Hu, Leland

Abstract INTRODUCTION Differentiation of glioblastoma recurrence from post treatment radiation effects (PTRE) would improve patient management. Dynamic susceptibility contrast (DSC) MRI can be used to generate fractional tumor burden (FTB) maps, via application of regional cerebral blood volume (RCBV) thresholds, to spatially differentiate recurrence from PTRE. FTB maps derived from a DSC-MRI protocol that includes a contrast agent preload in addition to the bolus injection and a moderate-flip angle (MFA) been histologically validated. Recently, a single-dose, low-flip angle (LFA) protocol was shown to provide RCBV measurements consistent with the MFA protocol and has been recommended as a consensus protocol. This study aims to identify the RCBV thresholds for the single-dose, LFA protocol that generates FTB maps, concordant with those obtained from the reference double-dose, MFA protocol. METHODS Two sets of DSC-MRI data were collected sequentially from 52 patients using a single-dose, low-flip angle (30o) protocol and a double-dose, moderate flip angle (60o) protocol using TR = 1.5s and TE = 30ms. Standardized RCBV maps (sRCBV) were obtained and co-registered with the post-contrast T1-weighted images using the FDA-approved clinical software, IB Neuro™ and IB Delta Suite™, respectively. The ground truth MFA-based FTB maps were computed using multiple sRCBV thresholds (1.0 and 1.75). An ROC analysis was conducted to identify the optimal, voxel-wise LFA sRCBV thresholds to produce FTB maps that best match the reference maps. RESULTS/ CONCLUSIONS The ROC analysis identified a lower LFA threshold of 1.0 to accurately distinguish PTRE from tumor recurrence and an upper threshold of 1.55 to differentiate aggressive tumor regions. The single-dose, LFA protocol eliminates variations in the contrast agent incubation time between the preload and bolus injection. This study provides motivation for the use of the single-dose LFA protocol in the management of patients with glioblastoma and is a reliable method for distinguishing tumor from PTRE.

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 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 are associated with lower grade tumors and portend a favorable prognosis relative to IDH-wildtype glioma. However, molecular profiling has identified additional alterations in lower grade gliomas that confer an aggressive high-grade phenotype and a worse clinical prognosis. Of these, the loss of CDKN2A/B in IDH-mutant astrocytoma is the strongest implicated alteration and is sufficient to define a grade 4 tumor regardless of histologic grade. However, there remain no effective therapies targeted at molecular subgroups in aggressive gliomas to date. Here, we sought to elucidate the biologic pathways and functional outcomes associated with the acquisition of high-grade behavior under loss of CDKN2A/B in IDH-mutant astrocytoma to inform future therapeutic strategies. We analyzed a cohort of patient IDH-mutant astrocytomas with whole exome and RNA sequencing data and found several candidate genes/pathways regulated concurrent with CDKN2A/B loss, including regulators of embryonic development, extracellular matrix organization and regulation of vascular processes. Moreover, we identified 1 tumor that displayed intratumoral heterogeneity of CDKN2A/B expression across biopsies. Using grade 4 IDH-mutant astrocytoma patient-derived (PDX) and established cell-lines harboring CDKN2A/B homozygous deletion, we then stably re-expressed cell cycle regulators p14, p15, and p16 using a Tet-inducible lentiviral system and confirmed their decreased proliferation as compared to parental lines. IDH-mutant cell lines with re-expression of p14, p15 or p16 were characterized by differential transcription factor activation and distinct vascular formation patterns as compared to parental lines. These lines also displayed an increased cell migration and invasive potential and were associated with enhanced integrin signaling through integrin beta-3. Together, our work supports a hypothesis of regional tumor evolution with distinct biologic phenotypes across IDH-mutant gliomas.

Abstract AIMS High-grade glioma continues to have dismal survival with current standard-of-care treatment, owing in part to its intra- and inter-patient heterogeneity. Typical diagnostic biopsies are taken from the dense tumor core to determine the presence of abnormal cells and the status of a few key genes (e.g. IDH1, MGMT). However, the tumor core is typically resected, leaving behind possibly genetically, transcriptomically and/or phenotypically distinct invasive margins that repopulate the disease. As these remaining populations are the ones ultimately being treated, it is important to know their compositional differences from the tumor core. We aim to identify the phenotypic niches defined by the relative composition of key cellular populations and understand their variation amongst patients. METHOD We have established an image-localized research biopsy study, that samples from both the invasive margin and tumor core. From this protocol, we currently have 202 samples from 58 patients with available bulk RNA-Seq, collected between Mayo Clinic and Barrow Neurological Institute. Using a single-cell reference dataset from our collaborators at Columbia University, we used CIBERSORTx, a deconvolution method, to predict relative abundances of 7 normal, 6 glioma, and 5 immune cell states for each sample. RESULTS We find that these cell state abundances connect to patient survival and show regional differences. For example, proneural glioma states were higher in invasive regions, whereas proliferative and mesenchymal states were higher in the tumor core. CONCLUSIONS Our analysis demonstrates a need to characterize the residual tissue following glioma resection to better understand the recurrent disease.

Abstract High grade glioma (HGG) represents a group of devastating diseases with dismal prognosis. Surgical resection of the contrast enhancing (CE) region of HGG remains the mainstay of treatment, but recurrence inevitably arises from the unresected non-contrast enhancing (NE) region, surgically inaccessible due to cancer cell invasion into healthy brain tissue. Due to its critical role in recurrence, understanding of the NE region is central to the improvement of clinical outcomes. We reveal the biological characteristics of this region through image localized multi-regional sampling. We linked microenvironmental characteristics measured by multi-parametric MRI to genomic mutations and transcriptional phenotypes using mixed effect modeling which allowed us to control for individualized patient effects. We first confirmed that T2 is a significant indicator of IDH mutation status in the NE region, being the first description of such a relationship in a HGG cohort. We found the combination of EGFR amplification and CDKN2A homozygous loss was associated with a significantly lower mean diffusivity (MD) compared to double wild type tumors in the NE region, indicating the presence of greater cellular packing and proliferation in EGFR amplification/CDKN2A loss regions. Finally, using single cell pathway based tumor classifications, we showed that nK2, a DSC-MRI metric representing cell size heterogeneity, correlated positively with neuronal signature and negatively with glycolytic/plurimetabolic signature within the NE tumor, indicating that glycolytic/plurimetobolic tumors possessed a high amount of cell size heterogeneity compared to neuronal samples. This hypothesis was supported using digital reference object (DRO) modeling which confirmed that cell size and heterogeneity drove the differential nK2 signal between neuronal and glycolytic/plurimetabolic samples. We identified immune cell infiltrate as one possible mechanism of increased cell size heterogeneity using transcriptomic signature analysis which found more immune cell signatures within glycolytic/plurimetobolic tumors compared to neuronal. Collectively this study demonstrates the central role of multi-parametric MRI as a non-invasive measure of tumor biology and a tool for understanding the clinically critical NE region which can then inform new therapies targeting this region of HGG recurrence. Citation Format: Matthew Flick, Taylor Weiskittel, Kevin Meng-Lin, Fulvio D'Angelo, Francesca Caruso, Shannon Ensign, Mylan Blomquist, Luija Wang, Christopher Sereduk, Gustavo De Leon, Ashley Nespodzany, Javier Urcuyo, Ashlynn Gonzalez, Lee Curtin, Kyle Singleton, Aliya Anil, Natenael Simmineh, Erika Lewis, Teresa Noviello, Reyna Patel, Panwen Wang, Junwen Wang, Jennifer Eschbacher, Andrea Hawkins-Daarud, Pamela Jackson, Kris Smith, Peter Nakaji, Bernard Bendok, Richard Zimmerman, Chandan Krishna, Devi Patra, Naresh Patel, Mark Lyons, Matthew Neal, Kliment Donev, Maciej Mrugala, Alyx Porter, Scott Beeman, Yuxiang Zhou, Leslie Baxter, Christopher Plaisier, Jing Li, Hu Li, Anna Lasorella, Chad Quarles, Kristin Swanson, Michele Ceccarelli, Antonio Iavarone, Nhan Tran, Leland Hu. Multi-parametric MRI maps regional heterogeneity of high grade glioma phenotypes. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5621.

Abstract High grade gliomas (HGG) are aggressive primary brain malignancies typified by diffuse invasion, genetic heterogeneity, and a universally fatal outcome. MRI-defined contrast-enhancing (CE) tumor burden serves as the clinical standard that guides maximal surgical resection and post-therapy response assessment. However, HGGs also comprise an invasive non-enhancing (NE) tumor margin that extends beyond the CE core and harbors the cells that contribute to recurrence. Sampling restrictions have hindered the comprehensive study of these NE HGG cell populations driving tumor progression. Herein, we present an integrated multi-omic analysis of 313 spatially matched multi-regional CE and NE tumor biopsies from 68 HGG patients, performing whole exome and RNA sequencing of both IDH wild-type and IDH mutant HGGs. We report spatially restricted molecular profiles in IDH-mutant HGG, highlighting a concern for sampling bias given the importance of molecular diagnosis and prognostication in IDH-mutant HGG. Regardless of IDH status, we found that NE tumor regions harbored the highest proportion of private mutations, which reflects an increased development of regional genomic complexity in infiltrative tumor. The multiregional genomic profiling of our IDH wild-type HGG cohort reveals that EGFR and NF1 somatic alterations occur as mutually exclusive events in 98.7% of tumors. However, we resolved rare low allele frequency co-alterations of EGFR and NF1 within the NE region. We find this co-occurrence enriched in recurrent tumors, pointing to the early emergence of NF1 inactivation in the NE regions. We constructed genomic models predictive of recurrent disease from both NE and CE regions, which highlight the occurrence of clonal EGFR copy number alterations and NF1 loss as clonal or subclonal events, respectively, emphasizing the regional and temporal complexity of well-studied canonical driver alterations. We detailed the spatially unique acquisition of multiple distinct EGFR alterations giving rise to intra-tumoral EGFR mosaicism, a challenge in the implementation of EGFR directed therapies. Our study also identified two transcriptomic clusters delineated by the significant overrepresentation of neuronal (NEU) and glycolytic/plurimetabolic (GPM) pathway-based functional states in the NE region. NE regions of the NEU subtype harbor the greatest proportion of private mutations, suggesting these infiltrative tumor cells accumulate alterations without clonal expansion. GPM populations conversely displayed a less branched phylogeny and were transcriptionally enriched in immune cell signatures. This phenotypic dichotomy between GPM and NEU populations supports the growing body of evidence that invasive GBM cells either take on a neuronal phenotype for active invasion or a more metabolic phenotype involving interaction with astrocytes, other glial cells, and infiltrating immune cells. Citation Format: Mylan R. Blomquist, Leland S. Hu, Fulvio D'Angelo, Taylor M. Weiskittel, Francesca P. Caruso, Shannon P. Fortin Ensign, Christopher Sereduk, Gustavo De Leon, Lee Curtin, Javier Urcuyo, Ashlynn Gonzalez, Ashley Nespodzany, Teresa Noviello, Jennifer M. Eschbacher, Kris A. Smith, Peter Nakaji, Bernard R. Bendok, Richard S. Zimmerman, Chandan Krishna, Devi Patra, Naresh Patel, Mark Lyons, Kliment Donev, Maciej Mrugala, Alyx Porter, Anna Lasorella, Kristin R. Swanson, Michele Ceccarelli, Antonio Iavarone, Nhan L. Tran. Multiregional sampling of high grade glioma identifies regional biologic signatures [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1507.

AbstractINTRODUCTIONResting‐state functional magnetic resonance imaging (fMRI) graph theory may help detect subtle functional connectivity changes affecting memory prior to impairment.METHODSCognitively normal apolipoprotein E (APOE) ε4 carriers/noncarriers underwent longitudinal cognitive assessment and one‐time MRI. The relationship of left/right hippocampal connectivity and memory trajectory were compared between carriers/noncarriers.RESULTSSteepness of verbal memory decline correlated with decreased connectivity in the left hippocampus, only among APOE ε4 carriers. Right hippocampal metrics were not correlated with memory and there were no significant correlations in the noncarriers. Verbal memory decline correlated with left hippocampal volume loss for both carriers and noncarriers, with no other significant volumetric findings.DISCUSSIONFindings support early hippocampal dysfunction in intact carriers, the AD disconnection hypothesis, and left hippocampal dysfunction earlier than the right. Combining lateralized graph theoretical metrics with a sensitive measure of memory trajectory allowed for detection of early‐stage changes in APOE ε4 carriers before symptoms of mild cognitive impairment are present.Highlights Graph theory connectivity detects preclinical hippocampal changes in APOE ε4 carriers. The AD disconnection hypothesis was supported in unimpaired APOE ε4 carriers. Hippocampal dysfunction starts asymmetrically on the left.

Abstract Characterizing intra- and inter-tumoral heterogeneity of glioblastoma has historically relied on discrete classifications of malignant cell populations leaving immune and other cell populations, known to exist admixed with the malignant tumor cells, relatively neglected. Manifold learning algorithms can manage deconvolving multiple cell populations and are often used to track cell state transitions in single cell transcriptomics. We applied a manifold learning approach to TCGA microarray data (Nf525) and bulk transcriptomics of 134 image localized biopsies across 30 patients with primary and 9 with recurrent glioblastoma to further elucidate how to organize biopsies across a spectrum of possible tissue states. The algorithm revealed a low-dimensional manifold graph for which each biopsy lives across 3 polarizing tissue states - one that is associated with diffusely invaded brain, one that is enriched in mesenchymal genes, and one that is enriched in classical proliferative tumor signatures. We deconvolved the bulk transcriptomics of the image-localized biopsies to reveal the relative abundance of 18 malignant, immune, and other cell subpopulations in each biopsy. Overlaying the cellular decomposition onto the manifold graph visualizing the tissue state distributions revealed that transitions between states correlate with changes in cellular cohabitation composition. The tumor cellular cohabitation ecologies have the lowest diversity, as inferred by ecological measures such as Shannon entropy and evenness, at the distal poles of the graph when compared to the transitional arms. Further, we found that the relationship between imaging appearance of contrast enhancement on T1-weighted MRI and the biopsy cellular composition varies with sex and primary vs recurrent biopsy status. The limited spectrum of possible tissue states revealed by the manifold learning is suggestive of a limited continuum along which tumor and non-tumoral cell populations can cohabitate. Such a limited low-dimensional biological space may constrain the dynamics of tumor biology in a predictable manner.