Migration Networks

Massachusetts Institute Of Technology, Cambridge MA

Investigators

Linked publications & trials

Abstract

The central step in cancer progression that leads to mortality is metastasis, the dissemination of cells from the primary tumor mass to distant organs. Research in the "EMT, Migration and Metastasis Networks" program is aimed at elucidating the regulatory pathways governing tumor progression to full metastatic disease with the goal of identifying new targets for therapeutic intervention in the treatment of malignant cancers. The proposed research focuses on four general aspects of metastasis: acquisition of a motile phenotype during epithelial to mesenchymal transition (EMT), motility responses to growth factor stimulation, dissemination of metastasis to the CNS and acquisition of resistance to therapy. We will use mathematical modeling to indentify novel regulatory pathways that control cell behavior as tumor cells develop an invasive, metastatic phenotype. Cell behavior during EMT and growth factor elicited motility will be measured quantitatively. The status of signaling pathways, gene expression and alternative splicing wall be interrogated and used to take an integrative systems approach to develop computational, data-driven models that relate these metrics to cell behavior. The models will be used to identify novel regulatory relationships governing the steps in carcinoma metastasis from initial invasion to tumor cell entry into blood or lymphatic vessels. Metastasis often leads to the acquisition of resistance both to conventional chemotherapy and to target treatments, possibly by allowing tumor cells to evade treatment by infiltrating a protected microenvironment such as the central nervous system. Tumor cell targeting of the central nervous system and acquisition of therapy resistant phenotypes will be explored using high-throughput RNAi screening approaches in vivo in a lymphoma model. The research program will employ mammary epithelia cells, breast cancer cells, xenografts and syngeneic tumor models, and lymphoma.