Selective genomic copy number imbalances and probability of recurrence in early-stage breast cancer

Patricia A. Thompson, Abenaa M. Brewster, Do Kim-Anh, Veerabhadran Baladandayuthapani, Bradley M. Broom, Mary E. Edgerton, Karin M. Hahn, James L. Murray, Aysegul Sahin, Spyros Tsavachidis, Yuker Wang, Li Zhang, Gabriel N. Hortobagyi, Gordon B. Mills, Melissa L. Bondy

Research output: Contribution to journalArticlepeer-review

39 Scopus citations


A number of studies of copy number imbalances (CNIs) in breast tumors support associations between individual CNIs and patient outcomes. However, no pattern or signature of CNIs has emerged for clinical use. We determined copy number (CN) gains and losses using high-density molecular inversion probe (MIP) arrays for 971 stage I/II breast tumors and applied a boosting strategy to fit hazards models for CN and recurrence, treating chromosomal segments in a dose-specific fashion (-1 [loss], 0 [no change] and +1 [gain]). The concordance index (C-Index) was used to compare prognostic accuracy between a training (n = 728) and test (n = 243) set and across models. Twelve novel prognostic CNIs were identified: losses at 1p12, 12q13.13, 13q12.3, 22q11, and Xp21, and gains at 2p11.1, 3q13.12, 10p11.21, 10q23.1, 11p15, 14q13.2-q13.3, and 17q21.33. In addition, seven CNIs previously implicated as prognostic markers were selected: losses at 8p22 and 16p11.2 and gains at 10p13, 11q13.5, 12p13, 20q13, and Xq28. For all breast cancers combined, the final full model including 19 CNIs, clinical covariates, and tumor marker-approximated subtypes (estrogen receptor [ER], progesterone receptor, ERBB2 amplification, and Ki67) significantly outperformed a model containing only clinical covariates and tumor subtypes (C-Index full model, train[test] = 0.72[0.71] ± 0.02 vs. C-Index clinical + subtype model, train[test] = 0.62[0.62] ± 0.02; p<10 -6). In addition, the full model containing 19 CNIs significantly improved prognostication separately for ER-, HER2+, luminal B, and triple negative tumors over clinical variables alone. In summary, we show that a set of 19 CNIs discriminates risk of recurrence among early-stage breast tumors, independent of ER status. Further, our data suggest the presence of specific CNIs that promote and, in some cases, limit tumor spread.

Original languageEnglish (US)
Article numbere23543
JournalPloS one
Issue number8
StatePublished - 2011
Externally publishedYes

ASJC Scopus subject areas

  • General


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