org/toc/2227-9059Personalized medicine aims to tailor treatment to patients based on their individual genetic or molecular background. Especially in diseases with a large molecular heterogeneity, such as diffuse large B-cell lymphoma (DLBCL), personalized medicine has the potential to improve outcome and/or to reduce resistance towards treatment. However, integration of patient-specific information into a computational model is challenging and has not been achieved for DLBCL. Here, we developed a computational model describing signaling pathways and expression of critical germinal center markers. The model integrates the regulatory mechanism of the signaling and gene expression network and covers more than 50 components, many carrying genetic lesions common in DLBCL. Using clinical and genomic data of 164 primary DLBCL patients, we implemented mutations, structural variants and copy number alterations as perturbations in the model using the CoLoMoTo notebook. Leveraging patient-specific genotypes and simulation of the expression of marker genes in specific germinal center conditions allows us to predict the consequence of the modeled pathways for each patient. Finally, besides modeling how genetic perturbations alter physiological signaling, we also predicted for each patient model the effect of rational inhibitors, such as Ibrutinib, that are currently discussed as possible DLBCL treatments, showing patient-dependent variations in effectiveness and synergies.Kirsten ThobeFabian KonrathBjörn ChapuyJana WolfMDPI AGarticlecancerpatient-specific treatmentpersonalized medicinelogical modelingDLBCLsignaling networksBiology (General)QH301-705.5ENBiomedicines, Vol 9, Iss 1655, p 1655 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	cancer patient-specific treatment personalized medicine logical modeling DLBCL signaling networks Biology (General) QH301-705.5
spellingShingle	cancer patient-specific treatment personalized medicine logical modeling DLBCL signaling networks Biology (General) QH301-705.5 Kirsten Thobe Fabian Konrath Björn Chapuy Jana Wolf Patient-Specific Modeling of Diffuse Large B-Cell Lymphoma
description	Personalized medicine aims to tailor treatment to patients based on their individual genetic or molecular background. Especially in diseases with a large molecular heterogeneity, such as diffuse large B-cell lymphoma (DLBCL), personalized medicine has the potential to improve outcome and/or to reduce resistance towards treatment. However, integration of patient-specific information into a computational model is challenging and has not been achieved for DLBCL. Here, we developed a computational model describing signaling pathways and expression of critical germinal center markers. The model integrates the regulatory mechanism of the signaling and gene expression network and covers more than 50 components, many carrying genetic lesions common in DLBCL. Using clinical and genomic data of 164 primary DLBCL patients, we implemented mutations, structural variants and copy number alterations as perturbations in the model using the CoLoMoTo notebook. Leveraging patient-specific genotypes and simulation of the expression of marker genes in specific germinal center conditions allows us to predict the consequence of the modeled pathways for each patient. Finally, besides modeling how genetic perturbations alter physiological signaling, we also predicted for each patient model the effect of rational inhibitors, such as Ibrutinib, that are currently discussed as possible DLBCL treatments, showing patient-dependent variations in effectiveness and synergies.
format	article
author	Kirsten Thobe Fabian Konrath Björn Chapuy Jana Wolf
author_facet	Kirsten Thobe Fabian Konrath Björn Chapuy Jana Wolf
author_sort	Kirsten Thobe
title	Patient-Specific Modeling of Diffuse Large B-Cell Lymphoma
title_short	Patient-Specific Modeling of Diffuse Large B-Cell Lymphoma
title_full	Patient-Specific Modeling of Diffuse Large B-Cell Lymphoma
title_fullStr	Patient-Specific Modeling of Diffuse Large B-Cell Lymphoma
title_full_unstemmed	Patient-Specific Modeling of Diffuse Large B-Cell Lymphoma
title_sort	patient-specific modeling of diffuse large b-cell lymphoma
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/fa11e8ee04db491da78735748c189709
work_keys_str_mv	AT kirstenthobe patientspecificmodelingofdiffuselargebcelllymphoma AT fabiankonrath patientspecificmodelingofdiffuselargebcelllymphoma AT bjornchapuy patientspecificmodelingofdiffuselargebcelllymphoma AT janawolf patientspecificmodelingofdiffuselargebcelllymphoma
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Patient-Specific Modeling of Diffuse Large B-Cell Lymphoma

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