Treatment Response In Glioblastoma Research Articles

TAK1 inhibition leads to RIPK1-dependent apoptosis in immune-activated cancers.

Poor survival and lack of treatment response in glioblastoma (GBM) is attributed to the persistence of glioma stem cells (GSCs). To identify novel therapeutic approaches, we performed CRISPR/Cas9 knockout screens and discovered TGFβ activated kinase (TAK1) as a selective survival factor in a significant fraction of GSCs. Loss of TAK1 kinase activity results in RIPK1-dependent apoptosis via Caspase-8/FADD complex activation, dependent on autocrine TNFα ligand production and constitutive TNFR signaling. We identify a transcriptional signature associated with immune activation and the mesenchymal GBM subtype to be a characteristic of cancer cells sensitive to TAK1 perturbation and employ this signature to accurately predict sensitivity to the TAK1 kinase inhibitor HS-276. In addition, exposure to pro-inflammatory cytokines IFNγ and TNFα can sensitize resistant GSCs to TAK1 inhibition. Our findings reveal dependency on TAK1 kinase activity as a novel vulnerability in immune-activated cancers, including mesenchymal GBMs that can be exploited therapeutically.

Exploring the Influence of tGLI-1 on Temozolomide Resistance in Glioblastomas: Unraveling Novel Therapeutic Targets

Background/Objective:Temozolomide (TMZ) is a standard chemotherapy treatment for patients with glioblastoma (GBM), but its effectiveness is limited, with only 50% of patients initially responding and developing resistance over time. Glioma stem cells (GSCs) have been implicated in TMZ resistance, particularly the mesenchymal subtype. The truncated form of GLI1, known as tGLI1, is highly expressed in mesenchymal GSCs and has been associated with poor patient outcomes in GBM. However, the role of tGLI1 in TMZ resistance remains unknown.
 Methods:The GBM cell line, U87MG, was utilized for this study. The IC50 of TMZ was determined using a cell viability assay. After successful transfection with vector, GLI1, and tGLI1, the cells were treated with the IC50 of TMZ to assess changes in cell viability between the groups.
 Results:The IC50 of TMZ is 290.1 μM, as averaged between replicate assays. Thus far, the results showed that tGLI1-expressing cells exhibited significantly higher cell viability (average: 39.09%) compared to Vector (average: 26.78%) and GLI1 (average: 27.11%). However, the tGLI1 group displayed higher variability in cell viability results, as evidenced by a larger standard deviation (0.2758) and standard error (0.1592) compared to vector (SD: 0.0325, SE: 0.0188) and GLI1 (SD: 0.1354, SE: 0.0781). The One-Way ANOVA, followed by Tukey's Multiple Comparison Test, results showed no statistically significant differences in cell viability between the groups.
 Conclusion/Impact:The increased cell viability observed in tGLI1-expressing cells suggests a potential association between tGLI1 and TMZ resistance, warranting additional research to fully comprehend its impact on GBM treatment response. Further investigation and replication studies are needed to establish the robustness of these results. This knowledge may contribute to the development oftargeted therapies aimed at inhibiting tGLI1 or its downstream signaling pathways, potentiallyimproving the response to TMZ and patient outcomes.

DNAR-10. HETEROGENEOUS MGMT PROMOTER METHYLATION IN A MULTIFOCAL GLIOBLASTOMA: A CASE REPORT

Abstract MGMT promoter methylation status is an important predictive biomarker for treatment response in glioblastoma. MGMT unmethylated glioblastoma has elevated levels of MGMT which ultimately leads to chemoresistance. Clinicians consider the MGMT methylation status when treatment recommendations are given, and clinical trials use this marker for the selection of a homogenous group of glioblastoma patients based on prognosis. Here, we present a case of multifocal glioblastoma with heterogeneous MGMT methylation status. A 56-year-old male presented with left leg weakness for 2 months and left arm weakness for 1 month. Imaging revealed a calcified, rim enhancing right temporoparietal cystic lesion and a heterogeneously enhancing right parietal mass. Perilesional edema surrounded and connected the two lesions. The patient underwent gross total resection of the parietal mass and pathology revealed a WHO grade 4 glioblastoma, IDH wildtype, unmethylated tumor. Postoperatively, the patient was neurologically intact. He completed a 6-week long course of concomitant chemoradiation without any significant side effects. Two weeks later the patient developed rapidly worsening headaches, nausea, vomiting, dizziness, and left side weakness. Imaging revealed significant enlargement of the previously unresected temporoparietal cystic lesion with marked increase in surrounding edema and a 2-3 mm right to left midline shift. Patient underwent craniotomy with cyst drainage and tumor resection of the right cystic temporoparietal lesion, demonstrating a WHO grade 4 glioblastoma, IDH wildtype, methylated tumor. Post-operatively the patient was again neurologically intact and started adjuvant temozolomide. MGMT methylation status of glioblastoma can alter recommendations for chemotherapy with temozolomide. MGMT methylation status might vary in distinct parts of the tumor as molecular heterogeneity is a hallmark of glioblastoma. This heterogeneity should be considered by clinicians when treatments are recommended and by clinical trials for glioblastoma patients, especially when multifocal lesions have a distinct radiographic appearance.

IMMU-05. CD8 IMMUNOPET IMAGING IS ABLE TO MONITOR THE IMMUNE RESPONSE SENSITIVELY AND SPECIFICALLY IN GBM PATIENTS FOLLOWING NEOADJUVANT CHECKPOINT BLOCKADE IMMUNOTHERAPY

Abstract The sensitive and specific evaluation of treatment response in glioblastoma (GBM) patients following immunotherapy is a clinical dilemma. Traditional MRI assessments to visualize and quantify intracranial tumor burden following immunotherapy cannot reliably differentiate tumor progression and pseudo progression remains a complicating factor. In this study, we provide an early report on the use of an investigational new functional imaging PET tracer (CD8 ImmunoPET, 89Zr-crefmirlimab berdoxam, ImaginAb) prior to and immediately following neoadjuvant checkpoint blockade immunotherapy in five patients with recurrent GBM. Tumor samples were collected from each patient at surgery following a two-week neoadjuvant checkpoint blockade to assess gene expression changes and TIL density. We then evaluated how such tumor measurements correlated with PET metrics obtained at baseline and within 2 weeks after treatment initiation which included standardized uptake values (SUVmax, SUVpeak, and SUVmean), and two proposed new metrics indicating the Volume of Immunometabolism (VIM), and the Total Lesion or Lymph nodes’ T cells called TLTc (TLTc = VI x SUVmean) in the tumor bed and lymph node basins (neck, axillae, and inguinal regions). CD8 ImmunoPET demonstrated tracer uptake in the intracranial tumor at both time points, as well as specific physiologic uptake in lymph node basins, spleen, liver, and bone marrow. Neoadjuvant checkpoint blockade with PD-1 +/- CTLA-4 mAb (Nivolumab, Ipilimumab, BMS) at 2 weeks induced variable changes in SUV, but in four of the five initial patients studied in this clinical trial, an increase in VI and TLTc was observed within the tumor bed. Tracer uptake was associated with elevated T cell infiltration into the tumor bed and gene expression changes indicative of interferon signaling and immune cells in these same patients. Our early findings identify a noninvasive functional imaging platform to evaluate the immunometabolism secondary to T cell infiltration and/or activation changes following immunotherapy in GBM patients.

Predictive value of diffusion MRI-based parametric response mapping for prognosis and treatment response in glioblastoma

BackgroundEarly detection of treatment response is important for the management of patients with malignant brain tumors such as glioblastoma to assure good quality of life in relation to therapeutic efficacy. AimTo investigate whether parametric response mapping (PRM) with diffusion MRI may provide prognostic information at an early stage of standard therapy for glioblastoma. Materials and methodsThis prospective study included 31 patients newly diagnosed with glioblastoma WHO grade IV, planned for primary standard postoperative treatment with radiotherapy 60Gy/30 fractions with concomitant and adjuvant Temozolomide. MRI follow-up including diffusion and perfusion weighting was performed at 3 T at start of postoperative chemoradiotherapy, three weeks into treatment, and then regularly until twelve months postoperatively. Regional mean diffusivity (MD) changes were analyzed voxel-wise using the PRM method (MD-PRM). At eight and twelve months postoperatively, after completion of standard treatment, patients were classified using conventional MRI and clinical evaluation as either having stable disease (SD, including partial response) or progressive disease (PD). It was assessed whether MD-PRM differed between patients having SD versus PD and whether it predicted the risk of disease progression (progression-free survival, PFS) or death (overall survival, OS). A subgroup analysis was performed that compared MD-PRM between SD and PD in patients only undergoing diagnostic biopsy. MGMT-promotor methylation status (O6-methylguanine-DNA methyltransferase) was registered and analyzed with respect to PFS, OS and MD-PRM. ResultsOf the 31 patients analyzed: 21 were operated by resection and ten by diagnostic biopsy. At eight months, 19 patients had SD and twelve had PD. At twelve months, ten patients had SD and 20 had PD, out of which ten were deceased within twelve months and one was deceased without known tumor progression. Median PFS was nine months, and median OS was 17 months. Eleven patients had methylated MGMT-promotor, 16 were MGMT unmethylated, and four had unknown MGMT-status. MD-PRM did not significantly predict patients having SD versus PD neither at eight nor at twelve months. Patients with an above median MD-PRM reduction had a slightly longer PFS (P = 0.015) in Kaplan-Maier analysis, as well as a non-significantly longer OS (P = 0.099). In the subgroup of patients only undergoing biopsy, total MD-PRM change at three weeks was generally higher for patients with SD than for patients with PD at eight months, although no tests were performed. MGMT status strongly predicted both PFS and OS but not MD-PRM change. ConclusionMD-PRM at three weeks was not demonstrated to be predictive of treatment response, disease progression, or survival. Preliminary results suggested a higher predictive value in non-resected patients, although this needs to be evaluated in future studies.

Diffusion-weighted imaging on an MRI-linear accelerator to identify adversely prognostic tumour regions in glioblastoma during chemoradiation

Background and purposeSurvival in glioblastoma might be extended by escalating the radiotherapy dose to treatment-resistant tumour and adapting to tumour changes. Diffusion-weighted imaging (DWI) on MRI-linear accelerators (MR-Linacs) could be used to identify a dose escalation target, but its prognostic value must be demonstrated. The purpose of this study was to determine whether MR-Linac DWI can assess treatment response in glioblastoma and whether changes in DWI show greater prognostic value than changes in the contrast-enhancing gross tumour volume (GTV). Materials and methodsSeventy-five patients with glioblastoma were treated with chemoradiotherapy, of which 32 were treated on a 1.5 T MRI-linear accelerator (MR-Linac). Patients were imaged with simulation MRI scanners (MR-sim) at treatment planning and weeks 2, 4, and 10 after treatment start. Twenty-eight patients had additional MR-Linac DWI sequences. Cox modelling was used to evaluate the correlation of overall and progression-free survival (OS and PFS) with clinical variables and volumetric changes in the GTV and low-ADC regions (ADC < 1.25 µm2/ms within GTV). ResultsIn total, 479 MR-Linac DWI and 289 MR-sim DWI datasets were analyzed. MR-Linac low-ADC changes between weeks 2 and 5 inclusive were prognostic for OS (hazard ratio lower limits ≥ 1.2, p-values ≤ 0.02). MR-sim low-ADC changes showed greater correlation with OS and PFS than GTV changes (e.g., OS hazard ratio at week 2 was 3.4 (p <0.001) for low-ADC versus 2.0 (p = 0.022) for GTV). ConclusionMR-Linac DWI can measure low-ADC tumour volumes that correlate with OS and PFS better than contrast-enhancing GTV. Low-ADC regions could serve as dose escalation targets.

A Novel Epigenetic Marker Predicts Treatment Response in Glioblastoma and Provides a Target for Overcoming Treatment Resistance

<h3>Purpose/Objective(s)</h3> Glioblastoma (GBM) is a highly aggressive primary brain tumor. A major challenge in GBM treatment is tumor resistance to radiation and chemotherapy. An important hallmark of GBM is the frequent mutation of epigenetic modifiers resulting in alteration of epigenetic signaling pathways in tumor cells. However, the effect of epigenetic signaling on chemotherapy response in GBM remains to be elucidated. SETD2 is a unique histone methyl transferase that facilitates H3K36 tri-methylation. Here, we unveil the role of SETD2 mutations that frequently occur in GBM in tumor resistance to temozolomide chemotherapy. <h3>Materials/Methods</h3> We have performed next-generation sequencing of the SETD2 gene and immunohistochemical staining if its product H3K36me3 in 129 GBM patient samples that was supplemented with TCGA database analysis of additional 279 GBM samples. We used primary patient-derived GBM lines to investigate the mechanism of chemotherapy resistance in SETD2-mutant GBM. <h3>Results</h3> We demonstrate that inactivating SETD2 mutations occur in up to 13% of GBM patients. Our investigation reveals that SETD2 mutations are associated with reduced progression-free (Median PFS 0.93 vs. 0.44 years, p=0.0022) and overall survival (Median survival 1.69 vs. 1.30 years, p=0.0044) in patients with methylated MGMT (methyl-guanine methyl transferase) promotor who received temozolomide suggesting that mutation of SETD2 is a predictive marker for treatment outcomes in GBM. Consequently, we demonstrate that loss of SETD2 results in reduced H3K36me3 levels in patient samples and a profound resistance to temozolomide in primary patient-derived GBM lines. MGMT-deficient tumors can acquire chemoresistance due to disrupted mismatch repair (MMR), a DNA repair pathway that plays a critical role in converting primary temozolomide-induced DNA lesions into toxic DNA double-strand breaks. Strikingly, we found that SETD2 loss abrogates the expression of the MMR factor MSH6 indicating that chemoresistance in SETD2-deficient cells us due to disrupted MMR. Mechanistically, we show that SETD2 regulates MMR by promoting transcription of the <i>MSH6</i> gene in GBM. Epigenetic modifiers have specific antagonists capable of reversing chromatin alterations induced by these modifiers. This provides a unique opportunity to restore chemotherapy response in SETD2-mutant GBM by targeting the antagonists of SETD2. We demonstrate that combined targeting of H3K36me3-specific histone de-methylases KDM4A and NO66 restores H3K36me3 levels along with MSH6 expression and sensitivity to temozolomide in SETD2-deficient GBM cells. <h3>Conclusion</h3> Our findings establish SETD2 mutation as a novel molecular marker predictive of treatment response in GBM and provide a framework for a novel approach to overcome chemotherapy resistance in this malignant brain tumor by targeting an epigenetic signaling pathway.

MR Electrical Properties Tomography (MREPT) at 9.4T to Assess Treatment Response of Choline Kinase Inhibition on GL261 Glioblastoma

Abstract AIMS To monitor changes in tissue-conductivity based MR parameter in response to JAS239 treatment in preclinical glioblastoma model. METHOD C57BL/6 mice were injected intracranially with 5x105 GL261 glioblastoma cells and tumours were observed on T2 weighted MRI. Mice were imaged on day 0 (baseline), day 3 , 6 of treatment and post-treatment day 10. For MREPT, multi-slice-multi-echo sequence was used. Laplacian algorithm was employed to obtain the phase-based MREPT image reconstruction. RESULTS Saline treated mice showed a greater increase in tumour volume compared to JAS239 treated animals with respect to baseline. Percentage changes in conductivity values with respect to baseline from saline treated mice were compared to JAS239 treated animals. JAS239 group showed a slight increase in mean conductivity on day 3 and 6 compared to saline group. There were no statistically (p&amp;gt; 0.05) significant differences between the two groups. 4 days after treatment completion (Day10), the mean of conductivity values of JAS239 treated animals compared to saline treated animals showed no statistically (p&amp;gt; 0.05) significant differences. These results may indicate that GL261 tumour model is treatment resistant. Our findings agree with a study conducted by Bhaduri et al, where tumours were treated with JAS239. CONCLUSION EPT technique provides unique data on tissue conductivity which can be used to improve the assessment of treatment response in glioblastoma. MREPT parameter might be insensitive to evaluate choline kinase inhibitor. For future work, this technique can be incorporated into multiparametric MRI to complement diffusion and perfusion-weighted imaging of assessing treatment response in glioblastoma.

Modulation of GABA Neurotransmitter Signalling Impacts Glioblastoma Spheroid Growth and Response to Standard Treatment

Abstract AIMS We have identified two subtypes within glioblastoma (GBM), defined by whether a specific subset of genes is upregulated (Up responders) or downregulated (Down responders) in response the standard treatment. The biology of therapy-driven transcriptional reprogramming in the two subtypes suggests the differential involvement of GABA neurotransmitter signalling in the underlying treatment resistance mechanisms. Our aim was to address our hypothesis that modulating GABA signalling will impact treatment response in GBM, in different ways for different responder subtypes. METHOD We identified two cell lines representing the different responder subtypes: established GBM cell line A172 is an Up responder; GBM63 is a patient derived Down responder. We have grown these cells as spheroids and inspected the response to standard treatment (2 Gy radiation and 30 mM temozolomide), via growth and cell viability assays, versus untreated controls in the presence and absence of a GABAA receptor (GABAAR) modulators. We have performed RNAseq to investigate the pathways affected by inhibition and treatment. RESULTS In all cases, modulation of GABAAR activity significantly alters both growth and response to standard treatment. However, this is most commonly in different directions in the Up vs Down responders. Furthermore, the majority of genes that are differentially expressed through treatment and inhibition are dysregulated in different directions. CONCLUSION Modulation of GABA neurotransmitter signalling impacts GBM spheroid growth and response to standard treatment. Identifying the key pathways affected in different responder subtypes may indicate how this can be used in the development of more effective combination treatments for GBM.

Imaging Glioblastoma Response to Radiotherapy Using 2H Magnetic Resonance Spectroscopy Measurements of Fumarate Metabolism.

2H magnetic resonance imaging of labeled fumarate metabolism can detect early evidence of tumor cell death following chemoradiation, meeting a clinical need to reliably detect treatment response in glioblastoma.

Abstract 2459: Automated VIF methods improve DCE-MRI parameterization repeatability in GBM

Abstract Introduction: Analysis of Dynamic Contrast Enhanced (DCE) MRI is widely used for measuring perfusion kinetics of cancerous tissue, though its implementation and results may vary between sites. Here we study the repeatability of quantitative DCE-MRI parameterization in a publicly available Glioblastoma (GBM) double-baseline dataset and set forth percent repeatability coefficients (%RC) which constitute a level of change at which a detectable biological change in biology may be differentiated from experimental noise. Methods: Double-baseline imaging data was previously acquired for The Cancer Imaging Archive, in the Quantitative Imaging Network (QIN) GBM Treatment Response (TR) dataset. This data includes dual baseline acquisitions of DCE-MRI MRI data performed within 5 days, for 29 patients. Each acquisition is analyzed using two vascular input function (VIF) methods: a manually segmented VIF from the superior sagittal sinus, and an automatically derived VIF determined from the top 10% of rapidly enhancing voxels. Each dynamic scan is analyzed the the Extended Tofts-Kety model (eTM; Ktrans, ve, vp) pharmacokinetic (PK) model. Bland-Altman analysis is then performed on the log-transformed PK parameters to determine the percent repeatability coefficients (%RC) of the data (95% confidence), as well as bias between first and second DCE scans. This analysis was repeated for two separate observers, writing their own analysis code, both of which are validated on a digital reference object to ensure consistency between analysis methods with zero noise. Results: For bothVIF determination methods, the 25-75th percentiles of the change in PK parameters was less than +/- 25%, the standard set forth by QIBA as a detectable biological change. For most parameters, VIF methods, and model combinations, the automatic VIF method has smallest %RC, indicating that automatic VIF determination may enhance the repeatability of the measurements between timepoints. The %RC was lowest for vp when using an automatically determined VIF. Using the automatically determined VIF and eTM, the %RCs of Ktrans, ve, and vp were measured as 72%, 82%, and 53%, respectively. Using the eTM and manually segmented VIF, the %RCs of Ktrans, ve, and vp were measured as 83%, 88%, and 66%. Conclusions: Using the QIN-GBM-TR dataset, the authors have determined that the %RC of Ktrans, ve, and vp to be 72%, 82%, and 53%, respectively (eTM, auto-VIF). Because these relative changes are large, we recommend that all sites abide by QIBA guidelines to enhance DCE parameterization reproducibility. We also recommend usage of an automated AIF determination method, as opposed to manual segmentation of large blood vessels. Citation Format: Ryan T. Woodall, Prativa Sahoo, Yujie Cui, Bihong Chen, Mark Shiroishi, Cristina Lavini, Paul Frankel, Margarita Gutova, Christine Brown, Jennifer Munson, Russell Rockne. Automated VIF methods improve DCE-MRI parameterization repeatability in GBM [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2459.

Evaluation of the response assessment criteria in newly diagnosed and recurrent glioblastoma.

2020 Background: The Response Assessment in Neuro-Oncology (RANO) and modified RANO (mRANO) criteria are the two most widely used criteria to evaluate treatment response in glioblastoma (GBM) clinical trials. Unlike RANO, mRANO omits the evaluation of FLAIR sequence and requires a repeat scan to confirm responses. It also uses the post-radiation (RT) MRI as a baseline MRI in the newly diagnosed setting instead of the pre-RT MRI used in RANO. We sought to compare the 2 response assessment criteria and evaluate the differences between them in a large patient population. We also sought to compare them to immunotherapy RANO (iRANO) in patients who received immunotherapy. Methods: We conducted a retrospective review of consecutive patients with newly diagnosed (nGBM) and recurrent (rGBM) IDH wild-type GBM treated at Dana-Farber Cancer Institute from 2014 to 2020. Bidimensional measurements of enhancing disease and evaluation of FLAIR sequences were performed by two independent readers on patients’ brain MRIs obtained before change of treatment, and discrepancies were evaluated by a third reader. Dates of disease progression (PD) were identified using RANO, mRANO, iRANO, and other response assessment criteria variations. Spearman’s correlations between PFS and OS were calculated using an iterative multiple imputation method to account for any right-censoring. Results: 526 and 580 patients were included in the newly diagnosed and recurrent cohorts, respectively. Spearman’s correlations were not significantly different between RANO and mRANO in the nGBM (0.69 [95% CI 0.62 to 0.75] vs. 0.67 [0.60, 0.73]) and rGBM (0.45 [0.37, 0.52] vs. 0.50 [0.42, 0.57]) cohorts. Evaluation of FLAIR sequences did not improve the correlation between PFS and OS in patients who received antiangiogenic therapy. Addition of confirmation scans was associated with stronger Spearman’s correlations only when PD was identified within 12 weeks of completion of RT in the nGBM cohort, but did not affect the Spearman’s correlations in the rGBM cohort. The use of the post-RT MRI as a baseline was associated with a higher Spearman’s correlation in nGBM than the use of pre-RT MRI (0.67 [0.60, 0.73] vs. 0.53 [0.42, 0.62]). Among 98 patients with nGBM and 175 patients with rGBM who received immunotherapy, the Spearman’s correlations (nGBM and rGBM) with iRANO (0.63 [0.44, 0.76] and 0.34 [0.17, 0.49]) were similar to RANO (0.73 [0.60, 0.82] and 0.42 [0.28, 0.54]) and mRANO (0.65 [0.48, 0.77] and 0.43 [0.28, 0.56]). Conclusions: RANO and mRANO demonstrated similar correlation between PFS and OS. The evaluation of FLAIR can be omitted, while confirmation scans appear to be only beneficial in the nGBM settings during the first 12 weeks of completion of RT. There was a nonsignificant trend in favor of the use of post-RT MRI as the baseline scan in the nGBM setting. The application of iRANO criteria did not add significant benefit in patients who received immunotherapy.

A systematic review of amino acid PET in assessing treatment response to temozolomide in glioma.

The response assessment in neuro-oncology (RANO) criteria have been the gold standard for monitoring treatment response in glioblastoma (GBM) and differentiating tumor progression from pseudoprogression. While the RANO criteria have played a key role in detecting early tumor progression, their ability to identify pseudoprogression is limited by post-treatment damage to the blood-brain barrier (BBB), which often leads to contrast enhancement on MRI and correlates poorly to tumor status. Amino acid positron emission tomography (AA PET) is a rapidly growing imaging modality in neuro-oncology. While contrast-enhanced MRI relies on leaky vascularity or a compromised BBB for delivery of contrast agents, amino acid tracers can cross the BBB, making AA PET particularly well-suited for monitoring treatment response and diagnosing pseudoprogression. The authors performed a systematic review of PubMed, MEDLINE, and Embase through December 2021 with the search terms “temozolomide” OR “Temodar,” “glioma” OR “glioblastoma,” “PET,” and “amino acid.” There were 19 studies meeting inclusion criteria. Thirteen studies utilized [18F]FET, five utilized [11C]MET, and one utilized both. All studies used static AA PET parameters to evaluate TMZ treatment in glioma patients, with nine using dynamic tracer parameters in addition. Throughout these studies, AA PET demonstrated utility in TMZ treatment monitoring and predicting patient survival.

CTNI-45. RESPONSE EVALUATION OF AR-67 FROM A PHASE-2 RECURRENT GLIOBLASTOMA TRIAL BY ARTIFICIAL INTELLIGENCE ASSISTED TUMOR VOLUMETRIC ESTIMATION: COMPARISON WITH THE SUM OF THE PERPENDICULAR DIAMETERS PRODUCT

Abstract BACKGROUND Modified Radiographic Response Assessment in Neuro-Oncology (“mRANO”) criteria based on SPDP form the basis for assessing treatment response in Glioblastoma but are subject to sampling bias and difficulty in differentiating between pseudo- and true disease progression. Volumetric image analysis using AI may overcome these limitations of standard techniques and improve our ability to detect changes earlier and more accurately. METHODS Images from eight reGBM patients enrolled in a Phase-2 reGBM study of Vivacitas Oncology’s drug, AR-67, were re-assessed using IAG’s AI-assisted volumetric measurements. A median of five MRI time points from each patient were included. The mRANO response was determined by central reading and tumor volumetric measurement using IAG’s proprietary platform. Statistical significance was set at p&amp;lt; .0001. RESULTS Four patients showed responses, two patients showed stable disease, and two patients showed progressive disease. Tumor volume was correlated (r=0.97) with SPDP, but was driven by high coefficients in large lesions. Standard SPDP overestimated tumor size in larger tumors using the Bland-Altman analysis (mean difference: 829; 95% CI: 704 to -2362) leading to discrepancies in response rates. For example, the mean response rate based on IAG’s volumetric criteria was +22% (1.29) compared with +17% using SPDP (0.81). Eight out of 45 time-points also differed in the directionality of responses (e.g., increase vs. decrease) with SPDP underestimating the positive effects of AR-67 compared to AI analysis. CONCLUSIONS IAG’s AI-assisted tumor volumetric analysis is feasible for clinical trials and may be more sensitive for evaluating treatment-related response rates vs. SPDP methodology. This is particularly true for measuring large lesions, and may also allow for more accurately differentiating between pseudo- and true disease progression. The data included eight patients’ MRI images from a Phase-2 reGBM study, showing that five patients achieved the primary end-point of six months Progression-Free Survival, suggesting AR-67’s therapeutic potential.

Artificial intelligence for early prediction of treatment response in glioblastoma

Abstract Aims Treatment response assessment in glioblastoma is challenging. Patients routinely undergo conventional magnetic resonance imaging (MRI), but it has a low diagnostic accuracy for distinguishing between true progression (tPD) and pseudoprogression (psPD) in the early post-chemoradiotherapy time period due to similar imaging appearances. The aim of this study was to use artificial intelligence (AI) on imaging data, clinical characteristics and molecular information within machine learning models, to distinguish between and predict early tPD from psPD in patients with glioblastoma. Method The study involved retrospective analysis of patients with newly-diagnosed glioblastoma over a 3.5 year period (n=340), undergoing surgery and standard chemoradiotherapy treatment, with an increase in contrast-enhancing disease on the baseline MRI study 4-6 weeks post-chemoradiotherapy. Studies had contrast-enhanced T1-weighted imaging (CE-T1WI), T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) sequences, acquired at 1.5 Tesla with 6-months follow-up to determine the reference standard outcome. 76 patients (mean age 55 years, range 18-76 years, 39% female, 46 tPD, 30 psPD) were included. Machine learning models utilised information from clinical characteristics (age, gender, resection extent, performance status), O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status and 307 quantitative imaging features; extracted from baseline study CE-T1WI/ADC and T2WI sequences using semi-automatically segmented enhancing disease and perilesional oedema masks respectively. Feature selection was performed within bootstrapped cross-validated recursive feature elimination with a random forest algorithm and Naïve Bayes five-fold cross-validation to validate the final model. Results Treatment response assessment based on the standard-of-care reports by clinical neuroradiologists showed an accuracy of 33% (sensitivity/specificity 52%/3%) to distinguish between tPD and psPD from the early post-treatment MRI study at 4-6 weeks. Machine learning-based models based on clinical and molecular features alone demonstrated an AUC of 0.66 and models using radiomic features alone from the early post-treatment MRI demonstrated an AUC of 0.46-0.69 depending on the feature and mask subset. A combined clinico-radiomic model utilising top common features demonstrated an AUC of 0.80 and an accuracy of 74% (sensitivity/specificity 78%/67%). The features in the final model were age, MGMT promoter methylation status, two shape-based features from the enhancing disease mask (elongation and sphericity), three radiomic features from the enhancing disease mask on ADC (kurtosis, correlation, contrast) and one radiomic feature from the perilesional oedema mask on T2WI (dependence entropy). Conclusion Current standard-of-care glioblastoma treatment response assessment imaging has limitations. In this study, the use of AI through a machine learning-based approach incorporating clinical characteristics and MGMT promoter methylation status with quantitative radiomic features from standard MRI sequences at early 4-6 weeks post-treatment imaging showed the best model performance and a higher accuracy to distinguish between tPD and psPD for early prediction of glioblastoma treatment response.

A Comparative Retrospective Study of Immunotherapy RANO Versus Standard RANO Criteria in Glioblastoma Patients Receiving Immune Checkpoint Inhibitor Therapy

ObjectivesReal-time assessment of treatment response in glioblastoma (GBM) patients on immune checkpoint blockade (ICB) remains challenging because inflammatory effects of therapy may mimic progressive disease, and the temporal evolution of these inflammatory findings is poorly understood. We compare GBM patient response during ICB as assessed with the Immunotherapy Response Assessment in Neuro-Oncology (iRANO) and the standard Response Assessment in Neuro-Oncology (RANO) radiological criteria.Methods49 GBM patients (seven newly diagnosed and 42 recurrent) treated with ICBs at a single institution were identified. Tumor burden was quantified on serial MR scans according to RANO criteria during ICB. Radiographic response assessment by iRANO and RANO were compared.Results82% (40/49) of patients received anti–PD-1, 16% (8/49) received anti-PD-L1, and 2% (1/49) received anti-PD-1 and anti-CTLA4 treatment. Change in tumor burden and best overall response ranged from −100 to +557% (median: +48%). 12% (6/49) of patients were classified as concordant non-progressors by both RANO and iRANO (best response: one CR, one PR, and four SD). Another12% (6/49) had discordant assessments: 15% (6/41) of RANO grade progressive disease (PD) patients had iRANO grade of progressive disease unconfirmed (PDU). The final classification of these discordant patients was pseudoprogression (PsP) in three of six, PD in two of six, and PDU in one of six who went off study before the iRANO assessment of PDU. iRANO delayed diagnosis of PD by 42 and 93 days in the two PD patients. 76% (37/49) patients were classified as concordant PD by both RANO and iRANO. 12% (6/49) of all patients were classified as PsP, starting at a median of 12 weeks (range, 4–30 weeks) after ICB initiation.ConclusionsStandard RANO and iRANO have high concordance for assessing PD in patients within 6 months of ICB initiation. iRANO was beneficial in 6% (3/49) cases later proven to be PsP, but delayed confirmation of PD by <3 months in 4% (2/49). PsP occurred in 12% of patients, starting at up to 7 months after initiation of ICB. Further study to define the utility of modified RANO compared with iRANO in ICB GBM patients is needed.

Genetic and environmental determinants of O6-methylguanine DNA-methyltransferase (MGMT) gene methylation: a 10-year longitudinal study of Danish twins

BackgroundEpigenetic inactivation of O6-methylguanine DNA-methyltransferase (MGMT) is associated with increased sensitivity to alkylating chemotherapeutic agents in glioblastoma patients. The genetic background underlying MGMT gene methylation may explain individual differences in treatment response and provide a clue to a personalized treatment strategy. Making use of the longitudinal twin design, we aimed, for the first time, to estimate the genetic contributions to MGMT methylation in a Danish twin cohort.MethodsDNA-methylation from whole blood (18 monozygotic (MZ) and 25 dizygotic (DZ) twin pairs) repeated 10 years apart from the Longitudinal Study of Aging Danish Twins (LSADT) were used to search for genetic and environmental contributions to DNA-methylation at 170 CpG sites of across the MGMT gene. Both univariate and bivariate twin models were applied. The intraclass correlations, performed on cross-sectional data (246 MZ twin pairs) from an independent study population, the Middle-Aged Danish Twins (MADT), were used to assess the genetic influence at each CpG site of MGMT for replication.ResultsUnivariate twin model revealed twelve CpG sites showing significantly high heritability at intake (wave 1, h2 > 0.43), and seven CpG sites with significant heritability estimates at end of follow-up (wave 2, h2 > 0.5). There were six significant CpG sites, located at the gene body region, that overlapped among the two waves (h2 > 0.5), of which five remained significant in the bivariate twin model, which was applied to both waves. Within MZ pair correlation in these six CpGs from MADT demarks top level of genetic influence. There were 11 CpGs constantly have substantial common environmental component over the 10 years.ConclusionsWe have identified 6 CpG sites linked to the MGMT gene with strong and persistent genetic control based on their DNA methylation levels. The genetic basis of MGMT gene methylation could help to explain individual differences in glioblastoma treatment response and most importantly, provide references for mapping the methylation Quantitative Trait Loci (meQTL) underlying the genetic regulation.

Imaging Glioblastoma Posttreatment: Progression, Pseudoprogression, Pseudoresponse, Radiation Necrosis.

Radiographic monitoring of posttreatment glioblastoma is important for clinical trials and determining next steps in management. Evaluation for tumor progression is confounded by the presence of treatment-related radiographic changes, making a definitive determination less straight-forward. The purpose of this article was to describe imaging tools available for assessing treatment response in glioblastoma, as well as to highlight the definitions, pathophysiology, and imaging features typical of true progression, pseudoprogression, pseudoresponse, and radiation necrosis.

Repeatability of tumor perfusion kinetics from dynamic contrast-enhanced MRI in glioblastoma.

BackgroundDynamic contrast-enhanced MRI (DCE-MRI) parameters have been shown to be biomarkers for treatment response in glioblastoma (GBM). However, variations in analysis and measurement methodology complicate determination of biological changes measured via DCE. The aim of this study is to quantify DCE-MRI variations attributable to analysis methodology and image quality in GBM patients.MethodsThe Extended Tofts model (eTM) and Leaky Tracer Kinetic Model (LTKM), with manually and automatically segmented vascular input functions (VIFs), were used to calculate perfusion kinetic parameters from 29 GBM patients with double-baseline DCE-MRI data. DCE-MRI images were acquired 2–5 days apart with no change in treatment. Repeatability of kinetic parameters was quantified with Bland–Altman and percent repeatability coefficient (%RC) analysis.ResultsThe perfusion parameter with the least RC was the plasma volume fraction (vp), with a %RC of 53%. The extra-cellular extra-vascular volume fraction (ve) %RC was 82% and 81%, for extended Tofts-Kety Model (eTM) and LTKM respectively. The %RC of the volume transfer rate constant (Ktrans) was 72% for the eTM, and 82% for the LTKM, respectively. Using an automatic VIF resulted in smaller %RCs for all model parameters, as compared to manual VIF.ConclusionsAs much as 72% change in Ktrans (eTM, autoVIF) can be attributable to non-biological changes in the 2–5 days between double-baseline imaging. Poor Ktrans repeatability may result from inferior temporal resolution and short image acquisition time. This variation suggests DCE-MRI repeatability studies should be performed institutionally, using an automatic VIF method and following quantitative imaging biomarkers alliance guidelines.

Quantitative CEST and MT at 1.5T for monitoring treatment response in glioblastoma: early and late tumor progression during chemoradiation.

Quantitative MRI (qMRI) was performed using a 1.5T protocol that includes a novel chemical exchange saturation transfer/magnetization transfer (CEST/MT) approach. The purpose of this prospective study was to determine if qMRI metrics at baseline, at the 10th and 20th fraction during a 30 fraction/6week standard chemoradiation (CRT) schedule, and at 1month following treatment could be an early indicator of response for glioblastoma (GBM). The study included 51 newly diagnosed GBM patients. Four regions-of-interest (ROI) were analyzed: (i) the radiation defined clinical target volume (CTV), (ii) radiation defined gross tumor volume (GTV), (iii) enhancing-tumor regions, and (iv) FLAIR-hyperintense regions. Quantitative CEST, MT, T1 and T2 parameters were compared between those patients progressing within 6.9months (early), and those progressing after CRT (late), using mixed modelling. Exploratory predictive modelling was performed to identify significant predictors of early progression using a multivariable LASSO model. Results were dependent on the specific tumor ROI analyzed and the imaging time point. The baseline CEST asymmetry within the CTV was significantly higher in the early progression cohort. Other significant predictors included the T2 of the MT pools (for semi-solid at fraction 20 and water at 1month after CRT), the exchange rate (at fraction 20) and the MGMT methylation status. We observe the potential for multiparametric qMRI, including a novel pulsed CEST/MT approach, to show potential in distinguishing early from late progression GBM cohorts. Ultimately, the goal is to personalize therapeutic decisions and treatment adaptation based on non-invasive imaging-based biomarkers.