bluuebunny/crossref_metadata_2025 huggingface dataset:
OS04.6.A ONE STEP CLOSER TO THE END OF LIFE - A QUALITATIVE STUDY ABOUT THE EXPERIENCES OF PATIENTS AND THEIR FAMILIES IN RELATION TO THE RECURRENCE OF A HIGH-GRADE GLIOMA
Authors: Sørensen von Essen H , Dahl Steffensen K , Rom Poulsen F , Piil K
Published: October 2024

BACKGROUND

High-grade gliomas (HGG) are the most aggressive and infiltrative sub-type of primary brain tumors. Despite intensive treatment, the average survival time from diagnosis is less than two years, and all patients experience recurrence after the initial resection and oncological treatment. However, our understanding of patients’ and their families’ experiences related to the inevitable recurrence remains limited. This qualitative study aimed to explore how patients and their close family members cope with their fear of recurrence, and to understand their experiences when faced with the reality of recurrence.

MATERIAL AND METHODS

The present study is part of a larger study exploring the experiences of patients and their families related to the recurrence of an HGG. Semi-structured individual interviews were conducted. The interviews were audio recorded and transcribed verbatim. Following an inductive phenomenological hermeneutical approach, the interviews were coded, and the findings were divided into themes.

RESULTS

The study included 15 patients and 14 family members between the ages of 22 and 79. The analysis identified three overall themes. Theme I: Navigating the fear of recurrence. Theme II: Facing recurrence and preparing for death. Theme III: Redefining hope in the era of recurrence. The three themes were interrelated and provided an overall insight into the complex experiences related to the diagnosis of an HGG recurrence, which will be further elaborated on in the presentation.

CONCLUSION

During the disease trajectory of living with an HGG, patients and their family members tried their best to live life to the fullest and cope with their fear of the inevitable recurrence and death. They expressed an overall awareness about the prognosis, but their hopes and coping mechanisms left them off guard, causing the recurrence diagnosis to strike them as a shock. The recurrence was experienced as taking one step closer to the end of life, leading them to redefine their hopes and goals for the future. This new knowledge can be used to tailor advance-care-planning consultations, emphasizing patients’ and family members’ readiness to discuss end-of-life issues.
OS07.2.A INTEGRATED SPATIALLY RESOLVED TRANSCRIPTOMICS AND ELECTROPHYSIOLOGY UNRAVELED THE ARCHITECTURAL HETEROGENEITY OF NEURONAL-GLIOMA INTERFACES
Authors: Grabis E , Hilfiger L , Kueckelhaus J , Neidert N , Zhang J , Villa G , Yabo Y , Benotmane J , Prinz M , Ricklefs F , Beck J , Schnell O , Vlachos A , Delev D , Heiland D
Published: October 2024

BACKGROUND

In recent years, spatially resolved, high-dimensional methodologies have elucidated substantial transcriptional heterogeneity within malignant brain tumors. However, the absence of functional readouts has impeded the linkage of emergent insights from cancer neuroscience to the structural heterogeneity observed. To address these challenges, we developed a novel methodology that combines spatially resolved electrophysiological recordings with array-based transcriptomics on surgical specimens. This approach enables an exploration of the transcriptional underpinnings of electrophysiological network communication in glioblastoma.

MATERIAL AND METHODS

We analyzed 12 surgical glioblastoma specimens, which were sectioned (350µm) and subjected to high-resolution multi-electrode array (HD-MEA) recordings (64 x 64 pillar electrodes at a 2µm resolution, 3Brain), followed by spatially resolved transcriptomics (at a 55µm resolution, Visium). Data co-registration was achieved through affine transformations and postprocessing using SPATA2 (github.com/theMILOlab/SPATA2).

RESULTS

Utilizing graph-based analysis, we identified prominent communication hubs and prevalent signaling pathways within tumor and peritumoral areas. Horizontal and vertical integration across these data modalities, combined with latent-space clustering, revealed spatial heterogeneity in electrophysiological communication and transcriptional responses. Notably, regions of infiltrative growth and tumor borders exhibited enhanced neuronal-glioma interactions, characterized by transcriptional up-regulation of synaptogenic signaling and a predominant presence of oligodendrocyte progenitor cell (OPC)- and neural progenitor cell (NPC)-like tumor cells. These regions displayed distinct, repetitive spatial signaling patterns. Conversely, areas with heightened inflammatory responses, marked by macrophage and T cell enrichment, as well as hypoxic alterations, demonstrated a lack of coherent communication patterns. Tumor peripheries with increased astrocytic presence or activated microglia exhibited diminished communication between tumor and neuronal systems compared to infiltrative areas with elevated oligodendrocytic abundance, underscoring the critical role of oligodendrocytes in facilitating neuronal-glioma communication.

CONCLUSION

Our investigations into the electrophysiological and transcriptomic architecture of gliomas provide novel insights into the intricate heterogeneity of the neuronal-glioma interface and identify key regulatory elements that drive the integration of glioma within neuronal circuits.
OS08.6.A MACHINE LEARNING BASED PREDICTIVE RADIOMICS FOR EVALUATION OF CANCER IMMUNE SIGNATURE IN GLIOBLASTOMA: THE PRECISE-GBM STUDY
Authors: Ghimire P , Modat M , Booth T
Published: October 2024

BACKGROUND

Immunotherapy is an effective personalized treatment for several cancers. Radiogenomics in glioblastoma patients may serve as pre-operative biomarkers of the tumor-host immune apparatus. Validated biomarkers would have the potential to stratify patients during immunotherapy clinical trials, and if trials are beneficial, facilitate personalized neo-adjuvant treatment. The aim of the study is to identify machine learning based radiological biomarkers that can predict immune status within the tumor microenvironment of IDH wildtype glioblastoma.

MATERIAL AND METHODS

This is a retrospective multicentre machine learning based study utilizing open access anonymized matched radiogenomic data of TCGA-GBM, CPTAC, Ivy GAP and REMBRANDT datasets. REMBRANDT data acted as a hold out dataset for validation. Imaging data consisted of pre-operative MRI based radiomics features extracted from deep learning based segmented tumours. Transcriptomic data-based overall immune scores and cell-specific (T cells and tumour associated macrophages-TAMS) scores were extracted using CIBERSORTx. Outliers were identified and filtered using principal component analysis. Combat and neuroCombat were utilized for data harmonization of genomic and imaging data respectively. Radiomic feature selection was performed using LASSO technique with cross validation. Support vector machine (SVM) was trained and tested with 80:20 radiomic data split, and binary labels based on (1) overall immune scores, and (2) immune cell-specific scores. Data query and analysis was performed using python.

RESULTS

One hundred and one patients with 15 in the hold out dataset were included in the study. Trained SVM models predicted the labels for immune score and cell-specific scores (T cells -CD4, CD8; TAMS - M0, M1, M2) with AUC of 0.93, 0.79, 0.86, 0.81, 0.95, 0.76; balanced accuracy of 0.72, 0.82, 0.93, 0.80, 0.86, 0.71 respectively. In the holdout set, the models predicted the labels with robust prediction sensitivity for overall immune scores (100%) and M2 (M2 pro-tumour subtype macrophages) scores (75%) and with robust prediction specificity for T cell scores (CD4: 100%, CD8: 90%) along with M0 (M0 precursor pro-tumour macrophages) (62.5%) and M1 (M1 anti-tumour subtype macrophages) (83%) scores. The radiomic signature employed for prediction models were a group of first order and second order radiomic features obtained after feature selection.

CONCLUSION

A radiogenomic SVM model non-invasively predicted immune status in wildtype glioblastoma microenvironment with good accuracy. This model has the potential to be utilized in a prospective glioblastoma clinical trial for immunotherapy.
P01.02.B CLONAL ARCHITECTURE, 3D TUMOR GROWTH, TRANSCRIPTIONAL STATES AND IN-VITRO DRUG RESPONSE IN GLIOBLASTOMA
Authors: Song Y , Miller-Michlits Y , Greutter L , Nenning K , Kiesel B , Loetsch-Gojo D , Redl V , Gesperger J , Jana Breu J , Nam D , Widhalm G , Sa J K , Woehrer A
Published: October 2024

BACKGROUND

Glioblastoma (GBM) is one of the deadliest types of cancer with median overall survival of little more than a year. It is the prototypic example of a molecularly diverse tumor associated with multiple subclones and high transcriptional plasticity. We here set out to map the clonal architecture and link it with growth patterns, transcriptional states and drug response in-vitro.

MATERIAL AND METHODS

Seventy-eight multiregional samples from 24 GBM patients (F/M ratio = 0.60, median age= 65.5ys) were profiled using deep whole-exome sequencing with a nominal coverage of >300x. Clonal complexity was assessed by the Euclidean distance based on somatic mutations between regions. Molecular distance was integrated with 3D neuronavigation-based physical distances and patient survival. Fresh tumor slices of 8 patients were cultured in minimal essential media and treated with 4.2Gy plus 200µM temozolomide for 48h. Spatial Transcriptomics was performed on matched tumor slices with /without treatment.

RESULTS

Integrative characterization revealed two evolutionary trajectories termed expansive and stochastic diversification based on the correlation between physical and molecular distance (RE=0.6, RS=-0.2). High molecular distance was associated with reduced PFS (R=-0.5538, p=0.026). Tumors with expansive evolution demonstrated volume-based growth that directly correlated with increased molecular diversification, while stochastic models showed irregular and discordant patterns. Patients with stochastic evolution were characterized by unfavorable prognosis (p= 0.35) and tumors appeared more frequently in the frontal lobe. In-vitro drug response varied considerably between patients based on cell proliferation (minus 0.5-13.8%), apoptosis (30.1-44.4%), and viability (minus 7.8-34.1%). Under treatment an increase of mesenchymal states with peculiar redistribution of transcriptional patterns and increased clonal complexity was observed.

CONCLUSION

In sum, we report on an ongoing study in glioblastoma that seeks to link the clonal architecture with tumor growth, transcriptional patterns and in-vitro drug response. Our study will provide new insights into the clinical relevance of the clonal composition of this deadly cancer.
P01.03.A DECIPHERING THE TUMOUR ECOSYSTEM IN GLIOMAS USING SPATIAL TRANSCRIPTOMICS
Authors: Seferbekova Z , Calafato D , Rukhovich G , Patel A , Ritter M , Ippen F , Suwala A , Sahm F , Gerstung M
Published: October 2024

BACKGROUND

Tumours are complex ecosystems comprised of tumour cells and various normal cell types. The interactions among these cells with diverse molecular characteristics lead to intratumoral heterogeneity and cancer evolution. However, little is known about the spatial organisation of the glioma tumour ecosystem, the cellular interactions therein, and their influence on patient outcomes. Spatially resolved transcriptomics, which measures the activity of hundreds of genes in situ, offers new avenues to address these long-standing questions.

MATERIAL AND METHODS

We assembled a clinically annotated cohort of glioma patients spanning 278 specimens from low- and high-grade IDH-mutant astrocytoma, IDH-mutant and 1p/19q co-deleted oligodendroglioma, ependymoma, and glioblastoma. Using tissue microarrays from archival FFPE, we performed spatial transcriptomics profiling with the 10x Xenium platform. A custom panel of 344 genes, selected based on publicly available single-cell RNA sequencing datasets of gliomas, was interrogated.

RESULTS

The spatial transcriptomics data enabled annotation and spatial mapping of more than 20 tumour microenvironment (TME) cell types and various tumour cell types present across the 5 glioma entities. We observed broadly conserved transcriptional profiles of TME cell types across gliomas, but TME cell type composition and spatial organisation varied depending on tumour entity. In particular, lower amounts of macrophages and microglia as well as oligodendrocytes and oligodendrocyte progenitor cells were observed in oligodendroglioma tumours compared to other entities. High-grade astrocytoma samples contained more myeloid dendritic cells and monocytes in comparison to low-grade astrocytomas. A lower proportion of vascular cells correlated with better overall survival for glioblastomas and low- and high-grade astrocytomas. Tumour cells exhibited mostly entity-specific gene expression, but also distinct tumour cell states including AC-, OPC-, NPC-, and MES-like cell states.

CONCLUSION

Our work demonstrates the utility of the 10x Xenium platform for high-resolution spatial profiling of expression in archival FFPE samples. Preliminary mapping of the TME and tumour cell types unravelled a complex spatial architecture of the glioma ecosystems. Distinct compositions of the TME were found in each glioma subtype, which may help understand different mechanisms of malignancy. Associations between cellular spatial patterns and clinical data available for FFPE samples have the potential to provide better insights for improved patient stratification and treatment.
P01.04.B DISSECT THE ROLE OF DRIVER GENE MUTATIONS IN THE INITIATION AND PROGRESSION OF GLIOMA
Authors: Tian A
Published: October 2024

BACKGROUND

Cancer initiation and progression are always associated with the acquisition and accumulation of genetic mutations which drive the malignant transformation of the cancer cells of origin. It has been shown that different driver mutations can generate tumors with distinct pathological features. How driver mutations interact with tumor cells of origin and how such interactions dictate the fate of the cell of origin and the tumor pathological features are the fundamental questions in cancer biology.

MATERIAL AND METHODS

We developed a set of novel “all-in-one” glioma lineage-tracing model system based the intra-brain ventricle electroporation and transposon-mediated conditional gene editing techniques. By using this system in genetically engineered mouse models, we generated glioma models with the top driver mutation combinations found in IDH-WT GBM patients and trace their initiation and progression with single-cell resolution, therefore we combined lineage tracing with multi-omics analysis almost recapitulating the full driver-mutation spectrum found in glioblastoma patients.

RESULTS

We found that driver gene mutations combinatorially dictate the early cell fate of tumor development. Driver gene mutations and cell fate combinatorially determine the prognosis and malignancy of glioma. Driver gene mutations combinatorially affect the molecular phenotypes and the treatment of end-stage tumor via deterministic CNVs. Synergistic or exclusive patterns of driver gene mutation combinations regulate the initiation and progression of gliomagenesis.

CONCLUSION

The results will help to address those fundamental questions pertinent to the relationships between driver mutations, tumor evolution pathways and the final tumor phenotypes. The results will also help to understand the nature of gliomagenesis and to develop novel approaches for glioma intervention and treatment.
P01.07.A HOW IS A RECURRENT GLIOBLASTOMA SELECTED?
Authors: Göhring K , Kässer A , Konig S , Strobel H , Karpel-Massler G , Mulaw M A , Debatin K M , Westhoff M A
Published: October 2024

BACKGROUND

Glioblastoma is the most common primary brain tumour in adults and one of the most lethal cancers per se. Despite aggressive therapeutic approaches, patient survival remains dismal, typically not exceeding 15 months. Standard treatment involves maximal safe resection followed by irradiation of the surgical cavity and chemotherapy with Temozolomide. However, recurrence is inevitable, and additional interventions provide only limited survival benefits.

MATERIAL AND METHODS

We conducted a study using two pairs of stem cell-like cell populations and their differentiated progeny obtained from the same patient, either before standard therapy or upon recurrence. Our objective was to investigate the selective forces driving the recurrent phenotype and identify potential therapeutic targets.

RESULTS

Our findings revealed an increased resistance to apoptosis in cells harvested after recurrence, albeit less pronounced than anticipated. The population doubling time was also reduced in post-recurrence cell populations, suggesting that they proliferate faster. However, the most striking changes we observed was a phenotypic shift towards a more mesenchymal phenotype. This was corroborated by increased expression of epithelial-mesenchymal transition (EMT) markers, the presence of oncostreams in differentiated progeny, and enhanced migratory velocity, suggesting heightened invasiveness post-standard intervention. Interestingly, detailed expression pattern analysis suggested that the recurrent phenotype might be expressed by cells more closely related to the cells of origin than those found in the primary tumour, implying recurrence may occur due to therapeutic clearance of the ecological niche.

CONCLUSION

Analysis of signalling networks identified potential druggable targets upregulated in cells from recurrent tumours, including phosphorylated STAT1 and various extracellular matrix components. Pharmacological inhibition of these targets exhibited a greater effect on recurrent tumour cells, indicating promise for inclusion in complex combination therapies. Our findings underscore the need to consider recurrent glioblastoma as an independent entity from the primary tumour and suggest further analysis may unveil additional therapeutic targets for novel combination therapies.
P01.13.B SPATIALLY RESOLVED FUNCTIONAL PROFILING FOR GLIOBLASTOMA
Authors: Buck A , Lee S , Wegmann R , Mena J , Vasella F , Friesen J , Regli L , Le Rhun E , Zeitlberger A , Neidert M , Tatari N , Hutter G , Weller M , Snijder B , Weiss T
Published: October 2024

BACKGROUND

Glioblastoma is the most common malignant primary brain tumor in adults with a poor survival and an unmet need for more effective therapies. Macroscopically, as observed through MR imaging and microscopy, these tumors are characterized by distinct tumor regions: a contrast-enhancing tumor core with a high cancer cell density and an infiltration zone that appears T2/FLAIR hyperintense without the uptake of contrast agent and consists of infiltrating tumor cells as well as non-malignant cells of the microenvironment. Previous genomic and transcriptomic studies revealed molecular differences between the tumor core and the infiltration zone. However, functional differences, such as drug sensitivity, between tumor regions are still largely unknown.

MATERIAL AND METHODS

To address this gap, we collected region-specific glioblastoma patient tissue samples from both the contrast-enhancing tumor core and the non-contrast-enhanced but 5-aminolevulinic acid (5-ALA) fluorescent infiltration zone directly from surgery. We utilized pharmacoscopy, a microscopy-based single-cell drug screening approach, to identify drug responses across regions and patients.

RESULTS

By profiling a library of 60 drugs across regions in a cohort of 23 patients, we identified region-specific drug responses and developed a complementarity score to pinpoint drug combinations capable of targeting both the tumor core and the infiltration zone. These combination treatments exhibited highly promising anti-tumor activity across regions and patients.

CONCLUSION

Given the high levels of functional spatial intra- and inter-tumor heterogeneity, relying on a single drug across patients may not be feasible for effective patient treatment. Thus, there is a strong rationale to administer drug combinations that not only consider regional heterogeneity but also account for patient-specific variations.
P02.01.B 3-PHOTON IN VIVO IMAGING REVEALS BREAKDOWN OF MICROGLIA SURVEILLANCE UPON GLIOMA INVASION IN THE CORPUS CALLOSUM
Authors: Nebeling F C , Fuhrmann F , Mittag M , Deli A , Stork M , Clements M , Garcia Diaz C , Parrinello S , Hölzel M , Herrlinger U , Salomoni P , Fuhrmann M
Published: October 2024

BACKGROUND

Glioblastomas are the most malignant primary brain tumors with median survival of about 17 month after diagnosis despite extensive treatment. Molecular heterogeneity and the tumor microenvironment contribute to the challenges in treating GBs. Glioma-associated microglia and macrophages, comprising 30-50% of non-neoplastic cells in the microenvironment, play a crucial role in facilitating tumor growth and inducing tissue necrosis. However, the intricate interplay between these immune and glioma cells remains poorly understood. This study focuses on understanding the dynamic role of microglia during glioblastoma invasion at the tumor’s invading front in corpus callosum.

MATERIAL AND METHODS

In order to investigate glioma and microglial dynamics, we use an autochthonous model of mesenchymal GBs employing a CRISPR/Cas mediated approach in recipient Cx3Cr1 knock-in mice. Previous studies using two-photon in vivo imaging were limited to superficial cortical regions, hindering investigations into deeper brain areas like the corpus callosum. Recently, three-photon microscopy has emerged as a powerful tool to image between 1100-1600µm deep into the intact brain.

RESULTS

By implanting chronic cortical windows and taking advantage of three photon in vivo imaging, we reveal differential regulation of microglial motility based on glioma infiltration levels in the corpus callosum. Moreover, we find distinct clusters of microglial movement in dependence to the distance of tumor cells. Conclusion: This approach provides insights into microglia-glioma interactions at the invading front in the corpus callosum, offering a valuable tool for studying deep-seated cellular dynamics and identifying potential therapeutic targets. Funding Source: CANcer TARgeting (CANTAR)
P02.03.A BLOOD-BRAIN BARRIER AFFECTING THE METABOLOMICS PROFILES IN THE TUMOR MICROENVIRONMENT, CSF AND BLOOD OF IN VIVO HUMAN GLIOBLASTOMA XENOGRAFT
Authors: Altendorfer-Kroath T , Schimek D , Monedeiro F , Zügner E , Prugger E , Magnes C , Birngruber T
Published: October 2024

BACKGROUND

Detection of glioblastoma (GBM) before clinical signs appear can significantly improve treatment outcome and patients’ quality of life. To date, there are various biomarker candidates for early detection of GBM but most of them can only be detected in cerebrospinal fluid (CSF) and in brain tissue biopsies. Brain tissue biopsies are very rarely taken in routine hospital practice while CSF or blood samples are part of the standard clinical routine in hospitals. However, no reliable biomarker has yet been identified for the early detection of most central nervous system (CNS) tumors in liquid biopsies, probably due to the limited ability of the biomarkers to cross the blood-brain barrier (BBB). In contrast to blood, CSF provides good access to the CNS. However, previous studies on pharmacokinetics of CSF and the interstitial fluid in the brain tissue (ISF) have shown that the equilibrium between the CSF and the ISF is slow and substance concentrations in the CSF do not reflect the concentrations that are locally present in brain tissue. We aimed to assess the metabolome from ISF collected with Open Flow Microperfusion (cOFM) from the tumor microenvironment (TME), CSF and blood from human GBM-bearing animals and compare it to respective samples collected from non-GBM-bearing animals (non-GBM controls) in order to identify potential metabolic biomarker or biomarker profiles that reflect the local situation of the TME.

MATERIAL AND METHODS

We applied a recently established atraumatic access to human GBM xenograft with cOFM that allows sampling of metabolites inside the TME with functional BBB. Tumor growth was monitored with magnetic-resonance-imaging. On day 18 post-inoculation, ISF from TME was collected with cOFM and blood, CSF and brain tissue were collected from the same animals at terminal time point. The collected samples were analyzed using a UHPLC-HRMS/MS metabolomics platform. Metabolomics data from GBM-bearing animals were analyzed and compared with that from non-GBM controls.

RESULTS

The metabolome from the GBM-bearing animals showed distinct metabolic patterns when compared to that from the non-GBM controls. GBM-relevant metabolites such as Kynurenine, Carnosine or Guanidinoacetate exhibited different levels in ISF from GBM-bearing animals than in CSF or blood samples from these mice.

CONCLUSION

This suggests that the BBB plays an important role when obtaining biomarkers from CSF or blood as it might affect the assessment of the local condition in the TME.
P02.12.B IMMUNOMODULATORY EFFECT OF MESENCHYMAL CELLS ON T-LYMPHOCYTES IN A 3D SPHEROID MODEL OF GLIOBLASTOMA
Authors: Šváblová T , Ternerová N , Houdová Megová M , Vymolová B , Balážiová E , Bušek P , Šedo A
Published: October 2024

BACKGROUND

Glioblastoma (GBM) represents the most aggressive form of primary brain tumors, typically resulting in a median survival of less than two years. Immunosuppression characterized by macrophage polarization into a protumorigenic phenotype and T cell dysfunction belong to key features of glioblastoma microenvironment. Recently, mesenchymal cells were identified in GBM, but their immunomodulatory effect has not been elucidated yet. This study aimed to establish a 3D GBM spheroid model to investigate the influence of mesenchymal cells derived from GBM patients on the phenotype of human T cells.

MATERIAL AND METHODS

Glioma stem-like cells (GSCs) and paired mesenchymal cell cultures were derived from 3 human GBMs. CD4+ T-lymphocytes were isolated from buffy coats of healthy donors using Ficoll Paque gradient separation, followed by negative magnetic isolation. T-lymphocytes were activated using anti-CD2, anti-CD3, and anti-CD28 antibodies. 3D spheroids were generated by encapsulating the GSC and T-lymphocytes in extracellular matrix (Geltrex) with or without mesenchymal cells. After co-culture for 5-days, the spheroids were dissociated, and the expression of T-cell activation markers CD69 and CD25, along with immune checkpoint molecules LAG3, TIM-3, and PD-1 was analyzed by flow cytometry. Immunohistochemistry was performed on frozen sections of the spheroids to investigate the spatial distribution of the cell populations.

RESULTS

In spheroids with mesenchymal cells, CD4+ T-lymphocytes exhibited elevated expression of the activation marker CD69, whereas no significant changes were observed in CD25 expression. PD-1 expression was higher in spheroids containing mesenchymal cells, whereas no difference was noted in TIM3 and LAG3 expression levels. Uniform distribution of glioblastoma-derived cells was revealed in spheroids containing glioma stem-like cells with and without mesenchymal cells and T-lymphocytes did not exhibit any specific spatial distribution pattern.

CONCLUSION

We have successfully established a 3D spheroid model to investigate the impact of mesenchymal cells on the GBM microenvironment. Our findings suggest that mesenchymal cells can influence the phenotype of CD4+ T-lymphocytes in glioblastoma. This research was funded by project National Institute for Cancer Research (Programme EXCELES, ID Project No. LX22NPO5102) by the European Union - Next Generation EU, and Charles University Cooperatio Program,,Oncology and Haematology“.
P02.13.B IMPLEMENTATION OF A MULTIPLEXED TISSUE IMAGING PROTOCOL FOR CHARACTERIZATION OF THE TUMOR MICROENVIRONMENT IN MELANOMA BRAIN METASTASES
Authors: Sagerer A , Westphal D , Meinhardt M , Schulz A , Meier F , Eyüpoglu I Y , Schackert G , Juratli T A
Published: October 2024

BACKGROUND

Melanoma-specific genetic changes are being identified for targeted therapy. Unfortunately, patients receiving such therapies often develop resistance and experience recurrence. Current data suggest that the tumor-specific microenvironment has a decisive influence on therapy resistance. We therefore established a multiplexed immunofluorescence staining procedure to precisely characterize the tumor microenvironment in melanoma brain metastases (BM).

MATERIAL AND METHODS

Co-detection by indexing (CODEX) technology relies on DNA-conjugated antibodies and the cyclic addition and removal of complementary fluorescently labeled DNA probes and has been used so far to simultaneously visualize up to 100 biomarkers in situ. It enables a deep view into the single-cell spatial relationships in tissues and is intended to spur discovery in disease and therapeutic design. Hence, CODEX was provided by Akoya Biosciences. To quickly handle a larger scale of samples, a tissue microarray (TMA) was created both of tumor bulk as well as positive and negative surgical margins.

RESULTS

At first, a panel of yet 30 antibodies were conjugated with its definite DNA oligonucleotide sequence (reporter). On our part, these antibodies were previously validated on conventional IHC using normal and gliotic brain along with tonsil tissue, including CD molecules (e.g. CD4, CD8, CD20, CD31, CD68), signal transducing antibodies (e.g. AKT, ERK, EGFR, IGFR and their phosphorylated isoforms) as well as tumor (e.g. HMB-45, Ki67, Melan-A) and neuronal (e.g. GFAP, NeuN, Olig2, PSD-95) markers. Subsequently, TMAs of different BM composition were generated (25 cores (1.5 mm), per slide 6x4 samples + 1 positive control). Within only a single working day TMAs were prepared and CODEX staining protocol fully assembled (single step staining to preserve sample integrity). CODEX run (3 reporter dyes imaged with complimentary barcodes) was performed overnight truly smooth. Lastly, analysis of spatial information has been received via QuPath (open source).

CONCLUSION

Once individually designed, multiplex fluorescence imaging platform is an easy and especially in case of TMAs fast-performing tool to gain insight into the tumor microenvironment of small sample volumes in particular. For the prediction of possible therapy resistance distinct further studies are warranted to define the potential of high throughput multiplexed imaging in a clinical routine.
P04.01.B DECIPHERING GLIOBLASTOMA METABOLIC SIGNATURE: MOLECULAR PROFILING AND NADH-FLIM IMAGING
Authors: Morelli M , Santonocito O , Lessi F , Ferri G , Menicagli M , Aretini P , Grimod G , Gambacciani C , Pieri F , Schweizer L , Ronellenfitsch M , Sanson M , Picca A , Garofano L , Iavarone A , Mazzanti C , Di Stefano A
Published: October 2024

BACKGROUND

Recent breakthroughs in single-cell analysis reveal distinct metabolic cellular states, leading to a novel classification of glioblastoma (GBM). An RNA Sequencing methodology optimized for FFPE tumor tissue of GBM allows for the detection of GBMs which are dependent on oxidative phosphorylation (OXPHOS). OXPHOS GBM show vulnerabilities to anti-mitochondrial agents, both in vitro and in vivo, and are currently the target population of clinical trials in newly diagnosed GBM patients (Optimum NCT04945148). To date, RNA Sequencing is the sole standardized method for the retrospective and prospective identification of OXPHOS GBM.

OBJECTIVE

To evaluate the efficacy of Fluorescence Lifetime Imaging (FLIM) microscopy of NADH, an intracellular enzymatic cofactor, in discerning the metabolic profile of FFPE GBM tumor tissue, with a focus on identifying OXPHOS GBM subtypes.

METHODS

We analyzed 3’ mRNA NGS FFPE tumor tissue from a retrospective series of IDHwt glioblastomas, newly diagnosed from 2021 to 2023. The OXPHOS logODDS ratio, GPM logODDS ratio, and the simplicity score were considered as quantitative criteria to select prototype cases. We selected OXPHOS mitochondrial and glycolytic/plurimetabolic cases for the NADH-FLIM microscopy study on FFPE slides. NADH-FLIM images were analyzed quantitatively. For each cases 10-16 subareas were analysed. Areas of necrosis were excluded from the quantitative analysis.

RESULTS

RNA Sequencing of 28 consecutive newly diagnosed IDHwt GBM patients from the Livorno Hospital found 9 OXPHOS, 6 glycolytic/plurimetabolic (GPM), 1 neuronal (NEU), 9 not classifiable, and 3 proliferative (PPR) cases. In the OXPHOS subgroup, 5 patients showed the highest score of association with the OXPHOS signature and were the subject of the NADH-FLIM study compared to the top 5 GPM cases. NADH bound/free distribution curves significantly differed between the OXPHOS’ and GPM’ subgroups, with higher values for GPM cases [GPM: median 0.21, range 0.0-0.93; OXPHOS: median 0.02, range 0.0-0.99; p<0.0001]. We noticed different skewness (0.53 vs 1.21) and kurtosis (-1.25 vs -0.15) parameters in NADH bound/free curves between GPMs and OXPHOSs, respectively.

CONCLUSIONS

OXPHOS and GPM glioblastoma clusters significantly differed in NADH bound/free distribution curves. Supplementary cases are currently under study to confirm preliminary results and to assess the power of NADH-FLIM to predict the metabolic status of GBM samples.
P08.06.B TARGETED PLASMA METABOLOMICS DETECTS A METABOLIC SIGNATURE POTENTIALLY RELEVANT FOR DIAGNOSIS AND PROGNOSIS IN PATIENTS WITH GLIOBLASTOMA
Authors: Pantalone M , Söderberg Naucler C , Stragliotto G
Published: October 2024

BACKGROUND

Glioblastoma diagnosis is made initially with radiological examination and pathological examination of the resected tumor. Debut symptoms may vary and be mild in the beginning therefore diagnosis can be delayed. Additional un-invasive diagnostic strategies can be useful for diagnosis and clinical follow up. Moreover, despite glioblastoma being almost invariably fatal, there are no clinically established plasma biomarkers to define prognosis in patients with glioblastoma.

MATERIAL AND METHODS

In this study we employed flow injection analysis tandem mass spectrometry to compare the plasma metabolic profile of patients with GBM (n=51) and healthy controls (n=24).

RESULTS

Principal Component Analysis (PCA) and Partial Least Discriminant Analysis (PLS-DA) highlighted a distinctive glioblastoma signature. The best confusion matrix from the ROC curve built on two metabolites, GLU and GLN-LYS, provided specificity of 87% and sensitivity of 92%, with positive and negative predictive values of 95% and 88%, respectively. Multivariate survival analyses revealed that higher levels of ALA, MET, ORN, PHE, TYR were to be associated with a shorter overall survival.

CONCLUSION

These results are currently being evaluated prospectively and at different stages of disease in a larger cohort of patients with glioblastoma to identify plasma metabolic markers relevant for recurrence development.
P09.06.A ELEVATED TRANSLOCATOR PROTEIN 18KDA BINDING IN LOW MEAN DIFFUSIVITY CLUSTERS OF THE TRANSFORMING GLIOMA MICROENVIRONMENT
Authors: Breese J , Agushi E , Lewis D , Hinz R , Alfaifi B , Jackson A , Coope D
Published: October 2024

BACKGROUND

Approximately 95% of low-grade gliomas (LGGs) undergo anaplastic de-differentiation into high-grade gliomas, a process only partially understood from the current suite of in vivo clinical imaging methods.

MATERIAL AND METHODS

11 patients with suspected transforming gliomas undertook a comprehensive magnetic resonance imaging (MRI)/positron emission tomography (PET) imaging protocol, incorporating post-contrast T1W/FLAIR acquisition for tumour delineation, and diffusion tensor imaging. PET imaging with the radiotracer [11C]-(R)-PK11195 assessed the distribution of translocator protein 18kDa (TSPO), whose upregulation is associated with inflammation and glioma progression. [11C]-(R)-PK11195 binding potential (BPND) maps were generated, utilising the simplified reference tissue model, with the grey-matter cerebellar time activity curve serving as the tissue input function. KMeans clustering of MD intensity values within the tumour FLAIR (minus contrast enhancement) region marked spatially distinct “habitats” of high/low MD voxel clusters. BPND values within the high and low MD clustering habitats were collected and subsequently compared between tumour grades.

RESULTS

Within the FLAIR (minus contrast enhancement) tumour region of grade 2 and 3 gliomas (n = 6), mean BPND values in low MD habitats (0.042 ± 0.125) were significantly higher than habitats of high MD (-0.066 ± 0.130) (p = 0.000007). In grade 4 tumours (n = 5), no significant difference was observed in the mean TSPO BPND values between habitats of low MD (0.498 ± 0.297) and high MD (0.509 ± 0.167) clusters (p = 0.892).

CONCLUSION

Our findings demonstrate a unique aspect of spatial heterogeneity in grade 2 and 3 tumours, absent from grade 4 gliomas, indicating differences in the underlying inflammatory processes. Regions of restricted diffusion with high TSPO signal in grade 2 and 3 tumours may signify heterogenous distributions of active immune cells and/or neoplastic cell populations in the microenvironment. Conversely, grade 4 tumours may exhibit uniform patterns of immune cell activation/neoplastic cell infiltration throughout their surrounding microenvironment.
P09.08.B GADOPICLENOL CAN HELP WITH GAMMA KNIFE LOCALIZATION OF BRAIN METASTASIS OVER OTHER CLASS II GADOLINIUM BASED CONTRAST AGENTS
Authors: Stevens G H J , Wu J
Published: October 2024

BACKGROUND

Brain metastases are common and affect up to 30% of adult cancer patients. An estimated 150,000 to 200,000 brain metastases are diagnosed annually and some form of stereotactic radiosurgery (SRS) is commonly employed as a treatment modality. The magnetic resonance imaging (MRI) localization exam for SRS typically involves a volumetric post contrast T1 3D Flash or T1 Space sequence with 1mm slices using either gadoterate meglumine or gadoteridol as the contrast agent. Acquisition time is approximately 7 minutes. A gadolinium-based contrast agent that has high stability (lower risk of nephrogenic systemic fibrosis by having a higher dissociation half-life), a high T1 relaxivity (greater signal to noise ratio to allow for a lower dose of gadolinium and hence reduce gadolinium exposure), and high tolerability (lower rate of adverse reaction) is most ideal for lesion localization. Imaging requires a high lesion to background contrast and contrast to noise to allow accurate lesion visualization. Gadopiclenol is a class II Gadolinium based contrast agent that provides these properties.

MATERIAL AND METHODS

We analyzed 50 consecutive brain metastases patients using two MRI localization exams (as defined above), performed within 10 days using the same MRI scanner and same imaging parameters. One scan was completed using the contrast agent gadoterate meglumine and the other gadopiclenol. The MRIs were interpreted by the same Neuroradiologist.

RESULTS

There were 30 females and 20 males with a mean age of 62.4 years. The most common pathology was lung (N=28). In 10% of patients, the brain metastases were more conspicuous on the gadopiclenol study and /or a greater number of metastases were seen resulting in a treatment change. No adverse reactions were noted.

CONCLUSION

Our institution has transitioned to using primarily gadopiclenol for MRI gamma knife localization exams as it appears to be more effective in lesion localization compared to other available agents, with similar safety profile. Multiple cases will be presented.
P09.11.A IMPACT OF FET-PET IMAGING IN SURVEILLANCE OF THERANOSTIC TREATMENT WITH 4-L-[131I]IODO-PHENYLALANINE ([131I]IPA) IN RELAPSED GLIOBLASTOMA PATIENTS
Authors: Pichler J , Leibetseder A , Höllmüller I , Feichtinger J , Dierneder J , Osipova O , Pichler R
Published: October 2024

BACKGROUND

Theranostic is an evolving field in oncology. IPAX1 and IPAX-Linz studies used the radioligand Iodine-131[131I] IPA in combination with external re-irradiation in glioblastoma (GBM) patients. Modality of choice for assessment after therapy is contrast-enhanced magnetic resonance imaging (CE-MRI). Further, O-(2-[18F]fluoroethyl-)-L-tyrosine (18F-FET) positron emission tomography (PET) has become a valuable tool for brain tumor surveillance by adding metabolic information. However, no real-world data exist in the joint application of RANO 2.0 and PET RANO 1.0 criteria in response assessment of GBM, especially in patients treated with a theranostic approach.

MATERIAL AND METHODS

12 patients with histological diagnosis of recurrent GBM treated in our center within study protocols IPAX 1 and IPAX Linz were evaluated to compare MRI with FET-PET in follow up after treatment with theranostic therapy. PET RANO 1.0 criteria were used to evaluate the validity and predictive value of additional FET-PET imaging in treatment surveillance. Primary aim of the study was the response assessment and comparison of progression-free survival following RANO 2.0 criteria and PET RANO 1.0 criteria.

RESULTS

Mean PFS according to MRI criteria was 20.5 weeks (4.7 months). Mean OS was 48.5 weeks (11.2 months). No partial or complete responses were seen in MRI follow up. 7/12 (58.3 %) patients show progression at first FU MRI two months after baseline. In 2nd FU 11/12 (91.7 %) patients progressed. Evaluation based on PET RANO 1.0 criteria at first FET-PET FU investigation resulted in different outcomes depending on which PET parameter was used (volume, TBRmax, TBRmean). Mean PFS calculated by PET volume is 77 days (11 weeks/2.5 mo). Mean PFS based on TBRmax or TBRmean is not assessable because in the 2 follow up investigations most patients were stable or had a partial response in these two PET parameters.

CONCLUSION

This pilot study demonstrated different outcome in respect of PFS depending on whether MRI or PET RANO criteria have been used for analysis. Further, application of PET RANO criteria showed varying results in terms of response for each individual parameter (TBRmax, TBRmean and PETvolume). Based on our data, the value of an additional PET scan for a valid prediction of response in recurrent GBM patients must be discussed. Disclosure: IPAX-1 was sponsored by Telix Pharmaceuticals. IPAX Linz is an investigator-initiated trial; IMP was provided by Telix Pharmaceuticals.
P09.17.B TUMOUR SIZE AND OVERALL SURVIVAL IN A COHORT OF PATIENTS WITH UNIFOCAL GLIOBLASTOMA. A UNI- AND MULTIVARIABLE PROGNOSTIC MODELLING AND RESAMPLING STUDY
Authors: Fatania K , Frood R , Mistry H , Short S C , O’Connor J , Scarsbrook A F , Currie S
Published: October 2024

BACKGROUND

Identifying prognostic factors for patients with glioblastoma (GBM) may better stratify patients into different risk groups, and accelerate individualised treatment strategies. One potential prognostic factor is tumour size. This is routinely assessed clinically usually by measurement of the diameter of the enhancing core, but there are now more readily available segmentation algorithms that allow estimation of tumour volume. Published models typically vary in their selection of tumour size in the final model. We aimed to investigate the effect of tumour size on prognosis in an institutional cohort of patients with GBM, and investigate the effect of log-transform and different sample sizes with bootstrap resampling.

MATERIAL AND METHODS

279 patients with unifocal IDH-wild type WHO grade 4 GBM (2014-2020) had their tumours segmented using deep-learning and manual correction and diameter measured manually. Institutional data was used to examine any prognostic effect between diameter, core, or whole tumour volume (CV, WV respectively) using Cox proportional hazards models. Data underwent bootstrap analysis (1,000,000 repetitions) to determine if log-transformation and/or changes in sample size had an impact on the chance of detecting a prognostic relationship of size. Multivariable models were adjusted for clinical predictors: age, gender, surgery (biopsy, gross-total, near-total or sub-total resection), MGMT promoter methylation and completion of adjuvant therapy (full Stupp, partial Stupp or none). P-value distribution across all resamples was assessed with a 2-sided Kolmogorov Smirnov (KS) test to ascertain the impact of log-transform and sample size on the chance of detecting a prognostic relationship.

RESULTS

All models adjusted for surgery were significant (p = 0.016, 0.013 and 0.018 respectively). Diameter adjusted for all clinical predictors was also significant (p=0.03). Log-transformation had a large effect on chances of detecting a prognostic effect of WV, by significantly decreasing the distribution of p-values towards 0.05 or below (KS test <0.05). Multivariable resampling experiments also demonstrated that with increased sample size, the chance of detecting a prognostic relationship of size (diameter, CV or WV) increased, which is the opposite of the expected behaviour for noise variables (i.e. predictors with random association to overall survival (OS)).

CONCLUSION

This study of a large single institutional cohort of patients with GBM,found: (1)diameter and WV are prognostic of OS in multivariable models, (2) log-transformation increases the chances of detecting a prognostic relationship of WTV with OS, and (3) greater sample sizes increase the chance of finding a prognostic role for tumour size.
P10.11.B CLINICAL APPLICATION OF MACHINE-BASED DEEP LEARNING IN PATIENTS WITH RADIOLOGICALLY PRESUMED ADULT-TYPE DIFFUSE GLIOMA GRADE 2 OR 3
Authors: Gómez Vecchio T , Neimantaite A , Solheim O , Pallud J , Berger M S , Widhalm G , Bartek jr. J , Gu I Y H , Jakola A S
Published: October 2024

BACKGROUND

Diffuse lower-grade gliomas (dLGG) are nowadays characterized by the IDH mutation. Presumed dLGG are typically non or minimal enhancing tumors, with hyperintensity in T2w-images. However, one third of the radiologically presumed dLGG turn out to be patients with IDH wild-type and have a disadvantaged prognosis. The aim of this study was to reduce clinical uncertainty applying deep learning (DL) to the prediction of IDH mutation in patients with radiologically presumed dLGG.

MATERIAL AND METHODS

314 patients were retrospectively recruited from six neurosurgical departments in Sweden, Norway, France, Austria, and the United States. Collected data included patient’s age, sex, tumor molecular characteristics (IDH, and 1p19q status), and routine preoperative radiological images. A clinical model was built using multivariable logistic regression with the variables age and voxel-based tumor location. Four DL architectures used in glioma research were chosen for comparison, the best performing DL model was selected with a grid search. In the final test, the clinical model and the DL model were scored with an unseen validation dataset consisting of 155 patients from the Erasmus Glioma Dataset.

RESULTS

The mean age in the training and validation cohorts were 45.0 (SD 14.3) and 44.3 years (SD 14.6). The training and validation cohorts were similar, except concerning the variables sex (53.5% vs 64.5% males, p-value 0.03) and IDH status (30.9% vs 12.9% IDH wild-type, p-value <0.01). Scored accuracy, sensitivity, specificity, positive predicted value, negative predicted value, and area under the curve for clinical model: 0.86, 0.90, 0.55, 0.93, 0.46, and AUC 0.86; DL model: 0.68, 0.66, 0.80, 0.96, 0.26, and 0.82; combined model: 0.78, 0.77, 0.85, 0.97, 0.35, and AUC 0.87; combined model (specificity tuned): 0.62, 0.58, 0.90, 0.98, 0.24, and AUC 0.87.

CONCLUSION

We present a large cohort of patients with non or minimal enhancing presumed dLGG studied with DL. Compared to acceptable results in the clinical model, the DL model showed lower sensitivity and higher specificity. Furthermore, when these models were combined, the AUC was slightly improved also showing good results at a high specificity cutoff. However, the DL model failed to deliver additional gain over the clinical model. DL models should be further improved if they are to become clinically applicable.