B70
Cell cycle phase progression analysis identifies unique phenotypes of major prognostic and predictive significance in breast cancer
Marco Loddo, Sarah Kingsbury, Mohammed Rashid, Craig Holt, Julie Young, Soha El-Sheikh, Mary Falzon, Kathryn. L Eward, Toby Prevost, Richard Sainsbury, Kai Stoeber, Gareth Williams
1Wolfson Institute for Biomedical Research, UCL, London, UK, 2Faculty of Science, Anglia Ruskin University, Cambridge, UK, 3Centre for Applied Medical Statistics, University of Cambridge Institute of Public Health, Cambridge, UK, 4Princess Ann Hospital, Southampton, UK
Aim
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.
Method
Protein expression profiles of key constituents of the DNA replication licensing pathway (Mcm2, geminin) and mitotic machinery (Plk1, Aurora A, Aurora substrate Histone H3S10ph) were generated for a cohort of 182 patients and linked to clinicopathological parameters.
Results
Arrested differentiation and genomic instability were associated with increased engagement of cells into the cell division cycle (p<0.0001). Three unique cell cycle phenotypes were identified: (I) well differentiated tumours composed predominantly of Mcm2 negative cells indicative of an out-of-cycle state (18% of cases), (II) 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), (III) high expressing Mcm2 tumours, but 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). Notably, high grade tumours with the G1 delayed/arrested phenotype showed an identical low risk of relapse to well differentiated out-of-cycle tumours (HR=1.00 [0.22-4.46], p=0.99).
Conclusion
Our biomarker algorithm provides novel insights into the cell cycle state of dynamic tumour cell populations in-vivo, information that is of major prognostic significance and that 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.
