S1-8: Molecular Signaling Distinguishes Early ER Positive Breast Cancer Recurrences Despite Adjuvant Tamoxifen.
Authors: Liu MC , Dixon JM , Xuan JJ , Riggins RB , Chen L , Wang C , Cho Y , Zhu Y , Jin L , Zwart A , Wang M , Klimach UM , Wang YJ , Renshaw L , Larionov A , Miller WR , Clarke R 
Published: June 2012

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.