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全部共 45 篇
临床队列临床研究Nature medicine

A health-system-scale, episode-resolved multimodal atlas of cancer cachexia.

Venise Jan Castillon、Amy X Xie、Sonia Boscenco 等DOI: 10.1038/s41591-026-04689-9
肿瘤与癌症多组学风险模型多中心队列研究
0 次引用趋势数据不足

《自然·医学》发表的一项研究利用纪念斯隆-凯特琳癌症中心2010至2024年间52,236例癌症患者的纵向临床基因组数据，构建了按发作解析的癌症恶病质多模态图谱。研究发现，恶病质性体重减轻发作集中于疾病进展期附近，并与几乎所有癌种的不良结局相关；不同恶性肿瘤的恶病质发作具有一致的血清学特征，即白蛋白和血红蛋白降低、肝酶升高。多种肿瘤体细胞基因型（包括多种疾病中的TP53突变）与恶病质发作更频繁相关。基于上述发现，研究者建立了多变量模型，用于预测肺癌、结直肠癌和前列腺癌患者即将发生恶病质性体重减轻的风险，并在丹娜法伯癌症研究院5,785例独立外部队列中完成验证。研究表明，常规临床基因组数据可作为癌症恶病质研究的可扩展资源。

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完整作者

Venise Jan Castillon、Amy X Xie、Sonia Boscenco、Ethan Tse、Neil Ruthen、Jamie Wang、Ziad Bakouny、Michele Waters、Chenlian Fu、Christopher Fong、Mustafa Saleh、Tarek Baroud、Wungki Park、Urvi A Shah、A Ari Hakimi、Puneeth Iyengar、Justin Jee、Eileen White、Alexander Gusev、Tobias Janowitz、Marcus D Goncalves、Ed Reznik

作者机构

  • Computational Oncology Service, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
  • Dana-Farber Cancer Institute, Boston, MA, USA.
  • Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
  • Weill Cornell Medical College, New York, NY, USA.
  • Urology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
  • Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
  • Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
  • Rutgers Cancer Institute, Rutgers University, New Brunswick, NJ, USA.
  • Ludwig Princeton Branch, Ludwig Institute for Cancer Research, Princeton University, Princeton, NJ, USA.
  • Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Harvard Medical School, Boston, MA, USA.
  • Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
  • Northwell Health Cancer Institute, Northwell Health, New Hyde Park, NY, USA.
  • Holman Division of Endocrinology, Diabetes & Metabolism, Department of Medicine and Radiation Oncology, NYU Langone Health, New York, NY, USA.
  • Computational Oncology Service, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA. reznike@mskcc.org.

技术路线

纵向临床基因组数据（MSK-IMPACT，52,236例患者，2010-2024）→定义共识性恶病质体重减轻的发作分辨轨迹并关联结局、血清学与肿瘤基因型→多变量模型预测肺癌、结直肠癌和前列腺癌的恶病质体重减轻风险→在Dana-Farber癌症研究所独立外部队列（n=5,785）中验证

最近 5 个完整年度引用趋势

0110202102022020230202402025

不含 2026 年，仅展示最近 5 个完整年度。

数据来源：openalex更新时间：2026/10/30 06:02
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临床队列临床研究Nature biotechnology

Decoding systemic vascular health and hypertensive disorders in pregnancy through retinal imaging and Visionary AI.

Srilaxmi Bearelly、Cassandra Areff、Matthew K Hoffman 等DOI: 10.1038/s41587-026-03303-0
深度学习风险模型孕产妇队列研究
0 次引用趋势数据不足

妊娠期高血压疾病源于早期胎盘和全身血管功能障碍，现有早孕期筛查不够精准且生物学机制不明确。研究提出可解释平台Visionary AI，将视网膜成像与基于图的血管建模相结合，把视网膜图像转化为微血管结构的拓扑和几何表征，基于血管特征而非通用图像嵌入或临床变量进行风险预测。在1267例妊娠的前瞻性开发队列中，子痫前期预测的曲线下面积为0.91，平均精度为0.81；经稳定性优化的模型无需重新训练即转移至独立外部验证队列，AUC为0.81，AP为0.68，优于临床和深度学习基准。视网膜血管拓扑、几何、网络复杂性和层级组织等重复出现的特征，使母体视网膜可作为全身血管健康的微创生物传感器，Visionary AI为妊娠风险分层提供了具有生物学可解释性的框架。

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编辑短评

摘要展示了基于视网膜图像和图建模的可解释AI用于妊娠高血压疾病预测。开发队列AUC 0.91，外部验证AUC 0.81且优于临床与深度学习基准，提示一定泛化性。但摘要未报告校准、决策曲线或亚组表现，且外部验证细节有限，临床效用仍需前瞻性影响研究确认。

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完整作者

Srilaxmi Bearelly、Cassandra Areff、Matthew K Hoffman、Daniel Sunko、Andrew F Laine、Bin Choi、Hanna Rodriguez Coleman、Bianca Cordazzo Vargas、Chloe Y Li、Jennifer J Dawkins、Emily Amir、Sahar Paz、Lisa A Hark、Whitney A Booker、April A Jauhal、Justin S Brandt、Ronald J Wapner、Liat Shenhav

作者机构

  • Department of Ophthalmology, Columbia University Irving Medical Center, New York, NY, USA.
  • Department of Obstetrics and Gynecology, Columbia University Irving Medical Center, New York, NY, USA.
  • Institute for Systems Genetics, NYU Grossman School of Medicine, New York University, New York, NY, USA.
  • Division of Obstetrics and Gynecology, ChristianaCare, Newark, DE, USA.
  • Department of Biomedical Engineering, Columbia University, New York, NY, USA.
  • Department of Radiology, Columbia University Irving Medical Center, New York, NY, USA.
  • Center for Health Analytics, Data Science Institute, Columbia University, New York, NY, USA.
  • Department of Obstetrics and Gynecology, NYU Grossman School of Medicine, New York University, New York, NY, USA.
  • Institute for Systems Genetics, NYU Grossman School of Medicine, New York University, New York, NY, USA. liat.shenhav@nyulangone.org.
  • Department of Microbiology, NYU Grossman School of Medicine, New York University, New York, NY, USA. liat.shenhav@nyulangone.org.
  • Department of Obstetrics and Gynecology, NYU Grossman School of Medicine, New York University, New York, NY, USA. liat.shenhav@nyulangone.org.
  • Department of Computer Science, Courant Institute of Mathematical Sciences, New York University, New York, NY, USA. liat.shenhav@nyulangone.org.

技术路线

数据/样本：前瞻性开发队列1,267例妊娠的视网膜图像；分析：将视网膜图像转换为微血管结构的拓扑与几何表示，结合基于图的血管建模进行风险预测；验证：稳定性优化模型未重新训练迁移至独立外部验证队列。

最近 5 个完整年度引用趋势

0110202102022020230202402025

不含 2026 年，仅展示最近 5 个完整年度。

数据来源：openalex更新时间：2026/10/30 06:02
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公共数据库研究类型未标注Nature genetics

Imputation of fluid intelligence scores reduces ascertainment bias and increases power for analyses of common and rare variants.

David M van den Berg、Daniel S Malawsky、Wei Huang 等DOI: 10.1038/s41588-026-02787-5
UK Biobank罕见病风险模型多中心队列研究
5 次引用趋势数据不足

本研究整合英国生物银行多项流体智力测试数据，并对未测量个体进行流体智力估算，使样本量从约27万人增至45.5万人以上。广泛验证显示，估算表型在遗传上类似实测流体智力，但仍可能残留部分非认知信号。合并实测与估算流体智力后，独立显著常见单核苷酸多态性关联数由390增至550，并减少了因测试参与者平均受教育程度较高而引入的确认偏倚。罕见变异分析识别出26个显著关联基因（错误发现率<1%），其中8个缺乏智力或神经发育疾病关联的强先验证据，并在外部队列中整体重复。结果表明，估算智力可提升遗传发现效能，并有助于发现新的神经发育疾病相关基因。

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完整作者

David M van den Berg、Daniel S Malawsky、Wei Huang、Petr Danecek、Klaudia Walter、Georgios Kalantzis、Isaac Wade、Duncan Palmer、Ewan Birney、Karin J H Verweij、Dirk J A Smit、Sarah J Lindsay、Matthew E Hurles、Abdel Abdellaoui、Hilary C Martin

作者机构

  • Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands.
  • Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK.
  • Nuffield Department of Population Health, Medical Sciences Division, University of Oxford, Oxford, UK.
  • Department of Statistics, University of Oxford, Oxford, UK.
  • The Pioneer Centre for SMARTbiomed, Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
  • European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK.
  • Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK. meh@sanger.ac.uk.
  • Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands. a.abdellaoui@amsterdamumc.nl.
  • Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK. hcm@sanger.ac.uk.

技术路线

数据/样本：整合UK Biobank多项流体智力(FI)测量，对未测量个体进行FI插补，样本量由约27万扩至超45.5万→分析：将实测与插补FI合并用于常见变异关联分析(独立显著SNP由390增至550)及罕见变异分析(识别26个FDR<1%显著基因)→验证：对插补表型进行广泛验证，显示其遗传上类似实测FI但可能残留部分非认知信号；罕见变异结果在外部队列中总体重复。

最近 5 个完整年度引用趋势

0110202102022020230202412025

不含 2026 年，仅展示最近 5 个完整年度。

数据来源：openalex更新时间：2026/10/29 12:36
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临床队列临床研究Immunity, inflammation and disease

Growth Hormone-Related Gene Signatures Reveal Diagnostic Biomarkers, Immune Landscapes, and Potential Therapeutic Targets in Sepsis Through WGCNA and Machine Learning Analyses.

Xiuhua Li、Ang Liu、Yingwei Ding 等DOI: 10.1002/iid3.70566
GEO机器学习风险模型队列研究
0 次引用趋势数据不足

脓毒症会重塑炎症、免疫和代谢活动，但生长激素相关基因在该反应中的表现尚不明确。研究整合两个GEO芯片队列构建模型，并以GSE26378和GSE28750独立验证；通过差异分析与WGCNA识别脓毒症相关表达信号，再与1205个生长激素相关基因取交集，经蛋白互作和四种机器学习方法筛选候选基因。结果获得2552个差异表达基因和1873个WGCNA模块基因，交集含40个基因，PPI分析缩减至23个枢纽候选，机器学习一致保留JAK1和GATA3；二者在脓毒症中均下调，在训练及外部验证队列中诊断性能稳定，联合模型判别能力优于单基因。功能分析显示两基因与T细胞信号、抗原加工、免疫调节、线粒体代谢及翻译过程相关；免疫图谱显示脓毒症中淋巴细胞相关特征减弱、髓系特征增强；按JAK1/GATA3表达可分两个免疫和转录谱不同的亚型。研究表明JAK1和GATA3是有前景的生长激素相关候选诊断生物标志物，与脓毒症免疫异质性相关，其联合特征或可用于分子分层，尚需进一步实验和临床验证。

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完整作者

Xiuhua Li、Ang Liu、Yingwei Ding、Gang Chen、Bingwen Zhang

作者机构

  • Department of Emergency Medicine,, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, Zhejiang, China.

技术路线

暂未整理

最近 5 个完整年度引用趋势

0110202102022020230202402025

不含 2026 年，仅展示最近 5 个完整年度。

数据来源：openalex更新时间：2026/11/01 03:57
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临床队列临床研究Nature

Sugar-rich foods exacerbate antibiotic-induced microbiome disruption.

Anqi Dai、Annamaria Ballweg、William Jogia 等DOI: 10.1038/s41586-026-11077-3
多组学风险模型队列研究
1 次引用趋势数据不足

一项研究通过精确追踪173名因造血细胞移植住院患者摄入的9 419餐饮食并分析随后微生物组变化，评估饮食与肠道微生物组动态的关系。对158名有配对纵向微生物组样本患者数据应用贝叶斯推断显示，抗生素暴露期间摄入甜食和糖类可预测微生物失调加重，表现为α多样性降低和致病共生菌肠球菌更大程度扩增。小鼠实验亦表明，补充蔗糖可增加并延长抗生素诱导的肠球菌扩增。研究提示，抗生素治疗期间避免富含单糖的饮食或可减轻微生物群破坏。

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编辑短评

评估了饮食（特别是甜食和糖类）与肠道微生物组动态的关系，抗生素治疗期间避免富含单糖的饮食或可减轻微生物群破坏。

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完整作者

Anqi Dai、Annamaria Ballweg、William Jogia、Madhumitha Rangesa、Peter A Adintori、Mirae Baichoo、Tyler Funnell、Teng Fei、Nicholas R Waters、Abrielle Swartz、Sukanya Sahu、Brianna Gipson、Sandeep S Raj、Eiko Hayase、Zoe Pierce、Kenton Wu、Natalie Smith、Justin Neuberger、Adam Warren、Marina Burgos da Silva、Oriana Miltiadous、Corrado Zuanelli Brambilla、Marissa Lubin Buchan、Tatanisha K Peets、Ana Gradissimo、Luigi A Amoretti、Caichen Duan、Chenzhen Zhang、Fanny Matheis、Alexis P Sullivan、John B Slingerland、Annelie G Clurman、Daniel G Brereton、Paul A Giardina、Antonio L C Gomes、Abigail J Johnson、Dan Knights、Robert R Jenq、Kate A Markey、Miguel-Angel Perales、Sergio A Giralt、Marcel R M van den Brink、Jonas Schluter、Jonathan U Peled

作者机构

  • Adult Bone Marrow Transplantation Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
  • Institute for Systems Genetics, NYU Langone Health Grossman School of Medicine, New York, NY, USA.
  • Department of Microbiology, NYU Langone Health Grossman School of Medicine, New York, NY, USA.
  • Department of Hematology and Hematopoietic Cell Transplantation, City of Hope National Medical Center, Los Angeles, CA, USA.
  • Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
  • Departments of Genomic Medicine and Stem Cell Transplantation Cellular Therapy, Division of Cancer Medicine, University of Texas MD Anderson Cancer Center, Houston, TX, USA.
  • Human Oncology & Pathogenesis Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
  • Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
  • Haematology Unit, Department of Cellular Therapy, Hematology and Laboratory Medicine, Azienda Ospedaliero-Universitaria Senese, Siena, Italy.
  • Food and Nutrition Services, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
  • Department of Biomedical Engineering, NYU Tandon School of Engineering, New York, NY, USA.
  • Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA.
  • Riddet Institute, Massey University, Palmerston North, New Zealand.
  • Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, USA.
  • Biotechnology Institute, University of Minnesota, Saint Paul, MN, USA.
  • Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, WA, USA.
  • Division of Medicine, University of Washington, Seattle, WA, USA.
  • Weill Cornell Medical College, New York, NY, USA.
  • City of Hope, Duarte, CA, USA. mvandenbrink@coh.org.
  • Institute for Systems Genetics, NYU Langone Health Grossman School of Medicine, New York, NY, USA. jonas.schluter@nyulangone.org.
  • Department of Microbiology, NYU Langone Health Grossman School of Medicine, New York, NY, USA. jonas.schluter@nyulangone.org.
  • Laura and Isaac Perlmutter Cancer Center, NYU Langone Health, New York, NY, USA. jonas.schluter@nyulangone.org.
  • Adult Bone Marrow Transplantation Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA. peledj@mskcc.org.
  • Weill Cornell Medical College, New York, NY, USA. peledj@mskcc.org.

技术路线

数据/样本：173例造血干细胞移植住院患者的9,419餐饮食记录及纵向肠道菌群样本（其中158例有配对纵向菌群样本用于贝叶斯推断）；分析：贝叶斯推断评估饮食与菌群动态关系；验证：小鼠实验验证蔗糖补充对抗生素诱导肠球菌扩张的影响。

最近 5 个完整年度引用趋势

0110202102022020230202402025

不含 2026 年，仅展示最近 5 个完整年度。

数据来源：openalex更新时间：2026/10/30 06:30
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临床队列临床研究Cell

Microbiome signatures linked to cancer and treatment adverse events in a real-world cohort.

Lu Yang、Vaidhvi Singh、Brent J Gawey 等DOI: 10.1016/j.cell.2026.09.009
肿瘤与癌症多组学风险模型多中心队列研究
0 次引用趋势数据不足

一项真实世界混合癌症队列研究（Mayo Clinic Cancer Microbiome）纳入1,364例癌症患者和287名健康对照，采用可校正癌症、合并症及人口学和临床变量相关非特异性微生物组关联的分析框架，在五类癌症中鉴定出341种最可能参与癌症发病的癌症相关菌种。在癌种内部，早期乳腺癌患者粪便胆汁酸及C. scindens水平较低，早期结直肠癌患者乳酸和Veillonella parvula升高；携带二氢嘧啶脱氢酶的Anaerostipes hadrus对5-氟尿嘧啶所致腹泻具有保护作用。研究提示该队列可用于发现癌症特异性微生物组特征和预测性生物标志物。

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完整作者

Lu Yang、Vaidhvi Singh、Brent J Gawey、Jason P Sinnwell、Stephen Johnson、Elle C Billings、Trena M Van Gorp、Jonathan J Harrington、Michael Q Slama、Lisa M Till、Manavjot Singh、Thoshik R Samineni、Mojun Zhu、Krishna R Kalari、Gianrico Farrugia、Jun Chen、Ruben A T Mars、Purna C Kashyap

作者机构

  • Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA; The Benioff Center for Microbiome Medicine, University of California San Francisco, San Francisco, CA, USA.
  • Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN, USA.
  • Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN, USA; Division of Gastroenterology, Mount Sinai, New York, NY, USA.
  • Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA.
  • Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA.
  • Division of Gastroenterology, Mount Sinai, New York, NY, USA.
  • Department of Oncology, Mayo Clinic, Rochester, MN, USA.
  • Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN, USA; Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA.
  • Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA; Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA. Electronic address: chen.jun2@mayo.edu.
  • Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN, USA; Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA. Electronic address: mars.ruben@mayo.edu.
  • Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN, USA; Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA. Electronic address: kashyap.purna@mayo.edu.

技术路线

真实世界混合癌症队列（1364例癌症患者和287例健康对照）的粪便微生物组数据 → 构建校正框架以排除与合并症、人口学和临床变量的非特异性关联，鉴定出五类癌症相关的341个菌种

最近 5 个完整年度引用趋势

0110202102022020230202402025

不含 2026 年，仅展示最近 5 个完整年度。

数据来源：openalex更新时间：2026/10/30 12:53
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临床队列临床研究Nature medicine

Werner helicase inhibitor for advanced microsatellite instability solid tumors: a phase 1 trial.

Timothy A Yap、Elisa Fontana、Natalie Cook 等DOI: 10.1038/s41591-026-04657-3
肿瘤与癌症风险模型成人队列研究
2 次引用趋势数据不足

一项正在进行的首次人体1期试验第1部分评估了WRN共价抑制剂RO7589831（VVD-133214）在88例微卫星不稳定（MSI）和/或DNA错配修复缺陷晚期实体瘤患者中的递增剂量。给药方案为每日一次150 mg、每日两次150~1,000 mg或每日三次200~600 mg，主要目标为安全性和耐受性及推荐2期剂量与方案（RP2D）。600 mg每日三次组发生1例剂量限制性2级恶心，但未确定最大耐受剂量。发生率≥10%的治疗中出现不良事件多为1~2级；≥30%者为恶心（54.5%）、腹泻（43.2%）、乏力（39.8%）、贫血（37.5%）和呕吐（30.7%）。导致停药的不良事件少见（3.4%），无5级事件。MSI可评估患者疾病控制率为74.2%（49/66），7例确认为部分缓解（客观缓解率10.6%），中位缓解持续时间10.2+个月；中位无进展生存期6.7个月，中位总生存期17.6个月。选择150 mg和600 mg每日两次作为RP2D。

为什么值得关注

编辑短评

WRN共价抑制剂RO7589831（VVD-133214）的首次人体1期试验第1部分，评估了患者中的递增剂量。

查看扩展信息

完整作者

Timothy A Yap、Elisa Fontana、Natalie Cook、Marwan Fakih、Oliver Bechter、Erica S Tsang、Emiliano Calvo、Kristoffer S Rohrberg、Javier Ros、Ignacio Matos、John H Strickler、Malaka Ameratunga、Meredith Pelster、Adam Sharp、Jaspreet Grewal、Susanna Ulahannan、Rajesh Belani、Matthew P Patricelli、Sophia M Blake、David Dejardin、Stephen Fowler、Christina Godfried Sie、Astrid Heller、Nina Henkel、Michael Hettich、Angelika Lahr、David Lamparter、Charlotte Lane、Ting Liu、Hélène Meistermann、Christophe Meille、Neil Parrott、Sotirios K Sotiriou、Denise Reeves、Barbara Romagnoli、Piergiorgio Pettazzoni、Daniel Waterkamp、Matthias Wittwer、Yali Fu、Sophie Postel-Vinay

作者机构

  • University of Texas MD Anderson Cancer Center, Houston, TX, USA. tyap@mdanderson.org.
  • Sarah Cannon Research Institute, London, UK.
  • The Christie NHS Foundation Trust and The University of Manchester, Manchester, UK.
  • City of Hope National Medical Center, Duarte, CA, USA.
  • Universitair Ziekenhuis (UZ) Leuven, Leuven, Belgium.
  • Princess Margaret Cancer Centre, Toronto, Ontario, Canada.
  • START Madrid-CIOCC, Centro Integral Oncológico Clara Campal, Madrid, Spain.
  • Rigshospitalet, Copenhagen, Denmark.
  • Vall d´Hebron Institut d'Oncologia (VHIO), Barcelona, Spain.
  • Clinica Universidad de Navarra, Madrid, Spain.
  • Duke University, Durham, NC, USA.
  • The Alfred, Melbourne, Victoria, Australia.
  • Sarah Cannon Research Institute, Nashville, TN, USA.
  • Royal Marsden Hospital, London, Institute of Cancer Research, London, UK.
  • Norton Cancer Institute, Louisville, KY, USA.
  • University of Oklahoma, Norman, OK, USA.
  • Vividion Therapeutics, San Diego, CA, USA.
  • Roche Pharma Research and Early Development, Roche Innovation Center Munich, Munich, Germany.
  • Roche Pharma Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland.
  • Roche Products Ltd., Welwyn Garden City, UK.
  • Roche Pharma Research and Early Development, Roche Innovation Center New York, New York, NY, USA.
  • Gustave Roussy, Villejuif and University College of London Cancer Institute, London, UK.

技术路线

88例MSI和/或dMMR晚期实体瘤患者→剂量递增（每日一次150 mg、每日两次150-1,000 mg、每日三次200-600 mg）→安全性/耐受性分析及RP2D确定→抗肿瘤活性分析（DCR、ORR、DOR、PFS、OS）→探索性ctDNA分子缓解和FDG-PET代谢缓解分析支持生物学活性（肿瘤组织直接靶点结合未能证实）

最近 5 个完整年度引用趋势

0110202102022020230202402025

不含 2026 年，仅展示最近 5 个完整年度。

数据来源：openalex更新时间：2026/10/30 06:03
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临床队列临床研究Cell

Opposing functions of gut immunomodulatory metabolites on CAR-T therapy.

Markus Perl、Severin Guetter、Dhyani Shah 等DOI: 10.1016/j.cell.2026.09.004
肿瘤与癌症多组学风险模型队列研究
0 次引用趋势数据不足

一项研究纳入德国三个中心的129例患者，采用鸟枪法宏基因组学和靶向质谱分析发现，CAR-T细胞治疗前短链脂肪酸（尤其是戊酸）水平降低与疾病进展风险增加相关；而吲哚类代谢物（包括吲哚-3-甲醛和吲哚-3-乙酸）及支链脂肪酸异戊酸水平升高与不良结局相关。在人源和小鼠CAR-T细胞模型中的功能验证表明，补充戊酸可增强CAR-T细胞疗效，而吲哚-3-甲醛和异戊酸则损害其疗效。研究揭示了免疫调节代谢物对CAR-T细胞治疗的对立作用，对设计代谢物引导的微生物组治疗策略具有重要意义。

为什么值得关注

暂未整理

查看扩展信息

完整作者

Markus Perl、Severin Guetter、Dhyani Shah、Simon Holzinger、Luisa Becker、Misbah Tariq、Philipp Menauer、Simon Delahais、Gloria Lutzny-Geier、Julian N Scherer、Sascha Göttert、Konstantin Herfeld、Paul Heinrich、Klaus Georg Kreitmeier、Claudia Gebhard、Wolfgang Herr、Marcel P Trefny、Janina Doerr、Saba Sameri、Dennis C Harrer、Kai Ziegler-Martin、Sarah Staudt、Leo Hansmann、Matthias Edinger、Daniel Wolff、Daniela Weber、Elisabeth Meedt、Alexander Denk、Andre Gessner、Ernst Holler、Tobias Wertheimer、Klaus Neuhaus、Tingting Zheng、David M Cordas Dos Santos、Sebastian Theurich、Melanie Schirmer、Karin Kleigrewe、Jonas Schluter、Marcel van den Brink、Michael Schmitt、Markus Feuerer、Christoph Heuser-Loy、Jeremy Baldwin、Luca Gattinoni、Michael Rehli、Michael Hudecek、Camille Bigenwald、Laurence Zitvogel、Marion Subklewe、Viktoria Blumenberg、Maria-Luisa Schubert、Christoph Stein-Thoeringer、Maik Luu、Sebastian Kobold、Matthias A Fante、Erik Thiele Orberg、Hendrik Poeck

作者机构

  • Department of Internal Medicine III, Hematology & Internal Oncology, University Hospital Regensburg, Regensburg, Germany; Bavarian Cancer Research Center (BZKF), Regensburg, Germany. Electronic address: markus.perl.ukr@gmail.com.
  • Department of Internal Medicine III, Hematology & Internal Oncology, University Hospital Regensburg, Regensburg, Germany; Leibniz Institute for Immunotherapy (LIT), Regensburg, Germany.
  • Department of Internal Medicine III, Hematology & Internal Oncology, University Hospital Regensburg, Regensburg, Germany.
  • Division of Clinical Pharmacology, Department of Medicine IV, University Hospital, Ludwig Maximilian University (LMU) of Munich, Munich, Germany.
  • Department of Internal Medicine 5, Hematology and Oncology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
  • Leibniz Institute for Immunotherapy (LIT), Regensburg, Germany.
  • Lehrstuhl für Zelluläre Immuntherapie, Medizinische Klinik und Poliklinik II, Universitätsklinikum Würzburg, Würzburg, Germany.
  • Institute of Clinical Microbiology and Hygiene, University Hospital Regensburg, Regensburg, Germany.
  • Division of Hematology and Oncology, Department of Medicine, Freiburg University, Freising, Germany.
  • Core Facility Microbiome, ZIEL - Institute for Food & Health, Technische Universität München, Freising, Germany.
  • Translational Microbiome Data Integration, School of Life Sciences, Technical University of Munich, Freising, Germany; ZIEL - Institute for Food & Health, Technische Universität München, Freising, Germany.
  • Gene Center and Department of Biochemistry, LMU Munich, Munich, Germany.
  • Department of Medicine III, Hematology/Oncology, LMU University Hospital and Gene Center, LMU Munich, Munich, Germany; German Cancer Consortium (DKTK), Partner Site Munich, a partnership between the DKFZ and BZKF at the University Hospital of the LMU, Germany.
  • Translational Microbiome Data Integration, School of Life Sciences, Technical University of Munich, Freising, Germany; ZIEL - Institute for Food & Health, Technische Universität München, Freising, Germany; Center for Organoid Systems, Technical University Munich, Garching, Germany.
  • Bavarian Center for Biomolecular Mass Spectrometry, TUM School of Life Sciences, Technical University Munich, Freising, Germany.
  • Department of Microbiology, New York University Grossman School of Medicine, New York, NY, USA; Institute for Systems Genetics, NYU Langone Health Grossman School of Medicine, New York, NY, USA.
  • Beckman Research Institute, City of Hope, Duarte, CA, USA.
  • Department of Medicine V, Hematology, Oncology and Rheumatology, University Hospital Heidelberg, Heidelberg, Germany.
  • Leibniz Institute for Immunotherapy (LIT), Regensburg, Germany; Department of Internal Medicine III, Haematology and Oncology, University Hospital Bonn, Bonn, Germany.
  • Lehrstuhl für Zelluläre Immuntherapie, Medizinische Klinik und Poliklinik II, Universitätsklinikum Würzburg, Würzburg, Germany; Branch Cellular Immunotherapy, Fraunhofer Institute for Cell Therapy and Immunology (IZI), Würzburg, Germany.
  • Gustave Roussy Cancer Campus, Villejuif, France.
  • Department of Medicine III, Hematology/Oncology, LMU University Hospital and Gene Center, LMU Munich, Munich, Germany; Laboratory for Translational Cancer Immunology, Gene Center LMU Munich, Munich, Germany; German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany; Bavarian Cancer Research Center (BZKF), Munich, Germany.
  • Department of Internal Medicine I, University Hospital Tuebingen, Tuebingen, Germany.
  • Division of Clinical Pharmacology, Department of Medicine IV, University Hospital, Ludwig Maximilian University (LMU) of Munich, Munich, Germany; Laboratory for Translational Cancer Immunology, Gene Center LMU Munich, Munich, Germany; Bavarian Cancer Research Center (BZKF), Munich, Germany; German Center for Lung Research (DZL), Partner Site Munich, Munich, Germany.
  • Department of Internal Medicine III, Hematology & Internal Oncology, University Hospital Regensburg, Regensburg, Germany; Department of Internal Medicine II, University Hospital of Würzburg, Würzburg, Germany.
  • Department of Internal Medicine III, Hematology & Internal Oncology, University Hospital Regensburg, Regensburg, Germany; Bavarian Cancer Research Center (BZKF), Regensburg, Germany. Electronic address: erik.orberg@ukr.de.
  • Department of Internal Medicine III, Hematology & Internal Oncology, University Hospital Regensburg, Regensburg, Germany; Bavarian Cancer Research Center (BZKF), Regensburg, Germany; Leibniz Institute for Immunotherapy (LIT), Regensburg, Germany. Electronic address: hendrik.poeck@ukr.de.

技术路线

粪便宏基因组与靶向质谱（129例三中心队列）→代谢物与疾病进展关联分析→人及小鼠CAR-T细胞模型功能验证

最近 5 个完整年度引用趋势

0110202102022020230202402025

不含 2026 年，仅展示最近 5 个完整年度。

数据来源：openalex更新时间：2026/10/30 06:03
探索相关选题直接查重
查看原文
临床队列临床研究Nature medicine

Large-scale esophageal cancer screening through noncontrast computed tomography and artificial intelligence.

Jian Zhou、Guangyu Guo、Jiawen Yao 等DOI: 10.1038/s41591-026-04656-4
肿瘤与癌症风险模型多中心队列研究
0 次引用趋势数据不足

研究开发了食管人工智能引导恶性病变评估（EAGLE）模型，可从胸部非增强CT中检测癌前病变和食管癌。模型在2个中心6813例患者中训练，并在3个国家12个中心80612例患者中验证，涵盖机会性筛查和人群筛查场景。多中心外部测试（8个中心，n=11466）特异性达98.5%，癌症敏感性90.0%，癌前病变敏感性52.5%；低剂量CT验证（2个中心，n=1607）表现相当。真实世界队列校准（3个中心，n=35402）使假阳性减少72.7%且保持敏感性；前瞻性医院验证（n=17446）阳性预测值为42.2%，真实世界低剂量筛查（n=10959）特异性达99.94%。配对CT-内镜队列（2个中心，n=702）中，较高敏感性操作点下癌前病变和Ⅰ期食管癌敏感性分别为65.0%和78.4%。前瞻性队列探索性分析提示，转诊高危个体接受内镜检查可提高筛查效率。EAGLE有望成为可扩展的早期食管癌筛查工具。

为什么值得关注

AI 辅助短评，基于标题和摘要整理

该研究针对食管癌缺乏无创可扩展筛查工具的问题，构建EAGLE模型从胸部平扫CT识别癌前病变与早期癌，并在多国多中心及多种筛查场景中验证。其价值在于展示了利用现有CT资源开展机会性筛查的可行性，为扩大食管癌早筛覆盖提供了新思路。

查看扩展信息

完整作者

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

作者机构

  • Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China.
  • DAMO Academy, Alibaba Group, Hangzhou, China.
  • Hupan Lab, Hangzhou, China.
  • Department of Surgical Oncology, Shantou Central Hospital, Shantou, China.
  • Department of Radiology, Shanghai Institution of Pancreatic Disease, Shanghai, China.
  • Department of Radiation Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
  • Zhejiang Key Laboratory of Imaging and Interventional Medicine, Department of Radiology, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui Central Hospital, Lishui, China.
  • Department of Radiotherapy, Cancer Hospital Affiliated to Xiangya Medical College, Central South University, Hunan Cancer Hospital, Changsha, China.
  • Department of Radiology, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic.
  • Department of radio-oncology, People's Hospital of Chenghai Shantou, Shantou, China.
  • DAMO Academy, Alibaba Group, Washington, DC, USA.
  • Department of Radiology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Chengdu, China.
  • Department of Endoscopy Center, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Chengdu, China.
  • Department of Radiation Oncology, Precision Radiation in Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Chengdu, China.
  • Department of Radiological Imaging, Suining Central Hospital, Suining, China.
  • Department of Radiotherapy, The Third Affiliated Teaching Hospital of Xinjiang Medical University (Affiliated Cancer Hospital), Urumqi, China.
  • Medical imaging center, The Third Affiliated Teaching Hospital of Xinjiang Medical University (Affiliated Cancer Hospital), Urumqi, China.
  • Department of Radiation Oncology, The Teaching Hospital of Fujian Medical University, Fujian Provincial Cancer Hospital, Fuzhou, China.
  • Guangdong Provincial Key Laboratory of Infectious Diseases and Molecular Immunopathology, Institute of Oncologic Pathology, Cancer Research Center, Shantou University Medical College, Shantou, China.
  • International Health Management Center, Shanghai Institution of Pancreatic Disease, Shanghai, China.
  • School of Artificial Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, China.
  • National Central Cancer Registry, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
  • Department of Cancer Prevention, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
  • Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China. zhangdingwen2006yyy@gmail.com.
  • Zhejiang Key Laboratory of Imaging and Interventional Medicine, Department of Radiology, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui Central Hospital, Lishui, China. jijiansong@zju.edu.cn.
  • Department of oncology, Suining Central Hospital, Suining, China. 1500921245@qq.com.
  • Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China. xchuanm@sysucc.org.cn.
  • Department of Radiology, Shanghai Institution of Pancreatic Disease, Shanghai, China. mdkaicao163@163.com.
  • Sichuan Provincial Engineering Research Center of Tumor Organoids and Clinical Transformation, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Chengdu, China. zhaoyuqian1211@scszlyy.org.cn.
  • Hupan Lab, Hangzhou, China. ling.z@alibaba-inc.com.
  • DAMO Academy, Alibaba Group, Washington, DC, USA. ling.z@alibaba-inc.com.
  • Department of Radiation Oncology, Precision Radiation in Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Chengdu, China. wangqifeng@scszlyy.org.cn.

技术路线

数据/样本（两中心6813例训练，三国家12中心80612例多场景验证，含机会性筛查11466例、低剂量CT 1607例、真实世界校准35402例、前瞻性医院验证17446例、真实世界低剂量筛查10959例、CT-内镜配对702例）→分析（构建EAGLE模型从胸部平扫CT检测癌前病变与食管癌，评估敏感性、特异性、PPV及校准后假阳性变化）

最近 5 个完整年度引用趋势

0110202102022020230202402025

不含 2026 年，仅展示最近 5 个完整年度。

数据来源：openalex更新时间：2026/11/01 03:52
探索相关选题直接查重
查看原文
临床队列临床研究Cancer cell

Chemotherapy-free phase 1/2 trial of HRS-4642 plus adebrelimab in previously treated metastatic KRAS G12D-mutant pancreatic cancer.

Si Shi、Miaoyan Wei、Nan Du 等DOI: 10.1016/j.ccell.2026.08.012
肿瘤与癌症风险模型队列研究
1 次引用趋势数据不足

一项1/2期试验（NCT06427239）评估选择性KRAS G12D抑制剂HRS-4642联合PD-L1阻断剂adebrelimab，用于48例既往接受过治疗的转移性KRAS G12D突变胰腺导管腺癌患者。剂量递增未出现剂量限制性毒性，确定了2期推荐剂量，未发生治疗相关死亡；最常见治疗相关不良事件为高胆固醇血症和贫血（各50%）。在2期推荐剂量队列（n=37）中，确认的客观缓解率为43.2%（95%CI 27.1-60.5），疾病控制率为81.1%（95%CI 64.8-92.0），中位缓解持续时间为6.9个月（95%CI 4.8-8.6），中位无进展生存期为5.7个月（95%CI 4.0-7.5），中位总生存期为11.5个月（95%CI 9.2-16.9）。该无化疗方案耐受性良好，在难治性KRAS G12D突变PDAC中显示出令人鼓舞的抗肿瘤活性，值得进一步研究。

为什么值得关注

编辑短评

经治 KRAS G12D 突变转移性胰腺癌后线治疗选择匮乏；该 Ⅰ/Ⅱ 期研究证实 HRS‑4642 联合 adebrelimab 的无化疗方案安全性可控、应答率高，为这类患者提供去化疗的靶向免疫联合新方向。

查看扩展信息

完整作者

Si Shi、Miaoyan Wei、Nan Du、Jialin Li、Boyue Han、Wei Wang、Xin Hu、Fei Luo、Jin Xu、Xianjun Yu

作者机构

  • Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China; Shanghai Pancreatic Cancer Institute, Shanghai, China; Pancreatic Cancer Institute, Fudan University, Shanghai, China. Electronic address: shisi@fudanpci.org.
  • Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China; Shanghai Pancreatic Cancer Institute, Shanghai, China; Pancreatic Cancer Institute, Fudan University, Shanghai, China.
  • Precision Cancer Medicine Center, Fudan University Shanghai Cancer Center, Shanghai, China.
  • Clinical Research & Development, Jiangsu Hengrui Pharmaceuticals Co., Ltd, Shanghai, China.
  • Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China; Shanghai Pancreatic Cancer Institute, Shanghai, China; Pancreatic Cancer Institute, Fudan University, Shanghai, China. Electronic address: xujin@fudanpci.org.
  • Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China; Shanghai Pancreatic Cancer Institute, Shanghai, China; Pancreatic Cancer Institute, Fudan University, Shanghai, China. Electronic address: yuxianjun@fudanpci.org.

技术路线

48例经治转移性KRAS G12D突变PDAC患者入组→剂量递增确定RP2D并汇总治疗相关不良事件→RP2D队列（n=37）疗效分析（确认ORR、DCR、DoR、PFS、OS及95%CI）

最近 5 个完整年度引用趋势

0110202102022020230202402025

不含 2026 年，仅展示最近 5 个完整年度。

数据来源：openalex更新时间：2026/11/01 03:52
探索相关选题直接查重
查看原文
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