Evaluating a predictive model of tyrosine kinase inhibitor therapy failure in a European-type cohort: a step towards population-specific tools Article Swipe
Sylvain Moinard
,
Benjamin Lebecque
,
Tom Lachaise
,
Hyacinthe Johnson‐Ansah
,
Charlotte Doublet
,
Gabrielle Roth-Guépin
,
Françoise Rigal‐Huguet
,
Lydia Roy
,
Anne Parry
,
Mathieu Meunier
,
Amélie Penot
,
Laurence Legros
,
Vanessa Pante
,
Martine Escoffre‐Barbe
,
Philippe Rousselot
,
Guillaume Denis
,
Hélène Monjanel
,
Dalil Hamroun
,
Abdessattar Khlaifia
,
Joévin Besombes
,
Bruno Pereira
,
Céline Bourgne
,
Marc Berger
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s41375-025-02703-6
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s41375-025-02703-6
Predicting therapeutic failure in patients with chronic phase-chronic myeloid leukemia (CP-CML) treated with tyrosine kinase inhibitors (TKI) remains a major challenge for personalized care management. The Sokal and EUTOS long-term survival scores were designed to predict CML-related mortality, but are also used to guide therapeutic choices, despite their poor performance for this purpose. A recent study proposed a refined predictive model of therapy failure specifically tailored for patients treated with imatinib and second-generation TKIs that showed promising results in a Chinese cohort. The present study evaluated the performance and applicability of this predictive model in a real-world, multicenter cohort from the French CML Observatory. The key differences identified between the Chinese and French cohorts (age, baseline hemoglobin levels, and treatment regimens) likely influenced the model performance. Specifically, the new model did not allow for discriminating risk groups effectively in the French cohort. However, the model reconstruction using this cohort identified other predictive variables (sex, leukocytosis, comorbidities, high-risk additional chromosomal abnormalities) that better stratified patients at risk of therapy failure. Our findings highlight the influence of demographic and clinical differences on predictive models and emphasize the need for local or population-specific tools to optimize risk stratification and therapeutic decision-making in CP-CML.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1038/s41375-025-02703-6
- https://www.nature.com/articles/s41375-025-02703-6.pdf
- OA Status
- hybrid
- References
- 38
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4412844229
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4412844229Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1038/s41375-025-02703-6Digital Object Identifier
- Title
-
Evaluating a predictive model of tyrosine kinase inhibitor therapy failure in a European-type cohort: a step towards population-specific toolsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-08-01Full publication date if available
- Authors
-
Sylvain Moinard, Benjamin Lebecque, Tom Lachaise, Hyacinthe Johnson‐Ansah, Charlotte Doublet, Gabrielle Roth-Guépin, Françoise Rigal‐Huguet, Lydia Roy, Anne Parry, Mathieu Meunier, Amélie Penot, Laurence Legros, Vanessa Pante, Martine Escoffre‐Barbe, Philippe Rousselot, Guillaume Denis, Hélène Monjanel, Dalil Hamroun, Abdessattar Khlaifia, Joévin Besombes, Bruno Pereira, Céline Bourgne, Marc BergerList of authors in order
- Landing page
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https://doi.org/10.1038/s41375-025-02703-6Publisher landing page
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https://www.nature.com/articles/s41375-025-02703-6.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://www.nature.com/articles/s41375-025-02703-6.pdfDirect OA link when available
- Concepts
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Tyrosine-kinase inhibitor, Medicine, Internal medicine, Oncology, Population, Cohort, Tyrosine kinase, Computational biology, Biology, Receptor, Cancer, Environmental healthTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
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38Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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