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Cell-cycle-phase progression analysis identifies unique phenotypes of major prognostic and predictive significance in breast cancer

Multiparameter analysis of core regulatory proteins involved in G1–S and G2–M cell-cycle transitions provides a powerful biomarker readout for assessment of the cell-cycle state. We have applied this algorithm to breast cancer to investigate how the cell cycle impacts on disease progression. Protein...

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Autores principales: Loddo, M, Kingsbury, S R, Rashid, M, Proctor, I, Holt, C, Young, J, El-Sheikh, S, Falzon, M, Eward, K L, Prevost, T, Sainsbury, R, Stoeber, K, Williams, G H
Formato: Texto
Lenguaje:English
Publicado: Nature Publishing Group 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2661794/
https://www.ncbi.nlm.nih.gov/pubmed/19240714
http://dx.doi.org/10.1038/sj.bjc.6604924
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author Loddo, M
Kingsbury, S R
Rashid, M
Proctor, I
Holt, C
Young, J
El-Sheikh, S
Falzon, M
Eward, K L
Prevost, T
Sainsbury, R
Stoeber, K
Williams, G H
author_facet Loddo, M
Kingsbury, S R
Rashid, M
Proctor, I
Holt, C
Young, J
El-Sheikh, S
Falzon, M
Eward, K L
Prevost, T
Sainsbury, R
Stoeber, K
Williams, G H
author_sort Loddo, M
collection PubMed
description Multiparameter analysis of core regulatory proteins involved in G1–S and G2–M cell-cycle transitions provides a powerful biomarker readout for assessment of the cell-cycle state. We have applied this algorithm to breast cancer to investigate how the cell cycle impacts on disease progression. Protein expression profiles of key constituents of the DNA replication licensing pathway (Mcm2, geminin) and mitotic machinery (Plk1, Aurora A and the Aurora substrate histone H3S10ph) were generated for a cohort of 182 patients and linked to clinicopathological parameters. Arrested differentiation and genomic instability were associated with an increased engagement of cells into the cell division cycle (P<0.0001). Three unique cell-cycle phenotypes were identified: (1) well-differentiated tumours composed predominantly of Mcm2-negative cells, indicative of an out-of-cycle state (18% of cases); (2) high Mcm2-expressing tumours but with low geminin, Aurora A, Plk1 and H3S10ph levels (S–G2–M progression markers), indicative of a G1-delayed/arrested state (24% cases); and (3) high Mcm2-expressing tumours and also expressing high levels of the S–G2–M progression markers, indicative of accelerated cell-cycle progression (58% of cases). The active cell-cycle progression phenotype had a higher risk of relapse when compared with out-of-cycle and G1-delayed/arrested phenotypes (HR=3.90 (1.81–8.40, P<0.001)), and was associated with Her-2 and triple negative subtypes (P<0.001). It is of note that high-grade tumours with the G1-delayed/arrested phenotype showed an identical low risk of relapse compared with well-differentiated out-of-cycle tumours (HR=1.00 (0.22–4.46), P=0.99). Our biomarker algorithm provides novel insights into the cell-cycle state of dynamic tumour cell populations in vivo. This information is of major prognostic significance and may impact on individualised therapeutic decisions. Patients with an accelerated phenotype are more likely to derive benefit from S- and M-phase-directed chemotherapeutic agents.
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spelling pubmed-26617942010-03-24 Cell-cycle-phase progression analysis identifies unique phenotypes of major prognostic and predictive significance in breast cancer Loddo, M Kingsbury, S R Rashid, M Proctor, I Holt, C Young, J El-Sheikh, S Falzon, M Eward, K L Prevost, T Sainsbury, R Stoeber, K Williams, G H Br J Cancer Molecular Diagnostics Multiparameter analysis of core regulatory proteins involved in G1–S and G2–M cell-cycle transitions provides a powerful biomarker readout for assessment of the cell-cycle state. We have applied this algorithm to breast cancer to investigate how the cell cycle impacts on disease progression. Protein expression profiles of key constituents of the DNA replication licensing pathway (Mcm2, geminin) and mitotic machinery (Plk1, Aurora A and the Aurora substrate histone H3S10ph) were generated for a cohort of 182 patients and linked to clinicopathological parameters. Arrested differentiation and genomic instability were associated with an increased engagement of cells into the cell division cycle (P<0.0001). Three unique cell-cycle phenotypes were identified: (1) well-differentiated tumours composed predominantly of Mcm2-negative cells, indicative of an out-of-cycle state (18% of cases); (2) high Mcm2-expressing tumours but with low geminin, Aurora A, Plk1 and H3S10ph levels (S–G2–M progression markers), indicative of a G1-delayed/arrested state (24% cases); and (3) high Mcm2-expressing tumours and also expressing high levels of the S–G2–M progression markers, indicative of accelerated cell-cycle progression (58% of cases). The active cell-cycle progression phenotype had a higher risk of relapse when compared with out-of-cycle and G1-delayed/arrested phenotypes (HR=3.90 (1.81–8.40, P<0.001)), and was associated with Her-2 and triple negative subtypes (P<0.001). It is of note that high-grade tumours with the G1-delayed/arrested phenotype showed an identical low risk of relapse compared with well-differentiated out-of-cycle tumours (HR=1.00 (0.22–4.46), P=0.99). Our biomarker algorithm provides novel insights into the cell-cycle state of dynamic tumour cell populations in vivo. This information is of major prognostic significance and may impact on individualised therapeutic decisions. Patients with an accelerated phenotype are more likely to derive benefit from S- and M-phase-directed chemotherapeutic agents. Nature Publishing Group 2009-03-24 2009-02-24 /pmc/articles/PMC2661794/ /pubmed/19240714 http://dx.doi.org/10.1038/sj.bjc.6604924 Text en Copyright © 2009 Cancer Research UK https://creativecommons.org/licenses/by/4.0/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 https://creativecommons.org/licenses/by/4.0/.
spellingShingle Molecular Diagnostics
Loddo, M
Kingsbury, S R
Rashid, M
Proctor, I
Holt, C
Young, J
El-Sheikh, S
Falzon, M
Eward, K L
Prevost, T
Sainsbury, R
Stoeber, K
Williams, G H
Cell-cycle-phase progression analysis identifies unique phenotypes of major prognostic and predictive significance in breast cancer
title Cell-cycle-phase progression analysis identifies unique phenotypes of major prognostic and predictive significance in breast cancer
title_full Cell-cycle-phase progression analysis identifies unique phenotypes of major prognostic and predictive significance in breast cancer
title_fullStr Cell-cycle-phase progression analysis identifies unique phenotypes of major prognostic and predictive significance in breast cancer
title_full_unstemmed Cell-cycle-phase progression analysis identifies unique phenotypes of major prognostic and predictive significance in breast cancer
title_short Cell-cycle-phase progression analysis identifies unique phenotypes of major prognostic and predictive significance in breast cancer
title_sort cell-cycle-phase progression analysis identifies unique phenotypes of major prognostic and predictive significance in breast cancer
topic Molecular Diagnostics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2661794/
https://www.ncbi.nlm.nih.gov/pubmed/19240714
http://dx.doi.org/10.1038/sj.bjc.6604924
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