Citations

Abstract Neuroblastoma (NB) accounts for 10% of pediatric cancer-related deaths. High-risk NB is frequently metastatic at diagnosis and has a poor 5-year survival rate (<50%) despite aggressive treatment. GD2, a disialoganglioside glycolipid that is expressed in nearly all NBs, has been validated as a promising immunotherapy target. Although GD2-CAR-T cells have been clinically evaluated, their efficacy has been limited by poor tumor infiltration and the immunosuppressive tumor microenvironment (TME), highlighting the need for alternative immune-based strategies. With inherent capacity of macrophages to infiltrate tumors, phagocytose cancer cells, and modulate TME, they represent a promising alternative strategy for immunotherapy in NB. In this study, we developed and evaluated GD2-CAR-macrophages (GD2-CAR-Ms) as a novel therapeutic approach for NB. GD2-specific CAR constructs were designed, validated, and introduced into multiple human and murine monocyte/macrophage cell lines via lentiviral transduction. Fluorescent microscopy and flow cytometry confirmed a significant enhancement in the phagocytosis of GD2-high NB cells by GD2-CAR-Ms containing Hu3F8 single-chain variable fragment (scFv). A strong positive correlation was observed between GD2 expression levels on the target cells and phagocytosis efficiency of GD2-CAR-Ms, indicating that the phagocytic activity of GD2-CAR-Ms is antigen-specific. Anti-tumor efficacy was further evaluated in vivo using an immunocompetent Mycn-expressing NB non-genetically engineered mouse model (MYCN-NGEMM). Mice treated with GD2-CAR-Ms exhibited significantly reduced tumor burden and prolonged survival compared to control groups. Notably, treatment of mice bearing GD2-high tumors resulted in markedly improved survival, including one complete remission among ten treated animals, underscoring the therapeutic potential of GD2-targeted macrophage therapy. Histological analysis showed increased deposition of connective tissues and decreased tumor cells density, suggesting TME remodeling. Furthermore, immunohistochemistry reveals increased immune cell infiltration, particularly of macrophages, neutrophils and T cells, indicating a favorable shift in the immune TME from “cold” to “hot” following GD2-CAR-M treatment. Additionally, inhibition of tumor growth was also observed in our zebrafish NB model after GD2-CAR-Ms treatment, further supporting their therapeutic potential. Together, our findings demonstrate that GD2-CAR-Ms exhibit potent antitumor activity and reshape the tumor immune microenvironment, supporting their translational potential as a therapeutic strategy for high-risk NB. Citation Format: Ke-En Tan, Kok Siong Yeo, Cheng Zhang, Caleb Baker, Shyang Hong Tan, Alexis M. Sosic, Yat-Yuen Lim, Choong-Yong Ung, Cristina Correia, Stephanie F. Polites, Yi Lin, William A. Weiss, Hu Li, Shizhen Zhu. Engineering macrophages as a novel therapeutic approach for high-risk neuroblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 7810.

Abstract Radiation is a cornerstone of glioblastoma (GBM) treatment, however, the majority of tumors progress within the radiation field due to inherent radiotherapy (RT) resistance. The integration of highly potent radiosensitizers, such as the ATM inhibitors AZD1390 or WSD0628, could reverse resistance and significantly improve local control of these tumors. However, the achievable brain tumor exposure of these inhibitors may be limited by drug exposure in skin and mucosa, leading to enhanced radiation toxicity. To support the clinical development of WSD0628, we developed pharmacokinetic (PK)-efficacy models to help interpret ongoing PK analyses. A model based on in vitro cell studies was based on the in vitro observations that 30 nM WSD0628 provided maximum ATM inhibition and the extent of radiosensitization was directly related to the duration of drug incubation after irradiation, up to a maximum of 24 hours. The unbound WSD0628 concentration, measured by rapid equilibrium dialysis, was 66% of the total concentration in cell culture media, and was used to define an optimal radiosensitizing target of 20 nM free WSD0628 for 4 to 24 hours. Previous PK analyses showed that the free, unbound fraction (fu) of WSD0628 in plasma and brain tumor tissue are 0.02 and 0.054, respectively, and the brain tumor-to-plasma ratio is 0.32 in GBM43 patient-derived xenografts (PDXs). From these data, a total drug level of 1160 nM in plasma is predicted to achieve unbound drug levels above the 20 nM WSD0628 target unbound concentration. To validate this prediction, parallel in vivo dose-ranging studies were performed in orthotopic PDXs. Mice with orthotopic GBM43 were randomized to treatment with RT (8 Gy x1) alone or in combination with a range of WSD0628 doses. RT combined with 0.25 mg/kg and 1 mg/kg WSD0628 had no impact on survival, compared to RT alone, while median survival increased by 1.3-fold for RT combined with 2.5 mg/kg, 1.7-fold with 5 mg/kg, and 3.2-fold with 10 mg/kg WSD0628. A similar dose-response was observed in an M12 melanoma brain metastasis PDX. Based on PK modeling of these drug doses, 2.5 mg/kg dosing will maintain total WSD0628 plasma levels above 1160 nM for 11.9 hours, 5 mg/kg for 23.6 hours, and 10 mg/kg for 40 hours. Taken together, we have developed a model to predict efficacious dosing of a brain tumor based on the free drug hypothesis, which allows integration of multiple aspects of in vitro modeling with measured drug levels in animal models to predict total plasma levels associated with robust sensitizing effects. Assuming similar tissue to plasma partitioning in human GBM, this model could be directly applied to interpret the ongoing Phase 1 PK analysis of WSD0628 in recurrent GBM. Citation Format: Ann Catherine Mladek, Juhee Oh, Sneha Rathi, Lily Liu, Danielle M. Burgenske, Brett L. Carlson, Katrina K. Bakken, Lauren L. Ott, Zeng Hu, Wei Zhong, William F. Elmquist, Jann N. Sarkaria. Modeling pharmacokinetics and efficacy of the ATM inhibitor WSD0628 in GBM and melanoma metastasis PDX models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 4629.

Abstract Background and Aims Whether hemodynamic and symptomatic benefits of the APTURE left atrial-to-coronary sinus shunt translate into clinical outcome benefit in heart failure with left ventricular ejection fraction >40% remains unknown. Methods In this post hoc analysis of ALT-FLOW EFS patients undergoing APTURE implantation, Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC) score-predicted 1-year all-cause mortality was compared with observed mortality by Kaplan-Meier analysis of adjudicated events. Heart failure hospitalization (HFH) rates were compared for the 12 months before vs after implantation. Additional analyses included stratification by baseline sodium-glucose cotransporter 2 inhibitor (SGLT2i) use and N-terminal pro-B-type natriuretic peptide (NT-proBNP), recurrent HFH cumulative hazard estimation (Nelson-Aalen), and H2FPEF score calculation. Results Among 95 patients (mean age 70.9 ± 8.5 years; 50% women; 93% New York Heart Association Class III; 85.9% with H2FPEF score >3), MAGGIC-predicted 1-year mortality was 13.0%. Observed 1-year survival was significantly higher than predicted (94.7% vs 87.0%; P=0.04), consistent across SGLT2i subgroups (log-rank P=0.59). The proportion of patients with HFH declined from 37.9% pre implantation to 9.6% post implantation (P<0.0001), with event rates falling from 0.61 to 0.21 per patient-year (P<0.001) and a 1-year cumulative hazard of recurrent HFH of 0.19 (95% confidence interval, 0.12-0.31). HFHs were evenly distributed across NT-proBNP strata. Conclusions In ALT-FLOW EFS, observed 1-year mortality was lower than MAGGIC-predicted mortality, and post implantation HFH rates were lower than pre implantation rates. These hypothesis-generating findings will require confirmation in the randomized, sham-controlled ALT-FLOW II trial.

Abstract Background: Chronic inflammation is implicated in ovarian carcinogenesis, but how different inflammation-related exposures individually or jointly affect histotype-specific associations remains unclear. Materials and Methods: We pooled data from 16 case-control studies in the Ovarian Cancer Association Consortium to evaluate associations of eight inflammation-related factors (anti-inflammatory: aspirin use, tubal ligation (TL); pro-inflammatory: endometriosis, obesity, lifetime ovulatory cycles (LOC), smoking, pelvic inflammatory disease (PID), polycystic ovary syndrome (PCOS)) with epithelial ovarian cancer (OvC) by histologic subtype. We examined individual associations and clustering of risk factors across histotypes and computed population attributable risk (PAR) for each factor. We assessed additive and multiplicative interactions for exposure combinations. Results: Associations with OvC risk differed by histotype (e.g., high-grade serous: aspirin: OR=0.90; 95%CI 0.82, 0.99; TL: OR=0.80; 95%CI 0.73, 0.88; overall serous: endometriosis: OR=1.17; 95%CI 1.03, 1.31; high LOC: OR=1.42; 95%CI 1.28, 1.58; obesity (low-grade serous): OR=1.50; 95%CI 1.14, 1.98). Clustering analyses showed highly correlated risk profiles in endometrioid and clear cell (r=0.91). High-grade serous and mucinous profiles were moderately correlated with endometrioid and clear cell (r=0.60) tumors. The profile for low-grade serous (r=0.36) tumors was distinct from other histotypes. PAR estimates suggested modifying aspirin use, TL, and LOCs could substantially reduce burdens of endometrioid, clear cell and mucinous tumors. Out of 28 exposure combinations tested in overall OvC and 189 by histotype, we observed 12 interactions. Not using aspirin regularly showed positive additive interactions with obesity and high LOCs, particularly in endometrioid tumors (obesity relative excess risk due to interaction (RERI)=0.74, 95%CI 0.31, 1.18; Pint=0.001 for; LOCs RERI=0.80, 95%CI 0.03, 1.56; Pint=0.04). Not using aspirin regularly also showed a positive additive interaction with endometriosis in clear cell tumors (RERI=1.77, 95%CI 0.03, 3.52; Pint=0.05). Lack of TL showed positive interactions with obesity in endometrioid (RERI=0.86, 95%CI 0.17, 1.53; Pint=0.01) and mucinous (RERI=1.10, 95%CI 0.23, 1.97; Pint=0.01) tumors, while negative additive interactions were observed for smoking and endometriosis in endometrioid tumors (RERI=-1.12, 95%CI -2.19, -0.05; Pint=0.04). A multiplicative interaction was observed between obesity and endometriosis in mucinous tumors (Pint=0.01). Conclusion: The findings suggest ovarian tumorigenesis is strongly shaped by pro- and anti-inflammatory pathways that act largely independently. Further examining these pathways may clarify the origins of histotype heterogeneity and guide prevention strategies. Citation Format: Maxwell Akonde, Britton Trabert, SHELLEY TWOROGER, Allan Jensen, Kathryn L. Terry, Joshua Sampson, Hoda Anton-Culver, David Bowtell, Elisa V. Bandera, Angela Brooks-Wilson, Andrew Berchuck, Daniel William Cramer, Linda S. Cook, Julie M. Cunningham, Jennifer A. Doherty, Ellen L. Goode, Marc T. Goodman, Holly Ruth Harris, Susanne K. Kjaer, Nhu Le, Alice Wen-Ron Lee, Francesmary Modugno, Kirsten B. Moysich, Celeste Pearce, Malcolm C. Pike, Harvey A. Risch, Mary A. Rossing, Joellen M. Schildkrau, Daniel O. Stram, Rebecca Sutphen, David Van Den Berg, Penelope M. Webb, Anna Wu, Argyrios Ziogas, Nicolas A. Wentzensen. Inflammation-related exposures and histotype- specific ovarian cancer risk in the Ovarian Cancer Association Consortium (OCAC) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 6261.

Abstract Introduction: Detectable circulating tumor DNA (ctDNA) is associated with inferior outcomes in Ewing sarcoma (ES); however, ctDNA clears in nearly all patients within 2 of the 6 chemotherapy cycles before radiotherapy (RT) or surgery. Given a 3-year EFS of 37% on AEWS 1221 (metastatic) and 5-year EFS of 78% on AEWS 1031 (localized), current ctDNA testing fails to predict most relapses. Extracellular vesicles (EVs) are microscopic particles studied as a biomarker in multiple cancer types. We hope to improve the sensitivity of ctDNA in ES and provide the first application of EVs in ES. Methods: cfDNA was isolated from 1mL plasma, quantified by Qubit, and sequenced as NEBNext Enzymatic Methyl-seq v2 libraries. We performed a systematic unsupervised clustering analysis of 27 ES plasma cfDNA profiles using 4 differentially methylated regions (DMR) subsets (63 strong DMRs, top 30, top 15, and 10 promoter-proximal loci), 3 clustering algorithms (k-means, Ward hierarchical, PAM), and k=2-5 clusters, testing associations with tumor burden and progression. EV analysis involved antibody detection against ES surface markers, including LINGO1, ENPP1, CDH11, and CD99. Concentrations of EVs were determined by standardized and calibrated high-resolution flow cytometry then analyzed with custom-built software to automatically determine and apply gates. Samples were de-identified, randomly coded, and run in triplicates. The senior research technologist responsible for EV analysis was blinded to patient information and clinical data. Results: EV analysis included 36 pre-RT samples from 16 metastatic and 20 localized ES patients from a prospectively maintained biobank, with 27 pre-RT samples in the cfDNA analysis and 19 healthy donor (HD) samples. 35 of 36 patients had chemotherapy before sample collection. cfDNA concentrations were markedly elevated in ES patients compared with HD (24.4 vs 4.6 ng/mL, p<0.0001), and 74% of ES samples exceeded the HD 95th-percentile detectability threshold (7.0 ng/mL). Across clustering configurations, ES cfDNA profiles consistently exhibited reproducible methylation substructure. One configuration (PAM clustering, k = 5, 63 strong DMRs) showed a modest but permutation-supported association with primary tumor size (nominal p ≈ 0.04; empirical p ≈ 0.03-0.04). ES samples had increased EV concentration compared to HD (p<0.001), with AUC 0.988 for distinguishing between ES and HD ENPP1 (p<0.0001). The highest ENPP1 EV values were in patients with large primary tumors (>8 cm vs. ≤ 8cm) and metastatic disease (p=0.066). Conclusions: cfDNA- and EV-based liquid biopsy provide encouraging signals for high ES detectability pre-RT. We also show the first study of EVs as a novel biomarker in ES, which may provide complementary information to ctDNA. Multimodal liquid biopsy in ES may identify candidate patients for systemic or local therapy escalation and facilitate personalized care. Citation Format: Roman O. Kowalchuk, Lilli J. Greiner, Yohan Kim, Rubia Noori, Jeffrey Szymanski, William S. Harmsen, Pradeep S. Chauhan, Adam C. Amundson, Andrea M. McMahon, Peter J. Schoettler, Wendy A. Allen-Rhoades, Stephanie F. Polites, Peter S. Rose, Kelly M. Bailey, Linda M. McAllister-Lucas, Anita Mahajan, Fabrice Lucien-Matteoni, Nadia N. Laack, Aadel A. Chaudhuri. Multimodal liquid biopsy of Ewing sarcoma reveals high sensitivity prior to radiotherapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 1149.

Abstract Background: Digital Drug Assignment (DDA) is a computational reasoning model that scores cancer therapies based on the complete molecular profile of a tumor, and stratifies them by predicted efficacy (Petak et al., 2021). In a prior study of 111 lung cancer patients, DDA-derived high-score molecularly targeted agents (MTAs) were associated with improved clinical outcomes (Dirner et al., 2025). Here, we extend this analysis to the GENIE BPC NSCLC cohort to assess the broader clinical validity of DDA. Methods: From the GENIE BPC NSCLC cohort data available on Synapse, we included 1,078 patients with a single-sample genomic profile, available primary treatment data and survival outcomes (total 2,103 treatment lines, therapies included: afatinib, erlotinib, osimertinib, crizotinib, nivolumab, pembrolizumab, atezolizumab, bevacizumab+chemo, ramucirumab+chemo; and chemotherapy alone). DDA scores were generated for all cases, and the individual score of the administered MTAs (incl. immune checkpoint inhibitors) was used to stratify outcomes into low (<0), intermediate, and high DDA-score (≥1000) tiers. Progression-free survival (PFS, by imaging) and overall survival (OS) were analyzed using Kaplan-Meier statistics. Results: Median PFS and OS differed significantly across DDA score tiers, increasing with higher scores (see table). Intermediate-tier drugs had similar mPFS values as chemotherapies (3.9 vs 4.2 months). Six-month PFS and twelve-month OS rates increased with DDA-tiers and were all significantly different by χ2 test. DDA-high therapies provided greater benefit across treatment types than lower-score counterparts. Conclusions: Across a large, real-world NSCLC cohort, DDA effectively distinguished therapies with higher clinical efficacy based on the full molecular profile of each patient. These results reinforce the potential of DDA to enhance personalized treatment selection based on NGS diagnostics in precision oncology. Citation Format: Barbara Vodicska, Eniko Kispeter, Dora Lakatos, Gabor Gy Kalmar, Robert Doczi, Dora Gorog-Tihanyi, Anna Dirner, Reka Szalkai-Denes, William T. Beck, Arkadiusz Z. Dudek, Christophe Le Tourneau, Istvan Petak. Molecularly-informed prediction of treatment efficacy in the GENIE BPC NSCLC cohort using computational reasoning [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 7.

Study Design: Retrospective analyses of the center of rotation (COR) in asymptomatic volunteers and symptomatic patients. Objective: Document (i) level-specific COR position at radiographically normal levels in asymptomatic volunteers, (ii) preoperative COR abnormalities in symptomatic patients, and (iii) correlations between COR and other intervertebral motion metrics. Summary of Background Data: The COR has been discussed in many prior publications; however, our understanding of the normal position of the COR, the types and prevalence of COR abnormalities in patients, and the association between COR and other more common measures of intervertebral motion remains limited. Methods: Using automated, FDA-cleared software, we quantified COR and standard motion/alignment metrics from cervical flexion-extension radiographs of 341 asymptomatic volunteers and 1263 symptomatic preoperative patients. Level-specific 95% reference ellipses were derived from volunteers. Abnormality was defined as COR outside the level-matched ellipse. Associations between COR (cranial-caudal and anteroposterior) and rotation, translation, sagittal plane offset, disc height change, and standardized instability indices were evaluated. Results: In volunteers, COR was level-dependent and well summarized by 95% reference ellipses. The cranial-caudal position of COR was chiefly determined by rotation and translation, whereas the anteroposterior position related to sagittal plane offset and disc height change. COR also correlated with translational (TI-Index) and vertical (AVI/PVI) instability metrics. In symptomatic patients, most levels fell within reference ellipses; when deviations occurred, they were infrequent and multidirectional. Conclusions: COR can be automatically derived from flexion-extension radiographs. Deviations from level-specific reference ellipses provide an intuitive visualization of abnormal kinematics, complementing established motion metrics and linking cranial-caudal and anteroposterior shifts to specific patterns of translation, alignment, and instability.

A bstract Alzheimer’s disease (AD) presents with substantial clinical and anatomical heterogeneity, including both typical amnestic and atypical variants such as posterior cortical atrophy and logopenic primary progressive aphasia. Although neurofibrillary tangle (NFT) burden is a defining pathological feature of AD, its regional distribution varies across clinical phenotypes, suggesting that selective neuronal vulnerability may shape disease presentation. However, the cellular and molecular determinants underlying this vulnerability remain incompletely understood. Here, we profiled single-nucleus transcriptomes across multiple brain regions, including hippocampal (CA1) and neocortical (superior temporal gyrus and occipital cortex) regions, from individuals with typical and atypical AD and healthy controls. Integrative analysis identified major cell classes and resolved diverse excitatory and inhibitory neuronal subpopulations that were reproducibly observed across regions and individuals. Using quasi-binomial regression models to assess compositional changes, we quantified subtype-specific vulnerability associated with AD pathology. We identified a distinct excitatory neuronal subpopulation characterized by NRGN and BEX1 expression, which showed reproducible depletion across multiple regions, with the strongest evidence in amnestic AD and in neocortical regions in lvPPA. This vulnerable population showed concordance with previously reported AD-associated excitatory neuron signatures, supporting a conserved transcriptional program of susceptibility. Genes enriched in this population were associated with chemical synaptic transmission and regulation of synaptic plasticity and formed interconnected networks in protein-protein interaction analyses. These findings suggest that intrinsic properties related to synaptic function may predispose specific neuronal populations to degeneration in AD. Together, our results define a conserved, transcriptionally distinct excitatory neuron subpopulation that is selectively vulnerable across AD phenotypes and brain regions. This work provides a framework for linking regional pathology to cell-type-specific susceptibility and highlights synaptic regulatory pathways as potential contributors to neuronal degeneration in Alzheimer’s disease.

Background: Pancreatitis significantly alters the microbial composition of the oral and intestinal compartments, causing dysbiosis that may contribute to disease mechanisms and potentially serve as a basis for diagnosis or treatment. Objective: To determine whether the oral or gut microbial signature can classify chronic pancreatitis (CP). Methods: Stool samples (n=707) were collected from participants in the Prospective Evaluation of Chronic Pancreatitis for Epidemiologic and Translational Studies (PROCEED). Samples were distributed among 200 healthy (HC), 310 CP, 49 acute pancreatitis (AP), and 148 recurrent acute pancreatitis (RAP). In addition, saliva samples were collected for a subset of participants (n=156). Whole genome sequencing was performed to assess microbiome composition. Machine learning algorithms were utilized to identify a signature with microbial features predictive of CP. Results: Gut alpha diversity was significantly decreased in AP, RAP, and CP compared with HC, with CP exhibiting the lowest diversity. In contrast, oral microbial diversity showed no significant variation across groups. Beta diversity analysis revealed distinct gut microbiome compositions between HC and pancreatitis subtypes, with CP showing the most pronounced differences. Random forest models using gut microbial species demonstrated robust predictive performance for CP using a minimum of 10 species (Area under the curve—AUC: 0.834; accuracy: 0.774). Despite similarities in gut microbiome composition across pancreatitis subtypes, a unique gut microbial signature for CP was identified highlighting the microbiome’s potential in CP diagnosis. Conclusion: Our study reveals a gut microbial signature predictive of CP using machine learning models in a large US multi-institutional cohort.