Citations

Abstract INTRODUCTION Women are increasingly entering the dementia research workforce, but they frequently fail to attain senior leadership positions in academia. Discrepancies in academic research outputs were investigated to guide equity‐focused policy recommendations. METHODS Bibliometrics was conducted on 400,482 original research articles (2003–2022) followed by surveying the AD/ADRD research community, to quantitatively and qualitatively evaluate discrepancies in publication and authorship trends. In addition, an unsupervised learning algorithm was developed to analyze underlying relationships between author groups. RESULTS Male author gender was consistently predictive of better publication and citation metrics across all bibliometrics outcomes, with senior male authors outperforming all other subgroups and fewer senior female authors in most research areas. Female survey respondents reported more barriers, but comparable productivity and citation strategies as male authors. DISCUSSION Multi‐pronged approaches by stakeholders across institutions, funders, and journals are necessary to provide career support for early‐career scientists working to transition to senior research roles.

Abstract Rationale Pulmonary hypertension (PH) is classified into five groups. Diagnosis requires multiple tests delaying treatment. This study compared patient and diagnostic characteristics for pulmonary arterial hypertension (PAH; Group 1 PH), Group 2-5 PH, and non-PH using machine-learning in an Electronic Health Record (EHR) dataset. This is novel as prior studies use claims data which may lack hemodynamic parameters. Methods This study analyzed adult patient records from a HIPPA-complaint EHR dataset from Mayo Clinic (2015-2024). Cohort definitions: PAH: right heart catheterization (RHC) data [mPAP>20 mmHg, PVR>2 WU, PCWP≤15 mmHg]; ≥2 PAH ICD diagnosis codes or positive natural language processing (NLP) sentiments separated by ≥ 30 days; and use of at least one PAH medication within six months after index date. Group 2-5 PH: RHC (mPAP>20mmHg) and ICD codes or positive NLP sentiment separated by ≥ 30 days for corresponding PH subtype. Non-PH: mPAP ≤ 20 mmHg and/or TRV ≤ 2.8 m/s. Index was the earliest NLP or ICD for PAH, earliest RHC for group 2-5 PH and most recent TRV/ mPAP for the non-PH cohort. Clinical variables were analyzed one-year before study index acknowledging data missingness as a limitation of retrospective dataset studies. Results 593 PAH, 817 Group 2-5 PH (67% with group 2 PH), and 11,600 non-PH patients (1:10 propensity-matched) were identified. Age, race, and symptoms were similar across cohorts. PAH had the highest proportion of females (60%) [Group 2-5 PH (50%) and non-PH cohort (53%)]. Clinical characteristic comparison is presented in Table 1. Comorbidities differed across cohorts. RHC: As expected, compared to Group 2-5, the PAH cohort had higher mPAP and PVR and lower PCWP. confirming a distinctly pre-capillary profile. ECHO: Compared to Group 2-5 PH, the PAH cohort had more pronounced right ventricular (RV) pressure overload and dysfunction and more impaired RV longitudinal strain, while TAPSE was similar. At the same time, left heart structure was less affected in PAH compared to Group 2-5. Among laboratory tests, serum creatinine was higher in Group 2-5 and ANA was higher in PAH cohort. Compared to the no-PH cohort, PAH and Group 2-5 cohorts exhibited clinically non-normal RHC and ECHO findings. Conclusion Although symptoms and demographic characteristics were similar among patients with and without PAH, unique RHC, ECHO, laboratory profiles and comorbidities differentiated PAH patients. Screening techniques using these distinct profiles could be considered to facilitate earlier diagnosis and treatment of PAH in clinical settings. This abstract is funded by: Merck & Co., Inc., Rahway, NJ, USA

Abstract Rationale Pulmonary arterial hypertension (PAH) is a rare and progressive disease, where the guideline-recommended diagnostic confirmation is via right heart catheterization (RHC). However, due to non-specific symptomology, patients often experience delays in diagnosis that potentially impact disease outcomes. We sought to develop a machine learning (ML) based approach for early and appropriate detection of PAH. Methods Three cohorts were derived from Mayo Clinic electronic health record (EHR): confirmed PAH (N = 1,169), No PH (N = 11,600) and Other PH (Group 2-5 PH, N = 817), based on 2022 ESC/ERS guidelines. The study index was defined as the earliest diagnosis dates for PH cohorts (PAH & Other PH) or the latest PH negative echocardiogram (ECHO) or RHC for the No-PH cohort. Patients included in the study had at least one symptom (dyspnea, fatigue, syncope, chest pain, or lower extremity swelling) within one year prior to the study index. A two-step sequential ML modelling strategy was applied: Step 1 differentiated PH (PAH + Other PH) from No PH and step 2 differentiated PAH from Other PH. Models were trained using a combination of structured data, unstructured clinical notes to detect symptoms using natural language processing and ECG waveforms captured one year prior to study index. Models were tested both with and without ECHO parameters. SHAP (SHapley Additive explanations) values were used to determine important features contributing to models (without ECG waveforms). Results Models including ECHO parameters performed better compared to those without (Figure 1). The top feature impacting model without ECHO parameters was dyspnea in the preceding three months followed by heart failure and systolic blood pressure for the PH vs no PH; and T wave peak in V1 and ANA level in PAH vs other PH cohort. The top feature impacting model with ECHO was tricuspid regurgitation velocity followed by heart failure and right atrial pressure for the PH vs no PH; and mean blood pressure and left ventricular diameter in the PAH vs other PH cohort. Figure 1: Model performance in the two-step sequential approach PR-AUC=Precision Recall-Area Under Curve Conclusion This study demonstrates that ML models can aid in the identification of PAH in symptomatic patient populations. While ECG can identify patients for further testing, ECHO can further increase the accuracy and the need to refer patients to RHC for confirming diagnosis of PAH. This abstract is funded by: Merck & Co., Inc., Rahway, NJ, USA

Abstract Rationale Sleep is an important dimension of COPD symptomology. Poor self-reported sleep quality has been independently associated with increased exacerbation risk and worse quality of life. There is limited evidence around the longitudinal sleep quality of patients with smoking-related lung disease. Methods Using the SPIROMICS longitudinal cohort of heavy smokers, we used group-based trajectory modeling to understand sleep quality over time as measured by serial retests of the Pittsburgh Sleep Quality Index (PSQI). We considered all participants of Black or Non-Hispanic White race who completed the PSQI in Visits 1-4, a span of 3 years. We considered race, gender, and social environmental exposures as time-stable risk factors for trajectory group membership. We adjusted for age, lung function, mood symptoms and self-reported COPD symptom severity were considered as time-varying factors. We selected the number of trajectory groups by minimizing Bayesian Information Criteria and polynomial order by maximizing the significance of exponential terms. For quality assurance we calculated average posterior probability of correct group assignment. Calculations were performed by complete case analysis and multiple imputation with chained equations. Results We analyzed 2,838 participants yielding 76,153 usable observations. Overall, 39.2% of participants had complete observations for all variables of interest at every measured visit, and 60.8% had at least one relevant observation missing. The largest single source of missingness in our dataset was the comorbidity count, for which 44% of participants had a missing observation. We identified two major trajectory groups. In one group, the PSQI was persistently high with little variation over time, containing 77.7% of the overall study population. The second group had low PSQI at baseline but experienced wide fluctuation over time. Risk factors for persistently poor sleep included female gender, Black race, and more adverse social conditions at either the neighborhood or individual level (Table). The average posterior probability of correct group assignment was 94.6% among the Low Variable group and 89.5% among the Persistent High group. Results were similar by complete case and imputed analysis, save that BMI did not independently predict group membership in the latter. Conclusion Historically underserved groups are at long term risk for poor sleep outcomes in smoking-related lung disease, even among those who are highly motivated and participate in clinical research. More work is needed to ensure equitable access to sleep medicine in order to improve the quality of care in COPD. This abstract is funded by: Nina Ireland Program for Lung Health

ABSTRACT Bispecific T cell engagers (BTEs) induce MHC‐independent cytotoxicity by bridging T cells to tumor cells via binding a T cell–activating receptor and a tumor‐associated antigen. BTEs have proven effective in hematologic malignancies and some solid tumors, yet their potential in glioblastoma (GBM) is largely unexplored. We developed a fully humanized BTE (hBTE) targeting interleukin‐13 receptor alpha 2 (IL13RA2), a tumor‐associated antigen widely expressed in GBM and associated with poor prognosis. In vitro, hBTE activated T cells and induced antigen‐dependent cytokine release and cytotoxicity against IL13RA2‐positive GBM cells. In vivo, hBTE showed robust target‐specific activity and markedly prolonged survival in primary and recurrent GBM xenograft models, without detectable off‐target local or systemic toxicity. Beyond GBM, hBTE also exhibited antitumor activity in IL13RA2‐expressing solid tumors, demonstrating selective tumor accumulation and therapeutic efficacy in models of breast cancer brain metastases and extracranial lung cancer. This work highlights the therapeutic potential of BTEs in IL13RA2‐expressing tumors and establishes a strong preclinical rationale for advancing hBTE therapy toward clinical translation in GBM and other tumors.

Abstract Chromosome 5p15.33 harbors several independent association signals which demonstrate antagonistic pleiotropy across cancer types, with causal mechanisms largely unresolved. To identify functional variants and enhancer elements at this locus, we performed statistical fine-mapping followed by massively parallel reporter assays (MPRA) and proliferation based CRISPRi screens. This approach identified eight multi-cancer functional variants (MCFVs) across three GWAS signals. Targeting rs421629 (part of the CLPTM1L signal marked by rs465498) with CRISPRi revealed opposing effects on TERT expression in pancreatic versus lung cancer cells, consistent with the antagonistic pleiotropy observed for this signal. Furthermore, CRISPRi nominated an intronic CLPTM1L variable number tandem repeat (VNTR) as a potent enhancer. Long-read sequencing established VNTR polymorphisms as potential causal variants for the rs465498 signal. We showed that Hippo-pathway transcription factors mediate VNTR enhancer activity in lung and pancreatic cancer cells. Together, these findings indicate that cancer susceptibility at 5p15.33 may be mediated by both SNPs and VNTRs and provide an integrated framework for resolving complex pleiotropic loci.

Abstract INTRODUCTION Glioblastoma is the most common malignant adult brain tumor with poor prognosis, largely due to its heterogeneous landscape. The AI-RANO and RANO-RGP groups organized the BraTS-Pathology 2024 challenge, providing a publicly-available dataset and a benchmarking environment for AI models to automatically identify six clinically-relevant histopathologic sub-regions in H&E-stained whole slide images (WSIs). METHODS We reclassified the TCGA-GBM and TCGA-LGG data according to the WHO 2021 criteria and identified diagnostic WSIs of 188 glioblastoma (IDH-wt, CNS WHO Gr.4) patients. A well-defined annotation protocol was communicated to eight neuropathologists, across eight institutions, who created “ground truth” annotations for Cellular Tumor (CT), Geographic Necrosis, Cortical Infiltration, Pseudopalisading Necrosis (PN), Microvascular Proliferation, and Penetration into White Matter (PWM). Comprehensive WSI curation for elimination of artifactual content and follow up partitioning resulted in approximately 300,000 data samples/patches. FINDINGS Inter-observer variability assessment highlighted difficulties in establishing a “gold standard”. The least annotated sub-regions (PWM and PN) were the most challenging to the participating methods, whereas all methods achieved their highest accuracy in the most annotated sub-region (CT). The highest average performance across sub-regions was obtained by a foundation model-based method. However, this top-ranked solution achieved an F1 score of 0.51 (Accuracy=0.65, Sensitivity=0.56, Specificity=0.93). CONCLUSIONS Our findings suggest that achieving state-of-the-art performance, in automatically identifying clinically-relevant glioblastoma sub-regions, remains an unsolved research problem. Analysis of the ground-truth annotations underscores the critical need for more precisely defined and neuropathologically standardized annotation protocol. Current AI algorithms are facing difficulties in addressing glioblastoma’s heterogeneous histologic landscape in the absence of large, comprehensively, and consistently annotated data. Building upon these findings, and towards developing more accurate AI algorithms, the 2025 iteration of the BraTS-Path challenge is currently on-going by leveraging two million data samples, aiming to enhance our disease understanding and improve diagnostic accuracy, ultimately contributing to improved patient outcomes.