Dataset Information

Spatial coupling of microbes and immune cells in solid malignancies [LCM_stroma_epithelium]

ABSTRACT: Solid tumors are composed of cancer cells and host immune cells that are distributed in a non-uniform pattern. Although this spatial heterogeneity may reflect mutational variations in cancer cells, mechanisms that direct the recruitment of immune cells to distinct regions within a tumor remain poorly understood. Here, we show that microbial-host interactions define tumor nests enriched in immune cells, and the distribution of microbes within tumors parallels the spatial heterogeneity of intratumoral lymphoid and myeloid cell populations. Analysis of human solid tumors revealed that the spatial distribution of immune cells, particularly CD8+ T cells, is markedly heterogeneous. Compared to T cell-poor (“cold”) tumor nests, T cell-rich (“hot”) tumor nests displayed a significantly higher number of myeloid cells, B cells, and plasma cells. We performed laser capture microdissection (LCM) followed by RNA sequencing to identify unique gene signatures that define tumor epithelium and stroma of cold and hot tumor nests. Cold tumor nests expressed genes that promote tumor proliferation and fibrosis, whereas hot tumor stroma and epithelium showed upregulation of immune-related processes, including responses to bacteria, and receptors that mediate mucosal immune responses to microbes, respectively. Consistent with these findings, we detected elevated levels of microbes within hot tumor nests in human pancreatic and lung cancers. Our data implicate host immune responses to microbes in defining intratumoral immune heterogeneity and highlight a potential role for crosstalks between microbes, cancer cells, and the host immune system in regulating cancer immunogenicity.

ORGANISM(S): Homo sapiens

PROVIDER: GSE155723 | GEO | 2023/12/01

REPOSITORIES: GEO

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Project description:Background: Immune checkpoint blockade improves survival in a subset of patients with non-small cell lung cancer (NSCLC), but robust biomarkers that predict response to PD-1 pathway inhibitors are lacking. Furthermore, our understanding of the diversity of the NSCLC tumor immune microenvironment remains limited. Methods: We performed comprehensive flow-cytometric immunoprofiling on both tumor and immune cells from 51 NSCLCs and integrated this analysis with clinical and histopathologic characteristics, next generation sequencing, mRNA expression, and PD-L1 immunohistochemistry (IHC). Results: Cytometric profiling identified an immunologically “hot” cluster with abundant CD8+ T cells expressing high levels of the PD-1 and TIM-3, and an immunologically “cold” cluster with lower relative abundance of CD8+ T cells and expression of inhibitory markers. The “hot” cluster was highly enriched for expression of genes associated with T cell trafficking and cytotoxic function, and high PD-L1 expression by IHC. There was no correlation between immunophenotype and KRAS or EGFR mutation, or patient smoking history, but we did observe an enrichment of squamous subtype and tumors with higher mutation burden in the “hot” cluster. Additionally, ~20% of cases had high B cell infiltrates with a subset producing IL-10. Conclusions: Our results support the use of immune-based metrics to study response and resistance to immunotherapy in lung cancer. Background: Immune checkpoint blockade improves survival in a subset of patients with non-small cell lung cancer (NSCLC), but robust biomarkers that predict response to PD-1 pathway inhibitors are lacking. Furthermore, our understanding of the diversity of the NSCLC tumor immune microenvironment remains limited. Methods: We performed comprehensive flow-cytometric immunoprofiling on both tumor and immune cells from 51 NSCLCs and integrated this analysis with clinical and histopathologic characteristics, next generation sequencing, mRNA expression, and PD-L1 immunohistochemistry (IHC). Results: Cytometric profiling identified an immunologically “hot” cluster with abundant CD8+ T cells expressing high levels of the PD-1 and TIM-3, and an immunologically “cold” cluster with lower relative abundance of CD8+ T cells and expression of inhibitory markers. The “hot” cluster was highly enriched for expression of genes associated with T cell trafficking and cytotoxic function, and high PD-L1 expression by IHC. There was no correlation between immunophenotype and KRAS or EGFR mutation, or patient smoking history, but we did observe an enrichment of squamous subtype and tumors with higher mutation burden in the “hot” cluster. Additionally, ~20% of cases had high B cell infiltrates with a subset producing IL-10. Conclusions: Our results support the use of immune-based metrics to study response and resistance to immunotherapy in lung cancer.

Dataset Information

Spatial coupling of microbes and immune cells in solid malignancies [LCM_stroma_epithelium]

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets