Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2026
Development and External Validation of a Transcriptome-Based Multivariable Prediction Model for Treatment-Free Remission in Chronic Myeloid Leukemia.
СтатьяМногоцентровое исследованиеВалидационное исследованиеНазвание и аннотация: Перевод в очереди
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Alcazer Vincent, Dulucq Stéphanie, Mosnier Isabelle, Chabane Kaddour, Bertin-Mourot Pauline, Derruau Sandrine, Balsat Marie, Labussiere-Wallet Hélène, Sujobert Pierre, Mahon François-Xavier, Etienne Gabriel, Nicolini Franck, Hayette Sandrine
Аннотация
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.
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.