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Accurate mantle cell lymphoma (MCL) stratification is crucial. Machine learning models integrating clinical and genomic data, including TP53 status, improve disease prediction and aid precision oncology.

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Area of Science:

  • Oncology
  • Bioinformatics
  • Genomics

Background:

  • Mantle cell lymphoma (MCL) is an incurable B-cell malignancy requiring precise pretreatment stratification.
  • Accurate prognostic models are essential for guiding treatment decisions in MCL patients.

Purpose of the Study:

  • To develop and validate machine learning models for improved disease stratification in MCL.
  • To identify key clinical and molecular features for predicting MCL patient outcomes.

Main Methods:

  • Curated a database of 862 MCL patients diagnosed between 2014 and 2022.
  • Developed a gradient-boosted machine learning model incorporating clinicopathologic, cytogenetic, and genomic data.
  • Utilized the model for feature selection in multivariate logistic and survival analyses, creating integrative MIPI (iMIPI) and iMIPI-s indices.

Main Results:

  • The ML model achieved an AUC ROC of 0.83 in discriminating between indolent and aggressive MCL.
  • The top 10 features for the iMIPI-s index include LDH, Ki-67%, platelet count, bone marrow involvement, hemoglobin, somatic mutations, TP53 status, ECOG performance, beta-2 microglobulin, and morphology.
  • The models highlight the importance of integrating molecular features, particularly TP53 mutational status, for prognostic accuracy.

Conclusions:

  • Machine learning models integrating clinical and genomic data offer robust disease stratification for MCL.
  • The developed iMIPI and iMIPI-s indices provide practical tools for clinical implementation in precision oncology.
  • Prognostic applications for MCL should incorporate molecular features to enhance predictive power.