Effective Response Metric: a Novel Tool to Predict Relapse in Childhood Acute Lymphoblastic Leukaemia Using Time‐series Gene Expression Profiling

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. 106%; P=2.5x10(-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=1.7x10(-3)). We conclude that our novel metric - ERM-D8 - based on time-series GEP after 8days of remission-induction therapy can independently predict relapse even after adjusting for NCI risk, genetics, Day 8 peripheral blood response and MRD.