GENETIC MARKERS IN PROGNOSIS AND RESIDUAL DISEASE

Fred Hutchinson Cancer Research Center, Seattle WA

Investigators

Linked publications & trials

Abstract

DESCRIPTION (provided by applicant): Relapse remains a substantial obstacle to cure after hematopoietic stem cell transplantation (HSCT). Our ability to define risk groups to help guide treatment is limited by the types of technology that we can bring to bear upon patient samples. Combinations of clinical features, cytogenetics, and flow cytometry can be used to stratify patients into risk groups, but these classifications use clinical and phenotypic surrogates rather than genotypic data. Studies of minimal residual disease (MRD) have enabled us in some diseases (especially CML) to predict patients at high risk for relapse after transplantation, but the application of MRD detection in the acute leukemias has been more problematic, owing to the dearth of frequent markers of malignancy. This project will strive to find molecular changes in tumors that predict response to therapy, and try to find more robust markers of disease for posttherapy monitoring of MRD. Thus, we will attempt to use state-of-the-art molecular biology to guide therapy up-front, and after initial therapy, in order to eliminate the specter of relapse. In Specific Aim 1, we will use gene expression microarray ("chip") technology to determine the genes associated with response in CML following HSCT. In Specific Aim 2, we will determine the genes associated with STI-571 and IFN response in CML. Lastly, in Specific Aim 3, we will detect and validate candidate genes that will be used for MRD detection in ALL and AML. The ultimate promise of these specific aims is an understanding of the molecular pathogenesis of leukemia in order to predict the progression and treatment response of a particular tumor in a particular patient. This will allow us to choose therapy, or intervene prior to relapse, based on the genetics of the particular tumor. These specific aims are a novel application of a cutting edge technology, and represent a new approach to determine risk groups and minimal residual disease detection. However, the combination of genomic discovery, technology, and informatics represents a revolution in medicine that has the potential to transform the application of genetics to clinical research.