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  • 1
    Online Resource
    Online Resource
    Integrated molecular and multiparametric MRI mapping of high-grade glioma identifies regional biologic signatures
    Springer Science and Business Media LLC ; 2023
    In:  Nature Communications Vol. 14, No. 1 ( 2023-09-28)
    Details
    In: Nature Communications, Springer Science and Business Media LLC, Vol. 14, No. 1 ( 2023-09-28)
    Abstract: Sampling restrictions have hindered the comprehensive study of invasive non-enhancing (NE) high-grade glioma (HGG) cell populations driving tumor progression. Here, we present an integrated multi-omic analysis of spatially matched molecular and multi-parametric magnetic resonance imaging (MRI) profiling across 313 multi-regional tumor biopsies, including 111 from the NE, across 68 HGG patients. Whole exome and RNA sequencing uncover unique genomic alterations to unresectable invasive NE tumor, including subclonal events, which inform genomic models predictive of geographic evolution. Infiltrative NE tumor is alternatively enriched with tumor cells exhibiting neuronal or glycolytic/plurimetabolic cellular states, two principal transcriptomic pathway-based glioma subtypes, which respectively demonstrate abundant private mutations or enrichment in immune cell signatures. These NE phenotypes are non-invasively identified through normalized K2 imaging signatures, which discern cell size heterogeneity on dynamic susceptibility contrast (DSC)-MRI. NE tumor populations predicted to display increased cellular proliferation by mean diffusivity (MD) MRI metrics are uniquely associated with EGFR amplification and CDKN2A homozygous deletion. The biophysical mapping of infiltrative HGG potentially enables the clinical recognition of tumor subpopulations with aggressive molecular signatures driving tumor progression, thereby informing precision medicine targeting.
    Type of Medium: Online Resource
    ISSN: 2041-1723
    URL: Article
    DOI: 10.1038/s41467-023-41559-1
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 2553671-0
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  • 2
    Online Resource
    Online Resource
    Inhibition of phosphatidylinositol 3-kinase by PX-866 suppresses temozolomide-induced autophagy and promotes apoptosis in glioblastoma cells
    Springer Science and Business Media LLC ; 2019
    In:  Molecular Medicine Vol. 25, No. 1 ( 2019-12)
    Details
    In: Molecular Medicine, Springer Science and Business Media LLC, Vol. 25, No. 1 ( 2019-12)
    Abstract: Temozolomide (TMZ) is the most commonly used chemotherapeutic agent used to treat glioblastoma (GBM), which causes significant DNA damage to highly proliferative cells. Our observations have added to accumulating evidence that TMZ induces stress-responsive cellular programs known to promote cell survival, including autophagy. As such, targeting these survival pathways may represent new vulnerabilities of GBM after treatment with TMZ. Methods Using the T98G human glioma cell line, we assessed the molecular signaling associated with TMZ treatment, the cellular consequences of using the pan-PI3K inhibitor PX-866, and performed clonogenic assays to determine the effect sequential treatment of TMZ and PX-866 had on colony formation. Additionally, we also use subcutaneous GBM patient derived xenograft (PDX) tumors to show relative LC3 protein expression and correlations between survival pathways and molecular markers which dictate clinical responsiveness to TMZ. Results Here, we report that TMZ can induce autophagic flux in T98G glioma cells. GBM patient-derived xenograft (PDX) tumors treated with TMZ also display an increase in the autophagosome marker LC3 II. Additionally, O 6 -methylguanine-DNA-methyltransferase (MGMT) expression correlates with PI3K/AKT activity, suggesting that patients with inherent resistance to TMZ (MGMT-high) would benefit from PI3K/AKT inhibitors in addition to TMZ. Accordingly, we have identified that the blood-brain barrier (BBB) penetrant pan-PI3K inhibitor, PX-866, is an early-stage inhibitor of autophagic flux, while maintaining its ability to inhibit PI3K/AKT signaling in glioma cells. Lastly, due to the induction of autophagic flux by TMZ, we provide evidence for sequential treatment of TMZ followed by PX-866, rather than combined co-treatment, as a means to shut down autophagy-induced survival in GBM cells and to enhance apoptosis. Conclusions The understanding of how TMZ induces survival pathways, such as autophagy, may offer new therapeutic vulnerabilities and opportunities to use sequential inhibition of alternate pro-survival pathways that regulate autophagy. As such, identification of additional ways to inhibit TMZ-induced autophagy could enhance the efficacy of TMZ.
    Type of Medium: Online Resource
    ISSN: 1076-1551 , 1528-3658
    URL: Article
    DOI: 10.1186/s10020-019-0116-z
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2019
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    detail.hit.zdb_id: 1283676-X
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  • 3
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    Online Resource
    Uncertainty quantification in the radiogenomics modeling of EGFR amplification in glioblastoma
    Springer Science and Business Media LLC ; 2021
    In:  Scientific Reports Vol. 11, No. 1 ( 2021-02-16)
    Details
    In: Scientific Reports, Springer Science and Business Media LLC, Vol. 11, No. 1 ( 2021-02-16)
    Abstract: Radiogenomics uses machine-learning (ML) to directly connect the morphologic and physiological appearance of tumors on clinical imaging with underlying genomic features. Despite extensive growth in the area of radiogenomics across many cancers, and its potential role in advancing clinical decision making, no published studies have directly addressed uncertainty in these model predictions. We developed a radiogenomics ML model to quantify uncertainty using transductive Gaussian Processes (GP) and a unique dataset of 95 image-localized biopsies with spatially matched MRI from 25 untreated Glioblastoma (GBM) patients. The model generated predictions for regional EGFR amplification status (a common and important target in GBM) to resolve the intratumoral genetic heterogeneity across each individual tumor—a key factor for future personalized therapeutic paradigms. The model used probability distributions for each sample prediction to quantify uncertainty, and used transductive learning to reduce the overall uncertainty. We compared predictive accuracy and uncertainty of the transductive learning GP model against a standard GP model using leave-one-patient-out cross validation. Additionally, we used a separate dataset containing 24 image-localized biopsies from 7 high-grade glioma patients to validate the model. Predictive uncertainty informed the likelihood of achieving an accurate sample prediction. When stratifying predictions based on uncertainty, we observed substantially higher performance in the group cohort (75% accuracy, n = 95) and amongst sample predictions with the lowest uncertainty (83% accuracy, n = 72) compared to predictions with higher uncertainty (48% accuracy, n = 23), due largely to data interpolation (rather than extrapolation). On the separate validation set, our model achieved 78% accuracy amongst the sample predictions with lowest uncertainty. We present a novel approach to quantify radiogenomics uncertainty to enhance model performance and clinical interpretability. This should help integrate more reliable radiogenomics models for improved medical decision-making.
    Type of Medium: Online Resource
    ISSN: 2045-2322
    URL: Article
    DOI: 10.1038/s41598-021-83141-z
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2021
    detail.hit.zdb_id: 2615211-3
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