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Lung cancer organoids analyzed on microwell arrays predict drug responses of patients within a week

While the potential of patient-derived organoids (PDOs) to predict patients’ responses to anti-cancer treatments has been well recognized, the lengthy time and the low efficiency in establishing PDOs hamper the implementation of PDO-based drug sensitivity tests in clinics. We first adapt a mechanica...

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Autores principales: Hu, Yawei, Sui, Xizhao, Song, Fan, Li, Yaqian, Li, Kaiyi, Chen, Zhongyao, Yang, Fan, Chen, Xiuyuan, Zhang, Yaohua, Wang, Xianning, Liu, Qiang, Li, Cong, Zou, Binbin, Chen, Xiaofang, Wang, Jun, Liu, Peng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8110811/
https://www.ncbi.nlm.nih.gov/pubmed/33972544
http://dx.doi.org/10.1038/s41467-021-22676-1
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author Hu, Yawei
Sui, Xizhao
Song, Fan
Li, Yaqian
Li, Kaiyi
Chen, Zhongyao
Yang, Fan
Chen, Xiuyuan
Zhang, Yaohua
Wang, Xianning
Liu, Qiang
Li, Cong
Zou, Binbin
Chen, Xiaofang
Wang, Jun
Liu, Peng
author_facet Hu, Yawei
Sui, Xizhao
Song, Fan
Li, Yaqian
Li, Kaiyi
Chen, Zhongyao
Yang, Fan
Chen, Xiuyuan
Zhang, Yaohua
Wang, Xianning
Liu, Qiang
Li, Cong
Zou, Binbin
Chen, Xiaofang
Wang, Jun
Liu, Peng
author_sort Hu, Yawei
collection PubMed
description While the potential of patient-derived organoids (PDOs) to predict patients’ responses to anti-cancer treatments has been well recognized, the lengthy time and the low efficiency in establishing PDOs hamper the implementation of PDO-based drug sensitivity tests in clinics. We first adapt a mechanical sample processing method to generate lung cancer organoids (LCOs) from surgically resected and biopsy tumor tissues. The LCOs recapitulate the histological and genetic features of the parental tumors and have the potential to expand indefinitely. By employing an integrated superhydrophobic microwell array chip (InSMAR-chip), we demonstrate hundreds of LCOs, a number that can be generated from most of the samples at passage 0, are sufficient to produce clinically meaningful drug responses within a week. The results prove our one-week drug tests are in good agreement with patient-derived xenografts, genetic mutations of tumors, and clinical outcomes. The LCO model coupled with the microwell device provides a technically feasible means for predicting patient-specific drug responses in clinical settings.
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spelling pubmed-81108112021-05-14 Lung cancer organoids analyzed on microwell arrays predict drug responses of patients within a week Hu, Yawei Sui, Xizhao Song, Fan Li, Yaqian Li, Kaiyi Chen, Zhongyao Yang, Fan Chen, Xiuyuan Zhang, Yaohua Wang, Xianning Liu, Qiang Li, Cong Zou, Binbin Chen, Xiaofang Wang, Jun Liu, Peng Nat Commun Article While the potential of patient-derived organoids (PDOs) to predict patients’ responses to anti-cancer treatments has been well recognized, the lengthy time and the low efficiency in establishing PDOs hamper the implementation of PDO-based drug sensitivity tests in clinics. We first adapt a mechanical sample processing method to generate lung cancer organoids (LCOs) from surgically resected and biopsy tumor tissues. The LCOs recapitulate the histological and genetic features of the parental tumors and have the potential to expand indefinitely. By employing an integrated superhydrophobic microwell array chip (InSMAR-chip), we demonstrate hundreds of LCOs, a number that can be generated from most of the samples at passage 0, are sufficient to produce clinically meaningful drug responses within a week. The results prove our one-week drug tests are in good agreement with patient-derived xenografts, genetic mutations of tumors, and clinical outcomes. The LCO model coupled with the microwell device provides a technically feasible means for predicting patient-specific drug responses in clinical settings. Nature Publishing Group UK 2021-05-10 /pmc/articles/PMC8110811/ /pubmed/33972544 http://dx.doi.org/10.1038/s41467-021-22676-1 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Hu, Yawei
Sui, Xizhao
Song, Fan
Li, Yaqian
Li, Kaiyi
Chen, Zhongyao
Yang, Fan
Chen, Xiuyuan
Zhang, Yaohua
Wang, Xianning
Liu, Qiang
Li, Cong
Zou, Binbin
Chen, Xiaofang
Wang, Jun
Liu, Peng
Lung cancer organoids analyzed on microwell arrays predict drug responses of patients within a week
title Lung cancer organoids analyzed on microwell arrays predict drug responses of patients within a week
title_full Lung cancer organoids analyzed on microwell arrays predict drug responses of patients within a week
title_fullStr Lung cancer organoids analyzed on microwell arrays predict drug responses of patients within a week
title_full_unstemmed Lung cancer organoids analyzed on microwell arrays predict drug responses of patients within a week
title_short Lung cancer organoids analyzed on microwell arrays predict drug responses of patients within a week
title_sort lung cancer organoids analyzed on microwell arrays predict drug responses of patients within a week
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8110811/
https://www.ncbi.nlm.nih.gov/pubmed/33972544
http://dx.doi.org/10.1038/s41467-021-22676-1
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