Baxter, Leslie C.

Abstract It has been widely acknowledged that limited access to the human brain, along with the importance of its structures, presents significant challenges in obtaining brain tumor tissue. This poses difficulties for clinical decision-making, as healthcare professionals often have to rely solely on imaging to address complex problems. There is currently no definitive method to make these decisions other than relying on informed yet subjective human judgment. That said, imaging abnormalities are a result of a presence of particular tumor-related biology. Because of this, and the heterogeneity frequently observed in brain tumors, we have developed an image-localized biopsy protocol that has been employed for research purposes at Mayo Clinic Arizona for over five years. This multidisciplinary approach involves the collaborative efforts from neurosurgeons, neuro-oncologists, scrub-techs, nurses, radiologists, as well as multiple research labs, all working within a well-established pipeline. The implementation of this protocol has allowed us to obtain image-localized biopsies during surgical procedures, enabling the correlation of local imaging findings with corresponding tissue features through the utilization of machine learning models. The primary objective of this endeavor is to enhance our understanding of the underlying biological mechanisms of brain tumors based on patients' imaging data. This protocol has been successful for our research purposes, and with minor adjustments, it can be readily adopted by any hospital in order to collect more data about these incredibly heterogeneous diseases. While the use of neuronavigation during surgery has become commonplace in many medical institutions, localization of tissue samples is not. Utilizing pre-existing infrastructure in the operating room is a small burden with big potential positive outcomes. Sample locations can be useful to clinicians during tumor board discussions, to better assess disease heterogeneity. These minor refinements in the surgical workflow have the capacity to provide clinicians with additional clinically-relevant data without disrupting patient care.

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 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.

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 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.

BackgroundAdvanced diffusion‐based MRI biomarkers may provide insight into microstructural and perfusion changes associated with neurodegeneration and cognitive decline.PurposeTo assess longitudinal microstructural and perfusion changes using apparent diffusion coefficient (ADC) and intravoxel incoherent motion diffusion‐weighted imaging (IVIM‐DWI) parameters in cognitively impaired (CI) and healthy control (HC) groups.Study TypeProspective/longitudinal.PopulationTwelve CI patients (75% female) and 13 HC subjects (69% female).Field Strength/Sequence3 T; Spin‐Echo‐IVIM‐DWI.AssessmentTwo MRI scans were performed with a 12‐month interval. ADC and IVIM‐DWI metrics (diffusion coefficient [D] and perfusion fraction [f]) were generated from monoexponential and biexponential fits, respectively. Additionally, voxel‐based correlations were evaluated between change in Montreal Cognitive Assessment (ΔMoCA) and baseline imaging parameters.Statistical TestsAnalysis of covariance with sex and age as covariates was performed for main effects of group and time (false discovery rate [FDR] corrected) with post hoc comparisons using Bonferroni correction. Partial‐η2 and Hedges' g were used for effect‐size analysis. Spearman's correlations (FDR corrected) were used for the relationship between ΔMoCA score and imaging. P < 0.05 was considered statistically significant.ResultsSignificant differences were found for the main effects of group (HC vs. CI) and time. For group effects, higher ADC, IVIM‐D, and IVIM‐f were observed in the CI group compared to HC (ADC: 1.23 ± 0.08.10−3 vs. 1.09 ± 0.07.10−3 mm2/sec; IVIM‐D: 0.82 ± 0.01.10−3 vs. 0.73 ± 0.01.10−3 mm2/sec; and IVIM‐f: 0.317 ± 0.008 vs. 0.253 ± 0.009). Significantly higher ADC, IVIM‐D, and IVIM‐f values were observed in the CI group after 12 months (ADC: 1.45 ± 0.05.10−3 vs. 1.50 ± 0.07.10−3 mm2/sec; IVIM‐D: 0.87 ± 0.01.10−3 vs. 0.94 ± 0.02.10−3 mm2/sec; and IVIM‐f: 0.303 ± 0.007 vs. 0.332 ± 0.008), but not in the HC group at large effect size. ADC, IVIM‐D, and IVIM‐f negatively correlated with ΔMoCA score (ρ = −0.49, −0.51, and −0.50, respectively).Data ConclusionThese findings demonstrate that longitudinal differences between CI and HC cohorts can be measured using IVIM‐based metrics.Level of Evidence2Technical Efficacy Stage2