I am a post doctorate scholar at the Tumor Immunology department at City of Hope
and a research scholar at the Electrical Engineering Faculty at Stanford University.
I am interested in revealing the role of the immune system in the setting of tumor stroma and finding key factors that affect immune cell function.
Tumor infiltrating immune cells and additional cells in the tumor microenvironment have key roles in both tumor progression and inhibition. The precise mechanisms by which these cells interact with tumor cells are still largely unknown. Advances in the filed will allow harnessing and re-directing the immune response to attack and destroy tumor cells not only in the primary tumor setting but also in the hard-to-detect metastasis locations.
Gene expression microarrays are used extensively, especially in cancer research. However, most studies profile whole tissue samples that consist of a mix of cell types. Tumor tissues typically consist not only of tumor cells but also infiltrating immune cells and additional microenvironmental cells. This greatly limits the conclusions derived from gene expression profiling of whole tissues. For example, the knowledge of which specific cell type a certain gene or pathway is up-regulated in, can contribute greatly to understanding specific mechanisms in that cell type or cell-cell interactions.
Public repositories such as the Gene Expression Omnibus (GEO) are replete with microarray gene expression samples from a myriad of cancer patients including experimental conditions and treatments never to be repeated. Most of these samples are whole tissue mixed-cell samples. Re-examination of these data for cell-type specific genes and pathways is a powerful tool for the discovery of phenomena otherwise not detected in the mixed tissue samples of individual experiments.
We have developed a novel deconvolution method that estimates, for mixed-cell tissue samples, the identity of cell types that exist in the tissue, the cell type specific gene expression profiles and their relative proportions per sample.
zuckerman at gmail dot com