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Single-Cell RNA Sequencing of Pediatric Ependymoma uncovers Subclonal Heterogeneity associated with Patient Survival

ABSTRACT: Ependymoma is a malignant glial tumor occurring throughout central nervous system, with one third of relapse rate. The increasing accessibility of single-cell transcriptome has accelerated our understanding of ependymoma. In this study, we generated a high-resolution single-cell dataset with a particular focus on the comparison of subclonal differences within tumor populations. As a proxy to traditional pseudotime analysis, we developed a novel trajectory scoring method to predict survival outcomes and specific molecular characteristics. Moreover, we identified putative cell-cell communication features between relapsed and primary samples and show upregulation of pathways associated with immune cell crosstalk. Our results reveal both inter- and intratumoral gene expression profiles and tumor differentiation and provide a framework for future studies on transcriptomic signatures of individual subclones in brain tumors at single-cell resolution, by which could complement existing published datasets and provide valuable insights into cell-type-specific properties and lay the foundation for therapeutic treatments of diseases.

ORGANISM(S): Homo sapiens

PROVIDER: GSE189939 | GEO | 2022/06/15

REPOSITORIES: GEO

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Project description:Intratumoral heterogeneity (ITH) arises from distinct subclonal expansion following genetic or epigenetic alterations, and profoundly influences how tumors response to their immune microenvironment. Tumor progression trees based on single-cell mutational profiles have made it possible to trace subclonal evolution; however, conventional tree-building methods can incorporate only a limited number of cells and mutations, restricting their application to larger single-cell data. To investigate the effect of ITH on the therapeutic response of melanoma, we have developed Trisicell (https://trisicell.rtfd.io), a computational toolkit for scalable inference of mutational ITH through assessment of single-cell genomic variant data. By applying Trisicell to genetically matched mouse melanoma datasets, we found that expressed mutations are sufficient to effectively drive subclonal evolution. On single-cell, full-length RNA sequencing data of mouse melanoma from preclinical immune checkpoint blockade (ICB) studies, the analysis showed that the subtree-seeding mutations in the trees identified distinct subclones associated with a specific developmental state and neural crest lineage markers. Using the tree to trace cell lineages, we found that neoantigens depleted by ICB were predominantly expressed in minor subclones, suggesting that post-treatment recurrence is driven by immunoediting. Moreover, these neoantigens were enriched with those derived from frameshift mutations and mutated nuclear genes. Importantly, recurrently mutated genes in ICB-responding human melanoma exhibited the same features. We next used Trisicell to analyze single-cell, full-length RNA data of brain metastases (BM) from melanoma patients treated with ICB, and discovered that relapsing BM from ICB-responding patients exhibited subclones that also expressed a higher fraction of frameshift mutations and were associated with elevated levels of infiltrated T cells. Notably, they also exhibited more mutated HLA genes and expressed high level of the novel immune checkpoint HLA-G, a putative local immunosuppressive mechanism. In summary, applying Trisicell to single-cell transcriptomic analyses allowed us to identify novel features of ICB-responding melanoma neoantigens and distinct mechanisms for adapting to immunotherapy at different sites. These results have important implications for both melanoma evolution and target identification for immunotherapies, including ICB and cancer vaccines. This is the first study to trace subclonal evolution of neoantigens at single-cell resolution, demonstrating that Trisicell and our datasets represent important resources for the field.

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Single-Cell RNA Sequencing of Pediatric Ependymoma uncovers Subclonal Heterogeneity associated with Patient Survival

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