Background: Estrogen receptor positive (ER+) breast cancers (BC) are heterogeneous with regard to their clinical behavior and response to therapies. The ER is currently the best predictor of response to the anti-estrogen agent tamoxifen, yet up to 30–40% of ER+BC will relapse despite tamoxifen treatment. New prognostic biomarkers and further biological understanding of tamoxifen resistance are required. We used gene expression profiling to develop an outcome-based predictor using a training set of 255 ER+ BC samples from women treated with adjuvant tamoxifen monotherapy. We used clusters of highly correlated genes to develop our predictor to facilitate both signature stability and biological interpretation. Independent validation was performed using 362 tamoxifen-treated ER+ BC samples obtained from multiple institutions and treated with tamoxifen only in the adjuvant and metastatic settings.Results: We developed a gene classifier consisting of 181 genes belonging to 13 biological clusters. In the independent set of adjuvantly-treated samples, it was able to define two distinct prognostic groups (HR 2.01 95%CI: 1.29–3.13; p = 0.002). Six of the 13 gene clusters represented pathways involved in cell cycle and proliferation. In 112 metastatic breast cancer patients treated with tamoxifen, one of the classifier components suggesting a cellular inflammatory mechanism was significantly predictive of response.Conclusion: We have developed a gene classifier that can predict clinical outcome in tamoxifen-treated ER+ BC patients. Whilst our study emphasizes the important role of proliferation genes in prognosis, our approach proposes other genes and pathways that may elucidate further mechanisms that influence clinical outcome and prediction of response to tamoxifen.

Purpose: A HOXB13-to-IL17BR expression ratio was previously identified to predict clinical outcome of breast cancer patients treated with adjuvant tamoxifen. However, this ratio may predict a tumor's response to tamoxifen, its intrinsic aggressiveness, or both. Patients and Methods: We have measured the HOXB13 and IL17BR expression levels by real-time polymerase chain reaction in 1,252 primary breast tumor specimens. Expression levels were normalized to housekeeper gene levels and related to clinicopathologic factors for all patients. The primary objective of this study was to determine the relationship of a HOXB13-to-IL17BR ratio with tumor aggressiveness and/or with response to tamoxifen therapy in estrogen receptor (ER) -positive disease. We selected ER-positive tumors, and clinical end points for the HOXB13-to-IL17BR ratio were disease-free survival (DFS) in patients with primary breast cancer (N = 619) and progression-free survival (PFS) in patients with recurrent breast cancer treated with first-line tamoxifen monotherapy (N = 193). The odds ratio (OR) and hazard ratio (HR) and their 95% CI were calculated, and all P values were two-sided. Results: The HOXB13-to-IL17BR ratio was significantly associated with DFS and PFS. In multivariate analysis, HOXB13-to-IL17BR ratio expression levels were associated with a shorter DFS for node-negative patients only. Corrected for traditional predictive factors, the dichotomized HOXB13-to-IL17BR ratio was the strongest predictor in multivariate analysis for a poor response to tamoxifen therapy (OR = 0.16; 95% CI, 0.06 to 0.45; P < .001) and a shorter PFS (HR = 2.97; 95% CI, 1.82 to 4.86; P < .001). Conclusion: High HOXB13-to-IL17BR ratio expression levels associate with both tumor aggressiveness and tamoxifen therapy failure.

Background: Patients with Estrogen Receptor α‐positive (ER+) Inflammatory Breast Cancer (IBC) are less responsive to endocrine therapy compared with ER+ non‐IBC (nIBC) patients. The study of ER+ IBC samples might reveal biomarkers for endocrine resistant breast cancer.Materials & methods: Gene expression profiles of ER+ samples from 201 patients were explored for genes that discriminated between IBC and nIBC. Classifier genes were applied onto clinically annotated expression data from 947 patients with ER+ breast cancer and validated with RT‐qPCR for 231 patients treated with first‐line tamoxifen. Relationships with metastasis‐free survival (MFS) and progression‐free survival (PFS) following adjuvant and first‐line endocrine treatment, respectively, were investigated using Cox regression analysis.Results: A metagene of six genes including the genes encoding for 4‐aminobutyrate aminotransferase (ABAT) and Stanniocalcin‐2 (STC2) were identified to distinguish 22 ER+ IBC from 43 ER+ nIBC patients and remained discriminatory in an independent series of 136 patients. The metagene and two genes were not prognostic in 517 (neo)adjuvant untreated lymph node‐negative ER+ nIBC breast cancer patients. Only ABAT was related to outcome in 250 patients treated with adjuvant tamoxifen. Three independent series of in total 411 patients with advanced disease showed increased metagene scores and decreased expression of ABAT and STC2 to be correlated with poor first‐line endocrine therapy outcome. The biomarkers remained predictive for first‐line tamoxifen treatment outcome in multivariate analysis including traditional factors or published signatures. In an exploratory analysis, ABAT and STC2 protein expression levels had no relation with PFS after first‐line tamoxifen.Conclusions: This study utilized ER+ IBC to identify a metagene including ABAT and STC2 as predictive biomarkers for endocrine therapy resistance.

For patients with metastatic breast cancer, we previously described that increased EZH2 expression levels were associated with an adverse outcome to tamoxifen therapy. Main objective of the present study is to investigate miR-26a and miR-101 levels, which both target EZH2, for their association with molecular pathways and with efficacy of tamoxifen as first-line monotherapy for metastatic breast cancer. Expression levels were measured using quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) in primary breast cancer specimens of 235 estrogen receptor-α (ER)-positive patients. Pathway analysis was performed on microarray data available for 65 of these tumors. Logistic regression and Cox uni- and multivariate analysis were performed to relate expression levels with clinical benefit and time to progression (TTP). Increasing levels of miR-26a were significantly (P < 0.005) associated with both clinical benefit and prolonged TTP, whereas miR-101 was not. Cell cycle regulation and CCNE1 and CDC2 were the only significant overlapping pathway and genes differentially expressed between tumors with high and low levels of miR-26a and EZH2, respectively. In addition, increasing mRNA levels of CCNE1 (P < 0.05) and CDC2 (P < 0.001) were related to poor outcome. Multivariate analysis revealed miR-26a and CDC2 as an optimal set of markers associated with outcome on tamoxifen therapy, independently of traditional predictive factors. To summarize, only miR-26a levels are related with treatment outcome. Cell cycle regulation is the only overlapping pathway linked to miR-26a and EZH2 levels. Low mRNA levels of EZH2, CCNE1, and CDC2, and high levels of miR-26a are associated with favorable outcome on tamoxifen.

Quantification of tumor‐specific variants (TSVs) in cell‐free DNA is rapidly evolving as a prognostic and predictive tool in patients with cancer. Currently, both variant allele frequency (VAF) and number of mutant molecules per mL plasma are used as units of measurement to report those TSVs. However, it is unknown to what extent both units of measurement agree and what are the factors underlying an existing disagreement. To study the agreement between VAF and mutant molecules in current clinical studies, we analyzed 1116 TSVs from 338 patients identified with next‐generation sequencing (NGS) or digital droplet PCR (ddPCR). On different study cohorts, a Deming regression analysis was performed and its 95% prediction interval was used as surrogate for the limits of agreement between VAF and number of mutant molecules per mL and to identify outliers. VAF and number of mutant molecules per mL plasma yielded greater agreement when using ddPCR than NGS. In case of discordance between VAF and number of mutant molecules per mL, insufficient molecular coverage in NGS and high cell‐free DNA concentration were the main responsible factors. We propose several optimization steps needed to bring monitoring of TSVs in cell‐free DNA to its full potential.