Differential Analysis of Ovarian and Endometrial Cancers Identifies a Methylator Phenotype

Despite improved outcomes in the past 30 years, less than half of all women diagnosed with epithelial ovarian cancer live five years beyond their diagnosis. Although typically treated as a single disease, epithelial ovarian cancer includes several distinct histological subtypes, such as papillary serous and endometrioid carcinomas. To address whether the morphological differences seen in these carcinomas represent distinct characteristics at the molecular level we analyzed DNA methylation patterns in 11 papillary serous tumors, 9 endometrioid ovarian tumors, 4 normal fallopian tube samples and 6 normal endometrial tissues, plus 8 normal fallopian tube and 4 serous samples from TCGA. For comparison within the endometrioid subtype we added 6 primary uterine endometrioid tumors and 5 endometrioid metastases from uterus to ovary. Data was obtained from 27,578 CpG dinucleotides occurring in or near promoter regions of 14,495 genes. We identified 36 locations with significant increases or decreases in methylation in comparisons of serous tumors and normal fallopian tube samples. Moreover, unsupervised clustering techniques applied to all samples showed three major profiles comprising mostly normal samples, serous tumors, and endometrioid tumors including ovarian, uterine and metastatic origins. The clustering analysis identified 60 differentially methylated sites between the serous group and the normal group. An unrelated set of 25 serous tumors validated the reproducibility of the methylation patterns. In contrast, >1,000 genes were differentially methylated between endometrioid tumors and normal samples. This finding is consistent with a generalized regulatory disruption caused by a methylator phenotype. Through DNA methylation analyses we have identified genes with known roles in ovarian carcinoma etiology, whereas pathway analyses provided biological insight to the role of novel genes. Our finding of differences between serous and endometrioid ovarian tumors indicates that intervention strategies could be developed to specifically address subtypes of epithelial ovarian cancer

Genome-wide meta-analysis of cerebral white matter hyperintensities in patients with stroke.

OBJECTIVE: For 3,670 stroke patients from the United Kingdom, United States, Australia, Belgium, and Italy, we performed a genome-wide meta-analysis of white matter hyperintensity volumes (WMHV) on data imputed to the 1000 Genomes reference dataset to provide insights into disease mechanisms. METHODS: We first sought to identify genetic associations with white matter hyperintensities in a stroke population, and then examined whether genetic loci previously linked to WMHV in community populations are also associated in stroke patients. Having established that genetic associations are shared between the 2 populations, we performed a meta-analysis testing which associations with WMHV in stroke-free populations are associated overall when combined with stroke populations. RESULTS: There were no associations at genome-wide significance with WMHV in stroke patients. All previously reported genome-wide significant associations with WMHV in community populations shared direction of effect in stroke patients. In a meta-analysis of the genome-wide significant and suggestive loci (p < 5 × 10(-6)) from community populations (15 single nucleotide polymorphisms in total) and from stroke patients, 6 independent loci were associated with WMHV in both populations. Four of these are novel associations at the genome-wide level (rs72934505 [NBEAL1], p = 2.2 × 10(-8); rs941898 [EVL], p = 4.0 × 10(-8); rs962888 [C1QL1], p = 1.1 × 10(-8); rs9515201 [COL4A2], p = 6.9 × 10(-9)). CONCLUSIONS: Genetic associations with WMHV are shared in otherwise healthy individuals and patients with stroke, indicating common genetic susceptibility in cerebral small vessel disease

Gene expression profiling of primary cultures of ovarian epithelial cells identifies novel molecular classifiers of ovarian cancer

In order to elucidate the biological variance between normal ovarian surface epithelial (NOSE) and epithelial ovarian cancer (EOC) cells, and to build a molecular classifier to discover new markers distinguishing these cells, we analysed gene expression patterns of 65 primary cultures of these tissues by oligonucleotide microarray. Unsupervised clustering highlights three subgroups of tumours: low malignant potential tumours, invasive solid tumours and tumour cells derived from ascites. We selected 18 genes with expression profiles that enable the distinction of NOSE from these three groups of EOC with 92% accuracy. Validation using an independent published data set derived from tissues or primary cultures confirmed a high accuracy (87–96%). The distinctive expression pattern of a subset of genes was validated by quantitative reverse transcription–PCR. An ovarian-specific tissue array representing tissues from NOSE and EOC samples of various subtypes and grades was used to further assess the protein expression patterns of two differentially expressed genes (Msln and BMP-2) by immunohistochemistry. This study highlights the relevance of using primary cultures of epithelial ovarian cells as a model system for gene profiling studies and demonstrates that the statistical analysis of gene expression profiling is a useful approach for selecting novel molecular tumour markers

Gene expression profiling of human ovarian tumours

There is currently a lack of reliable diagnostic and prognostic markers for ovarian cancer. We established gene expression profiles for 120 human ovarian tumours to identify determinants of histologic subtype, grade and degree of malignancy. Unsupervised cluster analysis of the most variable set of expression data resulted in three major tumour groups. One consisted predominantly of benign tumours, one contained mostly malignant tumours, and one was comprised of a mixture of borderline and malignant tumours. Using two supervised approaches, we identified a set of genes that distinguished the benign, borderline and malignant phenotypes. These algorithms were unable to establish profiles for histologic subtype or grade. To validate these findings, the expression of 21 candidate genes selected from these analyses was measured by quantitative RT–PCR using an independent set of tumour samples. Hierarchical clustering of these data resulted in two major groups, one benign and one malignant, with the borderline tumours interspersed between the two groups. These results indicate that borderline ovarian tumours may be classified as either benign or malignant, and that this classifier could be useful for predicting the clinical course of borderline tumours. Immunohistochemical analysis also demonstrated increased expression of CD24 antigen in malignant versus benign tumour tissue. The data that we have generated will contribute to a growing body of expression data that more accurately define the biologic and clinical characteristics of ovarian cancers

E2F5 status significantly improves malignancy diagnosis of epithelial ovarian cancer

<p>Abstract</p> <p>Background</p> <p>Ovarian epithelial cancer (OEC) usually presents in the later stages of the disease. Factors, especially those associated with cell-cycle genes, affecting the genesis and tumour progression for ovarian cancer are largely unknown. We hypothesized that over-expressed transcription factors (TFs), as well as those that are driving the expression of the OEC over-expressed genes, could be the key for OEC genesis and potentially useful tissue and serum markers for malignancy associated with OEC.</p> <p>Methods</p> <p>Using a combination of computational (selection of candidate TF markers and malignancy prediction) and experimental approaches (tissue microarray and western blotting on patient samples) we identified and evaluated E2F5 transcription factor involved in cell proliferation, as a promising candidate regulatory target in early stage disease. Our hypothesis was supported by our tissue array experiments that showed E2F5 expression only in OEC samples but not in normal and benign tissues, and by significantly positively biased expression in serum samples done using western blotting studies.</p> <p>Results</p> <p>Analysis of clinical cases shows that of the E2F5 status is characteristic for a different population group than one covered by CA125, a conventional OEC biomarker. E2F5 used in different combinations with CA125 for distinguishing malignant cyst from benign cyst shows that the presence of CA125 or E2F5 increases sensitivity of OEC detection to 97.9% (an increase from 87.5% if only CA125 is used) and, more importantly, the presence of both CA125 and E2F5 increases specificity of OEC to 72.5% (an increase from 55% if only CA125 is used). This significantly improved accuracy suggests possibility of an improved diagnostics of OEC. Furthermore, detection of malignancy status in 86 cases (38 benign, 48 early and late OEC) shows that the use of E2F5 status in combination with other clinical characteristics allows for an improved detection of malignant cases with sensitivity, specificity, F-measure and accuracy of 97.92%, 97.37%, 97.92% and 97.67%, respectively.</p> <p>Conclusions</p> <p>Overall, our findings, in addition to opening a realistic possibility for improved OEC diagnosis, provide an indirect evidence that a cell-cycle regulatory protein E2F5 might play a significant role in OEC pathogenesis.</p

Prediction of ovarian cancer prognosis and response to chemotherapy by a serum-based multiparametric biomarker panel

Currently, there are no effective biomarkers for ovarian cancer prognosis or prediction of therapeutic response. The objective of this study was to examine a panel of 10 serum biochemical parameters for their ability to predict response to chemotherapy, progression and survival of ovarian cancer patients. Sera from ovarian cancer patients were collected prior and during chemotherapy and were analysed by enzyme-linked immunosorbent assay for CA125, kallikreins 5, 6, 7, 8, 10 and 11, B7-H4, regenerating protein IV and Spondin-2. The odds ratio and hazard ratio and their 95% confidence interval (95% CI) were calculated. Time-dependent receiver-operating characteristic (ROC) curves were utilised to evaluate the prognostic performance of the biomarkers. The levels of several markers at baseline (c0), or after the first chemotherapy cycle (rc1), predicted chemotherapy response and overall or progression-free survival in univariate analysis. A multiparametric model (c0 of CA125, KLK5, KLK7 and rc1 of CA125) provided predictive accuracy with area under the ROC curve (AUC) of 0.82 (0.62 after correction for overfitting). Another marker combination (c0 of KLK7, KLK10, B7-H4, Spondin-2) was useful in predicting short-term (1-year) survival with an AUC of 0.89 (0.74 after correction for overfitting). All markers examined, except KLK7 and regenerating protein IV, were powerful predictors of time to progression (TTP) among chemotherapy responders. Individual and panels of biomarkers from the kallikrein family (and other families) can predict response to chemotherapy, overall survival, short-term (1-year) survival, progression-free survival and TTP of ovarian cancer patients treated with chemotherapy

Measuring progress from 1990 to 2017 and projecting attainment to 2030 of the health-related Sustainable Development Goals for 195 countries and territories: a systematic analysis for the Global Burden of Disease Study 2017

Background Efforts to establish the 2015 baseline and monitor early implementation of the UN Sustainable Development Goals (SDGs) highlight both great potential for and threats to improving health by 2030. To fully deliver on the SDG aim of “leaving no one behind”, it is increasingly important to examine the health-related SDGs beyond national-level estimates. As part of the Global Burden of Diseases, Injuries, and Risk Factors Study 2017 (GBD 2017), we measured progress on 41 of 52 health-related SDG indicators and estimated the health-related SDG index for 195 countries and territories for the period 1990–2017, projected indicators to 2030, and analysed global attainment. Methods We measured progress on 41 health-related SDG indicators from 1990 to 2017, an increase of four indicators since GBD 2016 (new indicators were health worker density, sexual violence by non-intimate partners, population census status, and prevalence of physical and sexual violence [reported separately]). We also improved the measurement of several previously reported indicators. We constructed national-level estimates and, for a subset of health-related SDGs, examined indicator-level differences by sex and Socio-demographic Index (SDI) quintile. We also did subnational assessments of performance for selected countries. To construct the healthrelated SDG index, we transformed the value for each indicator on a scale of 0–100, with 0 as the 2·5th percentile and 100 as the 97·5th percentile of 1000 draws calculated from 1990 to 2030, and took the geometric mean of the scaled indicators by target. To generate projections through 2030, we used a forecasting framework that drew estimates from the broader GBD study and used weighted averages of indicator-specific and country-specific annualised rates of change from 1990 to 2017 to inform future estimates. We assessed attainment of indicators with defined targets in two ways: first, using mean values projected for 2030, and then using the probability of attainment in 2030 calculated from 1000 draws. We also did a global attainment analysis of the feasibility of attaining SDG targets on the basis of past trends. Using 2015 global averages of indicators with defined SDG targets, we calculated the global annualised rates of change required from 2015 to 2030 to meet these targets, and then identified in what percentiles the required global annualised rates of change fell in the distribution of country-level rates of change from 1990 to 2015. We took the mean of these global percentile values across indicators and applied the past rate of change at this mean global percentile to all health-related SDG indicators, irrespective of target definition, to estimate the equivalent 2030 global average value and percentage change from 2015 to 2030 for each indicator. Findings The global median health-related SDG index in 2017 was 59·4 (IQR 35·4–67·3), ranging from a low of 11·6 (95% uncertainty interval 9·6–14·0) to a high of 84·9 (83·1–86·7). SDG index values in countries assessed at the subnational level varied substantially, particularly in China and India, although scores in Japan and the UK were more homogeneous. Indicators also varied by SDI quintile and sex, with males having worse outcomes than females for non-communicable disease (NCD) mortality, alcohol use, and smoking, among others. Most countries were projected to have a higher health-related SDG index in 2030 than in 2017, while country-level probabilities of attainment by 2030 varied widely by indicator. Under-5 mortality, neonatal mortality, maternal mortality ratio, and malaria indicators had the most countries with at least 95% probability of target attainment. Other indicators, including NCD mortality and suicide mortality, had no countries projected to meet corresponding SDG targets on the basis of projected mean values for 2030 but showed some probability of attainment by 2030. For some indicators, including child malnutrition, several infectious diseases, and most violence measures, the annualised rates of change required to meet SDG targets far exceeded the pace of progress achieved by any country in the recent past. We found that applying the mean global annualised rate of change to indicators without defined targets would equate to about 19% and 22% reductions in global smoking and alcohol consumption, respectively; a 47% decline in adolescent birth rates; and a more than 85% increase in health worker density per 1000 population by 2030. Interpretation The GBD study offers a unique, robust platform for monitoring the health-related SDGs across demographic and geographic dimensions. Our findings underscore the importance of increased collection and analysis of disaggregated data and highlight where more deliberate design or targeting of interventions could accelerate progress in attaining the SDGs. Current projections show that many health-related SDG indicators, NCDs, NCD-related risks, and violence-related indicators will require a concerted shift away from what might have driven past gains—curative interventions in the case of NCDs—towards multisectoral, prevention-oriented policy action and investments to achieve SDG aims. Notably, several targets, if they are to be met by 2030, demand a pace of progress that no country has achieved in the recent past. The future is fundamentally uncertain, and no model can fully predict what breakthroughs or events might alter the course of the SDGs. What is clear is that our actions—or inaction—today will ultimately dictate how close the world, collectively, can get to leaving no one behind by 2030

Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980–2017: a systematic analysis for the Global Burden of Disease Study 2017

Data sharing: To download the data used in these analyses, please visit the Global Health Data Exchange at https://ghdx.healthdata.org/gbd–2017 .Supplementary Materials are available online at: https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(18)32203-7/fulltext#supplementary-material .Correction: 1 Errata: Volume 392, Issue 10160, P2170, November 9, 2018: GBD 2017 Causes of Death Collaborators. Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 2018; 392: 1736–88—The bottom row in figure 7 was cut off. This correction has been made to the online version as of Nov 9, 2018, and has been made to the printed Article. Correction 2: Errata: Volume 393, Issue 10190, E44, June 20, 2019: GBD 2017 Causes of Death Collaborators. Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 2018; 392: 1736–88—In this Global Health Metrics paper, the affiliations have been amended for Yasin Jemal Yasin, Joseph Adel Mattar Banoub, and Abdullatif Husseini; and the declaration of interests statement has been amended for Boris Bikbov. These corrections have been made to the online version as of June 20, 2019.Background: Global development goals increasingly rely on country-specific estimates for benchmarking a nation's progress. To meet this need, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2016 estimated global, regional, national, and, for selected locations, subnational cause-specific mortality beginning in the year 1980. Here we report an update to that study, making use of newly available data and improved methods. GBD 2017 provides a comprehensive assessment of cause-specific mortality for 282 causes in 195 countries and territories from 1980 to 2017. Methods: The causes of death database is composed of vital registration (VR), verbal autopsy (VA), registry, survey, police, and surveillance data. GBD 2017 added ten VA studies, 127 country-years of VR data, 502 cancer-registry country-years, and an additional surveillance country-year. Expansions of the GBD cause of death hierarchy resulted in 18 additional causes estimated for GBD 2017. Newly available data led to subnational estimates for five additional countries—Ethiopia, Iran, New Zealand, Norway, and Russia. Deaths assigned International Classification of Diseases (ICD) codes for non-specific, implausible, or intermediate causes of death were reassigned to underlying causes by redistribution algorithms that were incorporated into uncertainty estimation. We used statistical modelling tools developed for GBD, including the Cause of Death Ensemble model (CODEm), to generate cause fractions and cause-specific death rates for each location, year, age, and sex. Instead of using UN estimates as in previous versions, GBD 2017 independently estimated population size and fertility rate for all locations. Years of life lost (YLLs) were then calculated as the sum of each death multiplied by the standard life expectancy at each age. All rates reported here are age-standardised. Findings: At the broadest grouping of causes of death (Level 1), non-communicable diseases (NCDs) comprised the greatest fraction of deaths, contributing to 73·4% (95% uncertainty interval [UI] 72·5–74·1) of total deaths in 2017, while communicable, maternal, neonatal, and nutritional (CMNN) causes accounted for 18·6% (17·9–19·6), and injuries 8·0% (7·7–8·2). Total numbers of deaths from NCD causes increased from 2007 to 2017 by 22·7% (21·5–23·9), representing an additional 7·61 million (7·20–8·01) deaths estimated in 2017 versus 2007. The death rate from NCDs decreased globally by 7·9% (7·0–8·8). The number of deaths for CMNN causes decreased by 22·2% (20·0–24·0) and the death rate by 31·8% (30·1–33·3). Total deaths from injuries increased by 2·3% (0·5–4·0) between 2007 and 2017, and the death rate from injuries decreased by 13·7% (12·2–15·1) to 57·9 deaths (55·9–59·2) per 100 000 in 2017. Deaths from substance use disorders also increased, rising from 284 000 deaths (268 000–289 000) globally in 2007 to 352 000 (334 000–363 000) in 2017. Between 2007 and 2017, total deaths from conflict and terrorism increased by 118·0% (88·8–148·6). A greater reduction in total deaths and death rates was observed for some CMNN causes among children younger than 5 years than for older adults, such as a 36·4% (32·2–40·6) reduction in deaths from lower respiratory infections for children younger than 5 years compared with a 33·6% (31·2–36·1) increase in adults older than 70 years. Globally, the number of deaths was greater for men than for women at most ages in 2017, except at ages older than 85 years. Trends in global YLLs reflect an epidemiological transition, with decreases in total YLLs from enteric infections, respiratory infections and tuberculosis, and maternal and neonatal disorders between 1990 and 2017; these were generally greater in magnitude at the lowest levels of the Socio-demographic Index (SDI). At the same time, there were large increases in YLLs from neoplasms and cardiovascular diseases. YLL rates decreased across the five leading Level 2 causes in all SDI quintiles. The leading causes of YLLs in 1990—neonatal disorders, lower respiratory infections, and diarrhoeal diseases—were ranked second, fourth, and fifth, in 2017. Meanwhile, estimated YLLs increased for ischaemic heart disease (ranked first in 2017) and stroke (ranked third), even though YLL rates decreased. Population growth contributed to increased total deaths across the 20 leading Level 2 causes of mortality between 2007 and 2017. Decreases in the cause-specific mortality rate reduced the effect of population growth for all but three causes: substance use disorders, neurological disorders, and skin and subcutaneous diseases. Interpretation: Improvements in global health have been unevenly distributed among populations. Deaths due to injuries, substance use disorders, armed conflict and terrorism, neoplasms, and cardiovascular disease are expanding threats to global health. For causes of death such as lower respiratory and enteric infections, more rapid progress occurred for children than for the oldest adults, and there is continuing disparity in mortality rates by sex across age groups. Reductions in the death rate of some common diseases are themselves slowing or have ceased, primarily for NCDs, and the death rate for selected causes has increased in the past decade.Bill & Melinda Gates Foundation

Measuring progress from 1990 to 2017 and projecting attainment to 2030 of the health-related Sustainable Development Goals for 195 countries and territories: a systematic analysis for the Global Burden of Disease Study 2017

Background: Efforts to establish the 2015 baseline and monitor early implementation of the UN Sustainable Development Goals (SDGs) highlight both great potential for and threats to improving health by 2030. To fully deliver on the SDG aim of “leaving no one behind”, it is increasingly important to examine the health-related SDGs beyond national-level estimates. As part of the Global Burden of Diseases, Injuries, and Risk Factors Study 2017 (GBD 2017), we measured progress on 41 of 52 health-related SDG indicators and estimated the health-related SDG index for 195 countries and territories for the period 1990–2017, projected indicators to 2030, and analysed global attainment. Methods: We measured progress on 41 health-related SDG indicators from 1990 to 2017, an increase of four indicators since GBD 2016 (new indicators were health worker density, sexual violence by non-intimate partners, population census status, and prevalence of physical and sexual violence [reported separately]). We also improved the measurement of several previously reported indicators. We constructed national-level estimates and, for a subset of health-related SDGs, examined indicator-level differences by sex and Socio-demographic Index (SDI) quintile. We also did subnational assessments of performance for selected countries. To construct the health-related SDG index, we transformed the value for each indicator on a scale of 0–100, with 0 as the 2·5th percentile and 100 as the 97·5th percentile of 1000 draws calculated from 1990 to 2030, and took the geometric mean of the scaled indicators by target. To generate projections through 2030, we used a forecasting framework that drew estimates from the broader GBD study and used weighted averages of indicator-specific and country-specific annualised rates of change from 1990 to 2017 to inform future estimates. We assessed attainment of indicators with defined targets in two ways: first, using mean values projected for 2030, and then using the probability of attainment in 2030 calculated from 1000 draws. We also did a global attainment analysis of the feasibility of attaining SDG targets on the basis of past trends. Using 2015 global averages of indicators with defined SDG targets, we calculated the global annualised rates of change required from 2015 to 2030 to meet these targets, and then identified in what percentiles the required global annualised rates of change fell in the distribution of country-level rates of change from 1990 to 2015. We took the mean of these global percentile values across indicators and applied the past rate of change at this mean global percentile to all health-related SDG indicators, irrespective of target definition, to estimate the equivalent 2030 global average value and percentage change from 2015 to 2030 for each indicator. Findings: The global median health-related SDG index in 2017 was 59·4 (IQR 35·4–67·3), ranging from a low of 11·6 (95% uncertainty interval 9·6–14·0) to a high of 84·9 (83·1–86·7). SDG index values in countries assessed at the subnational level varied substantially, particularly in China and India, although scores in Japan and the UK were more homogeneous. Indicators also varied by SDI quintile and sex, with males having worse outcomes than females for non-communicable disease (NCD) mortality, alcohol use, and smoking, among others. Most countries were projected to have a higher health-related SDG index in 2030 than in 2017, while country-level probabilities of attainment by 2030 varied widely by indicator. Under-5 mortality, neonatal mortality, maternal mortality ratio, and malaria indicators had the most countries with at least 95% probability of target attainment. Other indicators, including NCD mortality and suicide mortality, had no countries projected to meet corresponding SDG targets on the basis of projected mean values for 2030 but showed some probability of attainment by 2030. For some indicators, including child malnutrition, several infectious diseases, and most violence measures, the annualised rates of change required to meet SDG targets far exceeded the pace of progress achieved by any country in the recent past. We found that applying the mean global annualised rate of change to indicators without defined targets would equate to about 19% and 22% reductions in global smoking and alcohol consumption, respectively; a 47% decline in adolescent birth rates; and a more than 85% increase in health worker density per 1000 population by 2030. Interpretation: The GBD study offers a unique, robust platform for monitoring the health-related SDGs across demographic and geographic dimensions. Our findings underscore the importance of increased collection and analysis of disaggregated data and highlight where more deliberate design or targeting of interventions could accelerate progress in attaining the SDGs. Current projections show that many health-related SDG indicators, NCDs, NCD-related risks, and violence-related indicators will require a concerted shift away from what might have driven past gains—curative interventions in the case of NCDs—towards multisectoral, prevention-oriented policy action and investments to achieve SDG aims. Notably, several targets, if they are to be met by 2030, demand a pace of progress that no country has achieved in the recent past. The future is fundamentally uncertain, and no model can fully predict what breakthroughs or events might alter the course of the SDGs. What is clear is that our actions—or inaction—today will ultimately dictate how close the world, collectively, can get to leaving no one behind by 2030