Citations

BACKGROUND: Burnout remains prevalent among anesthesiology residents. Contributing workplace factors include limited control over scheduling, unpredictable hours, critical patient care needs, and other residency challenges. The Family Anesthesia Experience (FAX) is a program designed to educate residents and their support persons on wellness and residency demands. The focus of this multisite study was to evaluate the impact of FAX on first year clinical anesthesiology (CA1, ie, PGY2s) residents’ wellness. METHODS: This was a prospective evaluation study involving 15 US anesthesiology residency programs, 13 of which completed FAX. CA1 wellness was evaluated through surveys assessing perceived stress as the primary outcome, with exploratory outcomes including burnout, depression, psychological wellness, and social support. Additionally, changes in support persons’ understanding and knowledge of residency demands were tracked over time. The study included a historical control group of 302 CA1s (2019-2021) who did not participate in FAX, and an intervention group of 88 CA1s that participated in FAX (2021-2022). Statistical analyses compared changes in metrics between groups using linear mixed models and generalized linear models. RESULTS: Significant reductions in perceived stress were observed in the intervention group at 6 months post-FAX (mean change = –2.14, 95% CI, –3.95 to –0.34; P = .021, effect size = 0.58) relative to the control group, but there was no sustained difference at 12 months. Compared to the control support persons, intervention support persons reported increased understanding of residency demands, with significant improvements at 6 (mean change = 2.80, 95% CI, 1.12–4.48; P = .001, effect size = 0.77) and 12 (mean change = 2.59, 95% CI, 0.12–5.05; P = .039, effect size = 0.71) months. Improvement in support persons’ understanding did not predict changes in CA1 wellness outcomes. First generation physician residents reported lower family support at baseline but showed significant improvements in family support at 6 months (mean change = 3.05 points; 95% CI, 0.25–5.84; P = .033; effect size = 1.23) compared to their non first generation peers. CONCLUSIONS: FAX participation was associated with reduced perceived stress in residents up to 6 months post-intervention. First generation physicians experienced greater improvements in perceived family support compared to their non first generation peers. Our findings suggest that resident wellness may be enhanced by engaging both residents and their support persons in an educational wellness program that outlines the professional expectations and demands of anesthesiology residency training.

ABSTRACT Objectives Cognitive behavioral therapy (CBT) is the most researched treatment for childhood anxiety disorders (CADs). Improving outcomes and efficiency of CBT will require identifying and optimizing the most efficacious therapeutic components. Towards this end, a recent randomized controlled trial (RCT) found that Parent Coached Exposure Therapy (PCET) led to greater symptom improvement in less time than traditional CBT. To better understand and maximize these advantages, the current study explores the timing and course of symptom improvement in these two treatments for CADs, CBT, and PCET. Methods In this secondary analysis of data from the RCT, 56 youth aged 7−17 years (73.2% female) and a parent provided weekly ratings of anxiety symptom severity. Results The mean anxiety severity ratings decreased significantly in both conditions, but a significant condition by time interaction suggested that the pace of change differed between treatment for parent report, F (6.2, 335) = 2.96, p = 0.007. A series of post‐hoc comparisons indicated that the ratings became significantly lower in the PCET group beginning in session 3 (effect size = 0.77) through post‐treatment assessment. In addition, within each treatment condition the largest change in severity ratings occurred after the introduction of exposure, effect sizes of 0.68 for PCET and 0.56 for CBT. The child severity ratings followed a similar pattern but did not reach statistical significance. Discussion The implications of these exploratory and hypothesis‐generating findings for designing treatment protocols to deliver exposure in a manner to maximize the amount, and pace, of improvement are discussed.

Abstract Background: Glioblastoma recurrence is inevitable despite aggressive surgical resection of contrast enhancing (CE) tumor. Unlike the deep molecular characterization of CE glioblastoma, unresected infiltrating tumor, representing minimal residual disease (MRD) has not been well profiled. Increasingly, clinical trials are being conducted in the MRD setting for glioblastoma patients, necessitating understanding of unresected infiltrating tumor for targeted therapeutic development. Methods: Visium HD spatial transcriptomics was used to profile a cohort of newly diagnosed, treatment-naïve glioblastoma patients that underwent supramaximal resection, with both core and infiltrating in situ components. Spatial profiles generated expression signatures of infiltrative and CE tumor. For in silico prediction of therapeutics targeting the MRD, expression signatures of infiltrative and CE tumor were compared with drug perturbation signatures from the NIH L1000 database using the sRGES method to predict reversal of disease expression patterns. IC50 data from human cell lines were aggregated from the Genomics of Drug Sensitivity in Cancer or individual study data while blood-brain barrier (BBB) penetrance was predicted using the CNS-MPO approach. Findings: Infiltrative tumor expression profiles were enriched for genes corresponding to neural and oligodendrocyte progenitor like cells. In contrast CE glioblastoma displayed mesenchymal and astrocytic programs, suggesting spatial concentration of glioblastoma states within MRD and CE tumor. For CE glioblastoma, proteasome inhibitors and glucocorticoid receptor agonists were predicted to have the highest antitumor activity. In contrast, histone deacetylase inhibitors (HDACi) were highly ranked for MRD while exhibiting low predicted efficacy in CE tumor. Synergy analysis of drug candidates provided multiple dual treatment options for both the CE and MRD contexts. Conclusions: Cumulatively, these data indicate that the resected tumor does not reflect the MRD state, providing clarity on why targeted therapeutics for glioblastoma have not been particularly successful. Spatial profiling of the MRD demonstrates vulnerability to unique drug targets, which if utilized in the correct setting could confer greater survival benefit. Citation Format: Harrshavasan Congivaram, Shashwat Tripathi, Mateo Gomez, Katy McCortney, Ching Man Wai, Ruochen Du, Thomas K. Sears, Jianzhong Zhang, Daniel J. Brat, Craig M. Horbinski, Mark W. Youngblood, Jared T. Ahrendsen, Adam M. Sonabend, Stephen T. Magill, Matthew C. Tate, Maciej S. Lesniak, Sean Sachdev, Timothy Sita, Priya Kumthekar, Karan Dixit, Robin Buerki, Ditte Primdahl, Mustafa Khasraw, John de Groot, David A. Reardon, Rimas V. Lukas, Roger Stupp, Amy B. Heimberger. In silico screening of therapeutics candidates targeting minimal residual disease in glioblastoma [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 2496.

Abstract Background: Epidemiologic and other studies have identified environmental, occupational, and genetic risk factors for lymphoid and myeloid malignancies, but most studies have been conducted in Western populations. Investigations in populations with differing exposure patterns, distributions of disease subtypes, and genetic architecture are needed to fully understand hematopoietic tumor etiology. Methods: We conducted a large, multicenter, hospital-based case-control study of lymphoid and myeloid neoplasms in East Asia (AsiaLymph), including 5,671 lymphoid cases, 1,879 myeloid cases, and 3,858 controls. Participants completed a computer-assisted personal interview and provided biospecimens. Cases underwent central pathology review and were coded into the WHO Classification. Herein, we describe the study methods in detail and report association results for education, body mass index, and family history. Results: Greater BMI at age 20 but not at age 40 was associated with odds of total lymphoid neoplasm (per 5 kg/m2 increase: OR [95% CI]: 1.18 [1.09-1.27]), total myeloid neoplasm (OR [95% CI]: 1.17 [1.05-1.29]), and several subtypes. Greater educational attainment was associated with increased odds of total lymphoid neoplasm (for college vs. less than primary education: OR [95% CI]: 1.41 [1.21-1.65]) but not myeloid neoplasm (OR [95% CI]: 1.17 [0.95-1.45]; p-heterogeneity=0.02). There were also positive associations between family history of hematologic cancer in first degree relatives and odds of lymphoid neoplasm (OR [95% CI]: 1.53 [1.15-2.03]) and myeloid neoplasm (OR [95% CI]: 1.65 [1.11-2.44]) and specific subtypes. None of the evaluated risk factors were associated with NK/T-cell lymphoma, which supports a distinct etiology for this subtype. Conclusion: The AsiaLymph Study is one of the largest molecular epidemiology studies of both lymphoid and myeloid neoplasms with standardized World Health Organization classification of histopathologic subtypes and will serve as a valuable resource for etiologic investigations into risk factors for these malignancies. Citation Format: Qing Lan, Lauren M. Hurwitz, John K. Chan, Tai Hing Lam, Kexin Chen, Yok Lam Kwong, Xu Caigang, Brian CH Chiu, Raymond Liang, Ip Dennis, Wei Hu, Bryan Bassig, Mark Purdue, Jun Xu, Sarah Locke, Sophia S. Wang, James R. Cerhan, Sonja Berndt, Jonathan N. Hofmann, Jianxin Shi, Kai Yu, Shahinaz Gadalla, Lisa J. McReynolds, Rena Jones, Hongji Dai, Zhangyan Lyu, Lugui Qiu, Wei Liu, Huilai Zhang, Xianhuo Wang, Lindsay M. Morton, Stephen Chanock, Martha Linet, Melissa C. Friesen, Roel Vermeulen, Nathaniel Rothman. A multi-center hospital-based case-control study of lymphoid and myeloid neoplasms (AsiaLymph): Study design and initial findings for education, body mass index, and family history [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 5051.

Abstract Large Language Models (LLM) are being widely adopted into the medical field for their impressive ability to analyze and summarize large amounts of text data. These models enable clinicians and researchers to extract meaningful insights from complex datasets and may assist with decision making. Here we present our workflows and application of LLM for the interpretation and summarization of clinical data related to CAR-T cell therapy. Using an LLM (Gemini 2.5 pro) in the Google Cloud Computing (GCP) environment, two applications were developed to analyze CAR-T cell therapy clinical data: 1) extracting and summarizing CRS and ICANS event-related data to streamline the compliance team workflow, and 2) identifying features available at time of CAR-T infusion able to classify patients into high- or low-monitoring needs 14 days post CAR-T. Patient data (vitals, labs, hematology notes, and EKGs) were extracted from the electronic medical record (EMR) using Google BigQuery into SQL tables in GCP. For each application, relevant data fields were retrieved, formatted into JSON objects, and embedded in the LLM prompt for context-aware processing. Both applications have undergone iterative prompt engineering after analyzing the LLM output against ground truth data in the EMR. For the application extracting and summarizing CRS and ICANS events, the LLM was optimized on Mayo Clinic Rochester data. When compared to the IEC compliance database, the LLM achieved 100% accuracy and F1 score for CRS events, and 96% accuracy and 82% F1 score for ICANS events. The match rate for CRS and ICANS grades were 83% and 89% respectively. We applied the same LLM to Mayo Clinic Arizona (MCA) and Mayo Clinic Florida (MCF) cases, who document clinical notes and toxicity flowsheet differently, and achieved an accuracy (MCA: 93%, MCF:93%) and F1 score (MCA: 96%, MCF: 96%) for CRS and accuracy (MCA: 81%, MCF:82%) and F1 score (MCA: 76%, MCF: 75%) for ICANS. LLM was able to capture events missed by manual reviews. For most of the discrepancies where compliance team final adjudication is needed, LLM will be updated to flag discrepancies for review by the compliance team. For the application related to the monitoring needs 14 days post CAR-T, the LLM identified 5 categories predictive of high or low monitoring needs post CAR-T infusion (disease status, inflammatory and tumor burden markers, hematologic status, renal function and performance status). Our model achieved a sensitivity of 85.7%, specificity of 23.8%, and F1 score of 65.5% for our first cohort, compared with data extracted by the LLM from the EMR. For our second cohort, with demographics statistically similar to cohort 1 and using the same 5 categories, the LLM achieved a sensitivity of 83.3%, a specificity of 28.6%, and F1 score of 65.4%. Our workflow and applications of LLM’s provide examples and guidance to others interested in applying LLM’s for clinical and research applications. Citation Format: Emmanuel Contreras Guzman, Matthew Jankowski, Andre De Menezes Silva Corraes, Malvika Gupta, Monica L. Shaw, Madiha Iqbal, Talal Hilal, Saurabh Chhabra, Ricardo Daniel Parrondo, Jody K. Mclean, Kim R. Riester, Kayla Joseph, Melinda Tan, Holly Ross, Cleyonia Barnett, Sylvia Carter, Semy Girmay, Rachel Wolan, Milana Ramsey, Christian Downhour, Kristy Morgan, Shae Sibley, Erica Rushing, Lucy Holmes, Allison Burgstahler, Stephen M. Ansell, Hassan Alkhateeb, Matthew Hathcock, Ramona Bruno, Allison C. Rosenthal, Hemant Murthy, Patrick B. Johnston, Jonas Paludo, Yi Lin. Applications of large language models to CAR-T cell therapy clinical data using Google cloud computing [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 2740.

Abstract Chimeric antigen receptor (CAR) T-cell therapy achieves high response rates in relapsed/refractory aggressive B-cell non-Hodgkin lymphoma, yet only 40% of patients achieve durable remission. Identifying biological programs linked to long-term response remains critical. As metabolic fitness underlies T-cell persistence and effector function, we profiled metabolic pathways associated with clinical outcomes following commercial CD19 CAR T-cell therapy. Single-cell RNA sequencing of peripheral blood mononuclear cells collected at baseline (BL), peak CAR-T expansion (PK), and one-month post-infusion (M1) was analyzed using CellRanger v7.0.1, immunopipe. Seurat v4.3.0 was applied for unsupervised clustering to delineate cell subsets based on top differentially expressed genes. Patients were categorized as durable complete remission (CR ≥6 months; n = 16), primary refractory (PD1; n = 4), or relapse after initial response (PD2; n = 12). Gene-set enrichment analysis defined metabolic pathway activity across T-cell, monocyte, dendritic cell (DC), and natural killer (NK) subsets. Oxidative phosphorylation (OXPHOS) emerged as the dominant metabolic program distinguishing clinical outcomes. At BL and PK, OXPHOS was consistently enriched in PD1 relative to CR across T-cell, monocyte, and DC subsets, suggesting early oxidative activation in non-responders. By M1, this pattern inverted, with higher OXPHOS activity in CR. PD2 largely paralleled PD1, but several subsets including CD8 T central memory, classical monocytes TGFβ, intermediate monocytes CD38, monocytic myeloid-derived suppressor cell (mMDSC) HIF1A, and mMDSC SIRPA, showed OXPHOS enrichment in CR at BL and/or PK that persisted through M1. In contrast, DC and NK subsets exhibited the opposite pattern: OXPHOS was enriched in CR at PK (conventional DC 2 (cDC2), plasmacytoid DC (pDC), NK, proliferating NK), but shifted toward enrichment in PD2 at M1 (cDC2, NK, NK CD56bright). Glycolysis (GLY) followed a similar trajectory in monocytes and NK cells, with enrichment in PD1/PD2 at BL and PK, followed by enrichment in CR at M1. In T-cells, PD1 maintained GLY enrichment from BL through PK, with no significant differences observed at M1. The inositol-phosphate metabolism pathway showed a more static pattern and was consistently enriched in multiple PD2 effector and memory T-cell subsets without reversal at M1. Taken together, these data reveal distinct, lineage-specific metabolic signatures that differentiate durable remission from early and late progression. Dynamic OXPHOS and GLY programming, characterized by lower activity early and enhanced activity at M1 in CR, may reflect adaptive metabolic programming that supports sustained antitumor immunity. Immune-metabolic profiling may therefore serve as a biomarker of response and highlight actionable metabolic pathways to enhance CAR T-cell durability. Citation Format: Melinda S.Y. Tan, Panwen Wang, Patrizia Mondello, Jacqueline Turner, Andre de Menezes Silva Corraes, Chen Wu, Zuoyi Shao, Kevin Regan, Ma Audrey, Arushi Khurana, Nora N. Benanni, Yucai Wang, Paul Hampel, Jonas Paludo, Saad J. Kenderian, Urshila Durani, Patrick B. Johnston, Jose Caetano Villasboas, Stephen M. Ansell, Ying Li, Haidong Dong, Hu Zeng, Yi Lin. Metabolic pathway signatures defining response to CD19 CAR T-Cell therapy in aggressive B-cell non-hodgkin lymphoma [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 3278.

Abstract Introduction: Therapeutic outcomes in B-cell non-Hodgkin’s lymphoma (B-NHL) have substantially improved with the introduction of chimeric antigen receptor T-cell (CAR-T) therapy. Yet, persistent immune dysfunction and underlying metabolic dysregulation remain major obstacles to achieving durable clinical responses in B-NHL. We hypothesize that mitochondrial pathways play a critical role in mediating anti-tumor immunity during CAR-T therapy. Methods: Peripheral blood mononuclear cells were collected from healthy controls (CNTRL, n=5) and patients with advanced stage lymphoma (LYM) who received FDA approved CAR-T (n=32). Samples were collected before lymphodepletion (BL), at peak CAR-T expansion (PK) and one-month post-infusion (M1). Single-cell RNA sequencing was used to interrogate differential mitochondrial gene expression. Samples were obtained from LYM patients with complete remission for >6 months (CR), primary refractory (PD1), or relapsed (PD2) disease. Results: Compared to CNTRLs, LYM patients exhibited reduced expression of cytochrome c oxidase. At BL, CR patients demonstrated broader mitochondrial differential gene expression across all T-cell subsets relative to PD1 and PD2 and showed higher expression of MT-ATP-6, MT-ATP-8, and MT-CO3. Following CAR-T infusion, global mitochondrial gene expression increased across patients corresponding with expansion of CD8 effector T cells (Tem tumor circulating) and activated peripheral memory T cells (Tpm). Certain mitochondrial genes, such as MT-CO1, were durably expressed across LYM patients at BL, PK, and M1 in peripheral and central memory T cells (Tpm, Tcm). At PK, cytochrome c oxidase and ATP synthase genes were upregulated in CR Tpm and Tcm. At M1, MT-ATP-6, MT-ATP-8, MT-CO3 were significantly upregulated in CD8 memory precursor effector T-cells (MPECs) of CR patients compared to both PD1 and PD2. While CR and PD2 patients appeared clinically responsive at M1, CR samples maintained significantly higher MT-ATP-8 expression across T cell subsets (CD8 MPECS, Tpm, Tcm) compared to PD2 (p values 0.008; <0.0001; <0.0001). Conclusions: We report a temporal shift in immunometabolism with CAR-T therapy in LYM. Clinical responders exhibited sustained mitochondrial activity characterized by persistent expression of ATP synthase in CD8 MPECs and cytochrome c oxidase in Tpm and Tcm. In sum, mitochondrial programming may distinguish effective versus dysfunctional T cells and identify targets for therapeutic development. Citation Format: Jacqueline Turner, Panwen Wang, Patrizia Mondello, Melinda Tan, Christoph Schaefers, Chen Wu, Andre de Menezes Silva Corraes, Kevin Regan, Zuoyi Shao, Ma Audrey, Arushi Khurana, Nora N. Benanni, Yucai Wang, Paul Hampel, Saad J. Kenderian, Jonas Paludo, Urshila Durani, Patrick B. Johnston, Jose Caetano Villasboas, Stephen M. Ansell, Haidong Dong, Ying Li, Zeng Hu, Yi Lin. Mitochondrial transcriptome profiles associated with clinical responses to CAR-T therapy in aggressive lymphoma [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 1213.

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.