Intratumoral Heterogeneity In Glioblastoma Research Articles

Resolving spatial response heterogeneity in glioblastoma.

Spatial intratumoral heterogeneity poses a significant challenge for accurate response assessment in glioblastoma. Multimodal imaging coupled with advanced image analysis has the potential to unravel this response heterogeneity. Based on automated tumor segmentation and longitudinal registration with follow-up imaging, we categorized contrast-enhancing voxels of 61 patients with suspected recurrence of glioblastoma into either true tumor progression (TP) or pseudoprogression (PsP). To allow the unbiased analysis of semantically related image regions, adjacent voxels with similar values of cerebral blood volume (CBV), FET-PET, and contrast-enhanced T1w were automatically grouped into supervoxels. We then extracted first-order statistics as well as texture features from each supervoxel. With these features, a Random Forest classifier was trained and validated employing a 10-fold cross-validation scheme. For model evaluation, the area under the receiver operating curve, as well as classification performance metrics were calculated. Our image analysis pipeline enabled reliable spatial assessment of tumor response. The predictive model reached an accuracy of 80.0% and a macro-weighted AUC of 0.875, which takes class imbalance into account, in the hold-out samples from cross-validation on supervoxel level. Analysis of feature importances confirmed the significant role of FET-PET-derived features. Accordingly, TP- and PsP-labeled supervoxels differed significantly in their 10th and 90th percentile, as well as the median of tumor-to-background normalized FET-PET. However, CBV- and T1c-related features also relevantly contributed to the model's performance. Disentangling the intratumoral heterogeneity in glioblastoma holds immense promise for advancing precise local response evaluation and thereby also informing more personalized and localized treatment strategies in the future.

The Role of Mesenchymal Reprogramming in Malignant Clonal Evolution and Intra-Tumoral Heterogeneity in Glioblastoma.

Glioblastoma (GBM) is the most common yet uniformly fatal adult brain cancer. Intra-tumoral molecular and cellular heterogeneities are major contributory factors to therapeutic refractoriness and futility in GBM. Molecular heterogeneity is represented through molecular subtype clusters whereby the proneural (PN) subtype is associated with significantly increased long-term survival compared to the highly resistant mesenchymal (MES) subtype. Furthermore, it is universally recognized that a small subset of GBM cells known as GBM stem cells (GSCs) serve as reservoirs for tumor recurrence and progression. The clonal evolution of GSC molecular subtypes in response to therapy drives intra-tumoral heterogeneity and remains a critical determinant of GBM outcomes. In particular, the intra-tumoral MES reprogramming of GSCs using current GBM therapies has emerged as a leading hypothesis for therapeutic refractoriness. Preventing the intra-tumoral divergent evolution of GBM toward the MES subtype via new treatments would dramatically improve long-term survival for GBM patients and have a significant impact on GBM outcomes. In this review, we examine the challenges of the role of MES reprogramming in the malignant clonal evolution of glioblastoma and provide future perspectives for addressing the unmet therapeutic need to overcome resistance in GBM.

Abstract A028: Subclonal lineage recording in glioblastoma using CRISPR base editing

Abstract Novel lineage tracing approaches are needed to undercover lineage relationships between diverse cells in complex tumors such as glioblastoma (GBM). Intratumoral heterogeneity in GBM drives treatment resistance and recurrence. This intratumoral heterogeneity is reflected by multiple cellular states seen within tumors and across patients. Unfortunately, little is known about their lineage relationships and shared origins. Here we’ve developed a novel lineage tracing technology to track the clonal and subclonal evolution in patient-derived GBM models. Our adenine base editor lineage tracing (ABELT) system uses a Cas9 adenine base editor (ABE) to induce heritable single-nucleotide changes in the 3’ untranslated region of endogenous transcripts. These heritable marks distinguish individual clonal populations and can be used to trace the relationship between progeny within each clone. We demonstrate our system's in vitro lineage tracing capacity and show that the ABELT system faithfully records lineage, which is recoverable with unmodified single-cell RNA sequencing approaches. We then track the relationships between evolving cellular states across multiple divisions in an evolving GBM model. Our ABELT technology enables the rapid engineering and lineage tracing of cancer models incompatible with previous approaches. This novel approach will enable our group and others to explore GBM intratumoral heterogeneity with subclonal resolution to determine transcriptional plasticity that ultimately leads to treatment resistance. Citation Format: Abigail C. Marshall, Aaron McKenna. Subclonal lineage recording in glioblastoma using CRISPR base editing [abstract]. In: Proceedings of the AACR Special Conference on Brain Cancer; 2023 Oct 19-22; Minneapolis, Minnesota. Philadelphia (PA): AACR; Cancer Res 2024;84(5 Suppl_1):Abstract nr A028.

Genomic Exploration of Distinct Molecular Phenotypes Steering Temozolomide Resistance Development in Patient-Derived Glioblastoma Cells

Chemotherapy using temozolomide is the standard treatment for patients with glioblastoma. Despite treatment, prognosis is still poor largely due to the emergence of temozolomide resistance. This resistance is closely linked to the widely recognized inter- and intra-tumoral heterogeneity in glioblastoma, although the underlying mechanisms are not yet fully understood. To induce temozolomide resistance, we subjected 21 patient-derived glioblastoma cell cultures to Temozolomide treatment for a period of up to 90 days. Prior to treatment, the cells’ molecular characteristics were analyzed using bulk RNA sequencing. Additionally, we performed single-cell RNA sequencing on four of the cell cultures to track the evolution of temozolomide resistance. The induced temozolomide resistance was associated with two distinct phenotypic behaviors, classified as “adaptive” (ADA) or “non-adaptive” (N-ADA) to temozolomide. The ADA phenotype displayed neurodevelopmental and metabolic gene signatures, whereas the N-ADA phenotype expressed genes related to cell cycle regulation, DNA repair, and protein synthesis. Single-cell RNA sequencing revealed that in ADA cell cultures, one or more subpopulations emerged as dominant in the resistant samples, whereas N-ADA cell cultures remained relatively stable. The adaptability and heterogeneity of glioblastoma cells play pivotal roles in temozolomide treatment and contribute to the tumor’s ability to survive. Depending on the tumor’s adaptability potential, subpopulations with acquired resistance mechanisms may arise.

Machine learning applications on intratumoral heterogeneity in glioblastoma using single-cell RNA sequencing data.

Artificial intelligence is revolutionizing all fields that affect people's lives and health. One of the most critical applications is in the study of tumors. It is the case of glioblastoma (GBM) that has behaviors that need to be understood to develop effective therapies. Due to advances in single-cell RNA sequencing (scRNA-seq), it is possible to understand the cellular and molecular heterogeneity in the GBM. Given that there are different cell groups in these tumors, there is a need to apply Machine Learning (ML) algorithms. It will allow extracting information to understand how cancer changes and broaden the search for effective treatments. We proposed multiple comparisons of ML algorithms to classify cell groups based on the GBM scRNA-seq data. This broad comparison spectrum can show the scientific-medical community which models can achieve the best performance in this task. In this work are classified the following cell groups: Tumor Core (TC), Tumor Periphery (TP) and Normal Periphery (NP), in binary and multi-class scenarios. This work presents the biomarker candidates found for the models with the best results. The analyses presented here allow us to verify the biomarker candidates to understand the genetic characteristics of GBM, which may be affected by a suitable identification of GBM heterogeneity. This work obtained for the four scenarios covered cross-validation results of $93.03\% \pm 5.37\%$, $97.42\% \pm 3.94\%$, $98.27\% \pm 1.81\%$ and $93.04\% \pm 6.88\%$ for the classification of TP versus TC, TP versus NP, NP versus TP and TC (TPC) and NP versus TP versus TC, respectively.

Systems Medicine for Precise Targeting of Glioblastoma.

Glioblastoma (GBM) is a malignant cancer that is fatal even after standard therapy and the effects of current available therapeutics are not promising due its complex and evolving epigenetic and genetic profile. The mysteries that lead to GBM intratumoral heterogeneity and subtype transitions are not entirely clear. Systems medicine is an approach to view the patient in a whole pictureintegrating systems biology and synthetic biology along with computational techniques. Since the GBM oncogenesis involves genetic mutations, various therapies including gene therapeutics based on CRISPR-Cas technique, MicroRNAs, and implanted synthetic cells endowed with synthetic circuits against GBM with neural stem cells and mesenchymal stem cells acting as potential vehicles carrying therapeutics via the intranasal route, avoiding the risks of invasive methods in order to reach the GBM cells in the brain are discussed and proposed in this review. Systems medicine approach is a rather novel strategy, and since the GBM of a patient is complex and unique, thus to devise an individualized treatment strategy to tailor personalized multimodal treatments for the individual patient taking into account the phenotype of the GBM, the unique body health profile of the patient and individual responses according to the systems medicine concept might show potential to achieve optimum effects.

Machine learning applications on intratumoral heterogeneity in glioblastoma using single-cell RNA sequencing data

Machine learning applications on intratumoral heterogeneity in glioblastoma using single-cell RNA sequencing data

Machine learning applications on intratumoral heterogeneity in glioblastoma using single-cell RNA sequencing data

Machine learning applications on intratumoral heterogeneity in glioblastoma using single-cell RNA sequencing data

Abstract 1685: Deep learning approaches to deciphering intra-tumoural heterogeneity in glioblastoma

Abstract BACKGROUND: Emerging evidence strongly implicates intra-tumoral heterogeneous biology in treatment resistance and disease progression across many cancer types. Thus, there is a need for workflows capable of systematically resolving and targeting distinct tumor subpopulations1. Using glioblastoma (GBM) as a prototype, I have aimed to leverage the computational power of Artificial Intelligence (AI) and deep learning to develop an autonomous workflow for the objective definition of biologically distinct tumor subpopulations2. OBJECTIVES: I hypothesize that AI may be leveraged as a tool to resolve spatial heterogeneity, by identifying tumoral subpopulations with unique molecular profiles and therapeutic targets. To highlight the need for routine analysis of tumor heterogeneity, I will address if: METHODS: I apply our developed image clustering workflows to quantify AI-defined subregions within a clinical cohort of 10 GBM patient tumors2,3. Laser capture microdissection and mass spectrometry-based proteomics are leveraged to address if AI-defined subregions show intra-tumoral molecular variation. Further, existing pharmacogenomic databases are utilized to carry out drug sensitivity and transcriptional clustering to define AI-defined region-specific therapeutic sensitivities and resistances across my clinical GBM cohort4. RESULTS: Preliminary data shows that region to region heterogeneity can be found in IDH wild-type GBM using our unbiased omics approach, in addition to predicting different pharmacogenomic sensitivities. CONCLUSIONS: This project aims to develop the first AI-driven tool to guide the routine and systematic molecular analysis of spatial morphogenomic heterogeneity. Further, this tool may have the potential to provide novel approaches for personalized care by selecting drug combinations that target a larger fraction of a tumor’s true biology. 1. Patel, A. P. et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science (80-. ). 344, 1396-1401 (2014). 2. Faust, K. et al. Unsupervised Resolution of Histomorphologic Heterogeneity in Renal Cell Carcinoma Using a Brain Tumor-Educated Neural Network. JCO Clin. Cancer Informatics 811-821 (2020) doi:10.1200/cci.20.00035. 3. Roohi, A., Faust, K., Djuric, U. & Diamandis, P. Unsupervised Machine Learning in Pathology: The Next Frontier. Surgical Pathology Clinics vol. 13 349-358 (2020). 4. Barretina, J. et al. The Cancer Cell Line Encyclopedia enables predictive modeling of anticancer drug sensitivity. Nat. 2012 4837391 483, 603-607 (2012). Citation Format: Anglin J. Dent, Kevin Faust, Brian Lam, Alberto J. Leon, Queenie Tsang, Phedias Diamandis. Deep learning approaches to deciphering intra-tumoural heterogeneity in glioblastoma [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 1685.

Abstract 3621: 3D spatial sampling and integrated omics reveal sources and patterning of intratumoral heterogeneity in glioblastoma

Abstract Treatment failure in glioblastoma, the most common and lethal primary brain tumor in adults, is often attributed to intratumoral heterogeneity as it fosters tumor evolution and selection of resistant clones. Sources of intratumoral heterogeneity may include genomic alterations, such as single-nucleotide and structural variants, and epigenomic alterations, such as changes in chromatin structure and transcriptional regulation. The relative extent and functional significance of these contributors to intratumoral heterogeneity in glioblastoma have yet to be elucidated. In collaboration with neurosurgeons and neuro-imaging experts, we have established a novel approach towards characterizing intratumoral heterogeneity in three-dimensional (3D) space. For patients undergoing tumor resection, we utilize 3D surgical neuronavigation to safely acquire ~10 samples representing maximal anatomical diversity. Samples are mapped by 3D spatial coordinates and integrated with patient MRI scans for 360º visualization of sample location in context of the brain. We have now conducted whole-exome sequencing (Exome-Seq), assay for transposase-accessible chromatin (ATAC-Seq), and RNA-sequencing (RNA-Seq) for 83 spatially mapped samples obtained from 8 patients with primary IDH-WT glioblastoma. Integrative data analysis provides unprecedented insight into sources of intratumoral heterogeneity in glioblastoma and their 3D spatial patterning. We find that tumor cells show aberrant transcription factor activity, activation of fetal brain programs, and variable chromatin accessibility at CTCF-binding loop anchors indicating plasticity in higher-order chromatin structure. Chromosome conformation capture analysis by Hi-C extends these findings and reveals intratumoral differences in long-range chromatin interactions due to structural variants and enhancer hijacking. Further, we use deconvolution to identify microenvironmental contributors to epigenomic intratumoral heterogeneity including neural, glial, and immune populations. We define chromatin signatures associated with microenvironmental cell types and states, revealing their 3D spatial patterning, and validate these findings by single-cell ATAC-Seq. Our work thus establishes both tumor and microenvironmental sources of intratumoral heterogeneity in glioblastoma, revealing their chromatin programs and 3D spatial patterning. As a resource for further investigation, we have developed an interactive data sharing platform that enables visualization of brain tumor intratumoral heterogeneity in 360 degrees. Citation Format: Radhika Mathur, Qixuan Wang, Patrick Schupp, Ana Nikolic, Takafumi Yamaguchi, Stephanie Hilz, Chibo Hong, Ivan Smirnov, Marisa LaFontaine, Joanna Phillips, Susan Chang, Yan Li, Janine Lupo, Paul Boutros, Marco Gallo, Michael Oldham, Feng Yue, Joseph Costello. 3D spatial sampling and integrated omics reveal sources and patterning of intratumoral heterogeneity in glioblastoma [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 3621.

ECOA-5. Integrative 3D spatial characterization of genomic and epigenomic intratumoral heterogeneity in glioblastoma

Treatment failure in glioblastoma is often attributed to intratumoral heterogeneity (ITH), which fosters tumor evolution and selection of therapy-resistant clones. While genomic alterations are known contributors to ITH, emerging studies highlight functional roles for epigenomic ITH which integrates differentiation status, stochastic events, and microenvironmental inputs. Here, we have established a novel platform for integrative characterization of genomic and epigenomic ITH of glioblastoma in three-dimensional (3-D) space. In collaboration with neurosurgeons and biomedical imaging experts, we utilize 3-D surgical neuro-navigation to safely acquire ~10 tumor samples per patient representing maximal anatomical diversity. We conduct whole-exome sequencing, RNA sequencing, and assay for transposase-accessible chromatin using sequencing (ATAC-Seq) on each sample. The spatial location of each sample is mapped by its 3-D coordinates, allowing 360-degree visualization of genomic and epigenomic ITH for each patient. We demonstrate this approach on 8 patients with primary IDH-WT glioblastoma (83 spatially mapped samples), providing unprecedented insight into their spatial organization at the genomic and epigenomic levels. We link genetically defined tumor subclones to patterns of open chromatin and gene regulation, revealing underlying transcription factor binding at active promoters and enhancers. We also identify ITH in whole-genome doubling and focal oncogene amplification events in multiple patients, which we then link with epigenomic ITH. Further, to study microenvironmental inputs and their contribution to epigenomic ITH, we conduct deconvolution of RNA sequencing and ATAC-Seq data by analyzing feature co-variation. We resolve the 3-D spatial organization of immune, neural, and other nontumor cell types present in glioblastoma, characterizing their functional states and interactions with tumor cells. This work provides the most comprehensive spatial characterization of genomic and epigenomic ITH to date in glioblastoma. As a resource for further investigation, we have developed an interactive data sharing platform – The 3D Glioma Atlas – that enables 360-degree visualization of both genomic and epigenomic ITH.

Mapping the Intratumoral Heterogeneity in Glioblastomas with Hyperspectral Stimulated Raman Scattering Microscopy.

Recent genomic studies on the glioblastoma (GBM) subtypes (e.g., mesenchymal, proneural, and classical) pave a way for effective clinical treatments of the recurrent brain tumor. However, identification of the GBM subtype is complicated by the intratumoral heterogeneity that results in coexistence of multiple subtypes within the tissue specimen. Here, we present the use of hyperspectral stimulated Raman scattering (SRS) microscopy for rapid, label-free molecular assessment of GBM intratumoral heterogeneity with submicron resolution. We develop a unique label-free Raman imaging diagnostic platform consisting of the spectral focusing hyperspectral SRS imaging of the large-area GBM tissue specimens, SRS images, and spectrum retrieval using the multivariate curve resolution algorithm and subtype classification based on the quadratic support vector machine model for rapid molecular subtyping of GBMs. Both the stain-free SRS histological images and 2D subtype maps can be obtained within 20-30 min which is superior to the days of the conventional single-cell RNA sequencing. While the SRS histology assesses the demyelination status as a new diagnostic feature, the SRS mapping provides a new insight into intratumoral heterogeneity across GBM tissue specimens. We find that the major proportions of the GBM tissues agree with the diagnostic results of the genomic analysis, but nontrivial portions of the remaining SRS image tiles in the specimens are found to belong to other molecular subtypes, implying the substantial degree of GBM heterogeneity. The rapid SRS imaging diagnostic platform developed has shown the ability of unveiling tumor heterogeneity in GBM tissues accurately, which would promote the improvement of the GBM-targeted therapy in near future.

EPCO-31. EPIGENOMIC INTRATUMORAL HETEROGENEITY OF GLIOBLASTOMA IN THREE-DIMENSIONAL SPACE

Abstract Treatment failure in glioblastoma is often attributed to intratumoral heterogeneity (ITH), which fosters tumor evolution and generation of therapy-resistant clones. While ITH in glioblastoma has been well-characterized at the genomic and transcriptomic levels, the extent of ITH at the epigenomic level and its biological and clinical significance are not well understood. In collaboration with neurosurgeons, neuropathologists, and biomedical imaging experts, we have established a novel topographical approach towards characterizing epigenomic ITH in three-dimensional (3-D) space. We utilize pre-operative MRI scans to define tumor volume and then utilize 3-D surgical neuro-navigation to intra-operatively acquire 10+ samples representing maximal anatomical diversity. The precise spatial location of each sample is mapped by 3-D coordinates, enabling tumors to be visualized in 360-degrees and providing unprecedented insight into their spatial organization and patterning. For each sample, we conduct assay for transposase-accessible chromatin using sequencing (ATAC-Seq), which provides information on the genomic locations of open chromatin, DNA-binding proteins, and individual nucleosomes at nucleotide resolution. We additionally conduct whole-exome sequencing and RNA sequencing for each spatially mapped sample. Integrative analysis of these datasets reveals distinct patterns of chromatin accessibility within glioblastoma tumors, as well as their associations with genetically defined clonal expansions. Our analysis further reveals how differences in chromatin accessibility within tumors reflect underlying transcription factor activity at gene regulatory elements, including both promoters and enhancers, and drive expression of particular gene expression sets, including neuronal and immune programs. Collectively, this work provides the most comprehensive characterization of epigenomic ITH to date, establishing its importance for driving tumor evolution and therapy resistance in glioblastoma. As a resource for further investigation, we have provided our datasets on an interactive data sharing platform – The 3D Glioma Atlas – that enables 360-degree visualization of both genomic and epigenomic ITH.

Immunohistochemically Characterized Intratumoral Heterogeneity Is a Prognostic Marker in Human Glioblastoma.

Simple SummaryIntratumoral heterogeneity is believed to contribute to the immense therapy resistance and recurrence rate of glioblastoma. The aim of this retrospective study was to analyze the heterogeneity of 36 human glioblastoma samples on a morphological level by immunohistochemistry. We confirmed that this method is valid for heterogeneity detection. 115 Areas of Interest were labelled. By cluster analysis, we defined two subtypes (“classical” and “mesenchymal”). The results of epigenomic analyses corroborated the findings. Interestingly, patients with tumors that consisted of both subtypes (“subtype-heterogeneous”) showed a shorter overall survival compared to patients with tumor that were dominated by one subtype (“subtype-dominant”). Furthermore, the analysis of 21 corresponding pairs of primary and recurrent glioblastoma demonstrated that, additionally to an intratumoral heterogeneity, there is also a chronological heterogeneity with dominance of the mesenchymal subtype in recurrent tumors. Our study confirms the prognostic impact of intratumoral heterogeneity in glioblastoma and makes this hallmark assessable by routine diagnostics.Tumor heterogeneity is considered to be a hallmark of glioblastoma (GBM). Only more recently, it has become apparent that GBM is not only heterogeneous between patients (intertumoral heterogeneity) but more importantly, also within individual patients (intratumoral heterogeneity). In this study, we focused on assessing intratumoral heterogeneity. For this purpose, the heterogeneity of 38 treatment-naïve GBM was characterized by immunohistochemistry. Perceptible areas were rated for ALDH1A3, EGFR, GFAP, Iba1, Olig2, p53, and Mib1. By clustering methods, two distinct groups similar to subtypes described in literature were detected. The classical subtype featured a strong EGFR and Olig2 positivity, whereas the mesenchymal subtype displayed a strong ALDH1A3 expression and a high fraction of Iba1-positive microglia. 18 tumors exhibited both subtypes and were classified as “subtype-heterogeneous”, whereas the areas of the other tumors were all assigned to the same cluster and named “subtype-dominant”. Results of epigenomic analyses corroborated these findings. Strikingly, the subtype-heterogeneous tumors showed a clearly shorter overall survival compared to subtype-dominant tumors. Furthermore, 21 corresponding pairs of primary and recurrent GBM were compared, showing a dominance of the mesenchymal subtype in the recurrent tumors. Our study confirms the prognostic impact of intratumoral heterogeneity in GBM, and more importantly, makes this hallmark assessable by routine diagnostics.

Abstract 1111: Verteporfin inhibits GBM growth and migration and confers survival benefit in xenograft models

Abstract The diffusely infiltrative growth and spread, complex molecular and oncogenic signaling aberrations, and inter-and intra-tumoral heterogeneity in glioblastoma (GBM) impedes gross-total resection and chemoradiation and highlight the need for improved tumor-specific brain-penetrant therapies that inhibit cell migration in addition to proliferation. Invasive GBM cells undergo an epithelial-mesenchymal transition (EMT) phenotypic switch, expressing higher levels of genes involved in EMT remodeling, survival, and immune response compared to non-invasive GBM tumor cells. Recently, our lab defined a regulatory chromatin accessibility signature centered around the TEAD transcriptional family, which relates specifically to tumor migration in uncultured, patient-derived GBM stem cell populations, and we functionally validated TEAD1 as a driver of GBM migration and EMT, both in vitro and in vivo. The TEAD family of transcription factors, along with their co-activators YAP/TAZ, are the main downstream effectors of the Hippo pathway, a regulator of tissue growth and cell fate whose dysregulation has been implicated in tumor invasion, metastasis, and chemoresistance in other solid tumors. Here we explored the therapeutic efficacy of Verteporfin (VP), an FDA-approved macular degeneration therapy, and a small-molecule inhibitor of the YAP/TEAD complex, in patient-derived GBM cells and orthotopic xenotransplant mouse models (PDX), assessing VP's impact on GBM proliferation and migration both in vitro and in vivo. VP treatment across three different cell lines inhibited not only glioma growth but also significantly impaired tumor migration in three different in vitro assays (live cell tracking, transwell invasion, and spheroid dispersion) in a dose-dependent manner. At the protein and whole transcriptomic levels, VP-treated cells showed dose-dependent downregulation of TEAD-target activity and significant downregulation of EMT and cell migration functional gene sets, compared to vehicle. In several independent in vivo experiments, intraperitoneal administration of VP in mice with aggressive PDX glioblastoma showed consistent drug penetration into the brain parenchyma without systemic toxicity, resulted in lower tumor burden both at the tumor core and the leading infiltrative edge (n=22 VP, n=21 VEH, p=0.02, each), showed decreased number of individual distal migratory tumor cells (n=6 VP, n=5 VEH, p=0.0001 / n=6 VP, n=5 VEH, p=0.04), and, notably, conferred a survival benefit in combination with TMZ and radiation (n=10 per condition, p=0.02). Furthermore, in PDX mice transplanted with a more typical GBM, daily administration of VP for up to 280 days conferred survival benefit even as monotherapy (n=8 per condition, p=0.02), in the absence of systemic toxicity. The inhibitory effect of Verteporfin on downstream YAP/TEAD signaling and GBM migration, and its brain penetrance at non-toxic levels, underscore potential future therapeutic value and repurposing of this drug in GBM patients. Citation Format: Anne Marie Barrette, Alexandros Bouras, German Nudelman, Dominique Bozec, Noshin Shakawat, Elena Zaslavsky, Constantinos Hadjipanayis, Marc R. Birtwistle, Nadejda M. Tsankova. Verteporfin inhibits GBM growth and migration and confers survival benefit in xenograft models [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1111.

Identification of functionally distinct and interacting cancer cell subpopulations from glioblastoma with intratumoral genetic heterogeneity.

BackgroundGlioblastomas display a high level of intratumoral heterogeneity with regard to both genetic and histological features. Within single tumors, subclones have been shown to communicate with each other to affect overall tumor growth. The aim of this study was to broaden the understanding of interclonal communication in glioblastoma.MethodsWe have used the U-343 model, consisting of U-343 MG, U-343 MGa, U-343 MGa 31L, and U-343 MGa Cl2:6, a set of distinct glioblastoma cell lines that have been derived from the same tumor. We characterized these with regard to temozolomide sensitivity, protein secretome, gene expression, DNA copy number, and cancer cell phenotypic traits. Furthermore, we performed coculture and conditioned media-based experiments to model cell-to-cell signaling in a setting of intratumoral heterogeneity.ResultsTemozolomide treatment of a coculture composed of all 4 U-343 cell lines presents a tumor relapse model where the least sensitive population, U-343 MGa 31L, outlives the others. Interestingly, the U-343 cell lines were shown to have distinct gene expression signatures and phenotypes although they were derived from a single tumor. The DNA copy number analysis revealed both common and unique alterations, indicating the evolutionary relationship between the cells. Moreover, these cells were found to communicate and affect each other’s proliferation, both via contact-dependent and -independent interactions, where NOTCH1, TGFBI, and ADAMTS1 signaling effects were involved, respectively.ConclusionsThese results provide insight into how complex the signaling events may prove to be in a setting of intratumoral heterogeneity in glioblastoma and provide a map for future studies.

Unifying models of glioblastoma's intratumoral heterogeneity.

Unifying models of glioblastoma's intratumoral heterogeneity.

Malignant Gliomas: Simplifying the Complexity.

The dramatic intertumoral and intratumoral heterogeneity in glioblastoma represents a formidable obstacle to finding effective combination targeted therapy for each individual patient. The article by Wang and colleagues in this issue of Cancer Discovery adds to a growing body of literature that points to these diverse aberrant genomes coalescing onto a very limited number of cellular states, reviving the hope of eventually identifying and therapeutically targeting just a few central gene-regulatory targets in most gliomas.See related article by Wang et al., p. 1708.

COMP-06. PROBING GLIOBLASTOMA AND ITS MICROENVIRONMENT USING SINGLE-NUCLEUS AND SINGLE-CELL SEQUENCING

Abstract Single-cell (scSeq) and single-nucleus sequencing (snSeq) are powerful tools to investigate cancer genomics at single cell resolution. Multiple studies have recently illuminated intratumoral heterogeneity in glioblastoma, however, the majority focused on molecular complexity of tumor cells, without taking into account unexplored host cell types that contribute to the microenvironment around tumor. To address the glioblastoma microenvironment composition and potential tumor-host interactions, we performed deep coverage sequencing of freshly resected primary GBM patient tissue without implementing any tumor enrichment strategies. The sequencing resulted in 902 cells and 1186 nuclei, respectively, passing quality control and with low mitochondrial gene percentage. We customized reference transcriptome by listing gene transcript loci as exons to take into account immature RNA, which greatly improved the alignment rate for single-nucleus data. We applied Cell Ranger pipelines (Version 3.0.2) and Seurat package (Version 2.3.1) and discovered 10 clusters in both scSEQ and snSEQ. Pathway analysis of each cluster signature in scSeq data along with known GBM microenvironment cell signatures revealed glioma tumor population along with surrounding microglia/macrophages, astrocytes, pericytes, oligodendrocytes, T cells and endothelial cells. The analysis of snSEQ was able to capture the majority of cell types from patient tissues (tumor and microenvironment cells), but interestingly presented different cell type composition in microenvironment cell types such as microglia/marcophages. Integrating single-cell and single-nucleus transcriptomic data using canonical correlation analysis facilitated a comparison of snSEQ and scSEQ, contrasting depiction for certain cell types (e.g. NKX6-2 gene in Oligodendrocytes). Differential analysis of pathways between tumor and microenvironment cells unveiled potentially rewired pathways such as double strand break repair pathway. Our results demonstrate the cellular diversity of brain tumor microenvironment and lay a foundation to further investigate the individual tumor and host cell transcriptomes that are influenced not only by their cell identity but also by their interaction with surrounding microenvironment.

Deciphering brain tumor heterogeneity, one cell at a time.

Single-cell RNA sequencing characterizes clonal dynamics and distinct cellular states contributing to intratumoral heterogeneity in glioblastoma and medulloblastoma.