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

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 A...

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Autores principales: Pearson, Alexander T., Ingram, Patrick, Bai, Shoumei, O'Hayer, Patrick, Chung, Jaehoon, Yoon, Euisik, Jackson, Trachette, Buckanovich, Ronald J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Impact Journals LLC 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5762314/
https://www.ncbi.nlm.nih.gov/pubmed/29340046
http://dx.doi.org/10.18632/oncotarget.22693
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author Pearson, Alexander T.
Ingram, Patrick
Bai, Shoumei
O'Hayer, Patrick
Chung, Jaehoon
Yoon, Euisik
Jackson, Trachette
Buckanovich, Ronald J.
author_facet Pearson, Alexander T.
Ingram, Patrick
Bai, Shoumei
O'Hayer, Patrick
Chung, Jaehoon
Yoon, Euisik
Jackson, Trachette
Buckanovich, Ronald J.
author_sort Pearson, Alexander T.
collection PubMed
description 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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spelling pubmed-57623142018-01-16 Sampling from single-cell observations to predict tumor cell growth in-vitro and in-vivo Pearson, Alexander T. Ingram, Patrick Bai, Shoumei O'Hayer, Patrick Chung, Jaehoon Yoon, Euisik Jackson, Trachette Buckanovich, Ronald J. Oncotarget Research Paper 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. Impact Journals LLC 2017-11-25 /pmc/articles/PMC5762314/ /pubmed/29340046 http://dx.doi.org/10.18632/oncotarget.22693 Text en Copyright: © 2017 Pearson et al. http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/) 3.0 (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Paper
Pearson, Alexander T.
Ingram, Patrick
Bai, Shoumei
O'Hayer, Patrick
Chung, Jaehoon
Yoon, Euisik
Jackson, Trachette
Buckanovich, Ronald J.
Sampling from single-cell observations to predict tumor cell growth in-vitro and in-vivo
title Sampling from single-cell observations to predict tumor cell growth in-vitro and in-vivo
title_full Sampling from single-cell observations to predict tumor cell growth in-vitro and in-vivo
title_fullStr Sampling from single-cell observations to predict tumor cell growth in-vitro and in-vivo
title_full_unstemmed Sampling from single-cell observations to predict tumor cell growth in-vitro and in-vivo
title_short Sampling from single-cell observations to predict tumor cell growth in-vitro and in-vivo
title_sort sampling from single-cell observations to predict tumor cell growth in-vitro and in-vivo
topic Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5762314/
https://www.ncbi.nlm.nih.gov/pubmed/29340046
http://dx.doi.org/10.18632/oncotarget.22693
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