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Introduction: We have completed two CAR T cell clinical trials for glioblastoma (GBM) and have identified several key challenges to therapeutic efficacy, including the inherently heterogenous genomic landscape and the immunosuppressive tumor microenvironment (TME) found in GBM. Our previous study showed that EGFR variant III (EGFRvIII)-targeting monovalent CAR T cells reduced target-positive tumor cell populations, but tumor recurrence resulted from target-negative tumor cells, highlighting the limitation of single-target approaches in heterogenous tumors. With regards to the highly immunosuppressive TME in GBM, we found that transforming growth factor-β (TGFβ) was present in the GBM TME as a major driver of suppression of the anti-GBM response in clinical samples. TGFb is consistently highly expressed in both GBM tumor cell lines and patient tumor tissues. Methods: We used two parallel scFv constructs, independently targeting both IL13Rα2 and EGFRvIII, in combination with a truncated dominant negative (dn) TGFβ receptor II. This trivalent construct was designed to explore possible additive effects in both in vitro and in vivo GBM model systems to limit tumor escape and overcome the immunosuppressive GBM TME. The CART-EGFR-IL13Rα2-dnTGFb construct broadened the targeted tumor cell repertoire, blocked TGFβ signaling, and served as a sink for free TGFβ in the GBM TME to overcome the suppressive function of TGFβ. Results: The tri-modular CAR T construct had an enhanced proliferative response when compared with the CART-EGFR-IL13Rα2 construct, in vitro. In co-culture assays, this construct led to reduced PD-1 expression and increased central memory phenotype, when compared to the bicistronic CAR T construct, which suggested a lower fraction of exhausted T cells. Tri-modular CAR T cells blocked the suppressive pSmad2/3 signaling pathway, leading to the increased tumor killing activity in co-culture experiments with both adherent and suspension GBM cell lines. In an immunodeficient mouse model, tri-modular CAR T cells eradicated tumor cells efficiently and mice had a longer median survival when compared those treated with the bicistronic CART-EGFR-IL13Rα2 cells, lacking the dnTGFb receptor II. Conclusion: Overcoming the adaptive changes in the local TME and addressing antigen heterogeneity will be required to improve the clinical efficacy of CAR T-directed strategies. Our combination work showed that bicistronic CART constructs cooperate with truncated TGFβ receptor II efficiently. In summary, the dominant-negative TGFβ RII CART-EGFR-IL13Rα2 structure is a promising strategy to address the clinical challenges of antigenic heterogeneity and the immunosuppressive TME in GBM we have observed in our two GBM CART cell trials at UPenn. Citation Format: Nannan Li, Jesse Rodriguez, Zev Binder, Donald O’Rourke. Armored bicistronic CAR T cells with dominant-negative TGF-β receptor II to alleviate antigenic heterogeneity and suppressive immune microenvironment in glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB097.

PURPOSE Glioblastoma is extremely infiltrative with malignant cells extending beyond the enhancing rim where recurrence inevitably occurs, despite aggressive multimodal therapy. We hypothesize that important characteristics of peritumoral tissue heterogeneity captured and analyzed by multi-parametric MRI and artificial intelligence (AI) methods are generalizable in the updated multi-institutional ReSPOND (Radiomics Signatures for PrecisiON Diagnostics) consortium and predictive of neoplastic infiltration and future recurrence. METHODS We used the most recent update of the ReSPOND consortium to evaluate and further refine generalizability of our methods with different scanners and acquisition settings. 179 de novo glioblastoma patients with available T1, T1Gd, T2, T2-FLAIR, and ADC sequences at pre-resection baseline and after complete resection with subsequent pathology-confirmed recurrence were included. To establish generalizability of the predictive models, training and testing of the refined AI model was performed through Leave-One-Institution-Out-Cross-Validation schema. The multi-institutional cohort consisted of the Hospital of the University of Pennsylvania (UPenn, 124), Case Western Reserve University/University Hospitals (CWRU/UH, 27), New York University (NYU, 13), Ohio State University (OSU, 13), and University Hospital Río Hortega (RH, 2). Features extracted from pre-resection MRI were used to build the model predicting the spatial pattern of subsequent tumor recurrence. These predictions were evaluated against regions of pathology-confirmed post-resection recurrence. RESULTS Our model predicted the locations that later harbored tumor recurrence with overall odds ratio (99% CI)/AUC (99% CI), 12.0(11.8-12.2)/0.80(0.76-0.85), and per institute, CWRU/UH, 11.0(10.7-11.3)/0.80 (0.64-0.97); NYU, 7.0(6.7-7.3)/0.78(0.56-1.00); OSU, 18.3(17.5-19.1)/0.83(0.54-1.00); RH, 40.0(35.3-45.5)/0.93(0.00-1.00); UPenn, 8.00(7.7-8.3)/0.80(0.75-0.84). CONCLUSION This study provides extensive multi-institutional validated evidence that machine learning tools can identify peritumoral neoplastic infiltration and predict location of future recurrence, by decrypting the MRI signal heterogeneity in peritumoral tissue. Our analyses leveraged the unique dataset of the ReSPOND consortium, which aims to develop and validate AI-based biomarkers for individualized prediction and prognostication and establish generalizability in a multi-institutional setting.

PURPOSE The significant heterogeneity of glioblastoma is typically displayed on both phenotypical and molecular levels. Non-invasive in vivo approaches to characterize this heterogeneity would potentially facilitate personalized therapies. Here we leverage advanced unsupervised machine learning to integrate radiomic imaging features and genomics to identify distinct subtypes of glioblastoma. METHODS A retrospective cohort of 571 IDH-wildtype glioblastoma patients were collected with pre-operative multi-parametric MRI (T1, T1CE, T2, T2-FLAIR, DSC, DTI) scans (available in 462/571 patients) and targeted next-generation sequencing (NGS) data (available in 355/571 patients). Radiomic features (n= 971) were extracted from these MRI scans and a subset of 12 features were selected by L21-norm minimization. A total of 14 key driver genes in the 5 main pathways that are most frequently altered in glioblastoma were chosen. Subtypes were identified by a joint learning approach called Anchor-based Partial Multi-modal Clustering (APMC) on both radiomic and genomic modalities. RESULTS Three distinct glioblastoma subtypes were discovered by APMC based on 14-dimension NGS data together with 12 selected radiomic features representing characteristics from histograms, shape, and volumetric measures for different tumor sub-regions. The identified subtypes were 1) high-risk; 2) medium-risk; and 3) low-risk, in terms of their overall survival outcome in Kaplan-Meier analysis (p= 5.52e-6, log-rank test; HR= 1.51, 95%CI:1.20-1.74, Cox proportional hazard model). The three subtypes also displayed different molecular characteristics: subtype 1 exhibited increased frequency of mutation in [EGFR, PIK3CA, PTEN, NF1], subtype 3 showed frequently mutated [PDGFRA, ATRX], while subtype 2 did not show significant differences for mutations in any of these genes. CONCLUSION Our results revealed the synergistic value of integrated radiomic signatures and molecular characteristics for glioblastoma subtyping. Joint learning on both modalities could help better understand the molecular basis of phenotypical signatures of glioblastoma and further provide insights into the biologic underpinnings of tumor formation and progression.

BACKGROUND GBM genomic profiling relies on sequencing a limited tumor tissue sample, which is invasive and may underrepresent molecular heterogeneity. CSF, which can be obtained less invasively, is in intimate contact with tumor lesions and may better capture the full genomic profile of the tumor. However, datasets with contemporaneously collected CSF and tissue to support this claim have been lacking. To evaluate the performance of CSF NGS, we conducted a pilot study in patients with GBM undergoing a resection for suspected recurrence following first-line chemoradiotherapy. METHODS Paired CSF and tissue samples were sequenced using a hybrid capture-based NGS assay. Clinically meaningful variants were defined as those that are potentially targetable using off-label or clinical trial options, exhibit prognostic value, or may predict response or resistance to specific treatments. RESULTS Eighteen patients were enrolled, and 13 of 18 CSF samples (72.2%) were sequenced successfully. At least one variant was detected in all CSF samples analyzed. A median of 7 variants (range 1—67) was detected per sample across 54 genes. The median variant allele fraction was 0.6% (range 0.2—72.4%). Among 38 clinically meaningful genes, 102 variants were detected; 25 (24.5%) were detected in both tissue and CSF, while 60 (58.8%) were detectable solely in CSF. Hypermutation was detected by CSF in one patient. Of the 82 variants detected in this patient’s tumor, 15 (18.3%) were identified in both tissue and CSF, 15 (18.3%) were identified only in the tissue, and 52 (63.2%) were identified only in the CSF. CONCLUSIONS CSF NGS detects clinically meaningful variants at a substantial rate and frequently identifies mutations not detected by matched tissue NGS. These results suggest that CSF may be a suitable source material for tumor profiling, overcoming the limitations of tissue, and may also provide a more comprehensive tumor profile.

PURPOSE Differentiation of tumor progression (TP) from pseudoprogression (PsP) is a major unmet need in post-treatment glioblastoma (GBM). 18F-Fluciclovine is a synthetic amino acid PET radiotracer with higher uptake in tumor tissue vs. areas of treatment-related change. We investigated the value of 18F-Fluciclovine PET for differentiating PsP from TP independent from and in combination with multi-parametric MRI. METHODS We prospectively enrolled 30 patients with GBM with a new or enlarging contrast-enhancing lesion on MRI after chemoradiotherapy who were planned for surgical resection of the lesion. Patients underwent pre-operative 18F-Fluciclovine PET and multi-parametric MRI. Following surgery, the relative percentages of viable tumor and therapy-related changes observed in histopathology were quantified. Patients were categorized as TP if viable tumor represented ≥ 50% of the specimen, mixed TP if < 50% and > 10%, and PsP if ≤ 10%. RESULTS 18 patients had TP, 4 had mixed TP, and 8 PsP. Patients with TP/mixed TP had a significantly higher 40-50 minutes SUVmax (6.64 + 1.88 vs 4.11± 1.52, p=0.009) and an SUVmax cut-off of 4.66 provided 90% sensitivity and 83% specificity for differentiation of TP/mixed TP from PsP (AUC=0.856). A maximum cerebral blood volume (CBVmax) cut-off of 3.67 provided 90% sensitivity and 71% specificity for differentiation of TP/mixed TP from PsP (AUC=0.779). Combining a 40-50 minutes SUVmax cut-off of 4.662 and a relative CBVmax cut-off of 3.67 provided 100% sensitivity and 80% specificity for differentiating TP/mixed TP from PsP (AUC=0.95). The time activity curve patterns and time to peaks were not different between the groups. Normalization of PET parameters to normal brain parenchyma were not helpful to differentiate the groups due to variability in radiotracer uptake in normal brain between subjects. CONCLUSION 18F-Fluciclovine PET uptake can accurately differentiate PsP from TP in GBM patients, with even more accurate differentiation achieved when combined with MRI.

PURPOSE Glioblastoma, IDH-wildtype, is the most common primary malignant adult brain tumor with median overall survival (OS) of ~14 months, with little improvement over the last 20 years. We hypothesize that AI-based integration of quantitative tumor characteristics, independent of acquisition protocol and equipment, can reveal accurate generalizable prognostic stratification. We seek an AI-based OS predictor using routine clinically acquired MRI sequences, quantitatively evaluated across institutions of the ReSPOND (Radiomics Signatures for PrecisiON Diagnostics) consortium. METHODS We identified a retrospective cohort of 2,293 diffuse glioma (IDH-wildtype/-NOS/-NEC) patients from 22 geographically distinct institutions across 3 continents, with preoperative structural MRI scans. The entire tumor burden was automatically segmented into 3 sub-compartments, i.e., enhancing, necrotic, peritumoral T2-FLAIR abnormality. We developed our AI predictor by multivariate integration of i)patient age, ii)tumor sub-compartment volume normalized to brain volume, iii)spatial distribution characteristics (tumor location, distance to the ventricles, and laterality), and iv)morphologic descriptors (major axes’ length, axes’ ratio, extent, and number of tumors). The AI predictor returns a continuous value between 0-1, defining short-, intermediate-, and long-survivors based on thresholds on the 25th and 75th percentiles. Leave-One-Site-Out-Cross-Validation was used to assess the generalizability of our stratification. Kaplan-Meier survival curves were computed for OS analysis and evaluated by a Cox proportional hazards model for statistical significance and hazard ratios. RESULTS Survival analysis yielded a hazard ratio of 2.07 (95%CI, 2.06-2.08, p-value= 4.8e-102) for patient stratification into short-, intermediate-, and long-survivors. Pearson correlation between the predicted and actual OS yielded an R= 0.49. CONCLUSION Multivariate integration of visually quantified tumor characteristics, agnostic to acquisition protocol/equipment, yields an accurate OS surrogate index. Validation of our AI model in the largest centralized glioblastoma imaging dataset, from the ReSPOND consortium, supports its generalizability across diverse patient populations and acquisition settings, potentially contributing to equitable improvements of personalized patient care.

CAR-T cell therapy is a promising new immunotherapy for a number of difficult-to-treat cancers, however, it has yet to yield broad success in glioblastoma (GBM). In particular, tumor heterogeneity presents a major therapeutic challenge, and a detailed understanding of the complex interplay between different neoplastic, non-neoplastic, and CAR-T cells is critical for developing better treatments. Using a patient-derived GBM organoid model of CAR-T cell therapy, we performed single-cell multi-omics to examine the longitudinal dynamics of the adaptive tumor response, changes in cell states, and evolution of cell-to-cell interaction networks. We find that all tumor cell types - neoplastic and non-neoplastic - respond to CAR-T cell activity, and they generate to an initially anti-tumor, but subsequently pro-tumor and immune-inhibitory microenvironment, which is accompanied by eventual CAR-T cell dysfunction and exhaustion. Unexpectedly, CAR-T cell activity also leads to attenuation of glioma stem-like states in both antigen-positive and antigen-negative neoplastic cells and reduces their proliferation via diffusible factors, of which IFNɣ is required but not sufficient. These findings are supported by analyses in patient samples from CAR-T cell therapy clinical trials, and they are consistent across both de novo and recurrent tumors with different somatic mutational landscapes. Our study unravels how the complex heterogeneity of GBM interacts with CAR-T cell therapy, and we identify previously unappreciated possibilities to affect antigen-negative neoplastic cells in ways that may be further augmented for enhanced therapeutic efficacy.

BACKGROUND We evaluated the combination of retifanlimab, INCAGN01876, and FSRT in patients with recurrent GBM. METHODS Phase II, single-center, 2 cohort study (A: single-arm non-surgical cohort; B, two-arm neoadjuvant/surgical cohort). Cohort A patients received pre-FSRT one-time doses of retifanlimab (500mg) and INCAGN01876 (300mg), FSRT (8 Gy x 3 fractions), and post-FSRT 28-day treatment cycles (retifanlimab, day 1; INCAGN01876, days 1, 15). Cohort B patients received pre-surgery doses of retifanlimab + INCAGN01876; subsequently, patients either went directly to resection (Sub-Arm 1) or to FSRT followed by resection (Sub-Arm 2). All patients resumed immunotherapy post-operatively. Primary endpoint: ORR in Cohort A. Data cut-off for this analysis was June 30, 2022. RESULTS Thirty-two evaluable patients: Cohort A, n=16; Cohort B, n=16 (Sub-Arm 1, n=8, Sub-Arm 2, n=8). Median follow-up time: Cohort A, 13.6 months; Cohort B, 8.8 months. Forty-four percent women, median age 64 (IQR, 55–65), 56% MGMT unmethylated. Most common grade 3/4 treatment-related AEs included cerebral edema (25%), lymphopenia (16%), and cognitive disturbance (13%). Efficacy in Cohort A: no objective responses observed, best response of stable disease achieved in 9/16 patients (56%), median PFS 3.9 months (95% CI 2.1 – 6.2 months), median OS 9.8 months (95% CI 8.3 months – not reached [NR]). Efficacy in Cohort B: median PFS NR, median OS 15.1 months (95% CI 8.5 months – NR). Median PFS and OS are longer in Cohort B Sub-Arm 2 compared to Cohort B Sub-Arm 1 (PFS, NR vs. 2.2 months, p = 0.009; OS, NR vs. 8.5 months, p=0.026). Results of tissue and blood-based immune correlative analyses will be presented. CONCLUSIONS The combination of retifanlimab, INCAGN01876, and FSRT is generally well-tolerated in patients with recurrent GBM when administered with or without surgical resection. Survival outcomes in the neoadjuvant cohort are encouraging, largely driven by patients that received neoadjuvant FSRT.

Somatic genomic alterations acquired during GBM growth enhance adaptation of tumor cells to their microenvironment and give rise to molecular heterogeneity. Radiogenomics could facilitate exploration of the underlying pathobiology of tumor growth in specific microenvironments and thereby, promote precision medicine for the patients. We derived radiogenomic signatures of key driver genes and evaluated molecular compositions of tumor groups with predisposition to specific brain regions. Pre-operative multiparametric conventional MRI scans of 357 IDH-wildtype GBM patients with available targeted NGS data were jointly segmented and registered into a common template. We constructed spatial distribution atlases for tumors harboring mutations in driver genes and identified four distinct groups of tumor locations with predilection to the left frontal cingulate region (Group1), right temporal (Group2), right parietal (Group3), and occipital pole (Group4). Evaluation of the differences in molecular features of the tumor groups included: (1) exploring similarities of genomic profiles across all four groups by evaluating cosine similarity metric (CSM) between mutational signatures; (2) quantification of molecular heterogeneity based on Mutant Allele Tumor Heterogeneity (MATH) scores; and (3) inference of the evolutionary trajectories. Groups 1 and 4 were the most different, and Groups 2 and 3 were the most similar tumors, molecularly. The mutational signatures between Groups 1 and 4 revealed a CSM of 0.35. Group1 showed significantly lower MATH score (less heterogeneity) compared to Group4 (p< 0.05). Evaluation of evolutionary patterns suggested NF1 mutation as an early event in Group1, without subsequent gain of function or mutation in EGFR. In contrast, in Group4, EGFR mutations were early events triggering PTEN mutations later in the evolutionary trajectory. Radiogenomic signatures revealed distinct molecular underpinnings for the tumors with predilection towards specific brain regions that may suggest existence of different tumor microenvironments in different brain regions that cause intra- and inter-patient heterogeneity in the molecular tumor composition.

The effect of head position on intracranial pressure (ICP) and intracranial compliance was determined in 19 consecutive ICU patients. Ten had lower ICPs with the head raised 60 degrees, two were lower at 0 degrees, and seven were unchanged. Compliance improved with head elevation in five patients, improved with head lowering in four, and was unchanged in 10. The use of subarachnoid screw devices for compliance measurements was validated by simultaneously recording intraventricular and subarachnoid pressures in four patients. Optimal head positioning for patients with raised ICP should be established individually rather than routinely caring for patients with the head elevated.