Nature Medicine最新文献

{"title":"An antibody–drug conjugate for small-cell lung cancer","authors":"Karen O’Leary","doi":"10.1038/d41591-026-00047-x","DOIUrl":"https://doi.org/10.1038/d41591-026-00047-x","url":null,"abstract":"","PeriodicalId":19037,"journal":{"name":"Nature Medicine","volume":"11 1","pages":""},"PeriodicalIF":82.9,"publicationDate":"2026-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149022302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sex and gender differences belong in treatment guidelines","authors":"Alexandra Kautzky-Willer","doi":"10.1038/s41591-026-04672-4","DOIUrl":"10.1038/s41591-026-04672-4","url":null,"abstract":"Clinical guidelines should state what is already known, where evidence is missing and what future trials must report.","PeriodicalId":19037,"journal":{"name":"Nature Medicine","volume":"32 10","pages":"3511-3511"},"PeriodicalIF":52.5,"publicationDate":"2026-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149022195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Practical lessons in the global scaling of clinical AI: from one hospital to over a million patients screened","authors":"Richa Tiwari, Rajroshan Sawhney, Kasumi Widner, Kim Ramasamy, Ravindran Padmanabhan, Paisan Ruamviboonsuk, Angus Turner, Jay Nayar, Jessica Williams, Naama Hammel, Xiang Yin, Theo Guidroz, Dave Watson, Jonathan Krause, Quang Duong, Yun Liu, Yossi Matias, Ronit Levavi Morad, Avinatan Hassidim, Dale Webster, Sunny Virmani","doi":"10.1038/s41591-026-04643-9","DOIUrl":"10.1038/s41591-026-04643-9","url":null,"abstract":"Scaling a successful deep learning tool across three highly distinct settings – in India, Thailand and Australia — offers cross-cutting insights that may inform the expansion of healthcare artificial intelligence globally.","PeriodicalId":19037,"journal":{"name":"Nature Medicine","volume":"32 10","pages":"3525-3527"},"PeriodicalIF":52.5,"publicationDate":"2026-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149022193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-based characterization of Alzheimer’s disease phenotypes from population-scale single-cell data","authors":"Chenfeng He, Athan Z. Li, Kalpana Hanthanan Arachchilage, Chirag Gupta, Xiang Huang, Xinyu Zhao, Carissa L. Sirois, PsychAD Consortium, Kiran Girdhar, Georgios Voloudakis, Gabriel E. Hoffman, Jaroslav Bendl, John F. Fullard, Donghoon Lee, Panos Roussos, Daifeng Wang","doi":"10.1038/s41591-025-04128-1","DOIUrl":"10.1038/s41591-025-04128-1","url":null,"abstract":"The complexity of Alzheimer’s disease (AD) manifests in diverse clinical phenotypes, including cognitive impairment and neuropsychiatric symptoms. However, the etiology of these phenotypes remains elusive. To address this, the PsychAD project generated a population-level single-nucleus RNA sequencing dataset comprising over 6 million nuclei from the prefrontal cortex of >1,000 individual brains, covering a variety of disease phenotypes. Here, leveraging this dataset, we developed a computational framework, called Phenotype Associated Single Cell encoder (PASCode), to score single-cell phenotype associations, and identified ∼1.5 million phenotype-associated cells (PACs) from 584 donors with AD-related phenotypes. PASCode ensembles multiple statistical methods into a graph neural model for robust scoring. Comparing PACs within 27 brain cell subclasses, we prioritized cell subpopulations and their expressed genes for various AD phenotypes. For instance, we identified microglia subpopulations implicated in AD pathology; reactive astrocyte subtypes with altered neuroprotective and neurotoxic gene expression that likely confer cognitive resilience; and enhanced excitatory/inhibitory imbalance and mitochondrial dysfunction in cognitively impaired AD donors. We also identified many PACs for multiple phenotypes, including the astrocytes between AD and depression showing specific gene expression patterns such as inflammation and endoplasmic reticulum stress pathways. These prioritized subpopulations, genes and pathways potentially offer valuable insights for precision diagnostic and therapeutic development. We also validated our findings in external population-scale datasets including AD and major depressive disorder, compiled an AD-phenotypic single-cell atlas and delivered the framework as an open-source tool with pre-trained models and a web application for community use. Based on single-cell data from a cohort of 584 brain donors including patients with Alzheimer’s disease (AD) and controls, a graph neural network is used to investigate differential patterns between disease and control, to identify those related to cognitive resilience and to insurgence of depression in patients with AD.","PeriodicalId":19037,"journal":{"name":"Nature Medicine","volume":"32 10","pages":"3682-3694"},"PeriodicalIF":52.5,"publicationDate":"2026-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s41591-025-04128-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149022192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Trastuzumab plus nivolumab with gemcitabine and cisplatin as first-line therapy for HER2-positive unresectable biliary tract cancer: a phase 1b/2 trial","authors":"Choong-kun Lee,Taek Chung,Il Hwan Kim,Hong Jae Chon,Jin Won Kim,Se Jun Park,Woo Kyun Bae,Junghun Kang,Changhoon Yoo,Dong-Hoe Koo,Sora Kang,Minsuk Kwon,Ju Won Kim,Beodeul Kang,Sejung Park,Woochan Hwang,Chang Ho Ahn,Chang Gon Kim,Hye Jin Choi","doi":"10.1038/s41591-026-04679-x","DOIUrl":"https://doi.org/10.1038/s41591-026-04679-x","url":null,"abstract":"","PeriodicalId":19037,"journal":{"name":"Nature Medicine","volume":"40 1","pages":""},"PeriodicalIF":82.9,"publicationDate":"2026-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149022196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Blood proteomics of menopause map to brain aging and dementia risk","authors":"Madeline Wood Alexander,Jennifer S. Rabin,Michelle Caunca,Allesandra Iadipaolo,Louisa Cornelis,Ria Warrier,Keenan A. Walker,Nina Miolane,Veronica Augustina Bot,Brendan Wood,Hamilton Se-Hwee Oh,Tony Wyss-Coray,Albert Pham,Julia Borger,Valentina Diaz,Emily W. Paolillo,Joel Kramer,Laura Pritschet,Caitlin Taylor,Matthew S. Panizzon,Ramiro Eduardo Rea Reyes,Marisa N. Denkinger,Nicholas J. Ashton,Sterling C. Johnson,Emily G. Jacobs,Rowan Saloner,Kaitlin B. Casaletto","doi":"10.1038/s41591-026-04648-4","DOIUrl":"https://doi.org/10.1038/s41591-026-04648-4","url":null,"abstract":"Abstract Menopause is a hallmark process in biological aging that has been implicated in later neurodegenerative risk, but the pathways underlying this connection remain unclear. Here we used blood proteomics data from several cohorts to identify biological changes associated with menopause and its links to brain aging. In n  = 80 rigorously staged (STRAW+10) pre-, peri- and postmenopausal women (aged 43–58 years) with serum NULISAseq proteomics, we show that spontaneous menopause is characterized by dysregulation in inflammatory, synaptic, metabolic and Alzheimer’s disease biologic processes, which tracked more strongly with hormones than with age. Validation analyses in age-matched pre-/peri- and postmenopausal women ( n  = 2,814) with plasma Olink proteomics replicated the observed proteomic shifts and revealed broader menopause-related upregulation of inflammatory and catabolic processes plus accelerated organ and cell aging, including brain aging. In four independent cohorts of older women (average age, 60.7–72.1 years; total n  = 11,925), higher menopause proteomic scores associated consistently with cognitive aging and dementia risk. The molecular signatures of menopause may inform the selection of biomarkers or therapeutic targets for brain health in midlife women.","PeriodicalId":19037,"journal":{"name":"Nature Medicine","volume":"10 1","pages":""},"PeriodicalIF":82.9,"publicationDate":"2026-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149021796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Some answers, more questions for multi-cancer early detection tests","authors":"Catherine R. Marinac, Timothy R. Rebbeck, Elizabeth K. O’Donnell","doi":"10.1038/s41591-026-04688-w","DOIUrl":"10.1038/s41591-026-04688-w","url":null,"abstract":"The PATHFINDER 2 and NHS-Galleri studies provide complementary evidence on the clinical performance, safety and implementation of multi-cancer early detection, while underscoring the next challenge: demonstrating population-level benefit.","PeriodicalId":19037,"journal":{"name":"Nature Medicine","volume":"32 10","pages":"3541-3542"},"PeriodicalIF":52.5,"publicationDate":"2026-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149022303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance and safety of a multi-cancer early detection test: the PATHFINDER 2 study","authors":"Nima Nabavizadeh, Charles McDonnell, Dax Kurbegov, Marc Matrana, Shirish Gadgeel, Raymond H. Kim, Gretchen Stipec, Kevin C. Oeffinger, Michael J. Demeure, James W. Lillard Jr., Rebecca Kaltman, Jennifer Tran, Alan Roth, Sami Tahhan, Andrew Poklepovic, Donald Richards, Margarita Lopatin, Celine Marquez, Margaret McCusker, Karthik V. Giridhar","doi":"10.1038/s41591-026-04618-w","DOIUrl":"10.1038/s41591-026-04618-w","url":null,"abstract":"Early cancer detection improves survival; however, most cancer types do not have guideline-recommended screening. We previously reported the first PATHFINDER study that established feasibility of cancer screening with a blood-based multi-cancer early detection (MCED) test that predicts cancer signal origin (CSO) to guide diagnostic workup. The prospective, interventional PATHFINDER 2 study expands clinical evidence for the MCED test by evaluating co-primary endpoints of MCED performance metrics and safety in participants aged 50 years or older without clinical suspicion of cancer. Performance measures included cancer detection rate, positive and negative predictive values, episode sensitivity, specificity and CSO prediction accuracy. Safety was evaluated in terms of number and type of invasive procedures performed and adverse events due to diagnostic testing, after 12 months of follow-up. Endpoints were analyzed descriptively; no hypothesis testing was performed. Between December 2021 and July 2024, 35,878 participants were enrolled. Among 32,007 performance-analyzable participants, cancer detection rate was 0.54% (95% confidence interval (CI): 0.47−0.63%), positive predictive value was 60.3% (95% CI: 54.5−65.8%), negative predictive value was 99.2% (95% CI: 99.1−99.3%) and specificity was 99.64% (95% CI: 99.57−99.70%). Episode sensitivity was 39.3% (95% CI: 34.9−44.0%; all cancers) and 69.8% (95% CI: 62.8−76.0%; prespecified 12-cancer subgroup). Positive and negative likelihood ratios were 108.9 (95% CI: 87.6−135.2) and 0.61 (95% CI: 0.56−0.66), respectively, and the number needed to screen to detect one cancer was 185 (95% CI: 159−215). CSO prediction accuracy was 91.3%. Among 35,335 safety-analyzable participants, 213 (0.6%) had invasive procedure(s) after a positive MCED test; 90.5% were nonsurgical. No serious study-related adverse events were reported by the time of this analysis. These results provide insights into performance and safety of the MCED test in an intended-use population. Randomized trials and longer follow-up are needed to assess the clinical utility of MCED tests. ClinicalTrials.gov identifier: NCT05155605 . The interventional PATHFINDER 2 study provides insights on the safety and performance of a blood-based multi-cancer early detection test in an intended-use population, including more than 35,000 participants aged 50 years or older.","PeriodicalId":19037,"journal":{"name":"Nature Medicine","volume":"32 10","pages":"3760-3769"},"PeriodicalIF":52.5,"publicationDate":"2026-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s41591-026-04618-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149021794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Large-scale esophageal cancer screening through noncontrast computed tomography and artificial intelligence","authors":"Jian Zhou, Guangyu Guo, Jiawen Yao, Qinji Yu, Chunpeng Zheng, Xiaochen Feng, Xianghua Ye, Yongjin Zhou, Pei Yang, Lukas Lambert, Shaojun Zheng, Qiong Li, Yingda Xia, Wenchao Guo, Yi Chen, Mengdi Zhang, Yu Jiang, Dandan Zheng, Jia Ge, Haomiao Qing, Wusong Liu, Peng Zhou, Mei Lan, Lei Wu, Yong Li, Haifeng Wang, Yong Zhou, Junqiang Chen, Liyan Xu, Chenying Lu, Lin Chen, Junwei Han, Chengwei Shao, Wenqiang Wei, Kai Zhang, Jianfeng Zhang, Dingwen Zhang, Jiansong Ji, Na Li, Chuanmiao Xie, Kai Cao, Yuqian Zhao, Ling Zhang, Qifeng Wang","doi":"10.1038/s41591-026-04656-4","DOIUrl":"10.1038/s41591-026-04656-4","url":null,"abstract":"The absence of accurate, noninvasive, scalable screening tools keeps early esophageal cancer (EC) detection a global health challenge. Although noncontrast computed tomography (NC CT) is widely accessible, the esophagus is a hollow tubular structure prone to collapse and motion artifacts, making small early malignant lesions difficult to distinguish from normal tissue. Here we developed the Esophageal AI-Guided malignant Lesion Evaluation (EAGLE) model to detect precancerous lesions and cancer from chest NC CT, a task historically considered impossible. EAGLE was trained on 6,813 patients from two centers and validated across 12 centers in three countries involving 80,612 patients in opportunistic and population-based screening settings. For opportunistic screening on existing CT scans, multicenter external test cohorts (eight centers, n = 11,466) achieved 98.5% specificity, with 90.0% sensitivity for cancer and 52.5% for precancerous lesions; low-dose CT (LDCT) validation (two centers, n = 1,607) showed comparable performance, supporting EC screening through lung-cancer screening programs. Calibration in a real-world cohort (three centers, n = 35,402) reduced false positives by 72.7% while preserving sensitivity; prospective hospital validation (n = 17,446) achieved a 42.2% PPV, and real-world low-dose screening (n = 10,959) reached 99.94% specificity. EAGLE also detected precancerous lesions—in paired CT–endoscopy cohorts (two centers, n = 702), sensitivities were 65.0% for precancerous lesions and 78.4% for stage I EC at a higher-sensitivity operating point. Exploratory analyses of a prospectively enrolled cohort suggest that referring high-risk individuals for endoscopy could improve screening efficiency. In conclusion, EAGLE has the potential to serve as a scalable tool for early EC screening. Chictr.org.cn identifier: ChiCTR2300074806 . In a large-scale study, a new tool called Esophageal AI-Guided malignant Lesion Evaluation uses artificial intelligence to enhance esophageal cancer detection through noncontrast computed tomography, achieving high sensitivity and specificity across diverse settings.","PeriodicalId":19037,"journal":{"name":"Nature Medicine","volume":"32 10","pages":"3748-3759"},"PeriodicalIF":52.5,"publicationDate":"2026-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s41591-026-04656-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149021797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance of a multi-cancer early detection test in the randomized controlled NHS-Galleri trial","authors":"Richard D. Neal, Saoirse Dolly, Peter Johnson, Helen Jones, Sir Harpal Kumar, Lennard Y. W. Lee, Yujin Lee, Wei Liang, Thomas Round, Rebecca Smittenaar, Jane Warwick, Peter Sasieni, Charles Swanton","doi":"10.1038/s41591-026-04652-8","DOIUrl":"10.1038/s41591-026-04652-8","url":null,"abstract":"The NHS-Galleri trial was a randomized controlled trial evaluating a multi-cancer early detection (MCED) test added to usual care. We reported elsewhere that the primary endpoint of a reduction in the incidence of stage III/IV cancer diagnoses in the intervention arm versus control arm was not met. Here we report prespecified secondary test performance endpoints among evaluable intervention arm participants across three annual screening rounds. These analyses were descriptive; there was no hypothesis testing. Participants aged 50−77 years (N = 142,250) were randomized 1:1 into intervention (MCED) or control arms. Intervention arm participants with positive MCED results were referred to National Health Service (NHS) standard-of-care pathways for diagnostic workup, with referrals informed by the predicted cancer signal origin (CSO). Positive test results were returned for 722 of 70,325 (1.03%), 518 of 64,498 (0.80%) and 561 of 62,323 (0.90%) participants in rounds 1−3, respectively. In aggregate, 937 participants had MCED-detected primary cancers. Respective by-round cancer detection rates were 0.60%, 0.40% and 0.41%; positive predictive values were 58.0% (419/722), 50.4% (261/518) and 45.8% (257/561); and negative predictive values were 98.98% (68,895/69,603), 98.90% (63,278/63,980) and 98.86% (61,058/61,762). Across rounds, specificity ranged from 99.50% to 99.60%; episode sensitivity ranged from 26.7% to 37.2% for all cancers and from 47.6% to 63.4% for 12 prespecified types; and CSO accuracy ranged from 91.1% to 93.6%. These data provide insights on the performance of the MCED test in population screening within the NHS setting. ClinicalTrials.gov: NCT05611632 ; ISRCTN: ISRCTN91431511 . Secondary endpoint analyses in the NHS-Galleri randomized controlled trial, testing a multi-cancer early detection test added to usual care, provide insights on the performance of the blood-based test in more than 142,000 participants without suspicion of cancer.","PeriodicalId":19037,"journal":{"name":"Nature Medicine","volume":"32 10","pages":"3770-3780"},"PeriodicalIF":52.5,"publicationDate":"2026-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s41591-026-04652-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149021795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}