Dataset Information

Evaluation of the Response of Intracranial Xenografts to VEGF Signaling Inhibition Using Multiparametric MRI.

ABSTRACT: Vascular endothelial growth factor A (VEGF-A) is considered one of the most important factors in tumor angiogenesis, and consequently, a number of therapeutics have been developed to inhibit VEGF signaling. Therapeutic strategies to target brain malignancies, both primary brain tumors, particularly in pediatric patients, and metastases, are lacking, but targeting angiogenesis may be a promising approach. Multiparametric MRI was used to investigate the response of orthotopic SF188^luc pediatric glioblastoma xenografts to small molecule pan-VEGFR inhibitor cediranib and the effects of both cediranib and cross-reactive human/mouse anti-VEGF-A antibody B20-4.1.1 in intracranial MDA-MB-231 LM2-4 breast cancer xenografts over 48 hours. All therapeutic regimens resulted in significant tumor growth delay. In cediranib-treated SF188^luc tumors, this was associated with lower K^trans (compound biomarker of perfusion and vascular permeability) than in vehicle-treated controls. Cediranib also induced significant reductions in both K^trans and apparent diffusion coefficient (ADC) in MDA-MB-231 LM2-4 tumors associated with decreased histologically assessed perfusion. B20-4.1.1 treatment resulted in decreased K^trans, but in the absence of a change in perfusion; a non-significant reduction in vascular permeability, assessed by Evans blue extravasation, was observed in treated tumors. The imaging responses of intracranial MDA-MB-231 LM2-4 tumors to VEGF/VEGFR pathway inhibitors with differing mechanisms of action are subtly different. We show that VEGF pathway blockade resulted in tumor growth retardation and inhibition of tumor vasculature in preclinical models of pediatric glioblastoma and breast cancer brain metastases, suggesting that multiparametric MRI can provide a powerful adjunct to accelerate the development of antiangiogenic therapies for use in these patient populations.

SUBMITTER: Boult JKR

PROVIDER: S-EPMC5547238 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Evaluation of the Response of Intracranial Xenografts to VEGF Signaling Inhibition Using Multiparametric MRI.

Boult Jessica K R JKR Box Gary G Vinci Maria M Perryman Lara L Eccles Suzanne A SA Jones Chris C Robinson Simon P SP

Neoplasia (New York, N.Y.) 20170804 9

Vascular endothelial growth factor A (VEGF-A) is considered one of the most important factors in tumor angiogenesis, and consequently, a number of therapeutics have been developed to inhibit VEGF signaling. Therapeutic strategies to target brain malignancies, both primary brain tumors, particularly in pediatric patients, and metastases, are lacking, but targeting angiogenesis may be a promising approach. Multiparametric MRI was used to investigate the response of orthotopic SF188<sup>luc</sup> p ...[more]

PMID: 28780387

Similar Datasets

Project description:High grade and metastatic brain tumours exhibit considerable spatial variations in proliferation, angiogenesis, invasion, necrosis and oedema. Vascular heterogeneity arising from vascular co-option in regions of invasive growth (in which the blood-brain barrier remains intact) and neoangiogenesis is a major challenge faced in the assessment of brain tumours by conventional MRI. A multiparametric MRI approach, incorporating native measurements and both Gd-DTPA (Magnevist) and ultrasmall superparamagnetic iron oxide (P904)-enhanced imaging, was used in combination with histogram and unsupervised cluster analysis using a k-means algorithm to examine the spatial distribution of vascular parameters, water diffusion characteristics and invasion in intracranially propagated rat RG2 gliomas and human MDA-MB-231 LM2-4 breast adenocarcinomas in mice. Both tumour models presented with higher ?R1 (the change in transverse relaxation rate R1 induced by Gd-DTPA), fractional blood volume (fBV) and apparent diffusion coefficient than uninvolved regions of the brain. MDA-MB-231 LM2-4 tumours were less densely cellular than RG2 tumours and exhibited substantial local invasion, associated with oedema, whereas invasion in RG2 tumours was minimal. These additional features were reflected in the more heterogeneous appearance of MDA-MB-231 LM2-4 tumours on T2 -weighted images and maps of functional MRI parameters. Unsupervised cluster analysis separated subregions with distinct functional properties; areas with a low fBV and relatively impermeable blood vessels (low ?R1 ) were predominantly located at the tumour margins, regions of MDA-MB-231 LM2-4 tumours with relatively high levels of water diffusion and low vascular permeability and/or fBV corresponded to histologically identified regions of invasion and oedema, and areas of mismatch between vascular permeability and blood volume were identified. We demonstrate that dual contrast MRI and evaluation of tissue diffusion properties, coupled with cluster analysis, allows for the assessment of heterogeneity within invasive brain tumours and the designation of functionally diverse subregions that may provide more informative predictive biomarkers.

Project description:Background:To evaluate the association of multiparametric and multiregional MRI-features with key molecular characteristics in patients with newly-diagnosed glioblastoma. Methods:Retrospective data evaluation was approved by the local ethics committee of the University of Heidelberg (ethics approval number: S-320/2012) and informed consent was waived. Preoperative MRI-features were correlated with key molecular characteristics within a single-institutional cohort of 152 patients with newly-diagnosed glioblastoma. Preoperative MRI-features (n=31) included multiparametric (anatomical, diffusion-, perfusion-, and susceptibility-weighted images) and multiregional (contrast enhancing and non-enhancing FLAIR-hyperintense) information with (histogram) quantification of tumor volumes, volume ratios, apparent diffusion coefficients, cerebral blood flow / volume (CBF / CBV) and intratumoral susceptibility signals. Molecular characteristics determined with the Illumina Infinium HumanMethylation450 array included global DNA-methylation subgroups (e.g. mesenchymal (MES), RTK I “PGFRA”, RTK II “classic”), MGMT-promoter methylation status and hallmark copy-number-variations (EGFR-, PDGFRA-, MDM4- and CDK4-amplification; PTEN-, CDKN2A-, NF1- and RB1-loss). Univariate analyses (voxel-lesion-symptom-mapping for tumor location, Wilcoxon-test for all other MRI-features) as well as machine-learning models were applied to study the strength of association and discriminative value of MRI-features for predicting underlying molecular characteristics. Results: There was no tumor location predilection for any of the assessed molecular parameters (permutation-adjusted p>0.05 each). Univariate imaging parameter associations were noted for EGFR amplification and CDKN2A loss, both demonstrating increased nrCBV and nrCBF values (performance of these parameters, as assessed by the area under the ROC curve ranged from 63 to 69%, FDR-adjusted p<0.05, respectively). Subjecting all MRI-features to machine-learning-based classification allowed to predict EGFR amplification status and the RTK II “classic” GB subgroup with a moderate, yet significantly greater accuracy (63% for EGFR [p<0.01] and 61% for RTK II [p=0.01]) than the prediction by chance, whereas prediction accuracy for all other molecular parameters was non-significant (p>0.05, all models). Conclusions: In summary, we found univariate associations between established MRI-features and molecular characteristics, however not of sufficient strength to allow the generation of machine-learning classification models for reliable and clinically meaningful prediction of the assessed molecular characteristics in patients with newly-diagnosed glioblastoma.

Dataset Information

Evaluation of the Response of Intracranial Xenografts to VEGF Signaling Inhibition Using Multiparametric MRI.

Publications

Evaluation of the Response of Intracranial Xenografts to VEGF Signaling Inhibition Using Multiparametric MRI.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets