Hu, Leland

Abstract INTRODUCTION DSC-MRI perfusion methods are commonly used to evaluate both primary and metastatic brain cancer with the creation of maps of relative cerebral blood volume (rCBV). Recently, guidelines were established to ensure a standard DSC-MRI acquisition protocol that reduces inter-site variability. The purpose of this study was to initiate the determination of rCBV benchmark values using the DSC-MRI consensus protocol in treatment-naïve brain metastases. METHODS Patients from three sites with treatment-naïve, contrast-enhancing brain metastases on MRI were considered for inclusion in this retrospective study. The MRIs included pre- and post-contrast T1w(T1+C) images obtained after administration of gadolinium-based contrast agent (GBCA) (0.1 mmol/kg), which serves as the recommended preload for the DSC-MRI data collection. A 2nd GBCA dose (0.1 mmol/kg) was administered 40-60sec after the collection of baseline GRE-EPI images using recommended settings (FA=60o, TE/TR=30ms/1100-1250ms) for 120s. Calibrated pre/post T1w difference maps (dT1) were used for delineation of enhancing lesion, and standardized (calibrated) rCBV (sRCBV) created. Mean sRCBV for metastases were compared to normal appearing white matter (NAWM) and treatment-naive glioblastoma (GBM) from a previous study. Pairwise comparisons were performed using the Mann-Whitney nonparametric test. RESULTS N=52 patients with primary histology of lung (n=27); breast (n=6); skin (n=7); gastrointestinal (GI: n=3) and genitourinary (GU: n=9) cancers were included in comparison to GBM (n=31). The mean sRCBV for all metastases (1.77+/-1.05) is significantly lower (p=0.0003) than for GBM (2.67+/-1.34) but with both statistically greater (p< 0.0001) than NAWM (0.706+/-0.163). Individually, lung (1.47+/- 0.61), breast (2.275+/-0.87), skin (2.10+/-1.22), GI (1.91+/- 0.64) and GU (2.01+/-0.63) mean sRCBV are statistically greater than for NAWM. CONCLUSION Using the consensus DSC-MRI acquisition protocol confirms use of sRCBV to identify biologically active, treatment-naive brain metastases setting benchmark values for future applications.

Abstract The brain is notoriously difficult to access because of its location and protective mechanisms, both internal and external. Imaging-based approaches, such as multiparametric magnetic resonance imaging (mpMRI), provide a method to bypass these obstacles. mpMRI can capture a wide range of data from tumors embedded in brain tissue through various imaging features. However, the biological function captured by each imaging feature remains largely unknown, particularly within the aggressive disease glioblastoma multiforme (GBM). We integrated our GBM SYstems Genetics Network AnaLysis (gbmSYGNAL) gene regulatory network (Plaisier, et al. 2016), based on the multi-omic patient data from The Cancer Genome Atlas (TCGA), with mpMRI-localized patient tumor biopsies. The gbmSYGNAL network was reconstructed within the new image-localized biopsies, and associations revealed a strong connection between the spoiled gradient recalled echo + contrast (SPGR+C) feature and the immune hallmarks of cancer. The SPGR+C method generates an image by employing a contrast agent that bypasses the blood brain barrier (BBB) through leaky blood vessels that have weakened from the force of the rapidly growing GBM tumor. Furthermore, a significant association between SPGR+C signal and the presence of both infiltrating macrophages and T cells indicated that peripheral immune cells may also cross the BBB at these leaky sites. Separately, the mean diffusivity (MD) & T2 features were linked to the sustained angiogenesis hallmark. These features displayed high association with angiogenic genes, such as FLT1, KDR, and PDGFRB, and correlated with the presence of histologically identified vessels. Detection of these disease-relevant functions via mpMRI features has powerful clinical applications, providing a non-invasive approach to GBM diagnosis, prognosis, and treatment direction.

Abstract Brain tumor associated edema is a significant cause of patient morbidity and mortality often requiring continual treatment with differing levels of success across patients. Edema is a broad term that can indicate increased local water as well as diffusely infiltrating tumor cells. Edema is visualized as hyperintense regions on T2-weighted (T2W) magnetic resonance imaging (MRI) and is typically associated with blood-brain barrier (BBB) breakdown and tumor aggressiveness. Understanding the molecular mechanisms driving imaging patterns of “edema” could provide insights into clinical imaging interpretation. We have an ongoing image-guided biopsy study that allows us to link biopsy molecular markers with locoregional MRI patterns of edema. Further, we previously developed a physics-based method to estimate edema abundance (i.e., edema map) from T2W MRIs. Our goal was to identify connections between BBB-associated molecular factors and edema abundance in brain tumors. Our cohort included 38 patients (female: 15, male: 23) with 129 image-guided biopsies (female: 62, male: 67). We correlated image-localized edema map values with the mean transcriptional frequency for 57 genes related to BBB function. Additionally, we examined correlations separately according to patient reported sex (i.e., male and female) and imaging phenotype (i.e., ENH: enhancing and NE: non-enhancing). We utilized multiple comparisons corrections with a 5% false discovery rate to determine significance. For the overall cohort, we observed significant positive correlations for the HIF1A (p< 0.001) and SOX2 markers with edema. For NE samples, significant correlations included APOE (p=0.001), HIF1A (p< 0.001), PIK3CA (p< 0.001), PTCH1 (p< 0.001), and SOX2 (p< 0.001). Amongst female samples, a significant correlation with PTCH1 (p=0.002) was observed. There were no significant correlations noted for male and enhancing sub-cohorts. Significant correlations between molecular markers of BBB and edema map values could lead to clinical biomarkers for edema or tumor aggressiveness.

Abstract High-grade gliomas represent the most common type of primary adult malignant brain tumor historically diagnosed and graded from histologic criteria alone. Gliomas harboring isocitrate dehydrogenase (IDH) 1 or 2 mutations, which are present in more than 80% of World Health Organization (WHO) grade 2 or 3 tumors, portend a favorable prognosis as compared to IDH-wildtype tumors. However, recent molecular profiling has identified the presence of additional alterations in these lower grade tumors that transform the biology toward an aggressive high-grade phenotype clinically that are independent of histologic grading and confer a worse clinical prognosis. Of these, the loss of CDKN2A/B is the strongest implicated alteration and is sufficient to define a grade 4 tumor in IDH-mutant astrocytomas regardless of histologic appearance. However, there remain no effective therapies targeted at molecular subgroups in aggressive gliomas to date. Here we have profiled the intratumoral heterogeneity via whole exome and RNA sequencing and copy number alteration analysis within a cohort of de-identified IDH-mutant astrocytoma patient tumors collected through the Mayo Clinic and Barrow Neurological Institute (BNI) biorepositories to determine the spatial evolution of aggressive molecular features. Within this cohort we have collected 42 image-localized intratumoral biopsy specimens representing 13 IDH-mutant patient tumors. We identified 11 biopsies from 3 tumors that harbored loss of CDKN2A/B which included 1 tumor that displayed intratumoral heterogeneity of CDKN2A/B expression across subclones. We performed an unsupervised cluster analysis to profile the transcriptional signatures in IDH-mutant astrocytomas across both differential CDKN2A/B status and tumor geographic location comparing contrast enhancing versus non-enhancing regions, and we analyzed the MRI features in IDH-mutant tumors across CDKN2A/B status. We found that tumors with CDKN2A/B alteration demonstrated lower signal intensity on delayed post-contrast T2*W images, which could represent lower pre-contrast T2*, unique contrast agent kinetics, or dissimilar diffusion properties related to increased cellular density and/or greater heterogeneity of cell size. Interestingly, we uncovered spatial heterogeneity of additional molecular drivers of high-grade glioma including KRAS, MYCN, and PDGFRA. While EGFR alterations are rarely reported in IDH-mutant tumors, we identified one treatment naïve patient tumor that harbored EGFR alteration only in the contrast non-enhancing region. Based on this work we propose there is early spatial evolution of aggressive tumor subclones prior to primary tumor diagnosis, and multiregional molecular profiling may better inform tumor biology and predict therapeutic strategies in IDH-mutant astrocytomas. Citation Format: Shannon P. Fortin Ensign, Fulvio D'Angelo, Francesca P. Caruso, Christopher Sereduk, Kyle Singleton, Jennifer Eschbacher, Gina Mazza, Maciej Mrugala, Alyx Porter, Bernard Bendok, Richard Zimmerman, Kris Smith, Peter Nakaji, Kamala Swanson, Junwen Wang, Nathenael Semmineh, Christopher Quarles, Anna Lasorella, Kristin Swanson, Michele Ceccarelli, Antonio Iavarone, Leland Hu, Nhan L. Tran. Multiregional imaging and genomic analysis in high-grade IDH-mutant astrocytoma identifies intratumor subclones with aggressive features [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 770.

2001 Background: PolyADP-ribose polymerase (PARP) is an important modulator of DNA repair following temozolomide (TMZ) therapy. Pre-clinical testing demonstrated significant survival benefit for the combination of TMZ and PARP inhibitor veliparib in a subset of GBM pt-derived xenografts with MGMT promoter hypermethylation. Methods: After central pathology review and MGMT testing, patients (pts) with newly diagnosed, MGMT promoter hypermethylated GBM who had completed concurrent radiation and TMZ were randomized to adjuvant therapy with TMZ (Days 1-5 q28 days) combined with either placebo or veliparib (Days 1-7 q28 days). Veliparib/placebo+TMZ treatment was continued for up to 6 cycles. Pts accrued on the phase II and III portions of the trial were included in the primary endpoint analysis of overall survival (OS), with 90% power to detect a hazard ratio of 0.71 using a one-sided log-rank test with type I error rate of 0.05. The planned phase III sample size was 400 pts with data maturity after 302 deaths. Results: The phase II and III portions of the trial were open to accrual from 12/15/2014 to 2/6/2017 and 11/8/2017 to 10/15/2018, respectively; 447 pts were accrued to the trial and used in this intention to treat analysis. The two treatment groups were well balanced for prognostic factors, 421 pts initiated treatment, median follow-up was 57.8 months (mos), 380 pts had disease progression and 335 pts have died. There was no difference in OS (p = 0.15; HR 0.89 (0.71-1.11), median OS 28.1 vs. 24.8 mo. for TMZ+veliparib vs. TMZ+placebo, respectively) and no difference in secondary endpoint progression free survival (PFS, p = 0.31; HR 1.05 (0.86-1.30), median 13.2 vs. 12.1 mo, respectively). There was a notable trend for extended OS with TMZ+veliparib treatment at intermediate time-points between 24 and 42 mos (3-year OS 36.6% vs. 28.9% with TMZ+placebo, p = 0.09). In an unplanned exploratory analysis, treatment with TMZ at the time of first recurrence was associated with extended post-recurrence OS (p = 0.03) for pts treated on the experimental arm; median post-recurrence OS with TMZ salvage was 17.0 mo in the TMZ+veliparib arm and 12.6 mo in the TMZ+placebo arm, as compared to 9.6 mo in either arm if TMZ salvage was not used. These data are consistent with a possible effect of veliparib limiting the emergence of TMZ resistance in a subset of GBM pts. Conclusions: Veliparib combined with adjuvant TMZ therapy was not associated with significant extension in OS or PFS in newly diagnosed, MGMT hypermethylated GBM pts. However, a subset of pts treated with TMZ+veliparib may have an extended survival following re-treatment with TMZ at first recurrence. Clinical trial information: NCT02152982.

Abstract Brain metastases occur commonly in patients with advanced solid malignancies. Yet, less is known about brain metastases than cancer-related entities of similar incidence. Advances in oncologic care have heightened the importance of intracranial management. Here, in this consensus review supported by the Society for Neuro-Oncology (SNO), we review the landscape of brain metastases with particular attention to management approaches and ongoing efforts with potential to shape future paradigms of care. Each coauthor carried an area of expertise within the field of brain metastases and initially composed, edited, or reviewed their specific subsection of interest. After each subsection was accordingly written, multiple drafts of the manuscript were circulated to the entire list of authors for group discussion and feedback. The hope is that the these consensus guidelines will accelerate progress in the understanding and management of patients with brain metastases, and highlight key areas in need of further exploration that will lead to dedicated trials and other research investigations designed to advance the field.

Abstract Background Magnetic Resonance Imaging (MRI) is the mainstay for neurosurgical oncology but not for informing us about glioma biology. An obstacle to developing MR-based glioma biomarkers is the absence of rigorous correlation between MRI features and glioma biology as assessed in multi-regional biopsies, within and across patients. Methods We directly addressed this obstacle by collating a unique cohort of 202 MRI-localized biopsies from 58 patients. We define a low-dimensional transcriptional pseudotime continuum along which heterogeneous high-grade glioma (HGG) samples organize both within and across patients. Results We observe three polarized transcriptional tissue states: infiltrated brain, immune/inflammatory, and proliferative associated with patterns of cohabitation of cellular subpopulations. The states and deconvolved populations show correlation with enhancement status on T1Gd MRI. Moreover, discrete MRI habitats, regions sharing common imaging features, defined as combinations of high or low signal intensity across multiparametric MRI revealed 14 MRI habitats. We order the MRI habitats according to the average pseudotime on the transcriptional continuum. We find that MRI habitats with low pseudotime (associated with early tumor development and diffusely invaded brain tissue) localized at the periphery of the tumor whilst high pseudotime either proliferative or immune/inflammatory states were towards the core of the lesion. We find that composition of MRI habitats are impacted by MGMT status and patient sex. Conclusion This suggests that ongoing aggregation of MRI-localized biopsies may augment our projection of biology onto MRI habitats to support tracking of the evolution of cellular ecologies within and across each patient’s tumor over time. Key Points Trajectory inference of HGG samples reveals a continuum connecting three polarized tissue states reflecting transitions in cellular population ecologies and gene set enrichment Combining multiparametric MRI features into regional MRI habitats reveal clinically-distinct patterns of cell co-habitation and tissue state MGMT methylation status shifts the cellular patterns of cohabitation within MRI habitats Importance of the Study Regional heterogeneity is a defining characteristic of high-grade glioma (HGG). Mapping the transcriptomic biology of multiregionally sampled MRI-localized biopsies from high-grade gliomas onto a low-dimensional space reveals a continuum between three primary tissue states: infiltrated brain, immune/inflammatory, and proliferative. Each state is associated with distinct cell cohabitation ecologies. Transitions between the tissue states coordinate with changes in tumor composition, revealing potential direct connections between tumor biology shifts and their manifestation on clinical imaging.

Unlike medulloblastoma (MB) in children, robust prospective trials have not taken place for older patients due to the low incidence of MB in adults and adolescent and young adults (AYA). Current MB treatment paradigms for older patients have been extrapolated from the pediatric experience even though questions exist about the applicability of these approaches. Clinical and molecular classification of MB now provides better prognostication and is being incorporated in pediatric therapeutic trials. It has been established that genomic alterations leading to activation of the sonic hedgehog (SHH) pathway occur in approximately 60% of MB in patients over the age of 16 years. Within this cohort, protein patched homolog (PTCH) and smoothened (SMO) mutations are commonly found. Among patients whose tumors harbor the SHH molecular signature, it is estimated that over 80% of patients could respond to SHH pathway inhibitors. Given the advances in the understanding of molecular subgroups and the lack of robust clinical data for adult/AYA MB, the Alliance for Clinical Trial in Oncology group developed the AMBUSH trial: Comprehensive Management of AYA and Adult Patients with Medulloblastoma or Pineal Embryonal Tumors with a Randomized Placebo Controlled Phase II Focusing on Sonic Hedgehog Pathway Inhibition in SHH Subgroup Patients (Adult & Adolescent MedulloBlastoma Using Sonic Hedgehog Trial). This trial will enroll patients 18 years of age or older with MB (any molecular subgroup and risk stratification) or pineal embryonal tumor. Patients will be assigned to one of three cohorts: (1) average risk non-SHH-MB, (2) average risk SHH-MB, and (3) high risk MB or pineal embryonal tumors. All patients will receive protocol-directed comprehensive treatment with radiation therapy and chemotherapy. Patients with SHH-MB in cohort 1 will be randomized to a smoothened inhibitor or placebo as maintenance therapy for one year.

Abstract INTRODUCTION Dendritic cells (DCs) are potent antigen presenting cells that can be exploited to initiate an adaptive anti-tumoral immune response. DC vaccine clinical trials for primary glioblastoma (GBM) have reported prolonged progression-free survival without any impact on overall survival (OS). We report a radiomics approach that identifies a subpopulation of patients with prolonged OS in clinical trial MC1272. METHODS Twenty adults with primary GBM undergoing standard-of-care therapy were enrolled in MC1272. Autologous DCs were pulsed with allogenic GBM cell lines to generate vaccines that were administered for up to twelve cycles. Standard brain imaging was obtained at the initiation of treatment and two months afterwards. An independent cohort of image-localized biopsies underwent RNA sequencing followed by cellular deconvolution to estimate T cell abundance. A machine learning model was trained to predict intratumoral T cell abundance from imaging features, and the model was applied to MC1272 patient imaging. RESULTS Voxelwise predictions of T cell abundance were generated for each patient’s pre- and post-treatment images. The difference in total intratumoral T cell abundance between imaging timepoints classified patients into increasing or decreasing T cell groups. Patients whose T cells increased had longer OS (median, 21 months) than those whose T cells decreased (median, 10 months; p=0.0035). The significance remained in a Cox proportional hazards analysis that adjusted for patient age and sex (p=0.011). CONCLUSIONS A spatially-resolved radiomics model detected that an intratumoral T cell influx after DC vaccine therapy was associated with prolonged OS. The “post-treatment” imaging in this study was obtained a mere two months after DC vaccine initiation, suggesting that our radiomics model can provide an early indication of treatment responsiveness and prognosis in these patients.

Abstract Intra-tumor genetic heterogeneity is an important cause of treatment failure of GBM. Using MRI and image-localized biopsies, it is possible to train machine learning (ML) models to predict regional genetic status. However, biopsy samples are limited, making it difficult to train a robust ML model. We proposed a data-inclusive model called Weakly Supervised Ordinal Support Vector Machines (WSO-SVM) which leverages the vast amount of MRI data outside the sparsely sampled biopsy regions to augment the biopsy samples to improve ML accuracy. Our study included a unique dataset of 104 image-localized biopsies with spatially matched multiparametric MRI from 30 untreated Glioblastoma (GBM) patients. Each biopsy sample went through genetic sequencing analysis and our study focused on two GBM hallmark genes, EGFR and PDGFRA. For each gene, a biopsy sample was labeled as “altered” if the copy number of this gene was amplified or a mutation was found, and “non-altered” otherwise. From the localized region of six MRI contrasts from T1gd, T2w, diffusion and perfusion imaging, over 300 texture features were extracted. To account for biopsy sample location uncertainty, six neighboring regions of the biopsy sample including four neighbors two pixels away from the biopsy location and two neighbors on adjacent slices were also included in model training. WSO-SVM achieved 0.83 accuracy, 0.77 sensitivity, and 0.86 specificity for classifying EGFR; 0.77 accuracy, 0.74 sensitivity, and 0.79 specificity for classifying PDGFRA, based on 10-fold cross validation. Furthermore, using the trained models, we generated regional EGFR and PDGFRA alteration maps for each patient within the enhancing and non-enhancing tumoral areas. On average we found a greater proportion of enhancing tumor with co-alteration than non-enhancing tumor, while this trend was reversed when considering only one altered gene. The ratio of EGFR vs PDGFRA alterations was higher in non-enhancing tumor than enhancing tumor.