Latexin deletion in the bone marrow niche suppresses leukemia via LIFR downregulation and immune activation
Cuiping Zhang, Xia Yao, Pinpin Sui, Liming Hou, Bowen Yan, Larry L. Luchsinger, Gang Huang, Sheng Tong, Youwen Zhang, Bojing Shao, Hong Qian, Hong Zheng, Hui Zhong, Feng-Chun Yang, Ying Liang
Cuiping Zhang, Xia Yao, Pinpin Sui, Liming Hou, Bowen Yan, Larry L. Luchsinger, Gang Huang, Sheng Tong, Youwen Zhang, Bojing Shao, Hong Qian, Hong Zheng, Hui Zhong, Feng-Chun Yang, Ying Liang
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Research Article Hematology Oncology

Latexin deletion in the bone marrow niche suppresses leukemia via LIFR downregulation and immune activation

Abstract

Acute myeloid leukemia (AML) is the most common adult leukemia diagnosis. Bone marrow (BM) niche significantly influences the initiation and progression of AML. However, our knowledge about the effect of leukemic niche on leukemia stem cells (LSC) and leukemogenesis is limited. In this study, we identified an extrinsic-regulatory function of latexin (Lxn) in leukemogenesis. Using a MLL-AF9–induced AML mouse model in WT and Lxn-KO (Lxn–/–) recipient mice, we found that Lxn deletion in the BM niche enhanced the survival of AML mice by suppressing LSCs and reducing blood blasts. Single-cell RNA-seq of stromal cells and cell communication analysis uncovered downregulation of the leukemia inhibitory factor receptor (LIFR) signaling pathway in the Lxn–/– niche, particularly within mesenchymal stromal cells (MSCs). Mechanistically, reduced LIFR level in Lxn–/– MSCs upregulated Cxcl9 expression, leading to increased recruitment of CD8 T cells and enhanced cytotoxicity against leukemic cells. Combination of Lxn niche deletion and immune checkpoint inhibitor PD-1 further prolonged survival. The findings have important clinical implications, suggesting that Lxn inhibition could improve the efficacy of AML therapies by targeting the leukemia niche and enhancing immune surveillance.

Authors

Cuiping Zhang, Xia Yao, Pinpin Sui, Liming Hou, Bowen Yan, Larry L. Luchsinger, Gang Huang, Sheng Tong, Youwen Zhang, Bojing Shao, Hong Qian, Hong Zheng, Hui Zhong, Feng-Chun Yang, Ying Liang

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Figure 3

scRNA-seq on stromal and hematopoietic (or leukemic) cells from WT and Lxn–/– homeostatic (non-AML) and AML mice.

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scRNA-seq on stromal and hematopoietic (or leukemic) cells from WT and L...
(A) The UMAP clustering of all 8 individual scRNA-seq samples on stromal and hematopoietic (or leukemic) cells from WT and Lxn–/– (KO) homeostatic (non-AML) (Pre-) and AML (Post-) mice. Three distinct cell populations were identified, including stromal cells, hematopoietic cells, and MLL-AF9 blast cells. (B) The proportional distribution of hematopoietic (Lin–), leukemic (GFP), and stromal clusters in the BM of WT and Lxn–/– (KO) homeostatic (non-AML) (Pre-) and AML (Post-) mice. (C) CellChat analysis reveals the differential interaction strength of stromal and leukemic cells between AML post-WT and post-KO cells. CellChat function netVisual_heatmap was used to visualize changes in cell-cell communication strength between WT and Lxn-KO samples. In this heatmap, the top bar represents the total incoming signaling strength (sum of column values) for each cell population, whereas the right bar represents the total outgoing signaling strength (sum of row values). Within the heatmap, red indicates increased interaction strength in the Lxn KO condition relative to WT, while blue indicates decreased interaction strength. The MSC1 population on the y axis and the MonoP blast population on the x axis are highlighted with red lines. Their interaction is indicated by the red arrow, representing the strongest cell-cell interaction. (D) Relative information flow comparison of ligand-receptor signaling pathways between WT and Lxn–/– cells. We compared signaling pathway activity between WT and Lxn–/– samples by calculating the information flow for each pathway, defined as the sum of communication probabilities across all interacting cell populations within the inferred signaling network. Signaling pathways were then ranked according to the difference in overall information flow between the 2 conditions. In the resulting plot, pathways with higher information flow in WT are displayed in red, whereas pathways with higher information flow in Lxn KO are displayed in green.