TFR prediction in chronic myeloid leukemia
Purpose: Treatment-free remission (TFR) is a major therapeutic objective in chronic myeloid leukaemia (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 to classical machine learning algorithms. The signature was then externally validated in an independent real-world cohort of patients attempting IMA or nilotinib (NIL) cessation (n=70). The biological 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 (AUROC 0.83 [95% CI, 0.73–0.93] and 0.75 [95% CI, 0.55–1.00] 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, 0.58–0.83] overall; 0.77 [95% CI, 0.61–0.92] in IMA-treated patients) and as a time-to-event predictor (log-rank p=0.0042). 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 biological insights into the mechanisms underlying sustained TFR.
Purpose: Treatment-free remission (TFR) is a major therapeutic objective in chronic myeloid leukaemia (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 to classical machine learning algorithms. The signature was then externally validated in an independent real-world cohort of patients attempting IMA or nilotinib (NIL) cessation (n=70). The biological 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 (AUROC 0.83 [95% CI, 0.73–0.93] and 0.75 [95% CI, 0.55–1.00] 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, 0.58–0.83] overall; 0.77 [95% CI, 0.61–0.92] in IMA-treated patients) and as a time-to-event predictor (log-rank p=0.0042). 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 biological insights into the mechanisms underlying sustained TFR.
Universite Claude Bernard Lyon 1