Phenotype stability under dynamic brain-tumor environment stimuli maps glioblastoma progression in patients

Unveiling the Dynamics behind Glioblastoma Multiforme Single-Cell Data Heterogeneity

Glioblastoma Multiforme is a brain tumor distinguished by its aggressiveness. We suggested that this aggressiveness leads single-cell RNA-sequence data (scRNA-seq) to span a representative portion of the cancer attractors domain. This conjecture allowed us to interpret the scRNA-seq heterogeneity as reflecting a representative trajectory within the attractor's domain. We considered factors such as genomic instability to characterize the cancer dynamics through stochastic fixed points. The fixed points were derived from centroids obtained through various clustering methods to verify our method sensitivity. This methodological foundation is based upon sample and time average equivalence, assigning an interpretative value to the data cluster centroids and supporting parameters estimation. We used stochastic simulations to reproduce the dynamics, and our results showed an alignment between experimental and simulated dataset centroids. We also computed the Waddington landscape, which provided a visual framework for validating the centroids and standard deviations as characterizations of cancer attractors. Additionally, we examined the stability and transitions between attractors and revealed a potential interplay between subtypes. These transitions might be related to cancer recurrence and progression, connecting the molecular mechanisms of cancer heterogeneity with statistical properties of gene expression dynamics. Our work advances the modeling of gene expression dynamics and paves the way for personalized therapeutic interventions.

Decoding molecular programs in melanoma brain metastases

Melanoma brain metastases (MBM) variably respond to therapeutic interventions; thus determining patient's prognosis. However, the mechanisms that govern therapy response are poorly understood. Here, we use a multi-OMICS approach and targeted sequencing (TargetSeq) to unravel the programs that potentially control the development of progressive intracranial disease. Molecularly, the expression of E-cadherin (Ecad) or NGFR, the BRAF mutation state and level of immune cell infiltration subdivides tumors into proliferative/pigmented and invasive/stem-like/therapy-resistant irrespective of the intracranial location. The analysis of MAPK inhibitor-naive and refractory MBM reveals switching from Ecad-associated into NGFR-associated programs during progression. NGFR-associated programs control cell migration and proliferation via downstream transcription factors such as SOX4. Moreover, global methylome profiling uncovers 46 differentially methylated regions that discriminate BRAF^mut and wildtype MBM. In summary, we propose that the expression of Ecad and NGFR sub- classifies MBM and suggest that the Ecad-to-NGFR phenotype switch is a rate-limiting process which potentially indicates drug-response and intracranial progression states in melanoma patients.

Spatial organization of heterogeneous immunotherapy target antigen expression in high-grade glioma

High-grade (WHO grades III-IV) glioma remains one of the most lethal human cancers. Adoptive transfer of tumor-targeting chimeric antigen receptor (CAR)-redirected T cells for high-grade glioma has revealed promising indications of anti-tumor activity, but objective clinical responses remain elusive for most patients. A significant challenge to effective immunotherapy is the highly heterogeneous structure of these tumors, including large variations in the magnitudes and distributions of target antigen expression, observed both within individual tumors and between patients. To obtain a more detailed understanding of immunotherapy target antigens within patient tumors, we immunochemically mapped at single cell resolution three clinically-relevant targets, IL13Rα2, HER2 and EGFR, on tumor samples drawn from a 43-patient cohort. We observed that within individual tumor samples, expression of these antigens was neither random nor uniform, but rather that they mapped into local neighborhoods - phenotypically similar cells within regions of cellular tumor - reflecting not well understood properties of tumor cells and their milieu. Notably, tumor cell neighborhoods of high antigen expression were not arranged independently within regions. For example, in cellular tumor regions, neighborhoods of high IL13Rα2 and HER2 expression appeared to be reciprocal to those of EGFR, while in areas of pseudopalisading necrosis, expression of IL13Rα2 and HER2, but not EGFR, appeared to reflect the radial organization of tumor cells around hypoxic cores. Other structural features affecting expression of immunotherapy target antigens remain to be elucidated. This structured but heterogeneous organization of antigen expression in high grade glioma is highly permissive for antigen escape, and combinatorial antigen targeting is a commonly suggested potential mitigating strategy. Deeper understanding of antigen expression within and between patient tumors will enhance optimization of combination immunotherapies, the most immediate clinical application of the observations presented here being the importance of including (wild-type) EGFR as a target antigen.

Glucose fluxes in glycolytic and oxidative pathways detected in vivo by deuterium magnetic resonance spectroscopy reflect proliferation in mouse glioblastoma

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We performed dynamic glucose enhanced (DGE) ²H-MRS in mouse GBM tumors.

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Marchenko-Pastur PCA denoising of ²H-MRS spectra improved kinetic quantification.

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Metabolic kinetics revealed differential glucose pathway fluxes in non-necrotic tumors.

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Modulation of glucose metabolism reflected tumor heterogeneity (proliferation).

Objectives

Glioblastoma multiforme (GBM), the most aggressive glial brain tumors, can metabolize glucose through glycolysis and mitochondrial oxidation pathways. While specific dependencies on those pathways are increasingly associated with treatment response, detecting such GBM subtypes in vivo remains elusive. Here, we develop a dynamic glucose-enhanced deuterium spectroscopy (DGE ²H-MRS) approach for differentially assessing glucose turnover rates through glycolysis and mitochondrial oxidation in mouse GBM and explore their association with histologic features of the tumor and its microenvironment.

Materials and methods

GL261 and CT2A glioma allografts were induced in immunocompetent mice and scanned in vivo at 9.4 Tesla, harnessing DGE ²H-MRS with volume selection and Marchenko-Pastur PCA (MP-PCA) denoising to achieve high temporal resolution. Each tumor was also classified by histopathologic analysis and assessed for cell proliferation (Ki67 immunostaining), while the respective cell lines underwent in situ extracellular flux analysis to assess mitochondrial function.

Results

MP-PCA denoising of in vivo DGE ²H-MRS data significantly improved the time-course detection (~2-fold increased Signal-to-Noise Ratio) and fitting precision (−19 ± 1 % Cramér-Rao Lower Bounds) of ²H-labelled glucose, and glucose-derived glutamate-glutamine (Glx) and lactate pools in GL261 and CT2A orthotopic tumors. Kinetic modeling further indicated inter-tumor heterogeneity of glucose consumption rate for glycolysis and oxidation during a defined epoch of active proliferation in both cohorts (19 ± 1 days post-induction), with consistent volumes (38.3 ± 3.4 mm³) and perfusion properties prior to marked necrosis. Histopathologic analysis of these tumors revealed clear differences in tumor heterogeneity between the two GBM models, aligned with metabolic differences of the respective cell lines monitored in situ. Importantly, glucose oxidation (i.e. Glx synthesis and elimination rates: 0.40 ± 0.08 and 0.12 ± 0.03 mM min⁻¹, respectively) strongly correlated with cell proliferation across the pooled cohorts (R = 0.82, p = 0.001; and R = 0.80, p = 0.002, respectively), regardless of tumor morphologic features or in situ metabolic characteristics of each GBM model.

Conclusions

Our fast DGE ²H-MRS enables the quantification of glucose consumption rates through glycolysis and mitochondrial oxidation in mouse GBM, which is relevant for assessing their modulation in vivo according to tumor microenvironment features such as cell proliferation. This novel application augurs well for non-invasive metabolic characterization of glioma or other cancers with mitochondrial oxidation dependencies.

Enhanced proton treatment with a LDLR-ligand peptide-conjugated gold nanoparticles targeting the tumor microenvironment in an infiltrative brain tumor model

The tumor microenvironment (TME) of glioblastoma malforms (GBMs) contains tumor invasiveness factors, microvascular proliferation, migratory cancer stem cells and infiltrative tumor cells, which leads to tumor recurrence in the absence of effective drug delivery in a Blood Brain Barrier (BBB)-intact TME and radiological invisibility. Low-density lipoprotein receptor (LDLR) is abundant in the blood brain barrier and overexpressed in malignant glioma cells. This study aimed to treat the TME with transmitted proton sensitization of LDLR ligand-functionalized gold nanoparticles (ApoB@AuNPs) in an infiltrative F98 glioma rat model. BBB-crossing ApoB@AuNPs were selectively taken up in microvascular endothelial cells proliferation and pericyte invasion, which are therapeutic targets in the glioma TME. Proton sensitization treated the TME and bulk tumor volume with enhanced therapeutic efficacy by 67-75% compared to that with protons alone. Immunohistochemistry demonstrated efficient treatment of endothelial cell proliferation and migratory tumor cells of invasive microvessels in the TME with saving normal tissues. Taken together, these data indicate that the use of LDLR ligand-functionalized gold nanoparticles is a promising strategy to treat infiltrative malignant glioma while overcoming BBB crossing.

Tracking the Adaptation and Compensation Processes of Patients' Brain Arterial Network to an Evolving Glioblastoma

The brain's vascular network dynamically affects its development and core functions. It rapidly responds to abnormal conditions by adjusting properties of the network, aiding stabilization and regulation of brain activities. Tracking prominent arterial changes has clear clinical and surgical advantages. However, the arterial network functions as a system; thus, local changes may imply global compensatory effects that could impact the dynamic progression of a disease. We developed automated personalized system-level analysis methods of the compensatory arterial changes and mean blood flow behavior from a patient's clinical images. By applying our approach to data from a patient with aggressive brain cancer compared with healthy individuals, we found unique spatiotemporal patterns of the arterial network that could assist in predicting the evolution of glioblastoma over time. Our personalized approach provides a valuable analysis tool that could augment current clinical assessments of the progression of glioblastoma and other neurological disorders affecting the brain.

A deep convolutional neural network for segmentation of whole-slide pathology images identifies novel tumour cell-perivascular niche interactions that are associated with poor survival in glioblastoma

BACKGROUND

Glioblastoma is the most aggressive type of brain cancer with high-levels of intra- and inter-tumour heterogeneity that contribute to its rapid growth and invasion within the brain. However, a spatial characterisation of gene signatures and the cell types expressing these in different tumour locations is still lacking.

METHODS

We have used a deep convolutional neural network (DCNN) as a semantic segmentation model to segment seven different tumour regions including leading edge (LE), infiltrating tumour (IT), cellular tumour (CT), cellular tumour microvascular proliferation (CTmvp), cellular tumour pseudopalisading region around necrosis (CTpan), cellular tumour perinecrotic zones (CTpnz) and cellular tumour necrosis (CTne) in digitised glioblastoma histopathological slides from The Cancer Genome Atlas (TCGA). Correlation analysis between segmentation results from tumour images together with matched RNA expression data was performed to identify genetic signatures that are specific to different tumour regions.

RESULTS

We found that spatially resolved gene signatures were strongly correlated with survival in patients with defined genetic mutations. Further in silico cell ontology analysis along with single-cell RNA sequencing data from resected glioblastoma tissue samples showed that these tumour regions had different gene signatures, whose expression was driven by different cell types in the regional tumour microenvironment. Our results further pointed to a key role for interactions between microglia/pericytes/monocytes and tumour cells that occur in the IT and CTmvp regions, which may contribute to poor patient survival.

CONCLUSIONS

This work identified key histopathological features that correlate with patient survival and detected spatially associated genetic signatures that contribute to tumour-stroma interactions and which should be investigated as new targets in glioblastoma. The source codes and datasets used are available in GitHub: https://github.com/amin20/GBM_WSSM .

Receptor Tyrosine Kinase Signaling and Targeting in Glioblastoma Multiforme

Glioblastoma multiforme (GBM) is amongst the deadliest of human cancers, with a median survival rate of just over one year following diagnosis. Characterized by rapid proliferation and diffuse infiltration into the brain, GBM is notoriously difficult to treat, with tumor cells showing limited response to existing therapies and eventually developing resistance to these interventions. As such, there is intense interest in better understanding the molecular alterations in GBM to guide the development of more efficient targeted therapies. GBM tumors can be classified into several molecular subtypes which have distinct genetic signatures, and they show aberrant activation of numerous signal transduction pathways, particularly those connected to receptor tyrosine kinases (RTKs) which control glioma cell growth, survival, migration, invasion, and angiogenesis. There are also non-canonical modes of RTK signaling found in GBM, which involve G-protein-coupled receptors and calcium channels. This review uses The Cancer Genome Atlas (TCGA) GBM dataset in combination with a data-mining approach to summarize disease characteristics, with a focus on select molecular pathways that drive GBM pathogenesis. We also present a unique genomic survey of RTKs that are frequently altered in GBM subtypes, as well as catalog the GBM disease association scores for all RTKs. Lastly, we discuss current RTK targeted therapies and highlight emerging directions in GBM research.

Glioma cells survival depends both on fatty acid oxidation and on functional carnitine transport by SLC22A5

Gliomas are the most common primary malignant brain tumor in adults, but current treatment for glioblastoma multiforme (GBM) is insufficient. Even though glucose is the primary energetic substrate of glioma cells, they are capable of using fatty acids to generate energy. Fatty acid oxidation (FAO) in mitochondria requires L-carnitine for the formation of acylcarnitines by carnitine palmitoylotransferase 1 (CPT1) and further transport of acyl carnitine esters to mitochondrial matrix. Carnitine can be delivered to the cell by an organic cation/carnitine transporter-SLC22A5/OCTN2. In this study, we show that SLC22A5 is up-regulated in glioma cells and that they vary in the amount of SLC22A5 in the plasma membrane. Research on glioma cells (lines U87MG, LN229, T98G) with various expression levels of SLC22A5 demonstrated a correlation between the FAO rate, the level of the transporter, and the carnitine transport. Inhibition of carnitine transport by chemotherapeutics, such as vinorelbine and vincristine, led to inhibition of FAO, which was further intensified by etomoxir-a CPT1 inhibitor. This led to reduced viability and increased apoptosis in glioma cells. Modulation of SLC22A5 level by either silencing or up-regulation of SLC22A5 also affected glioma cell survival in a FAO-dependent way. These observations suggest that the survival of glioma cells is heavily reliant on both FAO and SLC22A5 activity, as well as that CPT1 and SLC22A5 might be possible drug targets.