Citations

Purpose This study aimed to develop and validate a tool to predict the primary tumor site in patients with metastatic cancer of unknown primary using demographic and clinical variables, including imaging-derived sites of metastasis, without pathology or laboratory data. Methods In this population-based study, data were extracted from the SEER database. Patients diagnosed between 2010 and 2020 with a known primary cancer site, metastatic staging, and metastasis in the bone, liver, brain, and lungs were included. Patients were randomly split into training (70%) and/or testing (30%) sets. A total of 27 primary tumor sites were included. A gradient boosting model was trained to predict the primary tumor site, with random forest and classification tree models evaluated as comparators. Random guess and frequency-weighted guessing models were used for baseline comparison. Results A total of 689,715 patients were included from 9,208,295 queried. The most common primary cancer sites were non–small cell lung cancer (NSCLC) (n = 211,338), intestines (n = 64,187), and pancreas (n = 60,310). The baseline models achieved Top-5 accuracies of 0.179 for a random guess and 0.476 for a frequency-weighted guess. The gradient boosting model achieved Top-1, Top-3, and Top-5 accuracies of 0.40 ± 0.001, 0.680 ± 0.001, and 0.790 ± 0.001, respectively, across 100 Monte-Carlo replicates. Top-5 accuracy was highest for primary NSCLC (0.990 ± 0.001) and intestinal cancer (0.900 ± 0.003). Our model was most influenced by sex (gain = 0.27) and metastasis to bone (gain = 0.20). A web app was constructed: primarycancerpredictor.netlify.app. Conclusion This study developed and evaluated a gradient boosting machine learning model and a user-friendly web tool to aid in the diagnosis of cancer of unknown primary.

ABSTRACT Aims Opioid use disorder remains a major public health crisis in the United States, driven largely by rising overdose deaths from synthetic opioids, such as fentanyl. Emerging population‐level evidence suggests that GLP‐1 receptor agonists (GLP‐1RAs) may reduce overdose risk. However, their underlying molecular mechanisms are not well understood. We investigated the transcriptional effects of fentanyl and GLP‐1RAs using human induced pluripotent stem cell (iPSC)‐derived forebrain organoids and neurons. Methods Organoids were treated with fentanyl, liraglutide, or exenatide, followed by RNA sequencing. We extended these analyses to iPSC‐derived neurons exposed to additional therapeutic candidates, including the anticonvulsants topiramate and gabapentin, and the metabolic modulator β‐hydroxybutyrate. All compounds were tested at clinically relevant concentrations, and transcriptomic and functional genomic assays were performed. Results Across models and drug classes, we identified modulation of endoplasmic reticulum (ER) stress signaling as a shared molecular mechanism. Fentanyl and GLP‐1RAs consistently downregulated ER stress‐related genes, with TRIB3 as the most strongly suppressed target. We further identified CEBPB as a key upstream regulator and confirmed reduced CEBPB DNA‐binding activity in ER stress‐related genes. Conclusion These findings highlight ER stress modulation as a convergent pathway with potential therapeutic relevance for substance use disorders.

Abstract Background: Sexual and gender minority (SGM) representation is growing in the United States. This study estimated the future SGM cancer burden amidst shifting cancer patterns. Methods: Age-specific incidence data for breast, prostate, colorectal, lung, and melanoma were obtained from the Surveillance, Epidemiology, and End Results Program’s 12-registry database. Nordpred age-period-cohort models projected incidence through 2040 to 2044. Rates were applied to US Census population estimates and generation- and sex-specific SGM identification proportions from Gallup’s 2023 to 2024 surveys. Prophet was used for sensitivity analysis. Results: At ages 55 to 59, breast cancer cases among Millennial SGMs were 3,672% higher than among Silent Generation SGMs (34,850 vs. 924 cases), melanoma cases were 1,061% higher (5,526 vs. 476 cases), and colorectal cancer cases were 2,206% higher (23,594 vs. 1,023 cases). Compared with Generation X, Millennial prostate cancer cases were 142% higher (8,100 vs. 3,344 cases), and lung cancer cases were 169% higher (5,451 vs. 2,029 cases). By 2040 to 2044, Millennials were projected to account for 68.4% of breast cancer cases and 59.7% of colorectal cancer cases among SGMs. Conclusions: At ages 55 to 59, the 5-year cancer burden was projected to be 15 times higher among Millennials than Silent Generation SGMs (77,521 vs. 5,134 cases), driven primarily by the larger SGM population in younger generations. Oncology systems will need to prepare for the increase in cancer care demand. Impact: These findings identify a substantial generational shift in the SGM cancer burden and may inform oncology workforce capacity, culturally responsive cancer care, and healthcare system preparation for this growing population.

Background. Rare diseases collectively affect an estimated 300 million people worldwide, yet patients wait an average of 4-8 years for a correct diagnosis after visiting multiple physicians, accumulating unexplained findings, and suffering preventable disease progression before a unifying diagnosis is reached. Electronic health records (EHRs) capture this pre-diagnostic trajectory in rich detail, but conventional methods typically collapse years of longitudinal phenotypic signals into a single static snapshot; our approach instead models this temporal structure directly. Methods. We conducted a retrospective cohort study using longitudinal EHR data from approximately 3 million patients at Mayo Clinic Platform_Accelerate. Cases were patients with confirmed diagnoses of 10 rare diseases; controls (~5,200 per disease) had no record of any rare disease. HPO phenotype terms were extracted from clinical notes and laboratory results using a medspaCy NLP pipeline; ICD codes were grouped at three-character level. We developed and trained two temporal models, GRU-Attn and Conformer, that encode patient histories as quarterly time-binned sequences spanning up to 30 years, and compared these against three static baselines (CatBoost, XGBoost, logistic regression). Models were evaluated systematically at prediction horizons h = 1-10 years before diagnosis. Results. In the full held-out cohort benchmark, temporal models outperformed static baselines across the large majority of diseases and prediction horizons, with the advantage widening substantially as the horizon lengthened. At 1-2 years prior to diagnosis, mean AUROC was 0.928 (GRU-Attn) versus 0.871 (CatBoost); at 6-10 years prior to diagnosis, 0.726 versus 0.693, with static models degrading to near-chance for some diseases. Combining HPO and ICD features yielded the strongest performance, with a mean gain of +0.063 AUROC over the best single source at 1-2 years prior to diagnosis. Separately, in a manually curated cohort of 143 confirmed-undiagnosed cases with extended EHR histories, GRU-Attn identified 138 cases before first disease mention and all 143 cases before formal diagnosis at the prespecified 99th-percentile specificity threshold, with median lead times of 1.1-8.3 years and 1.9-21.7 years, respectively. Conclusions. By learning from the temporal trajectory of phenotypic signals rather than their static aggregate, temporal EHR models improved retrospective early detection performance across several rare diseases, most powerfully for conditions where pre-diagnostic findings accrue gradually and heterogeneously over years. These results show that longitudinal EHR trajectories carry rare-disease signal beyond static phenotypic burden and support prospective evaluation of temporal EHR modeling for rare-disease risk stratification.

Abstract Colorectal cancer develops through a normal-adenoma-carcinoma sequence, yet only 5-10% of adenomas progress to malignancy, and the cellular programs governing that sequence remain poorly defined. Here we generate a spatial multi-omics atlas of human colon adenomas, combining Visium CytAssist and protein co-detection across 24 nonadvanced and advanced tubular adenomas with single-cell resolution Xenium Prime 5K profiling of 101 patient-matched normal, adenoma, and carcinoma cores from 16 patients. Integrating whole-transcriptome and 31-plex protein data identifies nine spatial clusters and two dysplastic epithelial populations that co-express stemness, proliferation, and senescence programs. These programs occupy a shared, spatially confined epithelial niche that expands from adenoma to carcinoma. Spatial analysis revealed GDF15, a senescence-associated secretory factor, mediated the coupling between senescence and stemness in advanced adenomas, and that GDF15-high epithelium locally excludes CD8+ T cells in adenoma and, more broadly, in carcinoma. These findings position senescence as a spatially instructive rather than merely tumor-suppressive program during colorectal carcinogenesis and suggest GDF15 might be a potential candidate target for cancer prevention and interception in the colon.

Deregulated inflammatory signaling via STAT family transcription factors, particularly STAT1, underlies a variety of immune-related diseases, including inflammatory bowel disease. Whereas activation of STATs by JAKs via canonical receptor-driven JAK–STAT signaling is well understood, little is known about JAK-independent mechanisms of STAT activation. Here, we identify the understudied nonreceptor tyrosine kinase TNK1 as a therapeutically targetable, JAK-independent activator of STAT signaling. Using a multiomics approach, we mapped a network of TNK1 substrates associated with protein condensates and proinflammatory signaling, including STAT1. We found that TNK1, but not its sister kinase ACK1, directly phosphorylates STATs at well described STAT-activating JAK sites. In cells, TNK1-mediated STAT1 phosphorylation and activation occurs independently of JAKs. Imaging and interactomics data suggest that TNK1 interacts with STAT1 in cytosolic condensates, which likely compartmentalize TNK1-substrate interactions. We show that an intrinsically disordered proline-rich region in TNK1, which includes a 14-3-3 docking phosphorylation site, is required for the formation of kinase-active TNK1 condensates and STAT1 phosphorylation. Mutations within the proline-rich region that eliminate 14-3-3 binding increase formation of TNK1 condensates, suggesting a model in which 14-3-3 acts as a clamp that constrains the flexible PRR to inhibit condensate formation and STAT1 activation. Finally, we show that TNK1 is a targetable driver of STAT1-mediated inflammation in the gut as inhibition of TNK1 reduces active STAT1 in the colon and ameliorates colitis symptoms in mice.

Importance Breast cancer is the most common cancer diagnosed in women. Therapies for breast cancer are evolving rapidly, and several are associated with cardiovascular adverse effects, can exacerbate cardiovascular risk factors, or can increase the risk of cardiovascular disease. Observations Advances in screening and treatment have led to earlier diagnosis and improved disease-free survival. For long-term survivors of breast cancer, cardiovascular adverse effects can pose a greater threat to overall long-term health than the cancer itself. Additionally, the increasing incidence of breast cancer in younger, premenopausal women has created a distinct demographic with unique medical needs. Conclusions and Relevance Cardiology practitioners need to remain updated on rapidly evolving breast cancer therapies to care for this growing, unique patient population. The field of cardio-oncology has evolved to address the cardiovascular needs of patients and survivors.

Importance Elevated low-density lipoprotein cholesterol (LDL-C) is a modifiable risk factor for cardiovascular disease, the leading cause of premature death worldwide. Assessing the LDL-C–related burden is critical for guiding prevention and treatment strategies. Objectives To estimate the global, regional, and national burden of ischemic heart disease and ischemic stroke attributable to elevated LDL-C (relative to 35-54 mg/dL) from 1990 to 2023 and to quantify the contributions of population growth, aging, risk-deleted burden, and exposure changes to burden trends. Design, Setting, and Population This comparative risk assessment, part of the Global Burden of Disease Study 2023, estimated population-level LDL-C exposure and associated health loss in 204 countries and territories. Mean LDL-C levels were estimated using spatiotemporal gaussian process regression based on 806 studies across 161 countries. Relative risks were derived from meta-analyses of 38 randomized clinical trials. Population-attributable fractions for deaths and disability-adjusted life-years (DALYs) were estimated by age and sex for adults aged 25 years or older from 1990 to 2023, with 95% uncertainty intervals. Exposure Population-level LDL-C concentrations. Main Outcomes and Measures Population-attributable fractions, counts, and rates (all ages and age standardized per 100 000) of LDL-C–attributable deaths and DALYs from ischemic heart disease and ischemic stroke, with uncertainty intervals. Results In 2023, elevated LDL-C accounted for 3.6 million deaths (95% uncertainty interval, 2.2-5.4 million; 6.0% of global mortality) and 90.7 million DALYs (95% uncertainty interval, 58.9-123.3 million; 3.2% of DALYs). Although global all-ages rates remained stable, age-standardized death and DALY rates decreased by 45.6% and 39.5%, respectively, since 1990. In 2023, age-standardized LDL-C–attributable DALY rates were highest in Eastern Europe and lowest in high-income Asia-Pacific. One-third of the global LDL-C burden occurred in India and China. Population growth and aging drove the increasing burden, with notable regional disparities in LDL-C exposure and risk-deleted DALY rates shifting toward middle-sociodemographic settings. Conclusions and Relevance Despite declining age-standardized rates, the absolute LDL-C burden has increased since 1990 due to demographic changes and has shifted toward middle-sociodemographic countries. Measurement and surveillance gaps persist. Strengthened prevention, diagnosis, and treatment access strategies are essential to mitigate the health burden of LDL-C.