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基于转录组的慢性髓性白血病无治疗缓解多变量预测模型的开发与外部验证

英文原题:Development and External Validation of a Transcriptome-Based Multivariable Prediction Model for Treatment-Free Remission in Chronic Myeloid Leukemia.

PubMed 2026/06/09(内容时间) J Clin Oncol Q1 · IF 44.7(JCR 2025)

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

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。

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。

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
原文标识
PubMed 42454992 · DOI 10.1200/JCO-25-02948