Monitoring of fusion gene transcripts to predict relapse in pediatric acute myeloid leukemia

Hidemasa Matsuo1,2,3, Yuka Iijima-Yamashita1, Miho Yamada1

  • 1Clinical Research Center, National Hospital Organization Nagoya Medical Center, Aichi, Japan.

Abstract

Insights

Monitoring MLL-AF9 transcripts aids in predicting relapse in pediatric acute myeloid leukemia (AML). While AML1-ETO and CBFB-MYH11 transcripts have limited utility, elevated AML1-ETO levels may indicate relapse risk.

Area of Science:

  • Hematology
  • Oncology
  • Molecular Biology

Background:

  • Accurate minimal residual disease (MRD) detection is crucial for risk-stratified therapy in acute myeloid leukemia (AML).
  • Limited studies exist on monitoring multiple fusion transcripts for MRD assessment at various time points.

Purpose of the Study:

  • To evaluate the accuracy of multiple fusion transcripts as indicators of MRD in pediatric AML.
  • To assess the prognostic value of these transcripts at multiple time points post-diagnosis.

Main Methods:

  • Retrospective analysis of RNA from 82 pediatric AML patients (JPLSG AML-05 study).
  • Analysis of six key fusion transcripts (AML1-ETO, CBFB-MYH11, MLL-AF9, MLL-ELL, MLL-AF6, FUS-ERG) at five time points post-diagnosis.

Main Results:

  • MLL-AF9 expression (>50 copies) was associated with relapse in 2/2 patients, while only 1/7 MLL-AF9-negative patients relapsed.
  • MRD-positive patients (AML1-ETO >3,000 copies; MLL-AF9 >50 copies) showed significantly lower relapse-free survival (RFS) and overall survival (OS).
  • AML1-ETO and CBFB-MYH11 transcripts persisted even in non-relapsed patients, suggesting limited utility for MRD monitoring.

Conclusions:

  • Real-time quantitative PCR detection of MLL-AF9 transcripts is a promising marker for predicting relapse in pediatric AML.
  • The clinical utility of AML1-ETO and CBFB-MYH11 detection is limited, though high AML1-ETO levels may predict relapse risk.
  • Optimal thresholds are needed for utilizing AML1-ETO expression as a relapse predictor.

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