Citations

Abstract Germinal center (GC)-derived lymphomas arise within a specialized immune ecosystem, yet whether malignant cells direct its remodeling remains unclear. Integrating genomic, transcriptomic, spatial, and functional analyses across follicular lymphoma and diffuse large B-cell lymphoma, we define a developmental framework linking malignant B-cell differentiation state to tumor microenvironment (TME) organization. Rather than segregating by histology, lymphomas align along a shared differentiation continuum in which proliferative dark zone (DZ) states associate with immune-depleted TMEs, whereas post-GC/memory B-cell (MBC) states drive inflamed but immunosuppressed TMEs. Spatial profiling reveals collapse of GC architecture and emergence of suppressive myeloid niches along this trajectory. Single-cell lineage reconstruction uncovers a DZ-to-MBC developmental axis underlying lymphoma evolution. Genetic reprogramming in vivo demonstrates that malignant differentiation state is sufficient to instruct TME remodeling. Collectively, these findings establish malignant differentiation state as a causal determinant of immune architecture across GC-derived lymphomas and identify lineage-directed immune reprogramming as a potential therapeutic strategy.

Background: Noninvasive tests are well validated in metabolic dysfunction–associated steatotic liver disease, but evidence in metabolic dysfunction and alcohol-associated liver disease (MetALD) and alcohol-associated liver disease (ALD) is limited. We evaluated noninvasive test–based clinical care pathways for fibrosis risk stratification in MetALD and ALD. Methods: This is a multinational, multicenter, cross-sectional study across 35 centers in 16 countries (2013–2025), including adults with MetALD or ALD. Fibrosis was assessed by liver biopsy and/or vibration-controlled transient elastography (VCTE). In biopsy-proven participants, we analyzed the sequential pathway (Fibrosis-4 [FIB-4] first, followed by VCTE for indeterminate FIB-4) to identify advanced fibrosis (≥F3). Diagnostic accuracy was assessed using the AUC. Results: Among 893 participants (41.3% MetALD and 58.7% ALD), the median age was 52 [43.0–61.0] years, and 79.4% were male. The estimated prevalence of advanced fibrosis was 45% (32.2% in MetALD and 54.0% in ALD). Participants with MetALD had a more dysmetabolic profile but lower fibrosis stages than ALD. Among biopsy-proven participants, FIB-4 AUC was 0.720, and VCTE AUC was 0.833. FIB-4 performed better in MetALD than ALD (AUC 0.773 vs. 0.540), while VCTE accuracy was similar across groups. In the sequential pathways, the false-negative rate for advanced fibrosis (misclassified as low risk) was 6.8% (4.9% MetALD and 11.3% ALD). Conclusions: In biopsy-proven patients, FIB-4 performance was acceptable in MetALD but substantially weaker in ALD. The standard 2-step pathway performed adequately in MetALD but missed a clinically meaningful proportion of advanced fibrosis in ALD.

Abstract Aims Cardiovascular disease (CVD) is the leading cause of mortality in chronic kidney disease (CKD). While CKD is known to give rise to systemic inflammation, its inciting factors remain poorly defined. Oxalate, long implicated in rare genetic kidney disorders, accumulates with decreased kidney function and has emerged as a driver of inflammation and independent risk factor for CVD. Here, we investigate the immunological mechanisms linking oxalate nephropathy to systemic inflammation, cardiac damage and kidney injury. Methods and results Oxalate nephropathy was induced in C57BL6/N mice through an oxalate-enriched diet. Oxalate induced systemic immune activation, renal fibrosis, and adverse cardiac remodelling, including pulmonary congestion with systolic and diastolic dysfunction. Flow cytometry analysis identified interleukin (IL)-17A as a dominant inflammatory effector, with expansion of Th17 and Th17-like Treg in the kidney, intestine, and spleen. Bulk mRNA sequencing confirmed these findings in kidney and heart. In line, plasma IL-17A was increased in oxalate-fed mice. Confirming the oxalate-IL-17A relationship, plasma IL-17A was elevated in patients with primary hyperoxaluria. Gut microbiome analysis by 16S amplicon sequencing showed only mild oxalate-induced alterations in mice. However, soluble oxalate directly enhanced Th17 polarization and disrupted mitochondrial respiration in vitro. In vivo, antibody-mediated IL-17A blockade improved kidney function, cardiac fibrosis, reduced neutrophil infiltration, and partially restored cardiac function in oxalate-fed mice. Conclusion Our study identifies oxalate as a systemic immunometabolic stressor and IL-17A as a central mediator of oxalate-induced cardiorenal injury. These findings establish the oxalate-IL-17A axis as a mechanistic link between CKD and CVD and suggest IL-17A inhibition as a potential therapeutic strategy to reduce cardiovascular damage in CKD.

Suppression of insulin-like growth factor-1 (IGF-1) signaling extends mammalian life span and protects against a range of age-related diseases. Unexpectedly, we found that reduced IGF-1 signaling fails to extend the life span of mitochondrial mutator mice. Most of the longevity pathways that are normally initiated by IGF-1 suppression were either blocked or blunted in the mutator mice. These observations suggest that the prolongevity effects of IGF-1 suppression critically depend on the integrity of the mitochondrial genome, revealing an unexpected hierarchy in the pathways that control mammalian aging. Together, these findings deepen our understanding of the interactions between the hallmarks of aging and underscore the need for interventions that preserve the integrity of the mitochondrial genome.

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 Latin America is undergoing rapid population aging alongside a rising burden of dementia. While the region holds substantial potential for dementia risk reduction, challenges remain, such as delayed diagnoses, limited access to specialized care and biomarker testing, persistent stigma, and deep‐rooted structural inequities. To address these gaps and foster regionally informed solutions, the Alzheimer's Association convened the 2025 Alzheimer's Association International Conference (AAIC) Satellite Symposium in Lima, Peru, on May 14–15, in collaboration with the Global Brain Health Institute (GBHI) and the Atlantic Fellows for Equity in Brain Health. The meeting aimed to bring core elements of the global AAIC meeting to regional Latin American settings, recognizing that national and cultural contexts demand tailored approaches to dementia prevention, risk reduction, treatment and care all aimed at promoting brain health in the region. This manuscript synthesizes the symposium's key discussions, scientific advances, and opportunities for collaboration across the region.

Sharing research code in an open access version-controlled repository offers significant benefits for both science as a whole and for individual researchers. In this article, we focus on this practice, which is fully aligned with the NIH’s Gold Standard Science (GSS) program as well as FAIR (findable, accessible, interoperable, reusable) and TRUST (transparency, responsibility, user focus, sustainability, technology) principles. Gold Standard Science supports open science by emphasizing transparency, reproducibility, and the use of best practices that enable others to verify and extend research. Pairing a research article’s cited data snapshot with a versioned, environment-specific code release, deposited in a companion code repository, ensures that, upon submission to a medical journal, readers and reviewers can directly verify results. An executable and updatable companion code repository complements, rather than replaces, established research data repositories. When code underlying medical research results is made openly available, then other scientists can inspect, run, and validate analyses. These activities enhance reproducibility, which is a core aim of GSS. Shared code also facilitates collaborative innovation by allowing researchers to extend the utility of the code to new datasets and applications. For researchers, code sharing can increase visibility, credibility, and citation impact. Demonstrating transparency through shared executable and updatable code builds trust with journal readers, peer reviewers, funders, and peers. Shared code in an open access repository signals adherence to high standards of scientific integrity and attracts opportunities for collaboration. A researcher who shares code receives recognition as a leader in reproducible, trustworthy research consistent with NIH’s GSS principles.

Abstract Introduction: CAR-T therapy has transformed cancer therapy. with its first Food and Drug Administration (FDA) approved product in August 2017. Given the expanding indications and growing experience, particularly with management of immediate toxicities requiring specialized expertise such as Cytokine Release Syndrome (CRS) and Immune Effector Cell-Associated Neurotoxicity syndrome (ICANS), FDA recently removed the Risk Evaluation and Mitigation Strategies (REMS) for CD19 and BCMA targeting CAR-T on June 26th2025. In particular, given that CRS and ICANS typically occur in the first 2 weeks post CAR-T infusion, the minimum required stay near CAR-T treatment center is reduced from one month to 2 weeks. While this could reduce treatment burden for patients (pts) who need to travel away from home to receive CAR-T, there is a need to update the transition of care from CAR-T center to pts' local oncology practice. Pts carry in their rate of recovery and have varying supportive care needs in the second half of the month post CAR-T infusion. To assist with care transition and planning, we examine the use of AI multimodal large language model (LLM) to predict patients' supportive care needs 2 weeks after CAR-T infusion based on the clinical data at the time of CAR-T evaluation, prior to leukapheresis. This timepoint was selected to also maximize travel, lodging and caregiver planning for pts and family as well. Methodology: Pts who were treated with FDA-approved CAR-T at Mayo Clinic Rochester between December 2022 and June 2025 were examined. Based on experiences with referral practices in the Midwest region, we defined patients with high monitoring needs as those who between day 14-30 post CAR-T had either hospital admission due to recurrent CRS or ICANS or required transfusion of more than 1 blood product per week (red blood cell or platelets). Others were classified as low monitoring needs. Clinical notes, labs, vital signs and electrocardiogram at the time of CAR-T evaluation were abstracted and summarized by Gemini LLM for association with patients with high versus low monitoring needs post CAR-T. Model was developed and tested for predicted needs and concordance with actual outcome of these pts. Results: Among the 362 pts who received FDA approved CAR-T in the timeframe, 84 pts had high monitoring needs. An additional 84 pts with low monitoring needs were randomly selected from 278 pts. These were divided into a training cohort and test cohort of 84 pts each, with 42 pts with high and 42 pts with low monitoring needs. Demographics of the two cohorts displayed no statistical difference. LLM analysis of the clinical data at the time of CAR-T eval identified 5 categories associated with risk for high monitoring needs: 1) Disease status (clinical notes about bulky, progressive disease or CNS involvement); 2) inflammatory and tumor burden markers (CRP, LDH); 3) hematologic status (platelet, plt; neutrophil count ANC); 4) renal function; 5) performance status. Using common clinical lab parameter convention (platelets <100, ANC< 1, CrCl< 30ml/min), and prioritizing for not missing pts with high monitoring needs, LLM model was defined to predict patients for high monitoring needs if high risk feature was identified in category 1 or any category combinations; whereas patients with no high risk feature or only 1 category that is not disease status was predicted as low monitoring needs. In this model, the sensitivity was 85.7%, specificity 23.8%, F1 score 65.5%. Applying this model to test cohort 2, the sensitivity was 83.3%, specificity 28.6%, and F1 score 65.4%. Discussion: We demonstrate proof-of-concept that LLM can be used to analyze clinical data at the time of evaluation for CAR-T eligibility to predict for supportive care needs post treatment. Not surprisingly, features identified by LLM to be associated with high monitoring needs are those previously reported by us and others at the time of CAR-T evaluation and or pre-lymphodepletion to be associated with risk for cytopenia, infection, hospitalization and non-relapse mortality. Based on the summary output generated by LLM, additional opportunities for classification methods were identified to improve specificity and balanced accuracy of the LLM model. This is a promising tool for further development to assist clinicians and patients to plan their treatment journey.

Abstract Introduction: Reporting patient outcomes by CAR-T treatment centers is part of the accreditation requirement under the FACT (Foundation for Accreditation of Cell Therapy) Immune Effector Cell (IEC) Program standard. Currently, this is a time-consuming and manual process, making it increasingly difficult to sustain with the growing CAR-T applications and patient volume. The objective of this study is to evaluate how a Large Language Model (LLM) can be used to automate the abstraction and summarization of medical record for CAR-T toxicities. Methods: Clinical data from patients treated with CAR-T between January 2018 and June 2025, including hematology clinical notes, lab results, flowsheets, vital signs, and medication administration record, were used as the source of patient information. The American Society of Transplant and Cell Therapy (ASTCT) CAR-T toxicity grading guideline for cytokine release syndrome (CRS) and immune effector cell associated neurologic syndrome (ICANS) were used to define these toxicities. With the large context window of the LLM (Gemini 2.5 pro), we created a prompt by embedding both the patient data and the ASTCT guidelines for CRS and ICANS, along with an outline of specific information we aimed to abstract from the data. Additionally, the LLM was instructed to provide contextual justification for the data it extracted from patient information. The LLM-generated output included event onset date, resolution date, maximum grade, date of maximum grade, and medications given for management. The CRS and ICANS outcome reports generated by the LLM were then compared to the IEC compliance program data. Any discrepancies identified were reviewed manually. Results: Among patients (pt) treated with CAR-T in the specified timeframe, 47 were selected as training cohort. This cohort included pt with toxicities events, no events, and a mix of events of varying grades. For the test cohort, 66 pts who received CAR-T from March to June 2025 were included. Demographics for the training and test cohorts are reflective of evolving clinical practice (training, test cohorts: median age 69, 65; male 55%, 74%; lymphoma/leukemia 66%, 48%; multiple myeloma 34%, 52%; CD19 CAR-T 46%, 50%). Compared to data from the IEC compliance program, the LLM-generated summaries for CRS events demonstrated an accuracy of 90%, precision 95%, sensitivity 100%, specificity 80%, F1 score 97%, and Matthew's correlation coefficient 87%. For ICANS event, the LLM achieved an accuracy of 91%, precision 92%, sensitivity 92%, specificity 90%, F1 score 92%, Matthew's correlation coefficient 83%. Maximum grade, for both CRS and ICANS, were identified with >90% accuracy. However, dates for CRS and ICANS were less accurate, with the lowest accuracy noted in identifying the event resolution date, 45% for CRS and 67% for ICANS. Medical records were reviewed for all discrepancies between the LLM and the IEC Compliance Program data. The most common discrepancy was a mismatch in event resolution date, determined to be due to differing criteria used by the compliance team and the LLM prompt. Of note, a few discrepancies were attributed to missed information by the IEC Compliance Team, which was correctly identified and categorised by the LLM. An updated LLM prompt (LLMv2) was developed and applied to the test cohort. In version 2, the prompt was revised to include clearer instructions on how to summarize CRS and ICANS events, as well as new instructions on additional data elements to be summarized. The performance of LLMv2 on this cohort for CRS event identification was 100% across all categories (accuracy, precision, sensitivity, specificity, F1 score, and Matthew's correlation coefficient). For ICANS the LLMv2 achieved accuracy of 96%, precision 69%, sensitivity 100%, specificity 93%, F1 score 82%, Matthew's correlation coefficient 80%. Accuracy in identifying event resolution date improved significantly from 64% to 99%. Conclusions: Our study demonstrates that LLMs can significantly reduce the time required to abstract and summarize CAR-T toxicities, while maintaining high accuracy. One limitation of our study is that it is based on data from a single institution. However, plans are already underway to evaluate the use of LLMs in a multi-institutional setting.

Abstract Introduction: Suboptimal response to therapy, early disease relapse, and limited long-term survival remain critical challenges in a substantial number of patients with multiple myeloma (MM). While cross-sectional imaging studies are commonly obtained in clinical practice, only limited information is currently incorporated into risk stratification efforts and clinical decision making. Here, we investigate the use of novel image-based biomarkers to identify patients at high risk for adverse outcomes. Methods: We obtained the pretreatment 18F-FDG PET/CT scans of patients with newly diagnosed MM with available clinical and laboratory data seen at Mayo Clinic between 2006 and 2022. In this analysis, we included 637 patients with detectable metabolic tumor volume on their pretreatment scan. The native DICOM images were pre-processed and subjected to automated image segmentation using nnU-Net (nnU-Net v1.0, inference using a pre-trained hematologic malignancies model). Limitations of this automated segmentation approach include heterogeneity introduced by varying image quality, technical variability in PET reconstruction, and potential inclusion of non-neoplastic FDG-avid lesions in segmented regions. PyRadiomics was used to extract quantitative radiomics features for clinical prediction modeling. We evaluated the predictive power of tumor size-, shape-, and texture-related radiomics features for identifying high-risk disease (advanced ISS stage at diagnosis, the presence of high-risk FISH abnormalities, failure to achieve a stringent complete response [sCR] to first-line therapy by IWMG criteria, and early mortality). All radiomics features were quantile-normalized for downstream modeling. Forward and backward feature selection as well as testing for multicollinearity were used to remove redundant features. Results: Median age at diagnosis of the 637 myeloma patients was 64 years (range 22–90), and 387 patients (61%) were male. After a median follow-up of 4.4 years (95% CI 0.2–11.7), median overall survival (OS) was 7.3 years (95% CI 6.3–8.3). Increased total tumor volume (upper tertile vs lower tertiles), measured by the total tumor surface area, was associated with advanced ISS stage (III vs I+II; OR 1.73, 95% CI 1.24–2.42, p=0.001, n=637). Increased tumor density (dichotomized at the median), measured by the median gray level intensity (OR 1.93, 95% CI 1.94–3.12, p=0.007, n=303), and decreased tumor heterogeneity (lower tertile vs upper tertiles), measured by the variability of gray-level intensities (OR 1.88, 95% CI 1.49–3.12, p=0.012, n=303), were associated with the presence of high-risk cytogenetics. Both increased total tumor volume (4.9% increase per tertile, 95% CI 2.2–7.6, p<0.001, n=600) and tumor density (8.9% increase per tertile, 95% CI 4.5–13.3, p<0.001, n=600) were independently associated with more extensive bone marrow involvement at diagnosis. Increased tumor heterogeneity (upper tertiles vs lower tertile), as measured by rapid changes of gray level intensities, was associated with failure to achieve a stringent complete response to first-line therapy (OR 0.50, 95% CI 0.30–0.82, p=0.006, n=351). Increased tumor volume was associated with shorter OS (HR 1.35 per tertile, 95% CI 1.11–1.63, p=0.002, n=301), independent of age, sex, ISS stage, and the presence of high-risk cytogenetics. A simple additive score consisting of tertiles of total tumor volume, ISS stage, and plasma cell labeling index ≥2% identified patients at risk of mortality within 3 years of diagnosis (OR 2.00, 95% CI 1.40–2.85, p<0.001, n=126), with moderate discrimination (AUC=0.71). Sensitivity analysis demonstrated total tumor volume and texture as most predictive of overall survival in a random forest survival model (C-index 0.712, 95% CI 0.649–0.775) and tumor texture as most predictive of response to therapy in a random forest classification model (C-index 0.693, 95% CI 0.607–0.779). Conclusions: Automated image segmentation and radiomics feature extraction from 18F-FDG PET/CT scans produces biomarkers that capture disease stage, burden, and risk in newly diagnosed MM. Total tumor volume and heterogeneity are strongly associated with important disease characteristics and can be used to identify patients at high risk for therapeutic resistance, early mortality, and inferior long-term outcomes.