Citations

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 Histones variant composition and post-translational modifications (PTMs) of core and linker histones jointly orchestrate chromatin architecture and tissue-specific gene regulation. However, capturing the full complexity of the “histone code” in native human tissues remains challenging. Here we present a human cardiac histone proteoform atlas—comprising intact histone variants with combinatorial PTMs—enabled by a streamlined top‑down proteomics workflow. In a single one‑dimensional reversed‑phase liquid chromatography (LC)-mass spectrometry (MS) run, we achieve baseline separation of all four core histone families (H2A, H2B, H3, and H4) together with the linker histone H1 directly from human myocardium. Notably, this intact-protein analysis preserves the connectivity of co-occurring PTMs to individual histone molecules to reveal the combinatorial histone code, enabling proteoform‑level quantification of histone‑variant composition and PTM stoichiometry at the chromatographic scale. Targeted MS/MS directly resolves co-occurring PTM combinations and distinguishes highly homologous histone isoforms. Using this approach, we assemble the first comprehensive cardiac histone proteoform atlas and uncover previously unobserved histone proteoforms. More broadly, this top-down proteomics workflow provides a robust framework for proteoform-resolved analysis of histone variants and PTMs in complex biological systems.

Abstract Enhancer is a critical epigenetic feature in head and neck squamous cell carcinoma (HNSCC), yet the functional roles of individual enhancers remain poorly understood. Here, we conducted an integrative multi-omics analysis based on publicly available ATAC-seq, H3K27ac ChIP-seq, transcriptomic profiling, and genetic association datasets to systematically map HNSCC-associated enhancers and their target genes. Integrating ATAC-seq and H3K27ac ChIP-seq, we identified 20,362 enhancers and 18,040 enhancer-associated genes, highlighting widespread regulatory complexity. Functional characterization of the TERT -associated enhancer GH05J001312 (GRCh38/hg38: chr5:1312099-1317743) revealed strong transcriptional activity in HNSCC. CRISPR-mediated deletion of a core sequence significantly reduced TERT expression, impaired cellular proliferation in vitro , and suppressed tumor growth in vivo , confirming its role as a key cis-regulatory element. RNA-seq analysis of enhancer-edited cells uncovered 742 differentially expressed genes enriched in cancer-related pathways, including MAPK and IL-17 signaling, indicating a broad transcriptional impact. Collectively, our findings establish GH05J001312 as a functional enhancer driving oncogenic programs in HNSCC and suggest enhancer-targeted strategies as a potential therapeutic avenue.

Abstract Background Electronic health record (EHR)–based prognostic modeling is increasingly used in oncology, yet incorporating pharmacogenomic (PGx) knowledge derived from experimental systems into clinical prediction frameworks remains challenging. This gap is driven by fundamental mismatches between controlled drug–mutation assays and heterogeneous, incomplete real-world clinical data. Methods We propose a representation transfer framework that integrates PGx embeddings learned from large-scale in vitro pharmacogenomic screens into patient-level EHR models. A frozen pharmacogenomic encoder is used to generate interaction-aware embeddings from patient mutation profiles and administered therapies, which are aggregated into a fixed-length PGx Complementarity Representation. These representations are incorporated into multimodal survival prediction models alongside standard clinical features. Performance was evaluated using systematic modality ablation analyses, attribution analyses, and exploratory unsupervised representation analyses. Results Integrating PGx embeddings yielded consistent performance improvements across all evaluated modality combinations. Relative gains were largest in modality-sparse settings, where baseline EHR features encode limited biological context, and were attenuated—but remained significant—in biologically enriched configurations. Attribution analyses indicated that PGx embeddings contributed non-redundant predictive signal beyond standard clinical features. Exploratory unsupervised analyses further demonstrated that the learned representations exhibit interpretable association patterns aligned with known therapeutic exposures and pathway-level associations. Conclusion These findings suggest that externally learned pharmacogenomic representations can be transferred into real-world EHR models as a context-dependent, non-redundant augmentation. By framing PGx knowledge as an interaction-aware representation rather than a mechanistic model, this work provides an informatics framework for integrating experimental pharmacogenomic data into clinical prediction tasks in a reproducible and interpretable manner.

Abstract Isolated/idiopathic rapid eye movement sleep behavior disorder (iRBD) is a usually prodromal manifestation of neurodegenerative disorders with α-synuclein pathology: Parkinson’s disease (PD), dementia with Lewy bodies (DLB), and multiple system atrophy (MSA). Clinical trials in the iRBD population face substantial barriers: limited access to well-characterized cohorts, inconsistent assessment protocols across centers, and the absence of validated biomarkers of disease burden. The North American Prodromal Synucleinopathy (NAPS) Consortium was established to address these challenges and facilitate clinical trials for neuroprotective therapies targeting synucleinopathy at the earliest known stages. In this multi-site, longitudinal, observational study, nine academic centers across North America will enroll and follow over 500 individuals with iRBD from existing sleep centers. Sixty control participants, matched for age, sex, and race will also be recruited. A harmonized protocol—including a standardized clinical battery assessing motor, cognitive, autonomic, psychiatric, sensory, and sleep function; structured diagnostic adjudication; biospecimen collection; and centralized analysis of both polysomnography and neuroimaging data—is outlined herein and reflects NAPS Stage 2. Each participants completes these assessments annually, and are replaced in the event of phenoconversion. By unifying assessments and expanding geographic reach, NAPS lays the groundwork for efficient, well-powered clinical trials designed to delay or prevent progression of iRBD to overt PD, DLB, or MSA—ultimately enabling earlier, more effective therapeutic intervention for neurodegenerative disease. Registered at clinicaltrials.gov (NCT05826457).

Abstract Background An intense search is underway for tau PET tracers with enhanced capability to visualize tau aggregates. Research indicates that [18F] AV1451 and [18F]MK 6240 exhibit comparable binding properties in terms of spatial distribution and severity of tau aggregates, as observed both in‐vivo and in‐vitro. This study focused on head‐to‐head pathological comparison of both the tracers binding specificity to tangle maturity levels (pretangles, mature tangles and ghost tangles) of tau aggregates as seen on autoradiography as compared to CP‐13 (early tangle maturity marker) and PHF‐1(middling tangle maturity marker) immunohistochemical (IHC) stains in Alzheimer's disease (AD) and non‐AD tauopathies. Method Study participants included included those with AD and non‐AD tauopathies. Analyses were performed on serial 5um formalin fixed paraffin embedded postmortem human brain sections acquired from Mayo clinic brain bank. Visual assessment of colocalization with IHC as well as quantitative analyses were used. IHC sections were incubated with antibodies CP‐13 (recognizing early tangle maturity) and PHF‐1 (recognizing middling tangle maturity). Result A total of 38 participants were examined. The participants included a spectrum from normal to AD, including mild cognitive impairment (MCI) and atypical AD. Participants with N279K (4R), P301L (4R), and R406W MAPT mutations (3R+4R) were included as well. Both the tracers showed strong binding to AD tau aggregates and no or minimal binding to non‐AD tauopathies. Generally, early tangle maturity tau accumulation participants had more tau demonstrated with CP‐13 than PHF‐1 and both the tracers [18F]MK 6240 and [18F]AV 1451 binding are well correlated with the PHF‐1 staining suggesting the preference for middling tangle level and minimal to early tangles. Conclusion The emergence and validation of these new tau PET tracers have opened new opportunities for the development of more accurate diagnostic and management tools for AD. In our study on post‐mortem sections, [ 18 F]MK‐6240 and [ 18 F]Flortaucipir showed similar binding profiles with preference to middling tangle maturity levels.

PURPOSE Manual extraction of treatment outcomes from unstructured oncology clinical notes is a significant challenge for real-world evidence (RWE) generation. This study aimed to develop and evaluate a robust natural language processing (NLP) system to automatically extract cancer treatments and their associated RECIST-based response categories (complete response, partial response, stable disease, and progressive disease) from non–small cell lung cancer (NSCLC) clinical notes. METHODS This retrospective NLP development and validation study used a corpus of 250 NSCLC oncology notes from University of Pittsburgh Medical Center (UPMC) Hillman Cancer Center, annotated by physician experts. An end-to-end NLP pipeline was designed, integrating a rule-based module for entity extraction (treatments and responses) and a machine learning module using biomedical clinical bidirectional encoder representations from transformers for relation classification. The system's performance was evaluated on a held-out test set, with partial external validation for relation extraction on a Mayo Clinic data set. RESULTS The NLP system achieved high overall accuracy. On the UPMC test set (64 notes), the relation classification model attained an area under the receiver operating characteristic curve of 0.94 and an F1 score of 0.92 for linking treatments with documented responses. The rule-based entity extraction demonstrated a macro-averaged F1 score of 0.87 (precision 0.98, recall 0.81). Although precision was high for chemotherapy and most response types (1.00), recall for cancer surgery was 0.45. External validation at Mayo Clinic showed moderate relation extraction F1 scores (range: 0.51-0.64). CONCLUSION The proposed NLP system can reliably extract structured treatment and response information from unstructured NSCLC oncology notes with high accuracy. This automated approach can assist in abstracting critical cancer treatment outcomes from clinical narrative text, thereby streamlining real-world data analysis and supporting the generation of RWE in oncology.

Abstract Background Alzheimer's disease (AD) exhibits significant genetic heterogeneity, with no single model fully explaining its inheritance pattern. Structural variants (SVs) may contribute to the missing heritability of AD, particularly in disease pathology and familial AD, which remain largely unexplored. Method Structural variants (SVs) were analyzed in unrelated 1,162 individuals from the ROSMAP study (687 cognitively normal (CN), 475 AD patients), as well as 197 Non‐Hispanic White (NHW) families (926 individuals, 58.5% with AD) and 214 Caribbean Hispanic (CH) families (1,340 individuals, 59.17% with AD). Large insertions and deletions were identified using Manta, Absinthe, and MELT from whole‐genome sequencing, supplemented by Sniffles2 and external SV call sets. This approach generated a unified VCF file containing 45,251 insertions and 76,566 deletions. After filtering for missingness rate and allele counts, a total of 9,290 insertions and 9,529 deletions were retained for analysis. Genome‐wide SV analyses were performed with AD‐related metrics in ROSMAP, adjusting for age, sex, and principal components, and validated in an independent ADSP replication cohort comprising 29,055 unrelated individuals (17,110 CN and 11,945 AD patients). Result We identified nine SVs (one insertion and eight deletions) segregating in AD families. An insertion near PDCD10 in NHW families reached genome‐wide significance ( p <5×10 ‐8 ) for AD risk in the replication cohort. Two deletions in KIF26B and SPHKAP were found in both CH and NHW families, while five appeared only in NHW and one in CH. All eight deletions showed genome‐wide significance for AD risk. SV GWAS on AD pathologies in ROSMAP revealed an insertion in INPP4B with the strongest association to global brain pathology (β=0.19; p = 2.32×10 ‐5 ), also linked to pathological AD, diffuse plaques, and Braak staging. A deletion near ADGRB1 (β=1.53; p = 5.05×10 ‐6 ) was associated with neuritic plaques, and another near ARMC2 (β=1.66; p = 2.17×10 ‐6 ) was linked to diffuse plaques. All three SVs associated with AD ( p <5×10 ‐6 ) in replication. Conclusion Our findings reveal numerous SVs segregating in familial AD across both CH and NHW families, as well as additional SVs linked to sporadic AD. Prioritizing these SVs based on their potential effects on gene function and expression will provide deeper insight into their contributions to both familial and sporadic AD.

Background: Carriers of a pathogenic variant (PV) in BRCA1 face a high risk of breast cancer. This study estimated the risk of developing breast cancer according to mutation type and location. Methods: BRCA1 carriers with no personal history of breast cancer or bilateral mastectomy were included. Detailed information on clinical and family history was collected by questionnaire. Survival analysis was used to estimate 15-year cumulative risk according to PV type and location. Results: A total of 3677 BRCA1 carriers were followed for a mean of 7.2 years (range 0.1–15.0 years); 481 incident breast cancers were documented. Overall, the 15-year cumulative incidence was 25%. Risk estimates varied by exon, ranging from 9% (exon 21) to 19% (exon 12) to 36% (exon 15); however, strata were small. Carriers of four founder mutations common in Eastern Europe (c.5263_5264insC, c.181T > G, c.66_67delAG and c.4034delA) experienced a lower-than-expected cancer risk (15.9–24.4%) compared to other PVs (28.8%) (p = 0.02). Conclusions: Although our data suggests some variability in penetrance based on specific BRCA1 PV, this was based on a large number of founder mutations. Breast cancer management strategies should continue to be based on comprehensive risk assessment.

Abstract Background Women with a pathogenic variant in BRCA1 or BRCA2 are at high risk of developing ovarian cancer, and it is often recommended that they undergo bilateral salpingo-oophorectomy at an early age, resulting in surgical menopause. Menopausal hormone replacement therapy (HRT) is an effective way to mitigate the adverse outcomes of early menopause; however, the safety of menopausal HRT on breast cancer risk in this population has not been established. Methods We conducted a prospective matched analysis of HRT use following menopause and breast cancer risk in BRCA carriers. Women who initiated HRT were matched one-to-one with women who had not initiated menopausal HRT by gene, year of birth, and age at menopause, resulting in 676 matched pairs. Menopausal HRT use collected by questionnaire included formulation and mode of administration. Results After a mean of 5.6 years, there were 87 (12.9%) incident breast cancer cases in the 676 exposed women and 128 (18.9%) cases in the 676 unexposed women (P = .002). Compared with unexposed matched control individuals, women who used estrogen alone experienced a statistically significantly decreased risk of breast cancer (hazard ratio = 0.37, 95% CI = 0.24 to 0.57). No protective or adverse effect was associated with the use of estrogen plus progestogen (hazard ratio = 0.94, 95% CI = 0.54 to 1.63). Conclusions Our findings suggest no substantial increase in the risk of breast cancer in BRCA carriers with the use of HRT and that estrogen alone might be protective.