Bioinformatics Unit, Spanish National Cancer Research Centre (CNIO), Calle Melchor Fernandez Almagro, 3, 28029 , Madrid, Spain.
Laboratorio de Oncología Clínico-Traslacional, Unidad de Investigación en tumores Digestivos, Instituto de Investigación I+12, Hospital 12 de Octubre, Av. de Córdoba, 28041, Madrid, Spain.
Genome Med. 2021 Dec 16;13(1):187. doi: 10.1186/s13073-021-01001-x.
We present Beyondcell, a computational methodology for identifying tumour cell subpopulations with distinct drug responses in single-cell RNA-seq data and proposing cancer-specific treatments. Our method calculates an enrichment score in a collection of drug signatures, delineating therapeutic clusters (TCs) within cellular populations. Additionally, Beyondcell determines the therapeutic differences among cell populations and generates a prioritised sensitivity-based ranking in order to guide drug selection. We performed Beyondcell analysis in five single-cell datasets and demonstrated that TCs can be exploited to target malignant cells both in cancer cell lines and tumour patients. Beyondcell is available at: https://gitlab.com/bu_cnio/beyondcell .
我们提出了 Beyondcell，这是一种计算方法，用于在单细胞 RNA-seq 数据中识别具有不同药物反应的肿瘤细胞亚群，并提出癌症特异性治疗方法。我们的方法计算了药物特征集中的富集分数，描绘了细胞群体中的治疗簇（TCs）。此外，Beyondcell 确定了细胞群体之间的治疗差异，并生成了基于敏感性的优先级排序，以指导药物选择。我们在五个单细胞数据集上进行了 Beyondcell 分析，结果表明 TC 可用于靶向癌细胞系和肿瘤患者中的恶性细胞。Beyondcell 可在以下网址获得：https://gitlab.com/bu_cnio/beyondcell。
