Decitabine enhances targeting of AML cells by NY-ESO-1-specific TCR-T cells and promotes the maintenance of effector function and the memory phenotype.
Synat KangLixin WangLu XuRuiqi WangQingzheng KangXuefeng GaoLiping DouPublished in: Oncogene (2022)
NY-ESO-1 is a well-known cancer-testis antigen (CTA) with re-expression in numerous cancer types, but its expression is suppressed in myeloid leukemia cells. Patients with acute myeloid leukemia (AML) receiving decitabine (DAC) exhibit induced expression of NY-ESO-1 in blasts; thus, we investigated the effects of NY-ESO-1-specific TCR-engineered T (TCR-T) cells combined with DAC against AML. NY-ESO-1-specific TCR-T cells could efficiently eliminate AML cell lines (including U937, HL60, and Kasumi-1cells) and primary AML blasts in vitro by targeting the DAC-induced NY-ESO-1 expression. Moreover, the incubation of T cells with DAC during TCR transduction (designated as dTCR-T cells) could further enhance the anti-leukemia efficacy of TCR-T cells and increase the generation of memory-like phenotype. The combination of DAC with NY-ESO-1-specific dTCR-T cells showed a superior anti-tumor efficacy in vivo and prolonged the survival of an AML xenograft mouse model, with three out of five mice showing complete elimination of AML cells over 90 days. This outcome was correlated with enhanced expressions of IFN-γ and TNF-α, and an increased proportion of central memory T cells (CD45RO<sup>+</sup>CD62L<sup>+</sup> and CD45RO<sup>+</sup>CCR7<sup>+</sup>). Taken together, these data provide preclinical evidence for the combined use of DAC and NY-ESO-1-specific dTCR-T cells for the treatment of AML.
Keyphrases
- acute myeloid leukemia
- regulatory t cells
- allogeneic hematopoietic stem cell transplantation
- induced apoptosis
- cell cycle arrest
- poor prognosis
- mouse model
- working memory
- binding protein
- signaling pathway
- papillary thyroid
- cell death
- endoplasmic reticulum stress
- oxidative stress
- drug induced
- adipose tissue
- cancer therapy
- machine learning
- insulin resistance
- metabolic syndrome
- pi k akt
- lymph node metastasis
- deep learning
- childhood cancer