Abstract
Simple SummaryAcute lymphoblastic leukemia minimal residual disease (MRD) refers to the presence of residual leukemia cells following the achievement of complete remission, but below the limit of detection using conventional morphologic assessment. Up to two thirds of children may have MRD detectable after induction therapy depending on the biological subtype and method of detection. Patients with detectable MRD have an increased likelihood of relapse. A rapid reduction of MRD reveals leukemia sensitivity to therapy and under this premise, MRD has emerged as the strongest independent predictor of individual patient outcome and is crucial for risk stratification. However, it is a poor surrogate for treatment effect on long term outcome at the trial level, with impending need of randomized trials to prove efficacy of MRD-adapted interventions.Acute lymphoblastic leukemia (ALL) is the most common pediatric cancer and advances in its clinical and laboratory biology have grown exponentially over the last few decades. Treatment outcome has improved steadily with over 90% of patients surviving 5 years from initial diagnosis. This success can be attributed in part to the development of a risk stratification approach to identify those subsets of patients with an outstanding outcome that might qualify for a reduction in therapy associated with fewer short and long term side effects. Likewise, recognition of patients with an inferior prognosis allows for augmentation of therapy, which has been shown to improve outcome. Among the clinical and biological variables known to impact prognosis, the kinetics of the reduction in tumor burden during initial therapy has emerged as the most important prognostic variable. Specifically, various methods have been used to detect minimal residual disease (MRD) with flow cytometric and molecular detection of antigen receptor gene rearrangements being the most common. However, many questions remain as to the optimal timing of these assays, their sensitivity, integration with other variables and role in treatment allocation of various ALL subgroups. Importantly, the emergence of next generation sequencing assays is likely to broaden the use of these assays to track disease evolution. This review will discuss the biological basis for utilizing MRD in risk assessment, the technical approaches and limitations of MRD detection and its emerging applications.
Highlights
Acute lymphoblastic leukemia (ALL) is the most common pediatric malignancy
Multiparametric flow cytometry (MPFC) is used to measure minimal residual disease (MRD) levels by identifying remaining leukemic cells based on surface protein expression
Similar to IgH/TCR identification required for real time quantitative polymerase chain reaction (RQ-Polymerase Chain Reaction (PCR)) analysis of MRD, Next generation sequencing (NGS) requires that patient specific clonal V(D)J rearrangements be identified at diagnosis to allow for tracking of these clones throughout disease progression
Summary
Acute lymphoblastic leukemia (ALL) is the most common pediatric malignancy. In the United States, its incidence is 1.4 cases per 100,000 people [1]. Many clinical and biological variables are associated with treatment outcome with the principal ones being age, white blood cell count, the presence of CNS involvement at diagnosis, blast genotype and initial response to therapy as measured by the kinetics of disease regression. These variables are used in a variety of algorithms to predict the risk of relapse. Rate of 90% and patients with high risk features can attain an 80% survival with augmented therapy [2,3] In spite of this success, certain subgroups such as infants, adolescents and young adults and patients who relapse have an inferior outcome, making ALL one of the principal causes of pediatric cancer related death. More sensitive and quantitative measurements are better at discriminating levels of residual tumor burden with currently available assays measuring minimal residual disease (MRD) at a maximal sensitivity detection capacity of one blast in a background of 1 million cells
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