Targeting chronic lymphocytic leukemia with N-methylated thrombospondin-1-derived peptides overcomes drug resistance
Chronic lymphocytic leukemia (CLL), the most common adulthood leukemia in Western countries, is a very heterogeneous disease characterized by a peripheral accumulation of abnormal CD5+ B lymphocytes in the immune system. Despite new therapeutic developments, there remains an unmet medical need for CLL. Here, we demonstrate that the use of N-methylated thrombospondin-1 (TSP-1)-derived peptides is an efficient way to kill the malignant CLL cells, including those from high-risk individuals with poor clinical prognosis, del11q, del17p, 2p gain, or complex karyotype. PKT16, our hit N-methylated peptide, triggers the elimination of the leukemic cells, sparing the nontumor cells, including the hematopoietic precursors, and reduces the in vivo tumor burden of a CLL-xenograft mice model. A complementary analysis underscores the improved cytotoxic efficiency of PKT16 compared with the previously described TSP-1-derived probes, such as PKHB1. PKT16 elicits an original caspase-independent programmed necrotic mode of cell death, different from necroptosis or ferroptosis, implicating an intracellular Ca2+ deregulation that provokes mitochondrial damage, cell cycle arrest, and the specific death of the malignant CLL cells. The activation of the Gαi proteins and the subsequent drop of cyclic adenosine monophosphate levels and protein kinase A activity regulate this cytotoxic cascade. Remarkably, PKT16 induces the molecular hallmarks of immunogenic cell death, as defined by the calreticulin plasma membrane exposure and the release of adenosine triphosphate and high-mobility group box 1 protein from the dying CLL cells. Thus, PKT16 appears to be able to stimulate an anticancer in vivo immune response. Collectively, our results pave the way toward the development of an efficient strategy against CLL.
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基因水平:PCR Array、RT-PCR、PCR、单细胞测序
蛋白水平:MSD、Luminex、CBA、Elispot、Antibody Array、ELISA、Sengenics
细胞水平:细胞染色、细胞分选、细胞培养、细胞功能
组织水平:空间多组学、多重荧光免疫组化、免疫组化、免疫荧光
数据分析:流式数据分析、组化数据分析、多因子数据分析
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