决定异体 CAR T 细胞排斥与扩增的细胞和分子机制
Cellular and molecular mechanisms determining allogeneic CAR T cell rejection and expansion.
我们评估了11例接受单一批次cemacabtagene ansegedleucel(cema-cel)治疗的大B细胞淋巴瘤患者,cemacabtagene ansegedleucel是一种异体抗CD19 CAR T产品。
英文原题:Forging New Pathways in Oncology: Strategic Insights from the 17th Annual Frontiers in Cancer Science Conference.
第17届癌症科学前沿(FCS)年会(2025)强调了多组学、计算生物学和祖先特异性基因组学的融合,以推进主动性癌症护理。
第17届癌症科学前沿(FCS)年会(2025)聚焦多组学、计算生物学与祖先特异性基因组学的融合,以推动主动性癌症照护。关键见解包括表观遗传可塑性在维持肿瘤增殖状态中的作用,以及代谢脆弱性的识别,如白血病中的 WNK1-mTORC1 轴和骨髓瘤中 MAF 驱动的谷氨酰胺代谢。会议强调了癌症的系统性本质,详述了“癌症教育”的中性粒细胞如何预置转移前微环境,以及空间排斥机制如何阻碍免疫治疗。治疗工程方面的突破得到展示,包括靶向 CD7 的CAR-T 细胞和不可逆 KRASG12C 抑制剂。一个关键焦点仍然是面向多样化人群的精准肿瘤学,倡导祖先感知数据集和长读长测序以解决亚洲队列中的基因组差异。此外,人工智能驱动的“片段组学”和机器学习的整合为早期检测和追踪疾病致死性提供了新路径。总体而言,FCS 2025 表明,肿瘤学的未来在于将高分辨率疾病模型与强大的数据科学相结合,以从反应性治疗转向个性化、干预性管理。
The 17th Annual Frontiers in Cancer Science (FCS) conference (2025) highlighted the convergence of multiomics, computational biology, and ancestry-specific genomics to advance proactive cancer care. Key insights included the role of epigenetic plasticity in maintaining tumor-propagating states and the identification of metabolic vulnerabilities, such as the WNK1-mTORC1 axis in leukemia and MAF-driven glutamine metabolism in myeloma. The meeting underscored the systemic nature of cancer, detailing how "cancer-educated" neutrophils prime premetastatic niches and how spatial exclusion mechanisms hinder immunotherapy. Breakthroughs in therapeutic engineering were showcased, including CD7-directed chimeric antigen receptor T cells and irreversible KRASG12C inhibitors. A critical focus remained on precision oncology for diverse populations, advocating for ancestry-aware datasets and long-read sequencing to address genomic disparities in Asian cohorts. Furthermore, the integration of artificial intelligence-driven "fragmentomics" and machine learning offers new pathways for early detection and tracking disease lethality. Collectively, FCS 2025 demonstrated that the future of oncology lies in integrating high-resolution disease models with robust data science to transition from reactive treatment to personalized, interceptive management.
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