A Coclinical Radiogenomic Validation Study: Conserved Magnetic Resonance Radiomic Appearance of Periostin-Expressing Glioblastoma in Patients and Xenograft Models

Radiomics is the extraction of multidimensional imaging features, which when correlated with genomics, is termed radiogenomics. However, radiogenomic biological validation is not sufficiently described in the literature. We seek to establish causality between differential gene expression status and...

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Veröffentlicht in:Clinical cancer research Jg. 24; H. 24; S. 6288
Hauptverfasser: Zinn, Pascal O, Singh, Sanjay K, Kotrotsou, Aikaterini, Hassan, Islam, Thomas, Ginu, Luedi, Markus M, Elakkad, Ahmed, Elshafeey, Nabil, Idris, Tagwa, Mosley, Jennifer, Gumin, Joy, Fuller, Gregory N, de Groot, John F, Baladandayuthapani, Veera, Sulman, Erik P, Kumar, Ashok J, Sawaya, Raymond, Lang, Frederick F, Piwnica-Worms, David, Colen, Rivka R
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States 15.12.2018
Schlagworte:
Adult
Aged
Aged, 80 and over
Animals
Biomarkers, Tumor
Brain Neoplasms - diagnostic imaging
Brain Neoplasms - genetics
Cell Adhesion Molecules - genetics
Data Analysis
Disease Models, Animal
Female
Gene Expression
Genomics - methods
Glioblastoma - diagnostic imaging
Glioblastoma - genetics
Humans
Image Interpretation, Computer-Assisted - methods
Image Processing, Computer-Assisted
Magnetic Resonance Imaging - methods
Magnetic Resonance Imaging - standards
Male
Mice
Middle Aged
Molecular Imaging - methods
Molecular Imaging - standards
Xenograft Model Antitumor Assays
ISSN:1078-0432, 1557-3265, 1557-3265
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Zusammenfassung:Radiomics is the extraction of multidimensional imaging features, which when correlated with genomics, is termed radiogenomics. However, radiogenomic biological validation is not sufficiently described in the literature. We seek to establish causality between differential gene expression status and MRI-extracted radiomic-features in glioblastoma. Radiogenomic predictions and validation were done using the Cancer Genome Atlas and Repository of Molecular Brain Neoplasia Data glioblastoma patients ( = 93) and orthotopic xenografts (OX; = 40). Tumor phenotypes were segmented, and radiomic-features extracted using the developed radiome-sequencing pipeline. Patients and animals were dichotomized on the basis of Periostin ( expression levels. RNA and protein levels confirmed RNAi-mediated knockdown in OX. Total RNA of tumor cells isolated from mouse brains (knockdown and control) was used for microarray-based expression profiling. Radiomic-features were utilized to predict expression status in patient, mouse, and interspecies. Our robust pipeline consists of segmentation, radiomic-feature extraction, feature normalization/selection, and predictive modeling. The combination of skull stripping, brain-tissue focused normalization, and patient-specific normalization are unique to this study, providing comparable cross-platform, cross-institution radiomic features. expression status was not associated with qualitative or volumetric MRI parameters. Radiomic features significantly predicted expression status in patients (AUC: 76.56%; sensitivity/specificity: 73.91/78.26%) and OX (AUC: 92.26%; sensitivity/specificity: 92.86%/91.67%). Furthermore, radiomic features in OX were significantly associated with patients with similar expression levels (AUC: 93.36%; sensitivity/specificity: 82.61%/95.74%; = 02.021E-15). We determined causality between radiomic texture features and expression levels in a preclinical model with clinical validation. Our biologically validated radiomic pipeline also showed the potential application for human-mouse matched coclinical trials.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 23
ISSN:1078-0432
1557-3265
1557-3265
DOI:10.1158/1078-0432.CCR-17-3420