RNF43 p.G659fs 通过 PI3K/AKT/mTOR 信号通路和 HLA-E 上调导致 MSI-high 结直肠癌中 NK 细胞功能障碍
RNF43 p.G659fs leads to natural killer cell dysfunction in MSI-high colorectal cancer through PI3K/AKT/mTOR signaling and HLA-E up-regulation.
CELL INTELLIGENCE · 肿瘤细胞治疗研究
肿瘤细胞治疗研究
英文原题:Cellular Composition and 5hmC Signature Predict the Treatment Response of AML Patients to Azacitidine Combined with Chemotherapy.
Cellular Composition and 5hmC Signature Predict the Treatment Response of AML Patients to Azacitidine Combined with Chemotherapy.
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阿扎胞苷(AZA)是一种DNA甲基转移酶抑制剂和表观遗传调节剂,与化疗联合用于高危急性髓系白血病(AML)患者时可成为有效药物。
然而,驱动此类低甲基化药物(HMA)治疗方案应答的生物学因素仍不清楚。在此,我们对来自一项1期临床试验的41例高危AML患者的转录组和/或全基因组5-羟甲基胞嘧啶(5hmC)进行了表征,这些患者接受了AZA表观遗传启动后序贯大剂量阿糖胞苷和米托蒽醌(AZA-HiDAC-Mito)治疗。数字细胞术显示,应答者具有升高的粒细胞-巨噬细胞祖细胞样(GMP样)恶性细胞,并表现出活跃的细胞周期程序。
此外,自然杀伤(NK)细胞的富集预测接受AZA-HiDAC-Mito治疗或其他基于AZA治疗的患者预后良好。比较AZA治疗五天前后的5hmC谱显示,AZA暴露诱导剂量依赖性5hmC变化,其幅度与总生存期相关(p = 0.015)。
我们开发了一个极端梯度提升(XGBoost)机器学习模型,基于11个基因的5hmC水平预测治疗应答,曲线下面积(AUC)达到0.860。这些结果表明,细胞组成显著影响治疗应答,并展示了5hmC特征在预测AML中基于HMA治疗结局方面的前景。
Azacitidine (AZA) is a DNA methyltransferase inhibitor and epigenetic modulator that can be an effective agent in combination with chemotherapy for patients with high-risk acute myeloid leukemia (AML).
However, biological factors driving the therapeutic response of such hypomethylating agent (HMA)-based therapies remain unknown.
Herein, the transcriptome and/or genome-wide 5-hydroxymethylcytosine (5hmC) is characterized for 41 patients with high-risk AML from a phase 1 clinical trial treated with AZA epigenetic priming followed by high-dose cytarabine and mitoxantrone (AZA-HiDAC-Mito). Digital cytometry reveals that responders have elevated Granulocyte-macrophage-progenitor-like (GMP-like) malignant cells displaying an active cell cycle program.
Moreover, the enrichment of natural killer (NK) cells predicts a favorable outcome in patients receiving AZA-HiDAC-Mito therapy or other AZA-based therapies. Comparing 5hmC profiles before and after five-day treatment of AZA shows that AZA exposure induces dose-dependent 5hmC changes, in which the magnitude correlates with overall survival (p = 0. 015).
An extreme gradient boosting (XGBoost) machine learning model is developed to predict the treatment response based on 5hmC levels of 11 genes, achieving an area under the curve (AUC) of 0. 860. These results suggest that cellular composition markedly impacts the treatment response, and showcase the prospect of 5hmC signatures in predicting the outcomes of HMA-based therapies in AML.
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