PO.MCB08.04 · 分子与细胞生物学
慢性髓系白血病中NK细胞的单细胞图谱鉴定出具有不同基因调控特征、与伊马替尼停药后不同结局相关的独特细胞状态
Single-cell profiling of NK cells in chronic myeloid leukemia identifies distinct cell states with gene regulatory signatures associated with differential outcomes after imatinib discontinuation
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摘要 Abstract
中文摘要
背景:无治疗缓解(TFR)是慢性髓系白血病(CML)一个新兴的治疗目标,约40%在维持深度分子学缓解后停用酪氨酸激酶抑制剂(TKI)治疗的患者可实现该目标。然而,区分持续缓解与分子学复发的免疫基因组机制仍未完全明确。先前的单细胞RNA测序(scRNA-seq)和TCR测序研究揭示,CML的特征是活化的CD56dim自然杀伤(NK)细胞和抗PR1 T细胞群体的扩增,它们介导抗白血病免疫。
方法:在这些发现的基础上,我们采用整合计算框架(结合无监督聚类、拟时序轨迹推断、基因调控网络(GRN)重建以及人工智能辅助的基因面板发现)重新分析了六例CML患者的NK细胞转录组,其中两例为早期复发、两例为晚期复发、两例维持持久TFR。对在TKI停药时以及治疗后6个月和12个月采集的纵向样本进行分析,以刻画免疫结局的转录动态和调控驱动因素。
结果:比较性GRN建模揭示了调控NK细胞活化、分化和耗竭的独特转录因子模块。维持TFR的患者在RUNX3、EOMES、ELK4和REL调控子中表现出更高活性,而复发病例则显示富集FOSL2和MAF、具有炎症/翻译相关靶点的模块。拟时序分析揭示功能性NK亚型之间的状态转变动力学发生改变,复发患者中耗竭轨迹加速。一个AI引导的基因优先级模型进一步鉴定出一个NK细胞基因调控面板,包括先前未被表征的转录介导因子,可稳健地区分TFR、晚期复发和早期复发组。通路富集分析将这些基因与IFN-gamma信号、代谢重编程和免疫调节反馈网络相关联。
结论:这项整合性再分析强调转录调控和调控网络重连是TKI停药后免疫持续与耗竭的关键决定因素。该AI衍生的基因面板为探索性生物标志物发现和CML缓解结局的机制分层提供了一个可扩展框架。总之,这些发现推进了我们对成功TFR背后免疫架构的理解,并鉴定出可改善CML缓解持久性的候选转录靶点。
查看英文原文 English abstract
Background: Treatment-free remission (TFR) is an emerging therapeutic goal in chronic myeloid leukemia (CML), achieved by ~40% of patients who discontinue tyrosine kinase inhibitor (TKI) therapy after maintaining a deep molecular response. However, the immunogenomic mechanisms that distinguish sustained remission from molecular relapse remain incompletely defined. Previous single-cell RNA (scRNA-seq) and TCR sequencing studies revealed that CML is characterized by an expanded population of activated CD56dim natural killer (NK) cells and anti-PR1 T cells that mediate anti-leukemic immunity.
Methods: Building on these findings, we reanalyzed NK cell transcriptomes from six CML patients, two with early relapse, two with late relapse, and two maintaining durable TFR, using an integrated computational framework combining unsupervised clustering, pseudotime trajectory inference, gene regulatory network (GRN) reconstruction, and artificial intelligence-assisted gene panel discovery. Longitudinal samples collected at TKI discontinuation and at 6- and 12-month post-therapy were analyzed to delineate transcriptional dynamics and regulatory drivers of immune outcomes.
Results: Comparative GRN modeling revealed distinct transcription factor modules governing NK cell activation, differentiation, and exhaustion. Patients maintaining TFR exhibited higher activity in RUNX3, EOMES, ELK4, and REL regulons, whereas relapse cases showed modules enriched for FOSL2 and MAF with inflammatory/translational targets. Pseudotime analysis revealed altered state-transition kinetics between functional NK subtypes, with accelerated exhaustion trajectories in relapsing patients. An AI-guided gene prioritization model further identified a gene NK cell regulatory panel, including previously uncharacterized transcriptional mediators that robustly separated TFR, late relapse, and early relapse groups. Pathway enrichment linked these genes to IFN-gamma signaling, metabolic reprogramming, and immunoregulatory feedback networks.
Conclusions: This integrative reanalysis highlights transcriptional control and regulatory network rewiring as key determinants of immune persistence versus exhaustion following TKI cessation. The AI-derived gene panel provides a scalable framework for exploratory biomarker discovery and mechanistic stratification of CML remission outcomes. Together, these findings advance our understanding of the immune architecture underlying successful TFR and identify candidate transcriptional targets to improve remission durability in CML.
利益披露 Disclosure
S. Borra, None..
D. Yan, None..
R. S. Welner, None..
Z. Yue, None.