? With the advent of modern combination chemotherapy and transplantation, significant advances have been made in the treatment of the acute leukemias, particularly in children. Yet despite these advances, 23,000 of the more than 33,000 children and adults diagnosed with leukemia in 2004 will ultimately die of resistant or relapsed disease. Particularly resistant forms of acute leukemia include: 1) relapsed acute lymphoblastic leukemia (ALL) in children; 2) newly diagnosed and relapsed ALL in adults; 3) acute leukemia arising in infants less than 1 year of age; and 4) acute myeloid leukemia (AML) in children and adults. The therapeutic advances that have been achieved have come in part through the development of risk classification schemes based on clinical features, the presence or absence of specific genetic abnormalities in leukemic cells, and measures of early therapeutic response that are used to target patients to specific regimens based on their relapse risk. Yet current risk classification schemes do not fully reflect the tremendous molecular heterogeneity of the acute leukemias and do not precisely identify those patients who are more prone to relapse, those who might be cured with less intensive regimens, or those who will respond to newer targeted therapeutic agents. It is our hypothesis that gene expression profiling of leukemic cells will yield systematic profiles or """"""""signatures"""""""" that can be used to improve outcome prediction, risk classification, and therapeutic targeting in the acute leukemias. Funded under the NCI Director's Challenge Program, we have worked with two NCI Cooperative Oncology Groups (COG and SWOG) to design retrospective patient cohorts from which we have derived rigorously cross-validated gene expression signatures and individual genes predictive of outcome in both children and adults with acute leukemia. We have particularly focused on more resistant forms of disease, where our results would have the most significant clinical impact. We now propose to refine and further validate these predictive genes and classifiers in new prospective studies: ? Aim 1) To improve risk classification, outcome prediction, and therapeutic targeting in pediatric and adult ALL by refining gene expression classifiers and testing the power and clinical utility of top predictive genes in new prospective patient cohorts. We will further refine and validate our gene expression classifier and top predictive genes in new prospective pediatric ALL cohorts, compare predictive profiles at initial diagnosis and at relapse, and determine the power of these classifiers in adult ALL. ? Aim 2) To improve molecular classification and therapeutic targeting in infant leukemia by prospectively testing a new gene expression classifier for initial diagnosis and refining classifiers for outcome prediction in a new prospective case-control study. We will refine and apply a new molecular classifier for initial diagnosis and therapeutic targeting in infants with leukemia. ? Aim 3) To improve risk classification, outcome prediction, and therapeutic targeting in pediatric and adult AML by refining the gene expression classifiers and the power and clinical utility of top predictive genes derived from our retrospective studies in new prospective patient cohorts. We will compare expression signatures for classification and outcome prediction in adult vs. pediatric AML and refine profiles that predict response or resistance to new targeted therapies (FLT3 and farnesvl transferase inhibitors). ? ?
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