Citations

Abstract The liver is organized into tightly regulated zones with distinct metabolic functions but zonation erodes with age. Cellular senescence contributes to aging and liver diseases, however, its impact on aging biology is ill-defined. As part of The Cellular Senescence Network Consortium, we used multiple spatial transcriptomics approaches (GeoMx, Visium, CosMx) with snRNA-seq to profile senescence signatures, zonation markers, and metabolic pathways in livers from wild-type (WT) mice of multiple ages. We observed a loss of canonical zone signatures in aged mouse livers characterized by “expansion” of midlobular (zone 2) marker gene expression, accompanied by diminished expression of zone 3 marker genes by middle-age (18 months), indicative of loss of cell identity. Multiple analytic approaches identified distinct age-, zone- and sex-specific senescence signatures, which were significantly associated with zonation markers changes. This was recapitulated in Ercc1 mutant models of accelerated senescence, supporting a causal role of senescent cells in liver aging. A “no-zone” hepatocyte-like cluster expanded with age and with the strongest Senescence-Associated Secretory Phenotype (SASP) profile. Gene expression profiles from senescent hepatocytes implicate decreased WNT signaling and increased BMP as contributing to age-related loss of zonation. Together, these data elucidate the role of senescent cells in driving aging biology in non-diseased liver through disruption of cell:cell signaling and the loss of metabolic and cell identity gene expression necessary for hepatocyte function.

Abstract Background Cardiac magnetic resonance elastography (MRE) allows the non-invasive quantification of myocardial stiffness, but long-interval reproducibility data at 3.0 T single-frequency 140-Hz remain limited. Purpose To evaluate the short- and long-interval reproducibility of 3.0 T cardiac MRE for left ventricular (LV) myocardial stiffness across healthy volunteers and patients with conditions associated with elevated myocardial stiffness. Materials and Methods This prospective single-center study included 69 participants: 60 healthy volunteers and 9 with cardiovascular disease (6 with aortic stenosis and 3 with long-standing hypertension). All participants underwent baseline cardiac MRE (scanner: Signa Premier 3.0 T, GE Healthcare, driver: Resoundant), an immediate short-interval rescan (SIR) within 5 minutes, and a long-interval rescan (LIR) after approximately 1 month. Left ventricular myocardial stiffness and octahedral shear strain signal-to-noise ratio were measured independently by 2 readers. Reproducibility was assessed using Bland-Altman analysis, intraclass correlation coefficients (ICCs), within-subject coefficient of variation, standard error of measurement, and smallest detectable change at the 95% confidence (SDC95) level. Results Baseline LV myocardial stiffness was higher in the disease group than in healthy volunteers, 11.73 ± 1.01 kPa and 7.80 ± 0.70 kPa (mean ± SD; P < .001), respectively. Inter-reader agreement was excellent across all timepoints, with ICCs of 0.970 at baseline (95% CI, 0.953-0.982), 0.966 for SIR (95% CI, 0.945-0.979), and 0.962 for LIR (95% CI, 0.939-0.976). Within-reader scan-rescan reproducibility was also excellent. For Reader 1, stiffness ICC was 0.979 for the SIR (95% CI, 0.966-0.987) and 0.957 for the LIR (95% CI, 0.932-0.973), with SDC95 values of 0.616 kPa (95% CI, 0.528-0.741) and 0.869 kPa (95% CI, 0.744-1.044), respectively. Reader 2 showed excellent reproducibility, with ICCs ≤0.943. Conclusions 3D single-frequency 140-Hz cardiac MRE at 3.0 T provides excellent short- and long-interval reproducibility for LV myocardial stiffness quantification. The SDC95 values may help distinguish true longitudinal stiffness changes from measurement variability.

Neuroendocrine neoplasms (NENs) represent a heterogeneous group of malignancies ranging from relatively indolent well-differentiated neuroendocrine tumors (NETs) to more aggressive poorly differentiated neuroendocrine carcinomas (NECs). The advent of somatostatin receptor (SSTR)–targeting therapies, particularly radioligand therapy (RLT), has transformed the therapeutic landscape of NETs. Ongoing efforts are aimed at maximizing the therapeutic benefit of RLT and include retreatment and combination strategies as well as novel α-emitter–based radiopharmaceuticals. The increasing use of molecular profiling has led to the identification of recurrent canonical alterations in pancreatic NETs, including in MEN1 , ATRX / DAXX , PI3K/Akt/mTOR, and angiogenic signaling pathways, facilitating the development of effective targeted therapies. However, despite the addition of newer targeted therapies to the therapeutic armamentarium, resistance remains inevitable and highlights the need for improved predictive markers to inform therapy sequencing and novel biomarker-selected therapies. In addition, the development of novel therapeutic strategies including SSTR antagonists, nonpeptide drug conjugates, and novel radiopharmaceutical constructs highlights the potential of biomarker-informed therapies in this space. By contrast, treatment options for NECs remain limited, with modest therapeutic benefit of platinum-based chemotherapy. The identification of delta-like ligand 3 (DLL3) as a therapeutic target in high-grade NENs has led to the development of novel targeted therapies such as T-cell engagers, which have demonstrated promising activity in early phase clinical trials. In addition, other DLL3-targeting agents such as antibody-drug conjugates, trispecific engagers, and cellular therapies remain under investigation. Collectively, these advances underscore the potential of biomarker-informed treatment approaches to personalize treatment decisions and improve long-term outcomes across the NEN spectrum.

Abstract Poly (ADP-ribose) polymerase (PARP) inhibitors (PARPi) are a mainstay therapy for homologous recombination (HR)-deficient cancers; however, resistance remains a major clinical challenge. Previously, through a genome wide CRISPR screen, we identified ZNF251 haploinsufficiency as a novel driver of PARPi resistance. In BRCA1 -mutant ( BRCA1 mut ) cells, ZNF251 deficiency led to HR hyperactivation, conferring PARPi resistance that could be reversed by RAD51 inhibition. In this study, we further show that ZNF251 deficiency induces replication stress and a pseudo-senescence state, in which cells exhibit molecular and phenotypic markers of senescence while retaining proliferative capacity. Because senomorphic and senolytic therapies can target senescent cells, we tested whether these approaches could overcome PARPi resistance in ZNF251 -deficient breast cancer cells. Critically, targeting this senescence-like state with either senomorphic agents, such as cytokine inhibitors, or senolytic agents, such as BCL-2 and BCL-XL inhibitors, overcame PARPi resistance ex vivo and in vivo . Importantly, pseudo-senescence was also observed in other PARPi-resistant contexts driven by HR hyperactivation, including 53BP1 - and Shieldin -mutant cells, suggesting that it may represent a broader mechanism underlying PARPi resistance in breast cancer. Furthermore, in two olaparib-resistant, BRCA -mutant triple-negative breast cancer organoid models, treatment with DT2216, a BCL-XL–targeting PROTAC, sensitized both models to olaparib. Together, our work defines a novel pathway linking HR hyperactivation, replication stress, and pseudo-senescence, and positions both senomorphic and senolytic therapies as promising strategies to overcome PARPi resistance in BRCA1 mut breast cancer. Highlights ZNF251 deficiency drives PARP inhibitor resistance through HR hyperactivation, replication stress, and pseudo-senescence in BRCA1 -mutant breast cancer. Senomorphic and senolytic therapies overcome PARP inhibitor resistance in vitro , in vivo , and in patient-derived organoid models. Pseudo-senescence represents a shared vulnerability of HR-hyperactivated PARPi-resistant cancers and can be therapeutically targeted.

Abstract One of the most significant unmet clinical challenges in paediatric oncology is the development of novel therapeutic strategies for recurrent medulloblastoma (R-MB). MYC-driven MBs are defined as classically cold tumours with a low incidence of infiltrating immune cells, resulting in a therapeutic challenge. The identification of cell-cell interactions between tumour and immune cells may provide insight into critical intercellular communications and manipulations occurring within the tumour immune microenvironment (TME). We hypothesised that the key cell-cell interactions in MYC-driven primary and recurrent MB may reveal dominant immune-suppressive mechanisms and uncover targetable therapeutic vulnerabilities. Paired primary-recurrent bulk RNA-sequencing data, confirmed myeloid cells as the most infiltrating immune cell type in group3-MB and group4-MB. Comprehensive spatial phenotypic and cell-cell communication analyses corroborated this discovery, validating an increased incidence of macrophages in the matched-recurrent tumours. Subsequently, we used innovative algorithms for 10X MB single-cell data to predict interactions between tumour-cell ligands and immune-cell receptors within the TME; macrophages emerged as the core immune-cells involved in interactions throughout the TMEs, with the most significant ligand-receptor interaction and inflammatory response between MIF and CD74. In-depth immunohistochemistry analyses of primary and recurrent group3 and group4 tumours, and exhaustive tissue microarrays demonstrated expression of CD74 and MIF, with limited expression of CD74 within the brain. To investigate the therapeutic potential of CD74, we developed recurrent, immune competent MYC-driven medulloblastoma mouse models. Comprehensive deconvolution analysis confirmed the TME integrity of our models to mirror that of the human disease. Locoregional delivery and repeat dosing of a bioactive-CD74 peptide demonstrated complete tumour clearance in our immune-competent mouse models of primary and recurrent-MB, demonstrating the significant therapeutic potential of targeting the CD74-MIF axis in MYC-driven primary and recurrent MB. Key therapeutic vulnerabilities within the TME of MYC-driven MB have been identified, highlighting the CD74-MIF axis as a target for next-generation immunotherapies.

TAR DNA binding protein 43 (TDP-43) pathology is a defining pathological hallmark of multiple neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). A major feature of TDP-43 pathology is its nuclear depletion, leading to the aberrant inclusion of cryptic exons during RNA splicing. STMN2 and UNC13A have emerged as prominent TDP-43 splicing targets, but the broader impact of TDP-43–dependent cryptic splicing on neuronal function remains unclear. Here, we report previously unidentified TDP-43 splicing targets critical for membrane excitability and synaptic function, including KALRN , RAP1GAP , SYT7 , and KCNQ2 . Using human stem cell–derived neurons, we showed that TDP-43 reduction induces cryptic splicing and down-regulation of these genes, resulting in impaired excitability and synaptic transmission. In postmortem brains from patients with FTD, these cryptic splicing events occurred selectively in neurons with TDP-43 pathology. Suppressing individual cryptic splicing events using antisense oligonucleotides partially restored neuronal function, and combined targeting almost fully rescued the synaptic deficit caused by TDP-43 loss. Together, our findings provide evidence that cryptic splicing in these synaptic and membrane excitability genes is not only a downstream marker but instead a direct driver of neuronal dysfunction, establishing a mechanistic link between TDP-43 pathology and neurodegeneration in ALS and FTD.

TPS2673 Background: The main challenge for developing CAR T therapies for solid tumors is the lack of targets that distinguish tumor from normal cells, resulting in on-target, off-tumor toxicity. Tmod logic-gated CAR T therapy addresses this challenge by incorporating 2 CARs on the same T cell: an activator targeting a marker on both tumor and normal cells, and a blocker targeting HLA-A*02 that inhibits CAR T activity against normal cells while allowing activation against tumor cells (with HLA-A*02 LOH), improving tumor selectivity and decreasing toxicity. Early safety results from 3 ongoing phase 1/2 clinical trials of logic-gated, Tmod CAR T therapy (EVEREST-1, EVEREST-2, and DENALI-1) have demonstrated manageable safety and tolerability in patients with advanced solid tumors (Grierson et al, SITC , 2024; Ward et al, SITC 2025; Specht et al, SABCS , 2025). Early efficacy results include the first ever reported complete response in a patient with non-small cell lung cancer following treatment with a CAR T-cell therapy (A2B694). A2B543 is an autologous Tmod CAR T therapy that contains the same Tmod construct as A2B694 with an added membrane-tethered IL-12 (memIL12) booster. Specifically, A2B543 is comprised of autologous Tmod cells transduced with 2 lentiviral vectors: one expressing both the HLA-A*02-targeted blocker and the mesothelin-targeted CAR activator; and a second expressing the memIL12 booster. Interleukin 12 (IL-12) is a potent, pro-inflammatory cytokine that plays a crucial role in inducing antitumor immune responses; however, systemic IL-12 can be prohibitively toxic (Jia et al, Front Immunol , 2022). In A2B543, expression of the memIL12 cassette is under the control of an NFAT promoter and is induced during antigen engagement or T cell activation. This inducible memIL12 is designed to reduce the toxicity associated with systemic IL-12 while enhancing the long-term potency and persistence of Tmod (Zhang et al, J Immunother Cancer , 2025). Methods: EVEREST-2 (NCT06051695) is a phase 1/2, open-label, nonrandomized study evaluating the safety and efficacy of A2B543 in adults with recurrent/metastatic mesothelin-expressing cancers with tumor-associated HLA-A*02 LOH, including mesothelioma, colorectal, non-small cell lung, pancreatic, or ovarian cancer. Patients are enrolled through BASECAMP-1 (NCT04981119), a master prescreening study that identifies patients with HLA LOH via next-generation sequencing and cryopreserves leukapheresis product. Upon progression, A2B543 is manufactured and then administered after lymphodepletion. The phase 1 primary objective is to evaluate the safety and tolerability of A2B543 and identify a recommended phase 2 dose (RP2D). The phase 2 primary objective is to assess overall response rate. Clinical trial information: NCT06051695 .

e16017 Background: Pancreatic ductal adenocarcinoma (PDAC) is the most common form of pancreatic cancer and remains one of the deadliest malignancies worldwide, largely due to late-stage diagnosis. Early-stage PDAC is frequently asymptomatic, limiting opportunities for timely detection and intervention. We aimed to develop prognostic models using routinely acquired pre-diagnostic clinical data to identify individuals at high risk for PDAC prior to clinical manifestation. Methods: We conducted a retrospective study of patients treated at Mayo Clinic sites in Rochester, MN; Phoenix, AZ; and Jacksonville, FL between January 2011 and February 2023. The cohort included 1,151 patients with PDAC and 3,745 noncancer controls. CT scans were standardized to axial abdominal acquisitions with slice thickness between 0.8–3.0 mm. Radiology reports were de-identified, normalized, and parsed into structured sections (Indications, Findings, Impression), with an additional pancreas-specific section aggregating pancreas-related sentences. Demographic and clinical variables, e.g., age, sex, race, etc., were encoded using a templated sentence representation. All textual inputs were embedded using a Sentence-BERT model. A multimodal deep learning survival model was developed to integrate CT imaging, radiology report embeddings, and demographic features. The model was trained using a negative log-likelihood loss to estimate patient-specific PDAC risk scores. Model performance was assessed using the concordance index (C-index). Internal validation included patients from MN and AZ sites, while external validation used the FL cohort. Results: We evaluated unimodal (imaging, text, and demographics) and multimodal inputs. Fusion of imaging, radiology report, and demographic features achieved the highest performance, with internal and external validation C-indices of 0.885 (95% CI, 0.856–0.908) and 0.751 (95% CI, 0.719–0.783), respectively. Subgroup analysis by tumor location demonstrated differential performance. For pancreatic head tumors, internal and external C-indices were 0.916 (95% CI, 0.841–0.960) and 0.778 (95% CI, 0.730–0.823). For pancreatic body tumors, C-indices were 0.954 (95% CI, 0.897–0.997) internally and 0.929 (95% CI, 0.897–0.953) externally. For pancreatic tail tumors, internal and external C-indices were 0.903 (95% CI, 0.830–0.974) and 0.789 (95% CI, 0.689–0.874). Conclusions: A multimodal deep survival model leveraging pre-diagnostic CT imaging, radiology report embeddings, and demographic data can effectively identify individuals at high risk for PDAC prior to clinical diagnosis. Differences in performance were observed across pancreatic head, body, and tail tumors. This approach highlights the potential of opportunistic cancer risk stratification from routine imaging to enable earlier detection and targeted intervention strategies.

Abstract Background High‐resolution MRI is essential for accurate diagnosis and treatment planning, but its clinical acquisition is often constrained by long scanning times, which increase patient discomfort and reduce scanner throughput. While super‐resolution (SR) techniques offer a post‐acquisition solution to enhance resolution, existing deep learning approaches face trade‐offs between reconstruction fidelity and computational efficiency, limiting their clinical applicability. Purpose This study aims to develop an efficient and accurate deep learning framework for MRI SR that preserves fine anatomical detail while maintaining low computational overhead, enabling practical integration into clinical workflows. Materials and Methods We propose a novel SR framework based on multi‐head selective state‐space models (MHSSM) integrated with a lightweight channel multilayer perceptron (MLP). The model employs 2D patch extraction with hybrid scanning strategies (vertical, horizontal, and diagonal) to capture long‐range dependencies while mitigating pixel forgetting. Each MambaFormer block combines MHSSM, depthwise convolutions, and gated channel mixing to balance local and global feature representation. The framework was trained and evaluated on two distinct datasets: 7T brain T1 MP2RAGE maps (142 subjects) and 1.5T prostate T2w MRI (334 subjects). Performance was compared against multiple baselines including Bicubic interpolation, GAN‐based (CycleGAN, Pix2pix, SPSR), transformer‐based (SwinIR), Mamba‐based (MambaIR), and diffusion‐based (I 2 SB, Res‐SRDiff) methods. Results The proposed model demonstrated superior performance across all evaluation metrics while maintaining exceptional computational efficiency. On the 7T brain dataset, our method achieved the highest structural similarity (SSIM: ) and peak signal‐to‐noise ratio (PSNR: dB), along with the best perceptual quality scores (LPIPS: ; GMSD: ). These results represented statistically significant improvements over all baselines (), including a 2.1% SSIM gain over SPSR and a 2.4% PSNR improvement over Res‐SRDiff. For the prostate dataset, the model similarly outperformed competing approaches, achieving SSIM of , PSNR of dB, LPIPS of , and GMSD of . Notably, our framework accomplished these results with only 0.9 million parameters and 57 GFLOPs, representing reductions of 99.8% in parameters and 97.5% in computational operations compared to Res‐SRDiff, while also substantially outperforming SwinIR and MambaIR in both accuracy and efficiency metrics. Conclusion The proposed framework provides a computationally efficient yet accurate solution for MRI SR, delivering well‐defined anatomical details and improved perceptual fidelity across anatomically distinct datasets. By significantly reducing computational demands while maintaining state‐of‐the‐art performance, the model offers strong potential for feasibility toward clinical translation and scalable integration into future imaging workflows.