An integrated bioinformatics approach identifies elevated cyclin E2 expression and E2F activity as distinct features of tamoxifen resistant breast tumors

EZH2 PROTACs target EZH2- and FOXM1-associated oncogenic nodes, suppressing breast cancer cell growth

Breast cancer (BC) remains the second leading cause of cancer-related mortalities in women. Resistance to hormone therapies such as tamoxifen, an estrogen receptor (ER) inhibitor, is a major hurdle in the treatment of BC. Enhancer of zeste homolog 2 (EZH2), the methyltransferase component of the Polycomb repressive complex 2 (PRC2), has been implicated in tamoxifen resistance. Evidence suggests that EZH2 often functions noncanonically, in a methyltransferase-independent manner, as a transcription coactivator through interacting with oncogenic transcription factors. Unlike methyltransferase inhibitors, proteolysis targeting chimeras (PROTAC) can suppress both activating and repressive functions of EZH2. Here, we find that EZH2 PROTACs, MS177 and MS8815, effectively inhibited the growth of BC cells, including those with acquired tamoxifen resistance, to a much greater degree when compared to methyltransferase inhibitors. Mechanistically, EZH2 associates with forkhead box M1 (FOXM1) and binds to the promoters of FOXM1 target genes. EZH2 PROTACs induce degradation of both EZH2 and FOXM1, leading to reduced expression of target genes involved in cell cycle progression and tamoxifen resistance. Together, this study supports that EZH2-targeted PROTACs represent a promising avenue of research for the future treatment of BC, including in the setting of tamoxifen resistance.

A Novel Mathematical Approach for Analysis of Integrated Cell-Patient Data Uncovers a 6-Gene Signature Linked to Endocrine Therapy Resistance

A significant number of breast cancers develop resistance to hormone therapy. This progression, while posing a major clinical challenge, is difficult to predict. Despite important contributions made by cell models and clinical studies to tackle this problem, both present limitations when taken individually. Experiments with cell models are highly reproducible but do not reflect the indubitable heterogenous landscape of breast cancer. On the other hand, clinical studies account for this complexity but introduce uncontrolled noise due to external factors. Here, we propose a new approach for biomarker discovery that is based on a combined analysis of sequencing data from controlled MCF7 cell experiments and heterogenous clinical samples that include clinical and sequencing information from The Cancer Genome Atlas. Using data from differential gene expression analysis and a Bayesian logistic regression model coupled with an original simulated annealing-type algorithm, we discovered a novel 6-gene signature for stratifying patient response to hormone therapy. The experimental observations and computational analysis built on independent cohorts indicated the superior predictive performance of this gene set over previously known signatures of similar scope. Together, these findings revealed a new gene signature to identify patients with breast cancer with an increased risk of developing resistance to endocrine therapy.

An Innovative Drug Repurposing Approach to Restrain Endometrial Cancer Metastatization

BACKGROUND

Endometrial cancer (EC) is the most common gynecologic tumor and the world's fourth most common cancer in women. Most patients respond to first-line treatments and have a low risk of recurrence, but refractory patients, and those with metastatic cancer at diagnosis, remain with no treatment options. Drug repurposing aims to discover new clinical indications for existing drugs with known safety profiles. It provides ready-to-use new therapeutic options for highly aggressive tumors for which standard protocols are ineffective, such as high-risk EC.

METHODS

Here, we aimed at defining new therapeutic opportunities for high-risk EC using an innovative and integrated computational drug repurposing approach.

RESULTS

We compared gene-expression profiles, from publicly available databases, of metastatic and non-metastatic EC patients being metastatization the most severe feature of EC aggressiveness. A comprehensive analysis of transcriptomic data through a two-arm approach was applied to obtain a robust prediction of drug candidates.

CONCLUSIONS

Some of the identified therapeutic agents are already successfully used in clinical practice to treat other types of tumors. This highlights the potential to repurpose them for EC and, therefore, the reliability of the proposed approach.

A Patient-Derived Xenograft Model of Dedifferentiated Endometrial Carcinoma: A Proof-of-Concept Study for the Identification of New Molecularly Informed Treatment Approaches

Simple Summary

Reliable animal models of human malignancies are paramount for preclinical studies of novel treatment approaches. Here, we successfully developed a patient-derived xenograft (PDX) model of dedifferentiated endometrial carcinoma (DEC)–an uncommon uterine malignancy that is generally unresponsive to standard chemo- and radiotherapy. The murine model–termed PDX-mLung–was established through the implantation of lung metastatic lesions obtained from a woman with DEC. Histologic and molecular findings revealed that PDX-mLung was highly similar to the parent human malignant lesions (both primary DEC and lung metastases). Importantly, molecular analyses revealed that PDX-mLung exhibited druggable alterations including a FGFR2 mutation and CCNE2 amplification. The former was targeted with the FGFR inhibitor lenvatinib while the latter with the cell cycle inhibitor palbociclib. The combination of the two drugs exhibited synergistic therapeutic effects against in vivo tumor growth. Collectively, these data illustrate the value of PDX models for preclinical testing of new molecularly informed therapies in difficult-to-treat gynecologic malignancies. Our results may also prompt further clinical research to examine whether the combination of lenvatinib and palbociclib has potential to improve clinical outcomes of women with DEC.

Abstract

Conventional treatment of dedifferentiated endometrial carcinoma (DEC)–an uncommon and highly aggressive uterine malignancy–is beset by high failure rates. A line of research that holds promise to overcome these limitations is tailored treatments targeted on specific molecular alterations. However, suitable preclinical platforms to allow a reliable implementation of this approach are still lacking. Here, we developed a patient-derived xenograft (PDX) model for preclinical testing of investigational drugs informed by molecular data. The model–termed PDX-mLung was established in mice implanted with lung metastatic lesions obtained from a patient with DEC. Histologic and whole-exome genetic analyses revealed a high degree of identity between PDX-mLung and the patient’s parental lesions (both primary DEC and lung metastases). Interestingly, molecular analyses revealed that PDX-mLung harbored druggable alterations including a FGFR2 mutation and CCNE2 amplification. Targeted combined treatment with the FGFR inhibitor lenvatinib and the cell cycle inhibitor palbociclib was found to exert synergistic therapeutic effects against in vivo tumor growth. Based on the results of RNA sequencing, lenvatinib and palbociclib were found to exert anti-tumor effects by interfering interferon signaling and activating hormonal pathways, respectively. Collectively, these data provide proof-of-concept evidence on the value of PDX models for preclinical testing of molecularly informed drug therapy in difficult-to-treat human malignancies. Further clinical research is needed to examine more rigorously the potential usefulness of the lenvatinib and palbociclib combination in patients with DEC.

Bioinformatics Approach for Data Capturing: The Case of Breast Cancer

Background:

With the rapid evolution in advanced computer systems and various statistical algorithms, it is now a days possible to analyze complex biological data. Bioinformatics is an interface between computational and biological assemblies. It is applied in various fields of biological as well as medical sciences.

Aim:

The manuscript aims to summarize the developments in the field of breast cancer research through the applications of bioinformatics.

Methods:

Various search engines like google, science direct, Scopus, PubMed, etc., were used for the literature survey.

Results:

It describes the bioinformatics analysis tools and models, which include mainly artificial neural network models.

Conclusion:

Bioinformatics is the evolutionary approach that is used for the capturing of data from the various case studies related to breast cancer.

An Integrative Transcriptome-Wide Analysis of Amyotrophic Lateral Sclerosis for the Identification of Potential Genetic Markers and Drug Candidates

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative neuromuscular disease. Although genome-wide association studies (GWAS) have successfully identified many variants significantly associated with ALS, it is still difficult to characterize the underlying biological mechanisms inducing ALS. In this study, we performed a transcriptome-wide association study (TWAS) to identify disease-specific genes in ALS. Using the largest ALS GWAS summary statistic (n = 80,610), we identified seven novel genes using 19 tissue reference panels. We conducted a conditional analysis to verify the genes’ independence and to confirm that they are driven by genetically regulated expressions. Furthermore, we performed a TWAS-based enrichment analysis to highlight the association of important biological pathways, one in each of the four tissue reference panels. Finally, utilizing a connectivity map, a database of human cell expression profiles cultured with bioactive small molecules, we discovered functional associations between genes and drugs to identify 15 bioactive small molecules as potential drug candidates for ALS. We believe that, by integrating the largest ALS GWAS summary statistic with gene expression to identify new risk loci and causal genes, our study provides strong candidates for molecular basis experiments in ALS.

Knockdown of ribonucleotide reductase regulatory subunit M2 increases the drug sensitivity of chronic myeloid leukemia to imatinib‑based therapy

Imatinib-based targeted treatment is the standard therapy for chronic myeloid leukemia (CML); however, drug resistance is an inevitable issue for imatinib-based CML treatment. Imatinib resistance can be ascribed to Bcr-Abl-dependent and independent resistance. In the present study, peripheral blood samples were collected from imatinib-sensitive (IS) and imatinib-resistant (IR) CML patients and transcriptome sequencing was carried out. From the RNA-seq data, a significantly altered IR-related gene (IRG), ribonucleotide reductase regulatory subunit M2 (RRM2) was identified. Using real-time quantitative fluorescence PCR (qF-PCR), we found that RRM2 was elevated in both IR CML patients and an IR cell line. Using reverse-transcription PCR (RT-PCR) and western blot analysis, we indicated that imatinib can increase RRM2 level in a dose-dependent manner in IR cells. We also demonstrated that RRM2 is involved in the Bcl-2/caspase cell apoptotic pathway and in the Akt cell signaling pathway, and therefore affects the cell survival following imatinib therapy. The present study, for the first time, indicates that RRM2 is responsible for drug resistance in imatinib-based therapy. Therefore, RRM2 gene can be considered as a potential therapeutic target in the clinical treatment of CML.

Antiproliferative activities of the second-generation antipsychotic drug sertindole against breast cancers with a potential application for treatment of breast-to-brain metastases

Epidemiological observations have shown that schizophrenia patients after long-term drug treatment exhibited reduced tumor incidences. The potential anticancer effects of antipsychotic drugs are subsequently demonstrated. These drugs are of great interest as agents against untreatable brain metastases because of their ability to traverse the blood-brain barrier (BBB). Most drugs tested thus far are the first-generation antipsychotics (FGAs). But their clinical application may be limited due to high risks of deaths in elderly patients. There is an urgent need to find additional BBB-traversing anticancer agents with lower risks of deaths. In this work, we investigated antitumor activities of eight second-generation-antipsychotic (SGA) drugs, since they exhibit lower mortality rates than FGAs. We discovered that sertindole showed broad antiproliferative activities against seven cancer types including 29 cell-lines and exhibited potent effects toward breast cancer cell-lines, with half maximal concentration to inhibit proliferation by 50% (IC₅₀) as low as 800 nM. We further found that sertindole caused cell death through autophagy-associated apoptosis and its directly-binding inhibition of 5-HT6 involved in this process. In xenotransplant mice, sertindole administration approaching maximal therapeutic dose attenuated breast-tumor growth by 22.7%. Therefore, our study reveals promising anticancer potentials of sertindole against breast cancers, with probable applications for breast-to-brain metastases.

Using Tumor Explants for Imaging Mass Spectrometry Visualization of Unlabeled Peptides and Small Molecules

Matrix assisted laser desorption ionization time-of-flight (MALDI-TOF) imaging mass spectrometry has emerged as a powerful, label-free technique to visualize penetration of small molecules in vivo and in vitro, including in 3D cell culture spheroids; however, some spheroids do not grow sufficiently large to provide enough area for imaging mass spectrometry. Here, we describe an ex vivo method for visualizing unlabeled peptides and small molecules in tumor explants, which can be divided into pieces of desired size, thus circumventing the size limitations of many spheroids. As proof-of-concept, a small molecule drug (4-hydroxytamoxifen), as well as a peptide drug (cyclosporin A) and peptide chemical probe, can be visualized after in vitro incubation with tumor explants so that this technique may provide a solution to robing cell penetration by unlabeled peptides.

Prognostic Values of CCNE1 Amplification and Overexpression in Cancer Patients: A Systematic Review and Meta-analysis

A number of studies revealed that CCNE1 copy number amplification and overexpression (on mRNA or protein expression level) were associated with prognosis of diverse cancers, however, the results were inconsistent among studies. So we conducted this systematic review and meta-analysis to investigate the prognostic values of CCNE1 amplification and overexpression in cancer patients. PubMed, Cochrane library, Embase, CNKI and WanFang database (last update by February 15, 2018) were searched for literatures. A total of 20 studies were included and 5 survival assessment parameters were measured in this study, which included overall survival (OS), progression free survival (PFS), recurrence free survival (RFS), cancer specific survival (CSS) and distant metastasis free survival (DMFS). Pooled analyses showed that CCNE1 amplification might predict poor OS (HR=1.59, 95% CI: 1.05-2.40, p=0.027) rather than PFS (HR=1.49, 95% CI: 0.83-2.67, p=0.177) and RFS (HR=0.982, 95% CI: 0.2376-4.059, p=0.9801) in various cancers; CCNE1 overexpression significantly correlated with poor OS (HR=1.52, 95% CI: 1.05-2.20, p=0.027), PFS (HR=1.20, 95% CI: 1.07-1.34, p=0.001) and DMFS (HR=1.62, 95% CI: 1.09-2.40, p=0.017) rather than RFS (HR=1.68, 95% CI: 0.81-3.50, p=0.164) and CSS (HR=1.54, 95% CI: 0.74-3.18, p=0.246). On the whole, these results indicated CCNE1 amplification and overexpression were associated with poor survival of patients with cancer, suggesting that CCNE1 might be an effective prognostic signature for cancer patients.

Giving Drugs a Second Chance: Overcoming Regulatory and Financial Hurdles in Repurposing Approved Drugs As Cancer Therapeutics

The repositioning or “repurposing” of existing therapies for alternative disease indications is an attractive approach that can save significant investments of time and money during drug development. For cancer indications, the primary goal of repurposed therapies is on efficacy, with less restriction on safety due to the immediate need to treat this patient population. This report provides a high-level overview of how drug developers pursuing repurposed assets have previously navigated funding efforts, regulatory affairs, and intellectual property laws to commercialize these “new” medicines in oncology. This article provides insight into funding programs (e.g., government grants and philanthropic organizations) that academic and corporate initiatives can leverage to repurpose drugs for cancer. In addition, we highlight previous examples where secondary uses of existing, Food and Drug Administration- or European Medicines Agency-approved therapies have been predicted in silico and successfully validated in vitro and/or in vivo (i.e., animal models and human clinical trials) for certain oncology indications. Finally, we describe the strategies that the pharmaceutical industry has previously employed to navigate regulatory considerations and successfully commercialize their drug products. These factors must be carefully considered when repurposing existing drugs for cancer to best benefit patients and drug developers alike.

Oestrogen receptor negativity in breast cancer: a cause or consequence?

Endocrine resistance, which occurs either by de novo or acquired route, is posing a major challenge in treating hormone-dependent breast cancers by endocrine therapies. The loss of oestrogen receptor α (ERα) expression is the vital cause of establishing endocrine resistance in this subtype. Understanding the mechanisms that determine the causes of this phenomenon are therefore essential to reduce the disease efficacy. But how we negate oestrogen receptor (ER) negativity and endocrine resistance in breast cancer is questionable. To answer that, two important approaches are considered: (1) understanding the cellular origin of heterogeneity and ER negativity in breast cancers and (2) characterization of molecular regulators of endocrine resistance. Breast tumours are heterogeneous in nature, having distinct molecular, cellular, histological and clinical behaviour. Recent advancements in perception of the heterogeneity of breast cancer revealed that the origin of a particular mammary tumour phenotype depends on the interactions between the cell of origin and driver genetic hits. On the other hand, histone deacetylases (HDACs), DNA methyltransferases (DNMTs), miRNAs and ubiquitin ligases emerged as vital molecular regulators of ER negativity in breast cancers. Restoring response to endocrine therapy through re-expression of ERα by modulating the expression of these molecular regulators is therefore considered as a relevant concept that can be implemented in treating ER-negative breast cancers. In this review, we will thoroughly discuss the underlying mechanisms for the loss of ERα expression and provide the future prospects for implementing the strategies to negate ER negativity in breast cancers.

Drug discovery using clinical outcome-based Connectivity Mapping: application to ovarian cancer

Background

Epithelial ovarian cancer (EOC) is the fifth leading cause of cancer death among women in the United States (5 % of cancer deaths). The standard treatment for patients with advanced EOC is initial debulking surgery followed by carboplatin-paclitaxel combination chemotherapy. Unfortunately, with chemotherapy most patients relapse and die resulting in a five-year overall survival around 45 %. Thus, finding novel therapeutics for treating EOC is essential. Connectivity Mapping (CMAP) has been used widely in cancer drug discovery and generally has relied on cancer cell line gene expression and drug phenotype data. Therefore, we took a CMAP approach based on tumor information and clinical endpoints from high grade serous EOC patients.

Methods

We determined tumor gene expression signatures (e.g., sets of genes) associated with time to recurrence (with and without adjustment for additional clinical covariates) among patients within TCGA (n = 407) and, separately, from the Mayo Clinic (n = 326). Each gene signature was inputted into CMAP software (Broad Institute) to determine a set of drugs for which our signature “matches” the “reference” signature, and drugs that overlapped between the CMAP analyses and the two studies were carried forward for validation studies involving drug screens on a set of 10 EOC cell lines.

Results

Of the 11 drugs carried forward, five (mitoxantrone, podophyllotoxin, wortmannin, doxorubicin, and 17-AAG) were known a priori to be cytotoxics and were indeed shown to effect EOC cell viability.

Conclusions

Future research is needed to investigate the use of these CMAP and similar analyses for determining combination therapies that might work synergistically to kill cancer cells and to apply this in silico bioinformatics approach using clinical outcomes to other cancer drug screening studies.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-3149-5) contains supplementary material, which is available to authorized users.

Targeting the RB-E2F pathway in breast cancer

Mutations of the retinoblastoma tumor-suppressor gene (RB1) or components regulating the CDK-RB-E2F pathway have been identified in nearly every human malignancy. Re-establishing cell cycle control through cyclin-dependent kinase (CDK) inhibition has therefore emerged as an attractive option in the development of targeted cancer therapy. The most successful example of this today is the use of the CDK4/6 inhibitor palbociclib combined with aromatase inhibitors for the treatment of estrogen receptor-positive breast cancers. Multiple studies have demonstrated that the CDK-RB-E2F pathway is critical for the control of cell proliferation. More recently, studies have highlighted additional roles of this pathway, especially E2F transcription factors themselves, in tumor progression, angiogenesis and metastasis. Specific E2Fs also have prognostic value in breast cancer, independent of clinical parameters. We discuss here recent advances in understanding of the RB-E2F pathway in breast cancer. We also discuss the application of genome-wide genetic screening efforts to gain insight into synthetic lethal interactions of CDK4/6 inhibitors in breast cancer for the development of more effective combination therapies.

Relationship between prolactin, breast cancer risk, and antipsychotics in patients with schizophrenia: a critical review

OBJECTIVE

A recent meta-analysis showed that breast cancer probably is more common in female patients with schizophrenia than in the general population (effect size = 1.25, P < 0.05). Increasing experimental and epidemiological data have alerted researchers to the influence of prolactin (PRL) in mammary carcinogenesis. We therefore investigated the possible relationship between antipsychotic-induced hyperprolactinemia (HPRL) and breast cancer risk in female patients with schizophrenia.

METHOD

A literature search (1950 until January 2015), using the MEDLINE database, was conducted for English-language published clinical trials to identify and synthesize data of the current state of knowledge concerning breast cancer risk (factors) in women with schizophrenia and its (their) relationship between HPRL and antipsychotic medication.

RESULTS

Although an increasing body of evidence supports the involvement of PRL in breast carcinogenesis, results of human prospective studies are limited, equivocal, and correlative (with risk ratios ranging from 0.70 to 1.9 for premenopausal women and from 0.76 to 2.03 for postmenopausal women). Moreover, these studies equally do not take into account the local production of PRL in breast epithelium, although amplification or overexpression of the local autocrine/paracrine PRL loop may be a more important mechanism in tumorigenesis. Until now, there is also no conclusive evidence that antipsychotic medication can increase the risk of breast malignancy and mortality.

CONCLUSION

Other breast risk factors than PRL, such as nulliparity, obesity, diabetes mellitus, and unhealthy lifestyle behaviours (alcohol dependence, smoking, low physical activity), probably are of greater relevance in individual breast cancer cases within the population of female patients with schizophrenia.

Advances in identification and validation of protein targets of natural products without chemical modification

This review focuses on and reports case studies of the latest advances in target protein identification methods for label-free natural products. The integration of newly developed technologies will provide new insights and highlight the value of natural products for use as biological probes and new drug candidates.

Gene-Set Local Hierarchical Clustering (GSLHC)--A Gene Set-Based Approach for Characterizing Bioactive Compounds in Terms of Biological Functional Groups

Gene-set-based analysis (GSA), which uses the relative importance of functional gene-sets, or molecular signatures, as units for analysis of genome-wide gene expression data, has exhibited major advantages with respect to greater accuracy, robustness, and biological relevance, over individual gene analysis (IGA), which uses log-ratios of individual genes for analysis. Yet IGA remains the dominant mode of analysis of gene expression data. The Connectivity Map (CMap), an extensive database on genomic profiles of effects of drugs and small molecules and widely used for studies related to repurposed drug discovery, has been mostly employed in IGA mode. Here, we constructed a GSA-based version of CMap, Gene-Set Connectivity Map (GSCMap), in which all the genomic profiles in CMap are converted, using gene-sets from the Molecular Signatures Database, to functional profiles. We showed that GSCMap essentially eliminated cell-type dependence, a weakness of CMap in IGA mode, and yielded significantly better performance on sample clustering and drug-target association. As a first application of GSCMap we constructed the platform Gene-Set Local Hierarchical Clustering (GSLHC) for discovering insights on coordinated actions of biological functions and facilitating classification of heterogeneous subtypes on drug-driven responses. GSLHC was shown to tightly clustered drugs of known similar properties. We used GSLHC to identify the therapeutic properties and putative targets of 18 compounds of previously unknown characteristics listed in CMap, eight of which suggest anti-cancer activities. The GSLHC website http://cloudr.ncu.edu.tw/gslhc/ contains 1,857 local hierarchical clusters accessible by querying 555 of the 1,309 drugs and small molecules listed in CMap. We expect GSCMap and GSLHC to be widely useful in providing new insights in the biological effect of bioactive compounds, in drug repurposing, and in function-based classification of complex diseases.

Monocyte-macrophage differentiation of acute myeloid leukemia cell lines by small molecules identified through interrogation of the Connectivity Map database

The transcription factor C/EBPα is required for granulocytic differentiation of normal myeloid progenitors and is frequently inactivated in acute myeloid leukemia (AML) cells. Ectopic expression of C/EBPα in AML cells suppresses proliferation and induces differentiation suggesting that restoring C/EBPα expression/activity in AML cells could be therapeutically useful. Unfortunately, current approaches of gene or protein delivery in leukemic cells are unsatisfactory. However, "drug repurposing" is becoming a very attractive strategy to identify potential new uses for existing drugs. In this study, we assessed the biological effects of candidate C/EBPα-mimetics identified by interrogation of the Connectivity Map database. We found that amantadine, an antiviral and anti-Parkinson agent, induced a monocyte-macrophage-like differentiation of HL60, U937, Kasumi-1 myeloid leukemia cell lines, as indicated by morphology and differentiation antigen expression, when used in combination with suboptimal concentration of all trans retinoic acid (ATRA) or Vit D3. The effect of amantadine depends, in part, on increased activity of the vitamin D receptor (VDR), since it induced VDR expression and amantadine-dependent monocyte-macrophage differentiation of HL60 cells was blocked by expression of dominant-negative VDR. These results reveal a new function for amantadine and support the concept that screening of the Connectivity Map database can identify small molecules that mimic the effect of transcription factors required for myelo-monocytic differentiation.

Effects of antipsychotics, antidepressants and mood stabilizers on risk for physical diseases in people with schizophrenia, depression and bipolar disorder

People with severe mental illness have a considerably shorter lifespan than the general population. This excess mortality is mainly due to physical illness. Next to mental illness-related factors, unhealthy lifestyle, and disparities in health care access and utilization, psychotropic medications can contribute to the risk of physical morbidity and mortality. We systematically reviewed the effects of antipsychotics, antidepressants and mood stabilizers on physical health outcomes in people with schizophrenia, depression and bipolar disorder. Updating and expanding our prior systematic review published in this journal, we searched MEDLINE (November 2009 - November 2014), combining the MeSH terms of major physical disease categories (and/or relevant diseases within these categories) with schizophrenia, major depressive disorder and bipolar disorder, and the three major psychotropic classes which received regulatory approval for these disorders, i.e., antipsychotics, antidepressants and mood stabilizers. We gave precedence to results from (systematic) reviews and meta-analyses wherever possible. Antipsychotics, and to a more restricted degree antidepressants and mood stabilizers, are associated with an increased risk for several physical diseases, including obesity, dyslipidemia, diabetes mellitus, thyroid disorders, hyponatremia; cardiovascular, respiratory tract, gastrointestinal, haematological, musculoskeletal and renal diseases, as well as movement and seizure disorders. Higher dosages, polypharmacy, and treatment of vulnerable (e.g., old or young) individuals are associated with greater absolute (elderly) and relative (youth) risk for most of these physical diseases. To what degree medication-specific and patient-specific risk factors interact, and how adverse outcomes can be minimized, allowing patients to derive maximum benefits from these medications, requires adequate clinical attention and further research.

Review of current methods, applications, and data management for the bioinformatics analysis of whole exome sequencing

The advent of next-generation sequencing technologies has greatly promoted advances in the study of human diseases at the genomic, transcriptomic, and epigenetic levels. Exome sequencing, where the coding region of the genome is captured and sequenced at a deep level, has proven to be a cost-effective method to detect disease-causing variants and discover gene targets. In this review, we outline the general framework of whole exome sequence data analysis. We focus on established bioinformatics tools and applications that support five analytical steps: raw data quality assessment, pre-processing, alignment, post-processing, and variant analysis (detection, annotation, and prioritization). We evaluate the performance of open-source alignment programs and variant calling tools using simulated and benchmark datasets, and highlight the challenges posed by the lack of concordance among variant detection tools. Based on these results, we recommend adopting multiple tools and resources to reduce false positives and increase the sensitivity of variant calling. In addition, we briefly discuss the current status and solutions for big data management, analysis, and summarization in the field of bioinformatics.

Functional Module Connectivity Map (FMCM): a framework for searching repurposed drug compounds for systems treatment of cancer and an application to colorectal adenocarcinoma

Drug repurposing has become an increasingly attractive approach to drug development owing to the ever-growing cost of new drug discovery and frequent withdrawal of successful drugs caused by side effect issues. Here, we devised Functional Module Connectivity Map (FMCM) for the discovery of repurposed drug compounds for systems treatment of complex diseases, and applied it to colorectal adenocarcinoma. FMCM used multiple functional gene modules to query the Connectivity Map (CMap). The functional modules were built around hub genes identified, through a gene selection by trend-of-disease-progression (GSToP) procedure, from condition-specific gene-gene interaction networks constructed from sets of cohort gene expression microarrays. The candidate drug compounds were restricted to drugs exhibiting predicted minimal intracellular harmful side effects. We tested FMCM against the common practice of selecting drugs using a genomic signature represented by a single set of individual genes to query CMap (IGCM), and found FMCM to have higher robustness, accuracy, specificity, and reproducibility in identifying known anti-cancer agents. Among the 46 drug candidates selected by FMCM for colorectal adenocarcinoma treatment, 65% had literature support for association with anti-cancer activities, and 60% of the drugs predicted to have harmful effects on cancer had been reported to be associated with carcinogens/immune suppressors. Compounds were formed from the selected drug candidates where in each compound the component drugs collectively were beneficial to all the functional modules while no single component drug was harmful to any of the modules. In cell viability tests, we identified four candidate drugs: GW-8510, etacrynic acid, ginkgolide A, and 6-azathymine, as having high inhibitory activities against cancer cells. Through microarray experiments we confirmed the novel functional links predicted for three candidate drugs: phenoxybenzamine (broad effects), GW-8510 (cell cycle), and imipenem (immune system). We believe FMCM can be usefully applied to repurposed drug discovery for systems treatment of other types of cancer and other complex diseases.

AKT-induced tamoxifen resistance is overturned by RRM2 inhibition

Acquired tamoxifen resistance develops in the majority of hormone-responsive breast cancers and frequently involves overexpression of the PI3K/AKT axis. Here, breast cancer cells with elevated endogenous AKT or overexpression of activated AKT exhibited tamoxifen-stimulated cell proliferation and enhanced cell motility. To gain mechanistic insight on AKT-induced endocrine resistance, gene expression profiling was performed to determine the transcripts that are differentially expressed post-tamoxifen therapy under conditions of AKT overexpression. Consistent with the biologic outcome, many of these transcripts function in cell proliferation and cell motility networks and were quantitatively validated in a larger panel of breast cancer cells. Moreover, ribonucleotide reductase M2 (RRM2) was revealed as a key contributor to AKT-induced tamoxifen resistance. Inhibition of RRM2 by RNA interference (RNAi)-mediated approaches significantly reversed the tamoxifen-resistant cell growth, inhibited cell motility, and activated DNA damage and proapoptotic pathways. In addition, treatment of tamoxifen-resistant breast cancer cells with the small molecule RRM inhibitor didox significantly reduced in vitro and in vivo growth. Thus, AKT-expressing breast cancer cells upregulate RRM2 expression, leading to increased DNA repair and protection from tamoxifen-induced apoptosis.

IMPLICATIONS

These findings identify RRM2 as an AKT-regulated gene, which plays a role in tamoxifen resistance and may prove to be a novel target for effective diagnostic and preventative strategies.

BRCA-Monet: a breast cancer specific drug treatment mode-of-action network for treatment effective prediction using large scale microarray database

BACKGROUND

Connectivity map (cMap) is a recent developed dataset and algorithm for uncovering and understanding the treatment effect of small molecules on different cancer cell lines. It is widely used but there are still remaining challenges for accurate predictions.

METHOD

Here, we propose BRCA-MoNet, a network of drug mode of action (MoA) specific to breast cancer, which is constructed based on the cMap dataset. A drug signature selection algorithm fitting the characteristic of cMap data, a quality control scheme as well as a novel query algorithm based on BRCA-MoNet are developed for more effective prediction of drug effects.

RESULT

BRCA-MoNet was applied to three independent data sets obtained from the GEO database: Estrodial treated MCF7 cell line, BMS-754807 treated MCF7 cell line, and a breast cancer patient microarray dataset. In the first case, BRCA-MoNet could identify drug MoAs likely to share same and reverse treatment effect. In the second case, the result demonstrated the potential of BRCA-MoNet to reposition drugs and predict treatment effects for drugs not in cMap data. In the third case, a possible procedure of personalized drug selection is showcased.

CONCLUSIONS

The results clearly demonstrated that the proposed BRCA-MoNet approach can provide increased prediction power to cMap and thus will be useful for identification of new therapeutic candidates.

Bioinformatics and Gene Network Analyses of the Swine Mammary Gland Transcriptome during Late Gestation

We used the newly-developed Dynamic Impact Approach (DIA) and gene network analysis to study the sow mammary transcriptome at 80, 100, and 110 days of pregnancy. A swine oligoarray with 13,290 inserts was used for transcriptome profiling. An ANOVA with false discovery rate (FDR < 0.15) correction resulted in 1,409 genes with a significant time effect across time comparisons. The DIA uncovered that Fatty acid biosynthesis, Interleukin-4 receptor binding, Galactose metabolism, and mTOR signaling were among the most-impacted pathways. IL-4 receptor binding, ABC transporters, cytokine-cytokine receptor interaction, and Jak-STAT signaling were markedly activated at 110 days compared with 80 and 100 days. Epigenetic and transcription factor regulatory mechanisms appear important in coordinating the final stages of mammary development during pregnancy. Network analysis revealed a crucial role for TP53, ARNT2, E2F4, and PPARG. The bioinformatics analyses revealed a number of pathways and functions that perform an irreplaceable role during late gestation to farrowing.

Luteolin sensitises drug-resistant human breast cancer cells to tamoxifen via the inhibition of cyclin E2 expression

Luteolin is a flavonoid that has been identified in many plant tissues and exhibits chemopreventive or chemosensitising properties against human breast cancer. However, the oncogenic molecules in human breast cancer cells that are inhibited by luteolin treatment have not been identified. This study found that the level of cyclin E2 (CCNE2) mRNA was higher in tumour cells (4.89-fold, (∗)P=0.005) than in normal paired tissue samples as assessed using real-time reverse-transcriptase polymerase chain reaction (RT-PCR) analysis (n=257). Further, relatively high levels of CCNE2 protein expression were detected in tamoxifen-resistant (TAM-R) MCF-7 cells. These results showed that the level of CCNE2 protein expression was specifically inhibited in luteolin-treated (5μM) TAM-R cells, either in the presence or absence of 4-OH-TAM (100nM). Combined treatment with 4-OH-TAM and luteolin synergistically sensitised the TAM-R cells to 4-OH-TAM. The results of this study suggest that luteolin can be used as a chemosensitiser to target the expression level of CCNE2 and that it could be a novel strategy to overcome TAM resistance in breast cancer patients.

Differential gene expression in tamoxifen-resistant breast cancer cells revealed by a new analytical model of RNA-Seq data

Resistance to tamoxifen (Tam), a widely used antagonist of the estrogen receptor (ER), is a common obstacle to successful breast cancer treatment. While adjuvant therapy with Tam has been shown to significantly decrease the rate of disease recurrence and mortality, recurrent disease occurs in one third of patients treated with Tam within 5 years of therapy. A better understanding of gene expression alterations associated with Tam resistance will facilitate circumventing this problem. Using a next generation sequencing approach and a new bioinformatics model, we compared the transcriptomes of Tam-sensitive and Tam-resistant breast cancer cells for identification of genes involved in the development of Tam resistance. We identified differential expression of 1215 mRNA and 513 small RNA transcripts clustered into ERα functions, cell cycle regulation, transcription/translation, and mitochondrial dysfunction. The extent of alterations found at multiple levels of gene regulation highlights the ability of the Tam-resistant cells to modulate global gene expression. Alterations of small nucleolar RNA, oxidative phosphorylation, and proliferation processes in Tam-resistant cells present areas for diagnostic and therapeutic tool development for combating resistance to this anti-estrogen agent.

Minireview: Inflammation: an instigator of more aggressive estrogen receptor (ER) positive breast cancers

Approximately 75% of breast tumors express the estrogen receptor (ER), and women with these tumors will receive endocrine therapy. Unfortunately, up to 50% of these patients will fail ER-targeted therapies due to either de novo or acquired resistance. ER-positive tumors can be classified based on gene expression profiles into Luminal A- and Luminal B-intrinsic subtypes, with distinctly different responses to endocrine therapy and overall patient outcome. However, the underlying biology causing this tumor heterogeneity has yet to become clear. This review will explore the role of inflammation as a risk factor in breast cancer as well as a player in the development of more aggressive, therapy-resistant ER-positive breast cancers. First, breast cancer risk factors, such as obesity and mammary gland involution after pregnancy, which can foster an inflammatory microenvironment within the breast, will be described. Second, inflammatory components of the tumor microenvironment, including tumor-associated macrophages and proinflammatory cytokines, which can act on nearby breast cancer cells and modulate tumor phenotype, will be explored. Finally, activation of the nuclear factor κB (NF-κB) pathway and its cross talk with ER in the regulation of key genes in the promotion of more aggressive breast cancers will be reviewed. From these multiple lines of evidence, we propose that inflammation may promote more aggressive ER-positive tumors and that combination therapy targeting both inflammation and estrogen production or actions could benefit a significant portion of women whose ER-positive breast tumors fail to respond to endocrine therapy.