The Stanford CCSB 2014 Summer Cancer Research Fellowship
|Principal Investigator:||Sylvia Plevritis, Ph.D.|
|Duration of Program:||June 23 through August 15, 2014|
Characterization of the leukemia stem cell regulatory program in Human AML
Acute myeloid leukemia (AML) is a malignant disease characterized by an increased number of myeloid precursor cells that fail to differentiate in the bone marrow. Growing evidence indicates that AML is organized as a hierarchy of functionally distinct subpopulations initiated and maintained by self-renewing cancer or leukemia stem cells (LSC). Because of this, AML stem cells are a critical target for the development of novel therapies. Previously, we investigated the association between clinical outcomes and LSC gene expression in four independent cohorts of more than 1000 AML patients. We found that increased expression of an LSC signature correlated with worse clinical outcomes.
The goals of this project are to investigate the function of specific genes in regulating AML LSC and to determine the molecular mechanisms by which they executes these functions using computational and/or experimental methods.
The project will have computational and experimental components, with the balance depending on the background of the student.
1. Computational methods: The student will learn to analyze the microarray data, supervised by Andrew Gentles. This will include applying standard methods as well as more complex algorithms such as network reconstruction and machine learning. Experience required: knowledge of statistics; programming in a language such as Matlab or R, or similar.
2. Molecular Biology methods: The student will learn basic laboratory techniques, supervised by Jinfeng Shen.
Lab reagents and experimental method:
a. Cell lines: KG1, HL60, THP-1, K562, Kasumi, Molm-13
b. Experimental methods: AML cell line culture, Proliferation WST-1 assay,
trypan blue cell counting, transient shRNA transfection, Western Blotting.
Gentles AJ, Plevritis SK, Majeti R, & Alizadeh AA (2010) Association of a leukemic stem cell gene expression signature with clinical outcomes in acute myeloid leukemia. (Jama 304(24):2706-2715.
Applicants for this project should have interests in applying computational methods to large scale data mining of cancer datasets. Experience with a high-level statistical programming language such as R, Matlab, or Perl is required – some experience with Unix systems would be advantageous. Knowledge of basic statistical methods will be useful. Although experience with single cell data such as flow cytometry data is not essential, an interest in learning about them and what they can tell us about cancer systems biology is essential.
2014 Fellow: Thomas David Hart, Hunter College