Cargando…

Multiparametric MRI enables for differentiation of different degrees of malignancy in two murine models of breast cancer

OBJECTIVE: The objective of this study was to non-invasively differentiate the degree of malignancy in two murine breast cancer models based on identification of distinct tissue characteristics in a metastatic and non-metastatic tumor model using a multiparametric Magnetic Resonance Imaging (MRI) ap...

Descripción completa

Detalles Bibliográficos
Autores principales: Gerwing, Mirjam, Hoffmann, Emily, Kronenberg, Katharina, Hansen, Uwe, Masthoff, Max, Helfen, Anne, Geyer, Christiane, Wachsmuth, Lydia, Höltke, Carsten, Maus, Bastian, Hoerr, Verena, Krähling, Tobias, Hiddeßen, Lena, Heindel, Walter, Karst, Uwe, Kimm, Melanie A., Schinner, Regina, Eisenblätter, Michel, Faber, Cornelius, Wildgruber, Moritz
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9667047/
https://www.ncbi.nlm.nih.gov/pubmed/36408159
http://dx.doi.org/10.3389/fonc.2022.1000036
_version_ 1784831642654736384
author Gerwing, Mirjam
Hoffmann, Emily
Kronenberg, Katharina
Hansen, Uwe
Masthoff, Max
Helfen, Anne
Geyer, Christiane
Wachsmuth, Lydia
Höltke, Carsten
Maus, Bastian
Hoerr, Verena
Krähling, Tobias
Hiddeßen, Lena
Heindel, Walter
Karst, Uwe
Kimm, Melanie A.
Schinner, Regina
Eisenblätter, Michel
Faber, Cornelius
Wildgruber, Moritz
author_facet Gerwing, Mirjam
Hoffmann, Emily
Kronenberg, Katharina
Hansen, Uwe
Masthoff, Max
Helfen, Anne
Geyer, Christiane
Wachsmuth, Lydia
Höltke, Carsten
Maus, Bastian
Hoerr, Verena
Krähling, Tobias
Hiddeßen, Lena
Heindel, Walter
Karst, Uwe
Kimm, Melanie A.
Schinner, Regina
Eisenblätter, Michel
Faber, Cornelius
Wildgruber, Moritz
author_sort Gerwing, Mirjam
collection PubMed
description OBJECTIVE: The objective of this study was to non-invasively differentiate the degree of malignancy in two murine breast cancer models based on identification of distinct tissue characteristics in a metastatic and non-metastatic tumor model using a multiparametric Magnetic Resonance Imaging (MRI) approach. METHODS: The highly metastatic 4T1 breast cancer model was compared to the non-metastatic 67NR model. Imaging was conducted on a 9.4 T small animal MRI. The protocol was used to characterize tumors regarding their structural composition, including heterogeneity, intratumoral edema and hemorrhage, as well as endothelial permeability using apparent diffusion coefficient (ADC), T1/T2 mapping and dynamic contrast-enhanced (DCE) imaging. Mice were assessed on either day three, six or nine, with an i.v. injection of the albumin-binding contrast agent gadofosveset. Ex vivo validation of the results was performed with laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS), histology, immunhistochemistry and electron microscopy. RESULTS: Significant differences in tumor composition were observed over time and between 4T1 and 67NR tumors. 4T1 tumors showed distorted blood vessels with a thin endothelial layer, resulting in a slower increase in signal intensity after injection of the contrast agent. Higher permeability was further reflected in higher K(trans) values, with consecutive retention of gadolinium in the tumor interstitium visible in MRI. 67NR tumors exhibited blood vessels with a thicker and more intact endothelial layer, resulting in higher peak enhancement, as well as higher maximum slope and area under the curve, but also a visible wash-out of the contrast agent and thus lower K(trans) values. A decreasing accumulation of gadolinium during tumor progression was also visible in both models in LA-ICP-MS. Tissue composition of 4T1 tumors was more heterogeneous, with intratumoral hemorrhage and necrosis and corresponding higher T1 and T2 relaxation times, while 67NR tumors mainly consisted of densely packed tumor cells. Histogram analysis of ADC showed higher values of mean ADC, histogram kurtosis, range and the 90(th) percentile (p90), as markers for the heterogenous structural composition of 4T1 tumors. Principal component analysis (PCA) discriminated well between the two tumor models. CONCLUSIONS: Multiparametric MRI as presented in this study enables for the estimation of malignant potential in the two studied tumor models via the assessment of certain tumor features over time.
format Online
Article
Text
id pubmed-9667047
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-96670472022-11-17 Multiparametric MRI enables for differentiation of different degrees of malignancy in two murine models of breast cancer Gerwing, Mirjam Hoffmann, Emily Kronenberg, Katharina Hansen, Uwe Masthoff, Max Helfen, Anne Geyer, Christiane Wachsmuth, Lydia Höltke, Carsten Maus, Bastian Hoerr, Verena Krähling, Tobias Hiddeßen, Lena Heindel, Walter Karst, Uwe Kimm, Melanie A. Schinner, Regina Eisenblätter, Michel Faber, Cornelius Wildgruber, Moritz Front Oncol Oncology OBJECTIVE: The objective of this study was to non-invasively differentiate the degree of malignancy in two murine breast cancer models based on identification of distinct tissue characteristics in a metastatic and non-metastatic tumor model using a multiparametric Magnetic Resonance Imaging (MRI) approach. METHODS: The highly metastatic 4T1 breast cancer model was compared to the non-metastatic 67NR model. Imaging was conducted on a 9.4 T small animal MRI. The protocol was used to characterize tumors regarding their structural composition, including heterogeneity, intratumoral edema and hemorrhage, as well as endothelial permeability using apparent diffusion coefficient (ADC), T1/T2 mapping and dynamic contrast-enhanced (DCE) imaging. Mice were assessed on either day three, six or nine, with an i.v. injection of the albumin-binding contrast agent gadofosveset. Ex vivo validation of the results was performed with laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS), histology, immunhistochemistry and electron microscopy. RESULTS: Significant differences in tumor composition were observed over time and between 4T1 and 67NR tumors. 4T1 tumors showed distorted blood vessels with a thin endothelial layer, resulting in a slower increase in signal intensity after injection of the contrast agent. Higher permeability was further reflected in higher K(trans) values, with consecutive retention of gadolinium in the tumor interstitium visible in MRI. 67NR tumors exhibited blood vessels with a thicker and more intact endothelial layer, resulting in higher peak enhancement, as well as higher maximum slope and area under the curve, but also a visible wash-out of the contrast agent and thus lower K(trans) values. A decreasing accumulation of gadolinium during tumor progression was also visible in both models in LA-ICP-MS. Tissue composition of 4T1 tumors was more heterogeneous, with intratumoral hemorrhage and necrosis and corresponding higher T1 and T2 relaxation times, while 67NR tumors mainly consisted of densely packed tumor cells. Histogram analysis of ADC showed higher values of mean ADC, histogram kurtosis, range and the 90(th) percentile (p90), as markers for the heterogenous structural composition of 4T1 tumors. Principal component analysis (PCA) discriminated well between the two tumor models. CONCLUSIONS: Multiparametric MRI as presented in this study enables for the estimation of malignant potential in the two studied tumor models via the assessment of certain tumor features over time. Frontiers Media S.A. 2022-11-02 /pmc/articles/PMC9667047/ /pubmed/36408159 http://dx.doi.org/10.3389/fonc.2022.1000036 Text en Copyright © 2022 Gerwing, Hoffmann, Kronenberg, Hansen, Masthoff, Helfen, Geyer, Wachsmuth, Höltke, Maus, Hoerr, Krähling, Hiddeßen, Heindel, Karst, Kimm, Schinner, Eisenblätter, Faber and Wildgruber https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
Gerwing, Mirjam
Hoffmann, Emily
Kronenberg, Katharina
Hansen, Uwe
Masthoff, Max
Helfen, Anne
Geyer, Christiane
Wachsmuth, Lydia
Höltke, Carsten
Maus, Bastian
Hoerr, Verena
Krähling, Tobias
Hiddeßen, Lena
Heindel, Walter
Karst, Uwe
Kimm, Melanie A.
Schinner, Regina
Eisenblätter, Michel
Faber, Cornelius
Wildgruber, Moritz
Multiparametric MRI enables for differentiation of different degrees of malignancy in two murine models of breast cancer
title Multiparametric MRI enables for differentiation of different degrees of malignancy in two murine models of breast cancer
title_full Multiparametric MRI enables for differentiation of different degrees of malignancy in two murine models of breast cancer
title_fullStr Multiparametric MRI enables for differentiation of different degrees of malignancy in two murine models of breast cancer
title_full_unstemmed Multiparametric MRI enables for differentiation of different degrees of malignancy in two murine models of breast cancer
title_short Multiparametric MRI enables for differentiation of different degrees of malignancy in two murine models of breast cancer
title_sort multiparametric mri enables for differentiation of different degrees of malignancy in two murine models of breast cancer
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9667047/
https://www.ncbi.nlm.nih.gov/pubmed/36408159
http://dx.doi.org/10.3389/fonc.2022.1000036
work_keys_str_mv AT gerwingmirjam multiparametricmrienablesfordifferentiationofdifferentdegreesofmalignancyintwomurinemodelsofbreastcancer
AT hoffmannemily multiparametricmrienablesfordifferentiationofdifferentdegreesofmalignancyintwomurinemodelsofbreastcancer
AT kronenbergkatharina multiparametricmrienablesfordifferentiationofdifferentdegreesofmalignancyintwomurinemodelsofbreastcancer
AT hansenuwe multiparametricmrienablesfordifferentiationofdifferentdegreesofmalignancyintwomurinemodelsofbreastcancer
AT masthoffmax multiparametricmrienablesfordifferentiationofdifferentdegreesofmalignancyintwomurinemodelsofbreastcancer
AT helfenanne multiparametricmrienablesfordifferentiationofdifferentdegreesofmalignancyintwomurinemodelsofbreastcancer
AT geyerchristiane multiparametricmrienablesfordifferentiationofdifferentdegreesofmalignancyintwomurinemodelsofbreastcancer
AT wachsmuthlydia multiparametricmrienablesfordifferentiationofdifferentdegreesofmalignancyintwomurinemodelsofbreastcancer
AT holtkecarsten multiparametricmrienablesfordifferentiationofdifferentdegreesofmalignancyintwomurinemodelsofbreastcancer
AT mausbastian multiparametricmrienablesfordifferentiationofdifferentdegreesofmalignancyintwomurinemodelsofbreastcancer
AT hoerrverena multiparametricmrienablesfordifferentiationofdifferentdegreesofmalignancyintwomurinemodelsofbreastcancer
AT krahlingtobias multiparametricmrienablesfordifferentiationofdifferentdegreesofmalignancyintwomurinemodelsofbreastcancer
AT hiddeßenlena multiparametricmrienablesfordifferentiationofdifferentdegreesofmalignancyintwomurinemodelsofbreastcancer
AT heindelwalter multiparametricmrienablesfordifferentiationofdifferentdegreesofmalignancyintwomurinemodelsofbreastcancer
AT karstuwe multiparametricmrienablesfordifferentiationofdifferentdegreesofmalignancyintwomurinemodelsofbreastcancer
AT kimmmelaniea multiparametricmrienablesfordifferentiationofdifferentdegreesofmalignancyintwomurinemodelsofbreastcancer
AT schinnerregina multiparametricmrienablesfordifferentiationofdifferentdegreesofmalignancyintwomurinemodelsofbreastcancer
AT eisenblattermichel multiparametricmrienablesfordifferentiationofdifferentdegreesofmalignancyintwomurinemodelsofbreastcancer
AT fabercornelius multiparametricmrienablesfordifferentiationofdifferentdegreesofmalignancyintwomurinemodelsofbreastcancer
AT wildgrubermoritz multiparametricmrienablesfordifferentiationofdifferentdegreesofmalignancyintwomurinemodelsofbreastcancer