Cargando…
Metabolic classification of non-small cell lung cancer patient-derived xenografts by a digital pathology approach: A pilot study
INTRODUCTION: Genetically characterized patient-derived tumor xenografts (PDX) are a valuable resource to understand the biological complexity of cancer and to investigate new therapeutic approaches. Previous studies, however, lack information about metabolic features of PDXs, which may limit testin...
Autores principales: | , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10011479/ https://www.ncbi.nlm.nih.gov/pubmed/36925926 http://dx.doi.org/10.3389/fonc.2023.1070505 |
_version_ | 1784906403012411392 |
---|---|
author | Ferrarini, Federica Zulato, Elisabetta Moro, Massimo Del Bianco, Paola Borzi, Cristina Esposito, Giovanni Zanin, Tiziana Sozzi, Gabriella Indraccolo, Stefano |
author_facet | Ferrarini, Federica Zulato, Elisabetta Moro, Massimo Del Bianco, Paola Borzi, Cristina Esposito, Giovanni Zanin, Tiziana Sozzi, Gabriella Indraccolo, Stefano |
author_sort | Ferrarini, Federica |
collection | PubMed |
description | INTRODUCTION: Genetically characterized patient-derived tumor xenografts (PDX) are a valuable resource to understand the biological complexity of cancer and to investigate new therapeutic approaches. Previous studies, however, lack information about metabolic features of PDXs, which may limit testing of metabolism targeting drugs. METHODS: In this pilot study, we investigated by immunohistochemistry (IHC) expression of five essential metabolism-associated markers in a set of lung adenocarcinoma PDX samples previously established and characterized. We exploited digital pathology to quantify expression of the markers and correlated results with tumor cell proliferation, angiogenesis and time of PDX growth in mice. RESULTS: Our results indicate that the majority of the analyzed PDX models rely on oxidative phosphorylation (OXPHOS) metabolism, either alone or in combination with glucose metabolism. Double IHC enabled us to describe spatial expression of the glycolysis-associated monocarboxylate transporter 4 (MCT4) marker and the OXPHOS-associated glutaminase (GLS) marker. GLS expression was associated with cell proliferation and with expression of liver-kinase B1 (LKB1), a tumor suppressor involved in the regulation of multiple metabolic pathways. Acetyl CoA carboxylase (ACC) was associated with the kinetics of PDX growth. CONCLUSION: Albeit limited by the small number of samples and markers analyzed, metabolic classification of existing collections of PDX by this mini panel will be useful to inform pre-clinical testing of metabolism-targeting drugs. |
format | Online Article Text |
id | pubmed-10011479 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-100114792023-03-15 Metabolic classification of non-small cell lung cancer patient-derived xenografts by a digital pathology approach: A pilot study Ferrarini, Federica Zulato, Elisabetta Moro, Massimo Del Bianco, Paola Borzi, Cristina Esposito, Giovanni Zanin, Tiziana Sozzi, Gabriella Indraccolo, Stefano Front Oncol Oncology INTRODUCTION: Genetically characterized patient-derived tumor xenografts (PDX) are a valuable resource to understand the biological complexity of cancer and to investigate new therapeutic approaches. Previous studies, however, lack information about metabolic features of PDXs, which may limit testing of metabolism targeting drugs. METHODS: In this pilot study, we investigated by immunohistochemistry (IHC) expression of five essential metabolism-associated markers in a set of lung adenocarcinoma PDX samples previously established and characterized. We exploited digital pathology to quantify expression of the markers and correlated results with tumor cell proliferation, angiogenesis and time of PDX growth in mice. RESULTS: Our results indicate that the majority of the analyzed PDX models rely on oxidative phosphorylation (OXPHOS) metabolism, either alone or in combination with glucose metabolism. Double IHC enabled us to describe spatial expression of the glycolysis-associated monocarboxylate transporter 4 (MCT4) marker and the OXPHOS-associated glutaminase (GLS) marker. GLS expression was associated with cell proliferation and with expression of liver-kinase B1 (LKB1), a tumor suppressor involved in the regulation of multiple metabolic pathways. Acetyl CoA carboxylase (ACC) was associated with the kinetics of PDX growth. CONCLUSION: Albeit limited by the small number of samples and markers analyzed, metabolic classification of existing collections of PDX by this mini panel will be useful to inform pre-clinical testing of metabolism-targeting drugs. Frontiers Media S.A. 2023-02-28 /pmc/articles/PMC10011479/ /pubmed/36925926 http://dx.doi.org/10.3389/fonc.2023.1070505 Text en Copyright © 2023 Ferrarini, Zulato, Moro, Del Bianco, Borzi, Esposito, Zanin, Sozzi and Indraccolo https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Oncology Ferrarini, Federica Zulato, Elisabetta Moro, Massimo Del Bianco, Paola Borzi, Cristina Esposito, Giovanni Zanin, Tiziana Sozzi, Gabriella Indraccolo, Stefano Metabolic classification of non-small cell lung cancer patient-derived xenografts by a digital pathology approach: A pilot study |
title | Metabolic classification of non-small cell lung cancer patient-derived xenografts by a digital pathology approach: A pilot study |
title_full | Metabolic classification of non-small cell lung cancer patient-derived xenografts by a digital pathology approach: A pilot study |
title_fullStr | Metabolic classification of non-small cell lung cancer patient-derived xenografts by a digital pathology approach: A pilot study |
title_full_unstemmed | Metabolic classification of non-small cell lung cancer patient-derived xenografts by a digital pathology approach: A pilot study |
title_short | Metabolic classification of non-small cell lung cancer patient-derived xenografts by a digital pathology approach: A pilot study |
title_sort | metabolic classification of non-small cell lung cancer patient-derived xenografts by a digital pathology approach: a pilot study |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10011479/ https://www.ncbi.nlm.nih.gov/pubmed/36925926 http://dx.doi.org/10.3389/fonc.2023.1070505 |
work_keys_str_mv | AT ferrarinifederica metabolicclassificationofnonsmallcelllungcancerpatientderivedxenograftsbyadigitalpathologyapproachapilotstudy AT zulatoelisabetta metabolicclassificationofnonsmallcelllungcancerpatientderivedxenograftsbyadigitalpathologyapproachapilotstudy AT moromassimo metabolicclassificationofnonsmallcelllungcancerpatientderivedxenograftsbyadigitalpathologyapproachapilotstudy AT delbiancopaola metabolicclassificationofnonsmallcelllungcancerpatientderivedxenograftsbyadigitalpathologyapproachapilotstudy AT borzicristina metabolicclassificationofnonsmallcelllungcancerpatientderivedxenograftsbyadigitalpathologyapproachapilotstudy AT espositogiovanni metabolicclassificationofnonsmallcelllungcancerpatientderivedxenograftsbyadigitalpathologyapproachapilotstudy AT zanintiziana metabolicclassificationofnonsmallcelllungcancerpatientderivedxenograftsbyadigitalpathologyapproachapilotstudy AT sozzigabriella metabolicclassificationofnonsmallcelllungcancerpatientderivedxenograftsbyadigitalpathologyapproachapilotstudy AT indraccolostefano metabolicclassificationofnonsmallcelllungcancerpatientderivedxenograftsbyadigitalpathologyapproachapilotstudy |