Exome sequencing identifies HELB as a novel susceptibility gene for non-mucinous, non-high-grade-serous epithelial ovarian cancer.

Rare, germline loss-of-function variants in a handful of genes that encode DNA repair proteins have been shown to be associated with epithelial ovarian cancer with a stronger association for the high-grade serous hiostotype. The aim of this study was to collate exome sequencing data from multiple epithelial ovarian cancer case cohorts and controls in order to systematically evaluate the role of coding, loss-of-function variants across the genome in epithelial ovarian cancer risk. We assembled exome data for a total of 2,573 non-mucinous cases (1,876 high-grade serous and 697 non-high grade serous) and 13,925 controls. Harmonised variant calling and quality control filtering was applied across the different data sets. We carried out a gene-by-gene simple burden test for association of rare loss-of-function variants (minor allele frequency < 0.1%) with all non-mucinous ovarian cancer, high grade serous ovarian cancer and non-high grade serous ovarian cancer using logistic regression adjusted for the top four principal components to account for cryptic population structure and genetic ancestry. Seven of the top 10 associated genes were associations of the known ovarian cancer susceptibility genes BRCA1, BRCA2, BRIP1, RAD51C, RAD51D, MSH6 and PALB2 (false discovery probability < 0.1). A further four genes (HELB, OR2T35, NBN and MYO1A) had a false discovery rate of less than 0.1. Of these, HELB was most strongly associated with the non-high grade serous histotype (P = 1.3×10-6, FDR = 9.1×10-4). Further support for this association comes from the observation that loss of function variants in this gene are also associated with age at natural menopause and Mendelian randomisation analysis shows an association between genetically predicted age at natural menopause and endometrioid ovarian cancer, but not high-grade serous ovarian cancer.

Baseline predictors of disease severity in immune checkpoint inhibitor-induced inflammatory arthritis.

OBJECTIVES: To determine baseline risk factors for requiring immunosuppression and having persistent arthritis in patients with immune checkpoint inhibitor-induced inflammatory arthritis (ICI-IA). METHODS: Participants were adults with rheumatologist diagnosed ICI-IA. The primary outcome was requirement of conventional synthetic (cs) or biologic (b) DMARDs; other outcomes were persistence of IA > 6 months after ICI cessation and requirement of corticosteroids. Logistic regression models evaluated associations between clinical features and primary and secondary outcomes, with adjustment for potential confounders, as appropriate. RESULTS: 126 patients with ICI-IA were included; 53 patients (42%) required a csDMARD/bDMARD. In univariate logistic regressions, higher CDAI, tenosynovitis, longer symptom duration before first rheumatology visit, and longer ICI duration were significantly associated with a higher likelihood of requiring DMARDs; there was a trend toward those treated with prior chemotherapy being less likely to need DMARDs. After adjustment, tenosynovitis, longer symptom duration, and higher CDAI remained associated with requiring DMARDs, while those with prior chemotherapy were significantly less likely to require DMARDs. Combination anti-CTLA-4/PD-1 therapy and steroid use at baseline were associated with a higher risk of persistent IA. CONCLUSION: Higher levels of disease activity, tenosynovitis, and longer symptom duration prior to rheumatology referral were associated with requiring DMARDs for ICI-IA, while those treated previously with chemotherapy were less likely to require additional immunosuppression. The presence of risk factors for severe disease at baseline may indicate a role for higher initial steroid dose, earlier rheumatology referral, and adoption of immunosuppression beyond steroids to improve outcomes.

DNA methylation and transcriptomic features are preserved throughout disease recurrence and chemoresistance in high grade serous ovarian cancers.

BACKGROUND: Little is known about the role of global DNA methylation in recurrence and chemoresistance of high grade serous ovarian cancer (HGSOC). METHODS: We performed whole genome bisulfite sequencing and transcriptome sequencing in 62 primary and recurrent tumors from 28 patients with stage III/IV HGSOC, of which 11 patients carried germline, pathogenic BRCA1 and/or BRCA2 mutations. RESULTS: Landscapes of genome-wide methylation (on average 24.2 million CpGs per tumor) and transcriptomes in primary and recurrent tumors showed extensive heterogeneity between patients but were highly preserved in tumors from the same patient. We identified significant differences in the burden of differentially methylated regions (DMRs) in tumors from BRCA1/2 compared to non-BRCA1/2 carriers (mean 659 DMRs and 388 DMRs in paired comparisons respectively). We identified overexpression of immune pathways in BRCA1/2 carriers compared to non-carriers, implicating an increased immune response in improved survival (P = 0.006) in these BRCA1/2 carriers. CONCLUSION: These findings indicate methylome and gene expression programs established in the primary tumor are conserved throughout disease progression, even after extensive chemotherapy treatment, and that changes in methylation and gene expression are unlikely to serve as drivers for chemoresistance in HGSOC.

CRISPR/Cas9-Mediated Editing of Autophagy Gene 6 in Petunia Decreases Flower Longevity, Seed Yield, and Phosphorus Remobilization by Accelerating Ethylene Production and Senescence-Related Gene Expression.

Developmental petal senescence is a type of programmed cell death (PCD), during which the production of ethylene is induced, the expression of PCD-related genes is upregulated, and nutrients are recycled. Autophagy is an intracellular mechanism involved in PCD modulation and nutrient cycling. As a central component of the autophagy pathway, Autophagy Gene 6 (ATG6) was previously shown as a negative regulator of petal senescence. To better understand the role of autophagy in ethylene biosynthesis and nutrient remobilization during petal senescence, we generated and characterized the knockout (KO) mutants of PhATG6 using CRISPR/Cas9 in Petunia × hybrida 'Mitchell Diploid.' PhATG6-KO lines exhibited decreased flower longevity when compared to the flowers of the wild-type or a non-mutated regenerative line (controls), confirming the negative regulatory role of ATG6 in petal senescence. Smaller capsules and fewer seeds per capsule were produced in the KO plants, indicating the crucial function of autophagy in seed production. Ethylene production and ethylene biosynthesis genes were upregulated earlier in the KO lines than the controls, indicating that autophagy affects flower longevity through ethylene. The transcript levels of petal PCD-related genes, including PhATG6, PhATG8d, PhPI3K (Phosphatidylinositol 3-Kinase), and a metacaspase gene PhMC1, were upregulated earlier in the corollas of PhATG6-KO lines, which supported the accelerated PCD in the KO plants. The remobilization of phosphorus was reduced in the KO lines, showing that nutrient recycling was compromised. Our study demonstrated the important role of autophagy in flower lifespan and seed production and supported the interactions between autophagy and various regulatory factors during developmental petal senescence.

Polygenic risk modeling for prediction of epithelial ovarian cancer risk.

Polygenic risk scores (PRS) for epithelial ovarian cancer (EOC) have the potential to improve risk stratification. Joint estimation of Single Nucleotide Polymorphism (SNP) effects in models could improve predictive performance over standard approaches of PRS construction. Here, we implemented computationally efficient, penalized, logistic regression models (lasso, elastic net, stepwise) to individual level genotype data and a Bayesian framework with continuous shrinkage, "select and shrink for summary statistics" (S4), to summary level data for epithelial non-mucinous ovarian cancer risk prediction. We developed the models in a dataset consisting of 23,564 non-mucinous EOC cases and 40,138 controls participating in the Ovarian Cancer Association Consortium (OCAC) and validated the best models in three populations of different ancestries: prospective data from 198,101 women of European ancestries; 7,669 women of East Asian ancestries; 1,072 women of African ancestries, and in 18,915 BRCA1 and 12,337 BRCA2 pathogenic variant carriers of European ancestries. In the external validation data, the model with the strongest association for non-mucinous EOC risk derived from the OCAC model development data was the S4 model (27,240 SNPs) with odds ratios (OR) of 1.38 (95% CI: 1.28-1.48, AUC: 0.588) per unit standard deviation, in women of European ancestries; 1.14 (95% CI: 1.08-1.19, AUC: 0.538) in women of East Asian ancestries; 1.38 (95% CI: 1.21-1.58, AUC: 0.593) in women of African ancestries; hazard ratios of 1.36 (95% CI: 1.29-1.43, AUC: 0.592) in BRCA1 pathogenic variant carriers and 1.49 (95% CI: 1.35-1.64, AUC: 0.624) in BRCA2 pathogenic variant carriers. Incorporation of the S4 PRS in risk prediction models for ovarian cancer may have clinical utility in ovarian cancer prevention programs.

Large-scale cross-cancer fine-mapping of the 5p15.33 region reveals multiple independent signals.

Genome-wide association studies (GWASs) have identified thousands of cancer risk loci revealing many risk regions shared across multiple cancers. Characterizing the cross-cancer shared genetic basis can increase our understanding of global mechanisms of cancer development. In this study, we collected GWAS summary statistics based on up to 375,468 cancer cases and 530,521 controls for fourteen types of cancer, including breast (overall, estrogen receptor [ER]-positive, and ER-negative), colorectal, endometrial, esophageal, glioma, head/neck, lung, melanoma, ovarian, pancreatic, prostate, and renal cancer, to characterize the shared genetic basis of cancer risk. We identified thirteen pairs of cancers with statistically significant local genetic correlations across eight distinct genomic regions. Specifically, the 5p15.33 region, harboring the TERT and CLPTM1L genes, showed statistically significant local genetic correlations for multiple cancer pairs. We conducted a cross-cancer fine-mapping of the 5p15.33 region based on eight cancers that showed genome-wide significant associations in this region (ER-negative breast, colorectal, glioma, lung, melanoma, ovarian, pancreatic, and prostate cancer). We used an iterative analysis pipeline implementing a subset-based meta-analysis approach based on cancer-specific conditional analyses and identified ten independent cross-cancer associations within this region. For each signal, we conducted cross-cancer fine-mapping to prioritize the most plausible causal variants. Our findings provide a more in-depth understanding of the shared inherited basis across human cancers and expand our knowledge of the 5p15.33 region in carcinogenesis.

Potential Pitfalls in Pre-implantation Genetic Diagnosis in a Patient with Tuberous Sclerosis and Isolated Mosaicism for a TSC2 Variant in Renal Tissue.

Tuberous sclerosis complex (TSC) is an autosomal dominant disorder that displays a wide spectrum of clinical manifestations, often affecting multiple organs including the kidneys, brain, lungs, and skin. A pathogenic mutation in either the TSC1 or TSC2 gene can be detected in almost 85% of the cases, with mosaicism accounting for about half of the remaining cases. We report a case of TSC diagnosed clinically, requesting genetic counselling regarding reproductive risks. No mutation was identified on initial testing of peripheral blood; however, mosaicism for a likely pathogenic frameshift variant in TSC2 was detected at a level of 15% in renal angiomyolipoma tissue. Despite widespread clinical manifestations of TCS, this variant was not detected in skin fibroblasts or saliva, raising the possibility this is an isolated somatic mutation in renal tissue with the underlying germline mutation not yet identified. This case highlights the difficulties when counselling patients with mosaicism regarding their reproductive risks and prenatal diagnostic options.

A Simple Optimization Workflow to Enable Precise and Accurate Imputation of Missing Values in Proteomic Data Sets.

Missing values in proteomic data sets have real consequences on downstream data analysis and reproducibility. Although several imputation methods exist to handle missing values, no single imputation method is best suited for a diverse range of data sets, and no clear strategy exists for evaluating imputation methods for clinical DIA-MS data sets, especially at different levels of protein quantification. To navigate through the different imputation strategies available in the literature, we have established a strategy to assess imputation methods on clinical label-free DIA-MS data sets. We used three DIA-MS data sets with real missing values to evaluate eight imputation methods with multiple parameters at different levels of protein quantification: a dilution series data set, a small pilot data set, and a clinical proteomic data set comparing paired tumor and stroma tissue. We found that imputation methods based on local structures within the data, like local least-squares (LLS) and random forest (RF), worked well in our dilution series data set, whereas imputation methods based on global structures within the data, like BPCA, performed well in the other two data sets. We also found that imputation at the most basic protein quantification level-fragment level-improved accuracy and the number of proteins quantified. With this analytical framework, we quickly and cost-effectively evaluated different imputation methods using two smaller complementary data sets to narrow down to the larger proteomic data set's most accurate methods. This acquisition strategy allowed us to provide reproducible evidence of the accuracy of the imputation method, even in the absence of a ground truth. Overall, this study indicates that the most suitable imputation method relies on the overall structure of the data set and provides an example of an analytic framework that may assist in identifying the most appropriate imputation strategies for the differential analysis of proteins.

Hyperprolactinemia and association with prolactinoma in transwomen receiving gender affirming hormone treatment.

PURPOSE: Approximately 0.6% of the adult population identifies as transgender. Gender affirming hormone treatment (GAHT) is required by many to develop physical and psychological characteristics that align with their gender identity. Once started, GAHT is continued lifelong and at higher doses than conventionally used in the management of cis-gendered women. Hyperprolactinemia and pituitary lactotroph adenomas are a potential consequence of GAHT. METHODS: Case series of three transfeminine women with hyperprolactinemia during gender affirming hormone treatment. RESULTS: We report two new cases of prolactinoma and one new case of marked hyperprolactinemia without pituitary adenoma associated with GAHT in transwomen at different stages of hormonal transition. Novel aspects of our case series include the first report of a prolactinoma in a transwoman associated with spironolactone and the alternate progestin medroxyprogesterone acetate and documentation of the transient changes in prolactin from baseline (prior to feminizing hormones) in two transwomen which demonstrate that marked hyperprolactinemia develops early in the course of GAHT. CONCLUSIONS: Transgender women receiving GAHT may be at increased risk for prolactinoma. As the number of transwomen seeking GAHT continues to increase, hyperprolactinemia and GAHT-associated prolactinoma may become an increasingly important component of endocrine practice. Screening of prolactin levels in transwomen receiving GAHT could potentially prevent morbidity related to hyperprolactinemia and allow for early detection of prolactin secreting pituitary adenomas.

Ovarian Cancer Risk Variants Are Enriched in Histotype-Specific Enhancers and Disrupt Transcription Factor Binding Sites.

Quantifying the functional effects of complex disease risk variants can provide insights into mechanisms underlying disease biology. Genome-wide association studies have identified 39 regions associated with risk of epithelial ovarian cancer (EOC). The vast majority of these variants lie in the non-coding genome, where they likely function through interaction with gene regulatory elements. In this study we first estimated the heritability explained by known common low penetrance risk alleles for EOC. The narrow sense heritability (hg2) of EOC overall and high-grade serous ovarian cancer (HGSOCs) were estimated to be 5%-6%. Partitioned SNP heritability across broad functional categories indicated a significant contribution of regulatory elements to EOC heritability. We collated epigenomic profiling data for 77 cell and tissue types from Roadmap Epigenomics and ENCODE, and from H3K27Ac ChIP-seq data generated in 26 ovarian cancer and precursor-related cell and tissue types. We identified significant enrichment of risk single-nucleotide polymorphisms (SNPs) in active regulatory elements marked by H3K27Ac in HGSOCs. To further investigate how risk SNPs in active regulatory elements influence predisposition to ovarian cancer, we used motifbreakR to predict the disruption of transcription factor binding sites. We identified 469 candidate causal risk variants in H3K27Ac peaks that are predicted to significantly break transcription factor (TF) motifs. The most frequently broken motif was REST (p value = 0.0028), which has been reported as both a tumor suppressor and an oncogene. Overall, these systematic functional annotations with epigenomic data improve interpretation of EOC risk variants and shed light on likely cells of origin.

Virus-Induced Gene Silencing for Functional Analysis of Flower Traits in Petunia.

Virus-induced gene silencing (VIGS) uses recombinant viruses to knock down the expression of endogenous plant genes, allowing for rapid functional analysis without generating stable transgenic plants. The Tobacco rattle virus (TRV) is a popular vector for VIGS because it has a wide host range that includes Petunia × hybrida (petunia), and it induces minimal viral symptoms. Using reporter genes like chalcone synthase (CHS) in tandem with a gene of interest (GOI; pTRV2-PhCHS-GOI), it is possible to visually identify silenced flowers so that phenotyping is more accurate. Inoculation methods and environmental conditions need to be optimized for each host plant-virus interaction to maximize silencing efficiency. This chapter will provide detailed protocols for VIGS in petunia, with an emphasis on the investigation of flower phenotypes.

A Polygenic and Phenotypic Risk Prediction for Polycystic Ovary Syndrome Evaluated by Phenome-Wide Association Studies.

CONTEXT: As many as 75% of patients with polycystic ovary syndrome (PCOS) are estimated to be unidentified in clinical practice. OBJECTIVE: Utilizing polygenic risk prediction, we aim to identify the phenome-wide comorbidity patterns characteristic of PCOS to improve accurate diagnosis and preventive treatment. DESIGN, PATIENTS, AND METHODS: Leveraging the electronic health records (EHRs) of 124 852 individuals, we developed a PCOS risk prediction algorithm by combining polygenic risk scores (PRS) with PCOS component phenotypes into a polygenic and phenotypic risk score (PPRS). We evaluated its predictive capability across different ancestries and perform a PRS-based phenome-wide association study (PheWAS) to assess the phenomic expression of the heightened risk of PCOS. RESULTS: The integrated polygenic prediction improved the average performance (pseudo-R2) for PCOS detection by 0.228 (61.5-fold), 0.224 (58.8-fold), 0.211 (57.0-fold) over the null model across European, African, and multi-ancestry participants respectively. The subsequent PRS-powered PheWAS identified a high level of shared biology between PCOS and a range of metabolic and endocrine outcomes, especially with obesity and diabetes: "morbid obesity", "type 2 diabetes", "hypercholesterolemia", "disorders of lipid metabolism", "hypertension", and "sleep apnea" reaching phenome-wide significance. CONCLUSIONS: Our study has expanded the methodological utility of PRS in patient stratification and risk prediction, especially in a multifactorial condition like PCOS, across different genetic origins. By utilizing the individual genome-phenome data available from the EHR, our approach also demonstrates that polygenic prediction by PRS can provide valuable opportunities to discover the pleiotropic phenomic network associated with PCOS pathogenesis.

Cervical adenocarcinoma in situ: Human papillomavirus types and incidence trends in five states, 2008-2015.

Primary prevention through the use of human papillomavirus (HPV) vaccination is expected to impact both cervical intraepithelial neoplasia (CIN) and adenocarcinoma in situ (AIS). While CIN is well described, less is known about the epidemiology of AIS, a rare cervical precancer. We identified AIS and CIN grade 3 (CIN3) cases through population-based surveillance, and analyzed data on HPV types and incidence trends overall, and among women screened for cervical cancer. From 2008 to 2015, 470 AIS and 6,587 CIN3 cases were identified. The median age of women with AIS was older than those with CIN3 (35 vs. 31 years; p < 0.01). HPV16 was the most frequently detected type in both AIS and CIN3 (57% in AIS; 58% in CIN3), whereas HPV18 was the second most common type in AIS and less common in CIN3 (38% vs. 5%; p < 0.01). AIS lesions were more likely than CIN3 lesions to be positive for high-risk types targeted by the bivalent and quadrivalent vaccines (HPV16/18, 92% vs. 63%; p < 0.01), and 9-valent vaccine (HPV16/18/31/33/45/52/58, 95% vs. 87%; p < 0.01). AIS incidence rates decreased significantly in the 21-24 year age group (annual percent change [APC] overall: -22.1%, 95% CI: -33.9 to -8.2; APC among screened: -16.1%, 95% CI: -28.8 to -1.2), but did not decrease significantly in any older age group. This report on the largest number of genotyped AIS cases to date suggests an important opportunity for vaccine prevention of AIS, and is the first to document a decline in AIS incidence rates among young women during the vaccine era.

Effectiveness of 1, 2, and 3 Doses of Human Papillomavirus Vaccine Against High-Grade Cervical Lesions Positive for Human Papillomavirus 16 or 18.

Before 2016, human papillomavirus (HPV) vaccination was recommended on a 3-dose schedule. However, many vaccine-eligible US females received fewer than 3 doses, which provided an opportunity to evaluate the real-world vaccine effectiveness (VE) of 1, 2, and 3 doses. We analyzed data on cervical intraepithelial neoplasia (CIN) grades 2-3 and adenocarcinoma in situ (designated CIN2+) from the HPV Vaccine Impact Monitoring Project (HPV-IMPACT; 2008-2014). Archived tissue from CIN2+ lesions was tested for 37 types of HPV. Women were classified by number of doses received ≥24 months before CIN2+ detection. Using a test-negative design, VE was estimated as 1 minus the adjusted odds ratio from a logistic regression model that compared vaccination history for women whose lesions tested positive for HPV-16/18 (vaccine-type cases) with that for women who had all other CIN2+ lesions (controls). Among 3,300 women with available data on CIN2+, typing results, and vaccine history, 1,561 (47%) were HPV-16/18-positive, 136 (4%) received 1 dose of HPV vaccine, 108 (3%) received 2 doses, and 325 (10%) received 3 doses. Adjusted odds ratios for vaccination with 1, 2, and 3 doses were 0.53 (95% confidence interval (CI): 0.37, 0.76; VE = 47%), 0.45 (95% CI: 0.30, 0.69; VE = 55%), and 0.26 (95% CI: 0.20, 0.35; VE = 74%), respectively. We found significant VE against vaccine-type CIN2+ after 3 doses of HPV vaccine and lower but significant VE with 1 or 2 doses.

GENAVi: a shiny web application for gene expression normalization, analysis and visualization.

BACKGROUND: The development of next generation sequencing (NGS) methods led to a rapid rise in the generation of large genomic datasets, but the development of user-friendly tools to analyze and visualize these datasets has not developed at the same pace. This presents a two-fold challenge to biologists; the expertise to select an appropriate data analysis pipeline, and the need for bioinformatics or programming skills to apply this pipeline. The development of graphical user interface (GUI) applications hosted on web-based servers such as Shiny can make complex workflows accessible across operating systems and internet browsers to those without programming knowledge. RESULTS: We have developed GENAVi (Gene Expression Normalization Analysis and Visualization) to provide a user-friendly interface for normalization and differential expression analysis (DEA) of human or mouse feature count level RNA-Seq data. GENAVi is a GUI based tool that combines Bioconductor packages in a format for scientists without bioinformatics expertise. We provide a panel of 20 cell lines commonly used for the study of breast and ovarian cancer within GENAVi as a foundation for users to bring their own data to the application. Users can visualize expression across samples, cluster samples based on gene expression or correlation, calculate and plot the results of principal components analysis, perform DEA and gene set enrichment and produce plots for each of these analyses. To allow scalability for large datasets we have provided local install via three methods. We improve on available tools by offering a range of normalization methods and a simple to use interface that provides clear and complete session reporting and for reproducible analysis. CONCLUSION: The development of tools using a GUI makes them practical and accessible to scientists without bioinformatics expertise, or access to a data analyst with relevant skills. While several GUI based tools are currently available for RNA-Seq analysis we improve on these existing tools. This user-friendly application provides a convenient platform for the normalization, analysis and visualization of gene expression data for scientists without bioinformatics expertise.

Soluble Markers of Antibody Secreting Cell Function as Predictors of Infection Risk in Rheumatoid Arthritis.

BACKGROUND: Rheumatoid arthritis (RA) is a systemic autoimmune disease associated with immune dysregulation and increased risk of infections. The presence of autoantibodies and immunoglobulin abnormalities indicates B-cell and antibody-secreting cell (ASC) dysfunction. We hypothesize that soluble factors associated with B-cell and ASC activity are decreased in RA patients and that this is linked to higher susceptibility to infections. METHODS: Using the Johns Hopkins Arthritis Cohort and Biorepository, we contrasted serum protein levels of soluble factors involved in B-cell activation (CD40, CD40L) and B-cell/ASC homing (CXCL10, CXCL11, and CXCL13) or survival (BAFF, APRIL, TACI, and BCMA) in 10 healthy subjects and 23 adult RA patients (aged 24-65 years). We subdivided RA patients into those with (n = 17) and those without infections (n = 6) within a 2-year period. In order to reduce the effect of RA treatment, we only included patients receiving methotrexate monotherapy or no RA treatments at baseline. Soluble serum protein levels of B-cell/ASC factors were quantified by multiplex immunoassays. RESULTS: We identified that (1) serum levels of soluble BCMA, APRIL, CD40, and CD40L were significantly decreased in RA patients relative to healthy individuals; (2) serum soluble BCMA, predominantly released by ASC, correlated with serum concentrations of class-switched immunoglobulins, IgG and IgA; and (3) RA patients with a history of infections had significantly lower soluble BCMA levels compared with healthy donors and with RA patients without infections. CONCLUSIONS: Our study using soluble factors linked to B-cell/ASC activation and survival suggests that there is a paucity of ASC in a subset of RA patients and that this may be linked to altered antibody production and increased risk of infections. Further delineating the link between ASC and infection susceptibility in RA may optimize disease management and provide novel insights into disease pathogenesis that are susceptible to intervention.

Trends in High-grade Cervical Lesions and Cervical Cancer Screening in 5 States, 2008-2015.

BACKGROUND: We describe changes in rates of cervical intraepithelial neoplasia grades 2, 3 and adenocarcinoma in situ (CIN2+) during a period of human papillomavirus (HPV) vaccine uptake and changing cervical cancer screening recommendations. METHODS: We conducted population-based laboratory surveillance for CIN2+ in catchment areas in 5 states, 2008-2015. We calculated age-specific CIN2+ rates per 100000 women by age groups. We estimated incidence rate ratios (IRR) of CIN2+ for 2-year periods among all women and among screened women to evaluate changes over time. RESULTS: A total of 16572 CIN2+ cases were reported. Among women aged 18-20 and 21-24 years, CIN2+ rates declined in all sites, whereas in women aged 25-29, 30-34, and 35-39 years, trends differed across sites. The percent of women screened annually declined in all sites and age groups. Compared to 2008-2009, rates among screened women were significantly lower for all 3 periods in women aged 18-20 years (2010-2011: IRR 0.82, 95% confidence interval [CI] 0.67-0.99; 2012-2013: IRR 0.63, 95% CI 0.47-0.85; 2014-2015: IRR 0.44, 95% CI 0.28-0.68) and lower for the latter 2 time periods in women aged 21-24 years (2012-2013: IRR 0.86, 95% CI 0.79-0.94; 2014-2015: IRR 0.61, 95% CI 0.55-0.67). CONCLUSIONS: From 2008-2015, both CIN2+ rates and cervical cancer screening declined in women aged 18-24 years. The significant decreases in CIN2+ rates among screened women aged 18-24 years are consistent with a population-level impact of HPV vaccination.

Large-scale genome-wide meta-analysis of polycystic ovary syndrome suggests shared genetic architecture for different diagnosis criteria.

Polycystic ovary syndrome (PCOS) is a disorder characterized by hyperandrogenism, ovulatory dysfunction and polycystic ovarian morphology. Affected women frequently have metabolic disturbances including insulin resistance and dysregulation of glucose homeostasis. PCOS is diagnosed with two different sets of diagnostic criteria, resulting in a phenotypic spectrum of PCOS cases. The genetic similarities between cases diagnosed based on the two criteria have been largely unknown. Previous studies in Chinese and European subjects have identified 16 loci associated with risk of PCOS. We report a fixed-effect, inverse-weighted-variance meta-analysis from 10,074 PCOS cases and 103,164 controls of European ancestry and characterisation of PCOS related traits. We identified 3 novel loci (near PLGRKT, ZBTB16 and MAPRE1), and provide replication of 11 previously reported loci. Only one locus differed significantly in its association by diagnostic criteria; otherwise the genetic architecture was similar between PCOS diagnosed by self-report and PCOS diagnosed by NIH or non-NIH Rotterdam criteria across common variants at 13 loci. Identified variants were associated with hyperandrogenism, gonadotropin regulation and testosterone levels in affected women. Linkage disequilibrium score regression analysis revealed genetic correlations with obesity, fasting insulin, type 2 diabetes, lipid levels and coronary artery disease, indicating shared genetic architecture between metabolic traits and PCOS. Mendelian randomization analyses suggested variants associated with body mass index, fasting insulin, menopause timing, depression and male-pattern balding play a causal role in PCOS. The data thus demonstrate 3 novel loci associated with PCOS and similar genetic architecture for all diagnostic criteria. The data also provide the first genetic evidence for a male phenotype for PCOS and a causal link to depression, a previously hypothesized comorbid disease. Thus, the genetics provide a comprehensive view of PCOS that encompasses multiple diagnostic criteria, gender, reproductive potential and mental health.

Immune Stimulation Using a Gut Microbe-Based Immunotherapy Reduces Disease Pathology and Improves Barrier Function in Ulcerative Colitis.

Background: Current ulcerative colitis (UC) treatments are focused on symptom management primarily via immune suppression. Despite the current arsenal of immunosuppressant treatments, the majority of patients with UC still experience disease progression. Importantly, aggressive long-term inhibition of immune function comes with consequent risk, such as serious infections and malignancy. There is thus a recognized need for new, safe and effective treatment strategies for people living with UC that work upstream of managing the symptoms of the disease. The objective of this study was to evaluate a microbial-based treatment, QBECO, that functions to productively activate rather than suppress mucosal immune function as a novel approach to treat UC. Methods: Two established models of experimental colitis, namely chemically-induced DSS colitis and the spontaneous colitis that develops in Muc2 deficient mice, were used to assess whether QBECO treatment could ameliorate gastrointestinal disease. A small exploratory 16-week QBECO open-label trial was subsequently conducted to test the safety and tolerability of this approach and also to determine whether similar improvements in clinical disease and histopathology could be demonstrated in patients with moderate-to-severe UC. Results: QBECO treatment successfully reduced inflammation and promoted mucosal and histological healing in both experimental models and in UC patients. The preclinical models of colitis showed that QBECO ameliorated mucosal pathology, in part by reducing inflammatory cell infiltration, primarily that induced by neutrophils and inflammatory T cells. The most rapid and noticeable change observed in QBECO treated UC patients was a marked reduction in rectal bleeding. Conclusion: Collectively, this work demonstrates for the first time that strategically activating immune function rather than suppressing it, not only does not worsen colitis induced-damage, but may lead to an objective reduction in UC disease pathology.