Citations

ABSTRACT Disease Overview Plasmablastic lymphoma (PBL) is a rare CD20‐negative aggressive lymphoma with poor prognosis when treated with standard treatment options. Although PBL is associated with HIV infection and other immunosuppressed states, it can also affect immunocompetent individuals. Diagnosis The diagnosis of PBL requires a high clinical suspicion and pathological confirmation. EBV‐encoded RNA (EBER) expression and MYC gene rearrangements are frequently detected in the malignant cells. The differential diagnosis includes EBV+ diffuse large B‐cell lymphoma (DLBCL), extracavitary primary effusion lymphoma, ALK+ DLBCL, and HHV8+ large B‐cell lymphoma, among others. Risk Stratification Age ≥ 60 years, advanced clinical stage, and high International Prognostic Index scores are associated with worse survival. Management Combination chemotherapy regimens, such as infusional EPOCH, are recommended. The addition of bortezomib or daratumumab might improve outcomes. B‐cell maturation antigen‐targeted therapies have shown early efficacy. The participation of patients with PBL in prospective clinical trials is warranted.

Abstract The growing availability of large-scale biomarker datasets has allowed data-driven methods to characterize Alzheimer’s disease biological heterogeneity. However, most prior studies have focused on cohorts of late-onset amnestic cases, leaving early-onset Alzheimer’s disease underexplored. We aimed to characterize tau-PET-based subtypes through a robust data-driven approach in the Longitudinal Early-Onset Alzheimer’s Disease Study. Baseline [18F]Flortaucipir PET scans from 365 amyloid-PET-positive participants with sporadic early-onset Alzheimer’s disease were quantified in the left and right medial temporal, lateral temporal, occipital, parietal, and frontal cortices. Tau PET values were z-scored against 85 amyloid-PET-negative cognitively normal age-matched participants and fitted into Subtype and Stage Inference (SuStaIn)—an unsupervised clustering algorithm that simultaneously models subtypes and progression from cross-sectional data. The derived subtypes were subsequently characterized by baseline and longitudinal clinical, cognitive, MRI, tau and amyloid PET features. We identified three tau-PET-based subtypes: on average, Subtype 1/Typical (n = 144, 40%) showed a predominant bilateral temporoparietal pattern typical of Alzheimer’s disease. Subtype 2/Left temporal (n = 111, 31%) showed predominant left temporal binding. Subtype 3/Posterior (n = 104, 29%) showed early and permeating occipitoparietal involvement. Subtypes did not differ in demographics or global amyloid burden, but were relatively more enriched for specific clinical presentations: S1/Typical for amnestic presentations, S2/Left Temporal for primary progressive aphasia, and S3/Posterior for posterior cortical atrophy. Baseline tau PET subtypes aligned with cortical atrophy patterns and domain-specific cognitive impairment. When follow-up tau PET scans were fitted to SuStaIn trained on baseline data, 85.6% (n = 172/201) of participants retained the same subtype classification, indicating subtype temporal stability, and progressed within subtypes by 0.56 ± 0.70 SuStaIn stage/year. Longitudinal voxel-wise linear mixed-effects modelling revealed tau accumulation patterns for each subtype in regions relatively spared at baseline: occipital lobe accumulation predominated in S1/Typical, bilateral frontal and right temporal in S2/Left Temporal, and bilateral frontotemporal lobes in S3/Posterior. All subtypes showed longitudinal increases in Clinical Dementia Rating-Sum of Boxes, but with slower worsening in S3/Posterior compared with the other subtypes. Our findings reveal robust subtypes in sporadic early-onset Alzheimer’s disease characterized by distinct spatiotemporal tau patterns that parallel differences in clinical presentations and trajectories of neurodegeneration. These subtypes extend beyond traditional clinical syndromes and support a more nuanced framework for individualized prognosis and care. Incorporating tau PET subtyping into clinical trial design could enable more targeted therapeutic approaches for this younger population.

Abstract Background Older adults remain underrepresented in prospective trials of immunotherapy for advanced lung cancer. To estimate pembrolizumab adverse events, we conducted a multicenter, nonrandomized, open-label phase 2 trial of oncologist-choice first-line pembrolizumab with or without chemotherapy for older adults with advanced lung adenocarcinoma. Methods Alliance for Clinical Trials in Oncology A171901 enrolled patients aged 70 years and older with stage IV or recurrent lung adenocarcinoma starting oncologist’s choice of first-line pembrolizumab with or without carboplatin and pemetrexed. The primary endpoint was the proportion of patients experiencing a solicited grade 3 or greater adverse event within 6 months. Secondary endpoints included overall survival, quality of life, and association of a geriatric assessment toxicity risk score with severe adverse events. Results Among 95 evaluable patients, 43 (45.3%) received pembrolizumab and 52 (54.7%) received pembrolizumab plus chemotherapy. Median age was 77 years (range = 70-92 years); 11.6% had an E Performance Status of 2 or higher. During 6 months of treatment, 25.6% (95% CI = 13.5% to 41.2%) experienced a solicited grade 3 or greater adverse event in the monotherapy group and 42.3% (95% CI = 28.7% to 56.8%) in the combination group. Median overall survival was 16.4 months (95% CI = 10.1 to not estimable) with monotherapy and 29.9 months (95% CI = 16.4 to not estimable) with combination therapy. Quality of life worsened during treatment. The geriatric assessment toxicity risk score was not associated with severe adverse events with pembrolizumab with or without chemotherapy. Conclusions Older adults with advanced lung adenocarcinoma receiving pembrolizumab with or without chemotherapy had comparable adverse event rates to those in prior registration trials that excluded medically frail patients. These findings can inform shared decision making and support inclusion of older adults in future trials. Clinical Trial NCT04533451 (https://clinicaltrials.gov/study/NCT04533451)

Abstract INTRODUCTION Remote neuropsychological testing can monitor brain health in individuals with or at risk of Alzheimer's disease (AD). We evaluated acceptability and validity of the Cogstate Brief Battery (CBB) for remote administration. METHODS Non‐demented adults from the Alzheimer's Disease Neuroimaging Initiative (ADNI) completed the CBB in‐clinic under supervision, then remotely every six months, with annual in‐clinic reassessments. Groups included cognitively unimpaired (CU) adults with normal amyloid (CU Aβ−) or elevated amyloid (CU Aβ+), and individuals with mild cognitive impairment with or without amyloid (MCI Aβ+, MCI Aβ−). Performance was compared across contexts and longitudinal changes were examined. RESULTS At baseline, MCI Aβ+ participants performed worse than CU Aβ−. CBB performance was similar across contexts, with consistent group differences. Over time, MCI Aβ+ participants showed greater decline on remote CBB measures than CU Aβ− adults. DISCUSSION Remote unsupervised CBB assessments are valid, reliable, and sensitive to AD‐related cognitive impairment.

Abstract Honoring a patient’s wishes for end-of-life care can be challenging if the patient loses decision-making capacity and the identified alternate decision-maker will not respect the patient’s wishes. This article discusses how to proceed ethically and legally when the alternate decision-maker and care team disagree about respecting a patient’s end-of-life preferences.

Abstract Genetic counseling assistants (GCAs) support genetic counselors (GCs) and genetics clinic workflows, but their potential roles in pretest genetic counseling for rare diseases have not been explored. A pilot within the Mayo Clinic Center for Individualized Medicine's Genetic Testing and Counseling Clinic (GTAC), which offers predefined tests for patients with rare disease, explored the impact of GCA pretest education on appointment time and patient questions. After training, GCAs met GTAC patients prior to the GC to provide scripted information on genetics concepts, the visit purpose, and the test including result types and disclosure plans. Data from the pilot and a control group were collected and analyzed using descriptive statistics and two‐sided t ‐tests. Patient cohort characteristics did not differ between the two models. When a GCA provided pretest education, the GC spent an average of 11.2 minutes less with the patient during their session compared to visits completed solely by the GC ( p < 0.0001). Total appointment time was not impacted by GCA education. Questions asked to GCAs were often not within GCA scope to answer (72.7%), and some patients asked repetitive questions to both the GC and GCA (63.2%). Reduction in GC time per patient could lead to increased accessibility by allowing additional patients to be seen in a day. Similar models may support GCA professional development while allowing GCs to remove repetitive education from their genetic counseling sessions, leading to less burnout and/or increased job satisfaction. Impact on administrative workflows, access, revenue, patient satisfaction and outcomes and GC/GCA satisfaction and benefits can continue to be explored when trialing models incorporating GCAs in pretest education roles.

Abstract Background: Immune checkpoint inhibitors (ICIs) have improved survival in non-small-cell lung cancer (NSCLC), yet only a subset of patients benefits, and biomarkers like PD-L1 remain limited. Here, we introduce a deep learning-based pathomics framework that utilizes routine H&E-stained slides to predict therapeutic response and survival outcomes in ICI-treated metastatic NSCLC. Methods: The study included 797 ICI-treated NSCLC patients from MD Anderson, with external validation in 280 patients from Mayo Clinic, Gustave Roussy, and the Phase III ICI-naïve LUSC Lung-MAP S1400I trial receiving nivolumab with or without ipilimumab. Path-IO (Pathology-Driven Immunotherapy Optimization) comprised of four major steps: (1) pathologist-verified tissue classifier segmented WSIs into eight compartments—Background, Bronchi, Immune, Lung, Necrosis, Stroma, Tumor, and Vessel—and was validated on TCGA and CPTAC dataset; (2) a survival prediction module generating patient-level risk scores in the MD Anderson cohort and validated across external datasets; (3) integration of Path-IO risk scores with radiomics and clinical features to improve prognostic accuracy; and (4) biological interpretability analyses correlating Path-IO risk immune contexture from multiplex immunofluorescence and transcriptomic signatures from NanoString profiling. Results: Path-IO effectively stratified patients into high and low-risk groups with significant survival differences. In the MD Anderson cohort, it achieved HR = 2.11 (p &lt; 0.001) and HR = 2.51 (p &lt; 0.001) for OS in the discovery and validation sets, and HR = 2.34 (p &lt; 0.001) and HR = 1.87 (p &lt; 0.001) for PFS, respectively. In the multicenter Phase III Lung-MAP S1400I trial, Path-IO achieved HR = 1.78 (p = 0.016) for OS and HR = 2.76 (p = 0.006) for PFS. In external validation, it predicted outcomes in the Gustave Roussy (OS = 1.97, p = 0.003; PFS = 1.51, p = 0.046) and Mayo Clinic (OS = 2.46, p = 0.007; PFS = 2.45, p = 0.027) cohorts. Path-IO outperformed PD-L1 and remained an independent predictor in multivariate analysis (p &lt; 0.001). It enabled data-driven frontline selection between anti-PD-1 monotherapy and chemo-immunotherapy, offering guidance toward more personalized treatment decisions. Integration with radiomics and clinical features further improved predictive accuracy (OS 0.63→0.75; PFS C-index 0.58→0.70), while high Path-IO risk scores correlated with immune-cold phenotypes identified by multiplex immunofluorescence and NanoString transcriptomics. Conclusions: Path-IO demonstrates that deep learning models rooted in histopathologic architecture can generate interpretable and biologically informed survival predictions in NSCLC treated with ICIs. By integrating pathology, radiology, and clinical data, Path-IO provides complementary predictive value beyond established biomarkers such as PD-L1. Citation Format: Rukhmini Bandyopadhyay, Linghzi Hong, Mihaela Aldea, Shenduo Li, Lodovica Zullo, Frank R. Rojas, Maliazurina B. Saad, Maricel C. Marin, Muhammad Waqas, Jiexin Zhang, Eman Showkatian, Claudio A. Arrechedera, Xiaoyu Han, Yuliya Kitsel, Sherif Ismail, Muhammad Aminu, Bo Zhu, Carol C. Wu, Brett W. Carter, Joe Y Chang, Zhongxing Liao, Maria R. Ghigna, Davide Soldato, Hai T. Tran, Xiuning Le, Tina Cascone, Bingnan Zhang, Haniel A. Araujo, Mehmet Altan, Simon Heeke, David Jaffray, Don L. Gibbons, Ara Vaporciyan, J Jack Lee, Neda Kalhor, Cara Haymaker, Ignacio Wistuba, John V. Heymach, Yanyan Lou, Natalie Vokes, Luisa M. Solis Soto, Jianjun Zhang, Jia Wu. Path-IO: A deep learning pathomics framework for personalized immunotherapy selection and outcome prediction in metastatic non-small cell lung cancer [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 4003.

ABSTRACT Objectives The increasing volume of medical education research necessitates efficient, reliable, and scalable methods for conducting quality appraisals. The Medical Education Research Study Quality Instrument (MERSQI) is a widely used tool, although its manual scoring process remains resource‐intensive. This study evaluated how well large language models (LLMs) appraise medical education research using the MERSQI tool in comparison with human judges. Methods Three LLMs (GPT‐5, Claude Sonnet 4, and Gemini 2.5 Pro) assigned MERSQI domain scores to 1423 medical education research articles. The authors compared AI‐generated scores with human‐generated scores using intraclass correlation coefficients (ICCs) across the six MERSQI domains. They evaluated the agreement between AI‐ and human‐generated MERSQI composite scores using Bland–Altman plots. Results Domain‐level ICC values ranged from fair (0.24) to near perfect (0.81), with the lowest agreement observed in the ‘sampling,’ ‘validity evidence,’ and ‘data analysis’ domains. No single LLM consistently outperformed the others across all domains. Composite score agreement with human ratings was substantial and similar across LLMs (ICC range: 0.65–0.69). GPT‐5 produced slightly lower composite scores than humans, while Claude Sonnet 4 and Gemini 2.5 Pro produced higher scores, with Gemini showing the largest deviation. The Bland–Altman plots for Gemini 2.5 Pro suggested proportional bias, indicating its agreement with human scores varied across the range of study quality. Conclusions These LLMs demonstrated substantial agreement with human raters for MERSQI composite scores, but domain‐level agreement varied. Systematic differences in scoring patterns highlight the need for human oversight and additional calibrations before integrating LLMs into systematic review appraisal workflows.