PET Imaging and Protein Expression of Prostate-Specific Membrane Antigen in Glioblastoma: A Multicenter Inventory Study.

Upregulation of prostate-specific membrane antigen (PSMA) in neovasculature has been described in glioblastoma multiforme (GBM), whereas vasculature in nonaffected brain shows hardly any expression of PSMA. It is unclear whether PSMA-targeting tracer uptake on PET is based on PSMA-specific binding to neovasculature or aspecific uptake in tumor. Here, we quantified uptake of various PSMA-targeting tracers in GBM and correlated this with PSMA expression in tumor biopsy samples from the same patients. Methods: Fourteen patients diagnosed with de novo (n = 8) or recurrent (n = 6) GBM underwent a preoperative PET scan after injection of 1.5 MBq/kg [68Ga]Ga-PSMA-11 (n = 7), 200 MBq of [18F]DCFpyl (n = 3), or 200 MBq of [18F]PSMA-1007 (n = 4). Uptake in tumor and tumor-to-background ratios, with contralateral nonaffected brain as background, were determined. In a subset of patients, PSMA expression levels from different regions in the tumor tissue samples (n = 40), determined using immunohistochemistry (n = 35) or RNA sequencing (n = 13), were correlated with tracer uptake on PET. Results: Moderate to high (SUVmax, 1.3-20.0) heterogeneous uptake was found in all tumors irrespective of the tracer type used. Uptake in nonaffected brain was low, resulting in high tumor-to-background ratios (6.1-359.0) calculated by dividing SUVmax of tumor by SUVmax of background. Immunohistochemistry showed variable PSMA expression on endothelial cells of tumor microvasculature, as well as on dispersed individual cells (of unknown origin), and granular staining of the neuropil. No correlation was found between in vivo uptake and PSMA expression levels (for immunohistochemistry, r = -0.173, P = 0.320; for RNA, r = -0.033, P = 0.915). Conclusion: Our results indicate the potential use of various PSMA-targeting tracers in GBM. However, we found no correlation between PSMA expression levels on immunohistochemistry and uptake intensity on PET. Whether this may be explained by methodologic reasons, such as the inability to measure functionally active PSMA with immunohistochemistry, tracer pharmacokinetics, or the contribution of a disturbed blood-brain barrier to tracer retention, should still be investigated.

Prognostic and Predictive Value of Integrated Qualitative and Quantitative Magnetic Resonance Imaging Analysis in Glioblastoma.

Glioblastoma (GBM) is the most malignant primary brain tumor for which no curative treatment options exist. Non-invasive qualitative (Visually Accessible Rembrandt Images (VASARI)) and quantitative (radiomics) imaging features to predict prognosis and clinically relevant markers for GBM patients are needed to guide clinicians. A retrospective analysis of GBM patients in two neuro-oncology centers was conducted. The multimodal Cox-regression model to predict overall survival (OS) was developed using clinical features with VASARI and radiomics features in isocitrate dehydrogenase (IDH)-wild type GBM. Predictive models for IDH-mutation, 06-methylguanine-DNA-methyltransferase (MGMT)-methylation and epidermal growth factor receptor (EGFR) amplification using imaging features were developed using machine learning. The performance of the prognostic model improved upon addition of clinical, VASARI and radiomics features, for which the combined model performed best. This could be reproduced after external validation (C-index 0.711 95% CI 0.64-0.78) and used to stratify Kaplan-Meijer curves in two survival groups (p-value < 0.001). The predictive models performed significantly in the external validation for EGFR amplification (area-under-the-curve (AUC) 0.707, 95% CI 0.582-8.25) and MGMT-methylation (AUC 0.667, 95% CI 0.522-0.82) but not for IDH-mutation (AUC 0.695, 95% CI 0.436-0.927). The integrated clinical and imaging prognostic model was shown to be robust and of potential clinical relevance. The prediction of molecular markers showed promising results in the training set but could not be validated after external validation in a clinically relevant manner. Overall, these results show the potential of combining clinical features with imaging features for prognostic and predictive models in GBM, but further optimization and larger prospective studies are warranted.

Mapping actionable pathways and mutations in brain tumours using targeted RNA next generation sequencing.

Many biology-based precision drugs are available that neutralize aberrant molecular pathways in cancer. Molecular heterogeneity and the lack of reliable companion diagnostic biomarkers for many drugs makes targeted treatment of cancer inaccurate for many individuals. Identifying actionable hyperactive biological pathways in individual cancers may improve this situation.To achieve this we applied a novel targeted RNA next generation sequencing (t/RNA-NGS) technique to surgically obtained glioma tissues. The test combines mutation detection with analysis of biological pathway activities that are involved in tumour behavior in many cancer types (e.g. tyrosine kinase signaling, angiogenesis signaling, immune response, metabolism), via quantitative measurement of transcript levels and splice variants of hundreds of genes. We here present proof of concept that the technique, which uses molecular inversion probes, generates a histology-independent molecular diagnosis and identifies classifiers that are strongly associated with conventional histopathology diagnoses and even with patient prognosis. The test not only confirmed known glioma-associated molecular aberrations but also identified aberrant expression levels of actionable genes and mutations that have so far been considered not to be associated with glioma, opening up the possibility of drug repurposing for individual patients. Its cost-effectiveness makes t/RNA-NGS to an attractive instrument to aid oncologists in therapy decision making.