Addressing dispersion in mis-measured multivariate binomial outcomes: A novel statistical approach for detecting differentially methylated regions in bisulfite sequencing data.

Motivated by a DNA methylation application, this article addresses the problem of fitting and inferring a multivariate binomial regression model for outcomes that are contaminated by errors and exhibit extra-parametric variations, also known as dispersion. While dispersion in univariate binomial regression has been extensively studied, addressing dispersion in the context of multivariate outcomes remains a complex and relatively unexplored task. The complexity arises from a noteworthy data characteristic observed in our motivating dataset: non-constant yet correlated dispersion across outcomes. To address this challenge and account for possible measurement error, we propose a novel hierarchical quasi-binomial varying coefficient mixed model, which enables flexible dispersion patterns through a combination of additive and multiplicative dispersion components. To maximize the Laplace-approximated quasi-likelihood of our model, we further develop a specialized two-stage expectation-maximization (EM) algorithm, where a plug-in estimate for the multiplicative scale parameter enhances the speed and stability of the EM iterations. Simulations demonstrated that our approach yields accurate inference for smooth covariate effects and exhibits excellent power in detecting non-zero effects. Additionally, we applied our proposed method to investigate the association between DNA methylation, measured across the genome through targeted custom capture sequencing of whole blood, and levels of anti-citrullinated protein antibodies (ACPA), a preclinical marker for rheumatoid arthritis (RA) risk. Our analysis revealed 23 significant genes that potentially contribute to ACPA-related differential methylation, highlighting the relevance of cell signaling and collagen metabolism in RA. We implemented our method in the R Bioconductor package called "SOMNiBUS."

STAT6 mutations enriched at diffuse large B-cell lymphoma relapse reshape the tumor microenvironment.

Diffuse large B-cell lymphoma (DLBCL) relapses in approximately 40% of patients following frontline therapy. We reported that STAT6D419 mutations are enriched in relapsed/refractory DLBCL (rrDLBCL) samples, suggesting that JAK/STAT signaling plays a role in therapeutic resistance. We hypothesized that STAT6D419 mutations can improve DLBCL cell survival by reprogramming the microenvironment to sustain STAT6 activation. Thus, we investigated the role of STAT6D419 mutations on DLBCL cell growth and its microenvironment. We found that phospho-STAT6D419N was retained in the nucleus longer than phospho-STAT6WT following IL-4 stimulation, and STAT6D419N recognized a more restricted DNA-consensus sequence than STAT6WT. Upon IL-4 induction, STAT6D419N expression led to a higher magnitude of gene expression changes, but in a more selective list of gene targets compared with STATWT. The most significantly expressed genes induced by STAT6D419N were those implicated in survival, proliferation, migration, and chemotaxis, in particular CCL17. This chemokine, also known as TARC, attracts helper T-cells to the tumor microenvironment, especially in Hodgkin's lymphoma. To this end, in DLBCL, phospho-STAT6+ rrDLBCL cells had a greater proportion of infiltrating CD4+ T-cells than phospho-STAT6- tumors. Our findings suggest that STAT6D419 mutations in DLBCL lead to cell autonomous changes, enhanced signaling, and altered composition of the tumor microenvironment.

Novel insights into systemic sclerosis using a sensitive computational method to analyze whole-genome bisulfite sequencing data.

BACKGROUND: Abnormal DNA methylation is thought to contribute to the onset and progression of systemic sclerosis. Currently, the most comprehensive assay for profiling DNA methylation is whole-genome bisulfite sequencing (WGBS), but its precision depends on read depth and it may be subject to sequencing errors. SOMNiBUS, a method for regional analysis, attempts to overcome some of these limitations. Using SOMNiBUS, we re-analyzed WGBS data previously analyzed using bumphunter, an approach that initially fits single CpG associations, to contrast DNA methylation estimates by both methods. METHODS: Purified CD4+ T lymphocytes of 9 SSc and 4 control females were sequenced using WGBS. We separated the resulting sequencing data into regions with dense CpG data, and differentially methylated regions (DMRs) were inferred with the SOMNiBUS region-level test, adjusted for age. Pathway enrichment analysis was performed with ingenuity pathway analysis (IPA). We compared the results obtained by SOMNiBUS and bumphunter. RESULTS: Of 8268 CpG regions of ≥ 60 CpGs eligible for analysis with SOMNiBUS, we identified 131 DMRs and 125 differentially methylated genes (DMGs; p-values less than Bonferroni-corrected threshold of 6.05-06 controlling family-wise error rate at 0.05; 1.6% of the regions). In comparison, bumphunter identified 821,929 CpG regions, 599 DMRs (of which none had ≥ 60 CpGs) and 340 DMGs (q-value of 0.05; 0.04% of all regions). The top ranked gene identified by SOMNiBUS was FLT4, a lymphangiogenic orchestrator, and the top ranked gene on chromosome X was CHST7, known to catalyze the sulfation of glycosaminoglycans in the extracellular matrix. The top networks identified by IPA included connective tissue disorders. CONCLUSIONS: SOMNiBUS is a complementary method of analyzing WGBS data that enhances biological insights into SSc and provides novel avenues of investigation into its pathogenesis.

GD2 and GD3 gangliosides as diagnostic biomarkers for all stages and subtypes of epithelial ovarian cancer.

Background: Ovarian cancer (OC) is the deadliest gynecological cancer, often diagnosed at advanced stages. A fast and accurate diagnostic method for early-stage OC is needed. The tumor marker gangliosides, GD2 and GD3, exhibit properties that make them ideal potential diagnostic biomarkers, but they have never before been quantified in OC. We investigated the diagnostic utility of GD2 and GD3 for diagnosis of all subtypes and stages of OC. Methods: This retrospective study evaluated GD2 and GD3 expression in biobanked tissue and serum samples from patients with invasive epithelial OC, healthy donors, non-malignant gynecological conditions, and other cancers. GD2 and GD3 levels were evaluated in tissue samples by immunohistochemistry (n=299) and in two cohorts of serum samples by quantitative ELISA. A discovery cohort (n=379) showed feasibility of GD2 and GD3 quantitative ELISA for diagnosing OC, and a subsequent model cohort (n=200) was used to train and cross-validate a diagnostic model. Results: GD2 and GD3 were expressed in tissues of all OC subtypes and FIGO stages but not in surrounding healthy tissue or other controls. In serum, GD2 and GD3 were elevated in patients with OC. A diagnostic model that included serum levels of GD2+GD3+age was superior to the standard of care (CA125, p<0.001) in diagnosing OC and early-stage (I/II) OC. Conclusion: GD2 and GD3 expression was associated with high rates of selectivity and specificity for OC. A diagnostic model combining GD2 and GD3 quantification in serum had diagnostic power for all subtypes and all stages of OC, including early stage. Further research exploring the utility of GD2 and GD3 for diagnosis of OC is warranted.

Sperm DNA methylome abnormalities occur both pre- and post-treatment in men with Hodgkin disease and testicular cancer.

BACKGROUND: Combination chemotherapy has contributed to increased survival from Hodgkin disease (HD) and testicular cancer (TC). However, questions concerning the quality of spermatozoa after treatment have arisen. While studies have shown evidence of DNA damage and aneuploidy in spermatozoa years following anticancer treatment, the sperm epigenome has received little attention. Our objectives here were to determine the impact of HD and TC, as well as their treatments, on sperm DNA methylation. Semen samples were collected from community controls (CC) and from men undergoing treatment for HD or TC, both before initiation of chemotherapy and at multiple times post-treatment. Sperm DNA methylation was assessed using genome-wide and locus-specific approaches. RESULTS: Imprinted gene methylation was not affected in the sperm of HD or TC men, before or after treatment. Prior to treatment, using Illumina HumanMethylation450 BeadChip (450 K) arrays, a subset of 500 probes was able to distinguish sperm samples from TC, HD and CC subjects; differences between groups persisted post-treatment. Comparing altered sperm methylation between HD or TC patients versus CC men, twice as many sites were affected in TC versus HD men; for both groups, the most affected CpGs were hypomethylated. For TC patients, the promoter region of GDF2 contained the largest region of differential methylation. To assess alterations in DNA methylation over time/post-chemotherapy, serial samples from individual patients were compared. With restriction landmark genome scanning and 450 K array analyses, some patients who underwent chemotherapy showed increased alterations in DNA methylation, up to 2 to 3 years post-treatment, when compared to the CC cohort. Similarly, a higher-resolution human sperm-specific assay that includes assessment of environmentally sensitive regions, or "dynamic sites," also demonstrated persistently altered sperm DNA methylation in cancer patients post-treatment and suggested preferential susceptibility of "dynamic" CpG sites. CONCLUSIONS: Distinct sperm DNA methylation signatures were present pre-treatment in men with HD and TC and may help explain increases in birth defects reported in recent clinical studies. Epigenetic defects in spermatozoa of some cancer survivors were evident even up to 2 years post-treatment. Abnormalities in the sperm epigenome both pre- and post-chemotherapy may contribute to detrimental effects on future reproductive health.

Patient preferences for features of HER2-targeted treatment of advanced or metastatic breast cancer: a discrete-choice experiment study.

BACKGROUND: We aimed to quantify patients' benefit-risk preferences for attributes associated with human epidermal growth factor receptor 2 (HER2)-targeted breast cancer treatments and estimate minimum acceptable benefits (MABs), denominated in additional months of progression-free survival (PFS), for given treatment-related adverse events (AEs). METHODS: We conducted an online discrete-choice experiment (DCE) among patients with self-reported advanced/metastatic breast cancer in the United States, United Kingdom, and Japan (N = 302). In a series of nine DCE questions, respondents chose between two hypothetical treatment profiles created by an experimental design. Profiles were defined by six attributes with varying levels: PFS, nausea/vomiting, diarrhea, liver function problems, risk of heart failure, and risk of serious lung damage and infections. Data were analyzed using an error component random-parameters logit model. RESULTS: Among the attributes, patients placed the most importance on a change in PFS from 5 to 26 months; change from no diarrhea to severe diarrhea was the least important. Avoiding a 15% risk of heart failure had the largest MAB (5.8 additional months of PFS), followed by avoiding a 15% risk of serious lung damage and infections (4.6 months), possible severe liver function problems (4.2 months), severe nausea/vomiting (3.7 months), and severe diarrhea (2.3 months) compared with having none of the AEs. The relative importance of 21 additional months of PFS (increasing from 5 to 26 months) increased for women with HER2-negative disease and those with children. CONCLUSIONS: Patients valued PFS gain higher than the potential risk of AEs when deciding between hypothetical breast cancer treatments.

Arsenic Exposure, Blood DNA Methylation, and Cardiovascular Disease.

BACKGROUND: Epigenetic dysregulation has been proposed as a key mechanism for arsenic-related cardiovascular disease (CVD). We evaluated differentially methylated positions (DMPs) as potential mediators on the association between arsenic and CVD. METHODS: Blood DNA methylation was measured in 2321 participants (mean age 56.2, 58.6% women) of the Strong Heart Study, a prospective cohort of American Indians. Urinary arsenic species were measured using high-performance liquid chromatography coupled to inductively coupled plasma mass spectrometry. We identified DMPs that are potential mediators between arsenic and CVD. In a cross-species analysis, we compared those DMPs with differential liver DNA methylation following early-life arsenic exposure in the apoE knockout (apoE-/-) mouse model of atherosclerosis. RESULTS: A total of 20 and 13 DMPs were potential mediators for CVD incidence and mortality, respectively, several of them annotated to genes related to diabetes. Eleven of these DMPs were similarly associated with incident CVD in 3 diverse prospective cohorts (Framingham Heart Study, Women's Health Initiative, and Multi-Ethnic Study of Atherosclerosis). In the mouse model, differentially methylated regions in 20 of those genes and DMPs in 10 genes were associated with arsenic. CONCLUSIONS: Differential DNA methylation might be part of the biological link between arsenic and CVD. The gene functions suggest that diabetes might represent a relevant mechanism for arsenic-related cardiovascular risk in populations with a high burden of diabetes.

Impact of clinical and demographic characteristics on patient preferences for psoriasis treatment features: Results from a discrete-choice experiment in a multicountry study.

OBJECTIVES: This study aimed to elicit preferences for psoriasis treatment features and to test for preference heterogeneity across groups of respondents. MATERIALS AND METHODS: A discrete-choice experiment was employed to elicit preferences of patients with plaque psoriasis in multiple countries. The survey instrument included a series of choice questions between three hypothetical treatments, each characterized by varying levels of six attributes (namely, lesion reduction, risk of impairing side effects, time to reach results, mode and frequency of administration, itching reduction, and side effects). Random parameters logit was used to model the data. Results were compared across a total of 18 subgroup sets. RESULTS: The data analysis from 1,123 respondents showed that, on average, respondents receive more utility gain from higher levels of lesion reduction and lower risks of impairing side effects than changes in other attributes included in the study. Systematic differences were detected for 13 sets; the most pronounced differences were observed based on disease severity, nail psoriasis, biologic experience, and quality-of-life scores. CONCLUSION: These many sources of preference heterogeneity identified by our analysis suggest that to improve patient satisfaction and, probably, adherence and persistence, clinicians should discuss options with patients when prescribing their treatment.

Exploring patient preference heterogeneity for pharmacological treatments for chronic pain: A latent class analysis.

BACKGROUND: Several pharmaceutical treatments for chronic pain caused by osteoarthritis (OA) and chronic low back pain (CLBP) are available or currently under development, each associated with different adverse events (AEs) and efficacy profiles. It is therefore important to understand what trade-offs patients are willing to make when choosing between treatments. METHODS: A discrete-choice experiment (DCE) was conducted with 437 adults with chronic pain caused by OA and/or CLBP. Respondents were presented with a series of scenarios and asked to choose between pairs of hypothetical treatments, each defined by six attributes: level of symptom control; risks of heart attack, rapidly progressive osteoarthritis and dependency; frequency and mode of administration and cost. Attributes were based on known profiles of oral nonsteroidal anti-inflammatory drugs, opioids and injected nerve growth factor inhibitors, the last of which were under clinical development at the time of the study. Data were analysed using a latent class (LC) model to explore preference heterogeneity. RESULTS: Overall, respondents considered improving symptom control and reducing risk of physical dependency to be the most important attributes. The LC analysis identified four participant classes: an 'efficacy-focused' class (33.7%), a 'cost-averse' class (29.4%), a 'physical-dependence-averse' class (19.6%) and a 'needle-averse' class (17.3%). Subgroup membership was incompletely predicted by participant age and their responses to comprehension questions. CONCLUSIONS: Preference heterogeneity across respondents indicates a need for a personalized approach to offering treatment options. Symptom improvement, cost, physical dependence and route of administration might be important to different patients. SIGNIFICANCE: Multiple treatment options that differ substantially in terms of efficacy and adverse events are available for the management of chronic pain. With a growing emphasis on a patient-centred care model that incorporates patients' priorities and values into treatment decisions, there is a need to understand how individuals with chronic musculoskeletal pain balance the benefits and risks of treatment and how treatment priorities vary among individuals. This study was designed to identify patient preferences for different characteristics of treatments for the management of chronic pain and to investigate how preferences differ among respondents.

Thousands of CpGs Show DNA Methylation Differences in ACPA-Positive Individuals.

High levels of anti-citrullinated protein antibodies (ACPA) are often observed prior to a diagnosis of rheumatoid arthritis (RA). We undertook a replication study to confirm CpG sites showing evidence of differential methylation in subjects positive vs. negative for ACPA, in a new subset of 112 individuals sampled from the population cohort and biobank CARTaGENE in Quebec, Canada. Targeted custom capture bisulfite sequencing was conducted at approximately 5.3 million CpGs located in regulatory or hypomethylated regions from whole blood; library and protocol improvements had been instituted between the original and this replication study, enabling better coverage and additional identification of differentially methylated regions (DMRs). Using binomial regression models, we identified 19,472 ACPA-associated differentially methylated cytosines (DMCs), of which 430 overlapped with the 1909 DMCs reported by the original study; 814 DMRs of relevance were clustered by grouping adjacent DMCs into regions. Furthermore, we performed an additional integrative analysis by looking at the DMRs that overlap with RA related loci published in the GWAS Catalog, and protein-coding genes associated with these DMRs were enriched in the biological process of cell adhesion and involved in immune-related pathways.

Risk tolerance in adjuvant and metastatic melanoma settings: a patient perspective study using the threshold technique.

Background: Adverse events (e.g., pyrexia) may affect treatment patterns and adherence. This study explored pyrexia risk tolerance among melanoma patients when treatment benefit is unknown versus known. Materials & methods: US respondents with stage III (n = 100) or stage III unresectable/stage IV melanoma (n = 125) chose between hypothetical melanoma treatments, defined by reoccurrence/progression-free survival and pyrexia risk, one resembling standard-of-care and one resembling dabrafenib + trametinib. Respondents chose first when efficacy was unknown and then when efficacy was known; pyrexia risk was varied systematically to define maximum acceptable risk. Results: Maximum acceptable risk of pyrexia was statistically significantly higher when efficacy was known versus unknown in stage III patients (85 vs 34%) and stage III unresectable/stage IV patients (66 vs 57%). Conclusion: Patients accepted higher levels of pyrexia risk when they understood treatment benefit.

High Systolic Blood Pressure at Hospital Admission Is an Important Risk Factor in Models Predicting Outcome of COVID-19 Patients.

BACKGROUND: The risk that coronavirus disease 2019 (COVID-19) patients develop critical illness that can be fatal depends on their age and immune status and may also be affected by comorbidities like hypertension. The goal of this study was to develop models that predict outcome using parameters collected at admission to the hospital. METHODS AND RESULTS: This is a retrospective single-center cohort study of COVID-19 patients at the Seventh Hospital of Wuhan City, China. Forty-three demographic, clinical, and laboratory parameters collected at admission plus discharge/death status, days from COVID-19 symptoms onset, and days of hospitalization were analyzed. From 157 patients, 120 were discharged and 37 died. Pearson correlations showed that hypertension and systolic blood pressure (SBP) were associated with death and respiratory distress parameters. A penalized logistic regression model efficiently predicts the probability of death with 13 of 43 variables. A regularized Cox regression model predicts the probability of survival with 7 of above 13 variables. SBP but not hypertension was a covariate in both mortality and survival prediction models. SBP was elevated in deceased compared with discharged COVID-19 patients. CONCLUSIONS: Using an unbiased approach, we developed models predicting outcome of COVID-19 patients based on data available at hospital admission. This can contribute to evidence-based risk prediction and appropriate decision-making at hospital triage to provide the most appropriate care and ensure the best patient outcome. High SBP, a cause of end-organ damage and an important comorbid factor, was identified as a covariate in both mortality and survival prediction models.

A novel statistical method for modeling covariate effects in bisulfite sequencing derived measures of DNA methylation.

Identifying disease-associated changes in DNA methylation can help us gain a better understanding of disease etiology. Bisulfite sequencing allows the generation of high-throughput methylation profiles at single-base resolution of DNA. However, optimally modeling and analyzing these sparse and discrete sequencing data is still very challenging due to variable read depth, missing data patterns, long-range correlations, data errors, and confounding from cell type mixtures. We propose a regression-based hierarchical model that allows covariate effects to vary smoothly along genomic positions and we have built a specialized EM algorithm, which explicitly allows for experimental errors and cell type mixtures, to make inference about smooth covariate effects in the model. Simulations show that the proposed method provides accurate estimates of covariate effects and captures the major underlying methylation patterns with excellent power. We also apply our method to analyze data from rheumatoid arthritis patients and controls. The method has been implemented in R package SOMNiBUS.

Preferences for Outcomes Among Adults with Type 1 Diabetes and Caregivers of Children with Type 1 Diabetes.

PURPOSE: Hemoglobin A1c (HbA1c) is the accepted measure of effectiveness for type 1 diabetes therapies. We investigated preferences for measures of diabetes control in addition to HbA1c among adults with type 1 diabetes and caregivers of children with type 1 diabetes. METHODS: Using discrete-choice experiment methodology, surveys for adults with type 1 diabetes and caregivers presented choices between hypothetical treatments described by six attributes with varying levels: HbA1c, time in optimal glucose range, weekly number and severity of hypoglycemic and hyperglycemic events, additional disease management time, and additional treatment cost. Choice data were analyzed using random-parameters logit. RESULTS: A total of 300 adults with type 1 diabetes and 400 caregivers completed the survey. Adults and caregivers placed the most importance on reducing hypoglycemic and hyperglycemic events. For adults, avoiding 1-5 mild-to-moderate hypoglycemic events (glucose 54-69 mg/dL)/week was five times more important than being a half-point above target HbA1c. Avoiding 1-5 hyperglycemic events (glucose >180 mg/dL)/week was seven times more important than being a half-point above target HbA1c. Additional time in optimal glucose range was as important as a reduction greater than a half-point in HbA1c. Avoiding hyperglycemic and hypoglycemic events was more important than all other outcomes for caregivers of younger children. Caregivers of children >12 years placed relatively more weight on avoiding hypoglycemic events <54 mg/dL than those with younger children and preferred avoiding additional costs. CONCLUSION: Adults with type 1 diabetes and caregivers prioritize controlling hypoglycemic and hyperglycemic events, including mild-to-moderate events. These preferences should be considered in drug development and regulatory decisions.

Parent Preferences for Delaying Insulin Dependence in Children at Risk of Stage III Type 1 Diabetes.

Background: Autoantibody screening in type 1 diabetes (T1D) may reduce the chances of potentially life-threatening diabetic ketoacidosis (DKA) at diagnosis by allowing individuals at risk of progression to more actively monitor for and/or manage progression to insulin dependence. We investigated parents' preferences for treatments to delay the onset of insulin dependence in children who are at high risk of developing Stage III T1D. Methods: A web-based survey (n = 1501) was administered to a stratified sample of parents (children <18 years) in the United States from an online panel. Parents were told to hypothetically assume that their youngest child would become insulin dependent within 6 months or 2 years and were offered a series of choices between no treatment and two hypothetical treatments that would delay insulin dependence. Random-parameters logit analysis and maximum acceptable risks were used to evaluate the relative importance of treatment benefits and risks. Results: Most parents chose at least one active treatment (2% always chose monitoring only). For parents of children without T1D (n = 901), delaying insulin dependence and reducing the risk of long-term health complications and serious infection were the most important treatment attributes. In addition, parents of children with T1D (n = 600) also valued reducing the risk of hospitalizations due to DKA. Conclusions: When told to assume their child would develop Stage III T1D, most parents considered active treatments to delay progression. For medicines under development to delay insulin dependence in T1D, the preferences expressed in this survey provide guidance on acceptable benefit-risk trade-offs.

Whole-genome bisulfite sequencing in systemic sclerosis provides novel targets to understand disease pathogenesis.

BACKGROUND: Systemic sclerosis (SSc) is a rare autoimmune connective tissue disease whose pathogenesis remains incompletely understood. Increasing evidence suggests that both genetic susceptibilities and changes in DNA methylation influence pivotal biological pathways and thereby contribute to the disease. The role of DNA methylation in SSc has not been fully elucidated, because existing investigations of DNA methylation predominantly focused on nucleotide CpGs within restricted genic regions, and were performed on samples containing mixed cell types. METHODS: We performed whole-genome bisulfite sequencing on purified CD4+ T lymphocytes from nine SSc patients and nine controls in a pilot study, and then profiled genome-wide cytosine methylation as well as genetic variations. We adopted robust statistical methods to identify differentially methylated genomic regions (DMRs). We then examined pathway enrichment associated with genes located in these DMRs. We also tested whether changes in CpG methylation were associated with adjacent genetic variation. RESULTS: We profiled DNA methylation at more than three million CpG dinucleotides genome-wide. We identified 599 DMRs associated with 340 genes, among which 54 genes exhibited further associations with adjacent genetic variation. We also found these genes were associated with pathways and functions that are known to be abnormal in SSc, including Wnt/ß-catenin signaling pathway, skin lesion formation and progression, and angiogenesis. CONCLUSION: The CD4+ T cell DNA cytosine methylation landscape in SSc involves crucial genes in disease pathogenesis. Some of the methylation patterns are also associated with genetic variation. These findings provide essential foundations for future studies of epigenetic regulation and genome-epigenome interaction in SSc.

Outcome-Related Differences in Gene Expression Profiles of High-Grade Serous Ovarian Cancers Following Neoadjuvant Chemotherapy.

Large-scale genomic studies have detailed the molecular landscape of tumors from patients with high-grade serous ovarian cancers (HGSC) who underwent primary debulking surgery and correlated the identified subgroups to survival. In recent years, there is increased use of neoadjuvant chemotherapy (NACT) for patients with HGSC and while abundant data exist for patients who underwent primary debulking, little data are available on the cancer cells remaining after NACT that could lead to recurrences. We aimed to analyze gene expression profiles of NACT-treated HGSC tumor samples, and correlate them to treatment response and outcome. Tumor samples were collected from patients with stage III or IV HGSC (NACT cohort, N = 57) at the time of surgery and diagnosis (biopsy samples N = 8). Tumor content was validated by histologic examination and bioinformatics. Gene expression analysis was performed using a tailored NanoString-based assay, while sequencing was performed using MiSeq. A cross-validated survival classifier revealed patient clusters with either a "Better" or "Worse" prognostic outcome. The association with overall survival remained significant after controlling for clinical variables, and differential gene expression, gene set enrichment analyses, and the appropriate survival models were used to assess the associations between alterations in gene expression in cancer cells remaining after NACT and outcome. Pathway-based analysis of the differentially expressed genes revealed comparatively high levels of cell cycle and DNA repair gene expression in the poor outcome group. IMPLICATIONS: Our work suggests mRNA expression patterns in key genes following NACT may reflect response to treatment and outcome in patient with HGSC.

Identification of a Radiosensitivity Molecular Signature Induced by Enzalutamide in Hormone-sensitive and Hormone-resistant Prostate Cancer Cells.

Prostate cancer (PCa) is the most common cancer amongst men. A novel androgen receptor (AR) antagonist, enzalutamide (ENZA) has recently been demonstrated to enhance the effect of radiation (XRT) by impairing the DNA damage repair process. This study aimed to identify a radiosensitive gene signature induced by ENZA in the PCa cells and to elucidate the biological pathways which influence this radiosensitivity. We treated LNCaP (AR-positive, hormone-sensitive PCa cells) and C4-2 (AR-positive, hormone-resistant PCa cells) cells with ENZA alone and in combination with androgen deprivation therapy (ADT) and XRT. Using one-way ANOVA on the gene expression profiling, we observed significantly differentially expressed (DE) genes in inflammation-and metabolism-related genes in hormone-sensitive and hormone-resistant PCa cell lines respectively. Survival analysis in both the TCGA PRAD and GSE25136 datasets suggested an association between the expression of these genes and time to recurrence. These results indicated that ENZA alone or in combination with ADT enhanced the effect of XRT through immune and inflammation-related pathways in LNCaP cells and metabolic-related pathways in C4-2 cells. Kaplan-Meier analysis and Cox proportional hazard models showed that low expression of all the candidate genes except for PTPRN2 were associated with tumor progression and recurrence in a PCa cohort.

College-Bound: Transition Planning Strategies for Students With Type 1 Diabetes.

Transition planning is mandated for students who receive special education services; however, it is not required for students with chronic conditions. Students with chronic conditions nearing graduation would benefit from more intensive attention to their post-high school self-care needs and responsibilities. Students with type 1 diabetes must be able to understand the necessary self-care of one of the most complicated and intensive chronic conditions yet there are no evidence-based strategies for how to help students transition from the support provided at school to independence at graduation. The need for a student with type 1diabetes to independently manage their diabetes is even greater as the student leaves home for college. School nurses, who are also certified diabetes educators, created a support group for seniors with type 1 diabetes. The group focused on life after high school and provided the students with a detailed checklist to help improve knowledge and prepare students for the transition to college. Participants felt better prepared to move on to college armed with their checklist and other resources to help when away from home.

Investigating potential causal relationships between SNPs, DNA methylation and HDL.

Using data on 680 patients from the GAW20 real data set, we conducted Mendelian randomization (MR) studies to explore the causal relationships between methylation levels at selected probes (cytosine-phosphate-guanine sites [CpGs]) and high-density lipoprotein (HDL) changes (ΔHDL) using single-nucleotide polymorphisms (SNPs) as instrumental variables. Several methods were used to estimate the causal effects at CpGs of interest on ΔHDL, including a newly developed method that we call constrained instrumental variables (CIV). CIV performs automatic SNP selection while providing estimates of causal effects adjusted for possible pleiotropy, when the potentially-pleiotropic phenotypes are measured. For CpGs in or near the 10 genes identified as associated with ΔHDL using a family-based VC-score test, we compared CIV to Egger regression and the two-stage least squares (TSLS) method. All 3 approaches selected at least 1CpG in 2 genes-RNMT;C18orf19 and C6orf141-as showing a causal relationship with ΔHDL.