Schupp Patrick G, Shelton Samuel J, Brody Daniel J, Eliscu Rebecca, Johnson Brett E, Mazor Tali, Kelley Kevin W, Potts Matthew B, McDermott Michael W, Huang Eric J, Lim Daniel A, Pieper Russell O, Berger Mitchel S, Costello Joseph F, Phillips Joanna J, Oldham Michael C
Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA.
Biomedical Sciences Graduate Program, University of California, San Francisco, San Francisco, CA 94143, USA.
Cancers (Basel). 2024 Jul 1;16(13):2429. doi: 10.3390/cancers16132429.
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 , 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，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、流行情况以及确定其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获得性靶向治疗耐药性提供了条件。在此，我们提出一种基于统计学的策略，通过对连续肿瘤切片进行多组学和多尺度分析（MOMA）来解构肿瘤内异质性。通过将异柠檬酸脱氢酶（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。通过对经单核RNA测序分析的细胞核进行主干突变基因分型，我们进一步表明，常用的从单细胞转录组中识别癌细胞的算法可能不准确。我们还证明，将批量样本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志物，并使用这种方法来识别一组由星形细胞瘤主干克隆持续表达的核心基因，包括 ，其表达与几种类型癌症的不良预后相关。总之，MOMA为精确解构肿瘤内异质性和阐明实体瘤中不同细胞群体的核心分子特性提供了一种强大且灵活的策略。
