Genome-based prediction of breast cancer risk in the general population: a modeling study based on meta-analyses of genetic associations

Targeting RLIP with CRISPR/Cas9 controls tumor growth

Breast cancer (BC) remains one of the major causes of cancer deaths in women. Over half of all BCs carry genetic defects in the gene encoding p53, a powerful tumor suppressor. P53 is known as the 'guardian of the genome' because it is essential for regulating cell division and preventing tumor formation. Ral-interacting protein (RLIP) is a modular protein capable of participating in many cellular functions. Blocking this stress-responsive protein, which is overexpressed during malignancy, enables BC cells to overcome the deleterious effects of p53 loss more effectively. In the clustered regularly interspaced short palindromic repeats/CRISPR-associated protein (CRISPR/Cas9) system, a single-guide RNA (sgRNA) recognizes a specific DNA sequence and directs the endonuclease Cas9 to make a double-strand break, which enables editing of targeted genes. Here, we harnessed CRISPR/Cas9 technology to target the RLIP gene in BC cells. We screened sgRNAs using a reporter system and lentivirally delivered them, along with Cas9, to BC cells for validation. We then assessed the survival, proliferation, and tumorigenicity of BC cells in vitro and the growth of tumors in vivo after CRISPR-mediated knockdown of RLIP. Doxycycline-inducible expression of Cas9 in BC cells transduced with lentiviral vectors encoding the sgRNAs disrupted the RLIP gene, leading to inhibition of BC cell proliferation both in vitro and in vivo, with resected tumors showing reduced levels of the survival and proliferation markers Ki67, RLIP, pAkt, and survivin, the cell cycle protein CDK4, and the mesenchymal marker vimentin, as well as elevated levels of the differentiation protein E-cadherin and pro-apoptotic protein Bim. Inducible Cas9/sgRNA-transduced BC cells without doxycycline treatment did not exhibit altered cell survival or proliferation in vitro or in vivo. Our study provides proof-of-concept that the CRISPR/Cas9 system can be utilized to target RLIP in vitro and in vivo.

A way forward for cancer prevention therapy: personalized risk assessment

Cancer is characterized by genetic and molecular aberrations whose number and complexity increase dramatically as cells progress along the spectrum of carcinogenesis. The pharmacologic application of agents in the context of a lower burden of dysregulated cellular processes constitutes an efficient strategy to enhance therapeutic efficacy, and underlies the rationale for using cancer prevention agents in high-risk populations. A longstanding barrier to implementing this strategy is that the risk in the general population is low for any given cancer, many people would have to be treated in order to benefit a few. Therefore, identifying and treating high-risk individuals will improve the risk: benefit ratio. Currently, risk is defined by considering a relatively low number of factors. A strategy that considers multiple factors has the ability to define a much-higher-risk cohort than the general population. This article will review the rationale for evaluating multiple risk factors so as to identify individuals at highest risk. It will use breast and lung cancer as examples, will describe currently available risk assessment tools, and will discuss ongoing efforts to expand the impact of this approach. The high potential of this strategy to provide a way forward for developing cancer prevention therapy will be highlighted.

Prevention of breast cancer-induced osteolytic bone resorption by benzyl isothiocyanate

Osteolytic bone resorption is the primary cause of pain and suffering (e.g. pathological bone fracture) in women with metastatic breast cancer. The current standard of care for patients with bone metastasis for reducing the incidence of skeletal complications includes bisphosphonates and a humanized antibody (denosumab). However, a subset of patients on these therapies still develops new bone metastasis or experiences adverse effects. Moreover, some bisphosphonates have poor oral bioavailability. Therefore, orally-bioavailable and non-toxic inhibitors of breast cancer-induced osteolytic bone resorption are still clinically desirable. We have shown previously that benzyl isothiocyanate (BITC) decreases the incidence of breast cancer in a transgenic mouse model without any side effects. The present study provides in vivo evidence for inhibition of breast cancer-induced osteolytic bone resorption by BITC. Plasma achievable doses of BITC (0.5 and 1 μM) inhibited in vitro osteoclast differentiation induced by co-culture of osteoclast precursor cells (RAW264.7) and breast cancer cells representative of different subtypes. This effect was accompanied by downregulation of key mediators of osteoclast differentiation, including receptor activator of nuclear factor-κB ligand and runt-related transcription factor 2 (RUNX2), in BITC-treated breast cancer cells. Doxycycline-inducible knockdown of RUNX2 augmented BITC-mediated inhibition of osteoclast differentiation. Oral administration of 10 mg BITC/kg body weight, 5 times per week, inhibited MDA-MB-231-induced skeletal metastasis multiplicity by ~81% when compared with control (P = 0.04). The present study indicates that BITC has the ability to inhibit breast cancer-induced osteolytic bone resorption in vivo.

Review of non-clinical risk models to aid prevention of breast cancer

A disease risk model is a statistical method which assesses the probability that an individual will develop one or more diseases within a stated period of time. Such models take into account the presence or absence of specific epidemiological risk factors associated with the disease and thereby potentially identify individuals at higher risk. Such models are currently used clinically to identify people at higher risk, including identifying women who are at increased risk of developing breast cancer. Many genetic and non-genetic breast cancer risk models have been developed previously. We have evaluated existing non-genetic/non-clinical models for breast cancer that incorporate modifiable risk factors. This review focuses on risk models that can be used by women themselves in the community in the absence of clinical risk factors characterization. The inclusion of modifiable factors in these models means that they can be used to improve primary prevention and health education pertinent for breast cancer. Literature searches were conducted using PubMed, ScienceDirect and the Cochrane Database of Systematic Reviews. Fourteen studies were eligible for review with sample sizes ranging from 654 to 248,407 participants. All models reviewed had acceptable calibration measures, with expected/observed (E/O) ratios ranging from 0.79 to 1.17. However, discrimination measures were variable across studies with concordance statistics (C-statistics) ranging from 0.56 to 0.89. We conclude that breast cancer risk models that include modifiable risk factors have been well calibrated but have less ability to discriminate. The latter may be a consequence of the omission of some significant risk factors in the models or from applying models to studies with limited sample sizes. More importantly, external validation is missing for most of the models. Generalization across models is also problematic as some variables may not be considered applicable to some populations and each model performance is conditioned by particular population characteristics. In conclusion, it is clear that there is still a need to develop a more reliable model for estimating breast cancer risk which has a good calibration, ability to accurately discriminate high risk and with better generalizability across populations.

Prediction of breast cancer risk using a machine learning approach embedded with a locality preserving projection algorithm

In order to automatically identify a set of effective mammographic image features and build an optimal breast cancer risk stratification model, this study aims to investigate advantages of applying a machine learning approach embedded with a locally preserving projection (LPP) based feature combination and regeneration algorithm to predict short-term breast cancer risk. A dataset involving negative mammograms acquired from 500 women was assembled. This dataset was divided into two age-matched classes of 250 high risk cases in which cancer was detected in the next subsequent mammography screening and 250 low risk cases, which remained negative. First, a computer-aided image processing scheme was applied to segment fibro-glandular tissue depicted on mammograms and initially compute 44 features related to the bilateral asymmetry of mammographic tissue density distribution between left and right breasts. Next, a multi-feature fusion based machine learning classifier was built to predict the risk of cancer detection in the next mammography screening. A leave-one-case-out (LOCO) cross-validation method was applied to train and test the machine learning classifier embedded with a LLP algorithm, which generated a new operational vector with 4 features using a maximal variance approach in each LOCO process. Results showed a 9.7% increase in risk prediction accuracy when using this LPP-embedded machine learning approach. An increased trend of adjusted odds ratios was also detected in which odds ratios increased from 1.0 to 11.2. This study demonstrated that applying the LPP algorithm effectively reduced feature dimensionality, and yielded higher and potentially more robust performance in predicting short-term breast cancer risk.

The more from East-Asian, the better: risk prediction of colorectal cancer risk by GWAS-identified SNPs among Japanese

BACKGROUND

Little is known about the difference of genetic predisposition for CRC between ethnicities; however, many genetic traits common to colorectal cancer have been identified. This study investigated whether more SNPs identified in GWAS in East Asian population could improve the risk prediction of Japanese and explored possible application of genetic risk groups as an instrument of the risk communication.

METHODS

558 Patients histologically verified colorectal cancer and 1116 first-visit outpatients were included for derivation study, and 547 cases and 547 controls were for replication study. Among each population, we evaluated prediction models for the risk of CRC that combined the genetic risk group based on SNPs from GWASs in European-population and a similarly developed model adding SNPs from GWASs in East Asian-population. We examined whether adding East Asian-specific SNPs would improve the discrimination.

RESULTS

Six SNPs (rs6983267, rs4779584, rs4444235, rs9929218, rs10936599, rs16969681) from 23 SNPs by European-based GWAS and five SNPs (rs704017, rs11196172, rs10774214, rs647161, rs2423279) among ten SNPs by Asian-based GWAS were selected in CRC risk prediction model. Compared with a 6-SNP-based model, an 11-SNP model including Asian GWAS-SNPs showed improved discrimination capacity in Receiver operator characteristic analysis. A model with 11 SNPs resulted in statistically significant improvement in both derivation (P = 0.0039) and replication studies (P = 0.0018) compared with six SNP model. We estimated cumulative risk of CRC by using genetic risk group based on 11 SNPs and found that the cumulative risk at age 80 is approximately 13% in the high-risk group while 6% in the low-risk group.

CONCLUSION

We constructed a more efficient CRC risk prediction model with 11 SNPs including newly identified East Asian-based GWAS SNPs (rs704017, rs11196172, rs10774214, rs647161, rs2423279). Risk grouping based on 11 SNPs depicted lifetime difference of CRC risk. This might be useful for effective individualized prevention for East Asian.

β-Adrenergic Receptors : New Target in Breast Cancer

BACKGROUND

Preclinical studies have demonstrated that β-adrenergic receptor antagonists could improve the prognosis of breast cancer. However, the conclusions of clinical and pharmacoepidemiological studies have been inconsistent. This review was conducted to re-assess the relationship between beta-adrenoceptor blockers and breast cancer prognosis.

MATERIALS AND METHODS

The literature was searched from PubMed, EMBASE and Web of Nature (Thompson Reuters) databases through using key terms, such as breast cancer and beta- adrenoceptor blockers.

RESULTS

Ten publications met the inclusion criteria. Six suggested that receiving beta- adrenoceptor blockers reduced the risk of breast cancer-specific mortality, and three of them had statistical significance (hazard ratio (HR)=0.42; 95% CI=0.18-0.97; p=0.042). Two studies reported that risk of recurrence and distant metastasis (DM) were both significantly reduced. One study demonstrated that the risk of relapse- free survival (RFS) was raised significantly with beta-blockers (BBS) (HR= 0.30; 95% CI=0.10-0.87; p=0.027). One reported longer disease-free interval (Log Rank (LR)=6.658; p=0.011) in BBS users, but there was no significant association between overall survival (OS) and BBS (HR= 0.35; 95% CI=0.12-1.0; p=0.05) in five studies.

CONCLUSIONS

Through careful consideration, it is suggested that beta-adrenoceptor blockers use may be associated with improved prognosis in breast cancer patients. Nevertheless, larger size studies are needed to further explore the relationship between beta-blocker drug use and breast cancer prognosis.

Prediction of non-muscle invasive bladder cancer outcomes assessed by innovative multimarker prognostic models

BACKGROUND

We adapted Bayesian statistical learning strategies to the prognosis field to investigate if genome-wide common SNP improve the prediction ability of clinico-pathological prognosticators and applied it to non-muscle invasive bladder cancer (NMIBC) patients.

METHODS

Adapted Bayesian sequential threshold models in combination with LASSO were applied to consider the time-to-event and the censoring nature of data. We studied 822 NMIBC patients followed-up >10 years. The study outcomes were time-to-first-recurrence and time-to-progression. The predictive ability of the models including up to 171,304 SNP and/or 6 clinico-pathological prognosticators was evaluated using AUC-ROC and determination coefficient.

RESULTS

Clinico-pathological prognosticators explained a larger proportion of the time-to-first-recurrence (3.1 %) and time-to-progression (5.4 %) phenotypic variances than SNPs (1 and 0.01 %, respectively). Adding SNPs to the clinico-pathological-parameters model slightly improved the prediction of time-to-first-recurrence (up to 4 %). The prediction of time-to-progression using both clinico-pathological prognosticators and SNP did not improve. Heritability (ĥ (2)) of both outcomes was <1 % in NMIBC.

CONCLUSIONS

We adapted a Bayesian statistical learning method to deal with a large number of parameters in prognostic studies. Common SNPs showed a limited role in predicting NMIBC outcomes yielding a very low heritability for both outcomes. We report for the first time a heritability estimate for a disease outcome. Our method can be extended to other disease models.

Cancer-selective death of human breast cancer cells by leelamine is mediated by bax and bak activation

The present study is the first to report inhibition of breast cancer cell growth in vitro and in vivo and suppression of self-renewal of breast cancer stem cells (bCSC) by a pine bark component (leelamine). Except for a few recent publications in melanoma, anticancer pharmacology of this interesting phytochemical is largely elusive. Leelamine (LLM) dose-dependently inhibited viability of MDA-MB-231 (triple-negative), MCF-7 (estrogen receptor-positive), and SUM159 (triple-negative) human breast cancer cells in association with apoptotic cell death induction. To the contrary, a normal mammary epithelial cell line derived from fibrocystic breast disease and spontaneously immortalized (MCF-10A) was fully resistant to LLM-mediated cell growth inhibition and apoptosis induction. LLM also inhibited self-renewal of breast cancer stem cells. Apoptosis induction by LLM in breast cancer cells was accompanied by a modest increase in reactive oxygen species production, which was not due to inhibition of mitochondrial electron transport chain complexes. Nevertheless, ectopic expression of manganese superoxide dismutase conferred partial protection against LLM-induced cell death but only at a lower yet pharmacologically relevant concentration. Exposure of breast cancer cells to LLM resulted in (a) induction and/or activation of multidomain proapoptotic proteins Bax and Bak, (b) caspase-9 activation, and (c) cytosolic release of cytochrome c. Bax and Bak deficiency in immortalized fibroblasts conferred significant protection against cell death by LLM. Intraperitoneal administration of LLM (7.5 mg/kg; 5 times/wk) suppressed the growth of orthotopic SUM159 xenografts in mice without any toxicity. In conclusion, the present study provides critical preclinical data to warrant further investigation of LLM. © 2016 Wiley Periodicals, Inc.

Integration of genetic and epigenetic markers for risk stratification: opportunities and challenges

Common genetic susceptibility variants could be used for risk stratification in risk-tailored cancer screening and prevention programmes. Combining genetic variants with environmental risk factors would improve risk stratification. Epigenetic changes are surrogate markers of environmental exposures during individual's lifetime. Integrating epigenetic markers, in lieu of environmental exposure data, with genetic markers would potentially improve risk stratification. Epigenetic changes are reversible and acquired gradually, providing potentials for prevention and early detection strategies. The epigenetic changes are tissue-specific and stage-of-development-specific, raising challenges in choice of sample and timing for evaluation of cancer-associated changes. The Horizon 2020 funded research programme, FORECEE, using empirical data, will investigate the value of integration of epigenomics with genomics for risk prediction and prevention of women-specific cancers.

Reclassification of genetic-based risk predictions as GWAS data accumulate

Background

Disease risk assessments based on common genetic variation have gained widespread attention and use in recent years. The clinical utility of genetic risk profiles depends on the number and effect size of identified loci, and how stable the predicted risks are as additional loci are discovered. Changes in risk classification for individuals over time would undermine the validity of common genetic variation for risk prediction. In this analysis, we quantified reclassification of genetic risk based on past and anticipated future GWAS data.

Methods

We identified disease-associated SNPs via the NHGRI GWAS catalog and recent large scale genome-wide association study (GWAS). We calculated the genomic risk for a simulated cohort of 100,000 individuals based on a multiplicative odds ratio model using cumulative GWAS-identified SNPs at four time points: 2007, 2009, 2011, and 2013. Individuals were classified as Higher Risk (population adjusted odds >2), Average Risk (between 0.5 and 2), and Lower Risk (<0.5) for each time point and we compared classifications between time points for breast cancer (BrCa), prostate cancer (PrCa), diabetes mellitus type 2 (T2D), and cardiovascular heart disease (CHD). We estimated future reclassification using the anticipated number of undiscovered SNPs.

Results

Risk reclassification occurred for all four phenotypes from 2007 to 2013. During the most recent interval (2011-2013), the degree of risk reclassification ranged from 16.3 % for CHD to 24.4 % for PrCa. Many individuals classified as Higher Risk at earlier time points were subsequently reclassified into a lower risk category. From 2011 to 2013, the degree of such downward risk reclassification ranged from 24.9 % for T2D to 55 % for CHD. The percent of individuals classified as Higher Risk increased as more SNPs were discovered, ranging from an increase of 5 % for CHD to 9 % for PrCa from 2007 to 2013. Reclassification continued to occur when we modeled the discovery of anticipated SNPs based on doubling current sample size.

Conclusion

Risk estimates from common genetic variation show large reclassification rates. Identifying disease-associated SNPs facilitates the clinically relevant task of identifying higher-risk individuals. However, the large amount of reclassification that we demonstrated in individuals initially classified as Higher Risk but later as Average Risk or Lower Risk, suggests that caution is currently warranted in basing clinical decisions on common genetic variation for many complex diseases.

Electronic supplementary material

The online version of this article (doi:10.1186/s13073-016-0272-5) contains supplementary material, which is available to authorized users.

A comparison between different prediction models for invasive breast cancer occurrence in the French E3N cohort

Breast cancer remains a global health concern with a lack of high discriminating prediction models. The k-nearest-neighbor algorithm (kNN) estimates individual risks using an intuitive tool. This study compares the performances of this approach with the Cox and the Gail models for the 5-year breast cancer risk prediction. The study included 64,995 women from the French E3N prospective cohort. The sample was divided into a learning (N = 51,821) series to learn the models using fivefold cross-validation and a validation (N = 13,174) series to evaluate them. The area under the receiver operating characteristic curve (AUC) and the expected over observed number of cases (E/O) ratio were estimated. In the two series, 393 and 78 premenopausal and 537 and 98 postmenopausal breast cancers were diagnosed. The discrimination values of the best combinations of predictors obtained from cross-validation ranged from 0.59 to 0.60. In the validation series, the AUC values in premenopausal and postmenopausal women were 0.583 [0.520; 0.646] and 0.621 [0.563; 0.679] using the kNN and 0.565 [0.500; 0.631] and 0.617 [0.561; 0.673] using the Cox model. The E/O ratios were 1.26 and 1.28 in premenopausal women and 1.44 and 1.40 in postmenopausal women. The applied Gail model provided AUC values of 0.614 [0.554; 0.675] and 0.549 [0.495; 0.604] and E/O ratios of 0.78 and 1.12. This study shows that the prediction performances differed according to menopausal status when using parametric statistical tools. The k-nearest-neighbor approach performed well, and discrimination was improved in postmenopausal women compared with the Gail model.

The role of polycomb group protein Bmi-1 and Notch4 in breast cancer stem cell inhibition by benzyl isothiocyanate

We showed previously that garden cress constituent benzyl isothiocyanate (BITC) inhibits self-renewal of breast cancer stem cells (bCSC) in vitro and in vivo. The present study offers novel insights into the mechanism by which BITC inhibits bCSC. Flow cytometry and mammosphere assay were performed to quantify bCSC fraction. Protein expression was determined by western blotting. Apoptosis was assessed by flow cytometry using Annexin V-propidium iodide method. Cell migration was determined by Boyden chamber assay. BITC treatment resulted in a marked decrease in protein level of polycomb group protein B-lymphoma Moloney murine leukemia virus insertion region-1 (Bmi-1) in cultured human breast cancer cells (MCF-7, SUM159, MDA-MB-231, and MDA-MB-361) and MDA-MB-231 xenografts in vivo. Overexpression (MCF-7) or knockdown (SUM159, and MDA-MB-231) of Bmi-1 protein had no meaningful impact on the BITC's ability to inhibit cell viability and cell migration and/or induce apoptosis. On the other hand, inhibition of bCSC markers (aldehyde dehydrogenase 1 activity and mammosphere frequency) resulting from BITC exposure was significantly altered by Bmi-1 overexpression and knockdown. BITC was previously shown to cause activation of Notch1, Notch2, and Notch4 in association with induction of γ-secretase complex component Nicastrin, which are also implicated in maintenance of cancer stemness. BITC-mediated inhibition of bCSC was augmented by knockdown of Notch4 and Nicastrin, but not by RNA interference of Notch1 or Notch2. The present study highlights important roles for Bmi-1 and Notch4 in BITC-mediated suppression of bCSC.

Notch2 activation is protective against anticancer effects of zerumbone in human breast cancer cells

We showed previously that zerumbone (ZER), a sesquiterpene isolated from subtropical ginger, inhibited in vitro (MCF-7 and MDA-MB-231cells) and in vivo (MDA-MB-231 cells) growth of human breast cancer cells in association with apoptosis induction. Here, we investigated the role of Notch receptors in anticancer effects of ZER (cell migration inhibition and apoptosis induction) using breast cancer cells. Western blotting was performed to determine protein expression changes. Effect of ZER on transcriptional activity of Notch was assessed by luciferase reporter assays. Transfection with small hairpin RNA or small interfering RNA was performed for knockdown of Notch2 or Presenilin-1 protein. Cell migration and apoptosis were quantitated by Boyden chamber assay and flow cytometry, respectively. Exposure of MDA-MB-231, MCF-7, and SUM159 cells to ZER resulted in increased cleavage of Notch2 in each cell line. On the other hand, levels of cleaved Notch1 and Notch4 proteins were decreased following ZER treatment. Increased cleavage of Notch2 in ZER-treated cells was accompanied by induction of Presenilin-1 protein and transcriptional activation of Notch. Inhibition of cell migration as well as apoptosis induction resulting from ZER exposure was significantly augmented by knockdown of Notch2 protein. ZER-mediated cleavage of Notch2 protein in MDA-MB-231 cells was markedly attenuated upon RNA interference of Presenilin-1. Knockdown of Presenilin-1 protein also resulted in escalation of ZER-induced apoptosis. The present study indicates that Notch2 activation by ZER inhibits its proapoptotic and anti-migratory response at least in breast cancer cells.

Implementing risk-stratified screening for common cancers: a review of potential ethical, legal and social issues

BACKGROUND

The identification of common genetic variants associated with common cancers including breast, prostate and ovarian cancers would allow population stratification by genotype to effectively target screening and treatment. As scientific, clinical and economic evidence mounts there will be increasing pressure for risk-stratified screening programmes to be implemented.

METHODS

This paper reviews some of the main ethical, legal and social issues (ELSI) raised by the introduction of genotyping into risk-stratified screening programmes, in terms of Beauchamp and Childress's four principles of biomedical ethics--respect for autonomy, non-maleficence, beneficence and justice. Two alternative approaches to data collection, storage, communication and consent are used to exemplify the ELSI issues that are likely to be raised.

RESULTS

Ultimately, the provision of risk-stratified screening using genotyping raises fundamental questions about respective roles of individuals, healthcare providers and the state in organizing or mandating such programmes, and the principles, which underpin their provision, particularly the requirement for distributive justice.

CONCLUSIONS

The scope and breadth of these issues suggest that ELSI relating to risk-stratified screening will become increasingly important for policy-makers, healthcare professionals and a wide diversity of stakeholders.

Whole genome prediction of bladder cancer risk with the Bayesian LASSO

To build a predictive model for urothelial carcinoma of the bladder (UCB) risk combining both genomic and nongenomic data, 1,127 cases and 1,090 controls from the Spanish Bladder Cancer/EPICURO study were genotyped using the HumanHap 1M SNP array. After quality control filters, genotypes from 475,290 variants were available. Nongenomic information comprised age, gender, region, and smoking status. Three Bayesian threshold models were implemented including: (1) only genomic information, (2) only nongenomic data, and (3) both sources of information. The three models were applied to the whole population, to only nonsmokers, to male smokers, and to extreme phenotypes to potentiate the UCB genetic component. The area under the ROC curve allowed evaluating the predictive ability of each model in a 10-fold cross-validation scenario. Smoking status showed the highest predictive ability of UCB risk (AUCtest = 0.62). On the other hand, the AUC of all genetic variants was poorer (0.53). When the extreme phenotype approach was applied, the predictive ability of the genomic model improved 15%. This study represents a first attempt to build a predictive model for UCB risk combining both genomic and nongenomic data and applying state-of-the-art statistical approaches. However, the lack of genetic relatedness among individuals, the complexity of UCB etiology, as well as a relatively small statistical power, may explain the low predictive ability for UCB risk. The study confirms the difficulty of predicting complex diseases using genetic data, and suggests the limited translational potential of findings from this type of data into public health interventions.

Personalized screening for cancers: should we consider polygenic profiling?

Polygenic profiling and risk stratification for population-based screening for cancer improve the efficiency of the screening programs. Translation of genomics into personalized screening programs requires evidence from empirical research on the balance of benefits and harms of personalized screening, and engagement with the public, professionals and policy makers.

The gene desert mammary carcinoma susceptibility locus Mcs1a regulates Nr2f1 modifying mammary epithelial cell differentiation and proliferation

Genome-wide association studies have revealed that many low-penetrance breast cancer susceptibility loci are located in non-protein coding genomic regions; however, few have been characterized. In a comparative genetics approach to model such loci in a rat breast cancer model, we previously identified the mammary carcinoma susceptibility locus Mcs1a. We now localize Mcs1a to a critical interval (277 Kb) within a gene desert. Mcs1a reduces mammary carcinoma multiplicity by 50% and acts in a mammary cell-autonomous manner. We developed a megadeletion mouse model, which lacks 535 Kb of sequence containing the Mcs1a ortholog. Global gene expression analysis by RNA-seq revealed that in the mouse mammary gland, the orphan nuclear receptor gene Nr2f1/Coup-tf1 is regulated by Mcs1a. In resistant Mcs1a congenic rats, as compared with susceptible congenic control rats, we found Nr2f1 transcript levels to be elevated in mammary gland, epithelial cells, and carcinoma samples. Chromatin looping over ∼820 Kb of sequence from the Nr2f1 promoter to a strongly conserved element within the Mcs1a critical interval was identified. This element contains a 14 bp indel polymorphism that affects a human-rat-mouse conserved COUP-TF binding motif and is a functional Mcs1a candidate. In both the rat and mouse models, higher Nr2f1 transcript levels are associated with higher abundance of luminal mammary epithelial cells. In both the mouse mammary gland and a human breast cancer global gene expression data set, we found Nr2f1 transcript levels to be strongly anti-correlated to a gene cluster enriched in cell cycle-related genes. We queried 12 large publicly available human breast cancer gene expression studies and found that the median NR2F1 transcript level is consistently lower in 'triple-negative' (ER-PR-HER2-) breast cancers as compared with 'receptor-positive' breast cancers. Our data suggest that the non-protein coding locus Mcs1a regulates Nr2f1, which is a candidate modifier of differentiation, proliferation, and mammary cancer risk.

Suppression of FOXQ1 in benzyl isothiocyanate-mediated inhibition of epithelial-mesenchymal transition in human breast cancer cells

We showed previously that breast cancer chemoprevention with benzyl isothiocyanate (BITC) in MMTV-neu mice was associated with induction of E-cadherin protein in vivo. Loss of E-cadherin expression and induction of mesenchymal markers (e.g. vimentin) are biochemical hallmarks of epithelial-mesenchymal transition (EMT), a developmental process implicated in progression of cancer to aggressive state. This study offers novel insights into the mechanism by which BITC inhibits EMT. Exposure of MDA-MB-231, SUM159 and MDA-MB-468 human breast cancer cells to BITC (2.5 and 5 µM) resulted in transcriptional repression of urokinase-type plasminogen activator (uPA) as well as its receptor (uPAR). However, ectopic expression of uPAR in MDA-MB-468 cells failed to confer protection against induction of E-cadherin and inhibition of cell invasion/migration resulting from BITC treatment. The BITC-mediated induction of E-cadherin and inhibition of cell migration was sustained in MDA-MB-231 and SUM159 cells transiently transfected with an uPAR-targeted small interfering RNA. Overexpression of Forkhead Box Q1 (FOXQ1), whose protein and messenger RNA levels were decreased by BITC treatment in cells and MDA-MB-231 xenografts, conferred marked protection against BITC-mediated inhibition of EMT and cell migration. In conclusion, this study implicates FOXQ1 suppression in BITC-mediated inhibition of EMT in human breast cancer cells.

Biomarkers of phenethyl isothiocyanate-mediated mammary cancer chemoprevention in a clinically relevant mouse model

BACKGROUND

Phenethyl isothiocyanate (PEITC) is a natural plant compound with chemopreventative potential against some cancers and the ability to induce apoptosis in breast cancer cells.

METHODS

Female mouse mammary tumor virus-neu mice were fed a control AIN-76A diet (n = 35) or the same diet supplemented with 3 µmol PEITC/g diet (n = 33) for 29 weeks, at which time they were killed. Breast tissue sections were stained with hematoxylin and eosin for histopathological assessments, and incidence and size of macroscopic mammary tumors were assessed. Cell proliferation (Ki-67 staining), apoptosis (terminal deoxynucleotidyl transferase-mediated dUTP nick-labeling), and neoangiogenesis (CD31 staining) were determined in tumor sections. Plasma levels of transthyretin were measured in treated and control mice. Expression of proteins in mammary tumor sections was determined by immunohistochemistry. Proteomic profiling was performed by two-dimensional gel electrophoresis followed by mass spectrometry. All statistical tests were two-sided.

RESULTS

Administration of PEITC for 29 weeks was associated with 53.13% decreased incidence of macroscopic mammary tumors (mean tumor incidence, PEITC-supplemented diet vs control diet, 18.75% vs 40.00%, difference = -21.25%, 95% confidence interval [CI] = -43.19% to 0.69%, P = .07) and with a 56.25% reduction in microscopic mammary carcinoma lesions greater than 2 mm(2) (mean incidence, PEITC-supplemented diet vs control diet, 18.75% vs 42.86%, difference = -24.11%, 95% CI = -46.35% to -1.86%, P = .04). PEITC-mediated mammary cancer growth inhibition was not because of suppression of human epidermal growth factor receptor-2 expression but was associated with reduced cellular proliferation and neoangiogenesis, increased apoptosis, and altered expression of several proteins, including decreased ATP synthase in the tumor and increased plasma levels of transthyretin.

CONCLUSIONS

PEITC inhibits the growth of mammary cancers in a mouse model with similarities to human breast cancer progression. ATP synthase and transthyretin appear to be novel biomarkers associated with PEITC exposure.

Potential usefulness of single nucleotide polymorphisms to identify persons at high cancer risk: an evaluation of seven common cancers

PURPOSE

To estimate the likely number and predictive strength of cancer-associated single nucleotide polymorphisms (SNPs) that are yet to be discovered for seven common cancers.

METHODS

From the statistical power of published genome-wide association studies, we estimated the number of undetected susceptibility loci and the distribution of effect sizes for all cancers. Assuming a log-normal model for risks and multiplicative relative risks for SNPs, family history (FH), and known risk factors, we estimated the area under the receiver operating characteristic curve (AUC) and the proportion of patients with risks above risk thresholds for screening. From additional prevalence data, we estimated the positive predictive value and the ratio of non-patient cases to patient cases (false-positive ratio) for various risk thresholds.

RESULTS

Age-specific discriminatory accuracy (AUC) for models including FH and foreseeable SNPs ranged from 0.575 for ovarian cancer to 0.694 for prostate cancer. The proportions of patients in the highest decile of population risk ranged from 16.2% for ovarian cancer to 29.4% for prostate cancer. The corresponding false-positive ratios were 241 for colorectal cancer, 610 for ovarian cancer, and 138 or 280 for breast cancer in women age 50 to 54 or 40 to 44 years, respectively.

CONCLUSION

Foreseeable common SNP discoveries may not permit identification of small subsets of patients that contain most cancers. Usefulness of screening could be diminished by many false positives. Additional strong risk factors are needed to improve risk discrimination.

Prevention of breast cancer in the context of a national breast screening programme

Breast cancer is not only increasing in the west but also particularly rapidly in eastern countries where traditionally the incidence has been low. The rise in incidence is mainly related to changes in reproductive patterns and lifestyle. These trends could potentially be reversed by defining women at greatest risk and offering appropriate preventive measures. A model for this approach was the establishment of Family History Clinics (FHCs), which have resulted in improved survival in younger women at high risk. New predictive models of risk that include reproductive and lifestyle factors, mammographic density and measurement of risk-associated single nucleotide polymorphisms (SNPs) may give more precise information concerning risk and enable better targeting for mammographic screening programmes and of preventive measures. Endocrine prevention using anti-oestrogens and aromatase inhibitors is effective, and observational studies suggest lifestyle modification may also be effective. However, referral to FHCs is opportunistic and predominantly includes younger women. A better approach for identifying older women at risk may be to use national breast screening programmes. Here were described pilot studies to assess whether the routine assessment of breast cancer risk is feasible within a population-based screening programme, whether the feedback and advice on risk-reducing interventions would be welcomed and taken up, and to consider whether the screening interval should be modified according to breast cancer risk.

Can we replace the microscope with microarrays for diagnosis, prognosis and treatment of early breast cancer?

In recent years, molecular research has translated into remarkable changes for breast cancer diagnostics and therapeutics. Molecular tests such as Oncotype DX® and MammaPrint® have revolutionized the predictive and prognostic tools in the clinic. By stratifying the risk of recurrence for patients, the tests are able to provide clinicians with more information on the treatment outcomes of using chemotherapy, endocrine therapy or combination therapies for patients with genetic expression patterns. However, it is still questionable for clinical applications as some areas remain unclear; the true benefit still needs prospective evaluation. In this paper, the limitation and the possibility to replace traditional histopathologic features of molecular tests are discussed. At the moment, it seems there are still limitations that prevent microarrays from replacing the microscope for diagnosis, prognosis and treatment of early breast cancer. However, additional important clinical information is added to traditional histology and IHC determination of ER, PR and HER2 in terms of prognostic and predictive power.

Perspectives on the use of multiple sclerosis risk genes for prediction

Objective

A recent collaborative genome-wide association study replicated a large number of susceptibility loci and identified novel loci. This increase in known multiple sclerosis (MS) risk genes raises questions about clinical applicability of genotyping. In an empirical set we assessed the predictive power of typing multiple genes. Next, in a modelling study we explored current and potential predictive performance of genetic MS risk models.

Materials and Methods

Genotype data on 6 MS risk genes in 591 MS patients and 600 controls were used to investigate the predictive value of combining risk alleles. Next, the replicated and novel MS risk loci from the recent and largest international genome-wide association study were used to construct genetic risk models simulating a population of 100,000 individuals. Finally, we assessed the required numbers, frequencies, and ORs of risk SNPs for higher discriminative accuracy in the future.

Results

Individuals with 10 to 12 risk alleles had a significantly increased risk compared to individuals with the average population risk for developing MS (OR 2.76 (95% CI 2.02–3.77)). In the simulation study we showed that the area under the receiver operating characteristic curve (AUC) for a risk score based on the 6 SNPs was 0.64. The AUC increases to 0.66 using the well replicated 24 SNPs and to 0.69 when including all replicated and novel SNPs (n = 53) in the risk model. An additional 20 SNPs with allele frequency 0.30 and ORs 1.1 would be needed to increase the AUC to a slightly higher level of 0.70, and at least 50 novel variants with allele frequency 0.30 and ORs 1.4 would be needed to obtain an AUC of 0.85.

Conclusion

Although new MS risk SNPs emerge rapidly, the discriminatory ability in a clinical setting will be limited.

Perspectives on the use of multiple sclerosis risk genes for prediction

OBJECTIVE

A recent collaborative genome-wide association study replicated a large number of susceptibility loci and identified novel loci. This increase in known multiple sclerosis (MS) risk genes raises questions about clinical applicability of genotyping. In an empirical set we assessed the predictive power of typing multiple genes. Next, in a modelling study we explored current and potential predictive performance of genetic MS risk models.

MATERIALS AND METHODS

Genotype data on 6 MS risk genes in 591 MS patients and 600 controls were used to investigate the predictive value of combining risk alleles. Next, the replicated and novel MS risk loci from the recent and largest international genome-wide association study were used to construct genetic risk models simulating a population of 100,000 individuals. Finally, we assessed the required numbers, frequencies, and ORs of risk SNPs for higher discriminative accuracy in the future.

RESULTS

Individuals with 10 to 12 risk alleles had a significantly increased risk compared to individuals with the average population risk for developing MS (OR 2.76 (95% CI 2.02-3.77)). In the simulation study we showed that the area under the receiver operating characteristic curve (AUC) for a risk score based on the 6 SNPs was 0.64. The AUC increases to 0.66 using the well replicated 24 SNPs and to 0.69 when including all replicated and novel SNPs (n = 53) in the risk model. An additional 20 SNPs with allele frequency 0.30 and ORs 1.1 would be needed to increase the AUC to a slightly higher level of 0.70, and at least 50 novel variants with allele frequency 0.30 and ORs 1.4 would be needed to obtain an AUC of 0.85.

CONCLUSION

Although new MS risk SNPs emerge rapidly, the discriminatory ability in a clinical setting will be limited.