Asbstract: Despite aggressive therapy, glioblastoma (GBM) recurs in almost all patients and treatment options are very limited. Despite our growing understanding of how cellular transitions associate with relapse in GBM, critical gaps remain in our ability to block these molecular changes and treat recurrent disease. In this study we combine computational biology, forward-thinking understanding of miRNA biology and cutting-edge nucleic acid delivery vehicles to advance targeted therapeutics for GBM. Computational analysis of RNA sequencing from clinical GBM specimens identified TGFβ type II receptor (TGFBR2) as a key player in the mesenchymal transition associated with worse outcome in GBM. Mechanistically, we show that elevated levels of TGFBR2 is conducive to reduced temozolomide (TMZ) sensitivity. This effect is, at least partially, induced by stem-cell driving events coordinated by the reprogramming transcription factors Oct4 and Sox2 that lead to open chromatin states. We show that blocking TGFBR2 via molecular and pharmacological approaches decreases stem cell capacity and sensitivity of clinical recurrent GBM (rGBM) isolates to TMZ in vitro. Network analysis uncovered miR-590-3p as a tumor suppressor that simultaneously inhibits multiple oncogenic nodes downstream of TGFBR2. We also developed novel biodegradable lipophilic poly(β-amino ester) nanoparticles (LiPBAEs) for in vivo microRNA (miRNAs) delivery. Following direct intra-tumoral infusion, these nanomiRs efficiently distribute through the tumors. Importantly, miR-590-3p nanomiRs inhibited the growth and extended survival of mice bearing orthotopic human rGBM xenografts, with an apparent 30% cure rate. These results show that miRNA-based targeted therapeutics provide new opportunities to treat rGBM and bypass the resistance to standard of care therapy.

Attempts to activate an anti-tumor immune response in glioblastoma (GBM) have been met with many challenges due to its inherently immunosuppressive tumor microenvironment. The degree and mechanisms by which molecularly and phenotypically diverse tumor-propagating glioma stem cells (GSCs) contribute to this state are poorly defined. In this study, our multifaceted approach combining bioinformatics analyses of clinical and experimental datasets, single-cell sequencing, and the molecular and pharmacologic manipulation of patient-derived cells identified GSCs expressing immunosuppressive effectors mimicking regulatory T cells (Tregs). We showed that this immunosuppressive Treg-like (ITL) GSC state is specific to the mesenchymal GSC subset and is associated with and driven specifically by TGFβ type II receptor (TGFBR2) in contrast to TGFBR1. Transgenic TGFBR2 expression in patient-derived GBM neurospheres promoted a mesenchymal transition and induced a six-gene ITL signature consisting of CD274 (PD-L1), NT5E (CD73), ENTPD1 (CD39), LGALS1 (galectin-1), PDCD1LG2 (PD-L2), and TGFB1. This TGFBR2-driven ITL signature was identified in clinical GBM specimens, patient-derived GSCs, and systemic mesenchymal malignancies. TGFBR2^high GSCs inhibited CD4+ and CD8+ T cell viability and their capacity to kill GBM cells, effects reversed by pharmacologic and shRNA-based TGFBR2 inhibition. Collectively, our data identify an immunosuppressive GSC state that is TGFBR2-dependent and susceptible to TGFBR2-targeted therapeutics.

Background/Objectives: CSCs are critical drivers of the tumor and stem cell phenotypes of glioblastoma (GBM) cells. Chromatin modifications play a fundamental role in driving a GBM CSC phenotype. The goal of this study is to further our understanding of how stem cell-driving events control changes in chromatin architecture that contribute to the tumor-propagating phenotype of GBM. Methods: We utilized computational analyses to identify a subset of clinically relevant genes that were predicted to be repressed in a Polycomb repressive complex 2 (PRC2)-dependent manner in GBM upon induction of stem cell-driving events. These associations were validated in patient-derived GBM neurosphere models using state-of-the-art molecular techniques to express, silence, and measure microRNA (miRNA) and gene expression changes. Advanced Poly(β-amino ester) nanoparticle formulations (PBAEs) were used to deliver miRNAs in vivo to orthotopic human GBM tumor models. Results: We show that glioma stem cell (GSC) formation and tumor propagation involve the crosstalk between multiple epigenetic mechanisms, resulting in the repression of the miRNAs that regulate PRC2 function and histone H3 lysine 27 tri-methylation (H3K27me3). We also identified miR-217-5p as an EZH2 regulator repressed in GSCs and showed that miR-217-5p reconstitution using advanced nanoparticle formulations re-activates the PRC2-repressed genes, inhibits GSC formation, impairs tumor growth, and enhances the effects of ionizing radiation in an orthotopic model of GBM. Conclusions: These findings suggest that inhibiting PRC2 function by targeting EZH2 with miR-217-5p advanced nanoparticle formulations could have a therapeutic benefit in GBM.

VIPoma is a rare neuroendocrine tumor (NET) that is challenging to diagnose. While VIP concentrations are elevated in VIPoma, the optimal threshold for diagnostic purposes is not well defined. We aimed to study this in a single-institution population. We obtained results from vasoactive intestinal peptide (VIP) tests from 2011 to 2023 and reviewed the medical record of patients who had concentrations greater than our established assay-specific reference limit of 75 pg/mL. We compared plasma VIP concentrations between patient cohorts with and without VIPoma and determined the optimal threshold for VIP concentrations to predict a VIPoma in this population. Seventy-six patients met the selection criteria. Of these, nine cases of VIPoma were diagnosed. All patients had chronic diarrhea, and five patients had a previous diagnosis of a pancreatic neuroendocrine tumor (PNET). VIP concentrations drawn for acute/episodic diarrhea or flushing/diaphoresis did not lead to a diagnosis of a VIPoma. Mean VIP concentration was increased in patients with a VIPoma relative to those without, but the difference was not statistically significant (508 pg/mL vs 223 pg/mL, P = 0.31). Using the threshold of 75 pg/mL, the positive predictive value for a VIPoma was 12%. The optimal VIP threshold was 442 pg/mL (OR: 11.96, 95% CI: 2.00–79.69, P = 0.01), with statistically significant odds ratios starting at 200 pg/mL. Our findings suggest that elevated VIP concentrations are not predictive for a VIPoma and most patients with elevated VIP do not have a VIPoma. We recommend that VIP only be drawn in certain clinical scenarios to avoid unnecessary medical investigations.

The objective of this study was to understand the utility of artificial intelligence-enabled electrocardiogram (AI-ECG) to assess the tolerability of anthracycline chemotherapy. From December 18, 2006 to October 15, 2020, we identified adults with sarcoma who were treated with anthracycline chemotherapy at our institution who had an ECG within 1 year prior to treatment initiation. Utilizing previously defined AI-ECG nomograms, we obtained age and ejection fraction (EF) predictions. Changes in AI-ECG age were correlated with chemotherapy tolerance (the rates of dose reductions, treatment delays, and early discontinuation). We measured the sensitivity and specificity of the ECG to predict an EF of less than 50% or 35% prior to treatment and compared how changes in the AI-ECG EF prediction related to changes in echocardiography-based EF. Forty patients met the eligibility criteria. Sixty-eight percent of the patients were men. The median age was 56.5 years (18-76 years). We did not find differences in chemotherapy tolerance between patients who had an elevated or decreased ECG age. There was a trend `toward higher rates of dose reductions in patients with high ECG aging (odds ratio, 5.13; P=.32). The AI-ECG low EF prediction had a sensitivity of 100% and a specificity of 94% to isolate patients with an EF of less than 50% prior to treatment. Two patients’ EF decreased more than 10% after treatment, and both cases showed significant increases in the low EF prediction. Overall, AI-based predictions on ECG tracings could be a way to monitor for decreases in EF during treatment with anthracycline chemotherapy. We recommend further studies to evaluate AI-ECG aging as a marker for chemotherapy tolerance.