Selection of Patients for Individual Acute Myeloid Leukemia Therapies

Abstract

Despite multiagent chemotherapy, allogeneic hematopoietic cell transplantation, and several newly-approved agents, acute myeloid leukemia (AML) remains difficult to cure. With a growing list of “standard” treatments and many investigational drug options, treatment decision-making has become complex. Particularly for a disease as molecularly diverse as AML and primarily affecting older people, many of whom will have comorbidities that could limit drug tolerance, predicting outcomes of individual therapies for individual patients would simplify management and optimize cure rates. There is no shortage in scoring systems aimed to identify patients at high risk of early death or treatment resistance after conventional AML induction chemotherapy. They offer an empiric approach of selecting patients who will do well with such therapy, but their accuracy is imperfect, highlighting our challenge in comprehensively capturing and mathematically modeling the factors relevant for outcomes of AML therapies. Moreover, tools to predict outcomes with newer, now standard, treatments are not available, another important limitation to be aware of when deciding which AML therapy to choose.