S1-8: Molecular Signaling Distinguishes Early ER Positive Breast Cancer Recurrences Despite Adjuvant Tamoxifen.

Abstract

Background: Unlike recurrences with other therapies, ER+ breast cancers (BC) can recur ≥10 yrs after an apparent successful period of adjuvant endocrine therapy. The molecular basis for this pattern of resistance is unresolved. We addressed the hypothesis that early recurrences during tamoxifen (TAM) treatment exhibit different biological characteristics than those that recur years later by assessing for variation in their respective transcriptomes. Methods: Appropriate tumors were identified from a set (BC030280) of snap-frozen tumor biopsies collected in Edinburgh from subjects with stage I-III BC before starting TAM (no chemotherapy); all had ≥10 yrs follow-up. Using rigorous standard operating procedures, high quality total RNA was extracted from samples with ≥50% malignant epithelium and arrayed on Affymetrix U133 Plus 2.0 GeneChips. Raw data were normalized using PLIER and analyzed with an adapted validated training and internal cross-validation workflow to avoid gene selection bias. A published dataset (Loi) was used for independent classifier validation that best fit our criteria for sample size, treatment (TAM only), data quality, and recurrence distribution. Early (E) vs late (L) recurrences were defined as distant relapse ≤3 vs ≥10 yrs from diagnosis. To explore putative mechanistic associations driving the transcriptome differences, a novel computational procedure was developed to integrate gene expression data with protein-protein interaction (PPI) data and create a statistical network model of the signaling. Metropolis sampling, a Markov Chain Monte Carlo method that can be implemented as a modified random walk procedure, identified ER network topology represented by the genes (nodes) and their predicted interconnections (edges). Results: A support vector machine with recursive feature elimination was used for the binary classification tasks on the BC030280 dataset. The optimized classifier for E vs L recurrence was independently tested on the Loi dataset with high levels of accuracy, specificity, and sensitivity. Classifier validation is supported by the respective survival curves (not shown). Gene set enrichment analysis reveals the top PPIs are primarily related to apoptosis (23/50; p=2.9e-13) and proliferation (14/50; p=6.8e-5). Substantial overlap of the network features and topology was seen between datasets. Specifically, increased relative expression of ESR1, ESR2, EGFR, BCL2, and AR was seen in L vs E recurrent tumors, and increased expression of CALM1, CALM2, CALM3, SRC, CDK1, and MAPK1 was seen in E vs L recurrent tumors. Several hubs (nodes with ≥5 edges) independently predicted for recurrence in additional public datasets of ER+ BC. Discussion: Our work provides clear evidence that robust molecular differences exist between ER+ BC that recur early vs. much later despite adjuvant TAM. Exploiting these differences will improve our understanding of involved signaling pathways, allow for the reliable prediction of early treatment failure, and guide use of novel therapeutics specifically directed at preventing E vs L recurrences on endocrine therapy. Citation Information: Cancer Res 2011;71(24 Suppl):Abstract nr S1-8.

Metastasis Dormancy in Estrogen Receptor–Positive Breast Cancer

“…The ratio of two individual mRNAs, HOXB13/IL17BR, has been demonstrated to be a prognostic factor in ER+ and node negative patients, which is the major population giving rise to late recurrences (54, 55). Gene expression signatures that are retrospectively associated with late recurrences have recently been derived by comparing the gene expression profiles of primary tumors of early vs. late recurrences (56, 57), or using dormant cancer cells in experimental systems (58). Whether these signatures can prospectively predict late recurrences remains to be tested.…”

Markers for the identification of late breast cancer recurrence

“…Tumors with mutations in PIK3CA have been shown to be associated with lower recurrence and mortality rates in the late time period [ 13 , 14 ]. Liu and colleagues [ 15 ] analyzed predictors of late relapse in early-stage ER-positive breast cancer, in which differences in the primary tumor tissue in patients with distant relapses occurring early (less than 3 years) versus late (after 7 years) have been compared. A set of genes was identified that were prognostic specifically for early relapse ( CALM1 , CALM2 , CALM3 , SRC , CDK1 , and MAPK1 ), but they also identified genes that appear to predict late relapse ( ESR1 , ESR2 , EGFR , BCL2 , and AR ).…”