Citations

Introduction Inhalation of organic dust from concentrated animal production operations induces inflammation in the brain and respiratory tract. We investigated the role of the potassium channel Kv1.3 in models of organic dust (OD)-induced neuroinflammation. Kv1.3 channels play multifaceted roles in microglial immune modulation, cancer, and neurodegenerative diseases, and represent a potential therapeutic target. Methods We used in vivo (C57BL/6 mice), in vitro (microglial cell line, and primary microglia), and ex vivo (brain slice culture) models of OD-induced neuroinflammation. Sterile OD extract (ODE) was prepared, and mice were exposed to either normal saline or ODE intranasally for 5 weeks (5 days/week) to simulate an occupational exposure scenario. Primary microglia were isolated from neonatal mice for total RNA sequencing (RNA-seq). Results The ODE-induced expression of Kv1.3 was quantified using in vitro and ex vivo models with and without PAP-1 treatment. Exposure-induced changes in cytokines and markers of reactive species were measured. Using western blot, we quantified phosphorylated p38 MAPK (p-p38 MAPK) and NOX2. We measured microglial Kv1.3 currents using whole-cell patch-clamp. Exposure to ODE increased the expression of Kv1.3 and p-p38 MAPK in mouse microglia without affecting the Kv1.3 currents at the cell surface. Exposure increased the levels of inflammatory cytokines and NOX2. Kv1.3 inhibition with PAP-1 decreased inflammatory markers (TNF-α in BV2 microglia and IL-6 in Brain slice cultures and BV2 microglia), levels of Kv1.3, p-p38 MAPK, NOX2, and nitrites. Conclusion Our study revealed that pharmacological inhibition of Kv1.3 potassium channels reduces ODE-induced neuroinflammation by decreasing inflammatory and oxidative stress markers.

Introduction. Aspergillus fumigatus phosphoglucomutase ( Af PGM) is a central enzyme in fungal carbohydrate metabolism that catalyses the interconversion of glucose-1-phosphate and glucose-6-phosphate, generating precursors such as uridine diphosphate glucose required for the synthesis of key cell wall polysaccharides. Hypothesis/Gap Statement. Although Af PGM has been implicated in fungal viability and cell wall integrity, further functional and molecular characterization is required to better define its role in cell wall-associated processes and to evaluate its potential as a selective antifungal target. Aim. This study aimed to further characterize the functional contribution of Af PGM to fungal growth, cell wall-related phenotypes, biofilm formation and enzymatic activity. Methodology. A conditional A. fumigatus P alcA ::pgm mutant was used to assess growth, soluble β -glucan levels and biofilm formation. Functional complementation was evaluated by heterologous expression of Afpgm in a Saccharomyces cerevisiae pgm2Δ strain. Enzymatic inhibition was examined using the isothiazolone compound ISFP10. Results. Reduced Af PGM activity was associated with impaired growth, altered cell wall-related phenotypes, decreased soluble β -glucan levels and diminished biofilm biomass. Expression of Afpgm restored growth and sedimentation defects in the yeast pgm2Δ mutant to WT levels, confirming functional conservation. ISFP10 inhibited Af PGM activity and disrupted associated fungal phenotypes. Conclusion. These findings further define the functional role of Af PGM in fungal carbohydrate metabolism and cell wall-associated biology. The selective inhibition of Af PGM relative to human phosphoglucomutase supports its potential as a promising target for the development of novel antifungal therapies.

Abstract Background: Alternative lengthening of telomeres (ALT) is common in isocitrate dehydrogenase (IDH) mutant astrocytomas, but how telomere length variability contributes to genome instability remains unclear. We hypothesized that chromosome arm–specific telomere heterogeneity underlies distinct patterns of somatic structural variation (SV), copy-number alteration (CNA), and extrachromosomal DNA (ecDNA) formation. Methods: Oxford Nanopore long-read whole-genome sequencing (∼20–40x coverage) was performed in a clinical cohort of n = 20 IDH-mutant astrocytomas. Somatic SVs were called with Severus and filtered to a high-confidence tumor-specific set. CNAs were inferred by read-depth segmentation (DNAcopy) and further refined using a long-read CNA framework (Wakhan). Chromosome arm–specific telomere length was estimated with Telogator, and SV breakpoints were annotated for enrichment near subtelomeric and pericentromeric regions. Associations between arm-level telomere length and SV/CNA features (rate and proportion) were tested using linear mixed-effects models with patient as a random effect. High-level amplification regions were defined directly from CNA segments with high copy number, and ecDNA-like circular amplicons were subsequently reconstructed from amplified intervals using Decoil. Results: IDH-mutant astrocytomas displayed pronounced SV heterogeneity, with complex breakends (BNDs) dominating the somatic SV landscape. SVs were spatially patterned, as deletions and insertions preferentially accumulated near subtelomeric and pericentromeric domains, implicating chromosome ends and repeat-rich regions as recurrent substrates for rearrangement. At the tumor level, markedly shortened telomeres were associated with increased SV burden and CNA burden, consistent with telomere attrition as a correlate of genome-wide structural instability in highly rearranged genomes. At higher resolution, telomere length varied substantially across chromosome arms and classified distinct structural outcomes: arms with shorter telomeres were enriched for complex insertional SVs, whereas arms with relatively elongated telomeres more frequently carried focal high-level amplifications and ecDNA-like circular amplicons. Conclusions: These findings reveal a link between arm-specific telomere heterogeneity and the emergence of structural and amplification architectures in IDH-mutant astrocytomas. Telomere remodeling may influence where rearrangements and ecDNA structures arise, offering potential biomarkers of genome instability and tumor evolution. Generative AI was used to improve language clarity; all content was verified by the authors. Citation Format: Maryam Jehangir, Kristen Drucker, T. Rhyker Ranallo-Benavidez, Thomas M. Kollmeyer, Ayse Keskus, Daniel Lachance, Jeanette E. Eckel-Passow, Ofer Shoshani, Mikhail Kolmogorov, Robert B. Jenkins, Floris P. Barthel. Telomere length heterogeneity shapes structural and amplification landscapes in IDH-mutant astrocytoma [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Brain Cancer; 2026 Mar 23-25; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2026;86(6_Suppl):Abstract nr PR009.

Abstract Advances in genomic DNA methylation profiling have revolutionized brain tumor classification, enhancing diagnostic accuracy and personalized medicine. This presentation will provide case-based examples of how methylation arrays have become an essential part of the workup for CNS tumors. It will also present two new approaches to methylation profiling, nanopore and TET-assisted pyridine borane sequencing (TAPS), and how these new technologies are poised to replace much of our current molecular armamentarium. Citation Format: Craig Horbinski. The present and future of methylation profiling in CNS tumors [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Brain Cancer; 2026 Mar 23-25; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2026;86(6_Suppl):Abstract nr IA007.

Abstract Glioblastoma (GBM) is the most common and aggressive primary brain tumor in adults with a poor prognosis of 15 months despite aggressive therapies. GBM malignancy is often driven by a high degree of inter- and intra-tumoral heterogeneity that is dynamic through time and regulated by interactions between tumor cells and their neural microenvironment. For instance, GBM tumors located near the lateral ventricles (LV) of the brain grow more aggressively and result in significantly worse patient survival, compared to tumors that grow far from the LV. This suggests that interactions between GBM and cells in the LV microenvironment may regulate tumor malignancy. Understanding the mechanisms underlying GBM malignancy near the LV is essential to determine if regulation of tumor biology is driven by the presence of healthy neural cells, and to ultimately develop therapeutic approaches that target GBM-LV interactions. Here, we tested the hypothesis that region-specific cellular identities and activation of unique transcriptional programs regulate GBM malignancy near the LV. Paired primary biopsies were collected from 14 GBM patients. Image-guided biopsies proximal and distal to the brain’s LV (LV-proximal GBM and LV-distal GBM, respectively) were processed for bulk RNA sequencing, single-nucleus RNA sequencing, and in situ spatial transcriptomic (Xenium) analyses. We found significant inter-patient and intra-tumoral transcriptional heterogeneity in GBM samples collected proximal and distal to the LV. After accounting for tumoral and patient heterogeneity, we found that proximal GBM tumors exhibit an increased immune-related gene signature, compared with tumors distal to the LV. Single-nucleus RNA sequencing and in situ spatial transcriptomic analyses showed no significant differences in the proportion of cellular states or identities between proximal and distal GBM samples. However, within each cellular identity, significant differences in gene expression were identified between proximal and distal GBM, suggesting that distinct cell-specific transcriptional pathways characterize regional differences in GBM biology. Our results suggest that GBM tumors near the LV have increased interactions with immune cells that lead to the enrichment of immune-related transcriptional pathways, compared to tumors growing far from the LV. This work will inform the development of therapies that target GBM-immune cell interactions to decrease tumor malignancy and improve individual patient outcomes. Citation Format: Maria F. Gonzalez-Aponte, Jhan C. Salazar, Aura Figueroa-Gonzalez, Emily S. Norton, Paola Suarez-Meade, Hugo Guerrero-Cazares. Integrating RNA sequencing and spatial transcriptomic analyses reveals drivers of glioblastoma malignancy near the brains’ lateral ventricles [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Brain Cancer; 2026 Mar 23-25; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2026;86(6_Suppl):Abstract nr B035.

Abstract Glioblastoma (GBM) remains the most common and lethal adult malignant primary brain cancer with few treatment options. A significant issue hindering GBM therapeutic development is intratumor heterogeneity and plasticity. GBM tumors contain neoplastic cells within a fluid spectrum of diverse transcriptional states. Identifying effective therapeutics requires a platform that predicts the differential sensitivity and resistance of these states to various treatments. Here, we develop scFOCAL (Single-Cell Framework for -Omics Connectivity and Analysis via L1000), to quantify the cellular drug sensitivity and resistance landscape. Using single-cell RNA sequencing of newly diagnosed and recurrent GBM tumors, we identify compounds from the LINCS L1000 database with transcriptional response signatures selectively discordant with distinct GBM cell states, and leverage this capability to predict combination synergy. We validate the significance of these findings in vitro, ex vivo, and in vivo, and use the Olig2 inhibitor CT-179 as a reference drug to identify additional small molecules that would maximize the cell-drug discordance across the GBM transcriptional landscape. Our analysis leads to the combination of Olig2 inhibition with treatment with Depatux-M, of with which we demonstrate synergy in vivo. Our studies suggest that scFOCAL identifies cell states that are sensitive and resistant to targeted therapies in GBM using a measure of cell and drug connectivity, which can be applied to identify new synergistic combinations. Citation Format: Robert K. Suter, Anna M. Jermakowicz, Rithvik Veeramachaneni, Matthew D'Antuono, Longwei Zhang, Rishika Chowdary, Simon Kaeppeli, Madison Sharp, Pravallika Palwai, Vasileios Stathias, Grace Baker, Luz Ruiz, Winston Walters, Maria Cepero, Danielle Burgenske, Edward B. Reilly, Anatol Oleksijew, Mark G. Anderson, Sion Ll. Williams, Michael E. Ivan, Ricardo J. Komotar, Macarena I. De La Fuente, Gregory Stein, Alexandre Wojcinski, Santosh Kesari, Jann N. Sarkaria, Stephan C. Schürer, Nagi G. Ayad. Drug and single-cell gene expression integration identifies heterogeneity-aware synergistic combinations for glioblastoma [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Brain Cancer; 2026 Mar 23-25; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2026;86(6_Suppl):Abstract nr B032.

Hospice and palliative medicine (HPM) simulation training primarily focuses on communication skills, rather than symptom management. Simulation can be an effective educational tool in high stakes clinical settings and provides a safe environment to improve skills in crisis management. HPM providers are often called to manage refractory symptoms in emotionally charged situations, especially at end-of-life care. These scenarios may present infrequently during their 1-year fellowship training. We describe a novel two-part simulation series ‘End of Life Symptom Management: Emergencies in Palliative Medicine’ that we have developed for our interdisciplinary HPM Fellowship. Our simulation series is conducted at our institution’s simulation centre and includes scenarios of (1) refractory pain, (2) palliative sedation, (3) initiation of buprenorphine-naloxone for cancer-associated pain in a patient with opioid use disorder (OUD), (4) acute pain crisis in a hospice patient with OUD on buprenorphine-naloxone, (5) terminal delirium, (6) compassionate extubation/respiratory distress, (7) status epilepticus and (8) terminal haemorrhage. Pre and postsession data collected from our HPM fellows reflect an increase in confidence levels after participating in the sessions. This simulation curriculum has allowed our fellows the opportunity to encounter and navigate these scenarios in a safe and controlled learning environment before encountering them in practice.

Abstract Cancer remains one of the most complex and devastating diseases, in part because tumors are not static entities but dynamic ecosystems that evolve in real time. Over the decades, researchers have proposed two major frameworks toexplain tumor development: 1) the clonal evolution model, which emphasizes the stepwise accumulation of mutations insingle cells, and 2) the cancer stem cell model, which posits a hierarchical tumor structure driven by a small population of stem-like cells capable of self-renewal and tumor initiation. In brain tumors, as in other cancer types, the presence of cancer stem cells has been demonstrated; however, their evolutionary role in glioblastoma, the most aggressive primary brain tumor, remains poorly understood. A key unanswered question is how cancer stem cells in the brain evolve intomalignant populations and how evolutionary processes shape this transition. Since Darwin, evolutionary biologists have sought to understand how traits evolve across lineages and have developed several tools to address questions regarding how lineages, species and populations have evolved through time. In recent years there has been an increase in the use of evolutionary statistical methods to study cancer evolution. These approaches remain niche in a few tumor types (e.g., blood and lung cancer), but the rapid growth of sequencing technologies and large-scale datasets has created new opportunities to address evolutionary questions across cancer types that have been understudied (e.g., brain tumor). Researchers are now using whole-genome, exome, single-cell, spatial, and multi-omic data to reconstruct tumor histories, test adaptive hypotheses, and in few cases, quantify rates and modes of evolution within tumors and between tumor types. However, there is not yet a consolidated framework for which evolutionary analyses are most appropriate to test cancer evolution, nor accessible guidance on how to implement these approaches in oncological datasets. We synthesize evolutionary statistical methods currently used in brain cancer research and propose additional approaches for future studies. We identify more than 30 evolutionary tools developed to study trait evolution, diversification, and lineage dynamics in biological systems, of which only one has been applied to brain cancer. We argue that adapting these methods offers a powerful opportunity to investigate cancer stem cell dynamics and tumor progression in glioblastoma and other understudied cancers. By bridging evolutionary biology and cancer genomics, this work provides a roadmap for expanding the evolutionary toolkit used to study tumor evolution and for generating testable hypotheses about malignant transformation. Citation Format: Jhan C. Salazar Salazar, Sean W. McHugh, Maria F. Gonzalez-Aponte, Hugo Guerrero-Cazares. From healthy brain to cancer cells: Understanding the origins of glioblastoma through the lens of Darwinian evolution [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Brain Cancer; 2026 Mar 23-25; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2026;86(6_Suppl):Abstract nr B017.

Abstract Background: High-grade astrocytoma with piloid features (HGAP) is a recently defined tumor type that is not well-understood. Prognostic factors of clinical outcome are not well-established. Methods: Samples were processed at the National Cancer Institute (NCI) Laboratory of Pathology, and published samples were included (n=421). Methylation classifier scores of ≥0.90 to the HGAP class on the NCI-Bethesda classifier were included. Clinical features were collected from the medical record. Kaplan-Meier survival analysis as well as Cox-proportional hazards was performed. Results: The cohort comprised 421 patients. There was a high rate of ATRX alteration (62%), CDKN2A/B loss (78%) and MGMT promoter methylation (53%). MAPK alterations were identified in 74% of samples. The median age was 46 years, and posterior fossa location was predominant (52%). The median overall survival (OS) was 88 months. Older age (p = 0.01) and the presence of an ATRX alteration (p = 0.04) were found to be poorly prognostic. The presence of cystic features on magnetic resonance imaging (MRI) was found to be favorably prognostic (p = 0.01). Factors that were not significantly associated with survival included histologic anaplasia, CDKN2A/B homozygous deletion, MGMT promoter methylation and surgery type. Conclusions: Utilizing a relatively large cohort of HGAP cases, this work establishes relative frequencies of several important markers, and additionally suggests older age, the presence of an ATRX alteration, and cystic features on MRI as prognostic. Our work may aid in identifying treatment regiments for patients with this tumor type. Citation Format: Shuodan Zhang, Omkar Singh, Karen Dazelle, HGAP manuscript co-authors, Kenneth Aldape. High-grade astrocytoma with piloid features: a clinical and genomic analysis of prognostic factors using a large cohort [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Brain Cancer; 2026 Mar 23-25; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2026;86(6_Suppl):Abstract nr A057.