tbuckley/cabot-search huggingface dataset:
Integrative Genome-wide Association Meta-analysis of Aortic Aneurysm and Dissection Identifies Five Novel Genes
Authors: Yifan Du, Yunlong Guan, Zhonghe Shao, Minghui Jiang, Minghan Qu, Yifan Kong, Hongji Wu, Da Luo, Shu Peng, Si Li, Xi Cao, Jing Chen, Ping Ye, Jiahong Xia, Xingjie Hao
Journal: Genomics Proteomics & Bioinformatics (2025)

Abstract Aortic aneurysm and dissection (AAD) is a multifaceted condition characterized by significant genetic predisposition and a considerable contribution to cardiovascular-related mortality. Previous studies have suggested that AAD subtypes share similar genetic mechanisms, however, these studies investigated the subtypes separately. Here, we performed a large genome-wide association study (GWAS) meta-analysis for AAD by combining its subtypes, including 11,148 cases and 708,468 controls of European ancestry. We identified 24 susceptibility loci, including four novel loci at 1p21.2 (PALMD), 2p22.2 (CRIM1), 6q22.1 (FRK), and 12q14.3 (HMGA2), which were partially validated in both internal and external populations. Cell type-specific analysis highlighted the artery as the most relevant tissue where the susceptibility variants may exert their effects in a tissue-specific manner. By using four approaches, we prioritized 53 genes, reinforcing the importance of elastic fiber formation and transforming growth factor-beta (TGF-β) signaling in the formation of AAD, and suggested potential target drugs for the treatment. Additionally, various cardiovascular diseases were genetically correlated with AAD, and several cardiovascular risk factors [e.g., body mass index (BMI), lipid levels, and pulse pressure] showed causal associations with AAD, underscoring their shared genetic structures and mechanisms underlying the comorbidity. Moreover, five prioritized genes (PALMD, CRIM1, FRK, HMGA2, and NT5DC1) at the novel loci were supported as regulators of smooth muscle and endothelial cell functions through ex vivo and in vitro experiments. Together, these findings enhance our understanding of the genetic architecture of AAD and provide novel insights into future biological mechanism studies and therapeutic strategies.
The Pathogen Adaptation of HLA Alleles and the Correlation with Autoimmune Diseases in the Han Chinese
Authors: Shuai Liu, Yanyan Li, Tingrui Song, Jingjing Zhang, Peng Zhang, Huaxia Luo, Sijia Zhang, Yiwei Niu, Tao Xu, Shunmin He
Journal: Genomics Proteomics & Bioinformatics (2025)

Abstract Human leukocyte antigen (HLA) genes play a crucial role in the adaptation of human populations to the dynamic pathogenic environment. Despite their significance, investigating the pathogen-driven evolution of HLAs and the implications for autoimmune diseases presents considerable challenges. Here, we genotyped over twenty HLA genes at 3-field resolution in 8278 individuals from diverse ethnic backgrounds, including 4013 unrelated Han Chinese. We focused on the adaptation of HLAs in the Han Chinese by analyzing their binding affinity for various pathogens, and explored the potential correlations between pathogen adaptation and autoimmune diseases. Our findings reveal that specific HLA alleles like HLA-DRB1*07:01 and HLA-DQB1*06:01 confer strong pathogen adaptability at the sequence level, notably for Corynebacterium diphtheriae and Bordetella pertussis. Additionally, alleles like HLA-C*03:02 demonstrate adaptive selection against pathogens like Mycobacterium tuberculosis and coronavirus at the gene expression level. Simultaneously, the aforementioned HLA alleles are closely related to some autoimmune diseases such as multiple sclerosis (MS). These exploratory discoveries shed light on the intricate coevolutionary relationships between pathogen adaptation and autoimmune diseases in the human population. These efforts led to an HLA database at http://bigdata.ibp.ac.cn/HLAtyping, aiding searches for HLA allele frequencies across populations.
A meta-analysis of persuasive design, engagement, and efficacy in 92 RCTs of mental health apps
Authors: Lee Valentine, Jordan D X Hinton, Kriti Bajaj, Larissa Boyd, Shaunagh O'Sullivan, Rory P Sorenson, Imogen H Bell, Miguel Sobredo Vega, Ping Liu, Wilma Peters, Shaminka N Mangelsdorf, Thomas W Wren, Carl Moller, Shane Cross, Carla McEnery, Sarah Bendall, Jennifer Nicholas, Mario Alvarez-Jimenez
Journal: npj Digital Medicine (2025)

This systematic review and meta-analysis examined the efficacy of digital mental health apps and the impact of persuasive design principles on intervention engagement and outcomes. Ninety-two RCTs and 16,728 participants were included in the meta-analyses. Findings indicate that apps significantly improved clinical outcomes compared to controls (g = 0.43). Persuasive design principles ranged from 1 to 12 per app (mode = 5). Engagement data were reported in 76% of studies, with 25 distinct engagement metrics identified, the most common being the percentage of users who completed the intervention and the average percentage of modules completed. No significant association was found between persuasive principles and either efficacy or engagement. With 25 distinct engagement metrics and 24% of studies not reporting engagement data, establishing overall engagement with mental health apps remains unfeasible. Standardising the definition of engagement and implementing a structured framework for reporting engagement metrics and persuasive design elements are essential steps toward advancing effective, engaging interventions in real-world settings.
A Phase II Randomized Study of Paclitaxel alone or combined with Pelareorep with or without Avelumab in Metastatic Hormone Receptor Positive Breast Cancer: the BRACELET-01/PrE0113 study
Authors: Amy S. Clark, Fengmin Zhao, Paula Klein, Alberto J. Montero, Carla Falkson, Elisa Krill-Jackson, Kendrith Rowland, Sagar Sardesai, Jason Incorvati, Patrick Dillon, Antonio C. Wolff, Richard Trauger, Thomas C. Heineman, Matthew C. Coffey, Kathy D. Miller
Journal: Clinical Cancer Research (2025)

Abstract Purpose: Pelareorep (Pel) is a type 3 oncolytic reovirus that upregulates PD-L1 expression. We determined the objective response rate (ORR) with paclitaxel (Pac), Pac + Pel, or Pac + Pel + avelumab (Ave). Patients and Methods: Patients with hormone receptor positive (HR+) HER2 negative metastatic breast cancer (MBC) who had progressed on at least one line of endocrine therapy with a CDK 4/6 inhibitor and had not received chemotherapy for MBC were eligible. Patients were randomized 1:1:1 to Pac, Pac/Pel or Pac/Pel/Ave after a three-patient run-in confirmed safety of the triplet. Response was assessed every 8 weeks until week 16 and then every 12 weeks using RECIST v1.1. The primary endpoint was 16-week ORR. Statistical comparison across arms was not planned. Results: 48 patients enrolled with 45 randomized, 16-week ORR was 20%, 31% and 14% in the Pac, Pac/Pel and Pac/Pel/Ave arms, respectively. Median progression-free survival (PFS) was 6.4 months (m), 12.1m and 5.8m in the Pac, Pac/Pel and Pac/Pel/Ave arms respectively. There were more adverse events, particularly infusion reactions, in the combination arms than the Pac arm. Expansion of peripheral T cell clones were observed by cycle (C)4 in Pac/Pel but not the Pac or Pac/Pel/Ave arms. Conclusions: The addition of Pel to Pac was associated with increased toxicity, expanded peripheral T-cell clones, and numerically increased ORR and PFS compared to Pac; Pac/Pel/Ave further increased toxicity and blunted T cell responses without obvious increase in efficacy. Investigation of the Pac/Pel combination warrants consideration with careful attention to acute toxicity.
Targeting the intracellular immune checkpoint CISH with CRISPR-Cas9-edited T cells in patients with metastatic colorectal cancer: a first-in-human, single-centre, phase 1 trial
Authors: Emil Lou, Modassir S Choudhry, Timothy K Starr, Timothy D Folsom, Jason Bell, Blaine Rathmann, Anthony P DeFeo, Jihyun Kim, Nicholas Slipek, Zhaohui Jin, Darin Sumstad, Christopher A Klebanoff, Katherine Ladner, Akshat Sarkari, R Scott McIvor, Thomas A Murray, Jeffrey S Miller, Madhuri Rao, Eric Jensen, Jacob Ankeny, Mahmoud A Khalifa, Anil Chauhan, Benjamin Spilseth, Ajay Dixit, Paolo P Provenzano, Wenjing Pan, Daniel Weber, Miranda Byrne-Steele, Tom Henley, David H McKenna, Matthew J Johnson, Beau R Webber, Branden S Moriarity
Journal: The Lancet Oncology (2025)
AASLD Practice Statement on the evaluation and management of metabolic dysfunction-associated steatotic liver disease in children
Authors: Stavra A. Xanthakos, Samar Ibrahim, Kathryn Adams, Rohit Kohli, Pushpa Sathya, Shikha Sundaram, Miram Vos, Ashish Dhawan, Sonia Caprio, Cindy Behling, Jeffrey B. Schwimmer
Journal: Hepatology (2025)

Given the high prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD) in children and its distinct epidemiological, clinical, and histopathological differences from adult disease, the American Association for the Study of Liver Diseases (AASLD) commissioned this pediatric-focused evidence-based practice statement. A multidisciplinary writing group—encompassing expertise in pediatric hepatology, gastroenterology, endocrinology, and liver pathology—conducted a comprehensive PubMed literature search for studies published through March 6, 2024, involving pediatric participants (ages 0–18 y) with NAFLD or MASLD. The review addressed epidemiology, pathophysiology, natural history, screening, diagnosis, treatment, comorbidity management, outcome monitoring, and transition of care. Using the highest available level of evidence—randomized controlled trials, large observational cohort studies, systematic reviews, and meta-analyses—30 evidence-guided practice statements were developed. Where high-quality evidence was lacking, expert consensus was employed, and critical knowledge gaps were identified to inform future research priorities. This document highlights key concepts relevant to pediatric MASLD, especially regarding diagnostic criteria, noninvasive assessment tools, and therapeutic approaches. It also discusses the implications of the 2023 nomenclature revision, which emphasizes evaluating both hepatic steatosis and cardiometabolic risk factors. With increasing recognition of MASLD’s cardiometabolic burden and long-term health consequences, this practice statement provides a structured framework to advance clinical care and research in pediatric MASLD.
A deep-learning algorithm using real-time collected intraoperative vital sign signals for predicting acute kidney injury after major non-cardiac surgeries: A modelling study
Authors: Sehoon Park, Soomin Chung, Yisak Kim, Sun-Ah Yang, Soie Kwon, Jeong Min Cho, Min Jae Lee, Eunbyeol Cho, Jiwon Ryu, Sejoong Kim, Jeonghwan Lee, Hyung Jin Yoon, Edward Choi, Kwangsoo Kim, Hajeong Lee
Journal: PLoS Medicine (2025)

Background Postoperative acute kidney injury (PO-AKI) prediction models for non-cardiac major surgeries typically rely solely on preoperative clinical characteristics. Methods and findings In this study, we developed and externally validated a deep-learning-based model that integrates preoperative data with minute-scale intraoperative vital signs to predict PO-AKI. Using data from three hospitals, we constructed a convolutional neural network-based EfficientNet framework to analyze intraoperative data and created an ensemble model incorporating 103 baseline variables of demographics, medication use, comorbidities, and surgery-related characteristics. Model performance was compared with the conventional SPARK model from a previous study. Among 110,696 patients, 51,345 were included in the development cohort, and 59,351 in the external validation cohorts. The median age of the cohorts was 60, 61, and 66 years, respectively, with males comprising 54.9%, 50.8%, and 42.7% of each cohort. The intraoperative vital sign-based model demonstrated comparable predictive power (AUROC (Area Under the Receiver Operating Characteristic Curve): discovery cohort 0.707, validation cohort 0.637 and 0.607) to preoperative-only models (AUROC: discovery cohort 0.724, validation cohort 0.697 and 0.745). Adding 11 key clinical variables (e.g., age, sex, estimated glomerular filtration rate (eGFR), albuminuria, hyponatremia, hypoalbuminemia, anemia, diabetes, renin-angiotensin-aldosterone inhibitors, emergency surgery, and the estimated surgery time) improved the model’s performance (AUROC: discovery cohort 0.765, validation cohort 0.716 and 0.761). The ensembled deep-learning model integrating both preoperative and intraoperative data achieved the highest predictive accuracy (AUROC: discovery cohort 0.795, validation cohort 0.762 and 0.786), outperforming the conventional SPARK model. The retrospective design in a single-nation cohort with non-inclusion of some potential AKI-associated variables is the main limitation of this study. Conclusions This deep-learning-based PO-AKI risk prediction model provides a comprehensive approach to evaluating PO-AKI risk prediction by combining preoperative clinical data with real-time intraoperative vital sign information, offering enhanced predictive performance for better clinical decision-making.
Adaptive Therapy Exploits Fitness Deficits in Chemotherapy-Resistant Ovarian Cancer to Achieve Long-Term Tumor Control
Authors: Helen Hockings, Eszter Lakatos, Weini Huang, Maximilian Mossner, Mohammed A. Khan, Nikolina Bakali, Jacqueline McDermott, Kane Smith, Ann-Marie Baker, Trevor A. Graham, Michelle Lockley
Journal: Cancer Research (2025)

Abstract Drug resistance results in poor outcomes for cancer patients. Adaptive therapy is a potential strategy to address drug resistance that exploits competitive interactions between sensitive and resistant subclones. Here, we showed that adapting carboplatin dose according to tumor response (adaptive therapy) significantly prolonged survival of murine ovarian cancer models compared to standard carboplatin dosing, without increasing mean daily drug dose or toxicity. Platinum resistant ovarian cancer cells exhibited diminished fitness when drug was absent in vitro and in vivo, which caused selective decline of resistant populations due to reduced proliferation and increased apoptosis. Conversely fitter, sensitive cells re-grew when drug was withdrawn. Using a bioinformatics pipeline that exploits copy number changes to quantify the emergence of treatment resistance, analysis of cell-free DNA obtained longitudinally from ovarian cancer patients during treatment showed subclonal selection through therapy, and measurements of resistant population growth correlated strongly with disease burden. These preclinical findings pave the way for future clinical testing of personalized adaptive therapy regimens tailored to the evolution of carboplatin resistance in individual ovarian cancer patients.
Integrating ctDNA Analysis and Radiomics for Dynamic Risk Assessment in Localized Lung Cancer
Authors: Everett J. Moding, Mohammad Shahrokh Esfahani, Cheng Jin, Angela B. Hui, Barzin Y. Nabet, Yufei Liu, Jacob J. Chabon, Michael S. Binkley, David M. Kurtz, Emily G. Hamilton, Aadel A. Chaudhuri, Chih Long Liu, Zhe Li, Rene F. Bonilla, Alice L. Jiang, Brianna C. Lau, Pablo Lopez, Jianzhong He, Yawei Qiao, Ting Xu, Luyang Yao, Saumil Gandhi, Zhongxing Liao, Millie Das, Kavitha J. Ramchandran, Sukhmani K. Padda, Joel W. Neal, Heather A. Wakelee, Michael F. Gensheimer, Billy W. Loo, Ruijiang Li, Steven H. Lin, Ash A. Alizadeh, Maximilian Diehn
Journal: Cancer Discovery (2025)

Abstract The complementarity and clinical utility of combining liquid biopsies and radiomic image analysis has not been demonstrated. ctDNA minimal residual disease after chemoradiotherapy (CRT) for non–small cell lung cancer (NSCLC) is highly prognostic, but on-treatment biomarkers are needed to enable response-adapted therapies. In this study, we analyzed 418 patients with NSCLC undergoing CRT to develop and validate a novel dynamic risk model that accurately predicts ultimate progression-free survival during treatment. We optimize tissue-free variant calling from plasma samples to facilitate ctDNA monitoring and demonstrate the importance of accounting for persistent clonal hematopoiesis variants. We show that mid-CRT ctDNA concentration is prognostic for disease progression and integrate additional pre-CRT risk factors, including radiomics, into a combined model that improves outcome prediction. Our results suggest that tumor features, radiomics, and mid-CRT ctDNA analysis are complementary and can identify patients at high and low risk of progression to potentially enable response-adapted therapies. Significance: This study demonstrates that combining tumor features, radiomics, and ctDNA analysis improves outcome prediction in NSCLC treated with CRT therapy. Our integrated model could enable personalized and response-adapted therapies to reduce toxicity and improve outcomes in patients.
Amplification of extrachromosomal MYC paralogs shapes immunosuppressive tumor microenvironment in small cell lung cancer
Authors: Jingwei Zhang, Yueqi Jin, Haodong Lin, Jiaming Deng, Yan Ju, Xueyan Hu, Jianqi She, Zhijian Liang, Kongxu Dai, Mantang Qiu, Kunkun Sun, Jun Wang, Fan Yang, Jian Chen, Ence Yang, Xiao Li
Journal: Clinical Cancer Research (2025)

Abstract Purpose: Immunotherapy has demonstrated promise in small cell lung cancer (SCLC), but certain patients encounter limited benefits, highlighting the need for immunosuppressive biomarkers. Extrachromosomal circular DNA (ecDNA) promotes amplification of MYC-paralogs (MYC, MYCN and MYCL), driving cross-resistance in SCLC. Here, we aim to investigate whether ecDNA-mediated MYC-paralogs amplification (ecMYC+) represents immunosuppressive features in SCLC. Experimental Design: Bulk RNA sequencing data were retrieved from public database and paraffin-embedded samples. The overexpression and amplification of MYC-paralogs were identified using immunohistochemistry and fluorescence in situ hybridization. Imaging mass cytometry and multiplex immunohistochemistry were used to characterize spatial distribution of tumor immune microenvironment (TIME). The copy number of MYC-paralogs was investigated using real-time polymerase chain reaction. RNA-sequencing and flow cytometry were performed in SCLC cell lines. Results: The mean copy number of ecDNAs and the frequency of ecMYC+ cell lines were higher in SCLC than that in the other lineages (SCLC 22/47 vs others 15/282). In ecMYC+ SCLC, multiple immune-related pathways were downregulated while nucleotide metabolism processes were upregulated. Inhibition of nucleotide metabolism induced ecDNA elimination, along with activated antigen presenting pathways. Highly dispersed MYC-paralogs amplifications were detected in resected treatment-naïve SCLC samples. Through the resolution of 103,341 cells from 24 pathological regions, we observed higher expression of MKI67, VEGFA, FAP and FOXP3 and reduced T cell infiltration in ecMYC+ samples. Moreover, ecMYC+ samples exhibited elevated cellular neighborhoods dominated by Ki67+ tumors, with reduced spatial interaction with immune cells. Conclusions: Extrachromosomal amplification of MYC-paralogs shapes suppressive TIME, identifying potential subgroup of immunotherapy resistant patients.
Clinical Implications of The Cancer Genome Atlas Molecular Classification System in Esophagogastric Cancer
Authors: Henry S. Walch, Raktim Borpatragohain, Justin Jee, Waleed Chatila, Christopher Fong, Steven B. Maron, Geoffrey Y. Ku, David H. Ilson, Yelena Y. Janjigian, Abraham J. Wu, Pari Shah, Daniel G. Coit, Manjit S. Bains, Valerie W. Rusch, Bernard J. Park, Matthew J. Bott, Katherine Gray, David R. Jones, Michael Berger, Nikolaus Schultz, Vivian E. Strong, Daniela Molena, Smita Sihag
Journal: Clinical Cancer Research (2025)

Abstract Purpose: The Cancer Genome Atlas (TCGA) project defined four distinct molecular subtypes of esophagogastric adenocarcinoma: microsatellite instable (MSI), Epstein–Barr virus (EBV)–associated, genomically stable (GS), and chromosomally instable (CIN). However, an association between molecular subtypes and clinical outcomes has not been clearly demonstrated. Given few actionable biomarkers, we investigated the clinical relevance of TCGA classification system. Experimental Design: We identified all patients with esophagogastric adenocarcinoma whose tumors underwent prospective next-generation sequencing using the Memorial Sloan Kettering–IMPACT assay from 2014 to 2023. We classified all tumors in accordance with TCGA methodology and correlated molecular subtypes with high-quality clinicopathologic data. Results: Among 1,438 included patients, 941 had CIN, 344 had GS, 103 had MSI, and 50 had EBV tumors. Accounting for the clinical stage and tumor grade, molecular classification was independently associated with overall cancer-specific survival (P &amp;lt; 0.001) on Cox multivariable analysis. Furthermore, genomic signatures, patient demographics, pathologic responses to neoadjuvant therapy, patterns of recurrence, and metastatic organotropism differed significantly by molecular subtype. Although most distal esophageal and gastroesophageal junction tumors were CIN, up to 25% of these included GS, MSI, or EBV subtypes in contrast to TCGA. Random forest machine learning demonstrated that the molecular subtype is more influential in predicting response to treatment than tumor location. Conclusions: Molecular classification is independently prognostic and may warrant inclusion in future staging and treatment guidelines. Routine molecular profiling is clinically feasible and may play a role in the management of patients to help guide appropriate treatment selection and clinical trial enrollment in the place of tumor location.
Eco-evolutionary Guided Pathomic Analysis Detects Biomarkers to Predict Ductal Carcinoma In Situ Upstaging
Authors: Yujie Xiao, Manal Elmasry, Ji Dong K. Bai, Andrew Chen, Yuzhu Chen, Brooke Jackson, Joseph O. Johnson, Prateek Prasanna, Chao Chen, Mehdi Damaghi
Journal: Cancer Research (2025)

Abstract Cancers evolve in a dynamic ecosystem. Thus, characterizing the ecological dynamics of cancer is crucial to understanding cancer evolution, which can lead to the discovery of biomarkers to predict disease progression. Ductal carcinoma in situ (DCIS) is an early-stage breast cancer characterized by abnormal epithelial cell growth confined within the milk ducts, and biomarkers are needed to predict which cases will progress to aggressive disease. In this study, we showed that ecological analysis of hypoxia and acidosis biomarkers can significantly improve prediction of DCIS upstaging. Quantitative analyses were performed on immuno-histological images from a retrospective cohort of DCIS specimens collected from biopsy samples. First, an eco-evolutionary designed approach was developed to define habitats in the tumor intra-ductal microenvironment based on oxygen diffusion distance. Then, cancer cells with metabolic phenotypes attributed to their habitats were identified, including a hypoxia-responding CA9+ phenotype and an acid-adapted LAMP2b+ phenotype. While these markers have traditionally shown limited, if any, predictive capabilities for DCIS progression, when analyzed from an ecological perspective, their power to differentiate between non-upstaged and upstaged DCIS increased significantly. Additionally, the distribution of distinct niches with specific spatial patterns of these biomarkers predicted patient upstaging. The niches were characterized by pattern analysis of both cellular and spatial features. A random forest classifier that was trained and underwent a 5-fold validation on the biopsy cohort achieved an area under curve (AUC) of 0.74 for predicting clinical outcome. These results affirm the importance of tumor ecological features in eco-evolutionary-designed approaches for biomarker discovery.