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Genomic features of rapid versus late relapse in triple negative breast cancer

Authors :
Yiqing Zhang
Sarah Asad
Zachary Weber
David Tallman
William Nock
Meghan Wyse
Jerome F. Bey
Kristin L. Dean
Elizabeth J. Adams
Sinclair Stockard
Jasneet Singh
Eric P. Winer
Nancy U. Lin
Yi-Zhou Jiang
Ding Ma
Peng Wang
Leming Shi
Wei Huang
Zhi-Ming Shao
Mathew Cherian
Maryam B. Lustberg
Bhuvaneswari Ramaswamy
Sagar Sardesai
Jeffrey VanDeusen
Nicole Williams
Robert Wesolowski
Samilia Obeng-Gyasi
Gina M. Sizemore
Steven T. Sizemore
Claire Verschraegen
Daniel G. Stover
Source :
BMC Cancer, Vol 21, Iss 1, Pp 1-13 (2021)
Publication Year :
2021
Publisher :
BMC, 2021.

Abstract

Abstract Background Triple-negative breast cancer (TNBC) is a heterogeneous disease and we have previously shown that rapid relapse of TNBC is associated with distinct sociodemographic features. We hypothesized that rapid versus late relapse in TNBC is also defined by distinct clinical and genomic features of primary tumors. Methods Using three publicly-available datasets, we identified 453 patients diagnosed with primary TNBC with adequate follow-up to be characterized as ‘rapid relapse’ (rrTNBC; distant relapse or death ≤2 years of diagnosis), ‘late relapse’ (lrTNBC; > 2 years) or ‘no relapse’ (nrTNBC: > 5 years no relapse/death). We explored basic clinical and primary tumor multi-omic data, including whole transcriptome (n = 453), and whole genome copy number and mutation data for 171 cancer-related genes (n = 317). Association of rapid relapse with clinical and genomic features were assessed using Pearson chi-squared tests, t-tests, ANOVA, and Fisher exact tests. We evaluated logistic regression models of clinical features with subtype versus two models that integrated significant genomic features. Results Relative to nrTNBC, both rrTNBC and lrTNBC had significantly lower immune signatures and immune signatures were highly correlated to anti-tumor CD8 T-cell, M1 macrophage, and gamma-delta T-cell CIBERSORT inferred immune subsets. Intriguingly, lrTNBCs were enriched for luminal signatures. There was no difference in tumor mutation burden or percent genome altered across groups. Logistic regression mModels that incorporate genomic features significantly outperformed standard clinical/subtype models in training (n = 63 patients), testing (n = 63) and independent validation (n = 34) cohorts, although performance of all models were overall modest. Conclusions We identify clinical and genomic features associated with rapid relapse TNBC for further study of this aggressive TNBC subset.

Details

Language :
English
ISSN :
14712407
Volume :
21
Issue :
1
Database :
Directory of Open Access Journals
Journal :
BMC Cancer
Publication Type :
Academic Journal
Accession number :
edsdoj.4d0a4dc2363b40cfb12c3b677a3f7f36
Document Type :
article
Full Text :
https://doi.org/10.1186/s12885-021-08320-7