研究概要
这些发现表明,在停用TKI时进行转录组分析可预测CML患者的TFR结局,并为持续TFR的潜在机制提供生物学见解。
研究思路结论见上方概要
目的
无治疗缓解(TFR)是慢性髓性白血病(CML)的主要治疗目标。然而,近50%的患者在停用酪氨酸激酶抑制剂(TKI)后复发,目前尚无可靠的预测性生物标志物。
方法
我们在多中心STIM2试验(n = 96)中,对伊马替尼(IMA)停药时患者的外周血细胞转录组进行了分析,以开发一个基于转录组的模型,预测2年时的TFR。将基于DESEQ2的机器学习方法与经典机器学习算法进行了比较。随后,该特征在一个独立的真实世界队列中进行了外部验证,该队列为尝试停用IMA或尼洛替尼的患者(n = 70)。进一步探索了与该特征相关的生物学过程。
结果
我们识别出一个50基因特征,可将持续2年TFR的患者与经历分子复发的患者区分开来(在训练队列和内部验证队列中,AUROC分别为0.83 [95% CI, 0.73至0.93]和0.75 [95% CI, 0.55至1.00])。该区分性能在外部测试队列中得到证实,既作为2年TFR的二元预测因子(总体AUROC为0.71 [95% CI, 0.58至0.83];在接受IMA治疗的患者中为0.77 [95% CI, 0.61至0.92]),也作为时间至事件预测因子(log-rank P = .0042)。高TFR特征组显示髓系免疫细胞和自然杀伤T细胞比例较高,并富集Hedgehog信号通路,而低TFR特征组显示淋系细胞比例较高,并富集mTOR信号通路及氧化磷酸化激活趋势。T细胞受体和免疫球蛋白重链库分析显示,高TFR特征组的多克隆性显著更高。
展开英文摘要原文
PURPOSE: Treatment-free remission (TFR) is a major therapeutic objective in chronic myeloid leukemia (CML). However, nearly 50% of patients relapse after tyrosine kinase inhibitor (TKI) discontinuation, and no robust predictive biomarker is currently available.
METHODS: We profiled peripheral blood cell transcriptomes at imatinib (IMA) discontinuation in patients from the multicenter STIM2 trial (n = 96) to develop a transcriptome-based model predicting TFR by 2 years. A DESEQ2-based machine learning approach was compared with classical machine learning algorithms. The signature was then externally validated in an independent real-world cohort of patients attempting IMA or nilotinib cessation (n = 70). The biologic processes associated with the signature were further explored.
RESULTS: We identified a 50-gene signature discriminating patients with sustained 2-year TFR from those experiencing molecular relapse (area under the receiver operating characteristic curve [AUROC], 0.83 [95% CI, 0.73 to 0.93] and 0.75 [95% CI, 0.55 to 1.00] in the training and internal validation cohorts, respectively). The discriminative performance was confirmed in the external test cohort, both as a binary predictor of 2-year TFR (AUROC, 0.71 [95% CI, 0.58 to 0.83] overall; 0.77 [95% CI, 0.61 to 0.92] in IMA-treated patients) and as a time-to-event predictor (log-rank P = .0042). The high TFR-signature group showed a higher proportion of myeloid immune cells and natural killer T cells, with an enrichment in Hedgehog signaling, whereas the low TFR-signature group demonstrated a higher proportion of lymphoid cells with an enrichment in mTOR signaling and a trend for oxidative phosphorylation activation. T-cell receptor and immunoglobulin heavy-chain repertoire analyses showed significantly greater polyclonality in the high TFR-signature group.
CONCLUSION: These findings demonstrate that transcriptomic profiling at TKI discontinuation can predict TFR outcomes in patients with CML and provide biologic insights into the mechanisms underlying sustained TFR.
论文信息
- 作者
- Alcazer V、Dulucq S、Mosnier I、Chabane K、Bertin-Mourot P、Derruau S、Balsat M、Labussiere-Wallet H
- 第一作者单位
- Service d'Hématologie Clinique, Hospices Civils de Lyon, Pierre-Bénite, France.France
- 通讯作者单位
- Centre International de Recherche en Infectiologie (CIRI), INSERM U1111, Lyon, France.France
- 文献类型
- 多中心研究 · 验证性研究
- 期刊
- Journal of clinical oncology : official journal of the American Society of Clinical Oncology2026 Jul 20