Citations

Abstract BACKGROUND Liquid biopsy in glioblastoma (GBM) is hindered by a lack of requisite circulating-free DNA (cfDNA) blood levels in due to the blood-brain barrier (BBB). Real-time image-guided low-intensity focused ultrasound (LIFU) with intravenously administered microbubble oscillators non-invasively causes BBB disruption (BBBD). This clinical trial aimed to evaluate the utility of LIFU for increasing cfDNA in blood for liquid biopsy in GBM. METHODS LIBERATE is an ongoing, prospective, multi-center, self-controlled, ongoing, pivotal trial evaluating the safety and technical efficacy of LIFU for BBBD to increase cfDNA in blood for GBM. Patients aged 18-80 years with suspected GBM planned for tumor biopsy or resection at ten centers in the US are being included. Patients are administered intravenous oscillating microbubbles for enhancing sonication, after which MR-guided BBBD using 220 kHz LIFU device is performed, with real-time acoustic feedback for effective sonication. Pre- and post-procedure, phlebotomies, and MRI brain are performed. The primary study endpoint is defined, per subject, as the ratio between cfDNA levels in blood 1-hour post-LIFU procedure compared to cfDNA level in blood pre-procedure. The primary study hypothesis is that BBBD with LIFU leads to ≥2-fold increase in cfDNA in blood. The secondary hypothesis is that there exists ≥75% concordance between biomarker patterns in the cfDNA sample from 1-hour post-LIFU sample and tumor tissue obtained later. Exploratory endpoints include (1) sensitivity of detection of known somatic mutations in cfDNA from blood samples collected before and after LIFU, (2) estimation of cfDNA levels in samples collected at 30 minutes, 1 hour, 2 hours, and 3 hours post-LIFU to determine the time of greatest yield, (3) correlation of MRI parameters related to BBBD grading and biomarkers in cfDNA from post-LIFU samples. Patient enrollment commenced in 2022, and 13 patients have been recruited by 06/09/2023. Clinical trial information: NCT05383872.

Abstract Glioblastoma (GBM) is the most aggressive and most common primary malignant brain tumor in adults, with a poor median survival time of 15 months. One of the key challenges in treating GBM is its highly heterogeneous nature, with multiple distinct subtypes that have been shown to occur on both inter- and intra-patient levels. Three main classifications, known as classical/proliferative, mesenchymal and proneural have become commonly demonstrated phenotypes. The cell cycle is a fundamental and highly conserved process that controls faithful division of cells; dysregulation of the cell cycle is known to be a key driver in many cancers. However, how the cell cycle is differently regulated between these subtypes has not been well classified in vivo. We investigate these three GBM subtypes using a recently published single nucleus RNAseq (snRNAseq) data set. We compare cell cycle regulation/dysregulation among these three subtypes using Tricycle, an R/Bioconductor package that utilises dimension reduction via principal component analysis and transfer learning to infer cell cycle position from any snRNAseq data set. We find that the classical GBM subtype has the highest proportion of actively dividing cells (cells in: S/G2/M phases), while the mesenchymal and proneural subtypes have a very low proportion of actively dividing cells. This supports the idea of a proliferation-migration dichotomy between GBM subtypes. We use this proportion of actively proliferating cells to calibrate a minimal spatiotemporal mathematical model for GBM tumor growth that accounts for the differences in cell cycle regulation between these three GBM subtypes.

Abstract RTOG 9402 demonstrated procarbazine (P), lomustine (CCNU/C), and vincristine (V), collectively PCV, added to radiation therapy significantly improved survival for patients with newly diagnosed anaplastic oligodendroglial tumors. The improvement was most pronounced in 1p19q codeleted cases, and to a lesser extent, IDH-mutant cases, with no survival benefit in cases with neither biomarker. Analogous results were observed in other phase III trials. However, vincristine (V) can cause substantial toxicity and requires intravenous administration, both negatively affecting quality of life, with debatable contribution to PCV efficacy. As a randomized trial comparing PCV to PC is unlikely to be conducted, we explored correlations between survival and V-dosing in patients randomized to PCV in RTOG 9402. We performed post-hoc analyses of all evaluable participants (n = 143) randomized to PCV on RTOG 9402, supplemented by restricting the analyses to the chemosensitive molecular subsets (IDH-mutant and/or 1p/19q codeleted, n = 118). Total V dose by quartile (Q) was calculated. Survival from six months post-randomization was compared among V dose groups, adjusting for age, extent of resection (EOR), and Karnofsky Performances Status (KPS) in a Cox proportional hazards model. Higher V dose received correlated with longer survival in multivariate analyses (n = 134, median-Q3 vs. < Q1, HR 0.40 (0.22, 0.73), p = 0.003; ≥Q3 vs. < Q1, HR 0.39 (0.22, 0.72), p = 0.002). Excluding chemoresistant cases (IDH wild-type/not codeleted), survival was also significantly longer in evaluable participants receiving the highest doses of vincristine (≥Q3 vs. < Q1 HR 0.42 (0.21,0.81), p = 0.01). In this post-hoc analysis, higher overall V-dose received correlated with longer survival in evaluable patients with anaplastic oligodendroglial tumors receiving PCV chemotherapy in RTOG 9402. Of note, as vincristine dosing was not assigned by randomization, confounding due to differences in patient characteristics among V dose groups cannot be ruled out. Caution should be exercised when considering discarding V from the PCV regimen.

Abstract INTRODUCTION: TTFields are electric fields that disrupt processes necessary for cancer cell survival and can stimulate an antitumor immune response in preclinical NSCLC models. TTFields therapy is a noninvasive and locoregional treatment approved for glioblastoma, which has recently demonstrated a survival benefit when applied to the thorax with standard of care (SOC) systemic therapy in patients with metastatic NSCLC that progressed on/after platinum. Here we describe the pivotal METIS study (EF-25, NCT02831959) evaluating the efficacy and safety of TTFields therapy applied to brain metastases from NSCLC. TRIAL DESIGN: Patients (270) with 1–10 brain metastases from NSCLC will receive stereotactic radiosurgery (SRS), followed by 1:1 randomization to TTFields (NovoTTF-200M [150 kHz] applied to the head ≥ 18 h/day) with best SOC, or best SOC alone. Key inclusion criteria are life expectancy ≥ 3 months, a new diagnosis of 1 inoperable or 2–10 supra and/or infratentorial brain metastases from NSCLC, that are amenable to SRS. Patients who had prior whole brain radiotherapy, or have single operable, leptomeningeal, or recurrent brain metastases, certain genetic mutations, or severe comorbidities or contraindications, are not eligible. The primary endpoint is time to first intracranial progression. Secondary endpoints include time to neurocognitive failure, overall survival, radiological response rate, time to second intracranial progression, time to first/second intracranial progression by modified RECIST v1.1, rate of intracranial progression, time to distant progression, rate of cognitive decline, quality of life, neurocognitive failure-free survival, and safety. The study is powered at 80% (2-sided alpha of 0.05) to detect a hazard ratio of 0.57. In July 2021, an independent Data Monitoring Committee reviewed the study data and recommended continuation. The study includes 128 sites in Europe, Israel, mainland China, North America, and Hong Kong. The final patient was enrolled in March 2023.

Abstract Glioblastoma (GBM), IDH-wildtype (WT) is currently defined as an IDH-WT and H3-WT diffuse astrocytic glioma with molecular diagnostic features including whole +7/−10, TERT promoter (TERTp) mutation, and EGFR amplification and/or morphological diagnostic features such as necrosis and microvascular proliferation. To further understand the diversity of copy number signatures of GBM, IDHWT and evaluate the diagnostic utility of other recurrent copy number changes, we analyzed paired chromosomal microarray and next-generation sequencing mutational profiling data from 418 adult IDH-WT and H3-WT tumors with GBM morphological diagnostic features. The most common molecular features were TERTp mutations (85%), CDKN2A/B heterozygous/homozygous deletion (78%/66%), +7/-10 (63%), EGFR amplification (41%), +19 (36%), PTEN mutations (35%), partial -13 (33%), partial -14 (33%), +20 (32%), TP53 mutations (32%), and partial -22 (29%). At least one molecular diagnostic biomarker was present in 92% (n=385) of cases, and 29% (n=121) of cases had all three. The +7/-10 biomarker was the most frequent chromosomes 7/10 copy number change (68%, n=265). Other +7/-10 patterns were seen in 30% (n=125) of cases and included isolated +7 or -10, combinations of +7 and -10 with partial +7, partial -10 or 10 copy neutral loss of heterozygosity (cnLOH). Overall, 93% (390/418) of cases had whole and other chromosome 7/10 copy number changes. In comparison to tumors with +7/-10, tumors with other +7/-10 patterns were significantly enriched for partial 13 loss (Bonferroni adjusted p=0.04) but were not associated with significant difference in patient overall survival (p=0.30, 158/418 had available survival data). Our findings reiterate and expand the spectrum of copy number changes associated with GBM, IDH-WT. The similar overall survival rates between cases with the +7/-10 biomarker and those with other +7/-10 patterns suggest that other +7/-10 patterns may be functionally equivalent to +7/-10 and diagnostically relevant as a molecular diagnostic biomarker for this tumor type.

Abstract High-grade glioma continues to have dismal survival owing in part to its intra- and inter-patient heterogeneity. Standard clinical protocol collects tumor samples with the aim of providing or confirming a diagnosis and determining the status of a few key genes (e.g. IDH1, MGMT). However, this protocol is unable to capture the diversity within tumor regions, immune expression or normal cell abundances that play key roles in the development of the disease. To overcome this, during surgery we collect image-localized multi-regional biopsies to characterize this disease heterogeneity. Data collection is ongoing, and we currently have 202 samples from 58 patients with available bulk RNA-Seq. With a single-cell reference dataset from Columbia University, we used CIBERSORTx, a deconvolution method, to predict relative abundances of 7 normal, 6 glioma, and 5 immune cell subpopulations for each sample. We used Cox proportional hazard models with first-order statistics (mean, minimum, maximum) of abundances within patients to determine whether these are prognostic, then used TCGA RNA-Seq data to validate these findings. We found that one glioma proneural subtype was significantly beneficial for patient survival relative to other glioma subtypes across all statistics (and showed significance in TCGA). Proliferative and mesenchymal glioma subtypes also showed significance for one or two of the calculated statistics. Oligodendrocyte progenitor cell abundances were consistently significantly beneficial for patient survival in our cohort, while an increased abundance of abnormal (reactive) astrocytes is associated with poor prognosis. We also ran these analyses within invasive margin and core tumor regions to determine location-specific population abundances associated with patient survival. In conclusion, understanding the in vivo diversity of cellular subpopulations within high grade glioma is important for treatment stratification and patient benefit.

Abstract BACKGROUND Leptomeningeal disease (LMD) carries poor prognosis with an average survival of 3-6 months after diagnosis. Ommaya reservoirs (OR) are an accessible alternative to serial lumbar punctures for delivery of intrathecal chemotherapy (IC) and disease monitoring but are not widely used. Intraventricular administration of chemotherapy may be associated with superior efficacy and improved patient comfort. The purpose of this study was to assess safety and efficacy of IC and disease monitoring via OR in routine clinical practice. METHODS Patients who received at least one administration of IC via OR for LMD between 2017 and 2022 were included. Demographics, treatment type, complications and outcomes were recorded. RESULTS We identified 22 patients (17 females, 5 males) with mean age 50.9 ± 14.8 years. The primary tumors were: breast (12), leukemia (3), ovarian carcinoma (3), CNS lymphoma (1), urothelial carcinoma (1), spinal melanocytoma (1), and high-grade glioma (1). A total of 208 OR injections were performed [median was 9 OR injections per patient, interquartile range (IQR) (5, 13)]. Five patients (23%) experienced mild adverse events of grade 2 or lower by Common Terminology Criteria for Adverse Events. Overall risk of adverse event from injection was 3.4% (7/208). Eight patients (36.3%) converted into negative CSF cytology (median 1 month following first injection). Eighteen patients (82%) had clinical or radiological progression of their LMD (median 2 months following first injection). Eleven patients (50%) died of their LMD during follow-up. Median OS and PFS from the first injection were 5.3 months [95% CI 4.8-NE (not estimable)] and 4.3 months [95% CI 1.8-16.0], respectively. CONCLUSION Use of OR in routine clinical practice is a safe and feasible option and should be strongly considered for treatment and monitoring of patients with LMD. The median OS in our cohort is comparable to prior published series.

Abstract Identification of key phenotypic regions such as necrosis, contrast enhancement, and edema on magnetic resonance imaging (MRI) is important for understanding disease evolution and treatment response in patients with glioma. Manual delineation is time intensive and not feasible for a clinical workflow. Automating phenotypic region segmentation overcomes many issues with manual segmentation, however, current glioma segmentation datasets focus on pre-treatment, diagnostic scans, where treat-ment effects and surgical cavities are not present. Thus, existing automatic segmenta-tion models are not applicable to post-treatment imaging that is used for longitudinal evaluation of care. Here, we present a comparison of three-dimensional convolutional neural networks (nnU-Net architecture) trained on large temporally defined pre-treatment, post-treatment, and mixed cohorts. We used a total of 1563 imaging timepoints from 854 patients curated from 13 different institutions as well as diverse public data sets to understand the capabilities and limitations of automatic segmenta-tion on glioma images with different phenotypic and treatment appearance. We as-sessed the performance of models using Dice coefficients on test cases from each group comparing predictions with manual segmentations generated by trained techni-cians. We demonstrate that training a combined model can be as effective as models trained on just one temporal group. The results highlight the importance of a diverse training set, that includes images from the course of disease and with effects from treatment, in the creation of a model that can accurately segment glioma MRIs at multiple treatment time points.

Abstract Image-based mathematical modeling is emerging as an advantageous tool to integrate into the care routine for patients with brain tumors. These tools can be used for a variety of tasks including predicting tumor growth, indicating spatial genomic alterations, and guiding radiation therapy. Often, the methods to produce these maps are developed in silos, using cohorts of retrospective patients and switching to a per patient map calculation can be an afterthought. Automatic pipelines are needed to enable creating mathematical maps in a clinically-relevant workflow. To this end, we developed a pipeline to create an image-based mathematical map of edema abundance based on T2-weighted (T2W) magnetic resonance imaging that includes preprocessing steps, abnormality and tissue type segmentation, brain extraction, map generation, and conversion between image file formats. The python-based pipeline was designed to take in T1-weighted (T1W), T1W with gadolinium contrast (T1Gd), T2W, and fluid attenuated inversion recovery (FLAIR) DICOM images for a single patient as input and convert them to NIfTIs for downstream steps. The pipeline preprocessed images by correcting bias field fluctuations using the N4 algorithm and normalizing intensities. The T1W, T1Gd, and FLAIR are then registered to the T2W. T1Gd and FLAIR are used in a deep learning algorithm to delineate the brain and abnormality including enhancing tumor, necrosis, and peripheral edema. Normal tissue segmentations are performed using the statistical parametric mapping (SPM) segmentation routine. The segmentations and preprocessed images are all utilized in the edema mathematical model. The resulting edema map is then reformatted as a DICOM and output for further use by clinicians and clinical imaging systems. Our future goals include integrating our pipeline in a clinical workstation and sending final maps to our hospital’s picture archiving and communication system (PACS) to allow for deployment of these models for patient care in a clinically efficient timeline.

Abstract BACKGROUND Leveraging external data has the potential to increase efficiency and precision of drug development in glioblastoma by complementing or replacing data from in-trial controls. We used an external control arm (ECA) to re-analyze data of the abemaciclib, neratinib and CC-115 arms of the Individualized Screening Trial of Innovative Glioblastoma Therapy (INSIGhT) trial. METHODS INSIGhT (NCT02977780) is an ongoing phase 2 adaptive platform trial in newly diagnosed MGMT unmethylated glioblastoma with a shared control arm of patients receiving radiation therapy with concurrent and maintenance temozolomide. The initial experimental arms (abemaciclib, neratinib, CC-115) have not shown a survival benefit relative to the shared control arm. We re-analyzed the experimental arm data and replaced the shared control arm with an ECA. The ECA was developed from a collection of trial datasets and real-world data available through the Glioblastoma External (GBM-X) Data Platform (). Propensity score matching was used to adjust for possible differences between pretreatment covariates (age, sex, resection, KPS) in the ECA and shared control arm. Cox proportional hazards models were used to estimate treatment effects. RESULTS On INSIGhT, patients were randomized to abemaciclib (n = 74), neratinib (n = 80), and CC-115 (n = 12). There were 687 unmethylated MGMT patients in the GBM-X ECA. After propensity score matching, there was no significant treatment effect associated with abemaciclib (HR 1.091, 95%CI:0.834-1.433, p = 0.536), neratinib (HR = 1.043, 95%CI:0.787-1.383, p = 0.770), or CC-115 (HR 1.088, 95%CI:0.834-1.433, p = 0.794) compared to the ECA. CONCLUSION In comparison to an ECA, there was no significant survival benefit with abemaciclib, neratinib and CC-115. Treatment effects estimates were similar to the primary analyses based on the original shared control arm of INSIGhT. The use of external control arms in early phase testing of new therapies is supported by our findings and could significantly reduce costs and time to conduct trials in newly diagnosed glioblastoma.