Tumors may contain billions of cells, including distinct malignant clones and nonmalignant cell types. Clarifying the evolutionary histories, prevalence, and defining molecular features of these cells is essential for improving clinical outcomes, since intratumoral heterogeneity provides fuel for acquired resistance to targeted therapies. Here we present a statistically motivated strategy for deconstructing intratumoral heterogeneity through multiomic and multiscale analysis of serial tumor sections (MOMA). By combining deep sampling of IDH-mutant astrocytomas with integrative analysis of single-nucleotide variants, copy-number variants, and gene expression, we reconstruct and validate the phylogenies, spatial distributions, and transcriptional profiles of distinct malignant clones. By genotyping nuclei analyzed by single-nucleus RNA-seq for truncal mutations, we further show that commonly used algorithms for identifying cancer cells from single-cell transcriptomes may be inaccurate. We also demonstrate that correlating gene expression with tumor purity in bulk samples can reveal optimal markers of malignant cells and use this approach to identify a core set of genes that are consistently expressed by astrocytoma truncal clones, includingAKR1C3, whose expression is associated with poor outcomes in several types of cancer. In summary, MOMA provides a robust and flexible strategy for precisely deconstructing intratumoral heterogeneity and clarifying the core molecular properties of distinct cellular populations in solid tumors.
肿瘤可能包含数十亿个细胞，涵盖不同的恶性克隆与非恶性细胞类型。阐明这些细胞的演化历程、分布频率及关键分子特征对改善临床预后至关重要，因为肿瘤内异质性为靶向治疗获得性耐药提供了条件。本研究提出一种基于统计学原理的策略，通过对连续肿瘤切片进行多组学多尺度分析来解构肿瘤内异质性。通过深度采样IDH突变型星形细胞瘤，并结合单核苷酸变异、拷贝数变异及基因表达数据的整合分析，我们成功重建并验证了不同恶性克隆的系统发育关系、空间分布特征及转录谱。通过对单核RNA测序样本进行主干突变核型分析，我们进一步发现当前广泛使用的单细胞转录组癌细胞识别算法可能存在误差。研究还证实，通过关联大样本中基因表达与肿瘤纯度可筛选恶性细胞的最佳标志物，并运用该方法鉴定出一组星形细胞瘤主干克隆稳定表达的核心基因，其中包括在多种癌症中与不良预后相关的AKR1C3。综上所述，多组学多尺度分析策略为精准解构实体瘤内异质性、阐明不同细胞群体的核心分子特征提供了稳健而灵活的研究框架。
肿瘤(癌症)患者之家