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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。

为什么值得关注

暂未整理

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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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