Recent advances in the clinical application of aromatase inhibitors

Aromatase inhibitors have evolved over a period of 20 years to well tolerated agents that can effectively obliterate aromatase activity in postmenopausal women. Breast cancer is the predominant clinical application and here the newer agents have established themselves as the preferred second-line agent after tamoxifen in the treatment of advanced disease. Recent data indicate that they be more efficacious than tamoxifen and, therefore, may replace it as the first-line agent of choice in the near future. On-going clinical trials in the adjuvant setting and prospective prevention studies will elucidate whether these drugs have a yet greater role in breast cancer.     

Progress in the prevention of breast cancer: concept to reality

In 1936, Professor Antoine Lacassagne suggested that breast cancer could be prevented by developing drugs to block estrogen action in the breast. Jensen discovered the physiologic target, the estrogen receptor, that regulates estrogen action in its target tissues and Lerner discovered the first nonsteroidal antiestrogen MER25. However, the success of tamoxifen as a treatment of breast cancer opened the door for the testing of the worth of tamoxifen to reduce breast cancer incidence in high-risk women. In 1998, Fisher showed that tamoxifen could reduce breast cancer incidence by 50%. Nevertheless, only half the women who develop breast cancer have risk factors other than age, so what can be done for women without risk factors? The recognition that nonsteroidal antiestrogens have the ability to modulate estrogen action selectively has advanced the design and development of new drug for multiple diseases. Tamoxifen and raloxifene maintain bone density and raloxifene is now used to prevent osteoporosis and is being tested as a preventive for coronary heart disease and breast cancer. The drug group is now known as selective estrogen receptor modulators (SERMs) and the challenge is to design new agents for multiple applications. If the 20th century was the era of chemotherapy, the 21st century will be the era of chemoprevention.     

Bayesian Mixture Models for Assessment of Gene Differential Behaviour and Prediction of pCR through the Integration of Copy Number and Gene Expression Data

We consider modeling jointly microarray RNA expression and DNA copy number data. We propose Bayesian mixture models that define latent Gaussian probit scores for the DNA and RNA, and integrate between the two platforms via a regression of the RNA probit scores on the DNA probit scores. Such a regression conveniently allows us to include additional sample specific covariates such as biological conditions and clinical outcomes. The two developed methods are aimed respectively to make inference on differential behaviour of genes in patients showing different subtypes of breast cancer and to predict the pathological complete response (pCR) of patients borrowing strength across the genomic platforms. Posterior inference is carried out via MCMC simulations. We demonstrate the proposed methodology using a published data set consisting of 121 breast cancer patients.     

Supervised two-dimensional functional principal component analysis with time-to-event outcomes and mammogram imaging data

Screening mammography aims to identify breast cancer early and secondarily measures breast density to classify women at higher or lower than average risk for future breast cancer in the general population. Despite the strong association of individual mammography features to breast cancer risk, the statistical literature on mammogram imaging data is limited. While functional principal component analysis (FPCA) has been studied in the literature for extracting image-based features, it is conducted independently of the time-to-event response variable. With the consideration of building a prognostic model for precision prevention, we present a set of flexible methods, supervised FPCA (sFPCA) and functional partial least squares (FPLS), to extract image-based features associated with the failure time while accommodating the added complication from right censoring. Throughout the article, we hope to demonstrate that one method is favored over the other under different clinical setups. The proposed methods are applied to the motivating data set from the Joanne Knight Breast Health cohort at Siteman Cancer Center. Our approaches not only obtain the best prediction performance compared to the benchmark model, but also reveal different risk patterns within the mammograms.     

Identification of a new tamoxifen-xanthene hybrid as pro-apoptotic anticancer agent

Breast cancer is the most diagnosed type of cancer among women for which an exhaustive cure has not been discovered yet. Nowadays, tamoxifen still represents the gold standard for breast cancer therapy; it acts on both estrogen receptor-positive and estrogen receptor-negative breast cancers. Unfortunately, its toxicity and the related chemoresistance undermine its antitumor potential. In this paper, new tamoxifen-based derivatives with a rigid structural motif in their structure were designed, synthesized, and evaluated to assess their antitumor behavior. All the tested compounds affected estrogen receptor-positive tumor (MCF-7) cell growth, even with different extents, among which, the most active ones proved also to induce mitochondria-mediated apoptosis through activation of PARP cleavage, decrease in Bax/Bcl-2 ratio and increase in Bim gene expression levels. Here we found that the compound 1, carrying a rigid xanthene core, turned out to be the most promising of the set showing an activity profile comparable to that of tamoxifen. Furthermore, a more favorable genotoxic profile than tamoxifen made compound 1 a promising candidate for further studies.     

Identification of eight genes that are potentially involved in tamoxifen sensitivity in breast cancer cells

Although the antiestrogen agent tamoxifen has long been used to treat women with hormone receptor positive invasive breast carcinoma, the mechanisms of its action and acquired resistance to tamoxifen during treatment are largely unknown. A number of studies have revealed that over-activation of some signaling pathways can cause tamoxifen resistance; however, very little information is available regarding the genes whose loss-of-function alternation contribute to tamoxifen resistance. Here we used a forward genetic approach in vitro to generate tamoxifen resistant cells from the tamoxifen sensitive breast cancer cell line ZR-75-1, and further identified the disrupted gene in different tamoxifen resistant clones. Retinol binding protein 7, DNA polymerase-transactivated protein 3, γ-glutamyltransferase-like activity 1,slit-robo RhoGTPase-activating protein, tetraspan NET-4, HSPC194, amiloride-sensitive epithelial sodium channel gene,and Notch2, were the eight mutated genes identified in different tamoxifen resistant clones, suggesting their requirement for tamoxifen sensitivity in ZR-75-1 cells. Since the functions of these genes are not related to each other, it suggests that multiple pathways can influence tamoxifen sensitivity in breast cancer ceils.     

Joint modelling of repeated transitions in follow-up data--a case study on breast cancer data

In longitudinal studies where time to a final event is the ultimate outcome often information is available about intermediate events the individuals may experience during the observation period. Even though many extensions of the Cox proportional hazards model have been proposed to model such multivariate time-to-event data these approaches are still very rarely applied to real datasets. The aim of this paper is to illustrate the application of extended Cox models for multiple time-to-event data and to show their implementation in popular statistical software packages. We demonstrate a systematic way of jointly modelling similar or repeated transitions in follow-up data by analysing an event-history dataset consisting of 270 breast cancer patients, that were followed-up for different clinical events during treatment in metastatic disease. First, we show how this methodology can also be applied to non Markovian stochastic processes by representing these processes as "conditional" Markov processes. Secondly, we compare the application of different Cox-related approaches to the breast cancer data by varying their key model components (i.e. analysis time scale, risk set and baseline hazard function). Our study showed that extended Cox models are a powerful tool for analysing complex event history datasets since the approach can address many dynamic data features such as multiple time scales, dynamic risk sets, time-varying covariates, transition by covariate interactions, autoregressive dependence or intra-subject correlation.     

Improving sandwich variance estimation for marginal Cox analysis of cluster randomized trials

Cluster randomized trials (CRTs) frequently recruit a small number of clusters, therefore necessitating the application of small-sample corrections for valid inference. A recent systematic review indicated that CRTs reporting right-censored, time-to-event outcomes are not uncommon and that the marginal Cox proportional hazards model is one of the common approaches used for primary analysis. While small-sample corrections have been studied under marginal models with continuous, binary, and count outcomes, no prior research has been devoted to the development and evaluation of bias-corrected sandwich variance estimators when clustered time-to-event outcomes are analyzed by the marginal Cox model. To improve current practice, we propose nine bias-corrected sandwich variance estimators for the analysis of CRTs using the marginal Cox model and report on a simulation study to evaluate their small-sample properties. Our results indicate that the optimal choice of bias-corrected sandwich variance estimator for CRTs with survival outcomes can depend on the variability of cluster sizes and can also slightly differ whether it is evaluated according to relative bias or type I error rate. Finally, we illustrate the new variance estimators in a real-world CRT where the conclusion about intervention effectiveness differs depending on the use of small-sample bias corrections. The proposed sandwich variance estimators are implemented in an R package CoxBcv .     

Approximate Bayesian evaluation of multiple treatment effects

We propose an approximate Bayesian method for comparing an experimental treatment to a control based on a randomized clinical trial with multivariate patient outcomes. Overall treatment effect is characterized by a vector of parameters corresponding to effects on the individual patient outcomes. We partition the parameter space into four sets where, respectively, the experimental treatment is superior to the control, the control is superior to the experimental, the two treatments are equivalent, and the treatment effects are discordant. We compute posterior probabilities of the parameter sets by treating an estimator of the parameter vector like a random variable in the Bayesian paradigm. The approximation may be used in any setting where a consistent, asymptotically normal estimator of the parameter vector is available. The method is illustrated by application to a breast cancer data set consisting of multiple time-to-event outcomes with covariates and to count data arising from a cross-classification of response, infection, and treatment in an acute leukemia trial.     

Constrained parametric model for simultaneous inference of two cumulative incidence functions

We propose a parametric regression model for the cumulative incidence functions (CIFs) commonly used for competing risks data. The model adopts a modified logistic model as the baseline CIF and a generalized odds‐rate model for covariate effects, and it explicitly takes into account the constraint that a subject with any given prognostic factors should eventually fail from one of the causes such that the asymptotes of the CIFs should add up to one. This constraint intrinsically holds in a nonparametric analysis without covariates, but is easily overlooked in a semiparametric or parametric regression setting. We hence model the CIF from the primary cause assuming the generalized odds‐rate transformation and the modified logistic function as the baseline CIF. Under the additivity constraint, the covariate effects on the competing cause are modeled by a function of the asymptote of the baseline distribution and the covariate effects on the primary cause. The inference procedure is straightforward by using the standard maximum likelihood theory. We demonstrate desirable finite‐sample performance of our model by simulation studies in comparison with existing methods. Its practical utility is illustrated in an analysis of a breast cancer dataset to assess the treatment effect of tamoxifen, adjusting for age and initial pathological tumor size, on breast cancer recurrence that is subject to dependent censoring by second primary cancers and deaths.     

Ano1/TMEM16A Overexpression Is Associated with Good Prognosis in PR-Positive or HER2-Negative Breast Cancer Patients following Tamoxifen Treatment

The calcium-activated chloride channel Ano1 (TMEM16A) is overexpressed in many tumors. Although Ano1 overexpression is found in breast cancer due to 11q13 amplification, it remains unclear whether signaling pathways are involved in Ano1 overexpression during breast cancer tumorigenesis in vivo. Estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) have been known to contribute to breast cancer progression. It is unclear whether Ano1 is associated with clinical outcomes in breast cancer patients with different ER, PR and HER2 status. In the present study, we investigated the Ano1 expression in 431 patients with invasive ductal breast carcinoma and 46 patients with fibroadenoma, using immunohistochemistry, and analyzed the association between Ano1 expression and clinical characteristics and outcomes of breast cancer patients with different ER, PR, and HER2 status. Ano1 was overexpressed in breast cancer compared with fibroadenoma. Ano1 was significantly more associated with breast cancer with the lower clinical stage (stage I or II), or triple-negative status. Mostly importantly, Ano1 overexpression was associated with good prognosis in patients with the PR-positive or HER2-negative status, and in patients following tamoxifen treatment. Multivariate Cox regression analysis showed that Ano1 overexpression was a prognostic factor for longer overall survival in PR-positive or HER2-negative patients, and a predictive factor for longer overall survival in patients following tamoxifen treatment. Our findings suggest that Ano1 may be a potential marker for good prognosis in PR-positive or HER2-negative patients following tamoxifen treatment. The PR and HER2 status defines a subtype of breast cancer in which Ano1 overexpression is associated with good prognosis following tamoxifen treatment.     

Lnc-DC promotes estrogen independent growth and tamoxifen resistance in breast cancer

Selective estrogen receptor modulators (SERMs) such as tamoxifen have proven to be effective in the treatment of estrogen receptor (ER) positive breast cancer. However, a major obstacle for such endocrine therapy is estrogen independent growth, leading to resistance, and the underlying mechanism is not fully understood. The purpose of this study was to determine whether long non-coding RNAs (lncRNAs) are involved in regulation of estrogen independent growth and tamoxifen resistance in ER positive breast cancer. Using a CRISPR/Cas9-based SAM (synergistic activation mediator) library against a focus group of lncRNAs, we identify Lnc-DC as a candidate lncRNA. Further analysis suggests that Lnc-DC is able to reduce tamoxifen-induced apoptosis by upregulation of anti-apoptotic genes such as Bcl2 and Bcl-xL. Furthermore, Lnc-DC activates STAT3 by phosphorylation (pSTAT3^Y705), and the activated STAT3 subsequently induces expression of cytokines which in turn activate STAT3, forming an autocrine loop. Clinically, upregulation of Lnc-DC is associated with poor prognosis. In particular, analysis of a tamoxifen-treated patient cohort indicates that Lnc-DC expression can predict the response to tamoxifen. Together, this study demonstrates a previously uncharacterized function of Lnc-DC/STAT3/cytokine axis in estrogen independent growth and tamoxifen resistance, and Lnc-DC may serve as a potential predictor for tamoxifen response.Subject terms: Breast cancer, Non-coding RNAs     

Identification of novel proteins induced by estradiol, 4-hydroxytamoxifen and acolbifene in T47D breast cancer cells

Tamoxifen is currently used as adjuvant therapy for estrogen receptor (ER) positive breast cancer patients and as a chemopreventative agent. Although ER is a predictive marker for tamoxifen response, ER status fails to predict tamoxifen response in a significant number of patients highlighting the need to identify new pathways for tamoxifen sensitivity/resistance. To identify novel proteins induced by tamoxifen in breast cancer cells sensitive to tamoxifen growth inhibition, two-dimensional (2D) gel electrophoresis was used to profile proteins in T47D breast cancer cells. Six proteins were identified that were differentially regulated by 17beta-estradiol, 4-hydroxytamoxifen and the pure antagonist acolbifene (EM-652); calreticulin, synapse associated protein 1 (SYAP1), CD2 antigen binding protein 2 (CD2BP2), nucleosome assembly protein 1 like 1 (NAP1L1), d-3-phosphoglycerate dehydrogenase (3-PHGDH) and pyridoxine 5' phosphate oxidase (PNPO). At the mRNA level, these ligands differentially regulated expression of mRNAs encoding the identified proteins in T47D and MCF7 cells but had no effect on mRNA in ERalpha-negative MDA-MB-231 breast cancer cells. These novel SERM-regulated proteins may participate in new or existing pathways for sensitivity or resistance to SERMs.     

Electromagnetic fields alter the expression of estrogen receptor cofactors in breast cancer cells

Breast cancer is the most common malignancy of women in Western societies. The increasing exposure to electromagnetic fields has been suspected to contribute to the rising incidence of breast cancer in industrialized countries. The majority of breast tumors is treated with the partial antiestrogen tamoxifen. Most tumors become resistant to tamoxifen in the course of treatment resulting in treatment failure. Electromagnetic fields reduce the efficacy of tamoxifen similar to tamoxifen resistance. In this study we investigated the mechanism by which electromagnetic fields influence the sensitivity to tamoxifen. In cells exposed to 1.2 microT of a 50 Hz electromagnetic field gene expression of cofactors of the estrogen receptors was compared to sham exposed cells. Using a gene array technology several cofactors were found to be differentially expressed. The expression of the coactivators, SRC-1 and AIB1, and of two corepressors, N-Cor and SMRT, was quantified by RT-PCR. Both coactivators were expressed more strongly in the exposed cells while the expression of two corepressors decreased. The RNA analysis was confirmed by Western blots. The contradirectional changes in gene expression of coactivators and corepressors by electromagnetic fields results in a lower sensitivity to tamoxifen. Electromagnetic fields may contribute to the induction of tamoxifen resistance in vivo.     

Genotoxicity of the some selective estrogen receptor modulators: a review

The objective of this article is to review genotoxicological profile of the major selective estrogen receptor modulators, including clomiphene, tamoxifen, toremifene, raloxifene. These drugs have been used for infertility treatment and breast cancer prevention in high risk-women. However, some studies reported that especially tamoxifen is a genotoxic agent and is related with endometrial cancer. Our review indicate that clomiphene and tamoxifen were found as genotoxic agent in majority of the tests. However published reports showed that toremifene is a weakly genotoxic agent. The genotoxic effects of raloxifene are still poorly known. Further genotoxicity studies should be conducted especially for raloxifene.     

Is there a better way to assess performance in breast cytology?

Is there a better way to assess performance in breast cytology? Results generated from a series of breast fine needle aspirates (BrFNA) are semi‐qualitative and therefore sensitivity, specificity and accuracy are not necessarily the best measures of effectiveness. This paper considers the likelihood ratio of cancer (LR +), which can provide the probabilities for cancer being present or absent for cancer across the range of diagnostic categories used in the National Health Service Breast Screening Programme (NHSBSP), i.e. C1 to C5. Using LR +, receiver operating characteristic (ROC) curves can be generated and used to compare performance between pathologists, laboratories or years. These are illustrated using test results from 1997 to 1999 for the Derbyshire Royal Infirmary. LR + and ROC are meaningful measures. They could replace the 12 calculations currently used for the evaluation of effectiveness of BrFNA in the National Health Service Breast Screening Programme (NHSBSP) and for validation of pathologists’ performance.     

Tamoxifen and flaxseed alter angiogenesis regulators in normal human breast tissue in vivo

The incidence of breast cancer is increasing in the Western world and there is an urgent need for studies of the mechanisms of sex steroids in order to develop novel preventive strategies. Diet modifications may be among the means for breast cancer prevention. Angiogenesis, key in tumor progression, is regulated by the balance between pro- and anti-angiogenic factors, which are controlled in the extracellular space. Sampling of these molecules at their bioactive compartment is therefore needed. The aims of this study were to explore if tamoxifen, one of the most used anti-estrogen treatments for breast cancer affected some of the most important endogenous angiogenesis regulators, vascular endothelial growth factor (VEGF), angiogenin, and endostatin in normal breast tissue in vivo and if a diet supplementation with flaxseed had similar effects as tamoxifen in the breast. Microdialysis was used for in situ sampling of extracellular proteins in normal breast tissue of women before and after six weeks of tamoxifen treatment or before and after addition of 25 g/day of ground flaxseed to the diet or in control women. We show significant correlations between estradiol and levels of VEGF, angiogenin, and endostatin in vivo, which was verified in ex vivo breast tissue culture. Moreover, tamoxifen decreased the levels of VEGF and angiogenin in the breast whereas endostatin increased significantly. Flaxseed did not alter VEGF or angiogenin levels but similar to tamoxifen the levels of endostatin increased significantly. We conclude that one of the mechanisms of tamoxifen in normal breast tissue include tipping of the angiogenic balance into an anti-angiogenic state and that flaxseed has limited effects on the pro-angiogenic factors whereas the anti-angiogenic endostatin may be modified by diet. Further studies of diet modifications for breast cancer prevention are warranted.     

Strong expression association between matriptase and its substrate prostasin in breast cancer

Breast cancer tumorigenesis is accompanied by increased levels of extracellular proteases that are capable of remodeling the extracellular matrix as well as cleaving and activating growth factors and signaling receptors that are critically involved in neoplastic progression. Multiple studies implicate the membrane anchored serine protease matriptase (also known as MT-SP1 and epithin) in breast cancer. The pro-form of the GPI-anchored serine protease prostasin has recently been identified as a physiological substrate of matriptase and the two proteases are co-expressed in multiple healthy tissues. In this study, the inter-relationship between the two membrane-anchored serine proteases in breast cancer was investigated using breast cancer cell lines and breast cancer patient samples to delineate the association between matriptase and prostasin. We used Western blotting to determine the expression of matriptase and prostasin proteins in a panel of breast cancer cell lines and immunohistochemistry to assess the expression in serial sections from breast cancer tissue arrays. We demonstrate that the expression of matriptase and prostasin is closely correlated in breast cancer cell lines as well as in breast cancer tissue samples. Furthermore, matriptase and prostasin display a near identical spatial expression pattern in the epithelial compartment of breast cancer tissue. These data suggest that the matriptase-prostasin cascade might play a critical role in breast cancer.