Abstract B2-12: A systems biology approach for the discovery of differentiation therapeutics

Abstract

Accurately assessing the changes in cellular differentiation status in response to drug treatments or genetic manipulations is crucial for understanding the tumorigenesis process and developing novel therapeutics for human cancer. Here we have developed a novel systems biology approach, called the Lineage Maturation Index (LMI), to define the changes in differentiation state of cancer cells based on their gene expression profiles. We have been focusing on hematopoietic malignancies, but this approach is applicable to many human cancer types. We define the expression profile of N genes within a cell to be a point in an N-dimensional space. A lineage vector is derived from a progenitor cell (such as a hematopoietic stem cell) to a mature cell (such as a granulocyte) based on the expression of these N genes. By projecting the expression profile of an unknown cell onto the lineage vector, an LMI can be computed for the differentiation state of this specific cell. Upon drug treatments or genetic manipulations, a shift in LMI from stem cells towards mature cell types indicates differentiation. We have confirmed that the LMI approach can track the differentiation of normal hematopoietic cells. We have further shown that differentiation is a major consequence of various targeted therapies against leukemia irrespective of the driving oncogenes. Most importantly, we have used this approach to analyze the Connectivity Map HL60 leukemia data set for drugs that can induce leukemia cells to differentiate. In addition to drugs known to have differentiation activities (such as all-trans retinoic acid and etoposide), we discovered that several novel drugs (such as dihydroergotamine, mebendazole, and clonidine) have potent differentiation activities in leukemia cells. Currently, we are expanding our study into multiple types of solid tumors, such as breast, prostate, and colon cancers. Our LMI approach is highly robust in characterizing differentiation of the cancer cells and discovering novel differentiation therapies for human cancer.

Citation Format: Yulin Li, Jun Seita, Dean Felsher, David L. Dill. A systems biology approach for the discovery of differentiation therapeutics. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B2-12.