Acute Myelogenous Leukemia

Acute myeloid leukemia (AML) is an aggressive cancer of the blood forming system. It is characterized by expansion of malignant cells and impairment of healthy blood cell formation. AML originates from a small population of malignant stem-like cells, referred to as leukemic stem cells (LSC) or leukemia initiating cells (LIC). A hallmark of AML is its poor prognosis and the high rate of relapse. The main reason for the high risk of relapse is the clonal heterogeneity of the disease. Sequencing studies reveal that the AML cell population is composed of multiple clones. Contributions of the individual clones to the total malignant cell burden vary over time. Personalized therapy is a major goal of modern oncology, as patient responses vary greatly even within a histologically defined cancer subtype. This is especially true in AML, which exhibits striking heterogeneity in molecular segmentation.

Computational Methods and Tools

• Computational models of acute myeloid leukemia.
• Bioinformatics methods in identifying disease mechanisms.
• Methods integrating medical images and sequencing data.
• Drug repositioning and drug target prediction.
• Validation of results from computational studies by experiments.

Computational simulations of cancer cell signaling have the potential to overcome both the limitation of cell line diversity and in vitro screening throughput. Computational modeling approaches can be used to capture and integrate knowledge with molecular and phenotypic data to better understand the genetic and signaling dependencies determining a drug's mechanism of action. Computational models based around Boolean networks, pioneered by Kauffman as a model for genetic regulatory networks, have been demonstrated for interpretation of large data sets as well as for drug discovery.  Another extension to Boolean networks is provided by Quantitative Modeling approaches, allowing variables to range over nondiscrete values and so capturing more complex relationships, but only feasible for much smaller, well-studied systems.

CD ComputaBio has multiple resources including academic research and preclinical works in the identification of a suitable disease target and its corresponding hit. Contact us for more service details.

References:

1. Dana Silverbush, et al. Cell-Specific Computational Modeling of the PIM Pathway in Acute Myeloid Leukemia. Cancer Res. 2017 (77) (4) 827-838.
2. Röllig, C., et al. Long-term prognosis of acute myeloid leukemia according to the new genetic risk classification of the European LeukemiaNet recommendations: evaluation of the proposed reporting system. J. Clin. Oncol. 2011. 29, 2758–2765.