bluuebunny/crossref_metadata_2025_split huggingface dataset:
EPCO-08. EPIGENETIC THERAPY POTENTIATES TRANSPOSABLE ELEMENT TRANSCRIPTION TO CREATE TUMOR-ENRICHED ANTIGENS IN GLIOBLASTOMA
Authors: Jang H Josh , Shah Nakul M , Maeng Ju Heon , Liang Yonghao , Basri Noah L , Ge Jiaxin , Qu Xuan , Mahlokozera Tatenda , Tzeng Shin-Cheng , Williams Russell B , Moore Michael J , Annamalai Devi , Chen Justin Y , Lee Hyung Joo , DeSouza Patrick A , Li Daofeng , Xing Xiaoyun , Kim Albert H , Wang Ting 
Published: November 2024

Abstract
Epigenetic treatment has been studied as a means to augment anti-tumor immune cell activity by increasing the expression of antiviral response genes. Previous studies, including our own, have revealed an additional consequence of epigenetic treatment: reactivation of transposable elements (TEs). Normally, most TEs are epigenetically repressed in healthy somatic tissues. However, epigenetic treatment can reactivate TEs as previously unannotated promoters, particularly in rapidly proliferating cells. These reactivated TEs could generate unique proteins that may act as novel antigens for immunotherapy. To study epigenetic treatment as a novel method to augment immunotherapy in cancers with a low number of somatic mutation-derived antigens, we chose glioblastoma as a model. Specifically, we investigated if drug-induced TE expression increases the antigen repertoire upon combinatorial treatment with DNMT inhibitor (Decitabine) and HDAC inhibitor (Panobinostat), agents currently being tested for clinical use in cancers, including CNS tumors. Using patient-derived primary glioblastoma stem cell (GSC) lines and control cell lines (astrocytes and fibroblasts), we first profiled treatment-induced changes in the epigenetic landscape using WGBS-seq and ATAC-seq. Then, using nanoCAGE-seq, both short-read and long-read RNA-seq, we identified treatment-induced, TE-derived transcripts that are predominantly expressed in glioblastoma cells along with upregulation of antiviral gene programs. Finally, using LC-MS/MS, we validated that tumor-enriched reactivated TEs, which mostly originate from the LTR class, encode HLA-I presented antigens in primary GSCs. Notably, epigenetic therapy also reactivated many TEs in proliferating control cell lines, and thus we provide evidence that targeted approaches like CRISPRa might reduce the side effects of activating nonspecific TE loci. Overall, our findings suggest that the therapeutic utilization of epigenetic treatment-induced, TE-derived antigens holds great promise but also provides a cautionary tale regarding the careful selection of tumor-enriched antigen targets.
EPCO-12. MENINGIOMA COPY NUMBER ANALYSIS IDENTIFIES OPTIMIZED SIZE THRESHOLDS AND CO-OCCURRENCE MODELS FOR INDIVIDUALIZED RISK-STRATIFICATION
Authors: Nguyen Minh , Chen William , Choudhury Abrar , Magill Stephen , Nitturi Vijay , Klisch Tiemo , Patel Akash , Raleigh David 
Published: November 2024

Abstract
Arm-level copy number alterations (CNAs) are common in meningiomas and are useful for risk-stratification, but there is no consensus regarding the optimal size threshold to define CNAs for prognostic models. Moreover, the prognostic importance of co-occurring CNA pairs and overall CNA burden in meningiomas remains incompletely understood. To address this, CNAs were analyzed in 691 retrospective meningiomas with clinical data and DNA methylation profiles from 3 international institutions. Arm-level CNAs were defined from DNA methylation profiles using iterative size thresholds ranging from 1-99% of each chromosome arm. The area under the curve (AUC) for 5-year local freedom from recurrence (LFFR) or overall survival (OS) was calculated using CNAs across size thresholds, and AUC standard deviation across thresholds was used as a measure of size-dependence. LASSO and Elastic Net Cox regressions were used to generate multi-CNA models for LFFR or OS and to identify prognostic co-occurring CNA pairs. These analyses identified 13 size-dependent prognostic CNAs in meningiomas, including loss of 1p, 3q, 4p, 4q, 6p, 6q, 9p, 10q, 12q, 14q,18q, and 22q, and gain of 1q. Regression models restricted to size-dependent CNAs improved risk stratification within strata of previously published models (Integrated score, Integrated grade), and identified prognostic CNAs not included in previous models, such as 1q gain, 7p loss, and 12q loss. Ontology analysis of genes in focal prognostic CNAs identified dysregulated STAT signaling and decreased expression of interferon-related genes from matched paired RNA sequencing. Models of size-dependent, prognostic, co-occurring CNA pairs identified 1p/22q codeletion, 1q gain/22q loss, and 9p/18q codeletion as significant predictors of LFFR. Increased CNA burden correlated with higher tumor grade and was significantly associated with worse LFFR and OS. In summary, chromosome-specific size thresholds identify prognostic arm-level CNAs and co-occurring CNA pairs in meningiomas. Thus, granular copy number analysis may improve risk stratification models for meningioma.
EPCO-21. ELUCIDATING THE FUNCTIONAL RELEVANCE OF ATRX-DEFICIENCY IN H3.3-G34-MUTANT DIFFUSE HEMISPHERIC GLIOMA
Authors: Machado Annette Alexandra , Malgulwar Prit Benny , Huse Jason T 
Published: November 2024

Abstract
Diffuse hemispheric gliomas (DHGs) account for 30% of aggressive cerebral tumors in children and young adults, exhibiting abysmal outcomes. Co-existing recurrent mutations in H3.3 histones at residue G34 (H3.3-G34R/V mutation) and concurrent inactivating mutations in TP53 and α-thalassemia/intellectual disability-X-linked gene (ATRX) implicate a coordinated molecular effort to drive oncogenesis. ATRX, a core component of the SWItch/Sucrose Non-Fermentable chromatin remodeling complex, regulates the incorporation of histone H3.3 into chromatin sites across the genome to maintain epigenome integrity. Yet, it is unclear how ATRX loss combines with H3.3-G34R/V mutations to influence cellular phenotype and dysregulate neuro-developmental programs. We propose that ATRX deficiency coordinates with key oncogenic partners by inducing oncogenic transcriptional programs and neuro-developmental pathways in DHGs. To investigate this, we employed CRISPR-editing to model H3.3-G34R followed by ATRX knockdown (ATRX-KD) in a TP53 mutant (TP53-mut) human-induced pluripotent stem cell (h-iPSC) line, developing an isogenic system to map contributions to epigenetic dysfunction made by specific molecular alterations. Upon differentiation of our h-iPSCs to neural stem cells (NSCs), we found unique transcriptional profiles associated with each genetic alteration, with upregulation of forebrain progenitor genes driven by ATRX-loss. Hierarchal clustering of normalized transcript counts revealed gene signatures from each model driving unique functional pathways, with the ATRX-KD and the triple mutant (TP53-mut, ATRX-KD, H3.3-G34R) signatures enriched for neural development pathways. Additionally, both signatures were associated with published transcriptional profiles of G34-mutant DHGs, emphasizing the disease relevance of our models. Further, time-course analysis using cerebral organoids uncovered distinct morphological changes with each alteration, with the triple mutant exhibiting impaired differentiation at later time points. These latter findings suggest that ATRX deficiency transcriptionally influences early neuronal differentiation, effects further refined by H3.3-G34 mutation. To conclude, our innovative models demonstrate the involvement of ATRX in neuro-developmental and differentiation phenotypes, illuminating underlying molecular features of aggressive DHGs.
EPCO-29. PATHOMICS ANALYSIS IDENTIFIES POTENTIAL SUBTYPES OF DIFFUSE GLIOMAS IN H&E-STAINED WHOLE-SLIDE IMAGES
Authors: Becker Aline , Chan Jany , Weible Jason , Machiraju Raghu , Chakravarti Arnab 
Published: November 2024

Abstract

IDH status is the first criteria of diagnostic definition for diffuse gliomas in the 2021 WHO classification. While sufficient number of molecular tools are available to identify IDH1 and/or IDH2 mutations, they impose a time and cost burden on patients. We previously trained deep learning models visually differentiate IDH wild-type (IDHwt) and IDH mutant (IDHmut) gliomas in a small set of H&E-stained whole image slides (WSIs) from TCGA using an Uncertainty-Aware Convolutional Neural Network (CNN) at 10x magnification. In this follow-up study, we aimed to improve our models by expanding our annotations of a more representative subset of the TCGA public data, increasing the sampling magnification to 20x, redesigning the preprocessing filters, adjusting the training pipeline to reduce overfitting, and assessing the confidence in model prediction via uncertainty metrics.

METHODS
By modifying the pre-processing methods from our pilot study, we increased the number of expert-annotated WSIs. Using a 2:1:2 train/validation/test split, we fine-tuned a ResNet18 deep learning model with a pre-trained feature extractor and Monte Carlo dropouts. To identify image features critical for separating by IDH status, we extracted the learned embeddings and performed post-hoc analyses (PCA, t-SNE, and UMAP).

RESULTS
The new set of annotations resulted in ~15-fold increase in the number of WSIs (i.e.,&gt;2.5 million tiles), without any data augmentation. Our prototype model demonstrated clear separation of cases according to IDH status (sensitivity = 0.63, specificity = 0.91, weighted F1 = 0.81).

CONCLUSION
We successfully improved the prediction of IDH status in H&E-stained WSIs and identified potential subtypes of IDHwt and IDHmut diffuse gliomas after significantly increasing the dataset size with appropriate expert annotations. We are currently addressing the data imbalance by annotating additional IDHmut glioma WSIs. Furthermore, we will integrate other biomarkers (1p19q, EGFR etc.) to increase the robustness of histo-pathomics as a surrogate for molecular tests.
EPCO-42. SINGLE-CELL MULTIOMIC ANALYSIS OF BRAIN METASTASES ACROSS MULTIPLE PRIMARY TUMOR TYPES
Authors: Blandford Emily , Delegard Jennifer , Hardigan Andrew , Mohan Aditya , Gregory Simon , Patel Anoop 
Published: November 2024

Abstract
Brain metastases are the most common form of intracranial tumor in adults, however the prognosis remains dismal due to lack of effective treatments and limited understanding of the underlying physiology allowing them to thrive in the foreign cell type environment of the brain. Here we characterized the transcriptomic and epigenomic landscape of 35 human brain metastases obtained from surgical resection using combined scRNA-seq and ATAC-seq spanning the most prevalent primary tumor types including lung adenocarcinoma (n=16), melanoma (n=14), breast (n=10), and renal cell carcinoma (n=6). Varying molecular subtypes across primary tumors were included allowing for a comprehensive analysis between and within primary tumor types. Approximately 200,000 cells total were profiled to a sequencing depth of 50,000 mean reads per cell with 2,500 median genes per cell. ATAC-seq libraries were sequenced to a depth of 25,000 reads per cell with 10-15,000 median fragments per cell. Unbiased clustering and integrated analysis identified various cell types within the tumor, immune microenvironment, and normal brain tissue. Tumor cell identities were confirmed throughout multiple clusters via inferred copy number alteration analyses. Integration with primary tumor atlases highlight differences between cell states of primary and metastatic tumors, revealing shifts of enhancer function in transcriptional regulation of metastatic cells. Overall, our findings describe the genetic and epigenetic states of metastatic cells contributing to the understanding of CNS metastatic colonization.
EPCO-43. DECONVOLUTION OF GLIOBLASTOMA RNA-SEQ REVEALS IMMUNE SUBPOPULATIONS PREDICTING THERAPEUTIC RESPONSE
Authors: Rauf Asad , Ahluwalia Manmeet , Dhawan Andrew 
Published: November 2024

Abstract
Bulk RNA-seq is now among the standard of care for patients with newly diagnosed glioblastoma (GBM), but whether this data can predict therapeutic response is unclear. In silico deconvolution of bulk RNA-seq data allows for quantification of cell types comprising the tumor and its microenvironment. Our study tests whether cell type proportions predict therapeutic response in patients with GBM and high-grade glioma (HGG) enrolled in trials of investigational agents. 127 patients with GBM/HGG underwent post-resection/biopsy whole exome RNA-seq and reads were post-processed using standard computational pipelines. In silico deconvolution was performed using GBMDeconvoluteR, which generates cellular proportions comprising the RNA-seq reads. Multivariate Cox regression models were used to examine each cell proportion as a predictor of progression-free (PFS) and overall (OS) survival. Statistical significance was ascertained using Bonferroni correction for multiple comparisons. Among 54 patients treated with a PD-L1 inhibitor (nivolumab) and VEGF inhibitor (bevacizumab), the proportions of NK cells (p=0.0024), radial glial cells (p=0.0010), and mural cells (p=0.0025), was associated with increased PFS, while proportion of endothelial cells (p=0.0008) was associated with decreased PFS. Astrocyte (p=0.0022), macrophage (p=0.0015), NK cell (p&lt;0.0001), and mural cell (p=0.0004) proportions were associated with longer OS, while endothelial cell proportion (p=0.0001) was associated with shorter OS. Among 21 patients treated with a tyrosine kinase inhibitor (ibrutinib) combined with radiation and temozolomide (RT/TMZ), cell proportions assessed at the time of diagnosis did not predict PFS or OS. In 52 patients with HGG treated with a JAK inhibitor (ruxolitinib) with RT/TMZ, only mural cell proportion was associated with a longer OS (p=0.0017). Our results highlight how bulk RNA-seq data can be used to predict therapeutic response. In GBM patients treated with immunotherapies, the proportion of NK cells may predict response. Further work will examine which gene programs are predictive of therapeutic response.
EPCO-48. RESOLVING INTRATUMORAL TRANSCRIPTIONAL HETEROGENEITY ACROSS THE CORE-EDGE SPATIAL AXIS IN GLIOBLASTOMA
Authors: Luo Adam , Casella Kelly , Baraban Ezra , Lucas Calixto-Hope , Shenderov Eugene 
Published: November 2024

Abstract
Intratumoral heterogeneity promotes tumor progression, treatment resistance, and recurrence in glioblastoma (GBM), and the cellular tumor core and infiltrating edge represent transcriptionally distinct cellular ecosystems. In this study, we characterize and validate the molecular differences across the core-edge spatial axis in GBM by applying whole-transcriptome GeoMx Digital Spatial Profiling (DSP) to 5 IDH-wildtype tumors. Three 3-micron tissue cores were extracted from each tumor and arranged into a tissue microarray (TMA). Core and edge regions of interest (ROIs) were chosen through coregistration of H&E and immunofluorescent TMA scans. Following DSP, data for 44 ROIs (30 core and 14 edge) and 8530 RNA targets were bioinformatically analyzed. Single-sample gene set enrichment analysis aligned the DSP core and edge regional signatures to those of publicly annotated datasets, substantiating our profiling methodology. Unsupervised analyses revealed ROI clusters unique to each tumor specimen for both pre- and post-normalized data. To mitigate a potential patient-level effect in subsequent differential gene expression and pathway analyses, we leveraged a linear mixed-effect model using core versus edge as a fixed effect and tumor specimen as a random effect. Our analyses showed upregulation of epigenomic markers in core ROIs and neuronal markers in edge ROIs. Further, core and edge signatures were incongruent with the canonical GBM subtypes (Mesenchymal, Classical, Proneural) and malignant cell states (OPC-like, NPC-like, AC-like, MES-like). Cellular deconvolution analysis highlighted differential enrichment of neoplastic cells in core ROIs and neuroglia (astrocytes and oligodendrocytes) in edge ROIs. In summary, we leverage spatial profiling to further validate the geographic molecular heterogeneity of GBM, which is characterized by divergent neuronal programs within the infiltrating edge.
EPCO-53. CHARACTERIZING THE FUNCTIONAL IMPACT OF GERMLINE-INHERITED RISK ALLELES IN GLIOMA EVOLUTION
Authors: Barathi Bhargavi Brahmendra , Malgulwar Prit , Liu Yang , Kannan Kasthuri , Huse Jason 
Published: November 2024

Abstract
Efforts to understand etiology of glioma led researchers to investigate associated genetic factors. Genome Wide Association Studies (GWAS) conducted to that end, uncovered an association of various single nucleotide polymorphisms (SNP) among hallmark oncogenes and tumor suppressors as risk variants in glioma incidence. Here, we perform in vitro modeling of four risk alleles/SNPs((rs723527 (EGFR), rs55705857 (MYC), rs7572263 (IDH1), rs78378222 (TP53)) mapped to non-coding regulatory regions of the genome, to assess their causal contribution to glioma evolution. To model the aforementioned risk alleles in an in-vitro system, we used a CRISPR-knock in strategy in human induced pluripotent stem cells (hiPSCs) and verified by Sanger sequencing. To study the effects of the risk alleles on brain cell types, we generated a cerebral organoid model from the edited and control lines and subjected them to downstream morphological and transcriptomic analyses. While we did not see morphological changes in the histological sections relative to controls at the early 14-day timepoint, we observed increased differentiation in both control and risk cohorts, along with an absence of neural rosettes, at the late 90-day timepoint. Bulk RNA-seq of the organoids at 14-days resulted in positive enrichment of oncogenic signaling pathways and negative enrichment of DNA repair pathways in the risk allele cohorts. Furthermore, single cell RNA seq of 14 and 90-day samples revealed a sharp decrease in neural stem cell clusters in both IDH and TP53 cohorts, while control organoids demonstrated robust production of immature neurons at 90 days. Overall, we observed a significant increase in differentiation into mature cell clusters in the control, relative to the risk allele organoids. Ongoing efforts are aimed at understanding this apparent differentiation block in the IDH and TP53 compartments, relative to control. Further analysis will lead to addressing key understudied areas of glioma etiology and associated mechanisms of risk incidence.
EPCO-55. SPATIAL HETEROGENEITY IN THE TUMOR CORE AND INFILTRATING MARGIN OFIDHWT GLIOBLASTOMA
Authors: Khan Saad , Wang Anthony , Desai Rupen , Foltz Jennifer , Leuthardt Eric , Patel Bhuvic , Dahiya Sonika , Kim Albert , Dunn Gavin , Petti Allegra 
Published: November 2024

Abstract
We integrated single-cell RNA-sequencing, Visium spatial transcriptomic, and Xenium in situ transcriptomic data to identify differences in cell composition between the tumor core and infiltrating edge in IDHWT glioblastoma (GBM). Using CellTrek, an algorithm that maps single cells to their most-likely locations in spatial transcriptomic data, we identified extensive spatial organization in GBM. Cell mapping revealed distinct regions enriched for tumor, immune, endothelial, neuronal, and glial cells. These were associated with the location of the tissue sample – edge regions were enriched for neurons and glia (as expected), while core regions were enriched for tumor cells and infiltrating immune cells, particularly macrophages. Within the core samples, the tumor and immune cells tended to occupy distinct spatial niches. Spatial clustering of mapped tumor cells across all samples revealed five spatially segregated tumor cell states, each associated with specific biological processes, including (1) histone methylation, (2) proliferation, (3) wound-healing, EMT, and antigen presentation, (4) hypoxia response, and (5) stem cell differentiation. These tumor cell states exhibited preferential colocalization with specific non-malignant cell types. For instance, antigen-presenting/wound-healing tumor cells tended to colocalize with diverse immune cells, while stem-like tumor cells tended to reside in neuron-rich regions at the tumor edge. Next, to identify ligand-receptor interactions governing this spatial organization, we analyzed known ligand-receptor pairs within all three data types. This revealed known and novel mediators of tumor cell interactions with vasculature, lymphoid cells, myeloid cells, neurons, and glia. While some interactions were shared across all tumor cell states (e.g. all tumor cell states interacted with myeloid cells via APP/CD74 interactions), others were specific to particular tumor cell states (e.g. wound-healing/antigen-presenting tumor cells interacted with macrophages via CSF1/CSF1R, while proliferating and stem-like tumor cells interacted with neurons through multiple known NRXN/NLGN interactions). These interactions suggest potential targets for clinical disruption of the tumor ecosystem.
EPCO-58. INTEGRATIVE PROTEOGENOMIC ANALYSES REVEAL INSIGHTS INTO SUBTYPE-SPECIFIC GLIOMA RISK
Authors: Nakase Taishi , Smith-Byrne Karl , Cario Clinton L , Ostrom Quinn T , Guerra Geno A , Melin Beatrice S , Rentoft Matilda , Wrensch Margaret , Wiencke John K , Jenkins Robert B , Eckel-Passow Jeanette E , Bondy Melissa L , Francis Stephen S , Kachuri Linda 
Published: November 2024

Abstract
Gliomas are heterogeneous brain tumors with prognostically-significant subtypes and few risk factors. IDH-wildtype glioblastoma, the most common subtype, carries a dismal prognosis. Using the largest GWAS dataset with histological (11,304 cases, 304,523 controls) and molecular (3,418 cases, 8,156 controls) subtypes, we analyzed the plasma and brain proteome to uncover proteins associated with glioma susceptibility. Using protein quantitative trait loci (pQTL) for 1,776 proteins in brain tissue, we identified 21 candidate proteins, including 9 associations with high probability (&gt;70%) of colocalization (shared causal genetic variants for glioma risk and protein abundance). We confirmed known risk genes such as EGFR (p=1.9×10-35) and D2HGDH (p=1.1×10-5). We also identified novel risk proteins, including ENPP6, a choline phosphodiesterase overexpressed in glioma, for IDH-wildtype glioblastoma (OR=0.62, p=7.6×10-5), and galectin-3, a lectin encoded by a tumor fitness gene in CRISPR screens of glioma proliferation and a clinical drug target, for glioma overall (OR=1.37, p=1.3×10-5). Analyses of the plasma proteome using pQTLs for 1,362 proteins from 3 cohorts (N=88,838) identified 9 colocalized associations in whole blood. Most risk signals were subtype-specific, such as CRELD1 (p=2.5×10-5) for triple-negative glioblastoma. We found three candidate risk proteins for IDH-mutant 1p19q-intact glioma: immunoglobulin glycoprotein BCAM (p=9.6×10-8), neurotrophin receptor SorCS2 (p=3.6×10-5) and immune checkpoint PD-1 (p=1.1×10-5). Interestingly, genetically-predicted PD-1 levels had a larger effect on IDH-mutant 1p19q-intact (OR=2.75, 95% CI=1.61-4.69) than IDH-wildtype (OR=0.73, 95% CI=0.51-1.05) tumors, suggesting germline differences in immune checkpoint mechanisms among subtypes. For all colocalized candidates, genetically-predicted protein abundance was correlated with cis-regulated transcriptional activity in brain tissue. For example, genetically-predicted SORCS2 gene expression in brain tissue was associated with plasma SorCS2 levels (Z=16.1, p=1.1×10-68). Our analysis identified candidate proteins involved in immune response and neuronal proliferation, which may enable prioritization of drug targets and provide insight into disease mechanisms for glioma.