Personalized tumor combination therapy optimization using the single-cell transcriptome

作者：Chen Tang, Shaliu Fu, Xuan Jin, Wannian Li, Feiyang Xing, Bin Duan, Xiaojie Cheng, Xiaohan Chen, Shuguang Wang, Chenyu Zhu, Gaoyang Li, Guohui Chuai, Yayi He, Ping Wang, Qi Liu · 发表于：Genome Medicine · 年份：2023 · DOI：10.1186/s13073-023-01256-6 · 被引用次数：31 · 研究领域：Single-cell and spatial transcriptomics、Cancer Immunotherapy and Biomarkers、Ferroptosis and cancer prognosis

BACKGROUND: The precise characterization of individual tumors and immune microenvironments using transcriptome sequencing has provided a great opportunity for successful personalized cancer treatment. However, the cancer treatment response is often characterized by in vitro assays or bulk transcriptomes that neglect the heterogeneity of malignant tumors in vivo and the immune microenvironment, motivating the need to use single-cell transcriptomes for personalized cancer treatment. METHODS: Here, we present comboSC, a computational proof-of-concept study to explore the feasibility of personalized cancer combination therapy optimization using single-cell transcriptomes. ComboSC provides a workable solution to stratify individual patient samples based on quantitative evaluation of their personalized immune microenvironment with single-cell RNA sequencing and maximize the translational potential of in vitro cellular response to unify the identification of synergistic drug/small molecule combinations or small molecules that can be paired with immune checkpoint inhibitors to boost immunotherapy from a large collection of small molecules and drugs, and finally prioritize them for personalized clinical use based on bipartition graph optimization. RESULTS: We apply comboSC to publicly available 119 single-cell transcriptome data from a comprehensive set of 119 tumor samples from 15 cancer types and validate the predicted drug combination with literature evidence, mining clinical trial...