Citations

Abstract The paradigm in the field of oncolytic virotherapy proposes that tumor cell killing by an oncolytic virus (OV) culminates in the priming of antitumor CD8+ T cells. However, this ignores the impact a highly immunodominant antiviral response against the OV has on the subdominant antitumor response, particularly with regard to the naturally low affinity of tumor antigens to their cognate T-cell receptors. To investigate this experimentally, we first studied the interaction between the two responses in B16 and MC38 tumor models, both expressing ovalbumin (OVA). Contrary to the accepted paradigm, the inflammatory vesicular stomatitis virus (VSV) failed to expand OVA-specific T cells in vivo unless armed with tumor-associated antigen (TAA). In fact, treatment with the control VSV led to the ablation of antitumor T cells and a shift toward dysfunction as determined by the upregulation of markers, including LAG3 and CD39, correlating with lower rates of survival. Combination with anti–PD-1 immune checkpoint blockade therapy significantly improved survival only when VSV expressed TAA. VSV-expressing altered peptide ligand variants of tumor antigens, which evade mechanisms of central tolerance, expanded near self-antigen–specific T cells with the ability to cross-react with tolerized tumor antigens, thus providing an antitumor effector population capable of proliferating in the presence of a potent antiviral response. Taken together, these findings offer a rational approach to OV design which preserves acute antitumor therapy provided by antiviral innate and T-cell responses while promoting the induction of tumor-specific T cells. Significance: Oncolytic viruses can induce an antiviral T-cell response that occludes an antitumor response, challenging the paradigm in the field and elucidating strategies to optimize the efficacy of oncolytic viroimmunotherapy.

ABSTRACT Background and Objectives Surgical management of ipsilateral breast tumor recurrence (IBTR) is often mastectomy. No guidelines specifically address contralateral prophylactic mastectomy (CPM) for this population. We assessed factors associated with CPM at the time of IBTR mastectomy. Methods Patients undergoing mastectomy for IBTR (10/2008‐5/2024) were identified. Demographics, tumor characteristics, and treatment details were assessed. Results Of the 381 eligible IBTR patients, 221 (58.0%) had a unilateral therapeutic mastectomy and 160 (42.0%) underwent mastectomy with CPM. CPM patients were younger ( p < 0.0001), with lower clinical T category tumors ( p < 0.001) and less HR + HER2‐ invasive disease ( p = 0.004). CPM patients were more likely to undergo germline genetic testing (63.1% vs 41.6%, p < 0.0001) and were twice as likely to have immediate breast reconstruction ( p < 0.0001). On multivariable analysis, younger age, HR‐HER2 + IBTR subtype, pathogenic variants known at the time of surgery (relative to negative testing and/or no genetic testing), immediate breast reconstruction, and prior radiation for index cancer were each associated with CPM (all p < 0.05). Conclusion In our series of 381 patients treated with mastectomy for IBTR, 42% also had a CPM. Multiple factors, including age, tumor stage and biology, immediate breast reconstruction, and genetic testing may impact CPM choice.

Abstract Objective To describe the development and implementation of an automated platform for genomic risk prediction that integrates multiple data types. Materials and methods Using the REDCap infrastructure, we constructed the R4 (Recruitment, Results, and Risk Reduction) platform to intake data from clinical sites, partner laboratories, participant surveys, and electronic health record (EHR) data across 13 institutions. Results The R4 Portal successfully integrated data to generate genome-informed risk assessments (GIRAs) across 11 conditions for a 23 840 person cohort. Testing phases and quality control led to network-wide protocols ensuring consistency and accuracy. Discussion As the science of estimating disease risk evolves, standardized and high-throughput methods of collecting and manipulating complex data are required. Platforms should be open-source, modular, and reusable, ensuring flexibility, security, and integration across healthcare environments. Conclusion The electronic MEdical Records and GEnomics (eMERGE) network successfully generated and returned comprehensive risk profiles using logic and data specific to 11 conditions in a secure and semi-automated fashion employing a customized REDCap database.

Abstract Since its introduction in 2002, left atrial appendage closure (LAAC) has rapidly expanded as an alternative to anticoagulation in patients with atrial fibrillation at high risk of stroke. Harmonized processes for data collection, analysis, and reporting in LAAC trials are essential to enhance research quality and improve clinical practice. The Left Atrial Appendage Academic Research Consortium (LAARC) initiative is an independent collaboration of academic research organizations, cardiology and neurology experts, clinical trialists, and regulatory authorities from the USA, Europe, and Asia. The consortium engaged clinical experts, regulators—including the US Food and Drug Administration (FDA), European Notified Bodies, and Japan’s Pharmaceuticals and Medical Devices Agency (PMDA)—and industry leaders to define standardized study elements and endpoints for LAAC trials. Key considerations included alignment with prior Academic Research Consortium initiatives, procedural and mechanistic insights, and clinical relevance. Consensus definitions were proposed for mortality, stroke, bleeding, and device performance, along with composite endpoints for safety and effectiveness. The proposed LAARC consensus definitions aim to standardize endpoint reporting, improve comparability across studies, and support regulatory and clinical trial applications for this evolving therapy through broad dissemination in the peer-reviewed literature.

Donation after circulatory death (DCD) has become a key avenue for expanding the organ donor pool. Many DCD protocols include premortem intravenous heparin to reduce microthrombus formation and preserve graft viability. This presents the plausible risk of premortem bleeding from an intervention that offers no direct therapeutic benefit to the donor. Ethical concerns and moral distress can arise when potential donors have a catastrophic intracranial hemorrhage with ongoing physiological concern for bleeding risk that is not survivable, but do not currently meet brain death criteria. While prior analyses describe premortem interventions in DCD in conceptual or protocol-driven terms, this case report considers the ethical concerns inherent in premortem heparin administration and illustrates how multidisciplinary teams can operationalize ethical safeguards, including transparent communication, attention to surrogate understanding, and separation of end-of-life decisions from donation-related interventions, to mitigate concerns and minimize moral distress. These issues are directly relevant to palliative care clinicians, who frequently support families during end-of-life crises and when there is consideration for organ donation. Through a multidisciplinary and ethically guided approach, this case illustrates how collaborative decision-making and transparent communication can uphold both donor dignity and transplant integrity.

566 Background: Endocrine therapy (ET) resistance (ETR) remains a primary challenge in ER+ breast cancer. Analyzing pretreatment tumor transcriptomes across trials with early response endpoints can reveal shared and specific ETR signatures. This study utilizes baseline RNA data from the Phase III ALTERNATE trial (Alliance A011106, NCT01953588; Anastrozole [A], Fulvestrant [F], or AF) and the ACOSOG Z1031B trial (NCT00824941) to identify predictors of early Ki67 response in postmenopausal ER+/HER2– patients. Methods: ETR was defined as week-4 on-treatment Ki67 >10%. Baseline gene expression from ALTERNATE was analyzed to identify differentially expressed (DE) genes (Wilcoxon test, P<0.05) and Hallmark pathways associated with ETR, both across and within individual treatment arms. Feature selection was performed using mixOmics. A Pan-Endocrine Therapy Signature (PETS) was developed by uniting DE genes identified across all three ALTERNATE arms and Z1031B. All statistical analyses were conducted in R (P<0.05). Results: Overall ETR rate in the ALTERNATE RNA-seq cohort (n=733) was 26%. ETR was associated with high Risk of Recurrence (ROR), Oncotype RS, and Mammaprint scores (calculated from RNA-seq data in research setting). In luminal tumors (n=649), ETR was linked to chr 3q13.33, 8q24.13, and 20q13.12 cytoband upregulation and 17q21, 18q23, 3p21.1, and 10q24.32 cytoband downregulation. ETR tumors showed T-cell, E2F target, and interferon-γ enrichment; sensitive tumors favored early estrogen response and muscle differentiation. At individual gene level, high MYBL2 , PIF1 , TROAP and with low HJURP predicted ETR across all samples (AUC>0.70). A deep learning model using all protein-coding genes achieved AUC 0.82 (training) and 0.79 (test) in predicting ETR. Cross-trial integration identified ETR-associated PETS, enriched for genomic instability. PETS performed comparably to established signatures and strongly correlated with MYBL2 signature (r=0.93). Top ETR predictors were MYBL2 , AURKB , and EME1 for Arm A and IL4I1 , TNFAIP6 , and ANLN for Arm AF. AF-resistant tumors were enriched for systemic lupus and RIG-I–like receptor signaling; sensitive tumors favored PI3K–AKT, EGFR TKI resistance, AMPK, and insulin signaling. Conclusions: Baseline transcriptomics identify shared and therapy-specific ETR markers. The 15-gene PETS defines a convergent resistance signature, performing similar to established signatures in predicting ETR and correlating with MYBL2. Enrichment of cell-cycle and immune pathways in resistant tumors may suggest patient stratification approach for alternative or combinatorial strategies to overcome early ETR in ER+ breast cancer. Acknowledgement: https://acknowledgments.alliancefound.org. Support: U10CA180821, U10CA180882, U24CA1. Clinical trial information: NCT01953588 .

Abstract Rationale In fungal endemic regions, differentiating benign from malignant pulmonary nodules on CT scans remains a major diagnostic challenge. Coccidioides nodules frequently resemble malignant lesions on CT and PET imaging, presenting significant challenges for differentiation, even among radiologists. Consequently, the benign biopsy and resection rate, especially in the Southwestern US, remains high. We previously developed [DOI:10.1371/journal.pone.0196910] and externally validated [DOI:10.1183/13993003.02485-2020] the Benign versus aggRessive nODule Evaluation using Radiomic Stratification (BRODERS) machine learning (ML) classifier to non-invasively distinguish between benign and malignant pulmonary nodules with an AUC of 0.87. However, its performance in certain benign nodules with malignant surface and texture characteristics like in coccidioidomycosis had a suboptimal AUC of 0.65. In this study, we redesigned our radiomics model by including numerous clinical features. Methods In this retrospective study, 113 pulmonary nodules (66 Coccidioides and 47 adenocarcinoma spectrum) detected on CTs with ground truth pathology diagnosis were identified by screening for key terms like “nodule”, “cocci”, and “adenocarcinoma” from both CT and pathology reports using a large language model trained on clinical textual data (Illuminate®). Clinical features such as demographics, smoking history, and laboratory studies were extracted by trained clinicians from the electronic medical record. All nodules were segmented and radiomic features extracted using a semi-automated seed growing algorithm previously described [DOI: 10.1097/JTO.0b013e3182843721]. The database was randomly split into an 80%-20% development and hold-out set prior to ML modeling. Feature selection for variables was conducted utilizing Elastic Net Regression, which provides a balanced approach between Lasso and Ridge regression. A logistic regression model was developed using the selected features and ground truth information of the training cohort. The true performance of the model was tested on the hold-out set. Results 160 radiomics and 22 clinical features were extracted. Elastic Net regression identified 44 features that contributed the most. Some of these clinical features include presence of satellite lesions, smoking history, and presence of positive IgG coccidiomycosis serology. The AUC of the ROC curve for the hold-out test set was 0.86. Conclusions The clinical features of significance that emerged during multimodal modeling are explainable. Designing explainable ML models is important, especially in the Southwestern US where Coccidioides nodules can appear to be malignant to both human experts and ML models. Although coccidioidomycosis serologies alone lack sensitivity and specificity to diagnose Coccidioides nodules, integrating them with a multimodal ML model can improve differentiation between benign Coccidioides and malignant adenocarcinoma nodules. This abstract is funded by: None

Abstract In 2011, a workgroup of the Alzheimer's Association Research Roundtable introduced recommendations for detecting and monitoring amyloid‐related imaging abnormalities (ARIA) in Alzheimer's disease (AD) clinical trials. Since then, anti‐amyloid immunotherapies have received regulatory approval for AD treatment and are beginning to enter clinical practice, underscoring the importance of informing healthcare providers, researchers, and patients about ARIA's implications in real‐world settings. In response, the Alzheimer's Association convened a new workgroup to review current knowledge of ARIA, including underlying mechanisms, clinical presentations, associated risk factors, mitigation strategies, radiologic detection methods, patients’ perspectives in treatment decision‐making, and outstanding challenges. Here, we outline key insights from this workgroup, highlighting that effective ARIA detection and monitoring in clinical practice requires adherence to robust protocols to mitigate risks and enhance patient safety. Limited availability of clinical and pathologic data on predictors of symptomatic and severe ARIA underscores the importance of continued real‐world data collection.