Abstract 6548: Bioinformatic characterization of dendritic and T cell subpopulations in the tumor microenvironment across different cancer types by single-cell RNA-Seq analysis

Abstract

Abstract Although the development of single-cell RNAseq (scRNAseq) has improved the knowledge of the tumor microenvironment (TME), there is not a clear understanding of specific cell subtypes, markers that define these cell populations, nor the interaction between them. Previously, we showed that inflammasome activation is correlated with antitumor responses triggered by immune checkpoint blockade (ICB). This highlights a central role for TMEM176B, mostly expressed in myeloid cells, that impairs the recruitment of cytotoxic immune cells in the TME by inhibiting NLRP3 inflammasome activation. Still, the roles of the different subsets of myeloid and T cells in the TME and to what extent they are connected remain controversial. Here, we used scRNAseq data from human tumors to identify and characterize cDC2, Th17 and exhausted CD8+ T cell subpopulations in cancer. Five scRNAseq data sets of melanoma, basal cell carcinoma, breast, lung, colorectal, and ovary cancer patients were integrated, accounting for 382,019 immune cells in total. We applied and compared different bioinformatic tools for scRNASeq data normalization and data sets integration to find conserved immune cell populations across samples, data sets, and cancer types. Then, we evaluated different parameters using well-established methods for clustering and cell type classification to identify subtypes of DCs and T cells in the integrated data. We combined automatic methods with manual analysis of cluster markers to improve robustness and confidence in the cell populations identified. Three clusters of cDC2 were defined and characterized using different scRNAseq analysis tools for pathway enrichment analysis and transcription factors (TF) activation prediction. We found a subset of inflammatory cDC2 with high gene expression of IL1B and NLRP3, in contrast with another cluster characterized by high expression of TMEM176B and CD14. A third cluster showed an intermediate transcriptomic profile. The TMEM176B+ CD14+ cluster was characterized by the activation of JAK-STAT and hypoxia pathways, and the TF FOXL2, ETS2, and ESR2. The NLRP3+ IL1B+ cluster showed more activation of MAPK, Trail, and PI3K pathways, and the TF CEBPE. Moreover, the activation of TNFα and NFkB signaling pathways was found in both clusters. In addition, a conserved population of Th17 cells in the integrated data was observed. Th17 cells were heterogeneous, making the subpopulations and transcriptome signature difficult to define. Further analysis is being performed to identify Th17 subsets and their potential interactions with the cDC2 subclusters. A robust identification and characterization of these immune cell subsets will build towards our understanding of the interactions between these subpopulations triggered by inflammasome activation within the TME and the mechanisms behind ICB response. Citation Format: Yamil D. Mahmoud, Natalia Rego, Marcela Vilarino, Florencia Veigas, Maria R. Girotti, Juan M. Perez Saez, Gabriel A. Rabinovich, Marcelo Hill. Bioinformatic characterization of dendritic and T cell subpopulations in the tumor microenvironment across different cancer types by single-cell RNA-Seq analysis. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6548.