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合成生物学基础研究Science translational medicine

Tumor-specific antibodies elicited by engineered bacteria promote bladder cancer immunotherapy in preclinical mouse models.

0 次引用趋势数据不足

研究者利用合成生物学改造益生菌大肠杆菌Nissle 1917(EcN),使其表达人趋化因子CXCL13,并在膀胱癌原位小鼠模型中经膀胱内给药。结果显示,工程菌可定植于膀胱肿瘤,并在肿瘤引流淋巴结中诱发生发中心反应;与PD-1阻断联合后,在两种侵袭性强、生长快且免疫“冷”的原位小鼠模型中增强抗肿瘤活性。该效应依赖CD8+ T细胞和CD4+滤泡辅助性T细胞,联合治疗提高肿瘤特异性抗体应答,促进长期生存及肿瘤再挑战后的保护性免疫。结论认为,表达CXCL13的工程化EcN可通过增强肿瘤特异性体液免疫提升PD-1阻断免疫治疗疗效。

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编辑短评

免疫冷膀胱癌对 PD‑1 抑制剂应答差;该研究用表达 CXCL13 的工程益生菌 EcN 膀胱灌注,在引流淋巴结诱导生发中心反应、产生肿瘤特异性抗体,与 PD‑1 阻断协同,实现肿瘤控制并建立长期抗肿瘤记忆Science

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完整作者

Mathieu Rouanne、Noah Chen、Dylan L Mariuzza、Zaofeng Yang、Fangda Li、Kenia de Los Santos-Alexis、Thomas M Savage、Rosa L Vincent、Cathy L Mendelsohn、Tal Danino、Nicholas Arpaia

作者机构

  • Department of Microbiology & Immunology, Columbia University, New York, NY 10032, USA.
  • Herbert Irving Comprehensive Cancer Center, Columbia University, New York, NY 10032, USA.
  • Department of Biomedical Engineering, Columbia University, New York, NY 10027, USA.
  • Department of Urology, Columbia University, New York, NY 10032, USA.
  • Data Science Institute, Columbia University, New York, NY 10027, USA.

技术路线

工程化CXCL13表达EcN经膀胱内递送定植膀胱肿瘤 → 膀胱肿瘤引流淋巴结生发中心应答分析

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 03:49
公共数据库基础研究Acta pharmaceutica Sinica. B

Oral artificial microbio-vaccine triggers synergistic immunogenic cell death induction and tumor microenvironment remodeling for liver cancer therapy.

暂无引用数据

研究人员采用分层组装策略构建了一种口服人工微生物疫苗(TP/GA-NYPs):将免疫原性细胞死亡诱导剂雷公藤甲素(TP)与免疫调节剂甘草酸(GA)共组装为纳米解毒核心,并负载于中空酿酒酵母微胶囊(YC)中,壳聚糖外壳保障包封效果。该设计利用YC实现胃部保护和肠道黏膜靶向。结果显示,TP/GA核心具有协同作用,TP有效诱导免疫原性细胞死亡,GA则通过抑制转移相关标志物、增强免疫刺激性细胞因子谱重塑免疫抑制性肿瘤微环境。该口服聚集体可激发肠道黏膜及全身免疫,抑制肝癌进展与转移,并减轻TP的全身毒性。研究提示功能性聚集体在协同抗肿瘤免疫方面具有治疗潜力,为口服免疫治疗提供了通用平台。

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完整作者

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作者机构

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技术路线

暂未整理

最近 5 个完整年度引用趋势

暂无引用数据

数据来源:暂未标注更新时间:暂无更新时间
AI 靶点发现基础研究Cell

AI-driven discovery of GPNMB CAR T cells as a multi-cancer therapy.

8 次引用趋势数据不足

研究开发了一种人工智能驱动的CAR-T细胞靶点发现方法,通过整合人皮肤癌与健康组织的单细胞RNA测序数据集筛选候选靶点,并利用公共数据集优化肿瘤组成、组织特异性及临床可行性,再以大语言模型优先排序和提名治疗潜力靶点。糖蛋白非转移性黑色素瘤蛋白B(GPNMB)为最常被提名的靶点,其在血液肿瘤和实体瘤中的表达得到验证。研究构建了靶向人GPNMB的CAR-T细胞,在小鼠单核母细胞白血病、黑色素瘤和结直肠腺癌模型中显示出强效抗肿瘤活性。研究建立了可扩展的CAR-T细胞靶点发现流程,并支持将GPNMB靶向CAR-T细胞作为多癌种治疗手段的转化。

为什么值得关注

暂未整理

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完整作者

Daniel J Baker、Leon M Frommer、Ugur Uslu、Kisha K Patel、Daniel Zhu、Nils W Engel、James M George、Wencao Zhao、Samuel I Kim、Lisa Sun、Christopher Roselle、Philipp C Rommel、Regina M Young、Jonathan A Epstein、Sikander Hayat、Zoltan Arany、Carl H June

作者机构

  • Center for Cellular Immunotherapies, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Cardiovascular Institute, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA. Electronic address: bakerdj@upenn.edu.
  • Department of Medicine 2, RWTH Aachen University, Medical Faculty, Aachen, Germany.
  • Center for Cellular Immunotherapies, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Department of Dermatology, Venereology and Allergology, University Hospital Würzburg, 97080 Würzburg, Germany.
  • Center for Cellular Immunotherapies, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.
  • Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI, USA.
  • Cardiovascular Institute, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
  • Cardiovascular Institute, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA; Institute for Regenerative Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA; Department of Cell and Developmental Biology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
  • Department of Medicine 2, RWTH Aachen University, Medical Faculty, Aachen, Germany; Cardiovascular Research Institute, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA. Electronic address: shayat@ukaachen.de.
  • Cardiovascular Institute, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA; Department of Physiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. Electronic address: zarany@upenn.edu.
  • Center for Cellular Immunotherapies, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Parker Institute for Cancer Immunotherapy at University of Pennsylvania, Philadelphia, PA 19104, USA. Electronic address: cjune@upenn.edu.

技术路线

单细胞RNA测序数据(人类皮肤癌与健康组织)整合→AI驱动候选靶点筛选,结合公共数据集优化肿瘤组成、组织特异性与临床可行性→大语言模型优先排序与提名→GPNMB跨血液与实体瘤表达验证→构建人源GPNMB CAR T细胞→单核细胞白血病、黑色素瘤和结直肠腺癌小鼠模型抗肿瘤活性验证

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:2026/10/29 12:32
AI 靶点发现综述Cancer cell

New horizons at the interface of artificial intelligence and translational cancer research.

55 次引用引用上升

人工智能正日益成为分析多组学数据的计算策略应用于癌症研究。单细胞与空间 profiling 技术的进步显著加深了对肿瘤生物学的理解,人工智能方法现被用于加速转化研究,包括靶点发现、生物标志物识别、患者分层和治疗反应预测。然而,人工智能融入临床工作流程仍然有限,既带来挑战也带来机遇。本综述讨论人工智能在多组学分析和转化肿瘤学中的应用,强调其在推进生物学发现和为临床决策提供信息方面的作用。重点关注领域包括细胞异质性、肿瘤微环境相互作用和人工智能辅助诊断。综述探讨了可重复性、人工智能模型可解释性和临床整合等挑战,并关注应对这些障碍的策略。这些进展共同凸显了人工智能与多组学在提升精准肿瘤学和推动癌症护理进步方面的潜力。

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完整作者

Josephine Yates、Eliezer M Van Allen

作者机构

  • Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Institute for Machine Learning, Department of Computer Science, ETH Zürich, Zurich, Switzerland; ETH AI Center, ETH Zurich, Zurich, Switzerland; Swiss Institute for Bioinformatics (SIB), Lausanne, Switzerland.
  • Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Cancer Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Division of Medical Sciences, Harvard University, Boston, MA, USA; Parker Institute for Cancer Immunotherapy, Dana-Farber Cancer Institute, Boston, MA, USA. Electronic address: eliezerm_vanallen@dfci.harvard.edu.

技术路线

多组学数据集(单细胞与空间 profiling 数据) → AI 计算分析(细胞异质性、肿瘤微环境互作、靶点发现、生物标志物识别、患者分层、治疗反应预测、AI 辅助诊断)

最近 5 个完整年度引用趋势

0101902021020220202302024192025

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 12:33
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