Citations

ABSTRACT Background The brain–heart axis is central to vascular health, yet no imaging biomarkers capture integrated dysfunction across neural and coronary microvascular networks. Although coronary microvascular dysfunction links to cognitive decline, neural correlates connecting cerebral efficiency with coronary physiology remain unclear. Objectives To determine whether the Unified Structural and Functional Connectivity (USFC)—a multimodal magnetic resonance imaging (MRI) “traffic map” of brain network efficiency—predicts coronary endothelial function and cognition in women with ischemia and no obstructive coronary artery disease (INOCA). Methods Thirty‐three women with suspected INOCA from the Women's Ischemia Syndrome Evaluation (WISE) study (NCT03876223) underwent invasive coronary function testing, cardiac MRI, cognitive evaluation, and multimodal brain MRI. USFC, structural connectivity (SC), and functional connectivity (FC) were computed for predefined 10 backbone pathways. Support vector regression and logistic classification assessed predictive performance. Results USFC explained 16%–20% more variance in coronary endothelial function, myocardial perfusion reserve, and cognition than SC or FC alone ( p < 0.05). Connectivity between the left caudate–superior medial orbital gyrus and right calcarine–inferior occipital gyrus emerged as robust predictors of crystallized cognition ( r = –0.78, p FDR < 0.05) and coronary endothelial function ( r = 0.70, p FDR < 0.05), respectively. USFC also best discriminated low versus high coronary blood flow (area under the ROC curve [AUC]: USFC 0.622 vs. SC 0.349 and FC 0.425; p < 0.05). Conclusions USFC identifies neuro–cardiac pathways linking cerebral efficiency with coronary endothelial function. These results introduce a sensitive biomarker of systemic vulnerability, highlighting occipital and frontostriatal pathways as shared substrates of dysfunction. USFC offers a mechanistic framework for detecting vascular risk across metabolically demanding tissues. Trial Registration ClinicalTrials.gov identifier: NCT03876223

Abstract Background An accurate discrete spot scanning (DSS) beam delivery time (BDT) model is essential, particularly as 4D dynamic dose accumulation calculations rely on machine‐specific time models for reliable interplay evaluation. Purpose To derive a BDT model for a synchrotron‐based Hitachi Probeat PBS proton system from oscilloscope measurements of key BDT structures, including multi‐energy extraction (MEE) characteristics. Methods Key BDT structures were characterized using oscilloscope measurements, supplemented by delivery log files where appropriate. Spot‐to‐spot scanning trajectories were visualized using a CROSSmini 2D strip ionization chamber (Liverage Biomedical Inc, Taiwan) via the dose‐driven continuous scanning (DDCS) mode. The MEE design and MEE charge recapture efficiencies were also characterized. In addition, the impact of the number of delivered spots on both the extractable fraction of charge and MEE recapture efficiency was investigated. Results An improved synchrotron‐based BDT model was derived that incorporates dead times between radiofrequency knockout (RFK) and high‐speed steering magnet (HSST) signals. Spot‐to‐spot movement follows a “hockey‐stick”‐like behavior where the effective spot‐to‐spot move time is governed by the slower axis. An energy‐dependent variation in the number of MEE layers deliverable per spill was also observed, with fewer MEE layers per spill (1–3) was observed in low and high energy ranges compared to the mid‐energy range (4–5), deviating from the nominal vendor value of five MEE layers per spill per block. A reduction in the extractable fraction of charge was observed with an increasing number of spots per spill, while increasing the number of delivered spots in the preceding layer significantly reduced the MEE recapture efficiency. Conclusion MEE characteristics should be incorporated into synchrotron‐based time models for accurate beam delivery modeling. The methodology presented here also provides a framework for centers seeking to derive machine‐specific delivery time models for their proton systems.

Abstract Early-phase clinical trials of BTK degraders in relapsed/refractory CLL have shown response rates of over 80%, including in patients with BTK mutations that confer resistance to conventional BTK inhibitors (BTKi). The molecular basis of resistance to BTK degraders is unknown and, given the promise of this class of drugs, would hold implications for therapy sequencing and the design of next-generation BTK degraders. To this end we identified five patients at Memorial Sloan Kettering who developed progressive disease while being treated on the phase 1 trials of zelebrudomide (NCT04830137, 4 patients) or bexobrutideg (NCT05131022, 1 patient). In two patients, one treated with zelebrudomide and the other with bexobrutideg, a pre-existing BTK A428D mutation had expanded at progression, rising from &lt;1.5% at the start of treatment to &gt;25%. In both cases, the increase in frequency of BTK A428D coincided with the loss of BTK degradation in peripheral blood B cells. No mutation in cereblon, the ligase utilized by these BTK degraders, was detected. To validate these clinical findings we generated wild-type and BTK A428D knock-in single-cell clones in TMD8 and HBL1 cells. BTK A428D conferred resistance to BTKi, including ibrutinib, acalabrutinib, zanubrutinib, and pirtobrutinib, as well as the clinical-stage BTK degraders zelebrudomide, bexobrutideg, BGB-16673, and AbbVie-1 (Compound 1, WO 2023183811). BTK A428D’s pan-resistance is unique compared to previously studied BTKi resistance mutations which remain susceptible to at least one BTKi and all clinical-stage BTK degraders tested. To understand how BTK A428D confers pan-resistance to BTKi and BTK degraders we conducted surface plasmon resonance studies which demonstrated that BTKi and all four clinical-stage BTK degraders tested are unable to bind BTK A428D. We next solved the crystal structure of apo BTK A428D to 1.8Å, which revealed that the mutant aspartate, which is larger and negatively charged compared to the wild-type alanine, sterically blocks BTKi and degraders from accessing residues in the hinge region of the kinase that are required for drug binding. We next asked why BTK A428D, despite conferring pan-resistance, has only been detected in 1.1% of patients treated with BTK inhibitors. Intriguingly, we noted that in one of our index patients, the frequency of BTK A428D declined and became undetectable after the withdrawal of zelebrudomide, suggesting a fitness defect. To assess this hypothesis, we compared the proliferation rates of five TMD8 wild-type and five BTK A428D single-cell clones; the BTK A428D clones grew more slowly and were consistently outcompeted in mixing studies. In summary, low-frequency BTK A428D mutations can expand under the selection pressure of BTK degraders and confer clinical resistance by preventing drug binding. The mutation, however, is outcompeted in the absence of BTK-targeted therapies. These findings provide rationale for the investigation of combination therapies and the development of next-generation BTK degraders capable of circumventing BTK A428D. Citation Format: Quinlan Sievers, Hao Lu, Allison Cool, Stefan Grajewski, Tim Kong, Mark Noviski, Xiaoli Mi, Emel Ficici, Ratul Mukerji, James Iuliano, Jordan Ye, Mateo Sanchez Garcia de los Rios, Hugo Bousquet, Sarah Whelan, May Tan, Christopher Phelps, Jahan Raman, Ryan Notti, Lizbeth Mendoza Navarrete, Samantha Sekeres, Eria Lamkin, Eduardo Bravo, Mark Ewalt, Prioty Islam, Anthony Mato, Lindsey Roeker, Justin Taylor, Meghan Thompson, Gwenn Hansen, Omar Abdel-Wahab. BTK A428D is a recurrent pan-resistance mutation to BTK degraders in clinical trials [abstract]. In: Proceedings of the Fifth AACR International Meeting on Advances in Malignant Lymphoma: From Discovery to Clinical Impact; 2026 Jun 24-27; Philadelphia, PA. Philadelphia (PA): AACR; Blood Cancer Discov 2026;7(3_Suppl):Abstract nr PR004.

21 Background: Large language models (LLMs) are becoming an increasing source of medical information for both patients and providers. Clinical decision making is occasionally complex and may use factors outside of traditional guidelines. We conducted a comparative performance evaluation of frontier models on standardized genitourinary (GU) oncology knowledge assessments derived from the American Society of Clinical Oncology Self-Evaluation Program 2025 (ASCO SEP 2025) in addition to challenging questions developed by GU oncology experts. Methods: Three frontier LLMs were selected: ChatGPT-5.2 (OpenAI, San Francisco, CA, USA), Claude Opus 4.6 (Anthropic, San Francisco, CA, USA), and Gemini 3 Pro (Google DeepMind, Mountain View, CA, USA). Model selection was based on publicly reported performance on “Humanity’s Last Exam,” a benchmark of advanced scientific reasoning (Scale AI leaderboard Jan-Feb 2026). The OpenEvidence (OpenEvidence Inc., MA, USA) search engine was also selected for evaluation. All models were asked 145 ASCO SEP 2025 GU oncology questions and 12 real world challenging scenarios using an uniform prompt. Subject matter experts peer reviewed LLM generated responses to challenging questions and credit was given only for correct rationale and option selection. Results: All models demonstrated high and comparable accuracy on Standardized knowledge assessment, with Gemini 3 Pro leading numerically at 97.24% (95% CI, 93.1% – 99.0%), OpenEvidence followed with an accuracy of 94.48% (95% CI, 89.5% – 97.3%), while Chat GPT -5.2 and Claude OPUS 4.6 scored 92.41% (95%, CI 86.9% – 95.7%) and 79.31% (95% CI, 71.9% – 85.1%) respectively. On challenging real world scenarios, ChatGPT-5.2 demonstrated the highest numerical accuracy at 100% (95% CI, 75.8% – 100%), Gemini 3 Pro and Open Evidence scored 75.0% (95% CI, 46.8%–91.1%) each, followed by Claude Opus 4.6 at 66.7% (39.1–86.3). Conclusions: All current frontier AI models appear to provide accurate medical information and rationale on a large majority of guideline based medical knowledge assessments for GU oncology. Frontier AI models also appear to have emerging competence in highly challenging real-world scenarios. AI based tools may become a valuable and reliable tool for patients and providers. Comparative LLM performance. Model ASCO SEP 2025 % (95% CI) Real World Scenarios % (95% CI) Gemini 3 Pro 97.24% (93.1–99.0) 75.0% (46.8–91.1) OpenEvidence 94.48% (89.5–97.3) 75.0% (46.8–91.1) ChatGPT-5.2 92.41% (86.9–95.7) 100% (75.8–100) Claude Opus 4.6 79.31% (71.9–85.1) 66.67% (39.1–86.3)

Abstract Studies have reported conflicting findings regarding the contribution of germline variants or somatic genomic drivers to racial disparities in multiple myeloma. To comprehensively investigate somatic drivers in relation to inherited genetics in multiple myeloma, we combined newly sequenced whole-genome sequencing data with publicly available datasets (total n = 1,286). Overall, we did not identify germline or somatic genomic differences that explain the different risk of developing multiple myeloma between patients with genetic similarity to African (AFR) or European (EUR) reference populations. A difference in the detectability and timing of APOBEC-associated and germinal center mutational activity was observed. Integrating epidemiologic data and mutational signature-based temporal estimates, we challenge the assumption that individuals in the AFR group develop multiple myeloma at a younger age. Finally, we demonstrate that, with equal access to efficacious therapies, patients in the AFR and EUR groups have equivalent clinical outcomes. Significance: Multiple myeloma is reported to occur at higher rates in individuals who self-identify as non-Hispanic Black. In this large dataset, genomic drivers occur at the same rate among ancestry groups, except for APOBEC mutagenesis. With equivalent therapy, clinical outcomes did not differ for patients grouped by genetic ancestry similarity.

The ever-changing landscape of cardiology necessitates continuous education of healthcare providers. Case-based learning (CBL) provides an inquiry-based form of learning where learners apply knowledge to realistic clinical scenarios under instructor guidance. However, current case-based learning curricula are limited to representing 3-dimensional content in 2-dimensions with distinct modalities utilized for various data sources. Virtual reality (VR) shows promise for CBL given it provides an immersive and interactive 3-dimensional medium in which multi-source content can be incorporated into a unified learning experience, potentially translating to improved learning efficiency and lower cognitive load. With VR case-based learning, learners can view medical imaging in 3-dimensions or seamlessly interact with various 2-dimensional images within one unified medium. Further, additional content including simulated patients or procedures can be incorporated with a higher level of fidelity. Herein, we summarize the current literature exploring VR-CBL, demonstrate a proof-of-concept VR application, and review the implications for cardiology education. The proof-of-concept exemplifies many of the key advantages unique to VR-CBL, such as medical imaging rendered in 3-dimensions, integration of multiple imaging sources, and novel interaction methodologies. Given the importance and rapid evolution of ongoing medical education, VR-CBL, with its immersive and engaging nature, appears to be a promising form of instruction that may serve a larger role in continuing medical education initiatives.

Objective Glaucoma and cancer are debilitating diseases with increasing global prevalence and morbidity. However, current literature lacks a systematic consolidation of their relationships and interactions. This systematic review aims to (A) summarize the association between intraocular/non-ocular cancers and their treatments with the prevalence and etiology of glaucoma, (B) explore glaucoma as a risk factor for complications or poor prognosis for intraocular cancer, and (C) explore the management of glaucoma secondary to cancer therapies. Methods A systematic search was performed from inception to July 2025 across three databases with variants of ‘cancer’ and ‘glaucoma’ as search terms. Findings were narratively synthesized. Results Of 8,103 identified studies, 44 were included in this review. Eighteen studies reported increased glaucoma prevalence amongst intraocular and non-ocular cancer patients. Fifteen studies suggested increased risk of regional invasion, metastases, and high-risk histopathologies when intraocular cancers co-occurred with glaucoma. Corticosteroids in cancer therapy have been linked to intraocular pressure (IOP) elevation, while androgen deprivation therapy was associated with lowered glaucoma risk. Conclusion Given the relationship between glaucoma and intraocular/non-ocular cancers, strategies to monitor and manage glaucoma risk following cancer diagnosis and/or therapy may be considered. Glaucoma may be an early sign of complications in intraocular cancers.

Abstract Purpose Modeling relative biological effectiveness (RBE) is central to carbon ion radiotherapy treatment planning. The modified Microdosimetric Kinetic Model (mMKM) is a clinically established RBE framework that has guided treatment protocols at many existing carbon centers, while the Mayo Clinic Florida Microdosimetric Kinetic Model (MCF MKM) is a recently developed alternative. This work aims to implement the MCF MKM in the open‐source treatment planning system matRad and to quantitatively compare its RBE‐weighted dose predictions with those of the clinically established mMKM using identical physical dose distributions across multiple disease sites. These findings will help assess their dosimetric equivalence and inform protocol development for carbon ion radiotherapy at MCF. Methods Monte Carlo simulations of the MCF carbon beamline were performed to generate physical (IDD, LET, lateral spread) and biological base data for integration of each RBE model into matRad . Treatment plans were generated for six patients, each corresponding to a different disease site, using clinical beam configurations with carbon‐reference dose prescriptions, and plans were optimized using the MCF MKM. To isolate differences attributable solely to the RBE model, the resulting physical dose distributions were held fixed and RBE‐weighted doses were recalculated using the mMKM. Dose volume histogram (DVH) metrics and spatial dose‐difference maps were used to compare target coverage and organ‐at‐risk doses between the two models. Results Across patient cases, RBE‐weighted dose distributions from MCF MKM and mMKM showed strong agreement. Differences in target coverage were small, with CTV D95% differing by less than 1.6% across all disease sites and maximum target dose differences not exceeding 0.88%. Organ‐at‐risk dose deviations were limited, with differences of 3.0% or less across evaluated DVH metrics. Spatial dose‐difference maps showed that the largest discrepancies occurred in regions of steep dose gradients near target to organ‐at‐risk interfaces, while overall dose conformity and plan quality remained comparable between the two models. Conclusions: This study served as the first systematic model comparison of the MCF MKM and mMKM within a treatment planning environment. These findings suggest that the MCF MKM and mMKM produce dosimetrically consistent RBE‐weighted dose predictions under realistic planning conditions using carbon‐reference parameters. Accordingly, the fractionation schemes developed from years of clinical experience with mMKM implementations may serve as a practical foundation for protocol development at MCF.

TPS7100 Background: Bruton tyrosine kinase covalent inhibitors (cBTKi) have been transformative in CLL treatment. Despite achieving extended remissions, in most patients disease still relapses despite cBTKi therapy, often associated with BTK C481 mutations. Non-covalent BTKi (ncBTKi), including pirtobrutinib, can overcome this resistance (Sharman et al. ASCO . 2025). Pirtobrutinib received FDA approval in Dec 2025, for R/R CLL/CLL patients pretreated with cBTKi. Rocbrutinib (LP-168) is a highly selective next-generation BTKi that can covalently bind wild-type and gatekeeper mutations (T474X, commonly seen in patients relapsed after pirtobrutinib) and non-covalently target C481-mutated BTK , with preclinical efficacy in treatment-naïve and BTKi-resistant CLL (Gordon et al. IWCLL . 2025). In the rocbrutinib phase 1 trial (NCT04775745), encouraging safety and efficacy have been observed in CLL patients with prior exposure to BTKi and/or BCL2 inhibitor (BCL2i) (Woyach et al. Blood. 2025). Presented here is the design of a phase 3 trial aiming to compare the efficacy of rocbrutinib versus pirtobrutinib in cBTKi-pretreated R/R CLL/SLL patients. Methods: ROCKET-CLL (NCT07342478) is a randomized, open-label, multicenter, phase 3 study comparing rocbrutinib (Arm 1) to pirtobrutinib (Arm 2) in R/R CLL/SLL subjects who previously received treatments including covalent BTKi. Eligible subjects must require treatment per 2018 iwCLL criteria, have measurable lesions by CT, an ECOG performance score of 0-2, and adequate hematologic and other organ functions. About 306 subjects will be randomized 1:1 based on stratification factors such as 17p deletion/ TP53 mutation presence, reasons for discontinuing prior cBTKi, prior BCL2i treatment, and geographic region. Participants assigned to each arm will receive rocbrutinib (200 mg QD) or pirtobrutinib (200 mg QD) tablets continuously until disease progression, unacceptable toxicity, withdrawal, or other study discontinuation criteria are met. The primary endpoint is progression-free survival (PFS), assessed by an independent review committee (IRC) per iwCLL 2018 criteria with CLL (Hallek et al. 2018) and Lugano 2014 criteria with SLL (Cheson et al. 2014). Important secondary endpoints include overall survival (OS), time to next treatment (TTNT), event-free survival (EFS), overall response rate (ORR), duration of response (DOR), PFS assessed by investigator (INV), concordance between IRC and INV PFS, safety and tolerability, and population PK. Exploratory endpoints include health-related quality of life and biomarker assessment. Recruitment is ongoing. Clinical trial information: NCT07342478 .