Sampling from single-cell observations to predict tumor cell growth in-vitro and in-vivo
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Alexander T. Pearson1, Patrick Ingram2, Shoumei Bai3, Patrick O'Hayer4, Jaehoon Chung5, Euisik Yoon6, Trachette Jackson7,* and Ronald J. Buckanovich3,*
1Section of Hematology/Oncology, Department of Medicine, University of Chicago, Chicago, IL, USA
2Department of Biomedical Engineering, University of Wisconsin, Madison, WI, USA
3Magee-Womens Research Institute, University of Pittsburgh, Pittsburgh, PA, USA
4University of Michigan School of Medicine, Ann Arbor, Michigan, MI, USA
5Institute of Microelectronics, Science and Engineering Research Council of the Agency for Science, Technology and Research, Singapore
6Department of Electrical Engineering and Computer Science, University of Michigan College of Engineering, Ann Arbor, MI, USA
7Department of Applied Mathematics, University of Michigan, Michigan, Ann Arbor, MI, USA
Ronald J. Buckanovich, email: email@example.com
Keywords: cancer modeling; ovarian cancer; cancer stem cell; EGFL6; microfluidics
Received: June 22, 2017 Accepted: October 16, 2017 Published: November 25, 2017
Cancer stem-like cells (CSCs) are a topic of increasing importance in cancer research, but are difficult to study due to their rarity and ability to rapidly divide to produce non-self-cells. We developed a simple model to describe transitions between aldehyde dehydrogenase (ALDH) positive CSCs and ALDH(-) bulk ovarian cancer cells. Microfluidics device-isolated single cell experiments demonstrated that ALDH+ cells were more proliferative than ALDH(-) cells. Based on our model we used ALDH+ and ALDH(-) cell division and proliferation properties to develop an empiric sampling algorithm and predict growth rate and CSC proportion for both ovarian cancer cell line and primary ovarian cancer cells, in-vitro and in-vivo. In both cell line and primary ovarian cancer cells, the algorithm predictions demonstrated a high correlation with observed ovarian cancer cell proliferation and CSC proportion. High correlation was maintained even in the presence of the EGF-like domain multiple 6 (EGFL6), a growth factor which changes ALDH+ cell asymmetric division rates and thereby tumor growth rates. Thus, based on sampling from the heterogeneity of in-vitro cell growth and division characteristics of a few hundred single cells, the simple algorithm described here provides rapid and inexpensive means to generate predictions that correlate with in-vivo tumor growth.
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