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The ensemble method random forests has become a popular classification tool in bioinformatics and related fields. The out-of-bag error is an error estimation technique often used to evaluate the accuracy of a random forest and to select appropriate values for tuning parameters, such as the number of candidate predictors that are randomly drawn for a split, referred to as mtry. However, for binary classification problems with metric predictors it has been shown that the out-of-bag error can overestimate the true prediction error depending on the choices of random forests parameters. Based on simulated and real data this paper aims to identify settings for which this overestimation is likely. It is, moreover, questionable whether the out-of-bag error can be used in classification tasks for selecting tuning parameters like mtry, because the overestimation is seen to depend on the parameter mtry. The simulation-based and real-data based studies with metric predictor variables performed in this paper show that the overestimation is largest in balanced settings and in settings with few observations, a large number of predictor variables, small correlations between predictors and weak effects. There was hardly any impact of the overestimation on tuning parameter selection. However, although the prediction performance of random forests was not substantially affected when using the out-of-bag error for tuning parameter selection in the present studies, one cannot be sure that this applies to all future data. For settings with metric predictor variables it is therefore strongly recommended to use stratified subsampling with sampling fractions that are proportional to the class sizes for both tuning parameter selection and error estimation in random forests. This yielded less biased estimates of the true prediction error. In unbalanced settings, in which there is a strong interest in predicting observations from the smaller classes well, sampling the same number of observations from each class is a promising alternative. [ABSTRACT FROM AUTHOR]

Breast cancer is an all too common disease in women, making how to effectively predict it an active research problem. A number of statistical and machine learning techniques have been employed to develop various breast cancer prediction models. Among them, support vector machines (SVM) have been shown to outperform many related techniques. To construct the SVM classifier, it is first necessary to decide the kernel function, and different kernel functions can result in different prediction performance. However, there have been very few studies focused on examining the prediction performances of SVM based on different kernel functions. Moreover, it is unknown whether SVM classifier ensembles which have been proposed to improve the performance of single classifiers can outperform single SVM classifiers in terms of breast cancer prediction. Therefore, the aim of this paper is to fully assess the prediction performance of SVM and SVM ensembles over small and large scale breast cancer datasets. The classification accuracy, ROC, F-measure, and computational times of training SVM and SVM ensembles are compared. The experimental results show that linear kernel based SVM ensembles based on the bagging method and RBF kernel based SVM ensembles with the boosting method can be the better choices for a small scale dataset, where feature selection should be performed in the data pre-processing stage. For a large scale dataset, RBF kernel based SVM ensembles based on boosting perform better than the other classifiers. [ABSTRACT FROM AUTHOR]

Purpose: Many studies have reported that carbohydrate antigen 153 (CA153) in breast secretions (BS) can discriminate breast cancer (BC) patients from healthy individuals, indicating CA153 in BS as a potential index for BC. This meta-analysis aimed to evaluate the actual diagnostic value of CA153 in BS. Methods: Related papers were obtained from Pubmed, Embase, Scopus, Ovid, Sciverse, the Cochrane library, Chinese Biomedical literature Database (CBM), Technology of Chongqing (VIP), Wan Fang Data, and Chinese National Knowledge Infrastructure (CNKI). Pooled sensitivity, specificity, and diagnostic odds ratio (DOR) of CA153 in BS for BC diagnosis were analyzed with the random effect model. SROC and the area under the curve (AUC) were applied to assess overall diagnostic efficiency. Results: This meta-analysis included five studies with a total of 329 BC patients and 381 healthy subjects. For CA153 in BS, the summary sensitivity, specificity, and DOR to diagnose BC were 0.63 (95% confidence interval (CI): 0.57∼0.68), 0.82 (95% CI: 0.78∼0.86), and 9.18 (95% CI: 4.22∼19.95), respectively. Furthermore, the AUC of BS CA153 in the diagnosis of BC was 0.8614. Conclusions: CA153 in BS is a valuable molecular marker in diagnosing BC and should be applied in standard clinical practices of BC screening upon confirmation of our findings in a larger prospective study. [ABSTRACT FROM AUTHOR]

Background: Evidence is growing that low-dose aspirin used as an adjuvant treatment of cancer is associated with an increased survival and a reduction in metastatic spread. We therefore extended up to August 2017 an earlier systematic search and meta-analyses of published studies of low-dose aspirin taken by patients with a diagnosis of cancer. Methods: Searches were completed in Medline and Embase to August 2017 using a pre-defined search strategy to identify reports of relevant studies. References in all the selected papers were scanned. Two reviewers independently applied pre-determined eligibility criteria and extracted data on cause-specific cancer deaths, overall mortality and the occurrence of metastatic spread. Meta-analyses were then conducted for different cancers and heterogeneity and publication bias assessed. Sensitivity analyses and attempts to reduce heterogeneity were conducted. Results: Analyses of 29 studies reported since an earlier review up to April 2015 are presented in this report, and these are then pooled with the 42 studies in our earlier publication. Overall meta-analyses of the 71 studies are presented, based on a total of over 120 thousand patients taking aspirin. Ten of the studies also give evidence on the incidence of metastatic cancer spread. There are now twenty-nine observational studies describing colorectal cancer (CRC) and post-diagnostic aspirin. Pooling the estimates of reduction by aspirin which are reported as hazard ratios (HR), gives an overall HR for aspirin and CRC mortality 0.72 (95% CI 0.64–0.80). Fourteen observational studies have reported on aspirin and breast cancer mortality and pooling those that report the association with aspirin as a hazard ratio gives HR 0.69 (0.53–0.90). Sixteen studies report on aspirin and prostate cancer mortality and a pooled estimate yields an HR of 0.87 (95% CI 0.73–1.05). Data from 12 reports relating to other cancers are also listed. Ten studies give evidence of a reduction in metastatic spread; four give a pooled HR 0.31 (95% CI 0.18, 0.54) and five studies which reported odds ratio of metastatic spread give OR 0.79 (0.66 to 0.95). Conclusion: Being almost entirely from observational studies, the evidence of benefit from aspirin is limited. There is heterogeneity between studies and the results are subject to important biases, only some of which can be identified. Nevertheless, the evidence would seem to merit wide discussion regarding whether or not it is adequate to justify the recommendation of low-dose therapeutic aspirin, and if it is, for which cancers? [ABSTRACT FROM AUTHOR]

Identification of miRNA-disease association is a fundamental challenge in human health clinic. However, the known miRNA-disease associations are rare and experimental verification methods are expensive and time-consuming. Therefore, there is a strong incentive to develop computational methods. In this paper, we calculate the similarity score for each miRNAs pair by integrating miRNA functional similarity and miRNA family information. We use the disease phenotype similarity data to construct the disease similarity network. Then we introduce a new miRNA-disease association prediction method (NETwork Group Similarity, NetGS) to explore the global network similarity, capturing the relationship between the disease and other diseases, the similarity between the potential disease-related miRNA and other miRNAs. Finally based on the consistency of diffusion profiles we get the miRNA-disease association scores. NetGS is tested by the leave-one-out cross validation and achieves an AUC value of 0.8450, which improves the prediction accuracy. NetGS can also be applied to solve the new miRNA-disease association and obtain reliable accuracy. Moreover, we use NetGS to predict new causing miRNAs of three cancers including breast cancer, lung cancer and Hepatocellular cancer. And the top predictions have been confirmed in the online databases. The encouraging results indicate that NetGS might play an essential role for future scientific research. [ABSTRACT FROM AUTHOR]

Background and Objectives: Current studies give us inconsistent results regarding the association of neoplasms and zinc(II) serum and tissues concentrations. The results of to-date studies using meta-analysis are summarized in this paper. Methods: Web of Science (Science citation index expanded), PubMed (Medline), Embase and CENTRAL were searched. Articles were reviewed by two evaluators; quality was assessed by Newcastle-Ottawa scale; meta-analysis was performed including meta-regression and publication bias analysis. Results: Analysis was performed on 114 case control, cohort and cross-sectional studies of 22737 participants. Decreased serum zinc level was found in patients with lung (effect size = −1.04), head and neck (effect size = −1.43), breast (effect size = −0.93), liver (effect size = −2.29), stomach (effect size = −1.59), and prostate (effect size = −1.36) cancers; elevation was not proven in any tumor. More specific zinc patterns are evident at tissue level, showing increase in breast cancer tissue (effect size = 1.80) and decrease in prostatic (effect size = −3.90), liver (effect size = −8.26), lung (effect size = −3.12), and thyroid cancer (effect size = −2.84). The rest of the included tumors brought ambiguous results, both in serum and tissue zinc levels across the studies. The association between zinc level and stage or grade of tumor has not been revealed by meta-regression. Conclusion: This study provides evidence on cancer-specific tissue zinc level alteration. Although serum zinc decrease was associated with most tumors mentioned herein, further – prospective - studies are needed. [ABSTRACT FROM AUTHOR]

Genetic risk prediction is an important goal in human genetics research and precision medicine. Accurate prediction models will have great impacts on both disease prevention and early treatment strategies. Despite the identification of thousands of disease-associated genetic variants through genome wide association studies (GWAS), genetic risk prediction accuracy remains moderate for most diseases, which is largely due to the challenges in both identifying all the functionally relevant variants and accurately estimating their effect sizes in the presence of linkage disequilibrium. In this paper, we introduce AnnoPred, a principled framework that leverages diverse types of genomic and epigenomic functional annotations in genetic risk prediction for complex diseases. AnnoPred is trained using GWAS summary statistics in a Bayesian framework in which we explicitly model various functional annotations and allow for linkage disequilibrium estimated from reference genotype data. Compared with state-of-the-art risk prediction methods, AnnoPred achieves consistently improved prediction accuracy in both extensive simulations and real data. [ABSTRACT FROM AUTHOR]

miRNAs are small non-coding RNAs that regulate gene expression by binding to the 3′-UTR of genes. Many recent studies have reported that miRNAs play important biological roles by regulating specific mRNAs or genes. Many sequence-based target prediction algorithms have been developed to predict miRNA targets. However, these methods are not designed for condition-specific target predictions and produce many false positives; thus, expression-based target prediction algorithms have been developed for condition-specific target predictions. A typical strategy to utilize expression data is to leverage the negative control roles of miRNAs on genes. To control false positives, a stringent cutoff value is typically set, but in this case, these methods tend to reject many true target relationships, i.e., false negatives. To overcome these limitations, additional information should be utilized. The literature is probably the best resource that we can utilize. Recent literature mining systems compile millions of articles with experiments designed for specific biological questions, and the systems provide a function to search for specific information. To utilize the literature information, we used a literature mining system, BEST, that automatically extracts information from the literature in PubMed and that allows the user to perform searches of the literature with any English words. By integrating omics data analysis methods and BEST, we developed Context-MMIA, a miRNA-mRNA target prediction method that combines expression data analysis results and the literature information extracted based on the user-specified context. In the pathway enrichment analysis using genes included in the top 200 miRNA-targets, Context-MMIA outperformed the four existing target prediction methods that we tested. In another test on whether prediction methods can re-produce experimentally validated target relationships, Context-MMIA outperformed the four existing target prediction methods. In summary, Context-MMIA allows the user to specify a context of the experimental data to predict miRNA targets, and we believe that Context-MMIA is very useful for predicting condition-specific miRNA targets. [ABSTRACT FROM AUTHOR]

Covariate selection is a fundamental step when building sparse prediction models in order to avoid overfitting and to gain a better interpretation of the classifier without losing its predictive accuracy. In practice the LASSO regression of Tibshirani, which penalizes the likelihood of the model by the L1 norm of the regression coefficients, has become the gold-standard to reach these objectives. Recently Lee and Oh developed a novel random-effect covariate selection method called the modified unbounded penalty (MUB) regression, whose penalization function can equal minus infinity at 0 in order to produce very sparse models. We sought to compare the predictive accuracy and the number of covariates selected by these two methods in several high-dimensional datasets, consisting in genes expressions measured to predict response to chemotherapy in breast cancer patients. These comparisons were performed by building the Receiver Operating Characteristics (ROC) curves of the classifiers obtained with the selected genes and by comparing their area under the ROC curve (AUC) corrected for optimism using several variants of bootstrap internal validation and cross-validation. We found consistently in all datasets that the MUB penalization selected a remarkably smaller number of covariates than the LASSO while offering a similar—and encouraging—predictive accuracy. The models selected by the MUB were actually nested in the ones obtained with the LASSO. Similar findings were observed when comparing these results to those obtained in their first publication by other authors or when using the area under the Precision-Recall curve (AUCPR) as another measure of predictive performance. In conclusion, the MUB penalization seems therefore to be one of the best options when sparsity is required in high-dimension. Further investigation in other datasets is however required to validate these findings. [ABSTRACT FROM AUTHOR]

One of the goals of cancer research is to identify a set of genes that cause or control disease progression. However, although multiple such gene sets were published, these are usually in very poor agreement with each other, and very few of the genes proved to be functional therapeutic targets. Furthermore, recent findings from a breast cancer gene-expression cohort showed that sets of genes selected randomly can be used to predict survival with a much higher probability than expected. These results imply that many of the genes identified in breast cancer gene expression analysis may not be causal of cancer progression, even though they can still be highly predictive of prognosis. We performed a similar analysis on all the cancer types available in the cancer genome atlas (TCGA), namely, estimating the predictive power of random gene sets for survival. Our work shows that most cancer types exhibit the property that random selections of genes are more predictive of survival than expected. In contrast to previous work, this property is not removed by using a proliferation signature, which implies that proliferation may not always be the confounder that drives this property. We suggest one possible solution in the form of data-driven sub-classification to reduce this property significantly. Our results suggest that the predictive power of random gene sets may be used to identify the existence of sub-classes in the data, and thus may allow better understanding of patient stratification. Furthermore, by reducing the observed bias this may allow more direct identification of biologically relevant, and potentially causal, genes. [ABSTRACT FROM AUTHOR]

Building prediction models based on complex omics datasets such as transcriptomics, proteomics, metabolomics remains a challenge in bioinformatics and biostatistics. Regularized regression techniques are typically used to deal with the high dimensionality of these datasets. However, due to the presence of correlation in the datasets, it is difficult to select the best model and application of these methods yields unstable results. We propose a novel strategy for model selection where the obtained models also perform well in terms of overall predictability. Several three step approaches are considered, where the steps are 1) network construction, 2) clustering to empirically derive modules or pathways, and 3) building a prediction model incorporating the information on the modules. For the first step, we use weighted correlation networks and Gaussian graphical modelling. Identification of groups of features is performed by hierarchical clustering. The grouping information is included in the prediction model by using group-based variable selection or group-specific penalization. We compare the performance of our new approaches with standard regularized regression via simulations. Based on these results we provide recommendations for selecting a strategy for building a prediction model given the specific goal of the analysis and the sizes of the datasets. Finally we illustrate the advantages of our approach by application of the methodology to two problems, namely prediction of body mass index in the DIetary, Lifestyle, and Genetic determinants of Obesity and Metabolic syndrome study (DILGOM) and prediction of response of each breast cancer cell line to treatment with specific drugs using a breast cancer cell lines pharmacogenomics dataset. [ABSTRACT FROM AUTHOR]

Wavelet Neural Networks are a combination of neural networks and wavelets and have been mostly used in the area of time-series prediction and control. Recently, Evolutionary Wavelet Neural Networks have been employed to develop cancer prediction models. The present study proposes to use ensembles of Evolutionary Wavelet Neural Networks. The search for a high quality ensemble is directed by a fitness function that incorporates the accuracy of the classifiers both independently and as part of the ensemble itself. The ensemble approach is tested on three publicly available biomedical benchmark datasets, one on Breast Cancer and two on Parkinson’s disease, using a 10-fold cross-validation strategy. Our experimental results show that, for the first dataset, the performance was similar to previous studies reported in literature. On the second dataset, the Evolutionary Wavelet Neural Network ensembles performed better than all previous methods. The third dataset is relatively new and this study is the first to report benchmark results. [ABSTRACT FROM AUTHOR]

Background: Recent studies demonstrate that interstitial inorganic phosphate is significantly elevated in the breast cancer microenvironment as compared to normal tissue. In addition it has been shown that breast cancer cells express high levels of the NaPi-IIb carrier (SLC34A2), suggesting that this carrier may play a role in breast cancer progression. However, the biochemical behavior of inorganic phosphate (Pi) transporter in this cancer type remains elusive. Methods: In this work, we characterize the kinetic parameters of Pi transport in the aggressive human breast cancer cell line, MDA-MB-231, and correlated Pi transport with cell migration and adhesion. Results: We determined the influence of sodium concentration, pH, metabolic inhibitors, as well as the affinity for inorganic phosphate in Pi transport. We observed that the inorganic phosphate is dependent on sodium transport (K0,5 value = 21.98 mM for NaCl). Furthermore, the transport is modulated by different pH values and increasing concentrations of Pi, following the Michaelis-Menten kinetics (K0,5 = 0.08 mM Pi). PFA, monensin, furosemide and ouabain inhibited Pi transport, cell migration and adhesion. Conclusions: Taken together, these results showed that the uptake of Pi in MDA-MB-231 cells is modulated by sodium and by regulatory mechanisms of intracellular sodium gradient. General Significance: Pi transport might be regarded as a potential target for therapy against tumor progression. [ABSTRACT FROM AUTHOR]

HER-2 belongs to the human epidermal growth factor receptor (HER) family. Via different signal transduction pathways, HER-2 regulates normal cell proliferation, survival, and differentiation. Recently, it was reported that MCF10A, BT474, and MDA-MB-231 cells bearing the HER2 K753E mutation were resistant to lapatinib. Present study revealed that HER-2 mutant K753E showed some contrasting behaviour as compared to wild, L768S and V773L HER-2 in complex with lapatinib while similar to previously known lapatinib resistant L755S HER-2 mutant. Lapatinib showed stable but reverse orientation in binding site of K753E and the highest binding energy among studied HER2-lapatinib complexes but slightly lesser than L755S mutant. Results indicate that K753E has similar profile as L755S mutant for lapatinib. The interacting residues were also found different from other three studied forms as revealed by free energy decomposition and ligplot analysis. [ABSTRACT FROM AUTHOR]

The potential prognostic value of GATA binding protein 3 (GATA3) in breast cancer has recently increased, although the evidence is inconclusive. This meta-analysis of 10 articles involving 5,080 breast cancer patients explored the prognostic and clinicopathological value of GATA3 in breast cancer. Time to tumor progression (TTP) and overall survival (OS) were primary endpoints. Pooled hazard ratio (HR), pooled risk ratio (RR), and 95% confidence interval (CI) were calculated to evaluate the association between GATA3, prognosis, and clinicopathological parameters. High GATA3 expression predicts breast cancer, with a HR (HR = 0.671; 95% CI = 0.475–0.947; P = 0.023) of TTP, but is not associated with OS (HR = 0.889; 95% CI = 0.789–1.001; P = 0.052). GATA3 overexpression is associated with positive ER (RR = 3.155; 95% CI = 1.680–5.923; P = 0.000), positive PR (RR = 3.949; 95% CI = 1.567–9.954, P = 0.004), lower nuclear grade (RR = 0.435; 95% CI = 0.369–0.514; P = 0.000), and smaller tumor size (RR = 0.816; 95% CI = 0.709–0.940; P = 0.005). High GATA3 expression may predict TTP in breast cancer, and such patients may show better clinicopathological features. [ABSTRACT FROM AUTHOR]

Purpose: The current meta-analysis aimed to summarize the available evidence for the efficacy and serious adverse events (AEs) associated with use of metronomic chemotherapy (MCT) in patients with metastatic breast cancer (MBC). Method: Electronic databases (PubMed, EMBASE database, Web of Knowledge, and the Cochrane database) were systematically searched for articles related to the use of MCT in MBC patients. Eligible studies included clinical trials of MBC patients treated with MCT that presented sufficient data related to tumor response, progression-free survival (PFS), overall survival (OS), and grade 3/4 AEs. A meta-analysis was performed using a random effects model. Results: This meta-analysis consists of 22 clinical trials with 1360 patients. The pooled objective response rate and clinical benefit rate of MCT were 34.1% (95% CI 27.4–41.5) and 55.6% (95% CI 49.2–61.9), respectively. The overall 6-month PFS, 12-month OS, and 24-month OS rates were 56.8% (95% CI 48.3–64.9), 70.3% (95% CI 62.6–76.9), and 40.0% (95% CI 30.6–50.2), respectively. The pooled incidence of grade 3/4 AEs was 29.5% (95% CI 21.1–39.5). There was no statistically significant difference observed in any endpoint between subgroups defined by concomitant anti-cancer therapies or chemotherapy regimens. After excluding one controversial study, we observed a trend showing lower toxicity rates with the use of MCT alone compared to use of MCT with other anti-cancer therapies (P = 0.070). Conclusions: Metronomic chemotherapy may be effective for use in patients with metastatic breast cancer. MCT used alone is possibly equally effective and less toxic than combination therapies. Well-designed RCTs are needed to obtain more evidence. [ABSTRACT FROM AUTHOR]

Objective: To investigate the replication validity of biomedical association studies covered by newspapers. Methods: We used a database of 4723 primary studies included in 306 meta-analysis articles. These studies associated a risk factor with a disease in three biomedical domains, psychiatry, neurology and four somatic diseases. They were classified into a lifestyle category (e.g. smoking) and a non-lifestyle category (e.g. genetic risk). Using the database Dow Jones Factiva, we investigated the newspaper coverage of each study. Their replication validity was assessed using a comparison with their corresponding meta-analyses. Results: Among the 5029 articles of our database, 156 primary studies (of which 63 were lifestyle studies) and 5 meta-analysis articles were reported in 1561 newspaper articles. The percentage of covered studies and the number of newspaper articles per study strongly increased with the impact factor of the journal that published each scientific study. Newspapers almost equally covered initial (5/39 12.8%) and subsequent (58/600 9.7%) lifestyle studies. In contrast, initial non-lifestyle studies were covered more often (48/366 13.1%) than subsequent ones (45/3718 1.2%). Newspapers never covered initial studies reporting null findings and rarely reported subsequent null observations. Only 48.7% of the 156 studies reported by newspapers were confirmed by the corresponding meta-analyses. Initial non-lifestyle studies were less often confirmed (16/48) than subsequent ones (29/45) and than lifestyle studies (31/63). Psychiatric studies covered by newspapers were less often confirmed (10/38) than the neurological (26/41) or somatic (40/77) ones. This is correlated to an even larger coverage of initial studies in psychiatry. Whereas 234 newspaper articles covered the 35 initial studies that were later disconfirmed, only four press articles covered a subsequent null finding and mentioned the refutation of an initial claim. Conclusion: Journalists preferentially cover initial findings although they are often contradicted by meta-analyses and rarely inform the public when they are disconfirmed. [ABSTRACT FROM AUTHOR]

Objective: Phosphorylated mammalian target of rapamycin (p-mTOR) is a promising prognostic marker in many types of cancer. However, its survival benefit in patients with breast carcinoma remains unknown. The aim of the present study was to assess the relationship between p-mTOR expression and prognosis in breast carcinoma based on a systematic review and meta-analysis. Materials and Methods: Electronic databases (including Pubmed, Embase, ISI web of science, and Cochrane Library) were searched up to November 24, 2015. The outcome measures were hazard ratios (HRs) with 95% confidence interval (CI) for the association between the prognosis of breast carcinoma patients and p-mTOR expression. Primary end points were disease-free survival (DFS), overall survival (OS), and recurrence-free survival (RFS). Statistical analysis was performed with STATA 12.0. Results: Nine cohort studies including 3051 patients met full eligibility criteria. The pooled HRs (95% CI) for OS, DFS, and RFS were 0.84 (0.27–2.63), 0.71 (0.40–1.23), and 0.48 (0.20–1.18), respectively. Conclusions: Our findings suggested that p-mTOR overexpression was not significantly related to prognosis in breast carcinoma regarding OS and disease recurrence. Prospective studies are warranted to examine the association between p-mTOR expression and survival outcomes in breast carcinoma. [ABSTRACT FROM AUTHOR]

Gene expression regulators, such as transcription factors (TFs) and microRNAs (miRNAs), have varying regulatory targets based on the tissue and physiological state (context) within which they are expressed. While the emergence of regulator-characterizing experiments has inferred the target genes of many regulators across many contexts, methods for transferring regulator target genes across contexts are lacking. Further, regulator target gene lists frequently are not curated or have permissive inclusion criteria, impairing their use. Here, we present a method called iterative Contextual Transcriptional Activity Inference of Regulators (icTAIR) to resolve these issues. icTAIR takes a regulator’s previously-identified target gene list and combines it with gene expression data from a context, quantifying that regulator’s activity for that context. It then calculates the correlation between each listed target gene’s expression and the quantitative score of regulatory activity, removes the uncorrelated genes from the list, and iterates the process until it derives a stable list of refined target genes. To validate and demonstrate icTAIR’s power, we use it to refine the MSigDB c3 database of TF, miRNA and unclassified motif target gene lists for breast cancer. We then use its output for survival analysis with clinicopathological multivariable adjustment in 7 independent breast cancer datasets covering 3,430 patients. We uncover many novel prognostic regulators that were obscured prior to refinement, in particular NFY, and offer a detailed look at the composition and relationships among the breast cancer prognostic regulome. We anticipate icTAIR will be of general use in contextually refining regulator target genes for discoveries across many contexts. The icTAIR algorithm can be downloaded from . [ABSTRACT FROM AUTHOR]

Purpose: miRNAs are stable and can be extracted from tissues, blood and other body fluid without degradation. miRNAs are abnormally expressed in the presence of a pathological status, including cancer. Therefore, miRNAs are ideal biomarkers for cancer diagnosis and prognosis. Patients with triple negative breast cancer (TNBC) suffer the worst prognosis, although great efforts have been made. Many studies have investigated the role of miRNAs in predicting the outcomes of TNBC patients for better adjustment of treatment. However, results were inconsistent. Thus, we performed a meta-analysis to summarize the published studies for conclusive results. Methods: Eligible studies from different database were retrieved from the online databases, and we used STSTA 12.0 to analysis the prognostic role of miRNAs in triple negative breast cancer. Results: Overall high miRNA expression indicated a worse survival with HR value of 1.78 (95% CI: 0.97–3.25). However, subtotal HRs of oncogenic miRNAs and tumor suppressive miRNAs were 2.73 (95% CI: 2.08–3.57; P<0.001) and 0.44 (95% CI: 0.21–0.90; P = 0.024), respectively, and no heterogeneity was observed within the subgroups. Conclusions: The miRNAs showed a slightly stronger prognostic value for disease-free survival, relapse-free survival and distant metastasis-free survival compared to the overall survival of TNBC patients. Circulating miRNAs could serve as potential biomarkers for the prognosis of TNBC patients and need further investigation. [ABSTRACT FROM AUTHOR]

Breast screening is an important tool for the early detection of breast cancers. However, tumours are typically present in less than 1% of mammograms. This low prevalence could cause radiologists to detect fewer tumours than they otherwise would, an issue known as the prevalence effect. The aim of our study was to investigate a novel breast screening protocol, designed to decrease the number of tumours missed by radiologists, without increasing their workload. We ran two laboratory-based experiments to assess the degree to which the novel protocol, called the catch trial (CT) protocol, resulted in greater sensitivity (d’) than the double screener protocol (DS), currently utilised in Australia. In our first experiment we found evidence that the CT protocol resulted in a criterion shift relative to the DS protocol but the evidence that sensitivity was greater in the CT protocol relative to the DS protocol was less clear. A second experiment, using more realistic stimuli that were more representative of actual tumours, also failed to find convincing evidence that sensitivity was greater in the CT protocol than in the DS protocol. This experiment instead found that both the hit rate and the false alarm rate increased in the CT protocol relative to the DS protocol. So while there was again evidence that the CT protocol induced a criterion shift, the sensitivity appeared to be approximately the same in both protocols. Our results suggest the CT protocol is unlikely to result in an improvement in sensitivity over the DS protocol, so we cannot recommend that it be trialled in a clinical setting. [ABSTRACT FROM AUTHOR]

Background: Many new biomedical research articles are published every day, accumulating rich information, such as genetic variants, genes, diseases, and treatments. Rapid yet accurate text mining on large-scale scientific literature can discover novel knowledge to better understand human diseases and to improve the quality of disease diagnosis, prevention, and treatment. Results: In this study, we designed and developed an efficient text mining framework called SparkText on a Big Data infrastructure, which is composed of Apache Spark data streaming and machine learning methods, combined with a Cassandra NoSQL database. To demonstrate its performance for classifying cancer types, we extracted information (e.g., breast, prostate, and lung cancers) from tens of thousands of articles downloaded from PubMed, and then employed Naïve Bayes, Support Vector Machine (SVM), and Logistic Regression to build prediction models to mine the articles. The accuracy of predicting a cancer type by SVM using the 29,437 full-text articles was 93.81%. While competing text-mining tools took more than 11 hours, SparkText mined the dataset in approximately 6 minutes. Conclusions: This study demonstrates the potential for mining large-scale scientific articles on a Big Data infrastructure, with real-time update from new articles published daily. SparkText can be extended to other areas of biomedical research. [ABSTRACT FROM AUTHOR]

The integration of data and knowledge from heterogeneous sources can be a key success factor in drug design, drug repurposing and multi-target therapies. In this context, biological networks provide a useful instrument to highlight the relationships and to model the phenomena underlying therapeutic action in cancer. In our work, we applied network-based modeling within a novel bioinformatics pipeline to identify promising multi-target drugs. Given a certain tumor type/subtype, we derive a disease-specific Protein-Protein Interaction (PPI) network by combining different data-bases and knowledge repositories. Next, the application of suitable graph-based algorithms allows selecting a set of potentially interesting combinations of drug targets. A list of drug candidates is then extracted by applying a recent data fusion approach based on matrix tri-factorization. Available knowledge about selected drugs mechanisms of action is finally exploited to identify the most promising candidates for planning in vitro studies. We applied this approach to the case of Triple Negative Breast Cancer (TNBC), a subtype of breast cancer whose biology is poorly understood and that lacks of specific molecular targets. Our “in-silico” findings have been confirmed by a number of in vitro experiments, whose results demonstrated the ability of the method to select candidates for drug repurposing. [ABSTRACT FROM AUTHOR]

Background: With the increased use of neoadjuvant chemotherapy (NAC) in breast cancer, the timing of sentinel lymph node biopsy (SLNB) has become increasingly important. In this study, we aimed to evaluate the feasibility and accuracy of SLNB for initially clinically node-negative breast cancer after NAC by conducting a systematic review and meta-analysis. Methods: We searched PubMed, Embase, and the Cochrane Library from January 1, 1993 to November 30, 2015 for studies on initially clinically node-negative breast cancer patients who underwent SLNB after NAC followed by axillary lymph node dissection (ALND). Results: A total of 1,456 patients from 16 studies were included in this review. The pooled identification rate (IR) for SLNB was 96% [95% confidence interval (CI): 95%-97%], and the false negative rate (FNR) was 6% (95% CI: 3%-8%). The pooled sensitivity, negative predictive value (NPV) and accuracy rate (AR) were 94% (95% CI: 92%-97%, I2 = 27.5%), 98% (95% CI: 98%-99%, I2 = 42.7%) and 99% (95% CI: 99%-100%, I2 = 32.6%), respectively. In the subgroup analysis, no significant differences were found in either the IR of an SLNB when different mapping methods were used (P = 0.180) or in the FNR between studies with and without immunohistochemistry (IHC) staining (P = 0.241). Conclusion: Based on current evidence, SLNB is technically feasible and accurate enough for axillary staging in initially clinically node-negative breast cancer patients after NAC. [ABSTRACT FROM AUTHOR]

Background: Neoadjuvant therapy is administered to breast cancer patients as an induction process before surgery or radiotherapy to reduce tumor size. Human epidermal growth factor receptor-2 (HER-2) negative breast cancer lacks effective standard target therapy. Bevacizumab has a controversial role in the treatment of breast cancer and we conduct a meta-analysis to evaluate the value of adding bevacizumab in neoadjuvant regimen. Methods: Potentially eligible studies were retrieved using PubMed, EMBASE and Medline. Clinical characteristics of patients and statistical data with pathological complete response (pCR) data were collected. Then a meta-analysis model was established to investigate the correlation between administration of bevacizumab in neoadjuvant therapy and pCR rates in HER-2 negative breast cancer. Results: Seven eligible studies and 5408 patients were yielded. The pCR rates for “breast” or “breast plus lymph node” were similar. In subgroup analysis, we emphasized on patients with triple-negative breast cancer (TNBC). In the criterion of “lesions in breast” the pooled ORs was 1.55 [1.29, 1.86], P<0.00001 and regarding to the evaluation criterion of “lesions in breast and lymph nodes”, the pooled ORs was 1.48 [1.23, 1.78], P<0.0001, in favor of bevacizumab administration. Conclusion: According to our pooled results, we finally find that bevacizumab addition as a neoadjuvant chemotherapy component, for induction use with limited cycle to improve the pCR rates and patients may avoid long-term adverse event and long-term invalid survival improvement. Especially in subgroup analysis, pCR rates could be improved significantly and physicians could consider bevacizumab with caution. As patients could avoid the adverse event caused by long-term using of bevacizumab, long-term quality of life improvement may be achieved, especially in TNBC. [ABSTRACT FROM AUTHOR]

Previous studies have indicated that carcinoembryonic antigen (CEA) and cancer antigen 15–3 (CA15-3) levels are both independent prognostic factors in breast cancer. However, the utility of CEA and CA15-3 levels as conventional cancer biomarkers in patients with triple-negative breast cancer (TNBC) remains controversial. The current study was performed to explore the predictive value of pre-therapeutic serum CEA and CA15-3 levels, and nomograms were developed including these serum cancer biomarkers to improve the prognostic evaluation of TNBC patients. Pre-therapeutic CA15-3 and CEA concentrations were measured in 247 patients with stage I–IV TNBC. Kaplan-Meier analysis showed that TNBC patients with high levels of both CEA and CA15-3 had shorter overall survival (OS) and disease-free survival (DFS) rates than those in the low-level groups (p<0.05). Multivariate analysis suggested that pre-therapeutic CA15-3 and CEA levels are independent predictive elements for OS (p = 0.022 and p = 0.040, respectively) and DFS (p = 0.023 and p = 0.028, respectively). In addition, novel nomograms were established and validated to provide personal forecasts of OS and DFS for patients with TNBC. These novel nomograms may help physicians to select the optimal treatment plans to ensure the best outcomes for TNBC patients. [ABSTRACT FROM AUTHOR]

Sirtuin-3 (SIRT3) is a major mitochondrial NAD(+)-dependent deacetylase and plays a key role in the progression and development of human cancers. Although the prognostic and clinicopathological features of SIRT3 expression in various cancers have been investigated by different research groups, however, inconsistent and opposing results can be observed. In this study, we therefore performed a meta-analysis to evaluate the significance of SIRT3 expression in various cancers. Systematic literature searching was performed in PubMed, Embase, China National Knowledge Infrastructure, and Wanfang Data up to November 2015. Total effect analyses and subgroup analyses were performed to evaluate the relationship between SIRT3 expression and overall survival, cancer/non-cancer tissues, lymph node metastasis, pathological differentiation, tumor node metastasis (TNM) stage, tumor size, and gender, in various cancer patients. Hazard ratios (HRs) or odds ratios (ORs) with 95% confidence intervals (CIs) were calculated to clarify the risk or hazard association. A total of 14 studies comprising 2165 cancer patients were included to assess the association between SIRT3 immunohistochemical expression and overall survival or clinicopathological characteristics. SIRT3 expression was significantly associated with overall survival in gastric cancer (HR = 0.62, 95% CI = 0.43–0.89, P = 0.009) and hepatocellular carcinoma patients (HR = 0.56, 95% CI = 0.42–0.74, P<0.0001), cancer/non-cancer tissues in hepatocellular carcinoma patients (OR = 0.04, 95% CI = 0.01–0.16, P<0.0001), lymph node metastasis in breast cancer patients (OR = 2.20, 95% CI = 1.49–3.26, P<0.0001), and also pathological differentiation in hepatocellular carcinoma patients (OR = 0.69, 95% CI = 0.48–0.98, P = 0.04) and gastric cancer patients (OR = 0.33, 95% CI = 0.21–0.50, P<0.00001), by subgroup analyses. Furthermore, SIRT3 expression was significantly associated with pathological differentiation in total effect analysis (OR = 0.46, 95% CI = 0.29–0.74, P = 0.001). No detectable relation between SIRT3 expression and other clinicopathological parameters were found. This meta-analysis indicates that SIRT3 expression level is associated with prognostic and clinical features in specific cancers. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate the performance of MRI for diagnosis of breast cancer in non-calcified equivocal breast findings. Materials and Methods: We performed a systematic review and meta-analysis of peer-reviewed studies in PubMed from 01/01/1986 until 06/15/2015. Eligible were studies applying dynamic contrast-enhanced breast MRI as an adjunct to conventional imaging (mammography, ultrasound) to clarify equivocal findings without microcalcifications. Reference standard for MRI findings had to be established by histopathological sampling or imaging follow-up of at least 12 months. Number of true or false positives and negatives and other characteristics were extracted, and possible bias was determined using the QUADAS-2 applet. Statistical analyses included data pooling and heterogeneity testing. Results: Fourteen out of 514 studies comprising 2,316 lesions met our inclusion criteria. Pooled diagnostic parameters were: sensitivity (99%, 95%-CI: 93–100%), specificity (89%, 95%-CI: 85–92%), PPV (56%, 95%-CI: 42–70%) and NPV (100%, 95%-CI: 99–100%). These estimates displayed significant heterogeneity (P<0.001). Conclusions: Breast MRI demonstrates an excellent diagnostic performance in case of non-calcified equivocal breast findings detected in conventional imaging. However, considering the substantial heterogeneity with regard to prevalence of malignancy, problem solving criteria need to be better defined. [ABSTRACT FROM AUTHOR]

Background: Breast cancer is the most frequent cancers and is the second leading cause of cancer death among women. Trastuzumab is an effective treatment, the first monoclonal antibody directed against the human epidermal growth factor receptor 2 (HER2). To inform the development of other effective treatments we report summary estimates of efficacy of trastuzumab on survival and tumour volume in animal models of breast cancer. Methods: We searched PubMed and EMBASE systematically to identify publications testing trastuzumab in animal models of breast cancer. Data describing tumour volume, median survival and animal features were extracted and we assessed quality using a 12-item checklist. We analysed the impact of study design and quality and evidence for publication bias. Results: We included data from 83 studies reporting 169 experiments using 2076 mice. Trastuzumab treatment caused a substantial reduction in tumour growth, with tumours in treated animals growing to 32.6% of the volume of tumours in control animals (95%CI 27.8%-38.2%). Median survival was prolonged by a factor of 1.45 (1.30–1.62). Many study design and quality features accounted for between-study heterogeneity and we found evidence suggesting publication bias. Conclusion: We have found trastuzumab to be effective in animal breast cancer models across a range of experimental circumstances. However the presence of publication bias and a low prevalence of measures to reduce bias provide a focus for future improvements in preclinical breast cancer research. [ABSTRACT FROM AUTHOR]

Objective: Published studies have evaluated the impact of stromal myofibroblasts on prognosis in solid cancers. However, the results of these studies remain controversial. We therefore performed a meta-analysis to address this issue. Methods: The PubMed, ISI Web of Science and Embase databases were searched through November 30th, 2015 by two investigators, and a total of 17 studies that contained 2606 patients were included. Stromal myofibroblasts were quantified in solid cancers using α-smooth muscle actin staining. Pooled Odds Ratio with 95% Confidence Intervals were calculated, and publication bias was analyzed. Results: The results of this study suggest that in solid cancers, a high density of stromal myofibroblasts is significantly associated with poor 3- and 5-year overall survival (pooled odds ratio (95% confidence interval): 1.33 (1.10–1.60) for 3-year overall survival and 1.68 (1.22–2.32) for 5-year overall survival). In addition, a high density of stromal myofibroblasts also predicted poor 3- and 5-year disease-free survival (1.30 (1.05–1.60) for 3-year disease-free survival and 1.36 (1.01–1.83) for 5-year disease-free survival). However, stromal myofibroblasts were not associated with 3- and 5-year cancer-specific survival. No publication bias was found for all analyses. Conclusions: The results of this study suggest that a high density of stromal myofibroblasts is associated with poor survival in solid cancers. More studies were required to investigate the prognostic value of stromal myofibroblasts in different types of solid cancers. [ABSTRACT FROM AUTHOR]

PIWI interacting RNAs (piRNAs), a member of non-coding RNA, originate from intergenic repetitive regions of the genome. piRNA expressions increase in various cancers and it is thought that this increase could be caused by hormones. We aimed to determine the effects of hormones on piRNA expression in breast and prostate cancer. High viability and a decrease in adhesion were observed at the concentrations of the highest proliferation. Furthermore, an increase in adhesion was also observed in MDA-MB-231 cells. After hormone treatment, while piR-651 expression had increased both breast and prostate cancer cell lines, piR-823 expressions increased in prostate cancer cell lines and only in the breast cancer cell line which was malignant. Thus, it was determined that piR-823 might show different expressions in different type of cancers. [ABSTRACT FROM AUTHOR]

During the last decades it has been established that breast cancer arises through the accumulation of genetic and epigenetic alterations in different cancer related genes. These alterations confer the tumor oncogenic abilities, which can be resumed as cancer hallmarks (CH). The purpose of this study was to establish the methylation profile of CpG sites located in cancer genes in breast tumors so as to infer their potential impact on 6 CH: i.e. sustained proliferative signaling, evasion of growth suppressors, resistance to cell death, induction of angiogenesis, genome instability and invasion and metastasis. For 51 breast carcinomas, MS-MLPA derived-methylation profiles of 81 CpG sites were converted into 6 CH profiles. CH profiles distribution was tested by different statistical methods and correlated with clinical-pathological data. Unsupervised Hierarchical Cluster Analysis revealed that CH profiles segregate in two main groups (bootstrapping 90–100%), which correlate with breast laterality (p = 0.05). For validating these observations, gene expression data was obtained by RealTime-PCR in a different cohort of 25 tumors and converted into CH profiles. This analyses confirmed the same clustering and a tendency of association with breast laterality (p = 0.15). In silico analyses on gene expression data from TCGA Breast dataset from left and right breast tumors showed that they differed significantly when data was previously converted into CH profiles (p = 0.033). We show here for the first time, that breast carcinomas arising on different sides of the body present differential cancer traits inferred from methylation and expression profiles. Our results indicate that by converting methylation or expression profiles in terms of Cancer Hallmarks, it would allow to uncover veiled associations with clinical features. These results contribute with a new finding to the better understanding of breast tumor behavior, and can moreover serve as proof of principle for other bilateral cancers like lung, testes or kidney. [ABSTRACT FROM AUTHOR]

A multigene expression assay corresponds to the likelihood of breast cancer recurrence after the initial diagnosis and can be used to guide the decision for additional chemotherapy. However, only few studies have investigated the associations between the imaging features of breast cancer and the results of multigene expression assays. Our study was to identify the relationship between imaging features on ultrasound (US) and the recurrence score (RS) on a 21-gene expression assay in patients with oestrogen receptor (ER)-positive, HER2-negative breast cancer. 267 patients with ER-positive, HER-negative invasive breast cancer who underwent examinations using US and Oncotype DX assay were included. US images were independently reviewed by dedicated breast radiologists who were blind to the RS. Tumour roundness was measured using a laboratory-developed software program. The pathological data were reviewed, including immunohistochemistry results. Univariate analysis was performed to assess the associations between the RS and each variable. Multiple logistic regression analysis was used to identify independent predictors of high RS. Of 267 patients, 147 (55%) had low, 96 (36%) intermediate, and 24 (9%) had high RS. According to the univariate analysis, parallel orientation, presence of calcification in the mass, and tumour roundness were positively associated with high RS. Multiple logistic regression analysis showed that parallel orientation (OR = 5.53) and tumour roundness (OR = 1.70 per 10 increase) were associated with high RS. Parallel orientation and tumour roundness are independent variables that may predict high RS in patients with ER-positive, HER2-negative breast cancer. [ABSTRACT FROM AUTHOR]

Mutation of p110 alpha-catalytic subunit of phosphatidylinositol 3-kinase (PIK3CA) has high predictive and prognostic values for breast cancer. Hence, there has been a marked interest in detecting and monitoring PIK3CA genotype with non-invasive technique, such as circulating free DNA (cfDNA). However, the diagnostic accuracy of PIK3CA genotyping by cfDNA is still a problem of controversy. Here, we conducted the first meta-analysis to evaluate overall diagnostic performance of cfDNA for PIK3CA mutation detection. Literature search was performed in Pubmed, Embase and Cochrane Central Register of Controlled Trials databases. Seven cohorts from five studies with 247 patients were included. The pooled sensitivity, specificity, positive and negative likelihood ratio, diagnostic odds ratio and area under summary receiver operating characteristic curve were calculated for accuracy evaluation. The pooled sensitivity and specificity were 0.86 (95% confidence interval [CI] 0.32–0.99) and 0.98 (95% CI 0.86–1.00), respectively; the pooled positive and negative likelihood ratio were 42.8 (95% CI 5.1–356.9) and 0.14 (95% CI 0.02–1.34), respectively; diagnostic odds ratio for evaluating the overall diagnostic performance was 300 (95% CI 8–11867); area under summary receiver operating characteristic curve reached 0.99 (95% CI 0.97–0.99). Subgroup analysis with metastatic breast cancer revealed remarkable improvement in diagnostic performance (sensitivity: 0.86–0.91; specificity: 0.98; diagnostic odds ratio: 300–428). This meta-analysis proved that detecting PIK3CA gene mutation by cfDNA has high diagnostic accuracy in breast cancer, especially for metastatic breast cancer. It may serve as a reliable non-invasive assay for detecting and monitoring PIK3CA mutation status in order to deliver personalized and precise treatment. [ABSTRACT FROM AUTHOR]

Background: The diagnostic performance of indocyanine green (ICG) fluorescence-guided sentinel lymph node biopsy (SLNB) for the presence of metastases in breast cancer remains unclear. Objective: We performed a meta-analysis to investigate the diagnostic performance of ICG-guided SLNB. Methods: Eligible studies were identified from searches of the databases PubMed and EMBASE up to September 2015. Studies that reported the detection rate of ICG fluorescence-guided SLNB with full axillary lymph node dissection and histological or immunohistochemical examinations were included. A meta-analysis was performed to generate pooled detection rate, sensitivity, specificity, false negative rate, diagnostic odds ratio (DOR) and a summary receiver operator characteristic curve (SROC). Results: Nineteen published studies were included to generate a pooled detection rate, comprising 2594 patients. The pooled detection rate was 0.98 (95% confidence interval [CI], 0.96–0.99). Six studies finally met the criteria for meta-analysis, which yielded a pooled sensitivity of 0.92 (95% CI, 0.85–0.96), specificity 1 (95% CI, 0.97–1), and DOR 311.47 (95% CI, 84.11–1153.39). The area under the SROC was 0.9758. No publication bias was found. Conclusion: ICG fluorescence-guided SLNB is viable for detection of lymph node metastases in breast cancer. Large-scale randomized multi-center trials are necessary to confirm our results. [ABSTRACT FROM AUTHOR]

Recently, the interest in programmed death ligand-1 (PD-L1) as a prognostic marker in several types of malignant tumors has increased. In the present meta-analysis, we aimed to explore the prognostic and clinicopathological value of PD-L1 in breast cancer. We searched Medline/PubMed, Web of Science, EMBASE, the Cochrane Library databases, and grey literature from inception until January 20, 2016. Studies concerning breast cancer that focused on PD-L1 expression and studies reporting survival data were included; two authors independently performed the data extraction. The pooled risk ratio (RR) and 95% confidence interval (CI) were assessed to determine the association between the clinicopathological parameters of patients and PD-L1 expression. Five studies involving 2061 patients were included in this meta-analysis. The results indicated that positive/higher PD-L1 expression was a negative predictor for breast cancer, with an RR of 1.64 (95% CI, 1.14–2.34) for the total mortality risk and an RR of 2.53 (95% CI, 1.78–3.59) for the mortality risk 10 years after surgery. Moreover, positive/higher PD-L1 expression was significantly associated with positive lymph node metastasis (RR, 1.33; 95% CI, 1.04–1.70), poor nuclear grade (RR, 1.24; 95% CI, 1.07–1.43), and negative estrogen receptor status (RR, 2.45; 95% CI, 1.31–4.60) in breast cancer patients. Our findings suggest that PD-L1 can serve as a significant biomarker for poor prognosis and the adverse clinicopathologic features of breast cancer and could facilitate the better management of individual patients. [ABSTRACT FROM AUTHOR]

Background: CDH13 (cadherin 13) is a special cadherin cell adhesion molecule, and the methylation of its promoter causes inactivation in a considerable number of human cancers. To explore the association between CDH13 promoter methylation and breast cancer risk and prognosis, we systematically integrated published articles to investigate the diagnostic performance of the CDH13 methylation test for breast cancer. An independent DNA methylation microarray dataset from The Cancer Genome Atlas project (TCGA) project was used to validate the results of the meta-analysis. Methods: The relevant literature was searched using the PubMed, Cochrane Library, Web of Science and Google Scholar databases for articles published in English up to May 2015. Data were analyzed using random effect or fixed effect models. The effect sizes were estimated by measuring an odds ratio (OR) or hazard ratio (HR) with a 95% confidence interval (CI). A chi-squared based Q test and sensitivity analysis were performed to examine the between-study heterogeneity and the contribution of single studies to the final results, respectively. Funnel plots were constructed to evaluate publication bias. Results: Seven hundred and twenty-six breast tumor samples and 422 controls were collected from 13 published studies. The data from the TCGA set include both tumor and normal samples. A significant association was observed between CDH13 promoter methylation and breast cancer, with an aggregated OR equal to 13.73 (95%CI: 8.09~23.31, z = 9.70, p<0.0001) as measured using the fixed effect model and 14.23 (95%CI: 5.06~40.05, z = 5.03, p<0.0001) as measured using a random effect model. The HR values were calculated as 0.77 (95%CI: 0.27~2.21, z = -0.49, p = 0.622) and 0.38 (95%CI: 0.09~1.69, z = -1.27, p = 0.20) for overall survival (OS) and disease-free survival (DFS), respectively, using the random effect model. This result indicated that breast cancer patients with CDH13 promoter methylation correlated non-significantly with prognosis and is therefore similar to the findings of the TCGA project. Conclusions: The methylation status of CDH13 promoter was strongly associated with breast cancer risk. However, CDH13 promoter methylation was not significantly related to the OS and DFS of breast cancer and may have limited prognostic value for breast cancer patients. [ABSTRACT FROM AUTHOR]

Background: Germline mutations in the BRCA1 and BRCA2 genes are the most frequent known hereditary causes of familial breast cancer. Little is known about the interaction of age at diagnosis, estrogen receptor (ER) and progesterone receptor (PgR) expression and outcomes in patients with BRCA1 or BRCA2 mutations. Methods: A PubMed search identified publications exploring the association between BRCA mutations and clinical outcome. Hazard ratios (HR) for overall survival were extracted from multivariable analyses. Hazard ratios were weighted and pooled using generic inverse-variance and random-effect modeling. Meta-regression weighted by total study sample size was conducted to explore the influence of age, ER and PgR expression on the association between BRCA mutations and overall survival. Results: A total of 16 studies comprising 10,180 patients were included in the analyses. BRCA mutations were not associated with worse overall survival (HR 1.06, 95% CI 0.84–1.34, p = 0.61). A similar finding was observed when evaluating the influence of BRCA1 and BRCA2 mutations on overall survival independently (BRCA1: HR 1.20, 95% CI 0.89–1.61, p = 0.24; BRCA2: HR 1.01, 95% CI 0.80–1.27, p = 0.95). Meta-regression identified an inverse association between ER expression and overall survival (β = -0.75, p = 0.02) in BRCA1 mutation carriers but no association with age or PgR expression (β = -0.45, p = 0.23 and β = 0.02, p = 0.97, respectively). No association was found for BRCA2 mutation status and age, ER, or PgR expression. Conclusion: ER-expression appears to be an effect modifier in patients with BRCA1 mutations, but not among those with BRCA2 mutations. [ABSTRACT FROM AUTHOR]

The number of published articles describing associations between mutations and diseases is increasing at a fast pace. There is a pressing need to gather such mutation-disease associations into public knowledge bases, but manual curation slows down the growth of such databases. We have addressed this problem by developing a text-mining system (DiMeX) to extract mutation to disease associations from publication abstracts. DiMeX consists of a series of natural language processing modules that preprocess input text and apply syntactic and semantic patterns to extract mutation-disease associations. DiMeX achieves high precision and recall with F-scores of 0.88, 0.91 and 0.89 when evaluated on three different datasets for mutation-disease associations. DiMeX includes a separate component that extracts mutation mentions in text and associates them with genes. This component has been also evaluated on different datasets and shown to achieve state-of-the-art performance. The results indicate that our system outperforms the existing mutation-disease association tools, addressing the low precision problems suffered by most approaches. DiMeX was applied on a large set of abstracts from Medline to extract mutation-disease associations, as well as other relevant information including patient/cohort size and population data. The results are stored in a database that can be queried and downloaded at . We conclude that this high-throughput text-mining approach has the potential to significantly assist researchers and curators to enrich mutation databases. [ABSTRACT FROM AUTHOR]

Background: The prognostic values of tumor-infiltrating lymphocytes (TILs) and TILs subsets in breast cancer (BC) are uncertain. Methods: A systematic literature search (MEDLINE, Web of Science, EMBASE, and the Cochrane Library to August 2014) was conducted for studies which met the eligibility criteria. The primary clinical outcome was defined as disease-free survival (DFS), overall survival (OS), and BC-specific survival (BCSS). Random or fixed-effects model was adopted to estimate the summary hazard ratio (HR). Results: Twenty-five published studies comprising 22,964 patients were reviewed. Pooled analysis indicated that TILs were not prognostic markers for DFS and OS in overall population, but related to improved DFS (HR, 0.82; 95% CI, 0.76–0.88) and OS (HR, 0.79; 95% CI, 0.71–0.87) in triple negative breast cancer (TNBC) patients. For TILs subsets, CD8+ lymphocytes were associated with improved DFS (HR, 0.69; 95% CI, 0.56–0.84) and BCSS (HR, 0.78; 95% CI, 0.71–0.86) in overall population, while FOXP3+ lymphocytes were associated with reduced DFS (HR, 1.47; 95% CI, 1.01–2.05) and OS (HR, 1.50; 95% CI, 1.15–1.97). In estrogen receptor (ER) negative patients, CD8+ lymphocytes was also related to better BCSS. In addition, the high density of CD20+, CD3+ or low level of PD-1+ or γδ T lymphocytes indicated increased OS in limited studies. Conclusion: TILs and TILs subsets are promising prognostic biomarkers in breast cancer, especially in TNBC. [ABSTRACT FROM AUTHOR]

Background: Single nucleotide polymorphisms (SNPs) in the estrogen receptor gene (ESR1) play critical roles in breast cancer (BC) susceptibility. Genome-wide association studies have reported that SNPs in ESR1 are associated with BC susceptibility; however, the results of recent studies have been inconsistent. Therefore, we performed this meta-analysis to obtain more accurate and credible results. Methods: We pooled published literature from PubMed, EMBASE, and Web of Science and calculated odds ratios (ORs) with 95% confidence intervals (CIs) to assess the strength of associations using fixed effects models and random effects models. Twenty relevant case-control and cohort studies of the 3 related SNPs were identified. Results: Three SNPs of the ESR1 gene, rs2077647:T>C, rs2228480:G>A and rs3798577:T>C, were not associated with increased BC risk in our overall meta-analysis. Stratified analysis by ethnicity showed that in Caucasians, the rs2228480 AA genotype was associated with a 26% decreased risk of BC compared with the GG genotype (OR = 0.740, 95% CI: 0.555–0.987). The C allele of the rs3798577:T>C variant was associated with decreased BC risk in Asians (OR = 0.828, 95% CI: 0.730–0.939), while Caucasians with this allele were found to experience significantly increased BC risk (OR = 1.551, 95% CI: 1.037–2.321). A non-significant association between rs2077647 and BC risk was identified in all of the evaluated ethnic populations. Conclusion: Rs3798577 was associated with an increased risk of BC in Caucasian populations but a decreased risk in Asians. Rs2228480 had a large protective effect in Caucasians, while rs2077647 was not associated with BC risk. [ABSTRACT FROM AUTHOR]

EGFR and cMET cross-talk is involved in breast cancer (BC) progression and resistance to different targeted therapies, however little is known about the co-expression patterns of EGFR and cMET or its prognostic significance in BC. Protein levels of EGFR, cMET and their phosphorylated proteins were measured in 825 BC samples using reverse phase protein array (RPPA). Given unimodal distribution of proteins, the median was selected as a cut-off after sensitivity analyses. Kaplan-Meier survival curves were used to estimate relapse-free (RFS) and overall survival (OS). Cox-proportional hazards models were utilized to determine associations between EGFR and cMET with outcomes. Mean age was 58 years with 457 (55%) hormone receptor (HR) positive, 211 (26%) triple-negative (TN) and 148 (18%) HER2 positive tumors (HER2+). HER2+ was associated with higher EGFR expression and phosphorylation, compared to HR and TN (p<0.05). High EGFR expression was associated with higher phosphorylated-cMET (p-cMET) but not cMET (ANOVA p-cMET p < 0.001; cMET p = 0.34). The same association was found with high phosphorylated-EGFR (p-EGFR) group at Tyr992 and Tyr1068 (both p < 0.001). High expressions in either of two p-EGFRs were linked with higher cMET as well (all p<0.001). For the TN subtype, high expression in EGFR and p-EGFR at Tyr992 but not at Tyr1068 was associated with higher p-cMET (p<0.00, p = 0.012, p = 0.4 respectively). Only high expression in p-EGFR at Tyr992 was linked with higher expression of cMET (p = 0.02). In contrast, among HER2 subtype, high expression in p-EGFR at Tyr1068 but not at Tyr992 was associated with higher cMET and p-cMET (cMET p = 0.023;p-cMET p<0.001). Four subgroups of patients defined by dichotomized EGFR/p-EGFR and cMET/p-cMET level demonstrated no significant differences in survival. In multivariate analyses, neither cMET nor EGFR expression/activation was found to be an independent prognostic factor in survival outcome. [ABSTRACT FROM AUTHOR]

Programmed cell death-1 (PD-1) plays an important inhibitory role in anti-tumor responses, so it is considered as a powerful candidate gene for individual’s genetic susceptibility to cancer. Recently, some epidemiological studies have evaluated the association between PD-1 polymorphisms and cancer risk. However, the results of the studies are conflicting. Therefore, a meta-analysis was performed. We identified all studies reporting the relationship between PD-1 polymorphisms and cancers by electronically searches. According to the inclusion criteria and the quality assessment of Newcastle-Ottawa Scale (NOS), only high quality studies were included. A total of twelve relevant studies involving 5,206 cases and 5,174 controls were recruited. For PD-1.5 (rs2227981) polymorphism, significantly decreased cancer risks were obtained among overall population, Asians subgroup and population-based subgroup both in TT vs. CC and TT vs. CT+CC genetic models. In addition, a similar result was also found in T vs. C allele for overall population. However, there were no significant associations between either PD-1.9 (rs2227982) or PD-1 rs7421861 polymorphisms and cancer risks in all genetic models and alleles. For PD-1.3 (rs11568821) polymorphism, we found different cancer susceptibilities between GA vs. GG and AA vs. AG+GG genetic models, and no associations between AA vs. GG, AA+AG vs. GG genetic models or A vs. G allele and cancer risks. In general, our results firstly indicated that PD-1.5 (rs2227981) polymorphism is associated a strongly decreased risk of cancers. Additional epidemiological studies are needed to confirm our findings. [ABSTRACT FROM AUTHOR]

Four of the most common limitations of the many available clustering methods are: i) the lack of a proper strategy to deal with outliers; ii) the need for a good a priori estimate of the number of clusters to obtain reasonable results; iii) the lack of a method able to detect when partitioning of a specific data set is not appropriate; and iv) the dependence of the result on the initialization. Here we propose Cross-clustering (CC), a partial clustering algorithm that overcomes these four limitations by combining the principles of two well established hierarchical clustering algorithms: Ward’s minimum variance and Complete-linkage. We validated CC by comparing it with a number of existing clustering methods, including Ward’s and Complete-linkage. We show on both simulated and real datasets, that CC performs better than the other methods in terms of: the identification of the correct number of clusters, the identification of outliers, and the determination of real cluster memberships. We used CC to cluster samples in order to identify disease subtypes, and on gene profiles, in order to determine groups of genes with the same behavior. Results obtained on a non-biological dataset show that the method is general enough to be successfully used in such diverse applications. The algorithm has been implemented in the statistical language R and is freely available from the CRAN contributed packages repository. [ABSTRACT FROM AUTHOR]

HOX transcript antisense intergenic RNA (HOTAIR) is a long non-coding RNA (lncRNA) that functions as an oncogenic molecule in different cancer cells. Genetic variants of HOTAIR may affect the activity of certain regulatory factors and further regulate the aberrant expression of HOTAIR, which might be underlying mechanisms that affect tumour susceptibility and prognosis. Recently, several studies have been performed to examine the possible link between polymorphisms in HOTAIR and cancer risk; however, the results have been inconclusive. Therefore, we performed a meta-analysis to estimate the associations between HOTAIR polymorphisms (rs920778, rs4759314 and rs1899663) and cancer risk. Eight studies comprising 7,151 cases and 8,740 controls were included in our study. Overall, no significant associations between the HOTAIR polymorphisms (rs920778, rs4759314 and rs1899663) and cancer risk were observed. However, in further stratified analyses, the variant T allele of rs920778 exhibited a significant increased risk of developing digestive cancers (dominant model: OR = 1.44; 95% CI = 1.31–1.59). These findings provided evidence that HOTAIR rs920778 may modify the susceptibility to certain cancer types. Further studies incorporating subjects with different ethnic backgrounds combined with re-sequencing of the marked region and functional evaluations are warranted. [ABSTRACT FROM AUTHOR]

Few studies have addressed the risk of recurrence by assessing proliferation markers in lymph node metastasis from breast cancer. Here, we aimed to examine Ki-67 expression and mitotic count in lymph nodes in comparison with primary tumors. A cohort of node positive breast cancer (n = 168) was studied as a part of the prospective Norwegian Breast Cancer Screening Program (1996–2009). The percentage of Ki-67 positivity was counted per 500 tumor cells in hot-spot areas (x630). Mitotic count was conducted in the most cellular and mitotic active areas in 10 high power fields (x400). Our results showed that Ki-67 and mitotic count were significantly correlated between primary tumor and lymph nodes (Spearman`s correlation 0. 56 and 0.46, respectively) and were associated with most of the histologic features of the primary tumor. Univariate survival analysis (log-rank test) showed that high Ki-67 and mitotic count in the primary tumor and lymph node metastasis significantly predicted risk of recurrence. In multivariate analysis, mitotic count in the lymph node metastasis was an independent predictor of tumor recurrence. In conclusion, proliferation markers in lymph node metastases significantly predicted disease free survival in node positive breast cancer. [ABSTRACT FROM AUTHOR]

Background: Prognostic factors and prognostic models play a key role in medical research and patient management. The Nottingham Prognostic Index (NPI) is a well-established prognostic classification scheme for patients with breast cancer. In a very simple way, it combines the information from tumor size, lymph node stage and tumor grade. For the resulting index cutpoints are proposed to classify it into three to six groups with different prognosis. As not all prognostic information from the three and other standard factors is used, we will consider improvement of the prognostic ability using suitable analysis approaches. Methods and Findings: Reanalyzing overall survival data of 1560 patients from a clinical database by using multivariable fractional polynomials and further modern statistical methods we illustrate suitable multivariable modelling and methods to derive and assess the prognostic ability of an index. Using a REMARK type profile we summarize relevant steps of the analysis. Adding the information from hormonal receptor status and using the full information from the three NPI components, specifically concerning the number of positive lymph nodes, an extended NPI with improved prognostic ability is derived. Conclusions: The prognostic ability of even one of the best established prognostic index in medicine can be improved by using suitable statistical methodology to extract the full information from standard clinical data. This extended version of the NPI can serve as a benchmark to assess the added value of new information, ranging from a new single clinical marker to a derived index from omics data. An established benchmark would also help to harmonize the statistical analyses of such studies and protect against the propagation of many false promises concerning the prognostic value of new measurements. Statistical methods used are generally available and can be used for similar analyses in other diseases. [ABSTRACT FROM AUTHOR]

Generally, cancer stem cells have epithelial-to-mesenchymal-transition characteristics and other aggressive properties that cause metastasis. However, there have been no confident markers for the identification of cancer stem cells and comparative methods examining adherent and sphere cells are widely used to investigate mechanism underlying cancer stem cells, because sphere cells have been known to maintain cancer stem cell characteristics. In this study, we conducted a meta-analysis that combined gene expression profiles from several studies that utilized tumorsphere technology to investigate tumor stem-like breast cancer cells. We used our own gene expression profiles along with the three different gene expression profiles from the Gene Expression Omnibus, which we combined using the ComBat method, and obtained significant gene sets using the gene set analysis of our datasets and the combined dataset. This experiment focused on four gene sets such as cytokine-cytokine receptor interaction that demonstrated significance in both datasets. Our observations demonstrated that among the genes of four significant gene sets, six genes were consistently up-regulated and satisfied the p-value of < 0.05, and our network analysis showed high connectivity in five genes. From these results, we established CXCR4, CXCL1 and HMGCS1, the intersecting genes of the datasets with high connectivity and p-value of < 0.05, as significant genes in the identification of cancer stem cells. Additional experiment using quantitative reverse transcription-polymerase chain reaction showed significant up-regulation in MCF-7 derived sphere cells and confirmed the importance of these three genes. Taken together, using meta-analysis that combines gene set and network analysis, we suggested CXCR4, CXCL1 and HMGCS1 as candidates involved in tumor stem-like breast cancer cells. Distinct from other meta-analysis, by using gene set analysis, we selected possible markers which can explain the biological mechanisms and suggested network analysis as an additional criterion for selecting candidates. [ABSTRACT FROM AUTHOR]

The role of adjuvant ovarian suppression or ablation (OS/OA) in premenopausal women with hormone receptor-positive breast cancer remains controversial. The purpose of our study was to examine which patients might benefit from the addition of OS/OA to tamoxifen. We analyzed the data of 2065 premenopausal patients with hormone receptor-positive invasive ductal carcinomas who were treated at Sun Yat-Sen University Cancer Center from 2000 to 2008. The five-year disease-free survival rate (DFSR) and overall survival rate (OSR) were compared by menstrual status and treatment. Compared with patients older than forty years of age, patients younger than forty years old had significant lower DFSRs and OSRs. The addition of OS/OA to tamoxifen increased the DFSR and OSR of patients with normal menstrual cycles after chemotherapy, regardless of their age at diagnosis. Patients with normal menstrual cycles after chemotherapy are the main beneficiaries of an adjuvant OS/OA. [ABSTRACT FROM AUTHOR]

Objective: Elevated γ-Glutamyltransferase serum levels are associated with increased risk of overall cancer incidence and several site-specific malignancies. In the present prospective study we report on the associations of serum γ-Glutamyltransferase with the risk of breast cancer in a pooled population-based cohort considering established life style risk factors. Methods: Two cohorts were included in the present study, i.e. the Vorarlberg (n = 97,268) and the Malmoe cohort (n = 9,790). Cox proportional hazards regression models were fitted to estimate HRs for risk of breast cancer. Results: In multivariate analysis adjusted for age, body mass index and smoking status, women with γ-Glutamyltransferase levels in the top quartile were at significantly higher risk for breast cancer compared to women in the lowest quartile (HR 1.21, 95% CI 1.09 to 1.35; p = 0.005). In the subgroup analysis of the Malmoe cohort, γ-Glutamyltransferase remained an independent risk factor for breast cancer when additionally considering alcohol intake. A statistically significant increase in risk was seen in women with γ-Glutamyltransferase-levels in the top versus lowest quartile in a multivariate model adjusted for age, body mass index, smoking status, physical activity, parity, oral contraceptive-use and alcohol consumption (HR 1.37, 95% CI 1.11–1.69, p = 0.006). Conclusion: Our findings identified γ-Glutamyltransferase as an independent risk factor for breast cancer beyond the consumption of alcohol and other life style risk factors. [ABSTRACT FROM AUTHOR]