Cancer precision medicine aims to improve patient outcomes by tailoring treatment to the unique genomic background of a tumor. However, efforts to develop prognostic and drug response biomarkers largely rely on bulk ‘omic’ data, which fails to capture intratumor heterogeneity (ITH) and deconvolve signals from normal versus tumor cells. These shortcomings in measuring clinically relevant features are being addressed with single-cell technologies, which provide a fine-resolution map of the genetic and phenotypic heterogeneity in tumors and their microenvironment, as well as an improved understanding of the patterns of subclonal tumor populations. Here we present recent advances in the application of single-cell technologies, towards gaining a deeper understanding of ITH and evolution, and potential applications in developing personalized therapeutic strategies.

癌症精准医学旨在通过根据肿瘤独特的基因组背景定制治疗方案来改善患者的治疗结果。然而，开发预后和药物反应生物标志物的努力在很大程度上依赖于大量“组学”数据，这些数据无法捕获肿瘤内异质性（ITH），也无法对正常细胞与肿瘤细胞的信号进行反卷积。单细胞技术正在解决测量临床相关特征的这些缺点，单细胞技术提供了肿瘤及其微环境的遗传和表型异质性的高分辨率图谱，并加深了对亚克隆肿瘤群体模式的理解。在这里，我们介绍单细胞技术应用的最新进展，以加深对 ITH 和进化的理解，以及开发个性化治疗策略的潜在应用。