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Generation and External Validation of a Histologic Transformation Risk Model for Patients with Follicular Lymphoma.

Ismael Fernández-Miranda1, Lucía Pedrosa1, Julia González-Rincón2

  • 1Department of Medical Oncology, Lymphoma Research Group, Hospital Universitario Puerta de Hierro-Majadahonda, IDIPHISA, Madrid, Spain.

Modern Pathology : an Official Journal of the United States and Canadian Academy of Pathology, Inc
|May 19, 2024
PubMed
Summary

A new clinicogenetic model predicts histologic transformation (HT) in follicular lymphoma (FL). This tool combines gene mutations and clinical factors to identify patients at high risk of transformation to diffuse large B-cell lymphoma (DLBCL).

Keywords:
follicular lymphomagenomicshistologic transformationpredictive model

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Area of Science:

  • Hematology
  • Oncology
  • Genetics

Background:

  • Follicular lymphoma (FL) is the most common indolent non-Hodgkin lymphoma.
  • A significant subset of FL patients (10%-15%) undergo histologic transformation (HT) to a more aggressive lymphoma, typically diffuse large B-cell lymphoma (DLBCL).
  • Accurate prediction of HT at diagnosis is crucial for optimizing patient management and treatment strategies.

Purpose of the Study:

  • To validate and enhance a genetic risk model for predicting histologic transformation (HT) in follicular lymphoma (FL) at the time of diagnosis.
  • To develop a nomogram integrating genetic mutations and clinical variables to estimate the risk of transformation to DLBCL.

Main Methods:

  • Collected mutational data from diagnostic biopsies of 64 FL patients.
  • Combined data with a prior cohort (total n=104) for nomogram development and risk model generation using Cox regression.
  • Validated the clinicogenetic model internally (bootstrapping) and externally in an independent cohort, assessing performance with concordance index and calibration curves.

Main Results:

  • The developed clinicogenetic nomogram, incorporating mutations in HIST1H1E, KMT2D, TNFRSF14, and high-risk Follicular Lymphoma International Prognostic Index (FLIPI), achieved a concordance index of 0.746 in the combined cohort.
  • The model effectively stratified patients into low- and high-risk groups for transformation, with distinct 24- and 60-month probabilities of HT.
  • External validation demonstrated the model's predictive capability, although with a lower concordance index (0.552).

Conclusions:

  • A novel clinicogenetic risk model combining specific gene mutations (HIST1H1E, KMT2D, TNFRSF14) and clinical factors (FLIPI) can predict FL transformation to DLBCL.
  • This model offers a valuable tool for improving the management of FL patients.
  • The risk stratification may guide treatment strategies aimed at preventing or delaying disease transformation.