Oscar M. Rueda
Dynamics of breast-cancer relapse reveal late-recurring ER-positive genomic subgroups
Rueda, Oscar M.; Sammut, Stephen-John; Seoane, Jose A.; Chin, Suet-Feung; Caswell-Jin, Jennifer L.; Callari, Maurizio; Batra, Rajbir; Pereira, Bernard; Bruna, Alejandra; Ali, H. Raza; Provenzano, Elena; Liu, Bin; Parisien, Michelle; Gillett, Cheryl; McKinney, Steven; Green, Andrew R.; Murphy, Leigh; Purushotham, Arnie; Ellis, Ian O.; Pharoah, Paul D.; Rueda, Cristina; Aparicio, Samuel; Caldas, Carlos; Curtis, Christina
Authors
Stephen-John Sammut
Jose A. Seoane
Suet-Feung Chin
Jennifer L. Caswell-Jin
Maurizio Callari
Rajbir Batra
Bernard Pereira
Alejandra Bruna
H. Raza Ali
Elena Provenzano
Bin Liu
Michelle Parisien
Cheryl Gillett
Steven McKinney
ANDREW GREEN andrew.green@nottingham.ac.uk
Associate Professor
Leigh Murphy
Arnie Purushotham
Professor IAN ELLIS IAN.ELLIS@NOTTINGHAM.AC.UK
Professor of Cancer Pathology
Paul D. Pharoah
Cristina Rueda
Samuel Aparicio
Carlos Caldas
Christina Curtis
Abstract
The rates and routes of lethal systemic spread in breast cancer are poorly understood owing to a lack of molecularly characterized patient cohorts with long-term, detailed follow-up data. Long-term follow-up is especially important for those with oestrogen-receptor (ER)-positive breast cancers, which can recur up to two decades after initial diagnosis1,2,3,4,5,6. It is therefore essential to identify patients who have a high risk of late relapse7,8,9. Here we present a statistical framework that models distinct disease stages (locoregional recurrence, distant recurrence, breast-cancer-related death and death from other causes) and competing risks of mortality from breast cancer, while yielding individual risk-of-recurrence predictions. We apply this model to 3,240 patients with breast cancer, including 1,980 for whom molecular data are available, and delineate spatiotemporal patterns of relapse across different categories of molecular information (namely immunohistochemical subtypes; PAM50 subtypes, which are based on gene-expression patterns10,11; and integrative or IntClust subtypes, which are based on patterns of genomic copy-number alterations and gene expression12,13). We identify four late-recurring integrative subtypes, comprising about one quarter (26%) of tumours that are both positive for ER and negative for human epidermal growth factor receptor 2, each with characteristic tumour-driving alterations in genomic copy number and a high risk of recurrence (mean 47–62%) up to 20 years after diagnosis. We also define a subgroup of triple-negative breast cancers in which cancer rarely recurs after five years, and a separate subgroup in which patients remain at risk. Use of the integrative subtypes improves the prediction of late, distant relapse beyond what is possible with clinical covariates (nodal status, tumour size, tumour grade and immunohistochemical subtype). These findings highlight opportunities for improved patient stratification and biomarker-driven clinical trials.
Citation
Rueda, O. M., Sammut, S., Seoane, J. A., Chin, S., Caswell-Jin, J. L., Callari, M., …Curtis, C. (2019). Dynamics of breast-cancer relapse reveal late-recurring ER-positive genomic subgroups. Nature, 567(7748), 399–404. https://doi.org/10.1038/s41586-019-1007-8
Journal Article Type | Article |
---|---|
Acceptance Date | Jan 31, 2019 |
Online Publication Date | Mar 13, 2019 |
Publication Date | Mar 13, 2019 |
Deposit Date | Mar 20, 2019 |
Publicly Available Date | Sep 14, 2019 |
Journal | Nature |
Print ISSN | 0028-0836 |
Electronic ISSN | 1476-4687 |
Publisher | Nature Publishing Group |
Peer Reviewed | Peer Reviewed |
Volume | 567 |
Issue | 7748 |
Pages | 399–404 |
DOI | https://doi.org/10.1038/s41586-019-1007-8 |
Public URL | https://nottingham-repository.worktribe.com/output/1668194 |
Publisher URL | https://www.nature.com/articles/s41586-019-1007-8 |
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