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Superior breast cancer metastasis risk stratification using an epithelial-mesenchymal-amoeboid transition gene signature

BACKGROUND: Cancer cells are known to display varying degrees of metastatic propensity, but the molecular basis underlying such heterogeneity remains unclear. Our aims in this study were to (i) elucidate prognostic subtypes in primary tumors based on an epithelial-to-mesenchymal-to-amoeboid transiti...

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Autores principales: Emad, Amin, Ray, Tania, Jensen, Tor W., Parat, Meera, Natrajan, Rachael, Sinha, Saurabh, Ray, Partha S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7341640/
https://www.ncbi.nlm.nih.gov/pubmed/32641077
http://dx.doi.org/10.1186/s13058-020-01304-8
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author Emad, Amin
Ray, Tania
Jensen, Tor W.
Parat, Meera
Natrajan, Rachael
Sinha, Saurabh
Ray, Partha S.
author_facet Emad, Amin
Ray, Tania
Jensen, Tor W.
Parat, Meera
Natrajan, Rachael
Sinha, Saurabh
Ray, Partha S.
author_sort Emad, Amin
collection PubMed
description BACKGROUND: Cancer cells are known to display varying degrees of metastatic propensity, but the molecular basis underlying such heterogeneity remains unclear. Our aims in this study were to (i) elucidate prognostic subtypes in primary tumors based on an epithelial-to-mesenchymal-to-amoeboid transition (EMAT) continuum that captures the heterogeneity of metastatic propensity and (ii) to more comprehensively define biologically informed subtypes predictive of breast cancer metastasis and survival in lymph node-negative (LNN) patients. METHODS: We constructed a novel metastasis biology-based gene signature (EMAT) derived exclusively from cancer cells induced to undergo either epithelial-to-mesenchymal transition (EMT) or mesenchymal-to-amoeboid transition (MAT) to gauge their metastatic potential. Genome-wide gene expression data obtained from 913 primary tumors of lymph node-negative breast cancer (LNNBC) patients were analyzed. EMAT gene signature-based prognostic stratification of patients was performed to identify biologically relevant subtypes associated with distinct metastatic propensity. RESULTS: Delineated EMAT subtypes display a biologic range from less stem-like to more stem-like cell states and from less invasive to more invasive modes of cancer progression. Consideration of EMAT subtypes in combination with standard clinical parameters significantly improved survival prediction. EMAT subtypes outperformed prognosis accuracy of receptor or PAM50-based BC intrinsic subtypes even after adjusting for treatment variables in 3 independent, LNNBC cohorts including a treatment-naïve patient cohort. CONCLUSIONS: EMAT classification is a biologically informed method that provides prognostic information beyond that which can be provided by traditional cancer staging or PAM50 molecular subtype status and may improve metastasis risk assessment in early stage, LNNBC patients, who may otherwise be perceived to be at low metastasis risk.
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spelling pubmed-73416402020-07-14 Superior breast cancer metastasis risk stratification using an epithelial-mesenchymal-amoeboid transition gene signature Emad, Amin Ray, Tania Jensen, Tor W. Parat, Meera Natrajan, Rachael Sinha, Saurabh Ray, Partha S. Breast Cancer Res Research Article BACKGROUND: Cancer cells are known to display varying degrees of metastatic propensity, but the molecular basis underlying such heterogeneity remains unclear. Our aims in this study were to (i) elucidate prognostic subtypes in primary tumors based on an epithelial-to-mesenchymal-to-amoeboid transition (EMAT) continuum that captures the heterogeneity of metastatic propensity and (ii) to more comprehensively define biologically informed subtypes predictive of breast cancer metastasis and survival in lymph node-negative (LNN) patients. METHODS: We constructed a novel metastasis biology-based gene signature (EMAT) derived exclusively from cancer cells induced to undergo either epithelial-to-mesenchymal transition (EMT) or mesenchymal-to-amoeboid transition (MAT) to gauge their metastatic potential. Genome-wide gene expression data obtained from 913 primary tumors of lymph node-negative breast cancer (LNNBC) patients were analyzed. EMAT gene signature-based prognostic stratification of patients was performed to identify biologically relevant subtypes associated with distinct metastatic propensity. RESULTS: Delineated EMAT subtypes display a biologic range from less stem-like to more stem-like cell states and from less invasive to more invasive modes of cancer progression. Consideration of EMAT subtypes in combination with standard clinical parameters significantly improved survival prediction. EMAT subtypes outperformed prognosis accuracy of receptor or PAM50-based BC intrinsic subtypes even after adjusting for treatment variables in 3 independent, LNNBC cohorts including a treatment-naïve patient cohort. CONCLUSIONS: EMAT classification is a biologically informed method that provides prognostic information beyond that which can be provided by traditional cancer staging or PAM50 molecular subtype status and may improve metastasis risk assessment in early stage, LNNBC patients, who may otherwise be perceived to be at low metastasis risk. BioMed Central 2020-07-08 2020 /pmc/articles/PMC7341640/ /pubmed/32641077 http://dx.doi.org/10.1186/s13058-020-01304-8 Text en © The Author(s) 2020 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/. The Creative Commons Public Domain Dedication waiver (http://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 Article
Emad, Amin
Ray, Tania
Jensen, Tor W.
Parat, Meera
Natrajan, Rachael
Sinha, Saurabh
Ray, Partha S.
Superior breast cancer metastasis risk stratification using an epithelial-mesenchymal-amoeboid transition gene signature
title Superior breast cancer metastasis risk stratification using an epithelial-mesenchymal-amoeboid transition gene signature
title_full Superior breast cancer metastasis risk stratification using an epithelial-mesenchymal-amoeboid transition gene signature
title_fullStr Superior breast cancer metastasis risk stratification using an epithelial-mesenchymal-amoeboid transition gene signature
title_full_unstemmed Superior breast cancer metastasis risk stratification using an epithelial-mesenchymal-amoeboid transition gene signature
title_short Superior breast cancer metastasis risk stratification using an epithelial-mesenchymal-amoeboid transition gene signature
title_sort superior breast cancer metastasis risk stratification using an epithelial-mesenchymal-amoeboid transition gene signature
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7341640/
https://www.ncbi.nlm.nih.gov/pubmed/32641077
http://dx.doi.org/10.1186/s13058-020-01304-8
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