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December 8, 2025Blood

CMML2AML: Machine-learning discovery of co-mutations predictive of blast transformation in chronic myelomonocytic leukemia

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Authors

MPMatteo Giovanni Della Porta

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Overview

Machine-learning uncovers significant genomic alterations impacting survival outcomes in chronic myelomonocytic leukemia, suggesting improved risk models.

Key Points

  • The presence of concurrent genomic alterations significantly predicts outcomes in chronic myelomonocytic leukemia.
  • Machine-learning algorithms identified relevant patient clusters influencing survival analysis with notable accuracy.
  • Cumulative incidence functions revealed distinct patterns in survival among identified patient subgroups based on genomic alterations.
  • Prognostic contributions of specific mutations highlight the need for improved risk stratification in future models.

Cite This Study

Matteo Giovanni Della Porta (2025) studied this question.

synapsesocial.com/papers/69362f4e4fa91c937236d839https://doi.org/10.1182/blood-2025-3844
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Myeloproliferative neoplasm driver mutations (JAK2/CALR/MPL) in chronic myelomonocytic leukemia are associated with an increased risk of blast transformation2025
  2. 2Risk-adjusted comparison of survival in chronic myelomonocytic leukemia with and without allogeneic stem cell transplant: Mayo Clinic experience in 775 consecutive patients2025
  3. 3Machine learning accurately predicts mortality in adult NPM1-mutant Acute Myeloid Leukemia using baseline clinical and genomic features2025
  4. 4Frequency and Impact of Somatic Co-occurring Mutations on Post-Transplant Outcomes in Acute Myeloid Leukemia: A Multicenter Registry Analysis on Behalf of the EBMT ALWP2025
  5. 5Molecular subclusters across the continuum of myelodysplastic neoplasms and acute myeloid leukaemia define distinct clinical entities2025