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Integration of gene mutations in risk prognostication for watch-and-wait follicular lymphoma patients initiating first-line treatment

  • 0Shanghai Institute of Hematology, State Key Laboratory of Medica Genomics, National Research Center for Translational Medicine at Shanghai Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China.
Hemasphere +

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May 8, 2025

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May 8, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

A new clinico-genetic model, m3-PRIMA-PI, improves risk assessment for follicular lymphoma (FL) patients on watch-and-wait (W&W). This model better predicts time to lymphoma treatment (TLT) compared to existing indexes.

Area Of Science

  • Hematology
  • Oncology
  • Genetics

Background

  • Follicular lymphoma (FL) patients with low tumor burden often receive a watch-and-wait (W&W) strategy.
  • Accurate risk stratification is crucial for managing these patients and predicting time to lymphoma treatment (TLT).

Purpose Of The Study

  • To develop and validate a clinico-genetic model for predicting TLT in FL patients managed with W&W.
  • To improve risk assessment beyond existing clinical prognostic indices.

Main Methods

  • Retrospective analysis of 214 FL patients on W&W (2016-2023), with 184 undergoing targeted sequencing.
  • Development of the m3-PRIMA-PI model using multivariate Cox analysis, incorporating clinical factors (bone marrow involvement, elevated β2-MG) and gene mutations (KMT2D, EP300, TP53).
  • Validation in an independent external cohort.

Main Results

  • The m3-PRIMA-PI model categorized patients into low, intermediate, and high-risk groups with distinct TLT probabilities at 1 and 2 years.
  • The model demonstrated superior predictive performance for TLT, with a C-index of 0.66, outperforming FLIPI (0.59) and FLIPI2 (0.59).
  • Results were confirmed in an external validation cohort.

Conclusions

  • The m3-PRIMA-PI model offers a promising tool for enhanced risk stratification in FL patients undergoing W&W.
  • Integrating clinical and genetic factors improves the prediction of time to treatment initiation.