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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...
Autores principales: | , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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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. |
format | Online Article Text |
id | pubmed-8110811 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
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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