Genome-wide shRNA screen revealed integrated mitogenic signaling between dopamine receptor D2 (DRD2) and epidermal growth factor receptor (EGFR) in glioblastoma

Glioblastoma remains one of the deadliest of human cancers, with most patients succumbing to the disease within two years of diagnosis. The available data suggest that simultaneous inactivation of critical nodes within the glioblastoma molecular circuitry will be required for meaningful clinical efficacy. We conducted parallel genome-wide shRNA screens to identify such nodes and uncovered a number of G-Protein Coupled Receptor (GPCR) neurotransmitter pathways, including the Dopamine Receptor D2 (DRD2) signaling pathway. Supporting the importance of DRD2 in glioblastoma, DRD2 mRNA and protein expression were elevated in clinical glioblastoma specimens relative to matched non-neoplastic cerebrum. Treatment with independent si-/shRNAs against DRD2 or with DRD2 antagonists suppressed the growth of patient-derived glioblastoma lines both in vitro and in vivo. Importantly, glioblastoma lines derived from independent genetically engineered mouse models (GEMMs) were more sensitive to haloperidol, an FDA approved DRD2 antagonist, than the premalignant astrocyte lines by approximately an order of magnitude. The pro-proliferative effect of DRD2 was, in part, mediated through a GNAI2/Rap1/Ras/ERK signaling axis. Combined inhibition of DRD2 and Epidermal Growth Factor Receptor (EGFR) led to synergistic tumoricidal activity as well as ERK suppression in independent in vivo and in vitro glioblastoma models. Our results suggest combined EGFR and DRD2 inhibition as a promising strategy for glioblastoma treatment

Quantitative Radiographic Measures Derived from Automatic Segmentation of Glioblastoma Medical Imaging Associate with Patient Survival and Tumor Genomics

Glioblastoma, the most common and deadly form of primary brain cancer, is characterized by rapid progression, heterogeneity, and defiance of therapy. The relentless nature of glioblastoma emphasizes the urgency of identifying improved methods to hasten the development of tailored treatments for patients afflicted by this malignancy. Genetic profiling of clinical glioblastoma specimens has revealed that glioblastoma, like other cancers, is composed of many different subtypes that may possess unique sensitivities to therapeutics. To improve the clinical outcome of glioblastoma patients, technologies must be developed to better define and discriminate the subtypes of glioblastomas in an affordable, accurate, and noninvasive manner. The heterogeneity of glioblastoma's genomic and molecular alterations mirror the diversity of its appearance in medical imaging. The emerging field of radiogenomics integrates methods common to neuroimaging, bioinformatics, and molecular biology to identify the radiographic correlates of tumor cellular and molecular processes. Application of radiogenomics to the study of glioblastoma may facilitate its understanding, especially when considering that magnetic resonance (MR) imaging is required for the modern clinical management of this disease. Unfortunately, radiogenomic progress demands accurate and high-throughput methods to reliably segment features from vast and varied imaging archives, and the careful design of metrics which capture biological phenotypes. In this context, we developed a robust algorithm for tumor segmentation and radiophenotype parameterization termed Iterative Probabilitic Voxel Labeling (IPVL). Application of IPVL to glioblastoma tumor images from The Cancer Imaging Archive (TCIA) with associated genomic profiling available via The Cancer Genome Atlas (TCGA) led to the topographic mapping of glioblastoma spatial distributions by molecular subtype, and the discovery of two survival-associated radiographic parameters. These parameters, tumor subventricular distance (SVZd), and lateral ventricle displacement (LVd), correlate with defined physiologic mechanisms, and associated genomic profiles. Together, these results provide proof of principle that quantitative radiographic assessment of glioblastoma is a viable and effective strategy capable of augmenting the power of molecular and genomic research. With further study in the clinical setting, application of these methodologies and novel imaging parameters could impact prognostic evaluation, identify tumor therapeutic subgroups, and hopefully improve the lives of patient

Themes in neuronavigation research: A machine learning topic analysis

Objective: To understand trends in neuronavigation we employed machine learning methods to perform a broad literature review which would be impractical by manual inspection. Methods: PubMed was queried for articles with “Neuronavigation” in any field from inception–2020. Articles were designated neuronavigation-focused (NF) if “Neuronavigation” was a major MeSH. The latent dirichlet allocation topic modeling technique was used to identify themes of NF research. Results: There were 3896 articles of which 1727 (44%) were designated as NF. Between 1999–2009 and 2010–2020, the number of NF publications experienced 80% growth. Between 2009–2014 and 2015–2020, there was a 0.3% decline. Eleven themes covered 1367 (86%) NF articles. “Resection of Eloquent Lesions” comprised the highest number of articles (243), followed by “Accuracy and Registration” (242), “Patient Outcomes” (156), “Stimulation and Mapping” (126), “Planning and Visualization” (123), “Intraoperative Tools” (104), “Placement of Ventricular Catheters” (86), “Spine Surgery” (85), “New Systems” (80), “Guided Biopsies” (61), and “Surgical Approach” (61). All topics except for “Planning and Visualization”, “Intraoperative Tools”, and “New Systems” exhibited a monotonic positive trend. When analyzing subcategories, there were a greater number of clinical assessments or usage of existing neuronavigation systems (77%) rather than modification or development of new apparatuses (18%). Conclusion: NF research appears to focus on the clinical assessment of neuronavigation and to a lesser extent on the development of new systems. Although neuronavigation has made significant strides, NF research output appears to have plateaued in the last decade

High-Throughput Oil-Encapsulated Nanodroplet Crystallisation for Organic-Soluble Small Molecule Structure Elucidation and Polymorph Screening (ENaCt)

Single crystal X-ray diffraction analysis (SCXRD) constitutes a universal approach for the elucidation of molecular structure and for the study of crystalline forms. However, the discovery of viable crystallisation conditions remains both experimentally challenging and resource intensive, in time and quantity of analyte(s). We report a robot-assisted, high-throughput method for the crystallisation of organic-soluble small molecules, employing only micrograms of analyte per experiment. This allows hundreds of crystallisation conditions to be screened in parallel, with minimal overall sample requirements. Crystals suitable for SCXRD analysis are grown from nanolitre droplets of a solution of analyte in organic solvent(s), each of which is encapsulated within an inert oil to control the rate of solvent loss. This encapsulated nanodroplet crystallisation methodology can also be used in the search for new crystal forms, as exemplified through both our discovery of a new (thirteenth) polymorph of the olanzapine precursor ROY and the SCXRD analysis of the “uncrystallisable” agrochemical dithianon

Quantification of glioblastoma mass effect by lateral ventricle displacement

Abstract Mass effect has demonstrated prognostic significance for glioblastoma, but is poorly quantified. Here we define and characterize a novel neuroimaging parameter, lateral ventricle displacement (LVd), which quantifies mass effect in glioblastoma patients. LVd is defined as the magnitude of displacement from the center of mass of the lateral ventricle volume in glioblastoma patients relative to that a normal reference brain. Pre-operative MR images from 214 glioblastoma patients from The Cancer Imaging Archive (TCIA) were segmented using iterative probabilistic voxel labeling (IPVL). LVd, contrast enhancing volumes (CEV) and FLAIR hyper-intensity volumes (FHV) were determined. Associations with patient survival and tumor genomics were investigated using data from The Cancer Genome Atlas (TCGA). Glioblastoma patients had significantly higher LVd relative to patients without brain tumors. The variance of LVd was not explained by tumor volume, as defined by CEV or FLAIR. LVd was robustly associated with glioblastoma survival in Cox models which accounted for both age and Karnofsky’s Performance Scale (KPS) (p = 0.006). Glioblastomas with higher LVd demonstrated increased expression of genes associated with tumor proliferation and decreased expression of genes associated with tumor invasion. Our results suggest LVd is a quantitative measure of glioblastoma mass effect and a prognostic imaging biomarker

Molecular physiology of contrast enhancement in glioblastomas: An analysis of The Cancer Imaging Archive (TCIA).

The physiologic processes underlying MRI contrast enhancement in glioblastoma patients remain poorly understood. MRIs of 148 glioblastoma subjects from The Cancer Imaging Archive were segmented using Iterative Probabilistic Voxel Labeling (IPVL). Three aspects of contrast enhancement (CE) were parametrized: the mean intensity of all CE voxels (CEi), the intensity heterogeneity in CE (CEh), and volumetric ratio of CE to necrosis (CEr). Associations between these parameters and patterns of gene expression were analyzed using DAVID functional enrichment analysis. Glioma CpG island methylator phenotype (G-CIMP) glioblastomas were poorly enhancing. Otherwise, no differences in CE parameters were found between proneural, neural, mesenchymal, and classical glioblastomas. High CEi was associated with expression of genes that mediate inflammatory responses. High CEh was associated with increased expression of genes that regulate remodeling of extracellular matrix (ECM) and endothelial permeability. High CEr was associated with increased expression of genes that mediate cellular response to stressful metabolic states, including hypoxia and starvation. Our results indicate that CE in glioblastoma is associated with distinct biological processes involved in inflammatory response and tissue hypoxia. Integrative analysis of these CE parameters may yield meaningful information pertaining to the biologic state of glioblastomas and guide future therapeutic paradigms