Effective Response Metric: a novel tool to predict relapse in childhood acute lymphoblastic leukaemia using time-series gene expression profiling

Abstract

Accurate risk assignment in childhood acute lymphoblastic leukaemia is essential to avoid under- or over-treatment. We hypothesized that time-series gene expression profiles (GEPs) of bone marrow samples during remission-induction therapy can measure the response and be used for relapse prediction. We computed the time-series changes from diagnosis to Day 8 of remission-induction, termed Effective Response Metric (ERM-D8) and tested its ability to predict relapse against contemporary risk assignment methods, including National Cancer Institutes (NCI) criteria, genetics and minimal residual disease (MRD). ERM-D8 was trained on a set of 131 patients and validated on an independent set of 79 patients. In the independent blinded test set, unfavourable ERM-D8 patients had >3-fold increased risk of relapse compared to favourable ERM-D8 (5-year cumulative incidence of relapse 38·1% vs. 10·6%; P = 2·5 × 10 −3 ). ERM-D8 remained predictive of relapse [P = 0·05; Hazard ratio 4·09, 95% confidence interval (CI) 1·03–16·23] after adjusting for NCI criteria, genetics, Day 8 peripheral response and Day 33 MRD. ERM-D8 improved risk stratification in favourable genetics subgroups (P = 0·01) and Day 33 MRD positive patients (P = 1·7 × 10 −3 ). We conclude that our novel metric – ERM-D8 – based on time-series GEP after 8 days of remission-induction therapy can independently predict relapse even after adjusting for NCI risk, genetics, Day 8 peripheral blood response and MRD.