Sampling from single-cell observations to predict tumor cell growth in-vitro and in-vivo

Alexander T. Pearson, Patrick Ingram, Shoumei Bai, Patrick O'Hayer, Jaehoon Chung, Euisik Yoon, Trachette Jackson, Ronald J. Buckanovich

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)111176-111189
Number of pages14
JournalOncotarget
Volume8
Issue number67
DOIs
Publication statusPublished - 2017

Bibliographical note

Funding Information:
5Institute of Microelectronics, Science and Engineering Research Council of the Agency for Science, Technology and Research, Singapore

Funding Information:
This work was supported by the Ovarian Cancer Research Fund and the Department of Defense Ovarian Cancer Research Program Idea Award W81XWH-14-1-0187. UMCC core facilities are supported in part by the NIH through the UMCC Support Grant (P30 CA046592). RB is supported by NIH R01CA163345-05. AP is supported by NIH K08 DE026500-01.

Publisher Copyright:
© Pearson et al.

All Science Journal Classification (ASJC) codes

  • Oncology

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