Pearson Alexander T, Ingram Patrick, Bai Shoumei, O'Hayer Patrick, Chung Jaehoon, Yoon Euisik, Jackson Trachette, Buckanovich Ronald J
Section of Hematology/Oncology, Department of Medicine, University of Chicago, Chicago, IL, USA.
Department of Biomedical Engineering, University of Wisconsin, Madison, WI, USA.
Oncotarget. 2017 Nov 25;8(67):111176-111189. doi: 10.18632/oncotarget.22693. eCollection 2017 Dec 19.
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, and . 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 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 tumor growth.
癌症干细胞（CSCs）是癌症研究中一个日益重要的课题，但由于其稀有性以及能够快速分裂产生非自身细胞，因此很难进行研究。我们开发了一个简单的模型来描述醛脱氢酶（ALDH）阳性癌症干细胞与ALDH（-）大量卵巢癌细胞之间的转变。微流控装置分离的单细胞实验表明，ALDH+细胞比ALDH（-）细胞更具增殖性。基于我们的模型，我们利用ALDH+和ALDH（-）细胞的分裂和增殖特性开发了一种经验采样算法，并预测了卵巢癌细胞系和原发性卵巢癌细胞的生长速率和癌症干细胞比例。在细胞系和原发性卵巢癌细胞中，该算法的预测结果与观察到的卵巢癌细胞增殖和癌症干细胞比例高度相关。即使存在表皮生长因子样结构域多重6（EGFL6），这种高相关性仍然保持，EGFL6是一种生长因子，它会改变ALDH+细胞的不对称分裂速率，从而影响肿瘤生长速率。因此，基于从几百个单细胞的细胞生长和分裂特征的异质性中进行采样，本文描述的简单算法提供了快速且廉价的方法来生成与肿瘤生长相关的预测。
