肿瘤细胞治疗研究
英文原题：The landscape of receptor-mediated precision cancer combination therapy via a single-cell perspective.
本研究提供了对选择性且精准靶向癌症所需联合靶点的身份和数量的计算估计。
通过挖掘大规模单细胞转录组学数据，我们在此采用组合优化技术来描绘癌症中最优联合疗法的全景。我们假设每种单一疗法可以靶向编码细胞表面受体的1269个基因中的任何一个，这些基因可能是CAR-T、偶联抗体或包被纳米颗粒疗法的靶点。我们发现，在大多数癌症类型中，由至多四个靶点组成的个性化组合足以杀死至少80%的肿瘤细胞，同时保护肿瘤微环境中至少90%的非肿瘤细胞。然而，随着需要更严格和更具选择性的杀伤，所需靶点数量迅速上升。新兴的单个靶点包括脑癌和头颈癌中的PTPRZ1以及多种肿瘤类型中的EGFR。总之，本研究提供了对选择性且精确靶向癌症所需组合靶点的身份和数量的计算估计。
Mining a large cohort of single-cell transcriptomics data, here we employ combinatorial optimization techniques to chart the landscape of optimal combination therapies in cancer. We assume that each individual therapy can target any one of 1269 genes encoding cell surface receptors, which may be targets of CAR-T, conjugated antibodies or coated nanoparticle therapies. We find that in most cancer types, personalized combinations composed of at most four targets are then sufficient for killing at least 80% of tumor cells while sparing at least 90% of nontumor cells in the tumor microenvironment. However, as more stringent and selective killing is required, the number of targets needed rises rapidly. Emerging individual targets include PTPRZ1 for brain and head and neck cancers and EGFR in multiple tumor types. In sum, this study provides a computational estimate of the identity and number of targets needed in combination to target cancers selectively and precisely.
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