Effects of Environmental Organochlorine Pesticides on Human Breast Cancer: Putative Involvement on Invasive Cell Ability

Human exposure to persistent organic pollutants (POPs) is a certainty, even to long banned pesticides like o,p′-dichlorodiphenyltrichloroethane (o,p′-DDT), and its metabolites p,p′-dichlorodiphenyldichloroethylene (p,p′-DDE), and p,p′-dichlorodiphenyldichloroethane (p,p′-DDD). POPs are known to be particularly toxic and have been associated with endocrine-disrupting effects in several mammals, including humans even at very low doses. As environmental estrogens, they could play a critical role in carcinogenesis, such as in breast cancer. With the purpose of evaluating their effect on breast cancer biology, o,p′-DDT, p,p′-DDE, and p,p′-DDD (50–1000 nM) were tested on two human breast adenocarcinoma cell lines: MCF-7 expressing estrogen receptor (ER) α and MDA-MB-231 negative for ERα, regarding cell proliferation and viability in addition to their invasive potential. Cell proliferation and viability were not equally affected by these compounds. In MCF-7 cells, the compounds were able to decrease cell proliferation and viability. On the other hand, no evident response was observed in treated MDA-MB-231 cells. Concerning the invasive potential, the less invasive cell line, MCF-7, had its invasion potential significantly induced, while the more invasive cell line MDA-MB-231, had its invasion potential dramatically reduced in the presence of the tested compounds. Altogether, the results showed that these compounds were able to modulate several cancer-related processes, namely in breast cancer cell lines, and underline the relevance of POP exposure to the risk of cancer development and progression, unraveling distinct pathways of action of these compounds on tumor cell biology.

Enoxacin inhibits growth of prostate cancer cells and effectively restores microRNA processing

Prostate cancer (PCa) is one of the most incident malignancies worldwide. Although efficient therapy is available for early-stage PCa, treatment of advanced disease is mainly ineffective and remains a clinical challenge. microRNA (miRNA) dysregulation is associated with PCa development and progression. In fact, several studies have reported a widespread downregulation of miRNAs in PCa, which highlights the importance of studying compounds capable of restoring the global miRNA expression. The main aim of this study was to define the usefulness of enoxacin as an anti-tumoral agent in PCa, due to its ability to induce miRNA biogenesis in a TRBP-mediated manner. Using a panel of five PCa cell lines, we observed that all of them were wild type for the TARBP2 gene and expressed TRBP protein. Furthermore, primary prostate carcinomas displayed normal levels of TRBP protein. Remarkably, enoxacin was able to decrease cell viability, induce apoptosis, cause cell cycle arrest, and inhibit the invasiveness of cell lines. Enoxacin was also effective in restoring the global expression of miRNAs. This study is the first to show that PCa cells are highly responsive to the anti-tumoral effects of enoxacin. Therefore, enoxacin constitutes a promising therapeutic agent for PCa.

Anti-tumoral effect of the non-nucleoside DNMT inhibitor RG108 in human prostate cancer cells

Background: Current therapeutic strategies for advanced prostate cancer (PCa) are largely ineffective. Because aberrant DNA methylation associated with inappropriate gene-silencing is a common feature of PCa, DNA methylation inhibitors might constitute an alternative therapy. In this study we aimed to evaluate the anti-cancer properties of RG108, a novel non-nucleoside inhibitor of DNA methyltransferases (DNMT), in PCa cell lines. Methods: The anti-tumoral impact of RG108 in LNCaP, 22Rv1, DU145 and PC-3 cell lines was assessed through standard cell viability, apoptosis and cell cycle assays. Likewise, DNMT activity, DNMT1 expression and global levels of DNA methylation were evaluated in the same cell lines. The effectiveness of DNA demethylation was further assessed through the determination of promoter methylation and transcript levels of GSTP1, APC and RAR-β2, by quantitative methylation-specific PCR and RT-PCR, respectively. Results: RG108 led to a significant dose and time dependent growth inhibition and apoptosis induction in LNCaP, 22Rv1 and DU145. LNCaP and 22Rv1 also displayed decreased DNMT activity, DNMT1 expression and global DNA methylation. Interestingly, chronic treatment with RG108 significantly decreased GSTP1, APC and RAR-β2 promoter hypermethylation levels, although mRNA re-expression was only attained GSTP1 and APC. Conclusions: RG108 is an effective tumor growth suppressor in most PCa cell lines tested. This effect is likely mediated by reversion of aberrant DNA methylation affecting cancer related-genes epigenetically silenced in PCa. However, additional mechanism might underlie the anti-tumor effects of RG108. In vivo studies are now mandatory to confirm these promising results and evaluate the potential of this compound for PCa therapy.

Electrochemical immunosensor for the analysis of the breast cancer biomarker HER2 ECD

Human epidermal growth factor receptor 2 (HER2) is a breast cancer biomarker that plays a major role in promoting breast cancer cell proliferation and malignant growth. The extracellular domain (ECD) of HER2 can be shed into the blood stream and its concentration is measurable in the serum fraction of blood. In this work an electrochemical immunosensor for the analysis of HER2 ECD in human serum samples was developed. To achieve this goal a screen-printed carbon electrode, modified with gold nanoparticles, was used as transducer surface. A sandwich immunoassay, using two monoclonal antibodies, was employed and the detection of the antibody–antigen interaction was performed through the analysis of an enzymatic reaction product by linear sweep voltammetry. Using the optimized experimental conditions the calibration curve (ip vs. log[HER2 ECD]) was established between 15 and 100 ng/mL and a limit of detection (LOD) of 4.4 ng/mL was achieved. These results indicate that the developed immunosensor could be a promising tool in breast cancer diagnostics, patient follow-up and monitoring of metastatic breast cancer since it allows quantification in a useful concentration range and has an LOD below the established cut-off value (15 ng/mL).

Using data mining techniques to support breast cancer diagnosis

More than ever, there is an increase of the number of decision support methods and computer aided diagnostic systems applied to various areas of medicine. In breast cancer research, many works have been done in order to reduce false-positives when used as a double reading method. In this study, we aimed to present a set of data mining techniques that were applied to approach a decision support system in the area of breast cancer diagnosis. This method is geared to assist clinical practice in identifying mammographic findings such as microcalcifications, masses and even normal tissues, in order to avoid misdiagnosis. In this work a reliable database was used, with 410 images from about 115 patients, containing previous reviews performed by radiologists as microcalcifications, masses and also normal tissue findings. Throughout this work, two feature extraction techniques were used: the gray level co-occurrence matrix and the gray level run length matrix. For classification purposes, we considered various scenarios according to different distinct patterns of injuries and several classifiers in order to distinguish the best performance in each case described. The many classifiers used were Naïve Bayes, Support Vector Machines, k-nearest Neighbors and Decision Trees (J48 and Random Forests). The results in distinguishing mammographic findings revealed great percentages of PPV and very good accuracy values. Furthermore, it also presented other related results of classification of breast density and BI-RADS® scale. The best predictive method found for all tested groups was the Random Forest classifier, and the best performance has been achieved through the distinction of microcalcifications. The conclusions based on the several tested scenarios represent a new perspective in breast cancer diagnosis using data mining techniques.

A novel antibody-like material for breast cancer antigen CA15-3, used to track breast cancer by potentiometric transduction

This work presents the development of a low cost sensor device for the diagnosis of breast cancer in point-of-care, made with new synthetic biomimetic materials inside plasticized poly(vinyl chloride), PVC, membranes, for subsequent potentiometric detection. This concept was applied to target a conventional biomarker in breast cancer: Breast Cancer Antigen (CA15-3). The new biomimetic material was obtained by molecularly-imprinted technology. In this, a plastic antibody was obtained by polymerizing around the biomarker that acted as an obstacle to the growth of the polymeric matrix. The imprinted polymer was specifically synthetized by electropolymerization on an FTO conductive glass, by using cyclic voltammetry, including 40 cycles within -0.2 and 1.0 V. The reaction used for the polymerization included monomer (pyrrol, 5.0×10-3 mol/L) and protein (CA15-3, 100U/mL), all prepared in phosphate buffer saline (PBS), with a pH of 7.2 and 1% of ethylene glycol. The biomarker was removed from the imprinted sites by proteolytic action of proteinase K. The biomimetic material was employed in the construction of potentiometric sensors and tested with regard to its affinity and selectivity for binding CA15-3, by checking the analytical performance of the obtained electrodes. For this purpose, the biomimetic material was dispersed in plasticized PVC membranes, including or not a lipophilic ionic additive, and applied on a solid conductive support of graphite. The analytical behaviour was evaluated in buffer and in synthetic serum, with regard to linear range, limit of detection, repeatability, and reproducibility. This antibody-like material was tested in synthetic serum, and good results were obtained. The best devices were able to detect 5 times less CA15-3 than that required in clinical use. Selectivity assays were also performed, showing that the various serum components did not interfere with this biomarker. Overall, the potentiometric-based methods showed several advantages compared to other methods reported in the literature. The analytical process was simple, providing fast responses for a reduced amount of analyte, with low cost and feasible miniaturization. It also allowed the detection of a wide range of concentrations, diminishing the required efforts in previous sample pre-treating stages.

Longitudinal analysis of tumor marker CEA of breast cancer patients from Braga's hospital

Allied to an epidemiological study of population of the Senology Unit of Braga’s Hospital that have been diagnosed with malignant breast cancer, we describe the progression in time of repeated measurements of tumor marker Carcinoembryonic antigen (CEA). Our main purpose is to describe the progression of this tumor marker as a function of possible risk factors and, hence, to understand how these risk factors influences that progression. The response variable, values of CEA, was analyzed making use of longitudinal models, testing for different correlation structures. The same covariates used in a previous survival analysis were considered in the longitudinal model. The reference time used was time from diagnose until death from breast cancer. For diagnostic of the models fitted we have used empirical and theoretical variograms. To evaluate the fixed term of the longitudinal model we have tested for a changing point on the effect of time on the tumor marker progression. A longitudinal model was also fitted only to the subset of patients that died from breast cancer, using the reference time as time from date of death until blood test.

High-Content Analysis of Breast Cancer Using Single-Cell Deep Transfer Learning

High-content analysis has revolutionized cancer drug discovery by identifying substances that alter the phenotype of a cell, which prevents tumor growth and metastasis. The high-resolution biofluorescence images from assays allow precise quantitative measures enabling the distinction of small molecules of a host cell from a tumor. In this work, we are particularly interested in the application of deep neural networks (DNNs), a cutting-edge machine learning method, to the classification of compounds in chemical mechanisms of action (MOAs). Compound classification has been performed using image-based profiling methods sometimes combined with feature reduction methods such as principal component analysis or factor analysis. In this article, we map the input features of each cell to a particular MOA class without using any treatment-level profiles or feature reduction methods. To the best of our knowledge, this is the first application of DNN in this domain, leveraging single-cell information. Furthermore, we use deep transfer learning (DTL) to alleviate the intensive and computational demanding effort of searching the huge parameter's space of a DNN. Results show that using this approach, we obtain a 30% speedup and a 2% accuracy improvement.