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Single cell sequencing of anti-PD-1 treated xenograft from mice bladder urothelium with Trp53, Rb1, PTEN mutations on C57 BL/6J

ABSTRACT: To determine the immune mechanism of anti-PD1 therapy, we carried out an anti-PD1 treatment on C57 BL/6J mice bearing mouse bladder urothelial carcinoma with three gene knockout. Anti-PD-1 immunotherapy resulted in a mixed pattern of treatment responses in individual tumors. Then, we performed single cell transcriptome profiling for tumor xenografts from Control IgG, anti-PD-1 non-responders and anti-PD-1 responders group. Duplicate tumors in each group were mixed and sequenced together as one representative sample. In results, we captured 4762 cells from Control IgG group, 4459 cells from anti-PD-1 non-responders group and 4861 cells from anti-PD-1 responders group. identified 8 clusters of immune cells in treated samples (basophils, B cells, dendritic cells, macrophages, monocytes, neutrophils, NK cells, and T cells). Responder xenografts displayed significantly increased immune cell infiltration (15.2%, 742 immune cells / 4861 total cells) compared to the non-responder tumors (10.1%, 452 immune cells/ 4459 total cells, Fisher Exact Test p<0.0001). Specifically, there were more T cells (46/4861 vs 18 /4459, p=0.002), and macrophages (420/4861 vs 287/4459, p=0.002) in responder xenografts than in non-responder xenografts. Compared to control IgG tumors, response tumors exhibited an immune-inflamed phenotype with significant infiltration of T cells and NKT cells, whereas non-responder tumors showed no significant change. The higher percentage of T cells and macrophages tumor infiltration in responders suggests a potential role of innate immune microenvironment for the Immune checkpoint inhibitor (ICI) treatment response. This study provides the insights of immune environments in ICI-treated bladder tumor xenograft on C57 mice.

ORGANISM(S): Mus musculus

PROVIDER: GSE200139 | GEO | 2022/06/01

REPOSITORIES: GEO

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Project description:Checkpoint blockade therapy using antibodies targeting CTLA4, PD1 or PDL1 have changed the way cancer patients with advanced disease are being treated, as evident by their FDA approval in a wide variety of malignancies, and their ability to induce high response rates when compared to conventional therapies (e.g. chemotherapy). However, even in melanoma, despite the high response rate, most patients are refractory to therapy or acquire resistance in 10-12 months. To identify key immunological elements coupled with response or resistance to checkpoint immunotherapy, we performed an unbiased analysis on 16,291 CD45+ immune cells from 32 patients (48 samples) treated with checkpoint therapy (anti-PD1=37; anti-PD1/CTLA4=11), using single cell transcriptomics. Initial unsupervised clustering of all CD45+ cells identified 11 clusters. When examining the association of these clusters with clinical outcome, we found that two clusters (B-cells, p=0.005; memory T-cells, p=0.03) were significantly enriched in responder lesions while 4 clusters (monocytes/macrophages, p=0.003; dendritic cells, p=0.015; exhausted CD8+ T-cells, p=0.005; and exhausted/cell-cycle lymphocytes, 1.3x10-5) are enriched in non-responder lesions. Next, by leveraging our unbiased single cell approach, we identified not only clusters but also specific markers associated with either responder lesions (PLAC8, LTB, LY9, SELL, TCF7, IGKC and CCR7) or non-responder ones (CCL3, CD38, HAVCR2, ENTPD1 and WARS), many of which were not previously reported to be associated with clinical outcome to checkpoint therapy. Due to the importance of CD8+ T-cells in controlling tumors, the significant association of T-cell states with clinical outcome, and their high abundance in melanoma tumors, we next focused our analysis on CD8+ T-cells and identified 2 main clusters CD8_G (with increased expression of genes linked to memory) and CD8_B (enriched for genes linked to cell dysfunction), that were significantly enriched in responder (CD8_G, p=1.4x10-6) and non-responder (CD8_B, p=0.005) lesions, respectively. Since cells with both states coexist in each of the responder and non-responder lesions, we decided to calculate the ratio between the number of cells in these 2 clusters and observed a significant separation between responders (CD8_G/CD8_B>1) and non-responders (CD8_G/CD8_B<1) when looking at all samples, baseline or post samples separately. Similar results were detected when looking at the expression of a single transcription factor, TCF7, in CD8 T-cells in an independent cohort (n=43) using a simple immunofluorescence assay. Additionally, we validated the identity and function of some of the newly identified cell states as well as their epigenetic landscape, and found that cells expressing the inhibitory molecules CD39 and TIM3, on top of being enriched in non-responder lesions, are likely to play a crucial role in T-cell exhaustion in melanoma. Collectively, our study provides extensive unbiased data in human tumors for discovery of predictors and therapeutic targets to checkpoint immunotherapy.

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Single cell sequencing of anti-PD-1 treated xenograft from mice bladder urothelium with Trp53, Rb1, PTEN mutations on C57 BL/6J

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