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Related Experiment Video

Updated: Jul 16, 2026

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets
03:37

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Published on: March 1, 2024

Development and External Validation of a Transcriptome-Based Multivariable Prediction Model for Treatment-Free

Vincent Alcazer1,2,3, Stéphanie Dulucq4, Isabelle Mosnier5

  • 1Service d'Hématologie Clinique, Hospices Civils de Lyon, Pierre-Bénite, France.

Journal of Clinical Oncology : Official Journal of the American Society of Clinical Oncology
|July 15, 2026
PubMed
Summary

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A new 50-gene signature predicts treatment-free remission (TFR) in chronic myeloid leukemia (CML) patients after tyrosine kinase inhibitor (TKI) discontinuation. This discovery offers hope for personalized CML management and achieving sustained TFR.

Area of Science:

  • Hematology
  • Molecular Biology
  • Genomics

Background:

  • Treatment-free remission (TFR) is a key goal for chronic myeloid leukemia (CML) patients.
  • Approximately 50% of patients relapse after tyrosine kinase inhibitor (TKI) cessation.
  • A reliable predictive biomarker for sustained TFR is currently lacking.

Purpose of the Study:

  • To develop and validate a transcriptome-based model for predicting 2-year TFR in CML patients.
  • To identify a gene signature that distinguishes patients achieving sustained TFR from those relapsing.
  • To explore the biological mechanisms associated with TFR outcomes.

Main Methods:

  • Peripheral blood cell transcriptomes were analyzed at imatinib discontinuation in the STIM2 trial (n=96).
  • A DESeq2-based machine learning approach was used to develop a predictive signature.

Related Experiment Videos

Last Updated: Jul 16, 2026

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets
03:37

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Published on: March 1, 2024

  • The signature was validated in an independent cohort (n=70) of patients discontinuing imatinib or nilotinib.

    • Main Results:

    • A 50-gene signature accurately predicted 2-year TFR, with an AUROC of 0.83 in training and 0.75 in internal validation.
    • External validation confirmed the signature's predictive performance (AUROC 0.71 overall).
    • Distinct immune cell profiles and signaling pathway enrichments (Hedgehog, mTOR) were observed between high and low TFR-signature groups.

    Conclusions:

    • Transcriptomic profiling at TKI discontinuation can predict TFR outcomes in CML patients.
    • The identified gene signature provides a potential tool for guiding TKI cessation decisions.
    • The study offers novel biological insights into the mechanisms supporting sustained TFR.