1. Deconstructing Intratumoral Heterogeneity through Multiomic and Multiscale Analysis of Serial Sections.
- Author
-
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., and Oldham, Michael C.
- Subjects
GLIOMAS ,RESEARCH funding ,MULTIOMICS ,CANCER patient medical care ,EARLY detection of cancer ,DESCRIPTIVE statistics ,GENE expression ,GENE expression profiling ,GENETIC mutation ,COMPARATIVE studies ,MULTIDETECTOR computed tomography ,GENETIC testing ,SINGLE nucleotide polymorphisms ,GENETICS ,GENOTYPES ,ALGORITHMS - Abstract
Simple Summary: Tumors contain cancerous cells with different mutations and different types of non-cancerous cells. It is important to understand the molecular properties of these cells in order to develop effective treatments for cancers. We describe a new strategy for studying tumor heterogeneity and apply it to human astrocytomas, a type of brain tumor. By slicing astrocytomas into a large number of serial sections and analyzing patterns of mutation frequencies, we reconstruct the evolutionary history of cancer cell mutations in each tumor. By comparing these patterns to gene activity measured in the same sections, we identify molecular features that optimally distinguish different types of cancerous and non-cancerous cells and validate these with different techniques, including analysis of individual nuclei. By integrating analyses of multiple molecular species at multiple scales, our strategy provides a powerful approach for precisely deconstructing intratumoral heterogeneity. 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. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF