Graduate Group in Genomics and Computational Biology, University of Pennsylvania, Philadelphia, PA, USA.
Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA.
Genome Biol. 2020 Jan 14;21(1):10. doi: 10.1186/s13059-019-1922-x.
Although scRNA-seq is now ubiquitously adopted in studies of intratumor heterogeneity, detection of somatic mutations and inference of clonal membership from scRNA-seq is currently unreliable. We propose DENDRO, an analysis method for scRNA-seq data that clusters single cells into genetically distinct subclones and reconstructs the phylogenetic tree relating the subclones. DENDRO utilizes transcribed point mutations and accounts for technical noise and expression stochasticity. We benchmark DENDRO and demonstrate its application on simulation data and real data from three cancer types. In particular, on a mouse melanoma model in response to immunotherapy, DENDRO delineates the role of neoantigens in treatment response.
虽然 scRNA-seq 现在在肿瘤异质性研究中被广泛采用，但从 scRNA-seq 中检测体细胞突变和推断克隆归属目前还不可靠。我们提出了 DENDRO，这是一种用于 scRNA-seq 数据的分析方法，它将单细胞聚类为遗传上不同的亚克隆，并重建亚克隆之间的系统发育树。DENDRO 利用转录点突变，并考虑了技术噪声和表达随机性。我们对 DENDRO 进行了基准测试，并在来自三种癌症类型的模拟数据和真实数据上展示了其应用。特别是，在针对免疫疗法的小鼠黑色素瘤模型中，DENDRO 描绘了新抗原在治疗反应中的作用。
