Radiotherapy For Glioblastoma Research Articles

Dosimetric comparison of glioblastoma radiotherapy treatment plans on a low-field MRI-guided linear accelerator compared to conventional C-arm linear accelerator

Magnetic resonance imaging (MRI)-guided radiation therapy has proven to provide many benefits such as real-time tracking, dose escalation, and the ability to perform online adaptive therapy. The objective of this study is to compare curative treatment plans for glioblastoma tumors on a low-field MR-guided linac vs a C-arm linac and evaluate if they are comparable in terms of coverage, organ at risk sparing, delivery time, and deliverability. This is a retrospective study that consisted of 15 previously treated patients who received radiation therapy for glioblastoma on a C-arm linac. The CT simulation data used for the original clinical plans was imported into the MR-linac treatment planning system (TPS) and utilized for MR-linac plan generation. The plans were evaluated utilizing the dose volumetric histogram (DVH) and isodose lines, then compared in terms of plan quality consisting of PTV coverage, dose distributions, and OAR constraints. Statistical analysis was performed to compare differences between the two planning techniques. QA was performed on a subset of the plans to verify deliverability. Plans generated on the MR-linac were more heterogenous compared to C-arm linac plans. A statistically significant difference was found in the homogeneity index (HI) and the PTV V105% volume (cc) values. The volume of the normal brain receiving 30 Gy also showed a statistically significant (p = 0.0479) difference, where on average an additional 41.5 cc's of the normal brain tissue received 30 Gy in the MR-linac plans. The maximum dose to the normal brain structure also increased in the MR-linac plans on average by 2.6 Gy (p = 0.0002). Similarly, the average maximum dose to the scalp 4 mm structure was 6.5 Gy higher in the MR-linac plans compared to C-arm linac plans (p = 0.0103). The total MU's were higher in the MR-linac plans compared to the C-arm linac plans (p = 0.0015). Both MR-linac and C-arm linac plans met constraints for PTV coverage and OAR sparing, were deliverable, and resulted to be clinically acceptable. However, our study showed that MR-linac plans were not as conformal or as homogenous as C-arm linac plans utilizing noncoplanar beams.

EPCO-12. IN VIVO PERTURB-SEQ DEFINES BIOLOGICAL DRIVERS AND THERAPEUTIC VULNERABILITIES UNDERLYING RADIOTHERAPY RESISTANCE IN GLIOBLASTOMA

Abstract Glioblastomas are comprised of dynamic malignant cell states and microenvironment cell types. Mechanistic and functional studies of glioblastoma resistance to therapy have been limited by a lack of in vivo approaches for multiplexed interrogation of single cells. Here we use genome-wide and targeted CRISPR interference (CRISPRi) to identify therapeutic vulnerabilities in glioblastomas in vivo by coupling convection enhanced delivery (CED) with functional genomics and single-cell transcriptomics (perturb-seq). Human (GBM43, LN18) or mouse (GL261, SB28) glioblastomas expressing CRISPRi machinery were established intracranially in mice, and unmodified mouse glioblastomas were established intracranially in immunocompetent mice expressing CRISPRi machinery in the tumor microenvironment. Perturb-seq gene prioritization was performed using genome-wide CRISPRi screens ± radiotherapy in cell cultures. Dual-sgRNA Perturb-seq lentivirus libraries were transduced in vivo using CED, and single-cell RNA sequencing with sgRNA capture was performed ± radiotherapy. RESULTS: were validated using RNA sequencing of 254,288 single nuclei from 43 pairs of primary and recurrent patient-matched human glioblastomas. Perturb-seq ± radiotherapy of 98 genes underlying glioblastoma radiotherapy responses was performed across 414,092 single cells in vivo. Genetic perturbations reprogrammed tumor and microenvironment cell states in response to radiotherapy, and high dimensional manifolds revealed genetic dependencies in metabolic and DNA damage response pathways. The DNA-dependent protein kinase (DNA-PK) component PRKDC was identified as a driver of glioblastoma radiotherapy resistance through regulation of malignant cell-intrinsic growth and stress pathways, and regulation of malignant cell-extrinsic interferon pathways that controlled cell-cell interactions with the tumor microenvironment. PRKDC inhibition with nedisertib sensitized glioblastomas to radiotherapy and extended survival in vivo, and single-cell RNA sequencing revealed hyperactivation of cell stress and cytokine signatures with combination treatment. In summary, we report in vivo perturb-seq as a platform for simultaneous discovery and functional interrogation of therapeutic vulnerabilities in glioblastoma, and show DNA-PK inhibition sensitizes glioblastomas to radiotherapy in vivo.

INNV-13. PREDICTION OF MGMT METHYLATION FOR GLIOBLASTOMAS USING AI MODELS. A COST-EFFECTIVE FRAMEWORK FOR LOW AND MIDDLE-INCOME COUNTRIES

Abstract BACKGROUND MGMT methylation (MGMTmet) is prognostic and predictive for patients with glioblastoma (GBM). However, financial constraints limit access to molecular tests, especially in low and middle-income countries. Artificial intelligence (AI) tools have emerged as a potentially cost-effective solution for identifying MGMTmet. Therefore, we are investigating if an AI model could select those at higher risk to present positive results for molecular confirmation, thus making a better resource allocation. METHODS We have selected GBM patients from the public image bank, The Cancer Imaging Archive, and we included all patients with a molecular evaluation of MGMTmet. We defined our volume of interest based on the ESTRO-ACROP 2016 guideline for GBM radiotherapy contouring and then extracted the radiomic features. The collected features underwent structured analysis, and a prediction model for MGMTmet was developed. We used a Random Forest algorithm to identify the most relevant features. Subsequently, the dataset was divided into training and validation sets. RESULTS We examined 100 patients with histologically classified high-grade gliomas and wild-type IDH, Sixty-one were unmethylated, and 39 were methylated. In this cohort, over 39,000 features were extracted. To avoid noise and overfitting, the 13 most predictive features were selected. They belong to the following feature classes: demographic, first order, Gray Level Size Zone Matrix, and Gray Level Co-occurrence Matrix. The preliminary model achieved an accuracy of 80% for predicting MGMTmet. CONCLUSION our preliminary results show a promising role for AI models in screening patients for molecular testing. This initiative demonstrates the utilization of research to bridge the public health gap. By sharing experiences and fostering collaboration, physicians can work towards mitigating healthcare inequalities in low-middle-income countries. In our future perspectives, we plan to enhance the algorithm's efficacy by incorporating qualitative human-generated features and expanding our sample size.

O-GlcNAcylation of melanophilin enhances radiation resistance in glioblastoma via suppressing TRIM21 mediated ubiquitination.

The molecular mechanism of glioblastoma (GBM) radiation resistance remains poorly understood. The aim of this study was to elucidate the potential role of Melanophilin (MLPH) O-GlcNAcylation and the specific mechanism through which it regulates GBM radiotherapy resistance. We found that MLPH was significantly upregulated in recurrent GBM tumor tissues after ionizing radiation (IR). MLPH induced radiotherapy resistance in GBM cells and xenotransplanted human tumors through regulating the NF-κB pathway. MLPH was O-GlcNAcylated at the conserved serine 510, and radiation-resistant GBM cells showed higher levels of O-GlcNAcylation of MLPH. O-GlcNAcylation of MLPH protected its protein stability and tripartite motif containing 21(TRIM21) was identified as an E3 ubiquitin ligase promoting MLPH degradation whose interaction with MLPH was affected by O-GlcNAcylation. Our data demonstrate that MLPH exerts regulatory functions in GBM radiation resistance by promoting the NF-κB signaling pathway and that O-GlcNAcylation of MLPH both stabilizes and protects it from TRIM21-mediated ubiquitination. These results identify a potential mechanism of GBM radiation resistance and suggest a potential therapeutic strategy for GBM treatment.

A Deep Learning Approach for Automatic Segmentation during Daily MRI-Linac Radiotherapy of Glioblastoma.

Glioblastoma changes during chemoradiotherapy are inferred from high-field MRI before and after treatment but are rarely investigated during radiotherapy. The purpose of this study was to develop a deep learning network to automatically segment glioblastoma tumors on daily treatment set-up scans from the first glioblastoma patients treated on MRI-linac. Glioblastoma patients were prospectively imaged daily during chemoradiotherapy on 0.35T MRI-linac. Tumor and edema (tumor lesion) and resection cavity kinetics throughout the treatment were manually segmented on these daily MRI. Utilizing a convolutional neural network, an automatic segmentation deep learning network was built. A nine-fold cross-validation schema was used to train the network using 80:10:10 for training, validation, and testing. Thirty-six glioblastoma patients were imaged pre-treatment and 30 times during radiotherapy (n = 31 volumes, total of 930 MRIs). The average tumor lesion and resection cavity volumes were 94.56 ± 64.68 cc and 72.44 ± 35.08 cc, respectively. The average Dice similarity coefficient between manual and auto-segmentation for tumor lesion and resection cavity across all patients was 0.67 and 0.84, respectively. This is the first brain lesion segmentation network developed for MRI-linac. The network performed comparably to the only other published network for auto-segmentation of post-operative glioblastoma lesions. Segmented volumes can be utilized for adaptive radiotherapy and propagated across multiple MRI contrasts to create a prognostic model for glioblastoma based on multiparametric MRI.

Nicotinamide Adenine Dinucleotide Phosphate Oxidase Promotes Glioblastoma Radiation Resistance in a Phosphate and Tensin Homolog-Dependent Manner.

Aims: The goal of this study was to determine whether nicotinamide adenine dinucleotide phosphate (NADPH) oxidase (NOX)-produced reactive oxygen species (ROS) enhance brain tumor growth of glioblastoma (GBM) under hypoxic conditions and during radiation treatment. Results: Exogenous ROS promoted brain tumor growth in gliomasphere cultures that expressed functional phosphate and tensin homolog (PTEN), but not in tumors that were PTEN deficient. Hypoxia induced the production of endogenous cytoplasmic ROS and tumor cell growth via activation of NOX. NOX activation resulted in oxidation of PTEN and downstream protein kinase B (Akt) activation. Radiation also promoted ROS production via NOX, which, in turn, resulted in cellular protection that could be abrogated by knockdown of the key NOX component, p22. Knockdown of p22 also inhibited tumor growth and enhanced the efficacy of radiation in PTEN-expressing GBM cells. Innovation: While other studies have implicated NOX function in GBM models, this study demonstrates NOX activation and function under physiological hypoxia and following radiation in GBM, two conditions that are seen in patients. NOX plays an important role in a PTEN-expressing GBM model system, but not in PTEN-nonfunctional systems, and provides a potential, patient-specific therapeutic opportunity. Conclusion: This study provides a strong basis for pursuing NOX inhibition in PTEN-expressing GBM cells as a possible adjunct to radiation therapy. Antioxid. Redox Signal. 39, 890-903.

Shooting the messenger: a systematic review investigating extracellular vesicle isolation and characterisation methods and their influence on understanding extracellular vesicles-radiotherapy interactions in glioblastoma

BackgroundExtracellular vesicles (EVs) hold promise for improving our understanding of radiotherapy response in glioblastoma due to their role in intercellular communication within the tumour microenvironment (TME). However, methodologies to study EVs are evolving with significant variation within the EV research community.MethodsWe conducted a systematic review to critically appraise EV isolation and characterisation methodologies and how this influences our understanding of the findings from studies investigating radiotherapy and EV interactions in glioblastoma. 246 articles published up to 24/07/2023 from PubMed and Web of Science were identified using search parameters related to radiotherapy, EVs, and glioblastoma. Two reviewers evaluated study eligibility and abstracted data.ResultsIn 26 articles eligible for inclusion (16 investigating the effects of radiotherapy on EVs, five investigating the effect of EVs on radiation response, and five clinical studies), significant heterogeneity and frequent omission of key characterisation steps was identified, reducing confidence that the results are related to EVs and their cargo as opposed to co-isolated bioactive molecules. However, the results are able to clearly identify interactions between EVs and radiotherapy bi-directionally within different cell types within the glioblastoma TME. These interactions facilitate transferable radioresistance and oncogenic signalling, highlighting that EVs are an important component in the variability of glioblastoma radiotherapy response.ConclusionsFuture multi-directional investigations interrogating the whole TME are required to improve subsequent clinical translation, and all studies should incorporate up to date controls and reporting requirements to increase the validity of their findings. This would be facilitated by increased collaboration between less experienced and more experienced EV research groups.

Inter-fractional Assessment during MR-guided Online Adaptive Radiotherapy for Glioblastoma

Magnetic resonance image (MRI) guided radiation therapy has the potential to improve outcomes for glioblastoma by adapting to tumor changes during radiation therapy. This study aimed to assess the feasibility and potential benefits of MR-guided online adaptive radiotherapy (MRgOART) for patients with glioblastoma. Twenty consecutive patients with glioblastoma were treated with MRgOART of 60 Gy in 30 fractions by the 1.5 T MR-Linac. The MRgOART fractions employed daily MR scans and the contours were utilized to create each adapted plan. The gross tumor volume (GTV) and clinical target volume (CTV) were delineated on MRI of pre-treatment simulation (Fx0) and all fractions (Fx1, Fx2, Fx3 ... Fx30) to evaluate the inter-fractional changes. These changes were quantified using absolute/relative volume (∆V), Dice similarity coefficient (DSC) and Hausdorff distance (HD) metrics. The reference treatment plans were generated using step-and-shoot IMRT and utilized 7-9 beam groups on original CT. Before the treatment, a synthetic CT (sCT) quality assurance (QA) process was performed to assess the dose accuracy of bulk relative electron density (rED) assignment for online MRI based treatment plan in terms of gamma analysis, point dose comparison and dose volume histogram (DVH) parameters. Then, the online adaptative treatment plans were obtained by re-optimizing based on the contours on daily pre-treatment MRI by "adapt to shape" workflow. Non-adaptive plans for each patient were generated by recalculating the dose from the reference plans on daily online MRI by "adapt to position" workflow. GTV and CTV coverage and organ at risk (OAR) constraints were used to compare non-adaptive and adaptive plans. For both criteria, the 1%/1mm (98.58%±0.15%) and 2%/2mm (99.88%±0.18%) gamma passing rate results were always clinically acceptable in sCT QA process. The differences on point dose and DVH parameters between the plans based on sCT and original CT were less than 1%. A total of 20 patients with 600 fractions were evaluated. The results showed that large inter-fractional changes for GTV limited the efficacy of radiation therapy (DSC: 0.78±0.08, HD: 20.94±3.64mm, ∆V: 2.92%±6.36%). The inter-fractional CTV changes were smaller (DSC: 0.91±0.04, HD: 15.31±3.09mm, ∆V: 1.41%±1.29%). GTV coverage of non-adaptive plans was below the prescribed coverage in 228/600 fractions (38%), with 90 (15%) failing by more than 10%. For CTV coverage of non-adaptive plans, the changes were less than 5%. Online adaptative plans improved GTV and CTV coverage significantly (p<0.001) to 99%. The adaptive plans also had lower dose to whole brain than non-adaptive plans (p<0.001). Significant inter-fractional tumor changes could be found during radiotherapy in patients with glioblastoma treated by the 1.5 T MR-Linac. Daily MR-guided re-optimization of treatment plans corrected for day-to-day anatomical variations and resulted in adequate target coverage in all fractions.

Prognostic Value of Weekly Delta-Radiomics during MR-Linac Radiotherapy of Glioblastoma

MRI after chemoradiotherapy (chemoRT) shows areas of presumed tumor growth in ≤ 50% of glioblastoma (GBM) patients, which can be true progression (TP) - tumor growth with poor treatment response, or pseudoprogression (PP) - edema and tumor necrosis with favorable treatment response. Patients with TP have median overall survival (OS) of only 7 months, while patients with PP have median OS of 36 months. However, on imaging, TP and PP are usually not discernible during treatment, making it difficult to adapt radiation for poor responders. The purpose of this study was to investigate the prognostic value of delta radiomic features from MR-Linac for GBM. Using an IRB-approved prospective cohort of GBM patients undergoing 30 fractions of chemoRT to 60 Gy on a 0.35T MR-Linac, 2 regions of interest (ROI) were contoured on daily T2-weighted treatment set-up scans: 1) tumor/edema (lesion) and 2) post-surgical resection cavity (RC). The lesion ROI were used to calculate texture features: second order radiomics features based on the gray-level co-occurrence matrix (GLCM), gray-level size zone matrix (GLSZM), gray-level run length matrix (GLRLM), and neighborhood gray-tone difference matrix (NGTDM). Each of these describe the probability of spatial relationships of gray levels occurring within the ROI. Features from fraction 1 (pre-radiation) were subtracted from fractions 5, 10, 15, 25, and 30 to create delta features at 5 timepoints (D5-D30). Patient response was retrospectively defined as no progression (NP), TP, or PP. Supervised machine learning was utilized using a 500-tree random forest (RF) classification model with TP or PP as the outcome. Variable importance analysis was conducted by calculating the out-of-bag errors with multiple bootstrapped data sets. The most prognostic features were selected using the RF importance scores. Thirty-six patients were screened for inclusion: 9 were excluded due to no T2 lesion (RC ROI only). Of the remaining 27 patients: 10 had NP, 11 had TP, and 6 had PP. Thirty-nine texture features, plus lesion volume and mean lesion intensity (for a total of 41 variables per time point) were calculated and included in the model. Of the 10 most prognostic features, 6 were from D10, suggesting that prognostic changes in the underlying lesion microenvironment are occurring within the first 10 fractions of treatment. The model selected GLSZM high gray-level zone emphasis (HGZE) D10, IBSI code 5GN9, as the most prognostic feature. The receiver operator characteristic (ROC) area under the curve (AUC) for GLSZM HGZE D10 was 0.94 (95% CI = 0.81-1.00). Delta radiomic features extracted from MR-Linac imaging may predict between PP and TP in GBM patients during treatment, which is earlier than current methods. This could allow physicians to adapt/intensify treatment in real time for poorly responding patients. Future directions include analysis with a larger patient cohort and with additional MRI contrasts (MR-Linac multiparametric MRI).

A Multicenter Study on Deep Learning for Glioblastoma Auto-Segmentation with Prior Knowledge in Multimodal Imaging

A precise radiotherapy plan is required to ensure accurate delineation of gross tumor volumes (GTV) and clinical target volumes (CTV1 and CTV2) of glioblastomas (GBMs). However, traditional manual delineation is labor intensive and highly dependent on oncologists' experience. To construct and evaluate a deep learning-based automatic delineation method using prior knowledge in multimodal medical imaging to automate precise GTV, CTV1 and CTV2 contouring in GBM patients. We retrospectively collected the CT and MRI scans of 55 eligible patients with histologically proven high-grade glioma (HGG) from an institute, these scans were performed with non-enhanced CT (CT), contrast-enhanced T1-weighted (T1C) and T2-FLAIR (T2F) sequences. We proposed a two-stage automatic segmentation framework (PKMI-Net) for GTV, CTV1 and CTV2 based on deep learning using prior knowledge in multimodal medical imaging, and its segmentation performance was evaluated with dice similarity coefficient (DSC), 95% Harsdorff distance (HD95), average surface distance (ASD) and relative volume difference (RVD). To further investigate the generalizability of our method, we designed and conducted two evaluation strategies (Mix and Cross) on four multicenter datasets (including 55 patients, 37 patients, 21 patients and 35 patients). The evaluation results with an 11-patient test set from the single institute were summarized in Table 1, the proposed method demonstrated the best accuracy in segmenting, respectively, GTV, CTV1 and CTV, achieving a DSC of 0.94, 0.95 and 0.92; HD95 of 2.07 mm, 1.18 mm and 3.80 mm; ASD of 0.69 mm, 0.39 mm and 1.13 mm and RVE of 5.50%, 3.97% and 9.68%. In the multicenter evaluation, the segmentation performance of our method implemented with the Cross strategy was comparable to that with the Mix strategy, demonstrating that our method had high and stable generalizability across multicenter datasets in automatically segmenting GTV, CTV1 and CTV2. Our proposed method achieved promising results in automatically segmenting gliomas across various datasets, which could improve the quality and efficiency of glioblastoma radiotherapy.

Identifying Gene-Treatment Interactions and Targetable Radiation Vulnerabilities in Glioblastoma through Coupling of In Vivo CRISPR Perturbation and Single Cell Transcriptomics

Glioblastoma (GBM) is an incurable brain tumor comprised of dynamic malignant cell states and microenvironment components that underlie treatment resistance. Here we use genome-wide CRISPR/Cas9 functional genomics to define biological drivers and therapeutic vulnerabilities across human and mouse GBM models. To interrogate these mechanisms in the context of the tumor microenvironment and in vivo physiology, we established in vivo Perturb-seq intracranially, a technique coupling functional genomics with single cell transcriptomics, where each cell is an individual experiment. Orthotopic intracranial tumor models were established using human (GBM6, GBM43) or mouse (GL261, SB28) GBM cells stably expressing CRISPR interference (CRISPRi) machinery. Perturb-seq target selection for phenotyping of gene-treatment interactions was performed using genome-wide CRISPRi screens ± radiotherapy in cell cultures. Dual sgRNA lentivirus libraries were transduced either ex vivo prior to intracranial GBM cell transplantation or in vivo using intratumor convection enhanced delivery (CED). Transcriptional phenotyping was performed using single-cell RNA-seq with CRISPR direct capture following focal brain radiotherapy (2 Gy x 5) or mock treatment. GBM cell states were validated using single-nucleus RNA-seq data from 86 primary-recurrent patient-matched GBMs. Mechanistic and functional validation was performed using small molecule inhibitors, immunohistochemistry, clonogenic assays, and in vivo survival experiments. In vivo Perturb-seq ± radiotherapy of 48 genes underlying GBM radiotherapy responses, which were enriched for DNA damage response and metabolic pathways, was performed in > 425,000 single cells. Radiotherapy induced 16 distinct GBM cell states, and genetic perturbations reprogrammed these cell states in a treatment-dependent fashion. Quantitative modeling of gene/radiotherapy interactions using high dimensional manifolds revealed in vivo-specific genetic dependencies. We revealed a critical role for Prkdc, the catalytic subunit of DNA-dependent protein kinase (DNA-PK), as a radiotherapy sensitizer through regulation of cell intrinsic growth and oxidative stress pathways, and cell extrinsic interferon and signaling pathways that altered cell-cell interactions in vivo. These pathways were also disrupted in single-nucleus RNA-seq analysis of post-radiotherapy human GBM tumors. Inhibition of Prkdc using a Food and Drug Administration approved small molecule sensitized GBM cells to radiotherapy and extended survival in mice harboring human intracranial xenografts. We establish in vivo Perturb-seq in orthotopic GBM models as a platform for simultaneous functional genomic discovery and characterization of therapeutic targets, revealing an underappreciated role for Prkdc in GBM tumors in vivo that is targetable using small molecules. These tools are adaptable for a wide range of disease models and treatment modalities.

PREDICTORS OF TUMOR DYNAMICS DURING A COURSE OF 30-FRACTION RADIOTHERAPY FOR GLIOBLASTOMA

Abstract We aim to identify predictors of tumor dynamics during radiotherapy for glioblastoma. METHODS: This is a prospective serial MRI study in patients with glioblastoma. MRIs were obtained at radiotherapy planning (F0), fraction-10 (F10), and fraction-20 (F20). The tumor dynamics metrics (relative to F0) assessed included: relative gross tumor volume (GTV) changes (Vrel) and migration distance (dM). RESULTS: 129 patients were included in this study. Median GTV was 20.9cc at F0, 17.6cc at F10 (Vrel 0.85), and 16.1cc at F20 (Vrel 0.78). Patients with no corpus-callosum involvement (CCI) (vs with CCI) had more marked GTV volume reduction: Vrel 0.82 vs 1.02 at F10 (P=0.05), and Vrel 0.77 vs 0.88 at F20 (P=0.03). Patients with gross total resection (GTR) had more marked GTV volume reduction (vs subtotal resection (STR) vs biopsy (Bx)): Vrel 0.78, 0.85 and 1.07 at F10 (P=0.001), and Vrel 0.69, 0.80, 1.04 at F20 (P=0.001). The median dM were 4.7mm at both F10 and F20. Patients with CCI had larger dM (vs no CCI): 41% vs 12% had dM&amp;gt;10mm at F10 (P=0.01), and 45% vs 9% had dM&amp;gt;10mm at F20 (P&amp;lt;0.001). At F20, 0%, 25% and 19% of patients with GTR, STR and Bx had dM&amp;gt;10mm (P=0.002). Age, sex, MGMT methylation and IDH-mutation status were not associated with tumor dynamics during treatment. CONCLUSION: CCI and extent of surgery predict for tumor dynamics, which may allow us to better select patients who benefit the most from re-planning/ treatment adaptation during radiotherapy.

Macrophage Membrane‐Coated Nanoparticles Sensitize Glioblastoma to Radiation by Suppressing Proneural–Mesenchymal Transformation in Glioma Stem Cells

AbstractRadiotherapy is identified as a crucial treatment for patients with glioblastoma, but recurrence is inevitable. The efficacy of radiotherapy is severely hampered partially due to the tumor evolution. Growing evidence suggests that proneural glioma stem cells can acquire mesenchymal features coupled with increased radioresistance. Thus, a better understanding of mechanisms underlying tumor subclonal evolution may develop new strategies. Herein, data highlighting a positive correlation between the accumulation of macrophage in the glioblastoma microenvironment after irradiation and mesenchymal transdifferentiation in glioblastoma are presented. Mechanistically, elevated production of inflammatory cytokines released by macrophages promotes mesenchymal transition in an NF‐κB‐dependent manner. Hence, rationally designed macrophage membrane‐coated porous mesoporous silica nanoparticles (MMNs) in which therapeutic anti‐NF‐κB peptides are loaded for enhancing radiotherapy of glioblastoma are constructed. The combination of MMNs and fractionated irradiation results in the blockage of tumor evolution and therapy resistance in glioblastoma‐bearing mice. Intriguingly, the macrophage invasion across the blood‐brain barrier is inhibited competitively by MMNs, suggesting that these nanoparticles can fundamentally halt the evolution of radioresistant clones. Taken together, the biomimetic MMNs represent a promising strategy that prevents mesenchymal transition and improves therapeutic response to irradiation as well as overall survival in patients with glioblastoma.

Improving Localized Radiotherapy for Glioblastoma via Small Molecule Inhibition of KIF11.

Glioblastoma, IDH-wild type (GBM) is the most common and lethal malignant primary brain tumor. Standard of care includes surgery, radiotherapy, and chemotherapy with the DNA alkylating agent temozolomide (TMZ). Despite these intensive efforts, current GBM therapy remains mainly palliative with only modest improvement achieved in overall survival. With regards to radiotherapy, GBM is ranked as one of the most radioresistant tumor types. In this study, we wanted to investigate if enriching cells in the most radiosensitive cell cycle phase, mitosis, could improve localized radiotherapy for GBM. To achieve cell cycle arrest in mitosis we used ispinesib, a small molecule inhibitor to the mitotic kinesin, KIF11. Cell culture studies validated that ispinesib radiosensitized patient-derived GBM cells. In vivo, we validated that ispinesib increased the fraction of tumor cells arrested in mitosis as well as increased apoptosis. Critical for the translation of this approach, we validated that combination therapy with ispinesib and irradiation led to the greatest increase in survival over either monotherapy alone. Our data highlight KIF11 inhibition in combination with radiotherapy as a new combinatorial approach that reduces the overall radioresistance of GBM and which can readily be moved into clinical trials.

Comparison of Dose Distribution to Target Volume and Organs at Risk using 3D CRT, IMRT, and VMAT Techniques in Glioblastoma Cases

The average lifetime risk of secondary cancer after irradiating glioblastoma radiation therapy to healthy organs shows that the technique of volumetric modulated arc therapy (VMAT) has a smaller risk of developing secondary cancer compared to intensity modulated radiation therapy (IMRT) and three-dimensional radiation therapy (3DCRT). Parameters of dose distribution, namely the target volume and dose received by the organs at risk (OAR), are used to compare these three techniques. The distribution of doses from TPS results in the form of DVH and isodose in the 3D CRT, IMRT, and VMAT techniques shows that the distribution of doses to healthy organs around the glioblastoma irradiation area is in the safe category and is still within tolerance limits. With VMAT, the PTV and CTV dose results were more optimal compared to the 3D CRT and IMRT techniques. 3D CRT showed a PTV dose value of 5551.8 cGy and a CTV of 5515.3 cGy. IMRT shows a PTV dose value of 6035.0 cGy and a CTV of 6018.8 cGy. VMAT shows PTV dose values of 6101.8 cGy and CTV of 6044.7 cGy. It can be seen that the distribution of doses to healthy organs in general in the VMAT technique is more optimal than the IMRT and 3D CRT techniques in protecting OAR.

Isotoxic dose escalated radiotherapy for glioblastoma based on diffusion-weighted MRI and tumor control probability-an in-silico study.

Glioblastoma (GBM) is the most common malignant primary brain tumor with local recurrence after radiotherapy (RT), the most common mode of failure. Standard RT practice applies the prescription dose uniformly across tumor volume disregarding radiological tumor heterogeneity. We present a novel strategy using diffusion-weighted (DW-) MRI to calculate the cellular density within the gross tumor volume (GTV) in order to facilitate dose escalation to a biological target volume (BTV) to improve tumor control probability (TCP). The pre-treatment apparent diffusion coefficient (ADC) maps derived from DW-MRI of ten GBM patients treated with radical chemoradiotherapy were used to calculate the local cellular density based on published data. Then, a TCP model was used to calculate TCP maps from the derived cell density values. The dose was escalated using a simultaneous integrated boost (SIB) to the BTV, defined as the voxels for which the expected pre-boost TCP was in the lowest quartile of the TCP range for each patient. The SIB dose was chosen so that the TCP in the BTV increased to match the average TCP of the whole tumor. By applying a SIB of between 3.60 Gy and 16.80 Gy isotoxically to the BTV, the cohort's calculated TCP increased by a mean of 8.44% (ranging from 7.19 to 16.84%). The radiation dose to organ at risk is still under their tolerance. Our findings indicate that TCPs of GBM patients could be increased by escalating radiation doses to intratumoral locations guided by the patient's biology (i.e., cellularity), moreover offering the possibility for personalized RT GBM treatments. A personalized and voxel level SIB radiotherapy method for GBM is proposed using DW-MRI, which can increase the tumor control probability and maintain organ at risk dose constraints.

Abstract 2825: Investigation of sponge property for enhanced concurrent tumor treating fields and radiotherapy for glioblastoma

Abstract Glioblastoma (GBM) is an aggressive primary brain tumor in adults featured with low overall survival (OS) of around 10-16 months, which is estimated to cause about 200,000 deaths related to the tumors of central nervous systems every year. Therefore, precise therapy on newly diagnosed GBM and increase median OS through concurrent tumor treating fields (TTFields) and chemoradiotherapy (the standard-of-care treatment proposed by NCCN) is of great significance. Though introducing latex-free sponge media was reported beneficial for improving concurrent TTFields and chemoradiotherapy efficacy on GBM, the specific enhancement mechanism and physical/chemical parameters of sponge media has not been studied as far as we know. Herein, different latex-free polyurethane sponges were selected based on SPARE phase II clinical research to investigate their impact on the concurrent TTFields and chemoradiotherapy efficacy on GBM. As computed tomography (CT) imaging of tumor and radiation dose are two vital factors for radiotherapy, the Hounsfield unit (HU) values of sponges were tested under Micro-CT firstly and the results suggest that the sponges with HU value lower than -750 have almost no impact on CT image, and clearer CT image is obtained upon the sponge with lower HU value. Moreover, eight clinical IMRT/VMAT plans were selected randomly and delivered into the bare (as a reference) or each-of-samples-surrounded CIRS head phantom with built-in EBT film to evaluate dosimetry effect. Film-based Gamma Passing Rate (2%/2 mm criteria, 20% threshold) analysis indicates that all the dose distributions upon three sponges (99.55±0.23, 99.54±0.22 and 99.59±0.19, respectively) are in good consistence with that over the reference (99.31%~99.98%). Importantly, the enhancement mechanism of latex-free sponge on concurrent TTFields and radiotherapy efficacy was studied and it corroborated that the introduced sponge could reduce the artifacts of target delineation generated in the presence of transducer array, decrease the position error of transducer array movement/replacement on scalp, lower constrictive injury during TTFields therapy (especially for locating transducer array position upon CT imaging), and reduce the adverse event of TTFields including irritant or allergic contact dermatitis caused by transducer array in the period of radiotherapy. This work corroborates that introducing low-HU-value sponge media into concurrent TTFields and chemoradiotherapy for GBM is a promising strategy to enhance therapy efficacy and thus improve or maintain the quality of patients’ life. Citation Format: Jiajun Zheng, Huanfeng Zhu, Wenjie Guo, Jiahao Guo, Chechen Cao, Li Sun, Geng Xu, Zhi Wang, Baoying Dai, Ning Gu, Xia He. Investigation of sponge property for enhanced concurrent tumor treating fields and radiotherapy for glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2825.

STING agonist-loaded, CD47/PD-L1-targeting nanoparticles potentiate antitumor immunity and radiotherapy for glioblastoma

As a key component of the standard of care for glioblastoma, radiotherapy induces several immune resistance mechanisms, such as upregulation of CD47 and PD-L1. Here, leveraging these radiotherapy-elicited processes, we generate a bridging-lipid nanoparticle (B-LNP) that engages tumor-associated myeloid cells (TAMCs) to glioblastoma cells via anti-CD47/PD-L1 dual ligation. We show that the engager B-LNPs block CD47 and PD-L1 and promote TAMC phagocytic activity. To enhance subsequent T cell recruitment and antitumor responses after tumor engulfment, the B-LNP was encapsulated with diABZI, a non-nucleotidyl agonist for stimulator of interferon genes. In vivo treatment with diABZI-loaded B-LNPs induced a transcriptomic and metabolic switch in TAMCs, turning these immunosuppressive cells into antitumor effectors, which induced T cell infiltration and activation in brain tumors. In preclinical murine models, B-LNP/diABZI administration synergized with radiotherapy to promote brain tumor regression and induce immunological memory against glioma. In summary, our study describes a nanotechnology-based approach that hijacks irradiation-triggered immune checkpoint molecules to boost potent and long-lasting antitumor immunity against glioblastoma.

Immunological and tumor-intrinsic mechanisms mediate the synergistic growth suppression of experimental glioblastoma by radiotherapy and MET inhibition

The hepatocyte growth factor (HGF)/MET signaling pathway has been proposed to be involved in the resistance to radiotherapy of glioblastoma via proinvasive and DNA damage response pathways.Here we assessed the role of the MET pathway in the response to radiotherapy in vitro and in vivo in syngeneic mouse glioma models. We find that the murine glioma cell lines GL-261, SMA-497, SMA-540 and SMA-560 express HGF and its receptor MET and respond to exogenous HGF with MET phosphorylation. Glioma cell viability or proliferation are unaffected by genetic or pharmacological MET inhibition using tepotinib or CRISPR/Cas9-engineered Met gene knockout and MET inhibition fails to sensitize glioma cells to irradiation in vitro. In contrast, the combination of tepotinib with radiotherapy prolongs survival of orthotopic SMA-560 or GL-261 glioma-bearing mice compared with radiotherapy or tepotinib treatment alone. Synergy is lost when such experiments are conducted in immunodeficient Rag1−/− mice, and, importantly, also when Met gene expression is disrupted in the tumor cells. Combination therapy suppresses a set of pro-inflammatory mediators including matrix metalloproteases that are upregulated by radiotherapy alone and that have been linked to poor outcome in glioblastoma. Several of these mediators are positively regulated by transforming growth factor (TGF)-β, and pSMAD2 levels as a surrogate marker of TGF-β pathway activity are suppressed by combination treatment. We conclude that synergistic suppression of experimental syngeneic glioma growth by irradiation and MET inhibition requires MET expression in the tumor as well as an intact immune system. Clinical evaluation of this combined strategy in newly diagnosed glioblastoma is warranted.

Fully automated clinical target volume segmentation for glioblastoma radiotherapy using a deep convolutional neural network.

Target volume delineation is a crucial step prior to radiotherapy planning in radiotherapy for glioblastoma. This step is performed manually, which is time-consuming and prone to intra- and inter-rater variabilities. Therefore, the purpose of this study is to evaluate a deep convolutional neural network (CNN) model for automatic segmentation of clinical target volume (CTV) in glioblastoma patients. In this study, the modified Segmentation-Net (SegNet) model with deep supervision and residual-based skip connection mechanism was trained on 259 glioblastoma patients from the Multimodal Brain Tumour Image Segmentation Benchmark (BraTS) 2019 Challenge dataset for segmentation of gross tumour volume (GTV). Then, the pre-trained CNN model was fine-tuned with an independent clinical dataset (n = 37) to perform the CTV segmentation. In the process of fine-tuning, to generate a CT segmentation mask, both CT and MRI scans were simultaneously used as input data. The performance of the CNN model in terms of segmentation accuracy was evaluated on an independent clinical test dataset (n = 15) using the Dice Similarity Coefficient (DSC) and Hausdorff distance. The impact of auto-segmented CTV definition on dosimetry was also analysed. The proposed model achieved the segmentation results with a DSC of 89.60 ± 3.56% and Hausdorff distance of 1.49 ± 0.65 mm. A statistically significant difference was found for the Dmin and Dmax of the CTV between manually and automatically planned doses. The results of our study suggest that our CNN-based auto-contouring system can be used for segmentation of CTVs to facilitate the brain tumour radiotherapy workflow.