Citations

Abstract INTRODUCTION Few African American (AA) donors have been included in post mortem Alzheimer's disease (AD) studies compared to European‐ancestry (EA) individuals. METHODS We generated transcriptome‐wide bulk pre‐frontal cortex (PFC) gene expression data from 125 AA donors with neuropathologically determined AD and 82 AA controls. RESULTS Transcriptome‐wide significant differential expression was observed with 482 genes. The most significant, ADAMTS2 , showed 1.52 times higher expression in AD cases ( p = 2.96x10 −8 ). Comparison of findings with those from a recent gene expression study of EA brain donors revealed substantial concordance, including ADAMTS2 . Other associations not observed in EA results may be especially relevant to AD risk in the AA population. Examination of AA AD GWAS‐implicated variants identified several expression quantitative trait loci. CONCLUSION This first large‐scale AA brain AD gene expression study identified many differentially expressed genes, including ADAMTS2 , and supports gene expression as a molecular pathway underlying the impact of several AA AD risk variants. Highlights We performed the largest African American brain tissue Alzheimer's disease (AD) gene expression study. Expression differences for 482 genes, notably ADAMTS2 , were study‐wide significant. Many significant differentially expressed genes are involved in energy metabolism. Several previously known AD‐associated variants in African Americans are eQTLs. These results advance knowledge of the genetic basis of AD in the AA population.

Background: Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death in the United States and worldwide. HCC screening among patients at elevated risk is associated with improved early detection and overall survival, but current ultrasound-based screening strategies are challenged by low adherence and suboptimal sensitivity. Blood-based screening tests have the potential to enhance adherence and improve early-stage HCC detection if they can demonstrate favorable performance compared with ultrasound-based screening. Here we present the design and rationale for ALTUS (A lternative to Ultrasound), a prospective, longitudinal, multicenter study in the United States to investigate the performance of the multi-target HCC blood test (mt-HBT) for the detection of HCC in a screening population. Methods: Adults with liver cirrhosis or chronic hepatitis B infection will be enrolled to undergo standard-of-care screening imaging and concurrent blood collection for the mt-HBT. All participants will undergo contrast-enhanced CT or MRI imaging with central radiology LI-RADS assessment as the reference method to determine HCC status. Participants without an identified malignancy will undergo a second screening visit and blood collection, and longitudinal clinical and imaging data will be collected up to 18 months from enrollment. The primary study objectives are to demonstrate that the mt-HBT is non-inferior to ultrasound for early-stage HCC sensitivity and to assess mt-HBT HCC specificity. The secondary objective is to assess mt-HBT overall sensitivity. Results: ALTUS is in progress with readout of the primary analysis expected in 2025. Conclusions: This prospective head-to-head comparison of the mt-HBT versus ultrasound will provide novel data regarding the performance and utility of the mt-HBT for HCC screening.

592 Background: While not all fevers occurring during hematopoietic stem cell transplantation (HCT) are dangerous, episodes of febrile neutropenia tend to be managed aggressively given the significant mortality and morbidity associated with infectious complications of transplantation. This can potentially lead to unnecessary diagnostic testing, antibiotic treatment, and prolonged hospital stays for non-infectious fever etiologies such as engraftment. Our group has previously demonstrated that high-frequency body temperature monitoring (HFTM) using a wearable device detects fevers earlier in transplant recipients (Flora et al, Cancer Cell, 2021). We hypothesized that applying supervised machine learning to HFTM data collected from inpatient allogeneic HCT patients would allow for accurately distinguishing between non-infectious and infectious etiologies of fever and could potentially guide safe de-escalation of antibiotics. Methods: We analyzed 74 independent fever events detected in HCT patients (n=42) outfitted with an FDA-cleared, wireless sensor measuring axillary temperatures every 2 minutes for the duration of their respective hospitalizations. After data filtering, independent fever events were then retrospectively grouped by fever etiology using physician-determined annotation as previously described (Flora et al., 2021). A split ratio of 80:20 was used to create training and test sets from the parent data set, consisting of 59 and 15 fever events for each split, respectively. Training sets were used to create a classification algorithm identifying bacterial infections requiring antibiotics using the machine learning model XGBoost. The performance of this model was then evaluated using test sets of the remaining fevers (n=15). Results: Averaging over 1000 test sets of 15 independent fevers comprising 5 bacterial infection-related fevers and 10 non-infectious fevers, our model correctly identified 82% (mean recall) of cases requiring antibiotics with a mean precision of 0.41. The model also correctly identified 42% of non-infectious fevers (mean true negative rate, 0.42) that would otherwise have been treated with unnecessary antibiotics. The mean AUCPR was 0.58, with the best performing individual model achieving an AUCPR of 0.97. Conclusions: The preliminary results of this study demonstrate that our classification model shows promise for differentiating fevers requiring antibiotics due to bacterial infections from other non-infectious fever etiologies. Prospective studies further developing supervised machine learning models for subcategorization of febrile neutropenia could pave the way for safely de-escalating antibiotics in HCT and other immunocompromised patients undergoing cancer treatment monitored using HFTM.

Objective The primary lateral sclerosis ( PLS ) consensus diagnostic criteria and functional rating scale ( PLSFRS ) were recently established to facilitate and optimize future PLS clinical trials. We examined the trajectory of the PLSFRS and other functional outcome measures and biomarkers in the PLS Natural History Study ( PLS NHS ) to understand their performance in this prospective cohort. Methods The PLS NHS is a prospective, longitudinal, multicenter study of people living with PLS in different diagnostic categories: early (disease duration <2 years); probable (2–4 years); and definite PLS (4–15 years). PLSFRS scores and other functional outcome measures were collected at baseline, 3‐, 6‐, 9‐, and 12‐month follow‐up visits. Baseline characteristics were compared between the groups. The slopes of the PLSFRS and other functional outcome measures over 12 months were examined in the overall cohort and subgroups using linear mixed‐effect models. The associations between baseline characteristics and the rate of PLSFRS decline were analyzed with linear regression models. Results A total of 76 participants were included: early (n = 6); probable (n = 26); and definite (n = 44) PLS . Baseline PLSFRS total scores were highest in the early PLS group, followed by the probable and definite PLS groups. In the overall cohort, the PLSFRS total score declined by 0.33 points/month (95% confidence interval [0.27–0.39], adjusted p < 0.05). The rate of decline was steepest in the early PLS group, followed by the probable and definite PLS groups. Baseline neurofilament light chain level was associated with the rate of PLSFRS decline over 1 year ( p = 0.001). Interpretation In PLS, the rate of functional decline, as measured by the PLSFRS total score, is faster during the early phase of the disease. Neurofilament light might serve as a prognostic biomarker in PLS. ANN NEUROL 2026;99:418–428

ABSTRACT Background Recent data show increased ventricular tachycardia (VT) related mortality. We aimed to investigate the trends and disparities of VT‐related mortality according to cardiomyopathy subtypes. Methods Mortality and demographic data were obtained from the CDC Wide‐ranging Online Data for Epidemiologic Research database between 1999 and 2020. VT‐related mortality was defined as the underlying cause of death and ischemic cardiomyopathy (ICM) or nonischemic cardiomyopathy (NICM) as the contributing cause of death. The direct method of standardization was utilized to estimate age‐adjusted mortality rates (AAMRs). Temporal trends were evaluated using log‐linear regression models. Results A total of 15 888 deaths were related to both VT and ICM, and 16 777 were due to both VT and NICM. There was a significant increase in VT and ICM‐related mortality between 2006 and 2020 with an APC of +1.38% ( p < 0.05). Similarly, VT and NICM‐related mortality increased between 2008 and 2020 with an APC of +0.60% ( p < 0.05). ICM had a higher AAMR in males [6.23 (6.12–6.34)], Whites [3.49 (3.43–3.54)], Hispanics [2.11 (1.95–2.26)], and the Midwest region [3.73 (3.61–3.85)] compared to NICM. In contrast, NICM had a higher AAMR in females [1.57 (1.52–1.61)], Black or African Americans [5.02 (4.84–5.20)], and the South region [3.10 (3.03–3.18)]. p for all trend < 0.05. Conclusions Real‐world data show significant differences in VT‐related mortality according to cardiomyopathy subtypes with prominent sex, race, and regional disparities. Clinical and public health strategies are needed to address inequities and improve outcomes.

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 Objective: The Pancreatic Cancer Detection Consortium (PCDC) performed a blinded EDRN defined phase 2 Biomarker Bakeoff study of eight promising biomarker panels using pancreatic cancer ductal adenocarcinoma (PDAC) case and general population healthy control samples from three institutions. The scientific questions were to determine whether panels could discriminate PDAC cases from controls with Receiver Operating Characteristic Area Under the Curve (AUC) &gt; 0.8, compare panel performance head-to-head via 95% confidence intervals (CI), and determine whether the panel improved upon serum CA19-9 alone. A secondary goal was to perform modeling to discover a new panel consisting of individual biomarkers. Methods: Eligible biomarkers were required to have had a prior EDRN defined phase 2 validation study published and be ready for validation in a sample set from multiple institutions. Three institutions submitted a total of 140 cases and 140 controls frequency matched to cases on age and sex. Four consortium member institutions submitted for validation eight panels consisting of ten plasma, serum and DNA genotyping biomarkers in addition to CA19-9. Locked panel formulas were submitted prior to specimen distribution. The primary metric was AUC with bootstrapped 95% CIs. LASSO penalized regression was used for exploratory panel building. Nested cross validation and internal-external validation were performed, and optimism-corrected performance estimates were computed. Results: The overall sample (N=280) was 51% female, median age of 67 years, and 94% were of non-Hispanic white race and ethnicity. Controls had median BMI 27.3, 56% never smokers, and 12% type II diabetes mellitus (DM2); cases (staged I to IV) had median BMI 26.6, 42% never smokers, and 26% DM2. Overall panel AUC estimates ranged from 89.9 to 96.3 and all were significantly larger than the null hypothesis value of 0.8 (p&lt;0.05). The apparent serum CA19-9 AUC was 91.7 (87.8, 95.6). Two panels were significantly different from serum CA19-9, a panel with FUT2/3 genotype with serum CA19-9 (p=0.002) and a panel containing tissue factor pathway inhibitor (TFPI), tenascin C (TNC), and plasma CA19-9 (p=0.01). In subset analyses by PDAC stage, no panel improved upon the null hypothesis value of 0.8 in stage I (p&gt;0.05), all panels improved upon the null hypothesis value of 0.8 in stage II and III (p&lt;0.05), and only serum CA19-9 did not improve upon the null hypothesis value of 0.8 in stage IV. All biomarkers but two microRNAs were retained in a LASSO model that had optimism corrected AUC=96.4 (95% CI: 94.0, 98.9). Conclusions: All submitted panels performed well overall in this blinded study, with the panel containing FUT2/3 genotype performing the best. Panel discovery improved upon serum CA19-9, though optimism corrected AUC 95% CIs overlap with those of apparent CA19-9 performance. Further work is needed to prioritize promising biomarkers and to improve detection of early stage PDAC. Citation Format: Ann L. Oberg, William R. Bamlet, Grant Izmirlian, Seetharaman Balasenthil, Surinder K. Batra, Masataka Hayashi, Daniel Herman, Michael A. Hollingsworth, Maneesh Jain, Ann M. Killary, Brianna M. Krusen, Suyu Liu, Gopalakrishnan Natarajan, Subrata Sen, Lynette M. Smith, Sudhir Srivastava, Brian M. Wolpin, Kenneth S. Zaret, Michael G. Goggins. Pancreatic Cancer Detection Consortium biomarker bake-off: A consortium Phase 2 blinded biomarker validation and panel discovery 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 B070.