A risk score including microdeletions improves relapse prediction for standard and medium risk precursor B-cell acute

Rosemary Sutton1,2, Nicola C Venn1, Tamara Law1

  • 1Children's Cancer Institute, Lowy Cancer Research Centre, UNSW, Sydney, Australia.

Insights

A new risk score combining genetic deletions, minimal residual disease (MRD), and National Cancer Institute (NCI) risk improves relapse prediction in childhood acute lymphoblastic leukaemia (ALL), enhancing treatment strategies.

Area of Science:

  • Pediatric Oncology
  • Hematology
  • Cancer Genomics

Background:

  • Intensive treatment is used for high-risk pediatric acute lymphoblastic leukemia (ALL) to prevent relapse.
  • However, relapses often occur in standard or medium-risk ALL patients with low minimal residual disease (MRD).
  • Accurate risk stratification is crucial for optimizing treatment and improving outcomes in pediatric ALL.

Purpose of the Study:

  • To identify recurrent microdeletions and clinical prognostic factors for relapse prediction in non-high-risk precursor B-cell ALL.
  • To develop a refined risk stratification model beyond current MRD and NCI classifications.
  • To improve therapeutic decisions and cure rates for childhood ALL.

Main Methods:

  • Analysis of microdeletions and clinical factors in 475 uniformly treated non-high-risk precursor B-cell ALL patients.
  • Development of a predictive risk score (RS) incorporating IKZF1 deletions, P2RY8-CRLF2 fusion, Day 33 MRD, and NCI risk.
  • Evaluation of the RS model's performance against MRD-based risk stratification.

Main Results:

  • Lower relapse-free survival (RFS) was significantly associated with IKZF1 deletions, P2RY8-CRLF2 fusion, elevated Day 33 MRD, and High NCI risk.
  • The developed risk score (RS0, RS1, RS2+) demonstrated distinct RFS (93%, 78%, 49%) and overall survival (OS) (99%, 91%, 71%) rates.
  • The RS model provided superior discrimination compared to standard MRD-based risk stratification.

Conclusions:

  • The novel risk score effectively stratifies relapse risk in non-high-risk pediatric ALL.
  • This RS model offers improved early therapeutic stratification, potentially enhancing cure rates.
  • Integrating genetic and MRD data into a comprehensive risk score is vital for personalized ALL treatment.

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