An Optimized Workflow to Evaluate Estrogen Receptor Gene Mutations in Small Amounts of Cell-Free DNA.

The detection of mutated genes in cell-free DNA (cfDNA) in plasma has emerged as an important minimally invasive way to obtain detailed information regarding tumor biology. Reliable determination of circulating tumor-derived DNA, often present at a low quantity amidst an excess of normal DNA in plasma, would be of added value for screening and monitoring of cancer patients and for hypothesis-generating studies in valuable retrospective cohorts. Our aim was to establish a workflow to simultaneously assess four hotspot estrogen receptor mutations (mESR1) in cfDNA isolated from only 200 µL of plasma by means of uniplex or multiplex pre-amplification combined with digital PCR. This workflow was then applied in metastatic breast cancer (MBC) patients receiving systemic therapies for MBC. In accordance with previous studies, estrogen receptor mutations were more frequently detected in endocrine-treated MBC patients at progressive disease [34.1% (15/44)] than before the start of endocrine therapy [3.9% (2/51); P = 0.001]. For a subset of samples, results were compared with analysis of these mutations by Oncomine-targeted next-generation sequencing, which, although requiring a higher cfDNA input, yielded concordant results. The data establish development and validation of a digital PCR workflow for the simultaneous detection of several tumor-derived mutations in minute amounts of cfDNA and show the potential of this workflow for use on archived volume-limited blood samples.

SIAH2 protein expression in breast cancer is inversely related with ER status and outcome to tamoxifen therapy.

Our previous study demonstrated that high mRNA levels for Seven in Absentia Homolog 2 (SIAH2) correlated with high Estrogen Receptor (ER) mRNA levels and with longer progression-free survival (PFS) after first-line tamoxifen. Others showed high SIAH2 protein levels in ER-negative breast cancer associated with an unfavorable relapse-free survival. In the current study, we investigated SIAH2 protein expression to clarify the discrepancy between protein and mRNA findings and to determine its diagnostic value in breast cancer patients. Tissue microarrays (TMAs) containing core specimens of primary breast tumors were immunohistochemically stained for SIAH2 protein. The TMAs analyzed a cohort of 746 patients with primary breast cancer (PBC) and a cohort of 245 patients with ER-positive metastatic breast cancer (MBC) treated with first-line tamoxifen. SIAH2 staining was scored for intensity and proportion of positive tumor cells and evaluated for its relationship with metastasis-free survival (MFS) and PFS. Multivariate survival analyses included traditional prognostic or predictive factors, respectively. The PBC-cohort had 263 patients with high SIAH2 protein expression and decreased expression of ER protein and mRNA levels (P = 0.005 and P = 0.003, respectively). High SIAH2 levels correlated with significant unfavorable MFS in lymph node negative, ER-positive breast cancer patients. The MBC-cohort had 86 patients with increased SIAH2 protein expression. High SIAH2 expression was associated with an unfavorable PFS after first-line tamoxifen in multivariate analyses (HR = 1.45; 95% CI, 1.07-1.96; P = 0.015). In conclusion, SIAH2 protein expression is especially observed in ER-negative tumors. Its prognostic value in breast cancer does not add to current prognostic markers. The proportion of SIAH2-positive cells can be used as biomarker to predict tamoxifen treatment failure in MBC patients.

An 8-gene mRNA expression profile in circulating tumor cells predicts response to aromatase inhibitors in metastatic breast cancer patients.

BACKGROUND: Molecular characterization of circulating tumor cells (CTC) is promising for personalized medicine. We aimed to identify a CTC gene expression profile predicting outcome to first-line aromatase inhibitors in metastatic breast cancer (MBC) patients. METHODS: CTCs were isolated from 78 MBC patients before treatment start. mRNA expression levels of 96 genes were measured by quantitative reverse transcriptase polymerase chain reaction. After applying predefined exclusion criteria based on lack of sufficient RNA quality and/or quantity, the data from 45 patients were used to construct a gene expression profile to predict poor responding patients, defined as disease progression or death <9 months, by a leave-one-out cross validation. RESULTS: Of the 45 patients, 19 were clinically classified as poor responders. To identify them, the 75% most variable genes were used to select genes differentially expressed between good and poor responders. An 8-gene CTC predictor was significantly associated with outcome (Hazard Ratio [HR] 4.40, 95% Confidence Interval [CI]: 2.17-8.92, P < 0.001). This predictor identified poor responding patients with a sensitivity of 63% and a positive predictive value of 75%, while good responding patients were correctly predicted in 85% of the cases. In multivariate Cox regression analysis, including CTC count at baseline, the 8-gene CTC predictor was the only factor independently associated with outcome (HR 4.59 [95% CI: 2.11-9.56], P < 0.001). This 8-gene signature was not associated with outcome in a group of 71 MBC patients treated with systemic treatments other than AI. CONCLUSIONS: An 8-gene CTC predictor was identified which discriminates good and poor outcome to first-line aromatase inhibitors in MBC patients. Although results need to be validated, this study underscores the potential of molecular characterization of CTCs.

Gene expression profiles of circulating tumor cells versus primary tumors in metastatic breast cancer.

Before using circulating tumor cells (CTCs) as liquid biopsy, insight into molecular discrepancies between CTCs and primary tumors is essential. We characterized CellSearch-enriched CTCs from 62 metastatic breast cancer (MBC) patients with ≥5 CTCs starting first-line systemic treatment. Expression levels of 35 tumor-associated, CTC-specific genes, including ESR1, coding for the estrogen receptor (ER), were measured by reverse transcription quantitative polymerase chain reaction and correlated to corresponding primary tumors. In 30 patients (48%), gene expression profiles of 35 genes were discrepant between CTCs and the primary tumor, but this had no prognostic consequences. In 15 patients (24%), the expression of ER was discrepant. Patients with ER-negative primary tumors and ER-positive CTCs had a longer median TTS compared to those with concordantly ER-negative CTCs (8.5 versus 2.1 months, P = 0.05). From seven patients, an axillary lymph node metastasis was available. In two patients, the CTC profiles better resembled the lymph node metastasis than the primary tumor. Our findings suggest that molecular discordances between CTCs and primary tumors frequently occur, but that this bears no prognostic consequences. Alterations in ER-status between primary tumors and CTCs might have prognostic implications.

Hallmarks of aromatase inhibitor drug resistance revealed by epigenetic profiling in breast cancer.

Aromatase inhibitors are the major first-line treatment of estrogen receptor-positive breast cancer, but resistance to treatment is common. To date, no biomarkers have been validated clinically to guide subsequent therapy in these patients. In this study, we mapped the genome-wide chromatin-binding profiles of estrogen receptor α (ERα), along with the epigenetic modifications H3K4me3 and H3K27me3, that are responsible for determining gene transcription (n = 12). Differential binding patterns of ERα, H3K4me3, and H3K27me3 were enriched between patients with good or poor outcomes after aromatase inhibition. ERα and H3K27me3 patterns were validated in an additional independent set of breast cancer cases (n = 10). We coupled these patterns to array-based proximal gene expression and progression-free survival data derived from a further independent cohort of 72 aromatase inhibitor-treated patients. Through this approach, we determined that the ERα and H3K27me3 profiles predicted the treatment outcomes for first-line aromatase inhibitors. In contrast, the H3K4me3 pattern identified was not similarly informative. The classification potential of these genes was only partially preserved in a cohort of 101 patients who received first-line tamoxifen treatment, suggesting some treatment selectivity in patient classification.