Dataset Information

Functional Characterization of Brain Tumor-Initiating Cells and Establishment of GBM Preclinical Models that Incorporate Heterogeneity, Therapy, and Sex Differences.

ABSTRACT: Glioblastoma (GBM) is the most common primary brain cancer in adults where tumor cell heterogeneity and sex differences influence clinical outcomes. Here, we functionally characterize three male and three female patient-derived GBM cell lines, identify protumorigenic BTICs, and create novel male and female preclinical models of GBM. Cell lines were evaluated on the following features: proliferation, stemness, migration, tumorigenesis, clinical characteristics, and sensitivity to radiation, TMZ, rhTNFSF10 (rhTRAIL), and rhBMP4 All cell lines were classified as GBM according to epigenetic subtyping, were heterogenous and functionally distinct from one another, and re-capitulated features of the original patient tumor. In establishing male and female preclinical models, it was found that two male-derived GBM cell lines (QNS108 and QNS120) and one female-derived GBM cell line (QNS315) grew at a faster rate in female mice brains. One male-derived GBM cell line (QNS108) decreased survival in female mice in comparison with male mice. However, no survival differences were observed for mice injected with a female-derived cell line (QNS315). In summary, a panel of six GBM patient-derived cell lines were functionally characterized, and it was shown that BTIC lines can be used to construct sex-specific models with differential phenotypes for additional studies.

SUBMITTER: Garcia CA

PROVIDER: S-EPMC8687628 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

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Functional Characterization of Brain Tumor-Initiating Cells and Establishment of GBM Preclinical Models that Incorporate Heterogeneity, Therapy, and Sex Differences.

Garcia Cesar A CA Bhargav Adip G AG Brooks Mieu M Suárez-Meade Paola P Mondal Sujan K SK Zarco Natanael N ReFaey Karim K Jentoft Mark M Middlebrooks Erik H EH Snuderl Matija M Carrano Anna A Guerrero-Cazares Hugo H Schiapparelli Paula P Sarabia-Estrada Rachel R Quiñones-Hinojosa Alfredo A

Molecular cancer therapeutics 20210831 12

Glioblastoma (GBM) is the most common primary brain cancer in adults where tumor cell heterogeneity and sex differences influence clinical outcomes. Here, we functionally characterize three male and three female patient-derived GBM cell lines, identify protumorigenic BTICs, and create novel male and female preclinical models of GBM. Cell lines were evaluated on the following features: proliferation, stemness, migration, tumorigenesis, clinical characteristics, and sensitivity to radiation, TMZ, ...[more]

PMID: 34465594

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Project description:Rationale: Developing novel pre-operative and intraoperative imaging approaches for glioblastoma multiforme (GBM) could aid therapeutic intervention while sparing healthy normal brain, which remains a significant clinical challenge. 5-aminolevulinic acid (5-ALA) is the only intraoperative imaging agent approved to aid the resection of GBM. Matrix metalloproteinase 14 (MMP14), which is overexpressed in GBM, is an attractive target for preoperative and intraoperative imaging of GBM. Prior studies have shown the feasibility of near-infrared fluorescence (NIRF) imaging and positron emission tomography (PET) imaging of GBM xenografts in mice using MMP-14 targeted peptide probes. The present studies assessed the tumor-specific localization and contrast of these MMP-14 targeted peptides relative to 5-ALA in GBM models. Methods: Fluorescence and PET imaging was performed after i.v. injection of 5-ALA and the MMP-14 targeted peptide probes (non-labeled or radiolabeled with 64Cu) in mice bearing human GBM orthotopic xenografts (U87, D54). Imaging signals were correlated to MMP-14 expression determined by immunofluorescence. Tumor-to-normal brain ratio (TBR) and Dice similarity coefficient (DSC) relative to tumor defined by ex vivo pathology or in vivo magnetic resonance imaging were determined for each imaging agent. Results: NIRF signals from the MMP-14 targeted peptide probes showed comparable TBR (p < 0.05) but significantly higher DSC (p < 0.05) relative to 5-ALA. NIRF signals from the peptide probes significantly correlated with MMP-14 expression (p < 0.05). MMP-14 binding peptide labeled with 64Cu showed moderate DSC (0.45) while PET signals significantly correlated (p < 0.05) with NIRF signals from a co-injected MMP-14 substrate peptide. NIRF and PET signals localized in residual tumor regions in the resection cavity during in situ resection. Conclusions: MMP-14 targeted peptides showed favorable TBR and higher tumor localization than 5-ALA in GBM orthotopic models. Further development of MMP-14 targeted peptide probes could lead to improved pre-operative and intraoperative management of GBM.

Project description:ObjectiveThe purpose of this study was to investigate the feasibility of applying handcrafted radiomics (HCR) and deep learning-based radiomics (DLR) for the accurate preoperative classification of glioblastoma (GBM) and solitary brain metastasis (BM).MethodsA retrospective analysis of the magnetic resonance imaging (MRI) data of 140 patients (110 in the training dataset and 30 in the test dataset) with GBM and 128 patients (98 in the training dataset and 30 in the test dataset) with BM confirmed by surgical pathology was performed. The regions of interest (ROIs) on T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and contrast-enhanced T1WI (T1CE) were drawn manually, and then, HCR and DLR analyses were performed. On this basis, different machine learning algorithms were implemented and compared to find the optimal modeling method. The final classifiers were identified and validated for different MRI modalities using HCR features and HCR + DLR features. By analyzing the receiver operating characteristic (ROC) curve, the area under the curve (AUC), accuracy, sensitivity, and specificity were calculated to evaluate the predictive efficacy of different methods.ResultsIn multiclassifier modeling, random forest modeling showed the best distinguishing performance among all MRI modalities. HCR models already showed good results for distinguishing between the two types of brain tumors in the test dataset (T1WI, AUC = 0.86; T2WI, AUC = 0.76; T1CE, AUC = 0.93). By adding DLR features, all AUCs showed significant improvement (T1WI, AUC = 0.87; T2WI, AUC = 0.80; T1CE, AUC = 0.97; p < 0.05). The T1CE-based radiomic model showed the best classification performance (AUC = 0.99 in the training dataset and AUC = 0.97 in the test dataset), surpassing the other MRI modalities (p < 0.05). The multimodality radiomic model also showed robust performance (AUC = 1 in the training dataset and AUC = 0.84 in the test dataset).ConclusionMachine learning models using MRI radiomic features can help distinguish GBM from BM effectively, especially the combination of HCR and DLR features.

Dataset Information

Functional Characterization of Brain Tumor-Initiating Cells and Establishment of GBM Preclinical Models that Incorporate Heterogeneity, Therapy, and Sex Differences.

Publications

Functional Characterization of Brain Tumor-Initiating Cells and Establishment of GBM Preclinical Models that Incorporate Heterogeneity, Therapy, and Sex Differences.

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