A genome-wide association scan on estrogen receptor-negative breast cancer

Prediction of the Endocrine disruption profile of fluorinated biphenyls and analogues: An in silico study

Fluorinated biphenyls and their analogues (FBAs) are considered new persistent organic pollutants, but their endocrine-disrupting effects are still unknown. To fill this gap, the binding probability of 44 FBAs to different nuclear hormone receptors (NHRs) was predicted using Endocrine Disruptome. And molecular similarity and network toxicology analysis were used to strengthen the docking screening. The docking results showed that FBAs could have high binding potential for various NHRs, such as estrogen receptors β antagonism (ERβ an), liver X receptors α (LXRα), estrogen receptors α (ERα), and liver X receptors β (LXRβ). The similarity analysis found that the degree of overlap of the NHR repertoire was related to the Tanimoto coefficient of FBAs. Network toxicology verified a part of docking screening results and identified endocrine-disrupting pathways worthy of attention. This study found out potential endocrine-disrupting FBAs and their vulnerable, and developed a workflow that would leverage in silico approaches including molecular docking, similarity, and network toxicology for risk prioritization of potential endocrine-disrupting compounds.

Class Balanced Multifactor Dimensionality Reduction to Detect Gene-Gene Interactions

Detecting gene-gene interactions in single-nucleotide polymorphism data is vital for understanding disease susceptibility. However, existing approaches may be limited by the sample size in case-control studies. Herein, we propose a balance approach for the multifactor dimensionality reduction (BMDR) method to increase the accuracy of estimates of the prediction error rate in small samples. BMDR explicitly selects the best model by evaluating the average of prediction error rates over k-fold cross-validation without cross-validation consistency selection. In this study, we used several epistatic models with and without marginal effects under different parameter settings (heritability and minor allele frequencies) to evaluate the performance of existing approaches. Using simulated data sets, BMDR successfully detected gene-gene interactions, particularly for data sets with small sample sizes. A large data set was obtained from the Wellcome Trust Case Control Consortium, and results indicated that BMDR could effectively detect significant gene-gene interactions.

Application of simulation-based CYP26 SNP-environment barcodes for evaluating the occurrence of oral malignant disorders by odds ratio-based binary particle swarm optimization: A case-control study in the Taiwanese population

Introduction

Genetic polymorphisms and social factors (alcohol consumption, betel quid (BQ) usage, and cigarette consumption), both separately or jointly, play a crucial role in the occurrence of oral malignant disorders such as oral and pharyngeal cancers and oral potentially malignant disorders (OPMD).

Material and methods

Simultaneous analyses of multiple single nucleotide polymorphisms (SNPs) and environmental effects on oral malignant disorders are essential to examine, albeit challenging. Thus, we conducted a case-control study (N = 576) to analyze the risk of occurrence of oral malignant disorders by using binary particle swarm optimization (BPSO) with an odds ratio (OR)-based method.

Results

We demonstrated that a combination of SNPs (CYP26B1 rs887844 and CYP26C1 rs12256889) and socio-demographic factors (age, ethnicity, and BQ chewing), referred to as the combined effects of SNP-environment, correlated with maximal risk diversity of occurrence observed between the oral malignant disorder group and the control group. The risks were more prominent in the oral and pharyngeal cancers group (OR = 10.30; 95% confidence interval (CI) = 4.58–23.15) than in the OPMD group (OR = 5.42; 95% CI = 1.94–15.12).

Conclusions

Simulation-based “SNP-environment barcodes” may be used to predict the risk of occurrence of oral malignant disorders. Applying simulation-based “SNP-environment barcodes” may provide insight into the importance of screening tests in preventing oral and pharyngeal cancers and OPMD.

Evaluation of significant genome-wide association studies risk - SNPs in young breast cancer patients

Purpose

Genome-wide-association studies (GWAS) have identified numerous single nucleotide polymorphisms (SNPs) that are associated with an increased risk of breast cancer. Most of these studies were conducted primarily in postmenopausal breast cancer patients. Therefore, we set out to assess whether or not these breast cancer variants are also associated with an elevated risk of breast cancer in young premenopausal patients.

Methods

In 451 women of European ancestry who had prospectively enrolled in a longitudinal cohort study for women diagnosed with breast cancer at or under age 40, we genotyped 44 SNPs that were previously associated with breast cancer risk. A control group was comprised of 1142 postmenopausal healthy women from the Nurses’ Health Study (NHS). We assessed if the frequencies of the adequately genotyped SNPs differed significantly (p≤0.05) between the cohort of young breast cancer patients and postmenopausal controls, and then we corrected for multiple testing.

Results

Genotyping of the controls or cases was inadequate for comparisons between the groups for seven of the 44 SNPs. 9 of the remaining 37 were associated with breast cancer risk in young women with a p-value <0.05: rs10510102, rs1219648, rs13387042, rs1876206, rs2936870, rs2981579, rs3734805, rs3803662 and rs4973768. The directions of these associations were consistent with those in postmenopausal women. However, after correction for multiple testing (Benjamini Hochberg) none of the results remained statistically significant.

Conclusion

After correction for multiple testing, none of the alleles for postmenopausal breast cancer were clearly associated with risk of premenopausal breast cancer in this relatively small study.

Genetic Epidemiology of Breast Cancer in Latin America

The last 10 years witnessed an acceleration of our understanding of what genetic factors underpin the risk of breast cancer. Rare high- and moderate-penetrance variants such as those in the BRCA genes account for a small proportion of the familial risk of breast cancer. Low-penetrance alleles are expected to underlie the remaining heritability. By now, there are about 180 genetic polymorphisms that are associated with risk, most of them of modest effect. In combination, they can be used to identify women at the lowest or highest ends of the risk spectrum, which might lead to more efficient cancer prevention strategies. Most of these variants were discovered in populations of European descent. As a result, we might be failing to discover additional polymorphisms that could explain risk in other groups. This review highlights breast cancer genetic epidemiology studies conducted in Latin America, and summarizes the information that they provide, with special attention to similarities and differences with studies in other populations. It includes studies of common variants, as well as moderate- and high-penetrance variants. In addition, it addresses the gaps that need to be bridged in order to better understand breast cancer genetic risk in Latin America.

Investigation of altered urinary metabolomic profiles of invasive ductal carcinoma of breast using targeted and untargeted approaches

INTRODUCTION

Invasive ductal carcinoma (IDC) is a type of breast cancer, usually detected in advanced stages due to its asymptomatic nature which ultimately leads to low survival rate. Identification of urinary metabolic adaptations induced by IDC to understand the disease pathophysiology and monitor therapy response would be a helpful approach in clinical settings. Moreover, its non-invasive and cost effective strategy better suited to minimize apprehension among high risk population.

OBJECTIVE

This study aims toward investigating the urinary metabolic alterations of IDC by targeted (LC-MRM/MS) and untargeted (GC-MS) approaches for the better understanding of the disease pathophysiology and monitoring therapy response.

METHODS

Urinary metabolic alterations of IDC subjects (63) and control subjects (63) were explored by targeted (LC-MRM/MS) and untargeted (GC-MS) approaches. IDC specific urinary metabolomics signature was extracted by applying both univariate and multivariate statistical tools.

RESULTS

Statistical analysis identified 39 urinary metabolites with the highest contribution to metabolomic alterations specific to IDC. Out of which, 19 metabolites were identified from targeted LC-MRM/MS analysis, while 20 were identified from the untargeted GC-MS analysis. Receiver operator characteristic (ROC) curve analysis evidenced 6 most discriminatory metabolites from each type of approach that could differentiate between IDC subjects and controls with higher sensitivity and specificity. Furthermore, metabolic pathway analysis depicted several dysregulated pathways in IDC including sugar, amino acid, nucleotide metabolism, TCA cycle etc. CONCLUSIONS: Overall, this study provides valuable inputs regarding altered urinary metabolites which improved our knowledge on urinary metabolomic alterations induced by IDC. Moreover, this study identified several dysregulated metabolic pathways which offer further insight into the disease pathophysiology.

Inference of patient-specific subpathway activities reveals a functional signature associated with the prognosis of patients with breast cancer

Breast cancer is one of the most deadly forms of cancer in women worldwide. Better prediction of breast cancer prognosis is essential for more personalized treatment. In this study, we aimed to infer patient-specific subpathway activities to reveal a functional signature associated with the prognosis of patients with breast cancer. We integrated pathway structure with gene expression data to construct patient-specific subpathway activity profiles using a greedy search algorithm. A four-subpathway prognostic signature was developed in the training set using a random forest supervised classification algorithm and a prognostic score model with the activity profiles. According to the signature, patients were classified into high-risk and low-risk groups with significantly different overall survival in the training set (median survival of 65 vs 106 months, P = 1.82e-13) and test set (median survival of 75 vs 101 months, P = 4.17e-5). Our signature was then applied to five independent breast cancer data sets and showed similar prognostic values, confirming the accuracy and robustness of the subpathway signature. Stratified analysis suggested that the four-subpathway signature had prognostic value within subtypes of breast cancer. Our results suggest that the four-subpathway signature may be a useful biomarker for breast cancer prognosis.

Genomic Disparities in Breast Cancer Among Latinas

BACKGROUND

Breast cancer is the most common cancer diagnosed among Latinas in the United States and the leading cause of cancer-related death among this population. Latinas tend to be diagnosed at a later stage and have worse prognostic features than their non-Hispanic white counterparts. Genetic and genomic factors may contribute to observed breast cancer health disparities in Latinas.

METHODS

We provide a landscape of our current understanding and the existing gaps that need to be filled across the cancer prevention and control continuum.

RESULTS

We summarize available data on mutations in high and moderate penetrance genes for inherited risk of breast cancer and the associated literature on disparities in awareness of and uptake of genetic counseling and testing in Latina populations. We also discuss common genetic polymorphisms and risk of breast cancer in Latinas. In the treatment setting, we examine tumor genomics and pharmacogenomics in Latina patients with breast cancer.

CONCLUSIONS

As the US population continues to diversify, extending genetic and genomic research into this underserved and understudied population is critical. By understanding the risk of breast cancer among ethnically diverse populations, we will be better positioned to make treatment advancements for earlier stages of cancer, identify more effective and ideally less toxic treatment regimens, and increase rates of survival.

Previous GWAS hits in relation to young-onset breast cancer

PURPOSE

Genome-wide association studies (GWAS) have identified dozens of single-nucleotide polymorphisms (SNPs) associated with breast cancer. Few studies focused on young-onset breast cancer, which exhibits etiologic and tumor-type differences from older-onset disease. Possible confounding by prenatal effects of the maternal genome has also not been considered.

METHODS

Using a family-based design for breast cancer before age 50, we assessed the relationship between breast cancer and 77 GWAS-identified breast cancer risk SNPs. We estimated relative risks (RR) for inherited and maternally mediated genetic effects. We also used published RR estimates to calculate genetic risk scores and model joint effects.

RESULTS

Seventeen of the candidate SNPs were nominally associated with young-onset breast cancer in our 1296 non-Hispanic white affected families (uncorrected p value <0.05). Top-ranked SNPs included rs3803662-A (TOX3, RR = 1.39; p = 7.0 × 10-6), rs12662670-G (ESR1, RR = 1.56; p = 5.7 × 10-4), rs2981579-A (FGFR2, RR = 1.24; p = 0.002), and rs999737-G (RAD51B, RR = 1.37; p = 0.003). No maternally mediated effects were found. A risk score based on all 77 SNPs indicated that their overall relationship to young-onset breast cancer risk was more than additive (additive-fit p = 2.2 × 10-7) and consistent with a multiplicative joint effect (multiplicative-fit p = 0.27). With the multiplicative formulation, the case sister's genetic risk score exceeded that of her unaffected sister in 59% of families.

CONCLUSIONS

The results of this family-based study indicate that no effects of previously identified risk SNPs were explained by prenatal effects of maternal variants. Many of the known breast cancer risk variants were associated with young-onset breast cancer, with evidence that TOX3, ESR1, FGFR2, and RAD51B are important for young-onset disease.

A study of the potential anticancer activity of Mangifera zeylanica bark: Evaluation of cytotoxic and apoptotic effects of the hexane extract and bioassay-guided fractionation to identify phytochemical constituents

The present study investigated the potential anticancer activity of the bark of Mangifera zeylanica, an endemic plant in Sri Lanka that has been traditionally used for cancer therapy. Cytotoxic and apoptotic effects were investigated in vitro using sulphorodamine assay, acridine orange and ethidium bromide staining, caspase-3 and -7 activity, DNA fragmentation and reverse transcription-quantitative polymerase chain reaction in estrogen receptor positive MCF-7 and triple-negative MDA-MB-231 breast cancer cell lines, SKOV-3 ovarian cancer cell line and MCF-10A normal mammary epithelial cells. Hexane extract demonstrated increased levels of cytotoxicity in cancer cells (IC₅₀, 86.6-116.5 µg/ml) compared with normal cells (IC₅₀, 217.2 µg/ml). Chloroform extract demonstrated increased cytotoxicity to normal cells (IC₅₀, 92.9 µg/ml) compared with cancer cells (IC₅₀, 280.1-506.5 µg/ml). Exposure to the hexane extract led to morphological changes characteristic of apoptosis and DNA fragmentation in the three cancer cell lines. Caspase-3 and -7 were significantly activated in MDA-MB-231 and SKOV-3 cells, indicating the occurrence of caspase-dependent apoptosis in these cells, and caspase-independent apoptosis in MCF-7 cells. Furthermore, upregulation of proapoptotic Bcl-2-associated X protein occurred in the three cancer cell lines, and antiapoptotic survivin was downregulated in MCF-7 and SKOV-3 cells; by contrast, tumor protein p53 was upregulated only in MCF-7 cells, suggesting p53-mediated apoptosis in MCF-7 cells and p53-independent apoptosis in the remaining cancerous cell lines. In addition, fraction M₁ obtained from bioactivity-guided fractionation of the hexane extract demonstrated increased cytotoxicity in cancer cells (IC₅₀, 15.4-38.7 µg/ml) compared with normal cells (IC₅₀, 114.6 µg/ml), with the highest cytotoxicity observed in MDA-MB-231 triple-negative breast cancer cells. The hexane extract of M. zeylanica bark contained polyphenols and flavonoids, and caused free radical scavenging activity. Its gas chromatography-mass spectrometry profile revealed the presence of long-chain hydrocarbons, including β-sitosterol and β-amyrin. Fraction M₁ contained seven unknown compounds and a small number of known non-cytotoxic compounds. Collectively, results obtained in the present study indicate that the hexane extract of M. zeylanica bark mediates cytotoxic activities through induction of apoptosis in three cancer cell lines; thus, the hexane extract may be used to isolate novel anti-cancer compounds.

The Combinational Polymorphisms of ORAI1 Gene Are Associated with Preventive Models of Breast Cancer in the Taiwanese

The ORAI calcium release-activated calcium modulator 1 (ORAI1) has been proven to be an important gene for breast cancer progression and metastasis. However, the protective association model between the single nucleotide polymorphisms (SNPs) of ORAI1 gene was not investigated. Based on a published data set of 345 female breast cancer patients and 290 female controls, we used a particle swarm optimization (PSO) algorithm to identify the possible protective models of breast cancer association in terms of the SNPs of ORAI1 gene. Results showed that the PSO-generated models of 2-SNP (rs12320939-TT/rs12313273-CC), 3-SNP (rs12320939-TT/rs12313273-CC/rs712853-(TT/TC)), 4-SNP (rs12320939-TT/rs12313273-CC/rs7135617-(GG/GT)/rs712853-(TT/TC)), and 5-SNP (rs12320939-TT/rs12313273-CC/rs7135617-(GG/GT)/rs6486795-CC/rs712853-(TT/TC)) displayed low values of odds ratios (0.409–0.425) for breast cancer association. Taken together, these results suggested that our proposed PSO strategy is powerful to identify the combinational SNPs of rs12320939, rs12313273, rs7135617, rs6486795, and rs712853 of ORAI1 gene with a strongly protective association in breast cancer.

Genome-wide association study of breast cancer in Latinas identifies novel protective variants on 6q25

The genetic contributions to breast cancer development among Latinas are not well understood. Here we carry out a genome-wide association study of breast cancer in Latinas and identify a genome-wide significant risk variant, located 5′ of the Estrogen Receptor 1 gene (ESR1; 6q25 region). The minor allele for this variant is strongly protective (rs140068132: odds ratio (OR) 0.60, 95% confidence interval (CI) 0.53–0.67, P=9 × 10⁻¹⁸), originates from Indigenous Americans and is uncorrelated with previously reported risk variants at 6q25. The association is stronger for oestrogen receptor-negative disease (OR 0.34, 95% CI 0.21–0.54) than oestrogen receptor-positive disease (OR 0.63, 95% CI 0.49–0.80; P heterogeneity=0.01) and is also associated with mammographic breast density, a strong risk factor for breast cancer (P=0.001). rs140068132 is located within several transcription factor-binding sites and electrophoretic mobility shift assays with MCF-7 nuclear protein demonstrate differential binding of the G/A alleles at this locus. These results highlight the importance of conducting research in diverse populations.

Genome-wide association studies (GWAS) have revealed gene variants associated with breast cancer, but their association with breast cancer development in Latinas is not clear. Here, the authors carry out a GWAS of breast cancer in Latinas and identify a significant protective variant of Indigenous American origin in the 6q25 region.

Approaches to integrating germline and tumor genomic data in cancer research

Cancer is characterized by a diversity of genetic and epigenetic alterations occurring in both the germline and somatic (tumor) genomes. Hundreds of germline variants associated with cancer risk have been identified, and large amounts of data identifying mutations in the tumor genome that participate in tumorigenesis have been generated. Increasingly, these two genomes are being explored jointly to better understand how cancer risk alleles contribute to carcinogenesis and whether they influence development of specific tumor types or mutation profiles. To understand how data from germline risk studies and tumor genome profiling is being integrated, we reviewed 160 articles describing research that incorporated data from both genomes, published between January 2009 and December 2012, and summarized the current state of the field. We identified three principle types of research questions being addressed using these data: (i) use of tumor data to determine the putative function of germline risk variants; (ii) identification and analysis of relationships between host genetic background and particular tumor mutations or types; and (iii) use of tumor molecular profiling data to reduce genetic heterogeneity or refine phenotypes for germline association studies. We also found descriptive studies that compared germline and tumor genomic variation in a gene or gene family, and papers describing research methods, data sources, or analytical tools. We identified a large set of tools and data resources that can be used to analyze and integrate data from both genomes. Finally, we discuss opportunities and challenges for cancer research that integrates germline and tumor genomics data.

How many etiological subtypes of breast cancer: two, three, four, or more?

Breast cancer is a heterogeneous disease, divisible into a variable number of clinical subtypes. A fundamental question is how many etiological classes underlie the clinical spectrum of breast cancer? An etiological subtype reflects a grouping with a common set of causes, whereas a clinical subtype represents a grouping with similar prognosis and/or prediction. Herein, we review the evidence for breast cancer etiological heterogeneity. We then evaluate the etiological evidence with mRNA profiling data. A bimodal age distribution at diagnosis with peak frequencies near ages 50 and 70 years is a fundamental characteristic of breast cancer for important tumor features, clinical characteristics, risk factor profiles, and molecular subtypes. The bimodal peak frequencies at diagnosis divide breast cancer overall into a "mixture" of two main components in varying proportions in different cancer populations. The first breast cancer tends to arise early in life with modal age-at-diagnosis near 50 years and generally behaves aggressively. The second breast cancer occurs later in life with modal age near 70 years and usually portends a more indolent clinical course. These epidemiological and molecular data are consistent with a two-component mixture model and compatible with a hierarchal view of breast cancers arising from two main cell types of origin. Notwithstanding the potential added value of more detailed categorizations for personalized breast cancer treatment, we suggest that the development of better criteria to identify the two proposed etiologic classes would advance breast cancer research and prevention.

A genome-wide association study of early-onset breast cancer identifies PFKM as a novel breast cancer gene and supports a common genetic spectrum for breast cancer at any age

Early-onset breast cancer (EOBC) causes substantial loss of life and productivity, creating a major burden among women worldwide. We analyzed 1,265,548 Hapmap3 single-nucleotide polymorphisms (SNP) among a discovery set of 3,523 EOBC incident cases and 2,702 population control women ages ≤ 51 years. The SNPs with smallest P values were examined in a replication set of 3,470 EOBC cases and 5,475 control women. We also tested EOBC association with 19,684 genes by annotating each gene with putative functional SNPs, and then combining their P values to obtain a gene-based P value. We examined the gene with smallest P value for replication in 1,145 breast cancer cases and 1,142 control women. The combined discovery and replication sets identified 72 new SNPs associated with EOBC (P < 4 × 10(-8)) located in six genomic regions previously reported to contain SNPs associated largely with later-onset breast cancer (LOBC). SNP rs2229882 and 10 other SNPs on chromosome 5q11.2 remained associated (P < 6 × 10(-4)) after adjustment for the strongest published SNPs in the region. Thirty-two of the 82 currently known LOBC SNPs were associated with EOBC (P < 0.05). Low power is likely responsible for the remaining 50 unassociated known LOBC SNPs. The gene-based analysis identified an association between breast cancer and the phosphofructokinase-muscle (PFKM) gene on chromosome 12q13.11 that met the genome-wide gene-based threshold of 2.5 × 10(-6). In conclusion, EOBC and LOBC seem to have similar genetic etiologies; the 5q11.2 region may contain multiple distinct breast cancer loci; and the PFKM gene region is worthy of further investigation. These findings should enhance our understanding of the etiology of breast cancer.

Genome-wide association studies and the clinic: a focus on breast cancer

Breast cancer is the most frequently diagnosed cancer among women worldwide, and has long been considered to be a genetic disease. A wide range of genetic variants, both rare mutations and more common variants, have been shown to influence breast cancer risk. In particular, recent studies have identified a number of common genetic variants, or single nucleotide polymorphisms, that are associated with breast cancer risk. In this review, we will briefly present the genetic epidemiology of breast cancer, genome-wide association study technology and how this technology may influence breast cancer screening in the clinic.

Significant overlap between human genome-wide association-study nominated breast cancer risk alleles and rat mammary cancer susceptibility loci

Introduction

Human population-based genome-wide association (GWA) studies identify low penetrance breast cancer risk alleles; however, GWA studies alone do not definitively determine causative genes or mechanisms. Stringent genome- wide statistical significance level requirements, set to avoid false-positive associations, yield many false-negative associations. Laboratory rats (Rattus norvegicus) are useful to study many aspects of breast cancer, including genetic susceptibility. Several rat mammary cancer associated loci have been identified using genetic linkage and congenic strain based-approaches. Here, we sought to determine the amount of overlap between GWA study nominated human breast and rat mammary cancer susceptibility loci.

Methods

We queried published GWA studies to identify two groups of SNPs, one that reached genome-wide significance and one comprised of SNPs failing a validation step and not reaching genome- wide significance. Human genome locations of these SNPs were compared to known rat mammary carcinoma susceptibility loci to determine if risk alleles existed in both species. Rat genome regions not known to associate with mammary cancer risk were randomly selected as control regions.

Results

Significantly more human breast cancer risk GWA study nominated SNPs mapped at orthologs of rat mammary cancer loci than to regions not known to contain rat mammary cancer loci. The rat genome was useful to predict associations that had met human genome-wide significance criteria and weaker associations that had not.

Conclusions

Integration of human and rat comparative genomics may be useful to parse out false-negative associations in GWA studies of breast cancer risk.

MDR-ER: balancing functions for adjusting the ratio in risk classes and classification errors for imbalanced cases and controls using multifactor-dimensionality reduction

Background

Determining the complex relationship between diseases, polymorphisms in human genes and environmental factors is challenging. Multifactor dimensionality reduction (MDR) has proven capable of effectively detecting statistical patterns of epistasis. However, MDR has its weakness in accurately assigning multi-locus genotypes to either high-risk and low-risk groups, and does generally not provide accurate error rates when the case and control data sets are imbalanced. Consequently, results for classification error rates and odds ratios (OR) may provide surprising values in that the true positive (TP) value is often small.

Methodology/Principal Findings

To address this problem, we introduce a classifier function based on the ratio between the percentage of cases in case data and the percentage of controls in control data to improve MDR (MDR-ER) for multi-locus genotypes to be classified correctly into high-risk and low-risk groups. In this study, a real data set with different ratios of cases to controls (1∶4) was obtained from the mitochondrial D-loop of chronic dialysis patients in order to test MDR-ER. The TP and TN values were collected from all tests to analyze to what degree MDR-ER performed better than MDR.

Conclusions/Significance

Results showed that MDR-ER can be successfully used to detect the complex associations in imbalanced data sets.

Germline variation in TP53 regulatory network genes associates with breast cancer survival and treatment outcome

Germline variation in the TP53 network genes PRKAG2, PPP2R2B, CCNG1, PIAS1 and YWHAQ was previously suggested to have an impact on drug response in vitro. Here, we investigated the effect on breast cancer survival of germline variation in these genes in 925 Finnish breast cancer patients and further analyzed five single nucleotide polymorphisms (SNPs) in PRKAG2 (rs1029946, rs4726050, rs6464153, rs7789699) and PPP2R2B (rs10477313) for 10-year survival in breast cancer patients, interaction with TP53 R72P and MDM2-SNP309, outcome after specific adjuvant therapy and correlation to tumor characteristics in 4,701 invasive cases from four data sets. We found evidence for carriers of PRKAG2-rs1029946 and PRKAG2-rs4726050 having improved survival in the pooled data (HR 0.53, 95% CI 0.3-0.9; p = 0.023 for homozygous carriers of the rare G-allele and HR 0.85, 95% CI 0.7-0.9; p = 0.049 for carriers of the rare G allele, respectively). PRKAG2-rs4726050 showed a significant interaction with MDM2-SNP309, with PRKAG2-rs4726050 rare G-allele having a dose-dependent effect for better breast cancer survival confined only to MDM2 SNP309 rare G-allele carriers (HR 0.45, 95% CI 0.2-0.7; p = 0.001). This interaction also emerged as an independent predictor of better survival (p = 0.047). PPP2R2B-rs10477313 rare A-allele was found to predict better survival (HR 0.82, 95% CI 0.6-0.9; p = 0.018), especially after hormonal therapy (HR 0.66, 95% CI 0.5-0.9; p = 0.048). These findings warrant further studies and suggest that genetic markers in TP53 network genes such as PRKAG2 and PPP2R2B might affect prognosis and treatment outcome in breast cancer patients.

Polymorphisms in oxidative stress-related genes and mortality in breast cancer patients--potential differential effects by radiotherapy?

We assessed whether variants in 22 oxidative stress-related genes are associated with mortality of breast cancer patients and whether the associations differ according to radiotherapy. Using a prospective cohort of 1348 postmenopausal breast cancer patients, we estimated hazard ratios (HR) and 95% confidence intervals (CI) for 109 single nucleotide polymorphisms (SNPs) using Cox proportional hazards regression. Validation of results was attempted using two Scandinavian studies. Eleven SNPs in MT2A, NFE2L2, NQO1, PRDX1, and PRDX6 were significantly associated with overall mortality after a median follow-up of 5.7 years. Three SNPs in NQO1 (rs2917667) and in PRDX6 (rs7314, rs4916362) were consistently associated with increased risk of dying across all three study populations (pooled: HRNQO1_rs2917667 1.20, 95% CI 1.00-1.44, p = 0.051; HRPRDX6_rs7314 1.16, 95% CI 1.00-1.35, p = 0.056, HRPRDX6_rs4916362 1.14 95% CI 1.00-1.32, p = 0.062). Potential effect modification by radiotherapy was found for CAT_rs769218. In conclusion, genetic variants in NQO1 and PRDX6 may modify breast cancer prognosis.

Fine mapping of breast cancer genome-wide association studies loci in women of African ancestry identifies novel susceptibility markers

Numerous single nucleotide polymorphisms (SNPs) associated with breast cancer susceptibility have been identified by genome-wide association studies (GWAS). However, these SNPs were primarily discovered and validated in women of European and Asian ancestry. Because linkage disequilibrium is ancestry-dependent and heterogeneous among racial/ethnic populations, we evaluated common genetic variants at 22 GWAS-identified breast cancer susceptibility loci in a pooled sample of 1502 breast cancer cases and 1378 controls of African ancestry. None of the 22 GWAS index SNPs could be validated, challenging the direct generalizability of breast cancer risk variants identified in Caucasians or Asians to other populations. Novel breast cancer risk variants for women of African ancestry were identified in regions including 5p12 (odds ratio [OR] = 1.40, 95% confidence interval [CI] = 1.11-1.76; P = 0.004), 5q11.2 (OR = 1.22, 95% CI = 1.09-1.36; P = 0.00053) and 10p15.1 (OR = 1.22, 95% CI = 1.08-1.38; P = 0.0015). We also found positive association signals in three regions (6q25.1, 10q26.13 and 16q12.1-q12.2) previously confirmed by fine mapping in women of African ancestry. In addition, polygenic model indicated that eight best markers in this study, compared with 22 GWAS-identified SNPs, could better predict breast cancer risk in women of African ancestry (per-allele OR = 1.21, 95% CI = 1.16-1.27; P = 9.7 × 10(-16)). Our results demonstrate that fine mapping is a powerful approach to better characterize the breast cancer risk alleles in diverse populations. Future studies and new GWAS in women of African ancestry hold promise to discover additional variants for breast cancer susceptibility with clinical implications throughout the African diaspora.

Evaluation of breast cancer susceptibility using improved genetic algorithms to generate genotype SNP barcodes

Genetic association is a challenging task for the identification and characterization of genes that increase the susceptibility to common complex multifactorial diseases. To fully execute genetic studies of complex diseases, modern geneticists face the challenge of detecting interactions between loci. A genetic algorithm (GA) is developed to detect the association of genotype frequencies of cancer cases and noncancer cases based on statistical analysis. An improved genetic algorithm (IGA) is proposed to improve the reliability of the GA method for high-dimensional SNP-SNP interactions. The strategy offers the top five results to the random population process, in which they guide the GA toward a significant search course. The IGA increases the likelihood of quickly detecting the maximum ratio difference between cancer cases and noncancer cases. The study systematically evaluates the joint effect of 23 SNP combinations of six steroid hormone metabolisms, and signaling-related genes involved in breast carcinogenesis pathways were systematically evaluated, with IGA successfully detecting significant ratio differences between breast cancer cases and noncancer cases. The possible breast cancer risks were subsequently analyzed by odds-ratio (OR) and risk-ratio analysis. The estimated OR of the best SNP barcode is significantly higher than 1 (between 1.15 and 7.01) for specific combinations of two to 13 SNPs. Analysis results support that the IGA provides higher ratio difference values than the GA between breast cancer cases and noncancer cases over 3-SNP to 13-SNP interactions. A more specific SNP-SNP interaction profile for the risk of breast cancer is also provided.

Proteome-wide analysis of nonsynonymous single-nucleotide variations in active sites of human proteins

An enzyme's active site is essential to normal protein activity such that any disruptions at this site may lead to dysfunction and disease. Nonsynonymous single-nucleotide variations (nsSNVs), which alter the amino acid sequence, are one type of disruption that can alter the active site. When this occurs, it is assumed that enzyme activity will vary because of the criticality of the site to normal protein function. We integrate nsSNV data and active site annotations from curated resources to identify all active-site-impacting nsSNVs in the human genome and search for all pathways observed to be associated with this data set to assess the likely consequences. We find that there are 934 unique nsSNVs that occur at the active sites of 559 proteins. Analysis of the nsSNV data shows an over-representation of arginine and an under-representation of cysteine, phenylalanine and tyrosine when comparing the list of nsSNV-impacted active site residues with the list of all possible proteomic active site residues, implying a potential bias for or against variation of these residues at the active site. Clustering analysis shows an abundance of hydrolases and transferases. Pathway and functional analysis shows several pathways over- or under-represented in the data set, with the most significantly affected pathways involved in carbohydrate metabolism. We provide a table of 32 variation-substrate/product pairs that can be used in targeted metabolomics experiments to assay the effects of specific variations. In addition, we report the significant prevalence of aspartic acid to histidine variation in eight proteins associated with nine diseases including glycogen storage diseases, lacrimo-auriculo-dento-digital syndrome, Parkinson's disease and several cancers.

An improved PSO algorithm for generating protective SNP barcodes in breast cancer

Background

Possible single nucleotide polymorphism (SNP) interactions in breast cancer are usually not investigated in genome-wide association studies. Previously, we proposed a particle swarm optimization (PSO) method to compute these kinds of SNP interactions. However, this PSO does not guarantee to find the best result in every implement, especially when high-dimensional data is investigated for SNP–SNP interactions.

Methodology/Principal Findings

In this study, we propose IPSO algorithm to improve the reliability of PSO for the identification of the best protective SNP barcodes (SNP combinations and genotypes with maximum difference between cases and controls) associated with breast cancer. SNP barcodes containing different numbers of SNPs were computed. The top five SNP barcode results are retained for computing the next SNP barcode with a one-SNP-increase for each processing step. Based on the simulated data for 23 SNPs of six steroid hormone metabolisms and signalling-related genes, the performance of our proposed IPSO algorithm is evaluated. Among 23 SNPs, 13 SNPs displayed significant odds ratio (OR) values (1.268 to 0.848; p<0.05) for breast cancer. Based on IPSO algorithm, the jointed effect in terms of SNP barcodes with two to seven SNPs show significantly decreasing OR values (0.84 to 0.57; p<0.05 to 0.001). Using PSO algorithm, two to four SNPs show significantly decreasing OR values (0.84 to 0.77; p<0.05 to 0.001). Based on the results of 20 simulations, medians of the maximum differences for each SNP barcode generated by IPSO are higher than by PSO. The interquartile ranges of the boxplot, as well as the upper and lower hinges for each n-SNP barcode (n = 3∼10) are more narrow in IPSO than in PSO, suggesting that IPSO is highly reliable for SNP barcode identification.

Conclusions/Significance

Overall, the proposed IPSO algorithm is robust to provide exact identification of the best protective SNP barcodes for breast cancer.

Centromere protein-A, an essential centromere protein, is a prognostic marker for relapse in estrogen receptor-positive breast cancer

INTRODUCTION

Centromere protein A (CENP-A), an essential centromere protein, has been associated with high grade cancers. This study was undertaken to determine if CENP-A is a prognostic factor for breast cancer patients not receiving systemic therapy or predictive of response to tamoxifen or neoadjuvant chemotherapy.

METHODS

mRNA levels of CENP-A and CENP-B, a centromere protein that binds independently of CENP-A, were measured in breast cancer specimens from 484 patients receiving no systemic therapy, 276 patients receiving tamoxifen, and 233 patients treated with neoadjuvant chemotherapy. Associations between CENP-A, CENP-B, Ki-67, relapse, and chemotherapy response were determined.

RESULTS

CENP-A but not CENP-B was higher in estrogen receptor (ER)-negative tumors than ER-positive tumors and positively correlated with Ki-67 expression. Among patients with ER-positive disease who received no systemic therapy or tamoxifen, higher levels of CENP-A were associated with lower rates of 5-year distant relapse free survival (DRFS). On multivariate analyses including Ki-67, high CENP-A expression had a hazard ratio of 10.9 for relapse in patients with ER-positive disease not receiving systemic therapy (95% CI, 2.86 to 41.78; P = 0.00047) and 1.64 for patients with ER-positive disease receiving tamoxifen (95% CI, 0.99 to 2.71; P = 0.054). CENP-A was not an independent prognostic marker in ER-negative tumors. For both ER-positive and ER-negative tumors, CENP-A was not a significant independent predictor of chemotherapy response.

CONCLUSIONS

CENP-A was a significant independent prognostic marker for patients with ER-positive breast cancer not treated with systemic therapy but had limited predictive value in tamoxifen treated patients and was not predictive of response to neoadjuvant chemotherapy.

Identification of colorectal cancer related genes with mRMR and shortest path in protein-protein interaction network

One of the most important and challenging problems in biomedicine and genomics is how to identify the disease genes. In this study, we developed a computational method to identify colorectal cancer-related genes based on (i) the gene expression profiles, and (ii) the shortest path analysis of functional protein association networks. The former has been used to select differentially expressed genes as disease genes for quite a long time, while the latter has been widely used to study the mechanism of diseases. With the existing protein-protein interaction data from STRING (Search Tool for the Retrieval of Interacting Genes), a weighted functional protein association network was constructed. By means of the mRMR (Maximum Relevance Minimum Redundancy) approach, six genes were identified that can distinguish the colorectal tumors and normal adjacent colonic tissues from their gene expression profiles. Meanwhile, according to the shortest path approach, we further found an additional 35 genes, of which some have been reported to be relevant to colorectal cancer and some are very likely to be relevant to it. Interestingly, the genes we identified from both the gene expression profiles and the functional protein association network have more cancer genes than the genes identified from the gene expression profiles alone. Besides, these genes also had greater functional similarity with the reported colorectal cancer genes than the genes identified from the gene expression profiles alone. All these indicate that our method as presented in this paper is quite promising. The method may become a useful tool, or at least plays a complementary role to the existing method, for identifying colorectal cancer genes. It has not escaped our notice that the method can be applied to identify the genes of other diseases as well.

Common breast cancer susceptibility loci are associated with triple-negative breast cancer

Triple-negative breast cancers are an aggressive subtype of breast cancer with poor survival, but there remains little known about the etiologic factors that promote its initiation and development. Commonly inherited breast cancer risk factors identified through genome-wide association studies display heterogeneity of effect among breast cancer subtypes as defined by the status of estrogen and progesterone receptors. In the Triple Negative Breast Cancer Consortium (TNBCC), 22 common breast cancer susceptibility variants were investigated in 2,980 Caucasian women with triple-negative breast cancer and 4,978 healthy controls. We identified six single-nucleotide polymorphisms, including rs2046210 (ESR1), rs12662670 (ESR1), rs3803662 (TOX3), rs999737 (RAD51L1), rs8170 (19p13.1), and rs8100241 (19p13.1), significantly associated with the risk of triple-negative breast cancer. Together, our results provide convincing evidence of genetic susceptibility for triple-negative breast cancer.

Screening of women at high risk for breast cancer

A woman may be at high risk of breast cancer because of a strong family history of breast cancer or because she carries a mutation in the BRCA1 or BRCA2 gene. The annual risk for women in this category is between 1% and 2% and the lifetime risk of breast cancer among gene carriers may approach 80%. Several recent trials have reported that the sensitivity of MRI for imaging breast cancer greatly exceeds that of conventional mammography, but no study has yet determined that annual MRI reduces breast cancer-specific mortality. Women with breast cancer and a BRCA1 mutation typically develop aggressive breast cancers and the prognosis is relatively poor for women with small node-negative breast cancers (compared to non-carriers) in particular, if chemotherapy is not given. It is hoped that annual MRI screening combined with appropriate treatment will result in decreased mortality for this and other groups of high-risk women. MRI-based screening for women at moderate risk is a topic of great interest--MRI has not yet been endorsed in moderate risk women because of the high cost of screening and because the specificity of the screening test is not yet determined in this subgroup.

Variation in base excision repair capacity

The major DNA repair pathway for coping with spontaneous forms of DNA damage, such as natural hydrolytic products or oxidative lesions, is base excision repair (BER). In particular, BER processes mutagenic and cytotoxic DNA lesions such as non-bulky base modifications, abasic sites, and a range of chemically distinct single-strand breaks. Defects in BER have been linked to cancer predisposition, neurodegenerative disorders, and immunodeficiency. Recent data indicate a large degree of sequence variability in DNA repair genes and several studies have associated BER gene polymorphisms with disease risk, including cancer of several sites. The intent of this review is to describe the range of BER capacity among individuals and the functional consequences of BER genetic variants. We also discuss studies that associate BER deficiency with disease risk and the current state of BER capacity measurement assays.