Citations

Abstract Objectives γ‐Aminobutyric acid B receptor (GABA B R)‐IgG (immunoglobulin G) is an intermediate‐risk paraneoplastic autoantibody often associated with seizures. We aimed to assess the clinical and oncological features of GABA B R‐IgG autoimmune encephalitis (AE) and evaluate the performance of antibody testing. Methods Patients testing positive for GABA B R‐IgG in serum/cerebrospinal fluid (CSF) at Mayo Clinic Neuroimmunology Laboratory were identified. Archived sera were retested by cell‐based assay (CBA) at 1:100 and 1:200 dilutions. A live‐cell flow cytometry–based assay (LCFBA) was developed and validated using archived sera and CSF. True positivity included patients with classic presentations of GABA B R‐IgG AE or oncological explanations for antibody presence. Results Eighty‐six patients (median age 63 years; 43 female) presented with classic presentations of GABA B R‐IgG AE: encephalopathy with prominent seizures ( n = 55), status epilepticus ( n = 23), and rapidly progressive dementia ( n = 8). In addition, 44 patients (33%) had a false‐positive result for GABA B R‐IgG characterized by non‐specific symptoms/alternate diagnoses. Malignancy was identified in 78% of true‐positive patients, predominantly small cell lung carcinoma (SCLC). Testing serum at 1:100 dilution on CBA and using tissue immunofluorescence assay (IFA) in serum and CSF improved the identification of true‐positive patients ( p < 0.001). CBA at 1:100 dilution performed better than conventional CBA (at 1:10 dilution, p < 0.001) and tissue IFA ( p = 0.031). An in‐house LCFBA showed 100% sensitivity and specificity in CSF, performing similarly to conventional CBA ( p = 0.125) in CSF, but better than tissue IFA ( p = 0.031). Furthermore, serum LCFBA performed better than conventional CBA ( p = 0.022) and tissue IFA ( p = 0.006). LCFBA had the highest diagnostic accuracy and was closely followed by CBA at 1:100 dilution. Significance GABA B R‐IgG AE often presents as encephalopathy with seizures or status epilepticus in the context of an underlying SCLC. Multimodal evaluation using tissue IFA and fixed CBA at higher dilutions improves detection of true cases. LCFBA performs very well as a diagnostic test with very high sensitivity and specificity.

Background and Aims: Cholangiocarcinoma (CCA) is one of the most lethal cancers, characterized by molecular heterogeneity and treatment resistance. To uncover new biological signals and therapeutic opportunities, we employed multiomic characterization combined with machine learning. Approach and Results: We profiled all anatomical CCA subtypes using whole exome sequencing, mRNA sequencing, and proteome/phosphoproteome analysis. Integrative dimensional reduction revealed RNA, protein, and phosphoprotein features driving tumor heterogeneity, enabling clustering. Machine learning algorithms identified molecular features for each cluster and mapped external datasets and patient-derived xenograft (PDX) models onto these clusters. Kinase enrichment analysis highlighted targetable kinases active in each cluster. In vivo validation was performed in cluster-specific PDX models using the selective TNK1 inhibitor, TP-5801. We identified 3 molecular clusters with distinct pathway characterization: immunomodulatory (cluster 1), metabolic (cluster 2), and gene regulation/cellular fate (cluster 3). Cluster assignment was independent of anatomic subtype but correlated with overall survival following curative-intent resection. We also identified multiomic features and pathways linked to overall survival and lymph node metastases, crucial for patient treatment selection. Kinase enrichment analysis pinpointed TNK1 as a highly active kinase in the metabolic cluster. Treatment with TP-5801 significantly reduced tumor growth in a metabolic PDX model, but not in models representing the other clusters. Combining internal data with publicly available datasets, we identified the immunomodulatory cluster as most responsive to gemcitabine/cisplatin therapy, confirmed in vivo using cluster-specific PDX models. Conclusions: Integrated multiomic characterization provides translational insights by defining unique molecular subtypes associated both with therapeutic response and overall clinical outcomes. This approach identified TNK1 as a previously unrecognized therapeutic target in a defined subset of CCA tumors.

Abstract Background: The Pancreatic Cancer Detection Consortium (PCDC) is an NCI-sponsored initiative to improve detection of early-stage pancreatic ductal adenocarcinoma (PDAC). A key strategy in this initiative is to develop prospective, multi-center, longitudinal biospecimen collections from individuals without PDAC who are at high-risk of PDAC due to family history, germline variants, or the presence of pancreatic cystic lesions. The biospecimen collections, linked to clinically relevant data, will enable Phase 3 biomarker validation studies. Methods: The PCDC currently includes 10 funded Research Units (RUs): City of Hope/TGen, Dana Farber Cancer Institute, Johns Hopkins Medical Institute, Mayo Clinic/University of Pennsylvania, MD Anderson Cancer Center, Oregon Health and Science University, University of Nebraska, University of Pittsburgh Medical Center, University of California (UC) San Diego, UC San Franciso. Non-protected patient health information and biospecimen data is logged into a central Medidata Rave® database managed by a Management and Data Coordination Unit (MDCU) at Mayo Clinic (NCT06271291). Biospecimens collected include blood and pancreatic cyst fluid. Two options for blood collections exist: 1) 20 mL EDTA, 10 mL no additive if samples can be frozen within 4 hours of collection; 2) 10 mL EDTA, 10 mL Streck, 10 mL no additive if samples cannot be frozen within 4 (but less than 12) hours. Cyst fluid (up to 1000 uL), if available as clinical residual, will be obtained at the time of clinically indicated operative resection or endoscopic ultrasound guided aspiration procedures. All samples must be processed per standardized methods outlined in the lab manual and aliquoted into 500 uL (blood) or 100 uL (cyst fluid) units and frozen (-70oC). Long-term biospecimen storage will be set up at NCI Frederick National Laboratory (FNL). Single Institutional Review Board (sIRB) oversight is provided by Mayo Clinic. A collaborative research agreement has been executed to allow sharing of data and biospecimens between and within PCDC RU members, the MDCU, NCI and NCI FNL. Results: The enrollment of patients began February 7, 2025 with additional sites joining as necessary sIRB reliance documents and training is completed. The protocol is approved for up to 30,000 patients, age 18 years and older. 14 have been enrolled from 1 site as of July 10, 2025. Study endpoints include diagnosis of PDAC, death or complete pancreatectomy for non-PDAC reasons. Biospecimens and questionnaires will be collected annually. Summary and Future Plans: The PCDC has designed a Phase 3 biobank following EDRN study design principles. Biospecimens will be stored at NCI FNL. Access to the biobank resource will be overseen by the PCDC Steering Committee. We anticipate this being a rich Phase 3 biospecimen resource for the pancreatic cancer early detection research community. Citation Format: Janet E. Olson, Shounak Majumder, Jennifer J. Brooks, Emily J. Kirsch, Bridget H. Schmidt, Bill R. Bamlet, Erin E. Carlson, Ann L. Oberg. On the development of the Pancreatic Cancer Detection Consortium (PCDC) Prospective Cohorts biobank [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research—Emerging Science Driving Transformative Solutions; Boston, MA; 2025 Sep 28-Oct 1; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(18_Suppl_3):Abstract nr B069.

Abstract Introduction: Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease with limited treatment options and poor survival. This study explores how a medically supervised ketogenic diet (MSKD) impacts the gut microbiome and treatment response in patients with newly diagnosed metastatic PDAC receiving gemcitabine, nab-paclitaxel, and cisplatin chemotherapy. Methods: As part of a randomized Phase II trial, 32 evaluable patients (median age 65.9 years; 53% male), 16 were randomized to either MSKD or standard diet (SD) combined with chemotherapy. Stool samples were collected from 4 patients in the MSKD group (13 samples) and 8 patients in the SD group (23 samples). Deep shotgun metagenomic sequencing was conducted using NovaSeq (Illumina). Taxonomical and functional microbiome characterization was performed using MetaPhlAn 4.0 and HUMAnN 3.6 (The Huttenhower Lab, Harvard). Comprehensive bioinformatics analyses, using QIIME2, were performed to analyze microbiome composition and function over time. Results: Patients in the MSKD group showed better treatment responses overall. Microbiome profiles between the two groups remained distinct throughout therapy. Though not statistically significant due to sample size, MSKD patients showed lower alpha diversity (Shannon diversity; p-value=0.1339), likely reflecting reduced intake of fermentable fiber. Differential abundance analysis using the ANCOM-BC method revealed a significant enrichment of beneficial taxa in the MSKD group compared to the SD group (p&lt;0.05, log-fold change [lfc]≥2), including Akkermansia muciniphila (linked to improved response to immune checkpoint inhibitors; p=0.017931, q=1), Intestinimonas butyriciproducens (a short-chain fatty acid [SCFA] and butyrate producer; p=3.09E-05, q=0.017), and Streptococcus thermophilus (a supporter of gut mucosal health; p=1.15E-05, q=0.006). During the course of the study, only MSKD patients showed sustained increases in Roseburia hominis (SFCA/butyrate producer; p=3.94E-05, q=0.021) and Lacticaseibacillus rhamnosus (with anti-inflammatory and immune-modulating properties; p=7.28E-19, q=3.98E-16), along with a marked reduction in potentially pro-inflammatory Actinomyces spp.(p≤4.47E-05, q≤0.024). Functional profiling showed increased abundance of cancer-relevant microbial pathways in MSKD patients, including phosphatidate metabolism (PWY-7039; involved in lipid signaling and immune regulation; p=4.36E-04, q=0.20) and allantoin degradation (PWY-5705; linked to redox balance; p=9.16E-06, q=0.004). Conversely, L-ascorbate biosynthesis V (PWY-6415) was reduced (p=3.80E-04, q=0.17), likely due to decreased D-galacturonate substrate availability from fiber restriction. Conclusion: These findings suggest that an MSKD may beneficially modulate the gut microbiome in ways that support and enhance therapeutic response in patients with PDAC. Further validation in a larger cohort is warranted to confirm these results and improve analytical resolution. Citation Format: Keehoon Lee, Derek Cridebring, Gayle S. Jameson, Denis J. Roe, Erkut Borazanci, Diana L. Hanna, Caroline G.P. Roberts, Meredith S. Pelster, Richard C. Frank, Angela T. Alistar, Alan M. Miller, J. Erin Wiedmeier-Nutor, Sandra D. Algaze, Alison R. Zoller, Sarah J. Hallberg, Betsy C. Wertheim, Joshua D. Rabinowitz, Stephen Gately, Jennifer Keppler, Sunil Sharma, Drew W. Rasco, Daniel D. Von Hoff. Ketogenic Diet Modulates Gut Microbiome Composition and Enhances Treatment Response in Patients with Advanced Pancreatic Cancer: A Phase II Study [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research—Emerging Science Driving Transformative Solutions; Boston, MA; 2025 Sep 28-Oct 1; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(18_Suppl_3):Abstract nr B094.

Abstract Objectives Emotional impairments in people with dementia can be taxing for family caregivers. However, caregivers differ in their resilience to these challenges. We previously found that, compared to healthy controls and people with Alzheimer’s disease, people with frontotemporal lobar degeneration (FTLD) have smaller physiological responses when anticipating upcoming emotional stimuli. This impairment may leave these people with FTLD unprepared for upcoming emotional stimuli because they cannot mount responses needed for appropriate behaviors, which may create stress and difficulties for caregivers. We examined (a) whether smaller preparatory physiological responses in people with dementia are associated with lower emotional well-being and physical functioning in caregivers; and (b) whether these associations are moderated by the dyads’ relationship quality. Methods A total of 158 individuals with FTLD or Alzheimer’s disease and their family caregivers participated. Preparatory physiological responses in people with dementia were quantified as decreases in cardiac interbeat intervals from baseline to an instruction period, during which they were told they would be watching an upcoming emotional film. Caregivers self-reported their emotional well-being, physical functioning, and relationship quality with the people with dementia. Results Smaller preparatory physiological responses in people with dementia were associated with lower emotional well-being (but not physical functioning) in caregivers. This association was moderated by relationship quality (i.e., weakened for caregivers in higher-quality relationships). Discussion Findings advance understanding of risk and resilience factors for negative caregiver outcomes. Positive interpersonal processes in high-quality relationships (e.g., positive appraisal of aberrant behaviors) may buffer caregivers from the impact of emotional impairments in people with dementia.

Abstract Hepatocellular carcinoma (HCC) mortality is increasing globally, partly due to the growing prevalence of nonviral liver diseases. Genome‐wide association studies (GWAS) have identified genetic variants associated with HCC development. Leveraging GWAS summary statistics and linkage disequilibrium score regression (LDSR), we investigated disease co‐development with hepatitis C virus‐negative (HCV‐negative) HCC to provide unique insights into HCC etiology and prioritize relationships for further causal inquiry. We utilized the LDSR statistical framework to estimate the genetic correlation and heritability between HCV‐negative HCC with 901 epidemiologic, behavioral, and clinical traits from the United Kingdom Biobank (UKBB). First, we set the threshold for observed scale heritability of each trait at 0.02 to ensure reliable inferences with adequate study power. Next, we observed significant positive genetic correlations between HCV‐negative HCC and blood‐based biomarkers of liver injury (ALT, GGT) and allostatic load (including glycated hemoglobin, blood pressure, and total albumin). We also identified a positive genetic correlation between HCV‐negative HCC and diseases associated with metabolic dysfunction‐associated steatotic liver disease (MASLD), including diabetes, hypertension, chronic ischemic heart disease, and others. Taken together, our results help to identify polygenic and pleiotropic signals related to different phenotypic traits associated with HCC and support further exploration of the predictive power of blood‐based biomarkers identified in this study for inferring HCC development among HCV‐negative individuals.

Abstract Background Delirium is often underdiagnosed in clinical practice and is not routinely coded for billing. While manual chart review can identify delirium, it is labor-intensive and impractical for large-scale studies. Natural language processing (NLP) can analyze unstructured text in electronic health records (EHRs) to extract meaningful clinical information. Methods To support national integration of NLP for EHR-based delirium identification across different institutions, we launched the Delirium Interest Group within the national Evolve to Next-Gen Accrual to Clinical Trials (ENACT) NLP Working Group. This paper outlines our initial efforts to standardize, evaluate, and translate an NLP-based delirium detection model into the i2b2/ENACT platform. Results Multisite contextual inquiry identified several key challenges, including variations in local screening practices (eg, tools used, documentation frequency, and quality control), the need for harmonized definitions in the context of EHRs, and the complexity of modeling temporal logic. Multisite NLP evaluation revealed variable performance degradation driven by differences in delirium screening practices, clinical documentation patterns and semantics, and note syntactic structures. Conclusion Our work represents an important first step toward enabling scalable and standardized NLP-based delirium detection across institutions. By engaging diverse institutions through the ENACT NLP Working Group, we identified shared challenges and site-specific variations that impact model implementation and performance. Our collaborative approach enabled the development of a more robust framework for delirium identification across heterogeneous EHR systems. Future efforts will build on this foundation to enhance the validity, usability, and translational impact of delirium detection.

We present the case of a 12-year-old female who presented with progressive back pain and was found to have a localized mass in the lumbar spine. The resected tissue was characterized by epithelioid cells with a papillary appearance and high mitotic activity. Initial immunohistochemical analysis was consistent with a diagnosis of atypical meningioma. However, comprehensive molecular profiling detected a CRTC1::NCOA2 fusion with a novel NCOA2 gene rearrangement, leading to a revised descriptive diagnosis of epithelioid mesenchymal neoplasm with CRTC1::NCOA2 fusion. This specific fusion has only been reported in 2 other cases involving mesenchymal neoplasms of the palate and urinary bladder. While CRTC1 is frequently associated with MAML2 fusion in mucoepidermoid carcinomas, NCOA2 rearrangements occur in a variety of solid and hematologic malignancies. There is a paucity of information on the prognostic relevance of these fusion partners. This is the first reported case of a CRTC1::NCOA2 fusion in a pediatric patient with a spinal cord malignancy. The mechanistic significance and prognostic implications of this fusion will be discussed.