Citations

Abstract Spatial transcriptomics analyses often require coordinating specialized Python and R methods. When analytical intent is translated through ad hoc scripts or unconstrained LLM generated code, workflows can be difficult to inspect, rerun, or compare. We present ChatSpatial, a schema enforced orchestration framework in which an LLM selects versioned tools and parameters rather than executable code. Built on the Model Context Protocol, ChatSpatial integrates 65 methods across 15 analytical categories into 20 typed tools with curated defaults. In two cancer case studies, ChatSpatial coordinated cross ecosystem workflows that recovered the analytical structure and central conclusions of published ovarian cancer and oral squamous cell carcinoma analyses. Across three LLMs, schema enforcement raised parameter validity from 20% to 98% and stabilized method selection. In end to end benchmarks against two code generation alternatives, it yielded higher execution success and output concordance.

Abstract Natural killer (NK) cells represent key effectors of antitumor immunity, yet emerging evidence highlights populations with distinct roles in cancer. Despite such expanded diversity within the NK cell repertoire, we lack an understanding of how this heterogeneity affects immune responses and downstream clinical outcomes. Using single-cell RNA sequencing, we systematically profiled NK cells across cancer and uncovered a dichotomous phenotypic and functional landscape of tumor-infiltrating NK cells shaped by opposing intrinsic signaling programs that drive the expression of IFNG or TGFB1. These divergent programs are associated with distinct transcription factor circuits that integrate cues within the tumor microenvironment and skew NK cells toward proinflammatory or suppressive functions. We found that the capacity for NK cells to engage in either functional direction is intrinsically linked to their phenotypic identity. Canonical NK cells recruited from circulation predominantly directed suppressive TGFB1 signals toward effector CD8+ T cells in tumors. Of note, these subsets exhibited higher TGFB1 expression than intratumoral myeloid cells across tumor types. In contrast, a tissue-resident (TR) adaptive subset exhibited exclusively proinflammatory IFNG-driven profiles and was associated with prolonged survival in both primary and metastatic tumor settings. Moreover, these TR adaptive NK cells, but not other subsets, were linked to response to immune checkpoint blockade. Collectively, our study reveals a previously unrecognized regulatory axis in NK cells that shapes NK cell diversity and augments broader antitumor immune responses.

Abstract Metabolic reprogramming is a fundamental determinant of immune cell fate and effector function during inflammation, yet how distinct metabolic pathways regulate pathogenic lung macrophage activity and contribute to fibrotic remodeling remains incompletely understood. Here, we integrated single-cell RNA sequencing, lineage-specific genetic mouse models, and fate-mapping approaches to define the metabolic pathways that regulate the pro-fibrotic activity of lung macrophages. We found that such cells undergo pronounced aerobic glycolysis in the bleomycin-induced mouse model of pulmonary fibrosis. Myeloid cell-specific depletion of lactate dehydrogenase A (Ldha), but not of mitochondrial pyruvate carrier (Mpc), markedly attenuated lung fibrosis and improved survival. Mechanistically, enhanced glycolytic flux increased lactate production, which promoted histone lysine lactylation and facilitated Arginase 1 (Arg1) expression in lung macrophages, thereby driving fibrotic progression. Using monocyte fate-mapping, we further demonstrated that Arg1 expression is largely restricted to recruited monocyte-derived macrophages rather than lung-resident macrophages. Notably, selective deletion of Ldha in granulocyte-monocyte progenitors and their progeny was sufficient to suppress Arg1 expression and reduce fibrosis severity. In human lung samples, we observed significantly elevated expression of ARG1 and key glycolytic enzymes in patients with idiopathic pulmonary fibrosis, despite species-specific differences in the immune cell types expressing ARG1. Together, these findings identify an aerobic glycolysis–lactate–histone lactylation axis that regulates pro-fibrotic myeloid cell function and represents a potential therapeutic target in pulmonary fibrosis.

ABSTRACT Meningiomas are the most common primary brain tumors and, despite their benign reputation, often behave aggressively. Meningiomas are morphologically heterogeneous, yet the full significance of their histologic diversity is unclear. This is in large part because many features are not readily quantifiable by traditional observer-based light microscopy. Molecular testing improves prognostic stratification, but is not universally accessible. We therefore sought to determine whether an artificial intelligence (AI)-trained program could predict specific genomic and epigenomic patterns in meningiomas, and whether it could extract more prognostic information out of standard hematoxylin and eosin (H&E) histopathology than the current WHO classification. To do this, we developed Morphologic Set Enrichment (MSE), an interpretable computational pathology framework that quantifies statistical enrichment of morphologic patterns, cells, and tissue architecture from H&E whole-slide images. The MSE meningioma histology program was able to accurately predict DNA methylation subtypes and concurrent chromosome 1p/22q losses, in the process identifying specific morphologic patterns associated with key genomic and epigenomic alterations. It also added prognostic value independent of standard clinical and pathological variables. These results demonstrate that AI-based quantitative morphologic profiling can capture clinically and biologically relevant information that redefines risk stratification for meningiomas, incorporating histological information not included in existing grading schemes.

Abstract Polycomb group (PcG) proteins form the Polycomb Repressive Complexes 1 and 2 (PRC1/2), which regulate gene expression through histone modifications. Their dysregulation is implicated in cancer. Genomic analysis of prostate cancer datasets revealed a striking divergence: PRC1 genes are frequently amplified in metastatic disease (SU2C), correlating with poor survival, higher tumor mutational burden (TMB), and enhanced androgen receptor (AR) signaling. In contrast, primary prostate cancer (PRAD) is characterized by recurrent deletion of the 3p13 locus, which harbors the gene for the non-canonical PRC1 subunit RYBP. Transcriptomic analysis indicated that RYBP loss derepresses gene expression, consistent with its role in transcriptional repression. Functionally, RYBP expression positively correlates with AR activity and negatively associates with neuroendocrine (NEPC) differentiation. Spatial and single-cell transcriptomics further show that RYBP is enriched in normal epithelial cells but downregulated in tumor cells. Besides, tumors with lower RYBP expression exhibit an immunosuppressive microenvironment, characterized by increased regulatory T cells. Collectively, these findings position RYBP deletion not only as a novel DNA-based biomarker for primary prostate cancer but also as a pivotal epigenetic deregulation event that coordinates oncogenic signaling and lineage plasticity.

Abstract Objective To investigate the ability of artificial intelligence‐enabled electrocardiogram (AI‐ECG) atrial fibrillation (AF) prediction model output and delta age, defined as AI‐estimated age minus chronological age, to predict adverse vascular outcomes in patients with migraine. Background Migraine, especially migraine with aura, is associated with an increased risk of cardiovascular events. Using normal sinus rhythm ECG, our institution has developed AI‐ECG models that predict the probability of AF and estimate patients' age, and from that, delta age (estimated age minus actual age) has been shown to reflect cardiovascular health and physiologic aging. Methods We conducted a retrospective cohort study that included adult patients with migraine who had at least one digital, standard 12‐lead ECG from 2000 to 2020 at any Mayo Clinic site. The first normal sinus rhythm ECG was defined as the index ECG, and those with adverse vascular events or AF prior to, or within 3 days after, index ECG were excluded. AI‐ECG AF prediction model output and delta age were obtained from the index ECG. The primary outcome was a composite endpoint of adverse vascular events that included acute myocardial infarction, acute ischemic stroke, venous thromboembolism, and death. New‐onset AF was also recorded as a secondary outcome. Results A total of 29,928 patients were included. The mean age was 44.3 ± 14.5 years, and the median follow‐up time was 54 months. During the study period, 4662 (15.6%) patients had adverse vascular events, and 1384 (4.6%) patients developed new‐onset AF. A higher AF prediction model output was associated with a higher risk of adverse vascular outcomes (adjusted hazard ratio [aHR]:1.15 [95% confidence interval: 1.12 to 1.18] for every 10% increase in prediction model output), as was a larger delta age (aHR:1.16 [1.12 to 1.21] for every 10‐year increase in delta age). Moreover, AI‐ECG AF prediction model outputs of ≥1% (aHR: 1.45 [1.36 to 1.54]) and delta age ≥−1 years (aHR: 1.09 [1.02 to 1.16]) were found to be the optimal cutoffs to dichotomize patients into high‐risk and low‐risk groups for adverse vascular events. Higher AI‐ECG AF prediction model output at baseline also predicted a higher likelihood of having new‐onset AF during the study period (aHR: 1.31 [1.26 to 1.37]), and the development of new‐onset AF was associated with a higher risk of adverse vascular events (aHR: 2.43 [2.17 to 2.73]). Conclusions For patients with migraine, a higher AI‐ECG AF prediction model output and delta age are predictive of adverse vascular outcomes. A higher AF prediction model output is also predictive of future AF development. Furthermore, we identified the cutoffs for both AI‐ECG models that can distinguish patients between high‐ and low‐risk groups. Our results showed that AI‐ECG models could inform clinical practice and identify patients with migraine at risk for future adverse vascular events.

Abstract Background: The Breast Cancer Index (BCI) is clinically validated to stratify patients with hormone receptor-positive (HR+), early-stage BC based on the risk of overall (0-10 years [y]) and late (5-10 y) distant recurrence (DR) with N0/N1 lymph node involvement. Validation of BCI in patients with HR+ HER2+ BC has been limited, especially in patients treated with adjuvant trastuzumab-based chemotherapy. The HOXB13 and IL17BR genes constitute the predictive component of BCI and are known to exhibit an ER-dependent correlation pattern with HER2. We evaluated the prognostic performance of BCI in patients with HR+ HER2+ disease enrolled in the NCCTG N9831 (Alliance) trial treated with the combination of chemotherapy and trastuzumab. Methods: All available tumor specimens from patients with HR+ HER2+ N0/N1 BC in arms B (sequential trastuzumab) and C (concurrent trastuzumab) of the trial were included. BCI testing was performed blinded to clinical outcomes. The primary endpoint was distant recurrence-free interval (DRFI). BCI (N0) and BCIN+ (N1) risk groups were calculated based on prespecified cut points. Kaplan-Meier analysis and log-rank testing were used to assess the prognostic significance of BCI/BCIN+ risk groups based on DR. Cox proportional hazard models with and without clinical covariates were used to estimate hazard ratios (HRs), and the associated 95% confidence intervals (CIs). Results: A total of 454 HR+ patients (234 arm B, 220 arm C), including 69 N0 and 385 N1 patients, were analyzed (median age was 47 y, 85% N1, 55% T2, 63% G3). BCIN+ scores were available for 378 of the 385 N1 patients due to missing tumor grade for 7 patients. The median follow-up was 12.7 years. In the overall cohort, BCI/BCIN+ stratified 51 (11%), 24 (5%) and 372 (83%) patients as low-, intermediate- and high-risk with 10-y DRFI rates of 96% (95% CI: 84-99%), 100% (100-100%) and 89% (86-92%), respectively. Since the low and intermediate risk groups had few patients and a similar risk of 10-y DRFI, they are combined into a single low-risk group hereafter. Thus, for overall 10-y DR, 75 (17%) and 372 (83%) of patients were classified by BCI/BCIN+ as low and high risk with 10-y DRFI rates of 97% (95% CI: 89-99%) and 89% (95% CI: 86-92%), respectively (HR=4.50, 95% CI: 1.09-18.61; p = 0.038). BCI/BCIN+ remained statistically significant in the multivariable analysis, adjusting for age, treatment, and nodal status (HR=7.54, 95% CI: 1.34-42.64; p = 0.022). For 69 N0 patients, BCI stratified 54 (78%) and 15 (22%) patients as low and high risk with 10-y DRFI rates of 100% (95% CI, 100-100%) and 77% (95% CI, 44-92%), respectively (p = 0.012). For 378 N1 patients, BCIN+ stratified 21 (6%) and 357 (94%) of patients as low and high risk with 10-y DRFI of 90% (95% CI, 66-97%) and 90% (95% CI, 86-93%), respectively (p = 0.07). Of the 21 patients classified as BCIN+ low-risk, two had BCIN+ scores of 6.75 and 6.73 in close proximity to the cut point (6.93) and experienced DR after 5 years, resulting in the same 10-y DRFI for both BCIN+ low- and high-risk patients. Conclusions: BCI is significantly prognostic for the risk of overall DR (0-10 y) in patients with HR+ HER2+ disease treated with adjuvant trastuzumab-based chemotherapy. Optimization of BCI cut points may further improve prognostic performance in HR+ HER2+. Additional validation in other HR+ HER2+ cohorts treated in the modern setting with anti-HER2-based therapy is warranted, given the small sample size of N0 patients and relatively low DR rate in the late recurrence cohort. In summary, these results provide further evidence of BCI’s utility to support clinical decision-making for adjuvant endocrine therapies for HR+ HER2+ BC. Support: U10CA180821, U10CA180882, U24CA196171, Genentech; Clinicaltrials.gov Id: NCT00005970; https://acknowledgments.alliancefound.org. Citation Format: S. Chumsri, E. Liu, K. Edwards, Y. Wen, Y. Zhang, A. K. Anderson, A. H. Partridge, L. Carey, K. Ballman, E. Perez, K. Treuner, M. Goetz, E. Thompson. Prognostic performance of Breast Cancer Index in patients with early-stage HR+ HER2+ breast cancer (BC) treated with adjuvant trastuzumab: NCCTG N9831 (Alliance) [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2025; 2025 Dec 9-12; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(4 Suppl):Abstract nr PS3-08-08.

Abstract Introduction: Accurate detection and prioritization of tumor-specific somatic variants is foundational to the development of personalized immunotherapies. Traditional pipelines for neoepitope identification rely heavily on short-read next-generation sequencing (NGS) and DNA-based variant calling, followed by RNA expression filtering and HLA-binding prediction. However, this approach is limited by time-intensive workflows, potential false positives from DNA-only calls, and poor capture of complex isoforms or RNA-level editing. Moreover, the accuracy of neoepitope prediction is constrained by conventional sequence-based HLA binding algorithms that do not account for structural and biophysical features of peptide-MHC interactions, particularly across underrepresented HLA alleles. Methods: To overcome these limitations, we developed a streamlined, sequencing-to-vaccine framework that leverages long-read RNA sequencing for direct detection of expressed somatic variants, including fusions, indels, and single nucleotide variants. Using Oxford Nanopore Technologies’ cDNA-PCR approach, we enable comprehensive, full-length transcript characterization from both fresh and frozen breast tumor samples. Our bioinformatic pipeline, NeoTarget, integrates alignment, variant calling, and neoepitope inference from tumor RNA in a single, modular workflow. A core innovation of this pipeline is HLA-Inception, a deep convolutional neural network trained to predict peptide-MHC class I binding using features derived from molecular dynamics-based modeling of electrostatic potential at the peptide-HLA interface, enhancing generalizability across diverse HLA subtypes. Results: Expanding on previous results, the current NeoTarget pipeline identifies, evaluates, and prioritizes single nucleotide variants, indels, and fusions. To validate this system, we used a set of 9 human breast cancer tumors benchmarked with short read sequencing. Identified neoantigens included most variants shown by clinical short read sequencing and also novel structural variants (i.e., fusions) only revealed with long read sequencing with high confidence. Conclusion: Here, we describe the design and performance of the NeoTarget pipeline, and show proof-of-concept feasibility of applying this fast and cost-effective workflow for HLA-restricted epitope discovery in breast tumors using long-read RNA sequencing. Overall, these methods can be applied to rapidly identify and prioritize neoantigens for personalized neoantigen-based immune therapies. Citation Format: F. Batalini, S. Henry, Y. Zhang, R. Foster, J. M. Gayton, A. Hostal, E. A. Wilson, A. Singharoy, M. Gustafson, B. Pockaj, J. Park, K. S. Anderson. Somatic variant detection using long read RNA sequencing reveals HLA-restricted targets for immunotherapy [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2025; 2025 Dec 9-12; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(4 Suppl):Abstract nr PS3-06-08.

Background Immune checkpoint inhibitors (ICIs) improve survival in advanced non-small cell lung cancer (NSCLC), yet current biomarkers such as programmed death-ligand 1 (PD-L1) expression and response criteria (Response Evaluation Criteria in Solid Tumors, RECIST, V.1.1) align poorly with long-term survival. Radiomics has been proposed as a source of novel biomarkers, but standard radiomic approaches suffer from limited biological interpretability and poor generalizability across treatment settings. We address these gaps by developing the Quantitative Vessel Tortuosity (QVT) score, a biologically interpretable imaging biomarker that quantifies tumor vascular complexity—a known mediator of immune evasion—from routine imaging. We hypothesized that QVT score would improve prognostication and enable treatment response monitoring in ICI-treated NSCLC, independent of current biomarkers. Methods This retrospective, multicenter study analyzed 1,301 CT scans from 682 patients with ICI-treated NSCLC. An automated pipeline segmented lesions and tumor-associated vasculature within each scan, extracting 910 QVT features measuring vascular shape and complexity. Unsupervised clustering of these features in a discovery cohort (N=375) was performed to identify fundamental vascular phenotypes. A continuous QVT score was then derived using regularized logistic regression to map patients along this phenotypic spectrum. QVT score was externally validated in ICI monotherapy (N=172) and chemoimmunotherapy (N=135) cohorts. In a longitudinal cohort (n=143), early on-treatment QVT score changes were evaluated for overall survival (OS) association. Results Two robust vascular phenotypes emerged in the discovery cohort: a highly vascularized, chaotic “QVT High” phenotype with poor post-ICI OS and a “QVT Low” phenotype with normalized vasculature and improved ICI outcomes. The continuous QVT score was prognostic for ICI monotherapy (HR=1.17 per 0.1 increase, p=0.0028) and chemoimmunotherapy (HR = 1.23 per 0.1 increase, p = 4.9×10⁻⁵). High QVT status remained prognostic for both treatments after adjustment for PD-L1 and clinical variables (adjusted HR range: 2.13–2.38, p≤0.002). Early decreases in QVT score during therapy, indicating vascular normalization, were associated with improved OS (HR=1.93, p=0.0022) independent of RECIST best overall response and tumor volume change. Conclusions QVT score is a novel, biologically interpretable imaging biomarker that quantifies vascular complexity. It enables automated, non-invasive prediction and monitoring of ICI outcomes by capturing treatment-induced vascular remodeling. Integrating QVT score into clinical decision-making and drug development can address critical gaps in precision oncology.

Abstract Background: Women younger than 40 generally fall outside the routine screening population for breast cancer (BC). We aimed to understand factors influencing diagnosis and treatment delays in these patients, and the impact of delays on disease-free survival (DFS). Methods: We included women aged ≤ 40 years at diagnosis of stage 0-IV BC enrolled in the Young Women’s BC Study from 2006-2016. Sociodemographics, initial BC symptoms, and disease status were collected via annual surveys. Medical record review was used to capture tumor characteristics and recurrence status. We used logistic regression to estimate associations between patient and tumor factors and three delay types: (1) self-delay (≥ 90 days from first symptom to presentation to a provider), (2) diagnosis delay (≥ 90 days from healthcare visit to diagnosis), and (3) treatment delay (≥ 60 days from diagnosis to first treatment). Odds ratios (OR) and 95 % confidence intervals (CI) were estimated in multivariable models after choosing factors via backwards selection (p≤0.05 retained). Hazard ratios (HR) and 95% CIs for the association between delays and DFS, overall and by stage, were estimated via Cox proportional hazard models, adjusted for tumor and treatment factors. Time to event was measured from diagnosis or treatment to recurrence, death, or last follow-up, whichever occurred first. Results: Of 1,297 patients, 1,071 who completed the long-form baseline survey were included. Median time from first symptom to presentation was 14 days (interquartile range (IQR)=2-45), presentation to diagnosis was 14 days (IQR=7-30), and diagnosis to treatment was 41 days (IQR=25-57). Overall, 14% (n=153) had a self-delay and 11% (n=120) had a diagnosis delay. Of 839 patients with self-detected tumors, 18% had a self-delay and 13% had a diagnosis delay. Among those with Stage 1-3 BC (N=928), 22% (n=205) had a treatment delay. In univariable models, income <50K (p=0.01), lower financial comfort (p=0.01), and not living with a partner (p=0.03) were associated with higher odds of self-delay, while pregnancy within 1 year pre-diagnosis (p=0.05) and first-degree family history of any cancer (p=0.04) or BC (p=0.03) were associated with lower odds. Self-delays were more likely for women diagnosed with stage 4 BC (p=0.004) or with HER2+ tumors (p=0.03) and less likely for those with triple negative (TN) BC (p<0.001). In the multivariable model, family history of BC (OR=0.40, 95% CI=0.17-0.80), income <50K (OR=1.97, 95% CI=1.22-3.13) and TN subtype (OR v. luminal A=0.30, 95% CI=0.13-0.60) remained associated. Odds of diagnosis delay were higher among women who were younger (p=0.01) or with Stage 4 BC (p=0.004), and lower for those with luminal B (p=0.04), TN (p=0.01), or high grade tumors (p=0.03). In the multivariable model, younger age (OR per 1-year=0.94, 95% CI=0.89-0.99), and subtype (luminal B v. A OR=0.60, 95% CI=0.38-0.95; TN v. luminal A OR=0.35, 95% CI=0.16-0.70) remained associated. Multivariable models for self-and diagnosis delays remained similar when restricted to self-detected patients.Treatment delays were less likely among unemployed individuals (p=0.001), and women with a positive germline BC risk variant (p=0.002), luminal B (p=0.005) or TN (p<0.001) subtypes, and high-grade tumors (p=0.03). Only tumor factors remained significant in the multivariable model, including subtype (luminal B v. A OR=0.59, 95% CI=0.41-0.86, TN v. luminal A OR=0.25, 95% CI=0.13-0.43), and stage (stage 3 v. 1 OR=0.37, 95% CI=0.20-0.65). Overall and by stage at diagnosis, delays were not statistically significantly associated with DFS before or after adjusting for tumor and treatment factors. Conclusions: Delays in breast cancer care are influenced by multiple psychosocial and clinical factors, and disparities exist by socioeconomic status. It is critical to decrease self- and diagnosis delays to prevent stage 4 breast cancer. Citation Format: B. X. Zhang, T. Sella, Y. Zheng, G. J. Kirkner, K. J. Ruddy, S. M. Rosenberg, V. F. Borges, L. Schapira, S. E. Come, J. Peppercorn, A. H. Partridge, K. D. Brantley. Delays in breast cancer diagnosis and treatment in young women [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2025; 2025 Dec 9-12; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(4 Suppl):Abstract nr PS3-02-16.