LEUKEMIAS

 

 

Acute myeloid leukemia remains the most rapidly lethal of hematologic disorders. Despite being a malignancy where genetic analytic tools were first applied and where there is clear definition of molecular abnormalities associated with the disease, the rate of cure has remained unsatisfactory. Remission is achieved in approximately 90% of patients with complete remission in ~70%, but cure rates are <25% [1]. Defining the basis for relapse is critical to ultimately change the outcome for the ~18,000 patients in the U.S. diagnosed annually with this disease [1]. The proposal seeks to deconvolute the complexity of AML into clonal subpopulations that can be molecularly, functionally and physically characterized to define the features associated with clonal dominance and clonal responsiveness to chemotherapy in vivo [2, 3]. Based on mathematical modeling of clone behavior in vivo, predictions for therapy modifications to achieve maximal malignant cell kill will be generated and tested. These studies will thereby provide new insight into the behavior of clones comprising leukemia and test novel physical sciences-based strategies to exploit that behavior to therapeutic advantage.

Retrospective analyses of AML are able to provide limited recreation of the processes involved in the evolution of the disease and its response to therapy. By combining the well-validated animal models of AML proposed here with clonal tracking methods, high resolution in vivo microscopy and unique indicators of in vivo cell functions, we will be able to capture a multi-parametric quantitative assessment of AML over time and test the relationships of those parameters to chemotherapy response. This will generate a highly novel and rich dataset for computational analysis. Integration of this data with novel mathematical modeling will create algorithms to enhance chemotherapy response that will be tested and iteratively refined. We have assembled an exceptionally innovative team with a proven track record in interdisciplinary research to perform these studies. Collectively, this assembly of expertise, tools, data and analytic methods are unique and will enable innovative, rationally designed strategies to improve outcomes in AML.

 

LITERATURE CITED

1. American Cancer Society, editor. Cancer Facts and Figures 2014. American Cancer Society, 2014; 2014; Atlanta, Ga, USA.

2. Klco JM, Spencer DH, Miller CA, Griffith M, Lamprecht TL, O'Laughlin M, Fronick C, Magrini V, Demeter RT, Fulton RS, Eades WC, Link DC, Graubert TA, Walter MJ, Mardis ER, Dipersio JF, Wilson RK, Ley TJ. Functional heterogeneity of genetically defined subclones in acute myeloid leukemia. Cancer Cell. 2014;25(3):379-92.

3. Ding L, Ley TJ, Larson DE, Miller CA, Koboldt DC, Welch JS, Ritchey JK, Young MA, Lamprecht T, McLellan MD, McMichael JF, Wallis JW, Lu C, Shen D, Harris CC, Dooling DJ, Fulton RS, Fulton LL, Chen K, Schmidt H, Kalicki-Veizer J, Magrini VJ, Cook L, McGrath SD, Vickery TL, Wendl MC, Heath S, Watson MA, Link DC, Tomasson MH, Shannon WD, Payton JE, Kulkarni S, Westervelt P, Walter MJ, Graubert TA, Mardis ER, Wilson RK, DiPersio JF. Clonal evolution in relapsed acute myeloid leukaemia revealed by whole-genome sequencing. Nature. 2012;481(7382):506-10.

 

 

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