The European Network for Breast Development and Cancer (ENBDC) Workshop on ‘Methods in Mammary Gland Development and Cancer’ has grown into the essential, international technical discussion forum for scientists with interests in the normal and neoplastic breast. The fifth ENBDC meeting was held in Weggis, Switzerland in April, 2013, and focussed on emerging, state-of-the-art techniques for the study of non-coding RNA, lineage tracing, tumor heterogeneity, metastasis and metabolism.
Both genome-wide association studies and candidate gene studies have reported that the major determinant of plasma levels of the Lipoprotein (a) [Lp(a)] reside within the LPA locus on chromosome 6. We have used data from the Human CVD bead chip to explore the contribution of other candidate genes determining Lp(a) levels.
48,032 single nucleotide polymorphisms (SNPs) from the Illumina Human CVD bead chip were genotyped in 5,059 participants of the Whitehall II study (WHII) of randomly ascertained healthy men and women. SNPs showing association with Lp(a) levels of p< 10−4 outside the LPA locus were selected for replication in a total of an additional 9,463 participants of five European based studies (EAS, EPIC-Norfolk, NPHSII, PROCARDIS, and SAPHIR)
In Whitehall II, apart from the LPA locus (where p values for several SNPs were < 10−30) there was significant association at four loci GALNT2, FABP1, PPARGC1A and TNFRSFF11A. However, a meta-analysis of the six studies did not confirm any of these findings.
Results from this meta analysis of 14,522 participants revealed no candidate genes from the Human CVD bead chip outside the LPA locus to have an effect on Lp(a) levels. Further studies with genome-wide and denser SNP coverage are required to confirm or refute this finding.
Lipoprotein(a); LPA; Illumina Human CVD bead chip; genetic association
In estrogen receptor positive (ER+) breast cancer cells, BCL2 overexpression contributes to antiestrogen resistance. Direct targeting of the antiapoptotic BCL2 members with GX15-070 (GX; obatoclax), a BH3-mimetic currently in clinical development, is an attractive strategy to overcome antiestrogen resistance in some breast cancers. Recently, GX has been shown to induce both apoptosis and autophagy, yet the underlying cell death mechanisms have yet to be elucidated. Here we show that GX is more effective in reducing the cell density of antiestrogen resistant breast cancer cells versus sensitive cells, and that this increased sensitivity of resistant cells to GX correlates with an accumulation of autophagic vacuoles. Formation of autophagosomes in GX-treated cells was verified by changes in expression of the lipidation of microtubule-associated protein-1 light chain-3 and both confocal and transmission electron microscopy. While GX treatment promotes autophagic vacuole and autolysosome formation, p62/SQSTM1, a marker for autophagic degradation, levels accumulate. Moreover, GX exposure leads to a reduction in cathepsin D and L protein expression that would otherwise digest autolysosome cargo. Thus, GX has dual roles in promoting cell death: (1) directly inhibiting antiapoptotic BCL2 family members, thereby inducing apoptosis; and (2) inhibiting downstream cathepsins D and L protein expression to limit the ability of cells to use degraded material to fuel cellular metabolism and restore homeostasis. Our data highlight a new mechanism of GX-induced cell death that could be used to design novel therapeutic interventions for antiestrogen resistant breast cancer.
GX15-070; antiestrogen; BCL2 proteins; autophagy; cathepsin
Autophagy (macroautophagy), a cellular process of “self-eating”, segregates damaged/aged organelles into vesicles, fuses with lysosomes, and enables recycling of the digested materials. The precise origin(s) of the autophagosome membrane is unclear and remains a critical but unanswered question. Endoplasmic reticulum, mitochondria, Golgi complex, and the plasma membrane have been proposed as the source of autophagosomal membranes.
Using electron microscopy, immunogold labeling techniques, confocal microscopy, and flow cytometry we show that mitochondria can directly donate their membrane material to form autophagosomes. We expand upon earlier studies to show that mitochondria donate their membranes to form autophagosomes during basal and drug-induced autophagy. Moreover, electron microscopy and immunogold labeling studies show the first physical evidence of mitochondria forming continuous structures with LC3-labeled autophagosomes. The mitochondria forming these structures also stain positive for parkin, indicating that these mitochondrial-formed autophagosomes represent a novel mechanism of parkin-associated mitophagy.
With the on-going debate regarding autophagosomal membrane origin, this report demonstrates that mitochondria can donate membrane materials to form autophagosomes. These structures may also represent a novel form of mitophagy where the mitochondria contribute to the formation of autophagosomes. This novel form of parkin-associated mitophagy may be a more efficient bio-energetic process compared with de novo biosynthesis of a new membrane, particularly if the membrane is obtained, at least partly, from the organelle being targeted for later degradation in the mature autolysosome.
Breast cancer; Mitochondria; Autophagy; Mitophagy; Parkin; Antiestrogen resistance; Fulvestrant; Imatinib; Estrogen receptor-α
Breast cancer stem-like cells (CSCs) are an important therapeutic target as they are purported to be responsible for tumor initiation, maintenance, metastases, and disease recurrence. Interleukin-8 (IL-8) is upregulated in breast cancer compared with normal breast tissue and is associated with poor prognosis. IL-8 is reported to promote breast cancer progression by increasing cell invasion, angiogenesis, and metastases and is upregulated in HER2-positive cancers. Recently, we and others have established that IL-8 via its cognate receptors, CXCR1 and CXCR2, is also involved in regulating breast CSC activity. Our work demonstrates that in metastatic breast CSCs, CXCR1/2 signals via transactivation of HER2. Given the importance of HER2 in breast cancer and in regulating CSC activity, a pathway driving the activation of these receptors would have important biological and clinical consequences, especially in tumors that express high levels of IL-8 and other CXCR1/2-activating ligands. Here, we review the IL-8 signaling pathway and the role of HER2 in maintaining an IL-8 inflammatory loop and discuss the potential of combining CXCR1/2 inhibitors with other treatments such as HER2-targeted therapy as a novel approach to eliminate CSCs and improve patient survival.
Reliable inference of transcription regulatory networks is still a challenging task in the field of computational biology. Network component analysis (NCA) has become a powerful scheme to uncover the networks behind complex biological processes, especially when gene expression data is integrated with binding motif information. However, the performance of NCA is impaired by the high rate of false connections in binding motif information and the high level of noise in gene expression data. Moreover, in real applications such as cancer research, the performance of NCA in simultaneously analyzing multiple candidate transcription factors (TFs) is further limited by the small sample number of gene expression data. In this paper, we propose a novel scheme, stability-based NCA, to overcome the above-mentioned problems by addressing the inconsistency between gene expression data and motif binding information (i.e., prior network knowledge). This method introduces small perturbations on prior network knowledge and utilizes the variation of estimated TF activities to reflect the stability of TF activities. Such a scheme is less limited by the sample size and especially capable to identify condition-specific TFs and their target genes. Experiment results on both simulation data and real breast cancer data demonstrate the efficiency and robustness of the proposed method.
transcription regulatory network; network component analysis; stability analysis; transcription factor activity; target genes identification
End-stage coagulation and the structure/function of fibrin are implicated in the pathogenesis of ischaemic stroke. We explored whether genetic variants associated with end-stage coagulation in healthy volunteers account for the genetic predisposition to ischemic stroke and examined their influence on stroke subtype.
Common genetic variants identified through genome-wide association studies of coagulation factors and fibrin structure/function in healthy twins (n=2,100 Stage 1) were examined in ischemic stroke (n=4,200 cases) using 2 independent samples of European ancestry (Stage 2). A third clinical collection having stroke subtyping (total 8,900 cases 55,000 controls) was used for replication (Stage 3).
Stage 1 identified 524 SNPs from 23 LD blocks having significant association (p<5 ×10-8) with one or more coagulation/fibrin phenotypes. Most striking associations included SNP rs5985 with factor XIII activity (p=2.6×10-186), rs10665 with FVII (p = 2.4×10-47) and rs505922 in the ABO gene with both von Willebrand Factor (vWF p=4.7×10-57) and factor VIII (p=1.2×10-36). In Stage 2, the 23 independent SNPs were examined in stroke cases/non-cases using MORGAM and WTCCC2 collections. SNP rs505922 was nominally associated with ischaemic stroke, odds ratio = 0.94 (95% confidence intervals, 0.88-0.99), p=0.023. Independent replication in Meta-Stroke confirmed the rs505922 association with stroke, beta=0.066 (0.02) p = 0.001, a finding specific to large vessel and cardioembolic stroke (p = 0.001 and p = <0.001 respectively) but not seen with small vessel stroke (p=0.811).
ABO gene variants are associated with large vessel and cardioembolic stroke but not small vessel disease. This work sheds light on the different pathogenic mechanisms underpinning stroke subtype.
GWAS; thrombosis; stroke; coagulation factor; stroke subtype
The reliability and reproducibility of gene biomarkers for classification of cancer patients has been challenged due to measurement noise and biological heterogeneity among patients. In this paper, we propose a novel module-based feature selection framework, which integrates biological network information and gene expression data to identify biomarkers not as individual genes but as functional modules. Results from four breast cancer studies demonstrate that the identified module biomarkers i) achieve higher classification accuracy in independent validation datasets; ii) are more reproducible than individual gene markers; iii) improve the biological interpretability of results; and iv) are enriched in cancer “disease drivers”.
Cancer biomarkers; systems biology; feature selection; disease classification
The objective was to evaluate the hypothesis that growth-differentiation factor 15 (GDF-15) is an independent marker of the long-term risk for both cardiovascular disease and cancer morbidity beyond clinical and biochemical risk factors. Plasma obtained at age 71 was available from 940 subjects in the Uppsala Longitudinal Study of Adult Men (ULSAM) cohort. Complete mortality and morbidity data were obtained from public registries. At baseline there were independent associations between GDF-15 and current smoking, diabetes mellitus, biomarkers of cardiac (high-sensitivity troponin-T, NT-proBNP) and renal dysfunction (cystatin-C) and inflammatory activity (C-reactive protein), and previous cardiovascular disease (CVD). During 10 years follow-up there occurred 265 and 131 deaths, 115 and 46 cardiovascular deaths, and 185 and 86 events with coronary heart disease mortality or morbidity in the respective total cohort (n=940) and non-CVD (n=561) cohort. After adjustment for conventional cardiovascular risk factors, one SD increase in log GDF-15 were, in the respective total and non-CVD populations, associated with 48% (95%CI 26 to 73%, p<0.001) and 67% (95%CI 28 to 217%, p<0.001) incremental risk of cardiovascular mortality, 48% (95%CI 33 to 67%, p<0.001) and 61% (95%CI 38 to 89%, p<0.001) of total mortality and 36% (95%CI 19 to 56%, p<0.001) and 44% (95%CI 17 to 76%, p<0.001) of coronary heart disease morbidity and mortality. The corresponding incremental increase for cancer mortality in the respective total and non-cancer disease (n=882) population was 46% (95%CI 21 to 77%, p<0.001) and 38% (95%CI 12 to 70%, p<0.001) and for cancer morbidity and mortality in patients without previous cancer disease 30% (95%CI 12 to 51%, p<0.001). In conclusion, in elderly men, GDF-15 improves prognostication of both cardiovascular, cancer mortality and morbidity beyond established risk factors and biomarkers of cardiac, renal dysfunction and inflammation.
Breast cancer is one of the most prevalent cancers in women, with more than 240,000 new cases reported in the United States in 2011. Classification of breast cancer based upon hormone and growth factor receptor profiling shows that approximately 70% of all breast cancers express estrogen receptor-α. Thus, drugs that either block estrogen biosynthesis (aromatase inhibitors like Letrozole), or compete with estrogen for estrogen receptor (ER) binding (selective ER modulators including tamoxifen; TAM) and/or cause ER degradation (selective estrogen receptor downregulators such as fulvestrant), are among the most prescribed targeted therapeutics for breast cancer. However, overall clinical benefit from the use of these drugs is often limited by resistance; ER+ breast cancers either fail to respond to endocrine therapies initially (de novo resistance), or they respond and then lose sensitivity over time (acquired resistance). While several preclinical studies postulate how antiestrogen resistance occurs, for the most part, the molecular mechanism(s) of resistance is unknown.
breast cancer; antiestrogen resistance; glucose-regulated protein 78; unfolded protein response; autophagy; AMP-activated protein kinase; MTOR
Understanding the molecular changes that drive an acquired antiestrogen resistance phenotype is of major clinical relevance. Previous methodologies for addressing this question have taken a single gene/pathway approach and the resulting gains have been limited in terms of their clinical impact. Recent systems biology approaches allow for the integration of data from high throughput “-omics” technologies. We highlight recent advances in the field of antiestrogen resistance with a focus on transcriptomics, proteomics and methylomics.
Systems biology; breast cancer; estrogens; antiestrogens
Telomerase is crucial for the maintenance of stem/progenitor cells in adult tissues and is detected in most malignant cancers, including osteosarcoma. However, the relationship between telomerase expression and cancer stem cells remains unknown. We observed that sphere-derived osteosarcoma cells had higher telomerase activity, indicating that telomerase activity might be enriched in osteosarcoma stem cells. We sorted subpopulations with high or low telomerase activity (TEL) using hTERT transcriptional promoter-induced green fluorescent protein (GFP). The TELpos cells showed an increased sphere and tumor propagating capacity compared to TELneg cells, and enhanced stem cell-like properties such as invasiveness, metastatic activity and resistance to chemotherapeutic agents both in vitro and in vivo. Furthermore, the telomerase inhibitor MST312 prevented tumorigenic potential both in vitro and in vivo, preferentially targeting the TELpos cells. These data support telomerase inhibition as a potential targeted therapy for osteosarcoma stem-like cells.
Osteosarcoma; Heterogeneity; Cancer stem cells; Telomerase; Metastasis; Drug resistance
Some countries fortify flour with folic acid to prevent neural tube defects but others do not, partly because of concerns about cancer risks. We aimed to assess the effects of folic acid supplementation on site-specific cancer rates in the randomised trials.
Meta-analyses of data on each individual in all placebo-controlled trials of folic acid for prevention of cardiovascular disease (10 trials, n=46,969) or colorectal adenoma (3 trials, n=2652) that recorded cancer incidence and recruited >500 participants. All trials were evenly randomised. Risk ratios (RRs) compare those allocated folic acid vs those allocated placebo, giving cancer incidence rate ratios (among those still free of cancer) during, but not after the scheduled treatment period.
During a weighted mean follow-up duration of 5.5 years, allocation to folic acid quadrupled plasma folate, but had no statistically significant effect on overall cancer incidence (1904 vs 1809 cancers, RR=1.06 [95%CI 0.99–1.13], p=0.10; trend with duration of treatment p=0.46). There was no significant heterogeneity between the results of individual trials (p=0.23), or between the cadiovascular prevention trials and the adenoma prevention trials (p=0.13). Moreover, there was no significant effect of folic acid supplementation on the incidence of cancer of the large intestine, prostate, lung, breast or any other specific site.
Folic acid supplementation does not substantially increase or decrease site-specific cancer incidence during the first 5 years of treatment.
British Heart Foundation, Medical Research Council, Cancer Research UK, Food Standards Agency.
E-selectin (SELE) mediates the rolling and adhesion of leukocytes on activated endothelial cells and plays a critial role in the pathogenesis of coronary artery disease (CAD). Associatons between the A561C and G98T polymorphisms of the SELE gene and CAD risk were investigated broadly, but the results were inconsistent. In the present study, we performed a meta-analysis to systematically evaluate the associations between the two polymorphisms and the risk of CAD.
Comprehensive research was conducted to identify relevant studies. The fixed or random effect model was selected based on the heterogeneity among studies, which was evaluated with Q-test and Ι2. Meta-regression was used to explore the potential sources of between-study heterogeneity. Peters's linear regression test was used to estimate the publication bias.
Overall, 24 articles involving 3694 cases and 3469 controls were included. After excluding articles deviating from Hardy–Weinberg equilibrium in controls and sensitive analysis, our meta-analysis showed a significant association between the A561C ploymprphism and CAD in dominant (OR = 1.84, 95% CI = 1.56–2.16) and codominant (OR = 1.74, 95% CI = 1.49–2.03) models. As for the G98T polymorphism, significantly increased CAD risk was observed in dominant (OR = 1.47, 95% CI = 1.16–1.87) and codominant (OR = 1.48, 95% CI = 1.18–1.86) models, but after subgroup analysis, the association was not significant among Caucasians in dominant (OR = 1.58, 95% CI = 0.73–3.41) and codominant (OR = 1.58, 95% CI = 0.79–3.20) models.
Despite some limitations, our meta-analysis suggested that the SELE gene polymorphisms (A561C, G98T) were significantly associated with increased risk of CAD. However, after subgroup analysis no significant association was found among Caucasians for the G98T polymorphism, which may be due to the small sample size and other confounding factors. Future investigations with multicenter, large-scale, and multi-ethnic groups are needed.
The prognostic importance of B-type natriuretic peptide (BNP) or N-terminal pro BNP (NT-proBNP) in patients with end-stage renal disease (ESRD) remains controversial.
We conducted an unrestricted search from the MEDLINE and EMBASE in all languages that were published between 1966 and Augest2013. Twenty-seven long-term prospective studies met our inclusion criterias. From the pooled analysis, elevated BNP/NT-proBNP was significantly associated with increased all cause mortality [odds ratio (OR), 3.85; 95% CI, 3.11 to 4.75], cardiovascular mortality (OR, 4.05; 95% CI, 2.53 to 6.84), and cardiovascular events (OR, 7.02; 95% CI, 2.21 to 22.33). The funnel plot showed no evidence of publication bias. The corresponding pooled positive and negative likelihood ratio for prediction of all cause mortality were 1.86 (95% CI, 1.66 to 2.08) and 0.48 (95% CI, 0.42 to 0.55), respectively.
BNP/NT-proBNP is a promising prognostic tool to risk-stratify the patients with ESRD. Further investigations are warranted to elucidate the specific pathogenic mechanisms and the impact of other potential prognostic factors.
Background The extent to which adult height, a biomarker of the interplay of genetic endowment and early-life experiences, is related to risk of chronic diseases in adulthood is uncertain.
Methods We calculated hazard ratios (HRs) for height, assessed in increments of 6.5 cm, using individual–participant data on 174 374 deaths or major non-fatal vascular outcomes recorded among 1 085 949 people in 121 prospective studies.
Results For people born between 1900 and 1960, mean adult height increased 0.5–1 cm with each successive decade of birth. After adjustment for age, sex, smoking and year of birth, HRs per 6.5 cm greater height were 0.97 (95% confidence interval: 0.96–0.99) for death from any cause, 0.94 (0.93–0.96) for death from vascular causes, 1.04 (1.03–1.06) for death from cancer and 0.92 (0.90–0.94) for death from other causes. Height was negatively associated with death from coronary disease, stroke subtypes, heart failure, stomach and oral cancers, chronic obstructive pulmonary disease, mental disorders, liver disease and external causes. In contrast, height was positively associated with death from ruptured aortic aneurysm, pulmonary embolism, melanoma and cancers of the pancreas, endocrine and nervous systems, ovary, breast, prostate, colorectum, blood and lung. HRs per 6.5 cm greater height ranged from 1.26 (1.12–1.42) for risk of melanoma death to 0.84 (0.80–0.89) for risk of death from chronic obstructive pulmonary disease. HRs were not appreciably altered after further adjustment for adiposity, blood pressure, lipids, inflammation biomarkers, diabetes mellitus, alcohol consumption or socio-economic indicators.
Conclusion Adult height has directionally opposing relationships with risk of death from several different major causes of chronic diseases.
Height; cardiovascular disease; cancer; cause-specific mortality; epidemiological study; meta-analysis
Breast cancer remains a significant scientific, clinical and societal challenge. This gap analysis has reviewed and critically assessed enduring issues and new challenges emerging from recent research, and proposes strategies for translating solutions into practice.
More than 100 internationally recognised specialist breast cancer scientists, clinicians and healthcare professionals collaborated to address nine thematic areas: genetics, epigenetics and epidemiology; molecular pathology and cell biology; hormonal influences and endocrine therapy; imaging, detection and screening; current/novel therapies and biomarkers; drug resistance; metastasis, angiogenesis, circulating tumour cells, cancer ‘stem’ cells; risk and prevention; living with and managing breast cancer and its treatment. The groups developed summary papers through an iterative process which, following further appraisal from experts and patients, were melded into this summary account.
The 10 major gaps identified were: (1) understanding the functions and contextual interactions of genetic and epigenetic changes in normal breast development and during malignant transformation; (2) how to implement sustainable lifestyle changes (diet, exercise and weight) and chemopreventive strategies; (3) the need for tailored screening approaches including clinically actionable tests; (4) enhancing knowledge of molecular drivers behind breast cancer subtypes, progression and metastasis; (5) understanding the molecular mechanisms of tumour heterogeneity, dormancy, de novo or acquired resistance and how to target key nodes in these dynamic processes; (6) developing validated markers for chemosensitivity and radiosensitivity; (7) understanding the optimal duration, sequencing and rational combinations of treatment for improved personalised therapy; (8) validating multimodality imaging biomarkers for minimally invasive diagnosis and monitoring of responses in primary and metastatic disease; (9) developing interventions and support to improve the survivorship experience; (10) a continuing need for clinical material for translational research derived from normal breast, blood, primary, relapsed, metastatic and drug-resistant cancers with expert bioinformatics support to maximise its utility. The proposed infrastructural enablers include enhanced resources to support clinically relevant in vitro and in vivo tumour models; improved access to appropriate, fully annotated clinical samples; extended biomarker discovery, validation and standardisation; and facilitated cross-discipline working.
With resources to conduct further high-quality targeted research focusing on the gaps identified, increased knowledge translating into improved clinical care should be achievable within five years.
To prospectively explore the association between non-high-density lipoprotein cholesterol (non-HDLC) and the risks of stroke and its subtypes.
A total of 95,916 participants (18-98 years old; 76,354 men and 19,562 women) from a Chinese urban community who were free of myocardial infarction and stroke at baseline time point (2006-2007) were eligible and enrolled in the study. The serum non-HDLC levels of participants were determined by subtracting the high-density lipoprotein cholesterol (HDLC) from total serum cholesterol. The primary outcome was the first occurrence of stroke, which was diagnosed according to the World Health Organization criteria and classified into three subtypes: ischemic stroke, intracerebral hemorrhage, or subarachnoid hemorrhage. The Cox proportional hazards models were used to estimate risk of stroke and its subtypes.
During the four-year follow-up, we identified 1614 stroke events (1,156 ischemic, 416 intracerebral hemorrhagic and 42 subarachnoid hemorrhagic). Statistical analyses showed that hazard ratios (HR) (95% Confidence Interval: CI) of serum Non-HDLC level for total and subtypes of stroke were: 1.08 (1.03-1.12) (total), 1.10 (1.05-1.16) (ischemic), 1.03 (0.96-1.10) (intracerebral hemorrhage) and 0.83 (0.66-1.05) (subarachnoid hemorrhage). HR for non-HDLC refers to the increase per each 20 mg/dl. For total and ischemic stroke, the risks were significantly higher in the fourth and fifth quintiles of non-HDLC concentrations compared to the first quintile after adjusting the confounding factors (total stroke: 4th quintile HR=1.33 (1.12-1.59); 5th quintile HR = 1.36 (1.15-1.62); ischemic stroke: 4th quintile HR =1.34 (1.09-1.66); 5th quintile HR = 1.53 (1.24-1.88)).
Our data suggest that serum non-HDLC level is an independent risk factor for total and ischemic stroke, and that higher serum non-HDLC concentrations are associated with increased risks for total stroke and ischemic stroke, but not for intracerebral and subarachnoid hemorrhage.
Breast cancer is the most commonly diagnosed cancer among women worldwide. Many women have become more aware of the benefits of increasing fruit consumption, as part of a healthy lifestyle, for the prevention of cancer. The mechanisms by which fruits, including berries, prevent breast cancer can be partially explained by exploring their interactions with pathways known influence cell-proliferation and evasion of cell-death. Two receptor pathways- estrogen receptor (ER) and tyrosine kinase receptors, especially the epidermal growth factor receptor (EGFR) family- are drivers of cell-proliferation and play a significant role in the development of both primary and recurrent breast cancer. There is strong evidence to show that several phytochemicals present in berries such as cyanidin, delphinidin, quercetin, kaempferol, ellagic acid, resveratrol and pterostilbene, interact with and alter the effects of these pathways. Further, they also induce cell death (apoptosis and autophagy) via their influence on kinase signaling. In this review, we summarize in vitro data regarding the interaction of berry polyphenols with the specific receptors and the mechanisms by which they induce cell death. Further, we also present in vivo data of primary breast cancer prevention by individual compounds and whole berries. Finally, we present a possible role for berries and berry compounds in the prevention of breast cancer and our perspective on the areas that require further research.
Berries; Berry Polyphenols; Breast Cancer; Ellagic Acid; Cyanidin; Delphinidin; Quercetin; Kaempherol; Resveratrol; Estrogen Receptor; Epidermal Growth Factor Receptor; Kinase Signaling; Apoptosis; Autophagy; ACI rats
Many computational methods for identification of transcription regulatory modules often result in many false positives in practice due to noise sources of binding information and gene expression profiling data. In this paper, we propose a multi-level strategy for condition-specific gene regulatory module identification by integrating motif binding information and gene expression data through support vector regression and significant analysis. We have demonstrated the feasibility of the proposed method on a yeast cell cycle data set. The study on a breast cancer microarray data set shows that it can successfully identify the significant and reliable regulatory modules associated with breast cancer.
transcription regulatory module; motif enrichment analysis; SVR; support vector regression; statistical significance analysis; multi-level regulator identification
Genome-wide association studies have shown that variance in the fat mass- and obesity- associated gene (FTO) is associated with risk of obesity in Europeans and Asians. Since obesity is associated with an increased risk of cardiovascular disease (CVD), several studies have investigated the association between variant in the FTO gene and CVD risk, with inconsistent results. In this study, we performed a meta-analysis to clarify the association of rs9939609 variant (or its proxies [r2>0.90]) in the FTO gene with CVD risk.
Published literature from PubMed and Embase was retrieved. Pooled odds ratios with 95% confidence intervals were calculated using the fixed- or random- effects model.
A total of 10 studies (comprising 19,153 CVD cases and 103,720 controls) were included in the meta-analysis. The results indicated that the rs9939609 variant was significantly associated with CVD risk (odds ratio = 1.18, 95% confidence interval = 1.07–1.30, p = 0.001 [Z test], I2 = 80.6%, p<0.001 [heterogeneity]), and there was an insignificant change after adjustment for body mass index (BMI) and other conventional CVD risk factors (odds ratio = 1.16, 95% confidence interval = 1.05–1.27, p = 0.003 [Z test], I2 = 75.4%, p<0.001 [heterogeneity]).
The present meta-analysis confirmed the significant association of the rs9939609 variant in the FTO gene with CVD risk, which was independent of BMI and other conventional CVD risk factors.
Lack of understanding of endocrine resistance remains one of the major challenges for breast cancer researchers, clinicians, and patients. Current reductionist approaches to understanding the molecular signaling driving resistance have offered mostly incremental progress over the past 10 years. As the field of systems biology has begun to mature, the approaches and network modeling tools being developed and applied therein offer a different way to think about how molecular signaling and the regulation of critical cellular functions are integrated. To gain novel insights, we first describe some of the key challenges facing network modeling of endocrine resistance, many of which arise from the properties of the data spaces being studied. We then use activation of the unfolded protein response (UPR) following induction of endoplasmic reticulum stress in breast cancer cells by antiestrogens, to illustrate our approaches to computational modeling. Activation of UPR is a key determinant of cell fate decision making and regulation of autophagy and apoptosis. These initial studies provide insight into a small subnetwork topology obtained using differential dependency network analysis and focused on the UPR gene XBP1. The XBP1 subnetwork topology incorporates BCAR3, BCL2, BIK, NFκB, and other genes as nodes; the connecting edges represent the dependency structures amongst these nodes. As data from ongoing cellular and molecular studies become available, we will build detailed mathematical models of this XBP1-UPR network.
Antiestrogen; autophagy; apoptosis; breast cancer; cell signaling; endoplasmic reticulum; estrogens; gene networks; unfolded protein response; computational modeling; mathematical modeling; systems biology
With the advent of high-throughput biotechnology capable of monitoring genomic signals, it becomes increasingly promising to understand molecular cellular mechanisms through systems biology approaches. One of the active research topics in systems biology is to infer gene transcriptional regulatory networks using various genomic data; this inference problem can be formulated as a linear model with latent signals associated with some regulatory proteins called transcription factors (TFs). As common statistical assumptions may not hold for genomic signals, typical latent variable algorithms such as independent component analysis (ICA) are incapable to reveal underlying true regulatory signals. Liao et al.  proposed to perform inference using an approach named network component analysis (NCA), the optimization of which is achieved by a least-squares fitting approach with biological knowledge constraints. However, the incompleteness of biological knowledge and its inconsistency with gene expression data are not considered in the original NCA solution, which could greatly affect the inference accuracy. To overcome these limitations, we propose a linear extraction scheme, namely regulatory component analysis (RCA), to infer underlying regulatory signals even with partial biological knowledge. Numerical simulations show a significant improvement of our proposed RCA over NCA, not only when signal-to-noise-ratio (SNR) is low, but also when the given biological knowledge is incomplete and inconsistent to gene expression data. Furthermore, real biological experiments on E. coli are performed for regulatory network inference in comparison with several typical linear latent variable methods, which again demonstrates the effectiveness and improved performance of the proposed algorithm.
Transcriptional regulatory network inference; Source extraction; Gene expression; Genomic signal processing
Motivation: Identification of transcriptional regulatory networks (TRNs) is of significant importance in computational biology for cancer research, providing a critical building block to unravel disease pathways. However, existing methods for TRN identification suffer from the inclusion of excessive ‘noise’ in microarray data and false-positives in binding data, especially when applied to human tumor-derived cell line studies. More robust methods that can counteract the imperfection of data sources are therefore needed for reliable identification of TRNs in this context.
Results: In this article, we propose to establish a link between the quality of one target gene to represent its regulator and the uncertainty of its expression to represent other target genes. Specifically, an outlier sum statistic was used to measure the aggregated evidence for regulation events between target genes and their corresponding transcription factors. A Gibbs sampling method was then developed to estimate the marginal distribution of the outlier sum statistic, hence, to uncover underlying regulatory relationships. To evaluate the effectiveness of our proposed method, we compared its performance with that of an existing sampling-based method using both simulation data and yeast cell cycle data. The experimental results show that our method consistently outperforms the competing method in different settings of signal-to-noise ratio and network topology, indicating its robustness for biological applications. Finally, we applied our method to breast cancer cell line data and demonstrated its ability to extract biologically meaningful regulatory modules related to estrogen signaling and action in breast cancer.
Availability and implementation: The Gibbs sampler MATLAB package is freely available at http://www.cbil.ece.vt.edu/software.htm.
Supplementary data are available at Bioinformatics online.
The American Cancer Society estimates that over 200,000 new breast cancer cases are diagnosed annually in the USA alone. Of these cases, the majority are invasive breast cancers and almost 70% are estrogen receptor-α positive. Therapies targeting the estrogen receptor-α are widely applied and include selective estrogen receptor modulators such as tamoxifen, a selective estrogen receptor downregulator such as Fulvestrant (Faslodex; FAS, ICI 182,780), or one of the third-generation aromatase inhibitors including letrozole or anastrozole. While these treatments reduce breast cancer mortality, many estrogen receptor-α-positive tumors eventually recur, highlighting the clinical significance of endocrine therapy resistance. The signaling leading to endocrine therapy resistance is poorly understood; however, preclinical studies have established an important role for autophagy in the acquired resistance phenotype. Autophagy is a cellular degradation process initiated in response to stress or nutrient deprivation, which attempts to restore metabolic homeostasis through the catabolic lysis of aggregated proteins, unfolded/misfolded proteins or damaged subcellular organelles. The duality of autophagy, which can be either pro-survival or pro-death, is well known. However, in the context of endocrine therapy resistance in breast cancer, the inhibition of autophagy can potentiate resensitization of previously antiestrogen resistant breast cancer cells. In this article, we discuss the complex and occasionally contradictory roles of autophagy in cancer and in resistance to endocrine therapies in breast cancer.
3-methyladenine; antiestrogen resistance; aromatase inhibitor; autophagy; bafilomycin A1; breast cancer; endoplasmic reticulum stress; fulvestrant; hydroxychloroquine; tamoxifen; unfolded protein response