Relapse prediction in childhood acute lymphoblastic leukemia by time-series gene expression profiling

Abstract

Childhood acute lymphoblastic leukemia (ALL) is the most common type of cancer in children. Contemporary management of patients with childhood ALL is based on the concept of tailoring the intensity of therapy to a patient?s risk of relapse, thereby maximizing the opportunity of cure and minimizing toxic side effects. However, practical protocols of relapse prediction remain imperfect. A significant number of patients with good prognostic characteristics relapse, while some with poor prognostic features survive. There is a demand to improve relapse prediction. High-throughput gene expression profiling (GEP) has been proved valuable in the diagnosis of childhood ALL. However, its application in relapse prediction falls short on 3 issues: 1) the lack of biological fundamental, 2) the improper selection of computational methodology, and 3) the limited clinical value. The treatment of childhood ALL is a process to gradually remove the leukemic cells in a patient. GEPs are capable of capturing leukemic genetic signatures in patients. Thus, we hypothesize that a leukemic sample consists of a mixture of leukemic cells and normal cells, where the intensity of the leukemic genetic signature measured by GEP could be used to infer the proportion of leukemic cells in the sample. In addition, as early response is known to have a great prognostic value in childhood ALL, we further expect to perform relapse prediction by the rate of the reduction of leukemic cells during treatment. To validate our hypothesis, for the first time, we generate time-series GEPs in a leukemia study. We demonstrate that the time-series GEPs are capable of mimicking the removal of leukemic cells in patients during disease treatment. By modeling our data, we propose to predict the relapses based on the change of GEPs between different time points, which is called genetic status shifting (GSS). Our relapse prediction results suggest the prognostic strength of GSS is superior to that of any other prognostic factors of childhood ALL, including minimal residual disease (MRD), which is considered as the most powerful relapse predictor among all biological and clinical features tested to date. In our study, GSS outperforms MRD for over 20% in the accuracy of relapse prediction. In addition, we prove the validity of GSS and its prognostic strength in acute myeloid leukemia (AML), a disease with only 40% of patients survived in 5 years. Our results suggest a new method to improve the prognosis of AML, and thus, probably, to increase the cure rate.