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Modeling T cell temporal response to cancer immunotherapy rationalize development of combinatorial treatments protocols

ABSTRACT: Successful immunotherapy relies on triggering complex responses involving T-cells dynamics in tumors and the periphery. Characterizing these responses remains challenging using static human single-cell atlases or mouse models. To address this, we developed a framework for in vivo tracking of tumor-specific CD8+ T cells over time and at single-cell resolution. Our tools facilitate the modeling of gene program dynamics in the tumor microenvironment (TME) and tumor-draining lymph node (tdLN). Using this approach, we characterize two modes of anti-PD1 (aPD1) activity, decoupling induced differentiation of tumor-specific activated precursor cells from cDC1-dependent proliferation and recruitment to the TME. We demonstrate that combining aPD1 with anti-4-1BB agonist enhances the recruitment and proliferation of activated precursors resulting in tumor control. These data suggest that effective response to aPD1 therapy is dependent on sufficient influx of activated precursor CD8+ cells to the TME, and highlight the importance of understanding system-level dynamics in optimizing immunotherapies.

ORGANISM(S): Mus musculus

PROVIDER: GSE249630 | GEO | 2024/03/01

REPOSITORIES: GEO

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Project description:Cervical cancer (CC) is one of the most common malignancy in women worldwide. It is characterized by a natural continuous phenomenon, that is, it is in the initial stage of HPV infection, progresses to intraepithelial neoplasia, and then develops into invasion and metastasis. Determining the complexity of tumor microenvironment (TME) can deepen our understanding of lesion progression and provide novel therapeutic strategies for CC. We performed the single-cell RNA sequencing on the normal cervix, intraepithelial neoplasia, primary tumor and metastatic lymph node tissues to describe the composition, lineage, and functional status of immune cells and mesenchymal cells at different stages of CC progression. A total of 59913 single cells were obtained and divided into 9 cellular clusters, including immune cells (T/NK cells, macrophages, B cells, plasma cells, mast cells and neutrophils) and mesenchymal cells (endothelial cells, smooth muscle cells and fibroblasts). Our results showed that there were distinct cell subpopulations in different stages of CC. High-stage intraepithelial neoplasia (HSIL) tissue exhibited a low, recently activated TME, and it was characterized by high infiltration of tissue-resident CD8 T cell, effector NK cells, Treg, DC1, pDC, and M1-like macrophages. Tumor tissue displayed high enrichment of exhausted CD8 T cells, resident NK cells and M2-like macrophages, suggesting immunosuppressive TME. Metastatic lymph node consisted of naive T cell, central memory T cell, circling NK cells, cytotoxic CD8+ T cells and effector memory CD8 T cells, suggesting an early activated phase of immune response. This study is the first to delineate the transcriptome profile of immune cells during CC progression using single-cell RNA sequencing. Our results indicated that HSIL exhibited a low, recently activated TME, tumor displayed immunosuppressive statue, and metastatic lymph node showed early activated phase of immune response. Our study enhanced the understanding of dynamic change of TME during CC progression and has implications for the development of novel treatments to inhibit the initiation and progression of CC.

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Modeling T cell temporal response to cancer immunotherapy rationalize development of combinatorial treatments protocols

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