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Zebrafish patient-derived xenograft models predict lymph node involvement and treatment outcome in non-small cell lung cancer

BACKGROUND: Accurate predictions of tumor dissemination risks and medical treatment outcomes are critical to personalize therapy. Patient-derived xenograft (PDX) models in mice have demonstrated high accuracy in predicting therapeutic outcomes, but methods for predicting tumor invasiveness and early...

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Autores principales: Ali, Zaheer, Vildevall, Malin, Rodriguez, Gabriela Vazquez, Tandiono, Decky, Vamvakaris, Ioannis, Evangelou, Georgios, Lolas, Georgios, Syrigos, Konstantinos N., Villanueva, Alberto, Wick, Michael, Omar, Shenga, Erkstam, Anna, Schueler, Julia, Fahlgren, Anna, Jensen, Lasse D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8827197/
https://www.ncbi.nlm.nih.gov/pubmed/35139880
http://dx.doi.org/10.1186/s13046-022-02280-x
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author Ali, Zaheer
Vildevall, Malin
Rodriguez, Gabriela Vazquez
Tandiono, Decky
Vamvakaris, Ioannis
Evangelou, Georgios
Lolas, Georgios
Syrigos, Konstantinos N.
Villanueva, Alberto
Wick, Michael
Omar, Shenga
Erkstam, Anna
Schueler, Julia
Fahlgren, Anna
Jensen, Lasse D.
author_facet Ali, Zaheer
Vildevall, Malin
Rodriguez, Gabriela Vazquez
Tandiono, Decky
Vamvakaris, Ioannis
Evangelou, Georgios
Lolas, Georgios
Syrigos, Konstantinos N.
Villanueva, Alberto
Wick, Michael
Omar, Shenga
Erkstam, Anna
Schueler, Julia
Fahlgren, Anna
Jensen, Lasse D.
author_sort Ali, Zaheer
collection PubMed
description BACKGROUND: Accurate predictions of tumor dissemination risks and medical treatment outcomes are critical to personalize therapy. Patient-derived xenograft (PDX) models in mice have demonstrated high accuracy in predicting therapeutic outcomes, but methods for predicting tumor invasiveness and early stages of vascular/lymphatic dissemination are still lacking. Here we show that a zebrafish tumor xenograft (ZTX) platform based on implantation of PDX tissue fragments recapitulate both treatment outcome and tumor invasiveness/dissemination in patients, within an assay time of only 3 days. METHODS: Using a panel of 39 non-small cell lung cancer PDX models, we developed a combined mouse-zebrafish PDX platform based on direct implantation of cryopreserved PDX tissue fragments into zebrafish embryos, without the need for pre-culturing or expansion. Clinical proof-of-principle was established by direct implantation of tumor samples from four patients. RESULTS: The resulting ZTX models responded to Erlotinib and Paclitaxel, with similar potency as in mouse-PDX models and the patients themselves, and resistant tumors similarly failed to respond to these drugs in the ZTX system. Drug response was coupled to elevated expression of EGFR, Mdm2, Ptch1 and Tsc1 (Erlotinib), or Nras and Ptch1 (Paclitaxel) and reduced expression of Egfr, Erbb2 and Foxa (Paclitaxel). Importantly, ZTX models retained the invasive phenotypes of the tumors and predicted lymph node involvement of the patients with 91% sensitivity and 62% specificity, which was superior to clinically used tests. The biopsies from all four patient tested implanted successfully, and treatment outcome and dissemination were quantified for all patients in only 3 days. CONCLUSIONS: We conclude that the ZTX platform provide a fast, accurate, and clinically relevant system for evaluation of treatment outcome and invasion/dissemination of PDX models, providing an attractive platform for combined mouse-zebrafish PDX trials and personalized medicine. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13046-022-02280-x.
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spelling pubmed-88271972022-02-10 Zebrafish patient-derived xenograft models predict lymph node involvement and treatment outcome in non-small cell lung cancer Ali, Zaheer Vildevall, Malin Rodriguez, Gabriela Vazquez Tandiono, Decky Vamvakaris, Ioannis Evangelou, Georgios Lolas, Georgios Syrigos, Konstantinos N. Villanueva, Alberto Wick, Michael Omar, Shenga Erkstam, Anna Schueler, Julia Fahlgren, Anna Jensen, Lasse D. J Exp Clin Cancer Res Research BACKGROUND: Accurate predictions of tumor dissemination risks and medical treatment outcomes are critical to personalize therapy. Patient-derived xenograft (PDX) models in mice have demonstrated high accuracy in predicting therapeutic outcomes, but methods for predicting tumor invasiveness and early stages of vascular/lymphatic dissemination are still lacking. Here we show that a zebrafish tumor xenograft (ZTX) platform based on implantation of PDX tissue fragments recapitulate both treatment outcome and tumor invasiveness/dissemination in patients, within an assay time of only 3 days. METHODS: Using a panel of 39 non-small cell lung cancer PDX models, we developed a combined mouse-zebrafish PDX platform based on direct implantation of cryopreserved PDX tissue fragments into zebrafish embryos, without the need for pre-culturing or expansion. Clinical proof-of-principle was established by direct implantation of tumor samples from four patients. RESULTS: The resulting ZTX models responded to Erlotinib and Paclitaxel, with similar potency as in mouse-PDX models and the patients themselves, and resistant tumors similarly failed to respond to these drugs in the ZTX system. Drug response was coupled to elevated expression of EGFR, Mdm2, Ptch1 and Tsc1 (Erlotinib), or Nras and Ptch1 (Paclitaxel) and reduced expression of Egfr, Erbb2 and Foxa (Paclitaxel). Importantly, ZTX models retained the invasive phenotypes of the tumors and predicted lymph node involvement of the patients with 91% sensitivity and 62% specificity, which was superior to clinically used tests. The biopsies from all four patient tested implanted successfully, and treatment outcome and dissemination were quantified for all patients in only 3 days. CONCLUSIONS: We conclude that the ZTX platform provide a fast, accurate, and clinically relevant system for evaluation of treatment outcome and invasion/dissemination of PDX models, providing an attractive platform for combined mouse-zebrafish PDX trials and personalized medicine. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13046-022-02280-x. BioMed Central 2022-02-09 /pmc/articles/PMC8827197/ /pubmed/35139880 http://dx.doi.org/10.1186/s13046-022-02280-x Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Ali, Zaheer
Vildevall, Malin
Rodriguez, Gabriela Vazquez
Tandiono, Decky
Vamvakaris, Ioannis
Evangelou, Georgios
Lolas, Georgios
Syrigos, Konstantinos N.
Villanueva, Alberto
Wick, Michael
Omar, Shenga
Erkstam, Anna
Schueler, Julia
Fahlgren, Anna
Jensen, Lasse D.
Zebrafish patient-derived xenograft models predict lymph node involvement and treatment outcome in non-small cell lung cancer
title Zebrafish patient-derived xenograft models predict lymph node involvement and treatment outcome in non-small cell lung cancer
title_full Zebrafish patient-derived xenograft models predict lymph node involvement and treatment outcome in non-small cell lung cancer
title_fullStr Zebrafish patient-derived xenograft models predict lymph node involvement and treatment outcome in non-small cell lung cancer
title_full_unstemmed Zebrafish patient-derived xenograft models predict lymph node involvement and treatment outcome in non-small cell lung cancer
title_short Zebrafish patient-derived xenograft models predict lymph node involvement and treatment outcome in non-small cell lung cancer
title_sort zebrafish patient-derived xenograft models predict lymph node involvement and treatment outcome in non-small cell lung cancer
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8827197/
https://www.ncbi.nlm.nih.gov/pubmed/35139880
http://dx.doi.org/10.1186/s13046-022-02280-x
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