Time-series Gene Expression Profiling of Childhood Acute Lymphoblastic Leukemia

ALL is the most common form of childhood cancer with >80% cured with contemporary treatment protocols. Accurate risk stratification in childhood ALL is essential to avoid under- and over-treatment. Currently, we use presenting clinical, biological features, and minimal residual disease (MRD) quantitation to risk stratify patients. Although whole genome gene expression profiling (GEP) can accurately classify patients with ALL into various WHO 2008 defined subgroups, its value in predicting relapse remained to be defined. We hypothesized that global time-series GEPs of bone marrow (BM) samples at diagnosis and specific points during initial remission-induction therapy can measure the success of cytoreduction and be used for relapse prediction. We generated time-series GEPs from 210 children with de novo ALL at diagnosis, and Day 8 of remission-induction therapy. We computed the time-series changes from diagnosis to follow-up time point of remission-induction, termed Effective Response Metric (ERM), that measures both the magnitude and direction of time-series change in multi-dimensional gene space towards the normal centroid, and we compared its ability to predict relapse against contemporary risk assignment methods including NCI criteria, cytogenetics and MRD. Gene expression profiling of 420 bone marrow or peripheral blood samples from 210 patiants at Day 0 and Day 8 was carried out using Affymetrix U133A or Affymetrix U133 Plus 2.0 arrays.