DICER governs characteristics of glioma stem cells and the resulting tumors in xenograft mouse models of glioblastoma

Hyperpolarized Magnetic Resonance Imaging, Nuclear Magnetic Resonance Metabolomics, and Artificial Intelligence to Interrogate the Metabolic Evolution of Glioblastoma

Glioblastoma (GBM) is a malignant Grade VI cancer type with a median survival duration of only 8-16 months. Earlier detection of GBM could enable more effective treatment. Hyperpolarized magnetic resonance spectroscopy (HPMRS) could detect GBM earlier than conventional anatomical MRI in glioblastoma murine models. We further investigated whether artificial intelligence (A.I.) could detect GBM earlier than HPMRS. We developed a deep learning model that combines multiple modalities of cancer data to predict tumor progression, assess treatment effects, and to reconstruct in vivo metabolomic information from ex vivo data. Our model can detect GBM progression two weeks earlier than conventional MRIs and a week earlier than HPMRS alone. Our model accurately predicted in vivo biomarkers from HPMRS, and the results inferred biological relevance. Additionally, the model showed potential for examining treatment effects. Our model successfully detected tumor progression two weeks earlier than conventional MRIs and accurately predicted in vivo biomarkers using ex vivo information such as conventional MRIs, HPMRS, and tumor size data. The accuracy of these predictions is consistent with biological relevance.

DICER1 mutations in primary central nervous system tumors: new insights into histologies, mutations, and prognosis

PURPOSE

We sought to characterize clinical outcomes for adult and pediatric patients with primary CNS tumors harboring DICER1 mutations or loss of DICER1.

METHODS

We conducted a retrospective cohort study of 98 patients who were treated between 1995 and 2020 for primary CNS tumors containing DICER1 mutations or loss of DICER1 on chromosome 14q, identified by targeted next generation sequencing. Kaplan-Meier plots and log rank tests were used to analyze survival. Cox proportional-hazards model was used for univariate and multivariable analyses for all-cause mortality (ACM).

RESULTS

Within our cohort, the most common malignancies were grade 3/4 glioma (61%), grade 1/2 glioma (17%), and CNS sarcoma (6%). Sarcoma and non-glioma histologies, and tumors with biallelic DICER1 mutations or deletions were common in the pediatric population. Mutations occurred throughout DICER1, including missense mutations in the DexD/H-box helicase, DUF283, RNaseIIIa, and RNaseIIIb domains. For patients with grade 3/4 glioma, MGMT methylation (Hazard ratio [HR] 0.35, 95% Confidence Interval [CI] 0.16-0.73, p = 0.005), IDH1 R132 mutation (HR 0.11, 95% CI 0.03-0.41, p = 0.001), and missense mutation in the DexD/H-box helicase domain (HR 0.06, 95% CI 0.01-0.38, p = 0.003) were independently associated with longer time to ACM on multivariable analyses.

CONCLUSION

DICER1 mutations or loss of DICER1 occur in diverse primary CNS tumors, including previously unrecognized grade 3/4 gliomas as the most common histology. While prior studies have described RNaseIIIb hotspot mutations, we document novel mutations in additional DICER1 functional domains. Within the grade 3/4 glioma cohort, missense mutation in the DexD/H-box helicase domain was associated with prolonged survival.

The Role of MicroRNAs in Therapeutic Resistance of Malignant Primary Brain Tumors

Brain tumors in children and adults are challenging tumors to treat. Malignant primary brain tumors (MPBTs) such as glioblastoma have very poor outcomes, emphasizing the need to better understand their pathogenesis. Developing novel strategies to slow down or even stop the growth of brain tumors remains one of the major clinical challenges. Modern treatment strategies for MPBTs are based on open surgery, chemotherapy, and radiation therapy. However, none of these treatments, alone or in combination, are considered effective in controlling tumor progression. MicroRNAs (miRNAs) are 18-22 nucleotide long endogenous non-coding RNAs that regulate gene expression at the post-transcriptional level by interacting with 3'-untranslated regions (3'-UTR) of mRNA-targets. It has been proven that miRNAs play a significant role in various biological processes, including the cell cycle, apoptosis, proliferation, differentiation, etc. Over the last decade, there has been an emergence of a large number of studies devoted to the role of miRNAs in the oncogenesis of brain tumors and the development of resistance to radio- and chemotherapy. Wherein, among the variety of molecules secreted by tumor cells into the external environment, extracellular vesicles (EVs) (exosomes and microvesicles) play a special role. Various elements were found in the EVs, including miRNAs, which can be transported as part of these EVs both between neighboring cells and between remotely located cells of different tissues using biological fluids. Some of these miRNAs in EVs can contribute to the development of resistance to radio- and chemotherapy in MPBTs, including multidrug resistance (MDR). This comprehensive review examines the role of miRNAs in the resistance of MPBTs (e.g., high-grade meningiomas, medulloblastoma (MB), pituitary adenomas (PAs) with aggressive behavior, and glioblastoma) to chemoradiotherapy and pharmacological treatment. It is believed that miRNAs are future therapeutic targets in MPBTs and such the role of miRNAs needs to be critically evaluated to focus on solving the problems of resistance to therapy this kind of human tumors.

Measuring the Metabolic Evolution of Glioblastoma throughout Tumor Development, Regression, and Recurrence with Hyperpolarized Magnetic Resonance

Rapid diagnosis and therapeutic monitoring of aggressive diseases such as glioblastoma can improve patient survival by providing physicians the time to optimally deliver treatment. This research tested whether metabolic imaging with hyperpolarized MRI could detect changes in tumor progression faster than conventional anatomic MRI in patient-derived glioblastoma murine models. To capture the dynamic nature of cancer metabolism, hyperpolarized MRI, NMR spectroscopy, and immunohistochemistry were performed at several time-points during tumor development, regression, and recurrence. Hyperpolarized MRI detected significant changes of metabolism throughout tumor progression whereas conventional MRI was less sensitive. This was accompanied by aberrations in amino acid and phospholipid lipid metabolism and MCT1 expression. Hyperpolarized MRI can help address clinical challenges such as identifying malignant disease prior to aggressive growth, differentiating pseudoprogression from true progression, and predicting relapse. The individual evolution of these metabolic assays as well as their correlations with one another provides context for further academic research.