Michael A. Black

Clinical risk prediction for pre-eclampsia in nulliparous women: development of model in international prospective cohort

Objectives To develop a predictive model for pre-eclampsia based on clinical risk factors for nulliparous women and to identify a subgroup at increased risk, in whom specialist referral might be indicated. Design Prospective multicentre cohort. Setting Five centres in Auckland, New Zealand; Adelaide, Australia; Manchester and London, United Kingdom; and Cork, Republic of Ireland. Participants 3572 “healthy” nulliparous women with a singleton pregnancy from a large international study; data on pregnancy outcome were available for 3529 (99%). Main outcome measure Pre-eclampsia defined as ≥140 mm Hg or diastolic blood pressure ≥90 mm Hg, or both, on at least two occasions four hours apart after 20 weeks’ gestation but before the onset of labour, or postpartum, with either proteinuria or any multisystem complication. Preterm pre-eclampsia was defined as women with pre-eclampsia delivered before 37+0 weeks’ gestation. In the stepwise logistic regression the comparison group was women without pre-eclampsia. Results Of the 3529 women, 186 (5.3%) developed pre-eclampsia, including 47 (1.3%) with preterm pre-eclampsia. Clinical risk factors at 14-16 weeks’ gestation were age, mean arterial blood pressure, body mass index (BMI), family history of pre-eclampsia, family history of coronary heart disease, maternal birth weight, and vaginal bleeding for at least five days. Factors associated with reduced risk were a previous single miscarriage with the same partner, taking at least 12 months to conceive, high intake of fruit, cigarette smoking, and alcohol use in the first trimester. The area under the receiver operating characteristics curve (AUC), under internal validation, was 0.71. Addition of uterine artery Doppler indices did not improve performance (internal validation AUC 0.71). A framework for specialist referral was developed based on a probability of pre-eclampsia generated by the model of at least 15% or an abnormal uterine artery Doppler waveform in a subset of women with single risk factors. Nine per cent of nulliparous women would be referred for a specialist opinion, of whom 21% would develop pre-eclampsia. The relative risk for developing pre-eclampsia and preterm pre-eclampsia in women referred to a specialist compared with standard care was 5.5 and 12.2, respectively. Conclusions The ability to predict pre-eclampsia in healthy nulliparous women using clinical phenotype is modest and requires external validation in other populations. If validated, it could provide a personalised clinical risk profile for nulliparous women to which biomarkers could be added. Trial registration ACTRN12607000551493.

The G Protein–Coupled Receptor GPR30 Inhibits Proliferation of Estrogen Receptor–Positive Breast Cancer Cells

The G protein-coupled receptor GPR30 binds 17beta-estradiol (E(2)) yet differs from classic estrogen receptors (ERalpha and ERbeta). GPR30 can mediate E(2)-induced nongenomic signaling, but its role in ERalpha-positive breast cancer remains unclear. Gene expression microarray data from five cohorts comprising 1,250 breast carcinomas showed an association between increased GPR30 expression and ERalpha-positive status. We therefore examined GPR30 in estrogenic activities in ER-positive MCF-7 breast cancer cells using G-1 and diethylstilbestrol (DES), ligands that selectively activate GPR30 and ER, respectively, and small interfering RNAs. In expression studies, E(2) and DES, but not G-1, transiently downregulated both ER and GPR30, indicating that this was ER mediated. In Ca(2+) mobilization studies, GPR30, but not ERalpha, mediated E(2)-induced Ca(2+) responses because E(2), 4-hydroxytamoxifen (activates GPR30), and G-1, but not DES, elicited cytosolic Ca(2+) increases not only in MCF-7 cells but also in ER-negative SKBr3 cells. Additionally, in MCF-7 cells, GPR30 depletion blocked E(2)-induced and G-1-induced Ca(2+) mobilization, but ERalpha depletion did not. Interestingly, GPR30-coupled Ca(2+) responses were sustained and inositol triphosphate receptor mediated in ER-positive MCF-7 cells but transitory and ryanodine receptor mediated in ER-negative SKBr3 cells. Proliferation studies involving GPR30 depletion indicated that the role of GPR30 was to promote SKBr3 cell growth but reduce MCF-7 cell growth. Supporting this, G-1 profoundly inhibited MCF-7 cell growth, potentially via p53 and p21 induction. Further, flow cytometry showed that G-1 blocked MCF-7 cell cycle progression at the G(1) phase. Thus, GPR30 antagonizes growth of ERalpha-positive breast cancer and may represent a new target to combat this disease.

Sleeping Beauty mutagenesis reveals cooperating mutations and pathways in pancreatic adenocarcinoma

Pancreatic cancer is one of the most deadly cancers affecting the Western world. Because the disease is highly metastatic and difficult to diagnosis until late stages, the 5-y survival rate is around 5%. The identification of molecular cancer drivers is critical for furthering our understanding of the disease and development of improved diagnostic tools and therapeutics. We have conducted a mutagenic screen using Sleeping Beauty (SB) in mice to identify new candidate cancer genes in pancreatic cancer. By combining SB with an oncogenic Kras allele, we observed highly metastatic pancreatic adenocarcinomas. Using two independent statistical methods to identify loci commonly mutated by SB in these tumors, we identified 681 loci that comprise 543 candidate cancer genes (CCGs); 75 of these CCGs, including Mll3 and Ptk2, have known mutations in human pancreatic cancer. We identified point mutations in human pancreatic patient samples for another 11 CCGs, including Acvr2a and Map2k4. Importantly, 10% of the CCGs are involved in chromatin remodeling, including Arid4b, Kdm6a, and Nsd3, and all SB tumors have at least one mutated gene involved in this process; 20 CCGs, including Ctnnd1, Fbxo11, and Vgll4, are also significantly associated with poor patient survival. SB mutagenesis provides a rich resource of mutations in potential cancer drivers for cross-comparative analyses with ongoing sequencing efforts in human pancreatic adenocarcinoma.

Multiple Gene Expression Classifiers from Different Array Platforms Predict Poor Prognosis of Colorectal Cancer

Purpose: This study aimed to develop gene classifiers to predict colorectal cancer recurrence. We investigated whether gene classifiers derived from two tumor series using different array platforms could be independently validated by application to the alternate series of patients. Experimental Design: Colorectal tumors from New Zealand (n = 149) and Germany (n = 55) patients had a minimum follow-up of 5 years. RNA was profiled using oligonucleotide printed microarrays (New Zealand samples) and Affymetrix arrays (German samples). Classifiers based on clinical data, gene expression data, and a combination of the two were produced and used to predict recurrence. The use of gene expression information was found to improve the predictive ability in both data sets. The New Zealand and German gene classifiers were cross-validated on the German and New Zealand data sets, respectively, to validate their predictive power. Survival analyses were done to evaluate the ability of the classifiers to predict patient survival. Results: The prediction rates for the New Zealand and German gene-based classifiers were 77% and 84%, respectively. Despite significant differences in study design and technologies used, both classifiers retained prognostic power when applied to the alternate series of patients. Survival analyses showed that both classifiers gave a better stratification of patients than the traditional clinical staging. One classifier contained genes associated with cancer progression, whereas the other had a large immune response gene cluster concordant with the role of a host immune response in modulating colorectal cancer outcome. Conclusions: The successful reciprocal validation of gene-based classifiers on different patient cohorts and technology platforms supports the power of microarray technology for individualized outcome prediction of colorectal cancer patients. Furthermore, many of the genes identified have known biological functions congruent with the predicted outcomes.

Mutations in genes encoding the cadherin receptor-ligand pair DCHS1 and FAT4 disrupt cerebral cortical development.

The regulated proliferation and differentiation of neural stem cells before the generation and migration of neurons in the cerebral cortex are central aspects of mammalian development. Periventricular neuronal heterotopia, a specific form of mislocalization of cortical neurons, can arise from neuronal progenitors that fail to negotiate aspects of these developmental processes. Here we show that mutations in genes encoding the receptor-ligand cadherin pair DCHS1 and FAT4 lead to a recessive syndrome in humans that includes periventricular neuronal heterotopia. Reducing the expression of Dchs1 or Fat4 within mouse embryonic neuroepithelium increased progenitor cell numbers and reduced their differentiation into neurons, resulting in the heterotopic accumulation of cells below the neuronal layers in the neocortex, reminiscent of the human phenotype. These effects were countered by concurrent knockdown of Yap, a transcriptional effector of the Hippo signaling pathway. These findings implicate Dchs1 and Fat4 upstream of Yap as key regulators of mammalian neurogenesis.

Early pregnancy prediction of preeclampsia in nulliparous women, combining clinical risk and biomarkers: the Screening for Pregnancy Endpoints (SCOPE) international cohort study

More than half of all cases of preeclampsia occur in healthy first-time pregnant women. Our aim was to develop a method to predict those at risk by combining clinical factors and measurements of biomarkers in women recruited to the Screening for Pregnancy Endpoints (SCOPE) study of low-risk nulliparous women. Forty-seven biomarkers identified on the basis of (1) association with preeclampsia, (2) a biological role in placentation, or (3) a role in cellular mechanisms involved in the pathogenesis of preeclampsia were measured in plasma sampled at 14 to 16 weeks’ gestation from 5623 women. The cohort was randomly divided into training (n=3747) and validation (n=1876) cohorts. Preeclampsia developed in 278 (4.9%) women, of whom 28 (0.5%) developed early-onset preeclampsia. The final model for the prediction of preeclampsia included placental growth factor, mean arterial pressure, and body mass index at 14 to 16 weeks’ gestation, the consumption of ≥3 pieces of fruit per day, and mean uterine artery resistance index. The area under the receiver operator curve (95% confidence interval) for this model in training and validation cohorts was 0.73 (0.70–0.77) and 0.68 (0.63–0.74), respectively. A predictive model of early-onset preeclampsia included angiogenin/placental growth factor as a ratio, mean arterial pressure, any pregnancy loss <10 weeks, and mean uterine artery resistance index (area under the receiver operator curve [95% confidence interval] in training and validation cohorts, 0.89 [0.78–1.0] and 0.78 [0.58–0.99], respectively). Neither model included pregnancy-associated plasma protein A, previously reported to predict preeclampsia in populations of mixed parity and risk. In nulliparous women, combining multiple biomarkers and clinical data provided modest prediction of preeclampsia. # Novelty and Significance {#article-title-41}More than half of all cases of preeclampsia occur in healthy first-time pregnant women. Our aim was to develop a method to predict those at risk by combining clinical factors and measurements of biomarkers in women recruited to the Screening for Pregnancy Endpoints (SCOPE) study of low-risk nulliparous women. Forty-seven biomarkers identified on the basis of (1) association with preeclampsia, (2) a biological role in placentation, or (3) a role in cellular mechanisms involved in the pathogenesis of preeclampsia were measured in plasma sampled at 14 to 16 weeks’ gestation from 5623 women. The cohort was randomly divided into training (n=3747) and validation (n=1876) cohorts. Preeclampsia developed in 278 (4.9%) women, of whom 28 (0.5%) developed early-onset preeclampsia. The final model for the prediction of preeclampsia included placental growth factor, mean arterial pressure, and body mass index at 14 to 16 weeks’ gestation, the consumption of ≥3 pieces of fruit per day, and mean uterine artery resistance index. The area under the receiver operator curve (95% confidence interval) for this model in training and validation cohorts was 0.73 (0.70–0.77) and 0.68 (0.63–0.74), respectively. A predictive model of early-onset preeclampsia included angiogenin/placental growth factor as a ratio, mean arterial pressure, any pregnancy loss <10 weeks, and mean uterine artery resistance index (area under the receiver operator curve [95% confidence interval] in training and validation cohorts, 0.89 [0.78–1.0] and 0.78 [0.58–0.99], respectively). Neither model included pregnancy-associated plasma protein A, previously reported to predict preeclampsia in populations of mixed parity and risk. In nulliparous women, combining multiple biomarkers and clinical data provided modest prediction of preeclampsia.

An Iron Regulatory Gene Signature Predicts Outcome in Breast Cancer

Changes in iron regulation characterize the malignant state. However, the pathways that effect these changes and their specific impact on prognosis remain poorly understood. We capitalized on publicly available microarray datasets comprising 674 breast cancer cases to systematically investigate how expression of genes related to iron metabolism is linked to breast cancer prognosis. Of 61 genes involved in iron regulation, 49% were statistically significantly associated with distant metastasis-free survival. Cases were divided into test and training cohorts, and the supervised principal component method was used to stratify cases into risk groups. Optimal risk stratification was achieved with a model comprising 16 genes, which we term the iron regulatory gene signature (IRGS). Multivariable analysis revealed that the IRGS contributes information not captured by conventional prognostic indicators (HR = 1.61; 95% confidence interval: 1.16-2.24; P = 0.004). The IRGS successfully stratified homogeneously treated patients, including ER+ patients treated with tamoxifen monotherapy, both with (P = 0.006) and without (P = 0.03) lymph node metastases. To test whether multiple pathways were embedded within the IRGS, we evaluated the performance of two gene dyads with known roles in iron biology in ER+ patients treated with tamoxifen monotherapy (n = 371). For both dyads, gene combinations that minimized intracellular iron content [anti-import: TFRC(Low)/HFE(High); or pro-export: SLC40A1 (ferroportin)(High)/HAMP(Low)] were associated with favorable prognosis (P < 0.005). Although the clinical utility of the IRGS will require further evaluation, its ability to both identify high-risk patients within traditionally low-risk groups and low-risk patients within high-risk groups has the potential to affect therapeutic decision making.

Interactions between immunity, proliferation and molecular subtype in breast cancer prognosis

BackgroundGene expression signatures indicative of tumor proliferative capacity and tumor-immune cell interactions have emerged as principal biology-driven predictors of breast cancer outcomes. How these signatures relate to one another in biological and prognostic contexts remains to be clarified.ResultsTo investigate the relationship between proliferation and immune gene signatures, we analyzed an integrated dataset of 1,954 clinically annotated breast tumor expression profiles randomized into training and test sets to allow two-way discovery and validation of gene-survival associations. Hierarchical clustering revealed a large cluster of distant metastasis-free survival-associated genes with known immunological functions that further partitioned into three distinct immune metagenes likely reflecting B cells and/or plasma cells; T cells and natural killer cells; and monocytes and/or dendritic cells. A proliferation metagene allowed stratification of cases into proliferation tertiles. The prognostic strength of these metagenes was largely restricted to tumors within the highest proliferation tertile, though intrinsic subtype-specific differences were observed in the intermediate and low proliferation tertiles. In highly proliferative tumors, high tertile immune metagene expression equated with markedly reduced risk of metastasis whereas tumors with low tertile expression of any one of the three immune metagenes were associated with poor outcome despite higher expression of the other two metagenes.ConclusionsThese findings suggest that a productive interplay among multiple immune cell types at the tumor site promotes long-term anti-metastatic immunity in a proliferation-dependent manner. The emergence of a subset of effective immune responders among highly proliferative tumors has novel prognostic ramifications.

A proteomic approach identifies early pregnancy biomarkers for preeclampsia: Novel linkages between a predisposition to preeclampsia and cardiovascular disease

Preeclampsia (PE) is a common, potentially life‐threatening pregnancy syndrome triggered by placental factors released into the maternal circulation, resulting in maternal vascular dysfunction along with activated inflammation and coagulation. Currently there is no screening test for PE. We sought to identify differentially expressed plasma proteins in women who subsequently develop PE that may perform as predictive biomarkers. In seven DIGE experiments, we compared the plasma proteome at 20 wk gestation in women who later developed PE with an appropriate birth weight for gestational age baby (n=27) or a small for gestational age baby (n=12) to healthy controls with uncomplicated pregnancies (n=57). Of the 49 differentially expressed spots associated with PE‐appropriate for gestational age, PE‐small for gestational age or both (p<0.05, false discovery rate corrected), 39 were identified by LC‐MS/MS. Two protein clusters that accurately (>90%) classified women at risk of developing PE were identified. Immunoblots confirmed the overexpression of fibrinogen γ chain and α‐1‐antichymotrypsin in plasma prior to PE. The proteins identified are involved in lipid metabolism, coagulation, complement regulation, extracellular matrix remodeling, protease inhibitor activity and acute‐phase responses, indicating novel synergism between pathways involved in the pathogenesis of PE. Our findings are remarkably similar to recently identified proteins complexed to high‐density lipoprotein and linked to cardiovascular disease.

Microsatellite Tandem Repeats Are Abundant in Human Promoters and Are Associated with Regulatory Elements

Tandem repeats are genomic elements that are prone to changes in repeat number and are thus often polymorphic. These sequences are found at a high density at the start of human genes, in the gene’s promoter. Increasing empirical evidence suggests that length variation in these tandem repeats can affect gene regulation. One class of tandem repeats, known as microsatellites, rapidly alter in repeat number. Some of the genetic variation induced by microsatellites is known to result in phenotypic variation. Recently, our group developed a novel method for measuring the evolutionary conservation of microsatellites, and with it we discovered that human microsatellites near transcription start sites are often highly conserved. In this study, we examined the properties of microsatellites found in promoters. We found a high density of microsatellites at the start of genes. We showed that microsatellites are statistically associated with promoters using a wavelet analysis, which allowed us to test for associations on multiple scales and to control for other promoter related elements. Because promoter microsatellites tend to be G/C rich, we hypothesized that G/C rich regulatory elements may drive the association between microsatellites and promoters. Our results indicate that CpG islands, G-quadruplexes (G4) and untranslated regulatory regions have highly significant associations with microsatellites, but controlling for these elements in the analysis does not remove the association between microsatellites and promoters. Due to their intrinsic lability and their overlap with predicted functional elements, these results suggest that many promoter microsatellites have the potential to affect human phenotypes by generating mutations in regulatory elements, which may ultimately result in disease. We discuss the potential functions of human promoter microsatellites in this context.