Fellowships
The Stanford CCSB 2013 Summer Cancer Research Fellowship
| Principal Investigator: | Sylvia Plevritis, Ph.D. |
| Duration of Program: | June 22 through August 16, 2013 |
Project description:
Drug perturbation analysis has become a method of choice for key steps in the development of therapeutic agents, from target discovery and validation to the inferences of the mechanisms of action of small molecules. Perturbation of single cells has recently been used to understand the effect of drug responses across different human hematopoietic cells (Bendal et al 2011). In AML for example, it is well established that there is a hierarchy of cells in the tumor. The signaling behavior in these cell types is cofounded by the heterogeneity in the data (Bodenmiller et al. 2012, Bendall et al. 2011). Computational methods to handle heterogeneous single cell high through put data from multiple perturbations are still under developed. Nested Effects Models (Markowetz et al. 2005) are a class of models developed to analyse high throughput perturbation data with nested molecular signaling or phenotypic effects. Using Nested Effects Models we aim to identify how signaling pathways interact with each other to promote tumor regression in cancer cells, but not in normal body tissue.
Our ICBP summer project involves applying computational tools to single cell perturbation data to gain insights into regulatory signaling mechanisms within different cancer cell types. For example, in AML the presence of large populations of leukemic stem cells is associated with much worse prognosis for patients, and frequent resistance to treatment. However, the underlying cellular signaling processes driving this are unknown. We plan to integrate clustering methods with Nested Effect Models in an optimal manner so as to account for cancer heterogeneity in data. We will apply the methods on AML single cell data.
Requirements:
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.
2013 Fellow: Brian Williamson, Pamona College
