肿瘤可能包含数十亿个细胞，包括不同的恶性克隆和非恶性细胞类型。阐明这些细胞的进化历史、患病率和定义分子特征对于改善临床结果至关重要，因为肿瘤内异质性为靶向治疗的获得性耐药性提供了燃料。在这里，我们提出了一种基于统计的策略，通过连续肿瘤切片（MOMA）的多组学和多尺度分析来解构肿瘤内异质性。通过将 IDH 突变型星形细胞瘤的深度采样与单核苷酸变异、拷贝数变异和基因表达的综合分析相结合，我们重建并验证了不同恶性克隆的系统发育、空间分布和转录谱。通过单核 RNA-seq 分析细胞核的躯干突变，我们进一步表明，从单细胞转录组中识别癌细胞的常用算法可能不准确。我们还证明，将大量样品中的基因表达与肿瘤纯度相关联可以揭示恶性细胞的最佳标记，并使用这种方法来识别星形细胞瘤干克隆一致表达的一组核心基因，包括 AKR1C3，其表达与不良结果相关在几种类型的癌症中。总之，MOMA 提供了一种稳健而灵活的策略，用于精确解构瘤内异质性并阐明实体瘤中不同细胞群的核心分子特性。

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, including AKR1C3, 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.