Chronic myeloid leukemia (CML) provides a unique disease model in which to apply a translational approach to the study of the biology of disease progression, therapeutic response, and disease relapse. Although the function of BCR-ABL is well described, events involved in disease progression remain largely unknown. Over the last 10 years, tyrosine kinase inhibitors such as imatinib mesylate (IM) have dramatically altered CML treatment. Yet despite the excellent responses in most early chronic phase (CP) patients, ~20% of CP patients fail IM therapy. This number is higher for late CP patients and outcomes for advanced disease or disease resistant to IM are significantly poorer. Thus, a clear need exists for the early identification of patients at risk for disease progression or therapeutic resistance. The proposed translational studies rely on the identification of risk factors in patients, followed by validation in independent patients, with the ultimate goal of applying these finding to patient care in the clinical setting. The success of these investigations relies on the ability to test the findings in a sufficiently large number of patients. We are actively participating in a number of clinical trials for CML, including SWOG S0325, which compares standard dose IM to high dose IM and to dasatinib.
In Specific Aim 1 diagnostic gene expression candidates associated with CML disease progression and therapy resistance that were previously identified in microarray studies will be validated using high throughput quantitative RT-PCR (QPCR) in 400 independent patient samples. Investigations will be expanded to identify microRNAs (miRNAs) associated with CML progression and therapy resistance using high throughput QPCR profiling for all currently known miRNAs.
In Specific Aim 2 these diagnostic studies will be integrated with enhanced monitoring studies in patients receiving tyrosine kinase inhibitors (TKIs). These investigations will include early detection and monitoring of the kinetics of T315I development, a mutation resistant to all currently approved TKIs. Enhanced QPCR monitoring of bcr-abl in patients maintaining the best molecular responses will also be performed using a novel nanofluidic platform that enhances sensitivity through sample partitioning into up to 10,000 independent reactions. The goal of the T315I point mutation studies is to determine whether early T315I mutation detection can be used to guide therapeutic choices;the goal of enhanced bcr-abl monitoring in the best molecular response patients is to determine a kinetic profile that may potentially identify patients in whom TKI therapy can be stopped. Lastly, in Specific Aim 3 we will examine PRAME and GLI2 for their role in disease progression and therapy resistance in cell lines and primary cells using lentiviral vector-based gene silencing and expression techniques. A main goal will be to identify new pathways involved in progression and resistance that can be targeted either by existing therapies or by novel strategies. A major future clinical application of these integrated biological and clinical studies is to identify profiles that can be used to determine risk-based treatment management in individual patients.
Despite the success of therapy with imatinib mesylate (Gleevec, IM) in early chronic phase chronic myeloid leukemia (CML) patients, treatment outcomes for patients with more advanced disease or IM resistant disease remain poor. Our published and preliminary data allow us to investigate in clinical trial samples several important questions that may change how we manage CML: can we define diagnostic predictors of response;can we identify the pan-resistant T315I mutation early in the treatment course;can we determine a bcr-abl mRNA pattern in the best responders that may identify a group of patients who may be able to stop therapy;and through a better understanding of the biology of CML progression can we identify targets for therapy and reasons for resistance? These findings will be used to create risk-based profiles that will ultimately be used in the clinical setting in individual patients to tailor therapy.
|Yeung, K Y; Gooley, T A; Zhang, A et al. (2012) Predicting relapse prior to transplantation in chronic myeloid leukemia by integrating expert knowledge and expression data. Bioinformatics 28:823-30|
|Ito, Takahiro; Kwon, Hyog Young; Zimdahl, Bryan et al. (2010) Regulation of myeloid leukaemia by the cell-fate determinant Musashi. Nature 466:765-8|