Loss of STARD10 expression identifies a group of poor prognosis breast cancers independent of HER2/Neu and triple negative status

Elevated levels of tumour apolipoprotein D independently predict poor outcome in breast cancer patients

AIMS

Apolipoprotein D (ApoD) is a protein that is regulated by androgen and oestrogen, and is a major constituent of breast cysts. Although ApoD has been reported to be a marker of breast cancer, its prognostic importance in invasive breast cancer is unclear. The aim of this study was to investigate the relationship between ApoD protein expression, oestrogen receptor-α (ERα) expression and androgen receptor (AR) expression in predicting breast cancer outcome.

METHODS AND RESULTS

ApoD levels were measured by the use of immunohistochemistry and video image analysis on tissue sections from a breast cancer cohort (n = 214). We assessed the associations of ApoD expression with disease-free survival (DFS), metastasis-free survival (MFS), and overall survival (OS). We also assessed the relationship between ApoD expression, AR expression and ERα expression in predicting OS. ApoD expression (>1% ApoD positivity) was found in 72% (154/214) of tissues. High ApoD positivity (≥20.7%, fourth quartile) was an independent predictor of MFS and OS, and conferred a 2.2-fold increased risk of developing metastatic disease and a 2.1-fold increased risk of breast cancer-related death. ApoD positivity was not associated with AR or ERα nuclear positivity. However, patients with (≥1%) ERα-positive cancers with low (<20.7%) ApoD positivity, or those showing high (≥78%) AR positivity and low (<20.7%) ApoD positivity had better OS than other patient groups.

CONCLUSIONS

ApoD expression could be used to predict breast cancer prognosis independently of ERα and AR expression.

A New Gene Expression Signature for Triple Negative Breast Cancer Using Frozen Fresh Tissue before Neoadjuvant Chemotherapy

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer tumors. Comparisons between TNBC and non-triple negative breast cancer (nTNBC) may help to differentiate key components involved in TNBC neoplasms. The purpose of the study was to analyze the expression profile of TNBC versus nTNBC tumors in a homogeneous population from northeastern Mexico. A prospective study of 50 patients was conducted (25 TNBC and 25 nTNBC). Clinic parameters were equally distributed for TNBC and nTNBC: age at diagnosis (51 vs 47 years, p=0.1), glucose levels (107 mg/dl vs 104 mg/dl, p=0.64), and body mass index (28 vs 29, p=0.14), respectively. Core biopsies were collected for histopathological diagnosis and gene expression analyses. Total RNA was isolated and expression profiling was performed. 40 genes showed differential expression pattern in TNBC tumors. Among these, 9 over-expressed genes (PRKX/PRKY, UGT8, HMGA1, LPIN1, HAPLN3, and ANKRD11), and one under-expressed (ANX9) gene are involved in general metabolism. Based on this biochemical peculiarity, and the over-expression of BCL11A and FOXC1 (involved in tumor growth and metastasis, respectively) we validated by qPCR the expression profile of 7 genes out of the signature. In this report, a new gene signature for TNBC is proposed. To our knowledge, this is the first TNBC signature which describes genes involved in general metabolism. The findings may be pertinent for Mexican patients and require to be evaluated in further ethnic groups and populations.

Dysregulation of miR-638 in hepatocellular carcinoma and its clinical significance

MicroRNAs (miRNAs/miRs) have been identified as important post-transcriptional regulators in healthy liver physiology and liver diseases. However, the clinical significance of miR-638 in hepatocellular carcinoma (HCC) remains unclear. The aim of the present study was to investigate the status of miR-638 expression in HCC and to determine its clinical significance. The expression of miR-638 was evaluated in 60 HCC tissues samples and HCC SMMC-7721, HepG2 and Hep3B cell lines using reverse transcription-quantitative polymerase chain reaction. The association between the expression of miR-638 and the clinicopathological characteristics of patients with HCC was analyzed. The proportion of HCC patients with low miR-638 expression was identified as 68.3% (41/60). Furthermore, significantly lower miR-638 expression was identified in HCC tissue samples compared with the healthy control group (P=0.031). miR-638 expression was significantly lower in SMMC-7721 (P=0.021), HepG2 (P=0.005) and Hep3B (P=0.003) cells compared with the healthy human hepatic HL-7702 cell line. In addition, miR-638 expression was correlated with α-fetoprotein levels (P=0.042) and portal vein invasion (P=0.025). The area under curve was identified as 0.71 (95% confidence interval=0.63-0.79; P=0.001). The cut-off value for miR-638 was the median 2^-Δ∆Cq=0.125. In conclusion, miR-638 may be involved in the progression of HCC and act as a potential biomarker for the prediction of HCC.

miR-661 expression in SNAI1-induced epithelial to mesenchymal transition contributes to breast cancer cell invasion by targeting Nectin-1 and StarD10 messengers

Epithelial to mesenchymal transition (EMT) is a key step toward metastasis. MCF7 breast cancer cells conditionally expressing the EMT master regulator SNAI1 were used to identify early expressed microRNAs (miRNAs) and their targets that may contribute to the EMT process. Potential targets of miRNAs were identified by matching lists of in silico predicted targets and of inversely expressed mRNAs. MiRNAs were ranked based on the number of predicted hits, highlighting miR-661, a miRNA with so far no reported role in EMT. MiR-661 was found required for efficient invasion of breast cancer cells by destabilizing two of its predicted mRNA targets, the cell-cell adhesion protein Nectin-1 and the lipid transferase StarD10, resulting, in turn, in the downregulation of epithelial markers. Reexpression of Nectin-1 or StarD10 lacking the 3'-untranslated region counteracted SNAI1-induced invasion. Importantly, analysis of public transcriptomic data from a cohort of 295 well-characterized breast tumor specimen revealed that expression of StarD10 is highly associated with markers of luminal subtypes whereas its loss negatively correlated with the EMT-related, basal-like subtype. Collectively, our non-a priori approach revealed a nonpredicted link between SNAI1-triggered EMT and the down-regulation of Nectin-1 and StarD10 through the up-regulation of miR-661, which may contribute to the invasion of breast cancer cells and poor disease outcome.

Traditional statistical methods for evaluating prediction models are uninformative as to clinical value: towards a decision analytic framework

Cancer prediction models are becoming ubiquitous, yet we generally have no idea whether they do more good than harm. This is because current statistical methods for evaluating prediction models are uninformative as to their clinical value. Prediction models are typically evaluated in terms of discrimination or calibration. However, it is generally unclear how high discrimination needs to be before it is considered "high enough"; similarly, there are no rational guidelines as to the degree of miscalibration that would discount clinical use of a model. Classification tables do present the results of models in more clinically relevant terms, but it is not always clear which of two models is preferable on the basis of a particular classification table, or even whether either model should be used at all. Recent years have seen the development of straightforward decision analytic techniques that evaluate prediction models in terms of their consequences. This depends on the simple approach of weighting true and false positives differently, to reflect that, for example, delaying the diagnosis of a cancer is more harmful than an unnecessary biopsy. Such decision analytic techniques hold the promise of determining whether clinical implementation of prediction models would do more good than harm.