Faculty Bibliography

STUDY OBJECTIVE/OBJECTIVE:Published information on the sexual health and well-being of individuals with complete androgen insensitivity syndrome (CAIS) remains limited. In this study, we aimed to evaluate the vaginal lengths, sexual well-being and body image findings of a relatively large, young cohort of individuals with CAIS, with the goal of guiding clinical practice toward more accurate and individualized patient-centered counseling. METHODS:In this interim analysis of a prospective IRB-approved AIS Natural History Study funded by the National Institutes of Health (NIH), we collected demographic information and vaginal length measurements from 23 individuals with CAIS between 2021 and 2025. Participants over 18 were invited to complete the PROMIS Sexual Function and Satisfaction (PROMIS SexFS) questionnaire and the Body Image Scale (BIS) questionnaire. RESULTS:The cohort (n = 23) ranged in age from 14 to 65 (median = 19). Fourteen participants (61%) identified as heterosexual, 7 (30%) had undergone complete gonadectomy, and 1 had undergone vaginoplasty. Three participants reported practicing serial vaginal dilations. Vaginal length measurements (n = 14) ranged from 25 to 90mm (median = 60mm). Of those who completed the PROMIS SexFS (n = 11), mean t-scores were within one standard deviation of normalized U.S. female population ranges. Of those who completed the BIS (n = 15), the average mean-item score was 0.9 on a Likert scale from 0 "not at all" to 3 "very much". CONCLUSION/CONCLUSIONS:Aside from one outlier, vaginal lengths in our cohort did not differ significantly from those reported in the general population, and most participants reported a positive body image and the ability to engage in fulfilling sexual activity.

PURPOSE/UNASSIGNED:The addition of MEK inhibition (MEKi) to programmed cell death ligand 1 (PD-L1) blockade improves progression-free survival (PFS) in patients with advanced biliary tract cancer. Although MEK inhibitors may increase tumor cell immunogenicity, they can impair T-cell priming/effector function, limiting combination efficacy. We hypothesized that the addition of a CD27 agonist could restore T-cell function and enhance antitumor immunity in this combination. PATIENTS AND METHODS/UNASSIGNED:We conducted a randomized, phase II trial evaluating atezolizumab (840 mg, intravenously, days 1 and 15) in combination with the CD27 costimulatory monoclonal antibody [CDX-1127/varlilumab (3 mg/kg, intravenously, days 1 and 15)], with/without the addition of an MEK inhibitor [cobimetinib (60 mg, orally, daily, days 1-21, off days 22-28)] in unresectable biliary tract cancer following at least one metastatic therapy. Overall response rate (ORR) and PFS were coprimary endpoints. Treatment-related changes in CD8+ tumor-infiltrating lymphocytes (TIL) were the primary correlative outcomes. RESULTS/UNASSIGNED:The trial was closed early following interim preplanned ORR analysis. At closure, 57 patients had been enrolled [n = 29 in the cobimetinib + atezolizumab + varlilumab (CAV) arm; n = 28 in the atezolizumab + varlilumab (AV) arm]. A majority (67%) had intrahepatic cholangiocarcinoma, and 32% were immunotherapy experienced. Both regimens were well tolerated without new safety signals. Objective responses were rare [0% (CAV); 3.8% (AV)]. The median PFS (mPFS) was 2.40 (CAV) and 1.84 (AV) months [hazard ratio (HR), 0.67; 95% confidence interval (CI), 0.38-1.18]. Among immunotherapy-experienced patients, the mPFS was 3.62 (CAV) and 1.84 (AV) months (HR, 0.54; 95% CI, 0.18-1.62). Treatment with CAV increased intratumoral CD8+ T-cell density compared with treatment with AV. CONCLUSIONS/UNASSIGNED:The combinations of atezolizumab and varlilumab with/without cobimetinib were safe, but neither meaningfully improved outcomes in biliary tract cancer treated in the later lines. Correlative tissue studies validated preclinical work that MEKi increases CD8+ TILs.

BACKGROUND/UNASSIGNED:Individuals with opioid use disorder (OUD) who return to the community from incarceration face a high risk of fatal drug overdose. This mortality risk is related to significant barriers to accessing health care, social support, and resources needed to transition to and thrive within the community safely. Digital health tools have effectively supported substance use treatment and recovery, but their potential to support OUD during the high-risk period of reentry is underexplored. OBJECTIVE/UNASSIGNED:This study aimed to examine how adults with OUD returning from incarceration perceive and engage with digital health tools, and identify barriers and facilitators to their use. METHODS/UNASSIGNED:Semistructured interviews were conducted with a purposive sample of 39 adults recently released from New Hampshire prisons and jails. Participants were recruited from a randomized clinical trial (EXIT-CJS) comparing the effectiveness of extended-release buprenorphine, naltrexone, and enhanced treatment as usual on treatment retention. Interviews were audio recorded, transcribed, and analyzed using a deductive-inductive approach to content analysis. The COREQ (Consolidated Criteria for Reporting Qualitative Research) criteria were used to guide the study reporting. RESULTS/UNASSIGNED:Most participants preferred digital health over in-person care for both OUD treatment and other health care, mainly due to its convenience and resilience against reentry challenges. Yet, most participants also underscored the irreplaceable role of in-person therapeutic human connection in recovery and recognized this as a trade-off of stand-alone digital health. Participants described using digital health tools to overcome barriers that otherwise would have prevented them from receiving in-person care, such as transportation shortages, limited provider availability, and work scheduling conflicts. Accessing digital health was acknowledged as contingent on access to internet-capable devices, affordable connectivity, and digital literacy. CONCLUSIONS/UNASSIGNED:The findings suggest that maximizing digital health's impact in reentry will require a multipronged approach: investing in infrastructure to close the digital divide, addressing upstream structural barriers to accessing care, and partnering with impacted persons to co-design hybrid care models that enhance the feasibility of engaging in care during reentry while enhancing therapeutic human connection and respecting patient preferences.

Histone lysine methylation, primarily mediated by the enhancer methyltransferase KMT2D (MLL4), regulates gene expression through H3K4 mono- and di-methylation. Dysregulation of KMT2D disrupts enhancer activation and contributes to tumorigenesis and cellular plasticity across multiple cancers, including lung, prostate, bladder, head and neck, and pancreatic tumors. KMT2D functions in a context-dependent manner, acting as either a tumor suppressor or oncogenic driver, and modulates key phenotypic transitions-such as epithelial-to-mesenchymal, squamous, endothelial, and neuroendocrine states-that underlie metastasis and therapeutic resistance. Beyond its tumor-intrinsic roles, KMT2D loss remodels the tumor immune microenvironment by enhancing antigen presentation and effector T-cell infiltration, thereby sensitizing tumors to immune checkpoint blockade. Understanding how KMT2D interfaces with signaling pathways such as PI3K/AKT, TGF-β, and NOTCH to regulate plasticity and immunogenicity will be critical for leveraging its biomarker and therapeutic potential. This review summarizes current insights into KMT2D's roles in cancer progression, lineage plasticity, therapy resistance, and immune regulation, highlighting its emerging relevance in precision oncology.

Current predictive models for pregnancy and infant outcomes often focus on limited endpoints and rely on costly tests or imaging. Here we developed the Mother-Child AI Agent (MoChiAgent), an LLM-based clinical assistant that orchestrates multiple tools to integrate sequential electronic health record (EHR) data, including routine laboratory tests, for forecasting maternal and infant diseases. MoChiAgent's core predictive engine, MoChiFormer, was developed and internally evaluated using 4,401,599 longitudinal clinical visits and externally validated using independent maternal and infant cohorts consisting of 263,452 and 23,192 visits, respectively. MoChiFormer reconstructs missing laboratory values, reduces batch effects and learns EHR representations that support gestational, fetal and infant age estimation, health-trajectory modelling and stratification of current and future disease risk. Subsequently, a Knowledge Search Tool utilizes these forecasts to retrieve evidence-based intervention and treatment recommendations from curated medical literature and authoritative guidelines. For maternal health, MoChiFormer accurately identified key gestational conditions, achieving AUROCs of 0.89 for placental abruption, 0.89 for premature rupture of membranes, and 0.91 for preterm labour. Analysis of paired mother-infant data further revealed transgenerational risk associations, with infants born to mothers in specific clusters showing substantially elevated risks of neonatal jaundice (HR = 2.81, 95% CI 2.60-3.03) and haematological diseases (HR = 2.83, 95% CI 2.62-3.05). Integrating maternal gestational EHRs with infant records improved prediction of infant conditions, including chromosomal abnormalities and respiratory disorders. These findings suggest that MoChiAgent can provide clinically relevant, actionable decision-support information to enhance risk-stratified care for mothers and infants.

BACKGROUND:Medication non-adherence contributes to the efficacy-effectiveness gap in real-world practice. Glucosuria, routinely measured on urinalysis, may serve as an objective proxy for assessing medication adherence to sodium-glucose cotransporter 2 (SGLT2) inhibitors, a class of medications that reduce the risks of kidney disease, heart failure, and mortality. METHODS:We leveraged two cohorts from the Optum Labs Data Warehouse real-world data (2014-2023): Cohort 1 included 3,987 patients with SGLT2 inhibitor pharmacy claims and a urinalysis performed both before and during active fill periods; cohort 2 included 45,711 patients prescribed SGLT2 inhibitors with urinalysis performed within 6 months after initiation. Glucosuria was defined as urine glucose 2+ or greater. Cohort 2 was followed for a mean of 3.3 years for all-cause mortality, heart failure hospitalization, end-stage kidney disease; fractures were used as a negative control outcome. RESULTS:In cohort 1, SGLT2 inhibitor use (vs. no use) was associated with 18.1-fold higher odds of glucosuria (95% CI, 16.4-20.0), adjusted for diabetes status. In cohort 2, 26,657 (58%) had glucosuria within 6 months of SGLT2 inhibitor initiation, suggesting adherence. After inverse probability of treatment weighting, glucosuria was associated with lower risks of all-cause mortality (HR, 0.78; 95% CI, 0.73-0.83), heart failure hospitalization (HR, 0.87; 95% CI, 0.78-0.98), and end-stage kidney disease (HR, 0.73; 95% CI, 0.58-0.91) compared to no glucosuria. No association was observed with fractures (HR, 1.00; 95% CI, 0.95-1.06). CONCLUSIONS:Glucosuria was associated with SGLT2 inhibitor adherence and, among patients prescribed SGLT2 inhibitors, is associated with reductions in risks of all-cause mortality, heart failure, and end-stage kidney disease.

BACKGROUND:Poor sleep after acute cardiovascular events is associated with increased mortality. Women are more likely than men to report sleep disturbances after cardiovascular events, but other sleep metrics linked to cardiometabolic health remain underexplored. OBJECTIVES/OBJECTIVE:We characterize multiple dimensions of sleep and examine factors associated with poor sleep among women with prior myocardial infarction (MI). METHODS:Participants completed 7 nights of wrist actigraphy, which measured sleep duration, sleep efficiency, and wake after sleep onset (WASO), and sleep diary, which measured self-reported sleep quality using a Likert scale. Multivariable regression modeled associations between sociodemographic, clinical, and psychosocial characteristics and sleep measures. RESULTS:Among 94 post-MI women (median days since MI 93; IQR: 69-709), mean age was 60 years, and 67.0% identified as non-Hispanic White. The mean sleep duration was 405.1 minutes (approximately 6.8 hours), sleep efficiency 86.3%, WASO 61.6 minutes, and self-reported sleep quality 3.3, indicating fair-to-good sleep quality. Overall, 64.9% of women had short (<7 hours) or long (>9 hours) sleep duration, 33% low sleep efficiency (<85%), and 88.3% long WASO (>30 minutes). In exploratory analyses, associations with poor sleep included identifying as other than non-Hispanic White (shorter sleep duration, lower efficiency, and longer WASO), being nonpartnered (shorter sleep duration), hypertension (lower sleep efficiency), and higher depressive symptoms (lower sleep quality). CONCLUSIONS:Many women with prior MI had suboptimal sleep (short or long sleep duration, low sleep efficiency, and/or long WASO). Our results highlight the importance of discussing multiple dimensions of sleep with women post-MI to optimize cardiovascular health.

PURPOSE/UNASSIGNED:Microhematuria may be a sign of underlying genitourinary pathology, but diagnoses of bladder cancer are rare among those who complete evaluations. This study determines the incidence of bladder cancer in a large cohort of patients with microhematuria, assesses the diagnostic potential of current guideline recommendations, and characterizes the relative risks of clinicodemographic factors to inform the need for microhematuria evaluation. MATERIALS AND METHODS/UNASSIGNED:microhematuria. The main outcome was a new bladder cancer diagnosis within 2 years of index urinalysis with microhematuria. Patients were stratified by American Urologic Association risk categories to compare guideline-based risk classification with model-based estimation. The Fine-Gray method was used to estimate adjusted associations between clinicodemographic characteristics and incident bladder cancer, accounting for the competing risk of death. RESULTS/UNASSIGNED:Among 267,133 patients with microhematuria, the two-year cumulative incidences of cystoscopy completion and a new bladder cancer diagnosis were 11.9% (95% CI 11.7, 12.0) and 1.5% (95% CI 1.4, 1.5), respectively. Older age, male sex, smoking history, and ≥25 RBC/hpf were associated with a new bladder cancer diagnosis when adjusted for competing risk of death. Performance of our bladder cancer prediction model was better (ROC AUC=0.63) than that of the current AUA guidelines (AUC=0.52, p<0.001) and allowed for further risk-estimation of patients designated "high risk" by AUA criteria. CONCLUSIONS/UNASSIGNED:Estimation of patient-level microhematuria-related bladder cancer risk is possible with readily available clinicodemographic factors. Current approaches lack nuance, particularly among "high risk" patients. Precision approaches that use relative risk-factor weights and/or novel diagnostic tools may improve individualized decision-making for evaluation.

BACKGROUND/UNASSIGNED:Radiation pneumonitis (RP) remains a significant treatment-related toxicity in patients with unresectable, locally advanced non-small cell lung cancer (NSCLC) undergoing chemoradiotherapy (CRT). Most existing predictive models rely on static baseline demographic or dosimetry variables and lack real-time clinical applicability. We developed a novel predictive framework that integrates longitudinal symptom data extracted from clinical notes using natural language processing (NLP) with clinical and dosimetry features to improve early RP prediction. METHODS/UNASSIGNED:We retrospectively identified 227 patients with locally advanced NSCLC treated with definitive CRT at a high-volume cancer center in the United States. We included all patients older than 18 years who were diagnosed between Jan 1, 2006, and Dec 31, 2022 with histologically or cytologically confirmed unresectable Stage 2 or 3 NSCLC and treated with conformal radiotherapy to a minimum dose of ≥45 Gy with or without chemotherapy. Of these, 31 RP events were identified through manual adjudication using radiologic criteria and chart review. NLP was used to extract the temporal relationship of 16 pre-specified symptoms with treatment from over 100,000 clinical notes spanning pre- and during-treatment intervals. We trained and validated machine learning models on combinations of baseline clinical data, radiation dosimetry, and NLP-derived symptom features. Model performance was evaluated using a nested cross-validation framework, with an outer cross-validation loop reserved for performance assessment and an inner cross-validation loop used for model training and integration, and summarized using area under the receiver operating characteristic curve (AUC) and partial AUC (pAUC) at high specificity thresholds. Clinical utility was evaluated using decision curve analysis (DCA). FINDINGS/UNASSIGNED:The best-performing model incorporated longitudinal NLP features and achieved a median AUC of 0.759 (90% confidence interval 0.753-0.766), significantly outperforming baseline models using only dosimetry (AUC 0.613) or clinical variables (AUC 0.635). NLP-based features such as cough trajectory, shortness of breath, and wheezing were among the most important predictors. Inclusion of NLP-derived symptom data improved early identification of high-risk patients, particularly in the clinically relevant high-specificity range (pAUC 0.021 vs. 0.010 for dosimetry alone). DCA showed that the calibrated MLP model provided greater net benefit than default strategies of treating all or no patients across clinically relevant threshold possibilities. INTERPRETATION/UNASSIGNED:In this early work, NLP-based extraction of longitudinal symptoms from routine clinical documentation meaningfully enhances RP prediction in patients undergoing CRT for NSCLC. This approach leverages existing electronic health record infrastructure to deliver real-time, scalable, and interpretable risk estimates, offering a pathway toward potential early intervention and personalized toxicity management. The model and DCA requires external and prospective validation before clinical deployment; as such, future work should focus on this validation and integration into clinical decision support systems. FUNDING/UNASSIGNED:AstraZeneca.