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

Summary Accurate risk assignment in childhood acute lymphoblastic leukaemia is essential to avoid under‐ or over‐treatment. We hypothesized that time‐series gene expression profiles ( GEP s) 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 .