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OBJECTIVE To distinguish among predictors of seroconversion, progression to multiple autoantibodies and from multiple autoantibodies to type 1 diabetes in young children. RESEARCH DESIGN AND METHODS Genetically high-risk newborns (n = 8,502) were followed for a median of 11.2 years (interquartile range 9.3–12.6); 835 (9.8%) developed islet autoantibodies and 283 (3.3%) were diagnosed with type 1 diabetes. Predictors were examined using Cox proportional hazards models. RESULTS Predictors of seroconversion and progression differed, depending on the type of first appearing autoantibody. Male sex, Finnish residence, having a sibling with type 1 diabetes, the HLA DR4 allele, probiotic use before age 28 days, and single nucleotide polymorphism (SNP) rs689_A (INS) predicted seroconversion to IAA-first (having islet autoantibody to insulin as the first appearing autoantibody). Increased weight at 12 months and SNPs rs12708716_G (CLEC16A) and rs2292239_T (ERBB3) predicted GADA-first (autoantibody to GAD as the first appearing). For those having a father with type 1 diabetes, the SNPs rs2476601_A (PTPN22) and rs3184504_T (SH2B3) predicted both. Younger age at seroconversion predicted progression from single to multiple autoantibodies as well as progression to diabetes, except for those presenting with GADA-first. Family history of type 1 diabetes and the HLA DR4 allele predicted progression to multiple autoantibodies but not diabetes. Sex did not predict progression to multiple autoantibodies, but males progressed more slowly than females from multiple autoantibodies to diabetes. SKAP2 and MIR3681HG SNPs are newly reported to be significantly associated with progression from multiple autoantibodies to type 1 diabetes. CONCLUSIONS Predictors of IAA-first versus GADA-first autoimmunity differ from each other and from the predictors of progression to diabetes.

Objective To examine if iron intake and genetically determined iron overload interact in predisposing to the development of childhood islet autoimmunity (IA) and type 1 diabetes (T1D). Research Design and Methods In “The Environmental Determinants of Diabetes in the Young” (TEDDY) study, 7,770 genetically high-risk children were followed from birth until the development of IA and progression to T1D. Exposures included energy-adjusted iron intake in the first 3 years of life and a genetic risk score (GRS) for increased circulating iron. Results We found a U-shaped association between iron intake and risk of GADA as first autoantibody. In subjects with GRS>=2 iron risk alleles, high iron intake was associated with increased risk of IA with insulin as first autoantibody [adjusted hazard ratio (HR, 95% CI): 1.71 (1.14; 2.58), as compared to moderate iron intake. Conclusions Iron intake may alter the risk of islet autoimmunity in children with high-risk HLA haplogenotypes.

AIMS/HYPOTHESIS: Prognostic factors and characteristics of children diagnosed with type 1 diabetes before 6years of age were compared with those diagnosed at 6-13years of age in the TEDDY study. METHODS: Genetically high-risk children (n = 8502) were followed from birth for a median of 9.9years; 328 (3.9%) were diagnosed with type 1 diabetes. Cox proportional hazard model was used to assess the association of prognostic factors with the risk of type 1 diabetes in the two age groups. RESULTS: Children in the younger group tended to develop autoantibodies earlier than those in the older group did (mean age 1.5 vs 3.5years), especially insulin autoantibodies (IAA), which developed earlier than GAD autoantibodies (GADA). Children in the younger group also progressed to diabetes more rapidly than the children in the older group did (mean duration 1.9 vs 5.4 years). Children with autoantibodies first appearing against insulinoma antigen-2 (IA-2A) were found only in the older group. The significant diabetes risk associated with the country of origin in the younger group was no longer significant in the older group. Conversely, the diabetes risk associated with HLA genotypes was statistically significant also in the older group. Initial seroconversion after and before 2years of age was associated with decreased risk for diabetes diagnosis in children positive for multiple autoantibodies, but the diabetes risk did not decrease further with increasing age if initial seroconversion occurred after age 2. Diabetes risk associated with the minor alleles of rs1004446 (INS) was decreased in both the younger and older groups compared with other genotypes (HR 0.67). Diabetes risk was significantly increased with the minor alleles of rs2476601 (PTPN22) (HR 2.04 and 1.72), rs428595 (PPIL2) (HR 2.13 and 2.10), rs113306148 (PLEKHA1) (HR 2.34 and 2.21) and rs73043122 (RNASET2) (HR 2.31 and 2.54) (HR values represent the younger and older groups, respectively). CONCLUSIONS/INTERPRETATIONS: Diabetes at an early age is likely to be preceded by IAA autoantibodies and is a more aggressive form of the disease. Among older children, once multiple autoantibodies have been observed there does not seem to be any association between progression to diabetes and the age of the child or family history. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT00279318.

BACKGROUND: Breastfeeding has beneficial effects on numerous health outcomes. OBJECTIVES: We investigated whether breastfeeding duration is associated with the development of early childhood autoimmunity, allergies, or obesity in a multinational prospective birth cohort. METHODS: Infants with genetic susceptibility for type 1 diabetes (n=8676) were followed for the development of autoantibodies to islet autoantigens or transglutaminase, allergies, and for anthropometric measurements to a median age of 8.3 y (IQR: 2.8-10.2y). Information on breastfeeding was collected at 3 mo of age and prospectively thereafter. A propensity score for longer breastfeeding was calculated from the variables that were likely to influence any or exclusive breastfeeding. The risks of developing autoimmunity or allergy were assessed using Cox proportional hazards models, and the risk of obesity at 5.5 y of age was assessed using logistic regression with adjustment by the propensity score. RESULTS: Breastfeeding duration was not associated with a lower risk of either islet or transglutaminase autoimmunity (any breastfeeding>6 mo, adjusted HR: 1.07; 95% CI: 0.96, 1.19; exclusive breastfeeding>3 mo, adjusted HR: 1.03; 95% CI: 0.92, 1.15). Exclusive breastfeeding>3 mo was associated with a decreased risk of seasonal allergic rhinitis (adjusted HR: 0.70; 95% CI: 0.53, 0.92; P6 mo and exclusive breastfeeding>3mo were associated with decreased risk of obesity (adjusted OR: 0.62; 95% CI: 0.47, 0.81; P&nbsp

Objective: To distinguish among predictors of seroconversion, progression to multiple autoantibodies and from multiple autoantibodies to type 1 diabetes in young children. Research Design and Methods: Genetically high-risk newborns (n=8502) were followed for a median of 11.2 y (IQR 9.3-12.6 y); 835 (9.8%) developed islet autoantibodies and 283 (3.3%) were diagnosed with type 1 diabetes. Predictors were examined using Cox proportional hazard models. Results: Predictors of seroconversion and progression differed, depending on the type of first appearing autoantibody. Male sex, Finnish residence, having a sibling with type 1 diabetes, the HLA DR4 allele, probiotic use before age 28 days, and SNP rs689_A (INS) predicted seroconversion to IAA-first. Increased weight at 12 months and SNPs rs12708716_G (CLEC16A) and rs2292239_T (ERBB3) predicted GADA-first. Having a father with type 1 diabetes, the SNPs rs2476601_A (PTPN22) and rs3184504_T (SH2B3) predicted both. Younger age at seroconversion predicted progression from single to multiple autoantibodies as well as progression to diabetes, except for those presenting with GADA-first. Family history of type 1 diabetes and the HLA DR4 allele predicted progression to multiple autoantibodies, but not diabetes. Sex did not predict progression to multiple autoantibodies, but males progressed more slowly than females from multiple autoantibodies to diabetes. SKAP2 and MIR3681HG SNPs are newly reported to be significantly associated with progression from multiple autoantibodies to type 1 diabetes. Conclusions: Predictors of IAA-first vs GADA-first autoimmunity differ from each other and from the predictors of progression to diabetes.

Type 1 diabetes (T1D) is an autoimmune disease, characterized by the presence of auto-antibodies to several protein and non-protein antigens. We report circulating levels of antibodies towards naturally occurring glycans using our customized glycan array and show that anti-carbohydrate antibodies (ACA) are associated with pathogenesis and progression to T1D. We compared circulatory levels of ACAs against 202 glycans in a cross-sectional cohort of T1D patients (n=278) and healthy controls (n=298), as well as in a longitudinal cohort (n=112). We identified 11 clusters of ACAs associated with glycan function class. Clusters enriched for aminoglycosides, blood group A and B antigens, glycolipids, ganglio-series, and O-linked glycans were associated with progression to T1D. ACAs against the aminoglycoside commonly used for neonatal sepsis, gentamicin, and its related structures, G418 and sisomicin, were associated with islet autoimmunity. ACAs improved discrimination of T1D status of individuals over a model with only clinical variables. ACAs are potential biomarkers for T1D.

The aim of this study was to investigate survival differences between equivalent residual disease [complete gross resection (CGR), minimal residual disease (MRD), suboptimal] at the time of primary debulking surgery (PDS) and interval debulking surgery (IDS). The National Cancer Database was used to identify patients from 2010 to 2015 with stage IIIC/IV primary peritoneal or ovarian cancer who had residual disease recorded. Propensity score matching (PSM) was used to correct for differences in characteristics between the PDS and IDS groups. Of 8683 patients with advanced ovarian cancer, 4493 (52%), 2546 (29%), and 1644 (19%) had CGR, MRD, or suboptimal resection, respectively. From 2010 to 2015, the number of patients undergoing IDS increased 27% (ptrend

To determine the long-term potential benefit of adjuvant chemotherapy in subgroups of high-risk stage I mucinous ovarian cancer patients using a predictive scoring algorithm.Data were collected from the National Cancer Database from 2004 to 2014. Based on demographic and surgical characteristics, a novel 10-year survival prognostic scoring system was developed using Cox regression.There were 2041 eligible patients with stage I mucinous ovarian cancer including 1362 (67%) with stage IA/IB disease, 598 (29%) with stage IC disease, and 81 (4%) with stage I disease not otherwise specified. Median age was 52 with a range of 13-90 years old. 737 (36%) patients were treated with adjuvant chemotherapy. Adjuvant chemotherapy was more common in patients with stage IC relative to stage IA/IB disease (69% vs. 21%, P 0.001) or with poorly-differentiated relative to well-differentiated tumors (69% vs. 23%, P 0.001). Unadjusted 10-year survival was 81% relative to 79% for patients treated with vs. without chemotherapy, respectively (P = 0.46). Patients were predicted to exhibit a low- or a high-risk of death using a multivariate Cox regression model with age, stage, grade, lymphovascular space invasion and ascites. Risk of death without vs. with adjuvant chemotherapy was similar in low-risk patients (88% vs. 84%; HR = 0.80, 95%CI = 0.56-1.15, P = 0.23) and worse in high-risk patients (51% vs. 74%; HR = 1.58, 95%CI: 1.05-2.38, P = 0.03) with stage I mucinous ovarian cancer.A predictive scoring algorithm may provide prognostic information on long-term survival and identify high-risk stage I mucinous ovarian cancer patients who might achieve a survival benefit from adjuvant chemotherapy.

Type 1 diabetes (T1D)—an autoimmune disease that destroys the pancreatic islets, resulting in insulin deficiency—often begins early in life when islet autoantibody appearance signals high risk1. However, clinical diabetes can follow in weeks or only after decades, and is very difficult to predict. Ketoacidosis at onset remains common2,3 and is most severe in the very young4,5, in whom it can be life threatening and difficult to treat6–9. Autoantibody surveillance programs effectively prevent most ketoacidosis10–12 but require frequent evaluations whose expense limits public health adoption13. Prevention therapies applied before onset, when greater islet mass remains, have rarely been feasible14 because individuals at greatest risk of impending T1D are difficult to identify. To remedy this, we sought accurate, cost-effective estimation of future T1D risk by developing a combined risk score incorporating both fixed and variable factors (genetic, clinical and immunological) in 7,798 high-risk children followed closely from birth for 9.3 years. Compared with autoantibodies alone, the combined model dramatically improves T1D prediction at ≥2 years of age over horizons up to 8 years of age (area under the receiver operating characteristic curve ≥ 0.9), doubles the estimated efficiency of population-based newborn screening to prevent ketoacidosis, and enables individualized risk estimates for better prevention trial selection. In a study of children with high genetic risk aged 2 years or older, a risk score integrating pancreatic islet autoantibodies, genetic factors and family history is highly predictive of type 1 diabetes in the subsequent 8 years.

OBJECTIVE: To measure anti-glycan antibodies (AGA) in cervical cancer (CC) patient sera and assess their effect on therapeutic outcome. PATIENTS AND METHODS: Serum AGA was measured in 276 stage II and 292 stage III Peruvian CC patients using a high content and throughput Luminex multiplex glycan array (LMGA) containing 177 glycans. Association with disease-specific survival (DSS) and progression free survival (PFS) were analyzed using Cox regression. RESULTS: AGAs were detected against 50 (28.3%) of the 177 glycans assayed. Of the 568 patients, 84.5% received external beam radiation therapy (EBRT) plus brachytherapy (BT), while 15.5% only received EBRT. For stage-matched patients (Stage III), receiving EBRT alone was significantly associated with worse survival (HR 6.4, p < 0.001). Stage III patients have significantly worse survival than Stage II patients after matching for treatment (HR = 2.8 in EBRT+BT treatment group). Furthermore, better PFS and DSS were observed in patients positive for AGA against multiple glycans belonging to the blood group H, Lewis, Ganglio, Isoglobo, lacto and sialylated tetrarose antigens (best HR = 0.49, best p = 0.0008). CONCLUSIONS: Better PFS and DSS are observed in cervical cancer patients that are positive for specific antiglycan antibodies and received brachytherapy.

Determine the utility of a clinical calculator to predict the benefit of chemotherapy in stage IA uterine papillary serous cancer (UPSC).Data were collected from NCDB from years 2010-2014. Based on demographic and surgical characteristics, a clinical score was developed using the random survival forest machine learning algorithm.Of 1,751 patients with stage IA UPSC, 1,012 (58%) received chemotherapy and 739 (42%) did not. Older age (HR 1.06), comorbidities (HR 1.31), larger tumor size (HR 1.27), lymphovascular invasion (HR 1.86), positive peritoneal cytology (HR 2.62), no pelvic lymph node dissection (HR 1.51), and no chemotherapy (HR 2.16) were associated with poorer prognosis. Compared to no chemotherapy, patients who underwent chemotherapy had a 5-year overall survival of 80% vs. 67%. To better delineate those who may derive more benefit from chemotherapy, we designed a clinical calculator capable of dividing patients into low, moderate, and high-risk groups with associated 5-year OS of 86%, 73%, and 53%, respectively. Using the calculator to assess the relative benefit of chemotherapy in each risk group, chemotherapy improved the 5-year OS in the high (42% to 64%; p 0.001) and moderate risk group (66% to 79%; p 0.001) but did not benefit the low risk group (84% to 87%; p = 0.29).Our results suggest a clinical calculator is useful for counseling and personalizing chemotherapy for stage IA UPSC.

Type 1 diabetes (T1D) is an autoimmune disease, characterized by the presence of autoantibodies to protein and non-protein antigens. Here we report the identification of specific anti-carbohydrate antibodies (ACAs) that are associated with pathogenesis and progression to T1D. We compare circulatory levels of ACAs against 202 glycans in a cross-sectional cohort of T1D patients (n = 278) and healthy controls (n = 298), as well as in a longitudinal cohort (n = 112). We identify 11 clusters of ACAs associated with glycan function class. Clusters enriched for aminoglycosides, blood group A and B antigens, glycolipids, ganglio-series, and O-linked glycans are associated with progression to T1D. ACAs against gentamicin and its related structures, G418 and sisomicin, are also associated with islet autoimmunity. ACAs improve discrimination of T1D status of individuals over a model with only clinical variables and are potential biomarkers for T1D.

Objective: The Environmental Determinants of Diabetes in the Young (TEDDY) study is uniquely capable of investigating age-specific differences associated with type 1 diabetes. As age is a primary driver of heterogeneity in type 1 diabetes, we sought to characterize by age, metabolic derangements prior to diagnosis and clinical features associated with diabetic ketoacidosis (DKA). Research Design and Methods: The 379 TEDDY children who developed type 1 diabetes were grouped by age at onset (0-4, 5-9, and 10-14 years; n=142, 151 and 86, respectively) with comparisons of autoantibody profiles, HLA, family history of diabetes, presence of DKA, symptomatology at onset, and adherence to TEDDY protocol. Time-varying analysis of those with OGTT data was compared to TEDDY children who did not progress to diabetes. Results: Increasing fasting glucose (HR=1.09 (95% CI 1.04-1.14), p=0.0003), stimulated glucose (HR=1.50 (1.42-1.59), p Conclusions: DKA at onset in TEDDY is uncommon, especially for FDRs. For those without familial risk, metabolic monitoring continues to provide a primary benefit of reduced DKA but requires regular follow-up. Clinical and laboratory features vary by age at onset adding to the heterogeneity of type 1 diabetes.

We present here detailed protocols for the newly developed multiplex glycan bead array (MGBA) for the high throughput and high content analyses of various glycan binding proteins including anti-glycan antibodies. This platform takes advantage of the commercially available Luminex beads to construct glycan arrays that are easily customizable at will and anytime by researchers. The platform allows the simultaneous analyses of up to 500 glycans and 384 samples at a time. By using multiple arrays, a researcher can analyze thousands of glycans and tens of thousands of samples within a short period. The assay is highly sensitive, specific, reproducible, economic, and fast. Furthermore, the bead array platform is approved for use in clinical settings, speeding up the translation of laboratory discoveries into patient care.

Fungal infections are a major health problem that often begin in the gastrointestinal tract. Gut microbe interactions in early childhood are critical for proper immune responses, yet there is little known about the development of the fungal population from infancy into childhood. Here, as part of the TEDDY (The Environmental Determinants of Diabetes in the Young) study, we examine stool samples of 888 children from 3 to 48 months and find considerable differences between fungi and bacteria. The metagenomic relative abundance of fungi was extremely low but increased while weaning from milk and formula. Overall fungal diversity remained constant over time, in contrast with the increase in bacterial diversity. Fungal profiles had high temporal variation, but there was less variation from month-to-month in an individual than among different children of the same age. Fungal composition varied with geography, diet, and the use of probiotics. Multiple Candida spp. were at higher relative abundance in children than adults, while Malassezia and certain food-associated fungi were lower in children. There were only subtle fungal differences associated with the subset of children that developed islet autoimmunity or type 1 diabetes. Having proper fungal exposures may be crucial for children to establish appropriate responses to fungi and limit the risk of infection: the data here suggests those gastrointestinal exposures are limited and variable.

Development of complications in type 1 diabetes patients can be reduced by modifying risk factors. We used a cross-sectional cohort of 1646 patients diagnosed with type 1 diabetes (T1D) to develop a clinical risk score for diabetic peripheral neuropathy (DPN), autonomic neuropathy (AN), retinopathy (DR), and nephropathy (DN). Of these patients, 199 (12.1%) had DPN, 63 (3.8%) had AN, 244 (14.9%) had DR, and 88 (5.4%) had DN. We selected five variables to include in each of the four microvascular complications risk models: age, age of T1D diagnosis, duration of T1D, and average systolic blood pressure and HbA1C over the last three clinic visits. These variables were selected for their strong evidence of association with diabetic complications in the literature and because they are modifiable risk factors. We found the optimism-corrected R2 and Harrell’s C statistic were 0.39 and 0.87 for DPN, 0.24 and 0.86 for AN, 0.49 and 0.91 for DR, and 0.22 and 0.83 for DN, respectively. This tool was built to help inform patients of their current risk of microvascular complications and to motivate patients to control their HbA1c and systolic blood pressure in order to reduce their risk of these complications.

Development of complications in type-1 diabetes patients can be reduced by modifying risk factors. We used a cross-sectional cohort of 1646 patients diagnosed with type 1 diabetes (T1D) to develop a clinical risk score for diabetic peripheral neuropathy (DPN), autonomic neuropathy (AN), retinopathy (DR), and nephropathy (DN). Of these patients, 199 (12.1%) had DPN, 63 (3.8%) had AN, 244 (14.9%) had DR, and 88 (5.4%) had DN. We selected five variables to include in each of the four microvascular complications risk models: age, age of T1D diagnosis, duration of T1D, and average systolic blood pressure and HbA1C over the last three clinic visits. These variables were selected for their strong evidence of association with diabetic complications in the literature and because they are modifiable risk factors. We found the optimism-corrected R2 and Harrell’s C statistic were 0.39 and 0.87 for DPN, 0.24 and 0.86 for AN, 0.49 and 0.91 for DR, and 0.22 and 0.83 for DN respectively.This tool (https://ptran25.shinyapps.io/Diabetic_Peripheral_Neuropathy_Risk) was built to help inform patients of their current risk of microvascular complications and to motivate patients to control their HbA1c and systolic blood pressure in order to reduce their risk of these complications.

Chromophobe renal cell carcinoma (chRCC) and oncocytoma (RO) are renal tumor types originating from alpha intercalated cells of the collecting ducts of the kidney. Both tumor types have similar gross histological morphology and increased mitochondria, which leads to difficulties differentiating between these tumors, especially with core biopsy samples. This study aims to apply a machine learning approach to develop a molecular classifier based on transcriptomics data. Here we generated a meta-data set containing 62 chRCC and 45 RO gene expression arrays. Arrays were subjected to quality control steps, and genes were selected based on differential expression and ROC analysis. The final gene list was evaluated with UMAP based dimension reduction followed by density-based clustering with 95.5% accuracy. Molecular profiling by KEGG pathway analysis identified enrichment of fatty acid oxidation pathway in RO. We finally identified and validated the 30-gene signature, with an accuracy of 94.4% to distinguish chRCC from RO on UMAP analysis. Our results show that chRCC and RO have a distinct gene signature that can differentiate these tumors and complement histology for routine diagnosis of these two tumors.

Multiple cross-sectional and longitudinal studies have shown that serum levels of the chemokine ligand 2 (CCL-2) are associated with type 1 diabetes (T1D), although the direction of effect differs. We assessed CCL-2 serum levels in a longitudinal cohort to clarify this association, combined with genetic data to elucidate the regulatory role of CCL-2 in T1D pathogenesis. The Diabetes Autoimmunity Study in the Young (DAISY) followed 310 subjects with high risk of developing T1D. Of these, 42 became persistently seropositive for islet autoantibodies but did not develop T1D (non-progressors); 48 did develop T1D (progressors). CCL-2 serum levels among the three study groups were compared using linear mixed models adjusting for age, sex, HLA genotype, and family history of T1D. Summary statistics were obtained from the CCL-2 protein quantitative trait loci (pQTL) and CCR2 expression QTL (eQTL) studies. The T1D fine mapping association data were provided by the Type 1 Diabetes Genetics Consortium (T1DGC). Serum CCL-2 levels were significantly lower in both progressors (p = 0.004) and non-progressors (p = 0.005), compared to controls. Two SNPs (rs1799988 and rs746492) in the 3p21.31 genetic locus, which includes the CCL-2 receptor, CCR2, were associated with increased CCR2 expression (p = 8.2e-5 and 5.2e-5, respectively), decreased CCL-2 serum level (p = 2.41e-9 and 6.21e-9, respectively), and increased risk of T1D (p = 7.9e-5 and 7.9e-5, respectively). The 3p21.31 genetic region is associated with developing T1D through regulatory control of the CCR2/CCL2 immune pathway., Highlights • Serum CCL-2 levels are lower in individuals with islet autoantibodies and type 1 diabetes compared to controls. • Serum CCL-2 levels are associated with the 3p21.31 genetic locus. • The 3p21.31 genetic locus is associated with type 1 diabetes. • The 3p21.31 genetic locus is associated with gene expression of the CCL-2 receptor, CCR2.

Viruses are implicated in autoimmune destruction of pancreatic islet beta cells, which results in insulin deficiency and type 1 diabetes (T1D)(1-4). Certain enteroviruses can infect beta cells in vitro(5), have been detected in the pancreatic islets of patients with T1D(6) and have shown an association with T1D in meta-analyses(4). However, establishing consistency in findings across studies has proven difficult. Obstacles to convincingly linking RNA viruses to islet autoimmunity may be attributed to rapid viral mutation rates, the cyclical periodicity of viruses(7) and the selection of variants with altered pathogenicity and ability to spread in populations. beta cells strongly express cell-surface coxsackie and adenovirus receptor (CXADR) genes, which can facilitate enterovirus infection(8). Studies of human pancreata and cultured islets have shown significant variation in enteroviral virulence to beta cells between serotypes and within the same serotype(9,10). In this large-scale study of known eukaryotic DNA and RNA viruses in stools from children, we evaluated fecally shed viruses in relation to islet autoimmunity and T1D. This study showed that prolonged enterovirus B rather than independent, short-duration enterovirus B infections may be involved in the development of islet autoimmunity, but not T1D, in some young children. Furthermore, we found that fewer early-life human mastadenovirus C infections, as well as CXADR rs6517774, independently correlated with islet autoimmunity.

OBJECTIVE Type 1 diabetes (T1D) is a highly heritable disease with much lower incidence but more adult-onset cases in the Chinese population. Although genome-wide association studies (GWAS) have identified >60 T1D loci in Caucasians, less is known in Asians. RESEARCH DESIGN AND METHODS We performed the first two-stage GWAS of T1D using 2,596 autoantibody-positive T1D case subjects and 5,082 control subjects in a Chinese Han population and evaluated the associations between the identified T1D risk loci and age and fasting C-peptide levels at T1D diagnosis. RESULTS We observed a high genetic correlation between children/adolescents and adult T1D case subjects (rg = 0.87), as well as subgroups of autoantibody status (rg ≥ 0.90). We identified four T1D risk loci reaching genome-wide significance in the Chinese Han population, including two novel loci, rs4320356 near BTN3A1 (odds ratio [OR] 1.26, P = 2.70 × 10−8) and rs3802604 in GATA3 (OR 1.24, P = 2.06 × 10−8), and two previously reported loci, rs1770 in MHC (OR 4.28, P = 2.25 × 10−232) and rs705699 in SUOX (OR 1.46, P = 7.48 × 10−20). Further fine mapping in the MHC region revealed five independent variants, including another novel locus, HLA-C position 275 (omnibus P = 9.78 × 10−12), specific to the Chinese population. Based on the identified eight variants, we achieved an area under the curve value of 0.86 (95% CI 0.85–0.88). By building a genetic risk score (GRS) with these variants, we observed that the higher GRS were associated with an earlier age of T1D diagnosis (P = 9.08 × 10−11) and lower fasting C-peptide levels (P = 7.19 × 10−3) in individuals newly diagnosed with T1D. CONCLUSIONS Our results extend current knowledge on genetic contributions to T1D risk. Further investigations in different populations are needed for genetic heterogeneity and subsequent precision medicine.

INTRODUCTION: Ovarian cancer is one of the most lethal neoplasms with 22,000 new cases and 14,000 deaths per year. Aggressive cytoreduction and chemotherapy have response rates as high as 80%. Most women are diagnosed with stage 3 disease or later and over 70% of women recur within 18 months. There is need for a test to determine prognosis of remission patients to allow for intervention and prolonged survival. METHODS AND MATERIALS: Women at Augusta University with ovarian cancer were enrolled between 2005 and 2015 (n=71). Blood was drawn at enrollment and follow-up visits. Patient serum collected at remission was analyzed using the SOMAscan array (n=35) to measure concentrations of 1129 proteins. The best 26 proteins were confirmed using Luminex assays in the same 35 patients and an additional 36 patients (n(total)=71) as orthogonal validation. The data from these proteins was combined with clinical factors using an elastic net multivariate model to find an optimized combination predictive of progression-free survival (PFS). RESULTS: Brain Derived Neurotrophic Factor and Platelet Derived Growth Factor molecules were significant for predicting PFS on univariate and multivariate analyses. The 26 proteins were combined with clinical factors using the elastic net algorithm. Ten components were determined to predict PFS (HR of 6.55, p-value 1.12 × 10(−6), CI 2.57 – 16.71). This model was named the Serous High Grade Ovarian Cancer (SHOC) Score. CONCLUSION: The SHOC score can predict patient prognosis in remission and will hopefully lead to early intervention and consolidation therapy strategies in remission patients destined to recur.

The risk for autoimmunity and subsequently type 1 diabetes is 10-fold higher in children with a first-degree family history of type 1 diabetes (FDR children) than in children in the general population (GP children). We analyzed children with high-risk HLA genotypes (n = 4,573) in the longitudinal TEDDY birth cohort to determine how much of the divergent risk is attributable to genetic enrichment in affected families. Enrichment for susceptible genotypes of multiple type 1 diabetes–associated genes and a novel risk gene, BTNL2, was identified in FDR children compared with GP children. After correction for genetic enrichment, the risks in the FDR and GP children converged but were not identical for multiple islet autoantibodies (hazard ratio [HR] 2.26 [95% CI 1.6–3.02]) and for diabetes (HR 2.92 [95% CI 2.05–4.16]). Convergence varied depending upon the degree of genetic susceptibility. Risks were similar in the highest genetic susceptibility group for multiple islet autoantibodies (14.3% vs .12.7%) and diabetes (4.8% vs. 4.1%) and were up to 5.8-fold divergent for children in the lowest genetic susceptibility group, decreasing incrementally in GP children but not in FDR children. These findings suggest that additional factors enriched within affected families preferentially increase the risk of autoimmunity and type 1 diabetes in lower genetic susceptibility strata.

OBJECTIVE Islet autoimmunity develops before clinical type 1 diabetes and includes multiple and single autoantibody phenotypes. The objective was to determine age-related risks of islet autoantibodies that reflect etiology and improve screening for presymptomatic type 1 diabetes. RESEARCH DESIGN AND METHODS The Environmental Determinants of Diabetes in the Young study prospectively monitored 8,556 genetically at-risk children at 3- to 6-month intervals from birth for the development of islet autoantibodies and type 1 diabetes. The age-related change in the risk of developing islet autoantibodies was determined using landmark and regression models. RESULTS The 5-year risk of developing multiple islet autoantibodies was 4.3% (95% CI 3.8–4.7) at 7.5 months of age and declined to 1.1% (95% CI 0.8–1.3) at a landmark age of 6.25 years (P < 0.0001). Risk decline was slight or absent in single insulin and GAD autoantibody phenotypes. The influence of sex, HLA, and other susceptibility genes on risk subsided with increasing age and was abrogated by age 6 years. Highest sensitivity and positive predictive value of multiple islet autoantibody phenotypes for type 1 diabetes was achieved by autoantibody screening at 2 years and again at 5–7 years of age. CONCLUSIONS The risk of developing islet autoimmunity declines exponentially with age, and the influence of major genetic factors on this risk is limited to the first few years of life.

Aims/hypothesis: Psychological stress has long been considered a possible trigger of type 1 diabetes, although prospective studies examining the link between psychological stress or life events during pregnancy and the child’s type 1 diabetes risk are rare. The objective of this study was to examine the association between life events during pregnancy and first-appearing islet autoantibodies (IA) in young children, conditioned by the child’s type 1 diabetes-related genetic risk. Methods: The IA status of 7317 genetically at-risk The Environmental Determinants of Diabetes in the Young (TEDDY) participants was assessed every 3months from 3months to 4years, and bi-annually thereafter. Reports of major life events during pregnancy were collected at study inception when the child was 3months of age and placed into one of six categories. Life events during pregnancy were examined for association with first-appearing insulin (IAA) (N = 222) or GAD (GADA) (N = 209) autoantibodies in the child until 6years of age using proportional hazard models. Relative excess risk due to interaction (RERI) by the child’s HLA-DR and SNP profile was estimated. Results: Overall, 65% of mothers reported a life event during pregnancy; disease/injury (25%), serious interpersonal (28%) and job-related (25%) life events were most common. The association of life events during pregnancy differed between IAA and GADA as the first-appearing autoantibody. Serious interpersonal life events correlated with increased risk of GADA-first only in HLA-DR3 children with the BACH2-T allele (HR 2.28, p < 0.0001), an additive interaction (RERI 1.87, p = 0.0004). Job-related life events were also associated with increased risk of GADA-first among HLA-DR3/4 children (HR 1.53, p = 0.04) independent of serious interpersonal life events (HR 1.90, p = 0.002), an additive interaction (RERI 1.19, p = 0.004). Job-related life events correlated with reduced risk of IAA-first (HR 0.55, p = 0.004), particularly in children with the BTNL2-GG allele (HR 0.48; 95% CI 0.31, 0.76). Conclusions/interpretation: Specific life events during pregnancy are differentially related to IAA vs GADA as first-appearing IA and interact with different HLA and non-HLA genetic factors, supporting the concept of different endotypes underlying type 1 diabetes. However, the mechanisms underlying these associations remain to be discovered. Life events may be markers for other yet-to-be-identified factors important to the development of first-appearing IA. Graphical abstract: [Figure not available: see fulltext.]

Gene expression profiling has been shown to be comparable to other molecular methods for glioma classification. We sought to validate a gene-expression based glioma classification method. Formalin-fixed paraffin embedded tissue and flash frozen tissue collected at the Augusta University (AU) Pathology Department between 2000&ndash, 2019 were identified and 2 mm cores were taken. The RNA was extracted from these cores after deparaffinization and bead homogenization. One hundred sixty-eight genes were evaluated in the RNA samples on the nCounter instrument. Forty-eight gliomas were classified using a supervised learning algorithm trained by using data from The Cancer Genome Atlas. An ensemble of 1000 linear support vector models classified 30 glioma samples into TP1 with classification confidence of 0.99. Glioma patients in TP1 group have a poorer survival (HR (95% CI) = 4.5 (1.3&ndash, 15.4), p = 0.005) with median survival time of 12.1 months, compared to non-TP1 groups. Network analysis revealed that cell cycle genes play an important role in distinguishing TP1 from non-TP1 cases and that these genes may play an important role in glioma survival. This could be a good clinical pipeline for molecular classification of gliomas.

Lung adenocarcinoma is one-half of all lung cancer and the third deadliest cancer in the world. The traditional classification of lung adenocarcinoma involves invasiveness, growth pattern, presence of mucin, and other histological features. However, this classification provides limited prognostic predictability. Molecular profiles based The Cancer genome Atlas lung adenocarcinoma cohort classified the tumors into the terminal respiratory unit (TRU), proximal proliferative (PP), and proximal infiltrative (PI) based on unsupervised classification, which did not address clinical applicability to the classification. Here, we developed an unsupervised classification with Density-based UMAP (DBU), which identified four transcriptomic profiles for lung adenocarcinoma (Transcriptomic profile, TP1-TP4). We identified a gene signature with 299 genes to develop a supervised classifier, called Ensemble Transcriptomic classifier (ETC), to identify these four subgroups within the total cohort. All TP1 is PI. TP2 is 49.0% TFU and 45.0% PP. TP3 is 76.2% PI and 23.8% PP. TP4 is 54.5% TFU and 45.5% PI. TP1 is characterized by neuroendocrine-like features and clusters with large cell neuroendocrine tumors in cluster analysis. TP3 is squamous-like enriched with cell cycle and DNA repair high and worse prognosis and groups with squamous cell carcinoma in cluster analysis. TP2 has high immune cell infiltration, and TP4 showed low mutational burden and low immune cell infiltration. We validated these subgroups identified in the TCGA cohort in a microarray meta-dataset with DBU, ETC. This classification showed four subgroups in lung adenocarcinoma, which shows potential clinical actionable findings. Citation Format: Khaled Bin Satter, Paul MH Tran, Lynn KH Tran, John Nechtman, Nikhil Patel, Bruno Santos, Diane Hopkins, Nagla Abdel Karim, Jin-Xiong She, Sharad B. Purohit. Transcriptomic classification of lung adenocarcinoma identifies a novel subgroup with a poor survival prognosis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3496.

Objectives: To determine the utility of a clinical calculator to predict the benefits of chemotherapy and outcomes in women with early stage clear cell ovarian cancer. Methods: Data from 2006-2015 was abstracted from the National Cancer Database. All patients had stage I clear cell carcinoma. Using a Cox regression model based on demographic, surgico-pathologic and laboratory characteristics, a clinical score was developed. Propensity score weighting was used to reduce bias between patients who did and did not receive chemotherapy. Results: Of 1,528 patients with stage I clear cell ovarian cancer, 1309 (85.7%) did receive and 219 (14.3%) did not receive chemotherapy. Patients who underwent chemotherapy had a 5-year overall survival (OS) of 85.6% vs. 84.0% in those without treatment (p=0.30). On univariate analysis, significant prognostic factors included older age (HR=1.021, 95% CI 1.002-1.021, p=0.03), malignant ascites (HR 1.608, 95% CI 1.054-2.453, p=0.03), and higher stage (1B or C) (HR 1.758, 95% CI 1.207-2.560, p=0.003). To better define those who may have an increased benefit from chemotherapy, we designed a clinical calculator capable of dividing patients into low (n=306), moderate (n=916), and high-risk (n=306) groups with associated 5-year overall survival of 90.3%, 86.8% and 76.1%, respectively (p Download : Download high-res image (121KB) Download : Download full-size image Conclusions: Our results suggest a clinical calculator can help direct chemotherapy decision making for women with early stage clear cell ovarian cancer.

Objectives: To determine the utility of a clinical calculator to predict the benefits of chemotherapy and outcomes in women with early stage high risk ovarian cancer. Methods: Data from 2006-2015 was abstracted from the National Cancer Database. All patients had stage I, grade 3; stage IC, stage II, or clear cell carcinoma. Using a Cox regression model, based on demographic, surgico-pathologic, and laboratory characteristics, a clinical score was developed. Propensity score matching was used to reduce bias between patients who did and did not receive chemotherapy. The clinical calculator was validated by both bootstrapping and in an independent validation set. Results: Of 8,289 patients with early stage high risk ovarian cancer, 6,990 (84%) did and 1,299 (16%) did not receive chemotherapy. Patients who underwent chemotherapy had a 5-year overall survival (OS) of 85% vs 80% in those without treatment (p Download : Download high-res image (40KB) Download : Download full-size image Conclusions: Our results suggest a clinical calculator can help direct chemotherapy decision making for women with high risk early stage ovarian cancer.

Gliomas are currently classified through integration of histology and mutation information, with new developments in DNA methylation classification. However, discrepancies exist amongst the major classification methods. This study sought to compare transcriptome-based classification to the established methods. RNAseq and microarray data were obtained for 1032 gliomas from the TCGA and 395 gliomas from REMBRANDT. Data were analyzed using unsupervised and supervised learning and other statistical methods. Global transcriptomic profiles defined four transcriptomic glioma subgroups with 91.4% concordance with the WHO-defined mutation subtypes. Using these subgroups, 168 genes were selected for the development of 1000 linear support vector classifiers (LSVC). Based on plurality voting of 1000 LSVC, the final ensemble classifier confidently classified all but 17 TCGA gliomas to one of the four transcriptomic profile (TP) groups. The classifier was validated using a gene expression microarray dataset. TP1 cases include IDHwt, glioblastoma high immune infiltration and cellular proliferation and poor survival prognosis. TP2a is characterized as IDHmut-codel, oligodendrogliomas with high tumor purity. TP2b tissue is mostly composed of neurons and few infiltrating malignant cells. TP3 exhibit increased NOTCH signaling, are astrocytoma and IDHmut-non-codel. TP groups are highly concordant with both WHO integrated histology and mutation classification as well as methylation-based classification of gliomas. Transcriptomic profiling provides a robust and objective method to classify gliomas with high agreement to the current WHO guidelines and may provide additional survival prediction to the current methods.

Children’s plasma metabolome, especially lipidome reflects gene regulation and dietary exposures, heralding the development of islet autoantibodies (IA) and type 1 diabetes (T1D). The TEDDY study enrolled 8676 newborns by screening HLA-DR-DQ genotypes at six clinical centers in four countries; profiled metabolome and measured concentrations of ascorbic acid, 25-hydroxyvitamin D (25(OH)D), erythrocyte membrane fatty acids following birth until IA seroconversion under nested case-control design. We grouped children having an initial autoantibody only against insulin (IAA-first) or glutamic acid decarboxylase (GADA-first) by unsupervised clustering of temporal lipidome, identifying a subgroup of children having early onset of each initial autoantibody, i.e., IAA-first by 12 months and GADA-first by 21 months, consistent with population-wide early seroconversion age. Differential analysis showed that infants having reduced plasma ascorbic acid and cholesterol experienced IAA-first earlier, while early onset of GADA-first was preceded by reduced sphingomyelins at infancy. Plasma 25(OH)D prior to either autoantibody was lower in T1D progressors compared to non-progressors, with simultaneous lower diglycerides, lysophosphatidylcholines, triglycerides, alanine before GADA-first. Plasma ascorbic acid and 25(OH)D at infancy were lower in HLA-DR3/DR4 children among IA cases but not in matched controls, implying gene expression dysregulation of circulating vitamins as latent signals for IA or T1D progression.

In the present study, we developed a transcriptomic signature capable of predicting prognosis and response to primary therapy in high grade serous ovarian cancer (HGSOC). Proportional hazard analysis was performed on individual genes in the TCGA RNAseq data set containing 229 HGSOC patients. Ridge regression analysis was performed to select genes and develop multigenic models. Survival analysis identified 120 genes whose expression levels were associated with overall survival (OS) (HR = 1.49-2.46 or HR = 0.48-0.63). Ridge regression modeling selected 38 of the 120 genes for development of the final Ridge regression models. The consensus model based on plurality voting by 68 individual Ridge regression models classified 102 (45%) as low, 23 (10%) as moderate and 104 patients (45%) as high risk. The median OS was 31 months (HR = 7.63, 95% CI = 4.85-12.0, P < 1.0(-10)) and 77 months (HR = ref) in the high and low risk groups, respectively. The gene signature had two components: intrinsic (proliferation, metastasis, autophagy) and extrinsic (immune evasion). Moderate/high risk patients had more partial and non-responses to primary therapy than low risk patients (odds ratio = 4.54, P < 0.001). We concluded that the overall survival and response to primary therapy in ovarian cancer is best assessed using a combination of gene signatures. A combination of genes which combines both tumor intrinsic and extrinsic functions has the best prediction. Validation studies are warranted in the future.

Molecular biomarkers that can predict survival and therapeutic outcome are still lacking for cervical cancer. Here we measured a panel of 19 serum proteins in sera from 565 patients with stage II or III cervical cancer and identified 10 proteins that have an impact on disease specific survival (DSS) (Hazzard&rsquo, s ratio, HR = 1.51&ndash, 2.1). Surprisingly, all ten proteins are implicated in senescence-associated secreted phenotype (SASP), a hallmark of cellular senescence. Machine learning using Ridge regression of these SASP proteins can robustly stratify patients with high SASP, which is associated with poor survival, and patients with low SASP associated with good survival (HR = 3.09&ndash, 4.52). Furthermore, brachytherapy, an effective therapy for cervical cancer, greatly improves survival in SASP-high patients (HR = 3.3, p &lt, 5 &times, 10&minus, 5) but has little impact on survival of SASP-low patients (HR = 1.5, p = 0.31). These results demonstrate that cellular senescence is a major determining factor for survival and therapeutic response in cervical cancer and suggest that senescence reduction therapy may be an efficacious strategy to improve the therapeutic outcome of cervical cancer.

OBJECTIVE The first-appearing β-cell autoantibody has been shown to influence risk of type 1 diabetes (T1D). Here, we assessed the risk of autoantibody spreading to the second-appearing autoantibody and further progression to clinical disease in The Environmental Determinants of Diabetes in the Young (TEDDY) study. RESEARCH DESIGN AND METHODS Eligible children with increased HLA-DR-DQ genetic risk for T1D were followed quarterly from age 3 months up to 15 years for development of a single first-appearing autoantibody (GAD antibody [GADA], insulin autoantibody [IAA], or insulinoma antigen-2 autoantibody [IA-2A]) and subsequent development of a single second-appearing autoantibody and progression to T1D. Autoantibody positivity was defined as positivity for a specific autoantibody at two consecutive visits confirmed in two laboratories. Zinc transporter 8 autoantibody (ZnT8A) was measured in children who developed another autoantibody. RESULTS There were 608 children who developed a single first-appearing autoantibody (IAA, n = 282, or GADA, n = 326) with a median follow-up of 12.5 years from birth. The risk of a second-appearing autoantibody was independent of GADA versus IAA as a first-appearing autoantibody (adjusted hazard ratio [HR] 1.12; 95% CI 0.88–1.42; P = 0.36). Second-appearing GADA, IAA, IA-2A, or ZnT8A conferred an increased risk of T1D compared with children who remained positive for a single autoantibody, e.g., IAA or GADA second (adjusted HR 6.44; 95% CI 3.78–10.98), IA-2A second (adjusted HR 16.33; 95% CI 9.10–29.29; P < 0.0001), or ZnT8A second (adjusted HR 5.35; 95% CI 2.61–10.95; P < 0.0001). In children who developed a distinct second autoantibody, IA-2A (adjusted HR 3.08; 95% CI 2.04–4.65; P < 0.0001) conferred a greater risk of progression to T1D as compared with GADA or IAA. Additionally, both a younger initial age at seroconversion and shorter time to the development of the second-appearing autoantibody increased the risk for T1D. CONCLUSIONS The hierarchical order of distinct autoantibody spreading was independent of the first-appearing autoantibody type and was age-dependent and augmented the risk of progression to T1D.

Children’s plasma metabolome, especially lipidome, reflects gene regulation and dietary exposures, heralding the development of islet autoantibodies (IA) and type 1 diabetes (T1D). The Environmental Determinants of Diabetes in the Young (TEDDY) study enrolled 8,676 newborns by screening of HLA-DR-DQ genotypes at six clinical centers in four countries, profiled metabolome, and measured concentrations of ascorbic acid, 25-hydroxyvitamin D [25(OH)D], and erythrocyte membrane fatty acids following birth until IA seroconversion under a nested case-control design. We grouped children having an initial autoantibody only against insulin (IAA-first) or GAD (GADA-first) by unsupervised clustering of temporal lipidome, identifying a subgroup of children having early onset of each initial autoantibody, i.e., IAA-first by 12 months and GADA-first by 21 months, consistent with population-wide early seroconversion age. Differential analysis showed that infants having reduced plasma ascorbic acid and cholesterol experienced IAA-first earlier, while early onset of GADA-first was preceded by reduced sphingomyelins at infancy. Plasma 25(OH)D prior to either autoantibody was lower in T1D progressors compared with nonprogressors, with simultaneous lower diglycerides, lysophosphatidylcholines, triglycerides, and alanine before GADA-first. Plasma ascorbic acid and 25(OH)D at infancy were lower in HLA-DR3/DR4 children among IA case subjects but not in matched control subjects, implying gene expression dysregulation of circulating vitamins as latent signals for IA or T1D progression.

Type 1 diabetes (T1D) is a disease of insulin deficiency that results from autoimmune destruction of pancreatic islet β cells. The exact cause of T1D remains unknown, although asymptomatic islet autoimmunity lasting from weeks to years before diagnosis raises the possibility of intervention before the onset of clinical disease. The number, type, and titer of islet autoantibodies are associated with long-term disease risk but do not cause disease, and robust early predictors of individual progression to T1D onset remain elusive. The Environmental Determinants of Diabetes in the Young (TEDDY) consortium is a prospective cohort study aiming to determine genetic and environmental interactions causing T1D. Here, we analyzed longitudinal blood transcriptomes of 2013 samples from 400 individuals in the TEDDY study before both T1D and islet autoimmunity. We identified and interpreted age-associated gene expression changes in healthy infancy and age-independent changes tracking with progression to both T1D and islet autoimmunity, beginning before other evidence of islet autoimmunity was present. We combined multivariate longitudinal data in a Bayesian joint model to predict individual risk of T1D onset and validated the association of a natural killer cell signature with progression and the model's predictive performance on an additional 356 samples from 56 individuals in the independent Type 1 Diabetes Prediction and Prevention study. Together, our results indicate that T1D is characterized by early and longitudinal changes in gene expression, informing the immunopathology of disease progression and facilitating prediction of its course.

The aim of this study was to investigate survival differences between equivalent residual disease [complete gross resection (CGR), minimal residual disease (MRD), suboptimal] at the time of primary debulking surgery (PDS) and interval debulking surgery (IDS).The National Cancer Database was used to identify patients from 2010 to 2015 with stage IIIC/IV primary peritoneal or ovarian cancer who had residual disease recorded. Propensity score matching (PSM) was used to correct for differences in characteristics between the PDS and IDS groups.Of 8683 patients with advanced ovarian cancer, 4493 (52%), 2546 (29%), and 1644 (19%) had CGR, MRD, or suboptimal resection, respectively. From 2010 to 2015, the number of patients undergoing IDS increased 27% (pThe use of IDS continues to rise in the US, and is associated with improved surgical outcomes but not necessarily similar oncologic outcomes. There should be continued efforts to improve cytoreductive outcomes in women with advanced ovarian and peritoneal malignancies.

Children at increased genetic risk for type 1 diabetes (T1D) after environmental exposures may develop pancreatic islet autoantibodies (IA) at a very young age. Metabolic profile changes over time may imply responses to exposures and signal development of the first IA. Our present research in The Environmental Determinants of Diabetes in the Young (TEDDY) study aimed to identify metabolome-wide signals preceding the first IA against GAD (GADA-first) or against insulin (IAA-first). We profiled metabolomes by mass spectrometry from children’s plasma at 3-month intervals after birth until appearance of the first IA. A trajectory analysis discovered each first IA preceded by reduced amino acid proline and branched-chain amino acids (BCAAs), respectively. With independent time point analysis following birth, we discovered dehydroascorbic acid (DHAA) contributing to the risk of each first IA, and γ-aminobutyric acid (GABAs) associated with the first autoantibody against insulin (IAA-first). Methionine and alanine, compounds produced in BCAA metabolism and fatty acids, also preceded IA at different time points. Unsaturated triglycerides and phosphatidylethanolamines decreased in abundance before appearance of either autoantibody. Our findings suggest that IAA-first and GADA-first are heralded by different patterns of DHAA, GABA, multiple amino acids, and fatty acids, which may be important to primary prevention of T1D.

KLHL family genes are noted for their involvement in the E3 ligase ubiquitination pathway through binding with Cullin-3 (CUL3) resulting in degradation of specific binding partners. KLHLs are thus intriguing genes for cancer as they can directly influence the degradation of therapeutically relevant cell cycle regulators such as Aurora Kinase, PLK1, or CDK1. However, most KLHL family members remain understudied within the literature. This study explores the relationship of expression of KLHL member, KLHL5, with the pharmacologic effect of anti-cancer drugs. KLHL5 knockdown decreased the proliferation and viability of cancer cells and sensitized cancer cells to numerous anti-cancer drugs. Drugs related to cell cycle including Akt/PI3K/mTOR inhibitors were especially sensitized by KLHL5 knockdown. The potential of KLHL5 as a prognostic or diagnostic cancer marker was compared to other KLHLs through a pan-cancer study of The Cancer Genome Atlas (TCGA) tumor groups. While KLHL5 expression shows marginal dysregulation in cancer, other KLHLs exhibit significant dysregulation in all cancer types, and exceptionally in renal carcinomas. This study advocates for further study of KLHLs as potential alternative therapeutic targets, since while KLHL5 is a novel gene impacting anticancer drug effects, others may have a similar impact on drug effect while having greater potential as diagnostic or prognostic markers.

The development of the microbiome from infancy to childhood is dependent on a range of factors, with microbial-immune crosstalk during this time thought to be involved in the pathobiology of later life diseases(1-9) such as persistent islet autoimmunity and type 1 diabetes(10-12). However, to our knowledge, no studies have performed extensive characterization of the microbiome in early life in a large, multi-centre population. Here we analyse longitudinal stool samples from 903 children between 3 and 46 months of age by 16S rRNA gene sequencing (n = 12,005) and metagenomic sequencing (n = 10,867), as part of the The Environmental Determinants of Diabetes in the Young (TEDDY) study. We show that the developing gut microbiome undergoes three distinct phases of microbiome progression: a developmental phase (months 3-14), a transitional phase (months 15-30), and a stable phase (months 31-46). Receipt of breast milk, either exclusive or partial, was the most significant factor associated with the microbiome structure. Breastfeeding was associated with higher levels of Bifidobacterium species (B. breve and B. bifidum), and the cessation of breast milk resulted in faster maturation of the gut microbiome, as marked by the phylum Firmicutes. Birth mode was also significantly associated with the microbiome during the developmental phase, driven by higher levels of Bacteroides species (particularly B. fragilis) in infants delivered vaginally. Bacteroides was also associated with increased gut diversity and faster maturation, regardless of the birth mode. Environmental factors including geographical location and household exposures (such as siblings and furry pets) also represented important covariates. A nested case-control analysis revealed subtle associations between microbial taxonomy and the development of islet autoimmunity or type 1 diabetes. These data determine the structural and functional assembly of the microbiome in early life and provide a foundation for targeted mechanistic investigation into the consequences of microbial-immune crosstalk for long-term health.

Background and purpose Macropinocytosis is involved in many pathologies, including cardiovascular disorders, cancer, allergic diseases, viral and bacterial infections. Unfortunately, the currently available pharmacological inhibitors of macropinocytosis interrupt other endocytic processes and have non-specific endocytosis-independent effects. Here we have sought to identify new, clinically relevant inhibitors of macropinocytosis, using an FDA-approved drug library. Experimental approach In the present study, 640 FDA-approved compounds were tested for their ability to inhibit macropinocytosis. A series of secondary assays were performed to confirm inhibitory activity, determine IC50 values and investigate cell toxicity. The ability of identified hits to inhibit phagocytosis and clathrin-mediated and caveolin-mediated endocytosis was also investigated. Scanning electron microscopy and molecular biology techniques were utilized to examine the mechanisms by which selected compounds inhibit macropinocytosis. Key results The primary screen identified 14 compounds that at ~10 μM concentration inhibit >95% of macropinocytotic solute internalization. Three compounds - imipramine, phenoxybenzamine and vinblastine - potently inhibited (IC50 ≤ 131 nM) macropinocytosis without exerting cytotoxic effects or inhibiting other endocytic pathways. Scanning electron microscopy imaging indicated that imipramine inhibits membrane ruffle formation, a critical early step leading to initiation of macropinocytosis. Finally, imipramine has been shown to inhibit macropinocytosis in several cell types, including cancer cells, dendritic cells and macrophages. Conclusions and implications Our results identify imipramine as a new pharmacological tool to study macropinocytosis in cellular and biological systems. This study also suggests that imipramine could be a good candidate for repurposing as a therapeutic agent in pathological processes involving macropinocytosis.

Despite evidences linking methylation changes in the cancer tissues, little is known about the methylation modification in the peripheral blood. With the current study, we identified differential methylation regions (DMRs) across human genome by collecting the blood samples of colorectal cancer (CRC) patients compared to that of their blood-related family who shared genetic inheritance and environmental influences, and unrelated obese and non-obese controls by accessing publicly available Gene Expression Omnibus data. We performed genome-wide analyses using the reduced representation bisulfite sequencing (RRBS) method covering about 25% of CpGs for whole human genome of the four groups (n = 5 each). In comparison to the non-obese controls, we observed significant DMRs in CRC for genes involved in tumorigenesis including MLH3, MSH2, MSH6, SEPT9, GNAS; and glucose transporter genes associated with obesity and diabetes including SLC2A1/GLUT1, and SLC2A3/GLUT3 that were reported on methylation being modified in cancer tissues. In addition, we observed significant DMRs in CRC for genes involved in the methylation pathways including PEMT, ALDH1L1, and DNMT3A. CRC and family members shared significant DMRs for genes of tumorigenesis including MSH2, SEPT9, GNAS, SLC2A1/GLUT1 and SLC2A3/GLUT3); and CAMK1, GLUT1/SLC2A1 and GLUT3/SLC2A3 genes involved in glucose and insulin metabolism that played vital role in development of obesity and diabetes. Our study provided evidences that these differentially methylated genes in the blood could potentially serve as candidate biomarkers for CRC diagnostic and may provide further understanding on CRC progression. Further studies are warranted to validate these methylation changes for diagnostic and prevention of CRC.

// Prashant D. Tailor 1, 2 , Sai Karthik Kodeboyina 2 , Shan Bai 2 , Nikhil Patel 4 , Shruti Sharma 2 , Akshay Ratnani 1, 2 , John A. Copland 5 , Jin-Xiong She 2 and Ashok Sharma 2, 3 1 Medical College of Georgia, Augusta University, Augusta, GA, USA 2 Center for Biotechnology and Genomic Medicine, Augusta University, Augusta, GA, USA 3 Department of Population Health Sciences, Augusta University, Augusta, GA, USA 4 Department of Pathology, Augusta University, Augusta, GA, USA 5 Mayo Clinic, Jacksonville, FL, USA Correspondence to: Ashok Sharma, email: assharma@augusta.edu Keywords: cancer; TCGA; kallikreins; gene expression; prognosis Received: September 21, 2017 Accepted: March 06, 2018 Published: April 03, 2018 ABSTRACT Purpose: The aim of this study was to compare and contrast the expression of all members of the Kallikrein (KLK) family of genes across 15 cancer types and to evaluate their utility as diagnostic and prognostic biomarkers. Results: Severe alterations were found in the expression of different Kallikrein genes across various cancers. Interestingly, renal clear cell and papillary carcinomas have similar kallikrein expression profiles, whereas, chromophobe renal cell carcinoma has a unique expression profile. Several KLK genes have excellent biomarker potential (AUC > 0.90) for chromophobe renal cell carcinoma ( KLK2, KLK3, KLK4, KLK7, KLK15 ), renal papillary carcinoma ( KLK1, KLK6, KLK7 ), clear cell renal cell carcinoma ( KLK1 , KLK6 ), thyroid carcinoma ( KLK2, KLK4, KLK13, KLK15 ) and colon adenocarcinoma ( KLK6, KLK7, KLK8, KLK10 ). Several KLK genes were significantly associated with mortality in clear cell renal cell carcinoma ( KLK2 : HR = 1.69; KLK4 : HR = 1.63; KLK8 : HR = 1.71; KLK10 : HR = 2.12; KLK11 : HR = 1.76; KLK14 : HR = 1.86), papillary renal cell carcinoma ( KLK6 : HR = 3.38, KLK7 : HR = 2.50), urothelial bladder carcinoma ( KLK5 : HR = 1.89, KLK6 : HR = 1.71, KLK8 : HR = 1.60), and hepatocellular carcinoma ( KLK13 : HR = 1.75). Methods: The RNA-seq gene expression data were downloaded from The Cancer Genome Atlas (TCGA). Statistical analyses, including differential expression analysis, receiver operating characteristic curves and survival analysis (Cox proportional-hazards regression models) were performed. Conclusions: A comprehensive analysis revealed the changes in the expression of different KLK genes associated with specific cancers and highlighted their potential as a diagnostic and prognostic tool.

OBJECTIVE: Cesarean section (C-section) is associated with various immune-mediated diseases in the offspring. We investigated the relationship between mode of delivery and celiac disease (CD) and CD autoimmunity (CDA) in a multinational birth cohort. METHODS: From 2004 to 2010, infants from the general population who tested positive for HLA DR3-DQ2 or DR4-DQ8 were enrolled in The Environmental Determinants for Diabetes in the Young (TEDDY) study. Children were annually screened for transglutaminase autoantibodies, if positive, they are retested after 3 to 6 months and those persistently positive defined as CDA. Associations of C-section with maternal (age, education level, parity, pre-pregnancy weight, diabetes, smoking, weight gain during pregnancy) and child characteristics (gestational age, birth weight) were examined by Fisher exact test or Wilcoxon rank-sum test. Hazard ratios (HRs) for CDA or CD were calculated by Cox proportional hazard regression models. RESULTS: Of 6087 analyzed singletons, 1600 (26%) were born by C-section (Germany 38%, United States 37%, Finland 18%, Sweden 16%), and the remaining were born vaginally without instrumental support; 979 (16%) had developed CDA and 343 (6%) developed CD. C-section was associated with lower risk for CDA (hazard ratio [HR] = 0.85; 95% confidence interval [CI] 0.73, 0.99 P = 0.032) and CD (HR = 0.75; 95% CI 0.58, 0.98; P = 0.034). After adjusting for country, sex, HLA-genotype, CD in family, maternal education, and breast-feeding duration, significance was lost for CDA (HR = 0.91; 95% CI 0.78, 1.06; P = 0.20) and CD (HR = 0.85; 95% CI 0.65, 1.11; P = 0.24). Presurgical ruptured membranes had no influence on CDA or CD development. CONCLUSION: C-section is not associated with increased risk for CDA or CD in the offspring.

OBJECTIVE To examine duration of breastfeeding and timing of complementary foods and risk of islet autoimmunity (IA). RESEARCH DESIGN AND METHODS The Environmental Determinants of Diabetes in the Young (TEDDY) study prospectively follows 8,676 children with increased genetic risk of type 1 diabetes (T1D) in the U.S., Finland, Germany, and Sweden. This study included 7,563 children with at least 9 months of follow-up. Blood samples were collected every 3 months from birth to evaluate IA, defined as persistent, confirmed positive antibodies to insulin (IAAs), GAD, or insulinoma antigen-2. We examined the associations between diet and the risk of IA using Cox regression models adjusted for country, T1D family history, HLA genotype, sex, and early probiotic exposure. Additionally, we investigated martingale residuals and log-rank statistics to determine cut points for ages of dietary exposures. RESULTS Later introduction of gluten was associated with increased risk of any IA and IAA. The hazard ratios (HRs) for every 1-month delay in gluten introduction were 1.05 (95% CI 1.01, 1.10; P = 0.02) and 1.08 (95% CI 1.00, 1.16; P = 0.04), respectively. Martingale residual analysis suggested that the age at gluten introduction could be grouped as 9 months. The risk of IA associated with introducing gluten before 4 months of age was lower (HR 0.68; 95% CI 0.47, 0.99), and the risk of IA associated with introducing it later than the age of 9 months was higher (HR 1.57; 95% CI 1.07, 2.31) than introduction between 4 and 9 months of age. CONCLUSIONS The timing of gluten-containing cereals and IA should be studied further.

β-cell autoantibodies against insulin (IAA), GAD65 (GADA) and IA-2 (IA-2A) precede onset of childhood type 1 diabetes (T1D). Incidence of the first appearing β-cell autoantibodies peaks at a young age and is patterned by T1D-associated genes, suggesting an early environmental influence. Here, we tested if gestational infections and interactions with child's human leukocyte antigen (HLA) and non-HLA genes affected the appearance of the first β-cell autoantibody. Singletons of mothers without diabetes (n = 7472) with T1D-associated HLA-DR-DQ genotypes were prospectively followed quarterly through the first 4 years of life, then semiannually until age 6 years, using standardized autoantibody analyses. Maternal infections during pregnancy were assessed via questionnaire 3–4.5 months post-delivery. Polymorphisms in twelve non-HLA genes associated with the first appearing β-cell autoantibodies were included in a Cox regression analysis. IAA predominated as the first appearing β-cell autoantibody in younger children (n = 226, median age at seroconversion 1.8 years) and GADA (n = 212; 3.2 years) in children aged ≥2 years. Gestational infections were not associated with the first appearing β-cell autoantibodies overall. However, gestational respiratory infections (G-RI) showed a consistent protective influence on IAA (HR 0.64, 95% CI 0.45–0.91) among CTLA4-(AG, GG) children (G-RI*CTLA4 interaction, p = 0.002). The predominant associations of HLA-DR-DQ 4-8/8-4 with IAA and HLA-DR-DQ 3-2/3-2 with GADA were not observed if a G-RI was reported (G-RI*HLA-DR-DQ interaction, p = 0.03). The role of G-RI may depend on offspring HLA and CTLA-4 alleles and supports a bidirectional trigger for IAA or GADA as a first appearing β-cell autoantibody in early life.

Introduction: Accurate diagnosis is essential for cancer treatment. Sometimes, it is difficult to confirm histological diagnosis due to inadequate tissue sample or preparation. Using molecular markers for classification would be ideal in such cases. For renal cell carcinoma(RCC), we developed a molecular classification based on transcriptomics to address these issues and aid current diagnostic workflow. Method: We obtained transcriptomic and phenotype data of renal cell carcinoma from The cancer genome atlas (TCGA) data repository. We developed an unsupervised algorithm, Density-based UMAP, based on two clustering methods, UMAP (Uniform manifold approximation and projection) and DBScan (Density-based spatial clustering of applications with noise). We iterated this algorithm 1000 times with random 1000 genes each time and classified each sample in each iteration. We used plurality voting of at least 70% of the iterations for consensus groups. The algorithm was able to identify all the major histological subtypes. We ran a differential expression analysis for each group. Finally, we developed a classification gene signature and a supervised algorithm to classify subtypes with fewer genes and implement this signature in an RCC metanalysis. Results: Density-based UMAP Algorithm classified the samples with 91.4% concordance with WHO 2016 classification. Among the discrepant cases, 4.4 % cases (46) were not diagnosed the same as their histological classification. 4.2 % (44) cases were shown a mixed expression profile. These cases are of further interest as they might respond better with a different treatment. Based on molecular characteristics they share with major subtypes, they are named ClCh, Clear 2, and Clear 3. ClCh and Clear 2 have shown to be indolent and, Clear 3 has shown to be very aggressive. We manually curated 328 genes for supervised learning from differential expression analysis between the groups and able to identify the groups in a metanalysis. Conclusion: Transcriptomic profiling is fast, robust and, simple. It can help in diagnosing histologically discrepant cases. This classifier would be a good edition in renal cancer diagnostic workflow and can complement current diagnostic guidelines for renal cancer. Citation Format: Khaled Bin Satter, Paul MH Tran, Sharad B. Purohit, Jin-Xiong She. Transcriptomic classification of renal cancer: a machine learning approach [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2192.

Both tumors and patients are complex and models that determine survival and toxicity of radiotherapy or any other treatment ideally must take into account this variability as well as its dynamic state. The genetic features of the tumor and the host, and increasingly also the epi-genetic and proteomic characteristics, are being unraveled. Multiple techniques, including histological examination, blood sampling, measurement of circulating tumor cells (CTCs), and functional and molecular imaging, can be used for this purpose. However, the effects of radiation on the tumor and on organs at risk (OARs) are also influenced by the applied dose and volume of irradiated tissues. Combining all these biological, clinical, imaging, and dosimetric parameters in a validated prognostic or predictive model poses a major challenge. Here we aimed to provide an objective review of the potential of blood markers to guide high precision radiation therapy. A combined biological-mathematical approach opens new doors beyond prognostication of patients, as it allows truly precise oncological treatment. Indeed, the core for individualized and precision medicine is not only selection of patients, but even more the optimization of the therapeutic window on an individual basis. A holistic model will allow for determination of an individual dose-response relationship for each organ at risk for each tumor in each individual patient for the complete oncological treatment package. This includes, but is not limited to, radiotherapy alone. Individualized dose-response curves will allow for consideration of different doses of radiation and combinations with other drugs to plan for both optimal toxicity and complete response. Insights into the interactions between a multitude of parameters will lead to the discovery of new pathways and networks that will fuel new biological research on target discovery.

Aims/hypothesis: During the A/H1N1 2009 (A/California/04/2009) pandemic, mass vaccination with a squalene-containing vaccine, Pandemrix®, was performed in Sweden and Finland. The vaccination was found to cause narcolepsy in children and young adults with the HLA-DQ 6.2 haplotype. The aim of this study was to investigate if exposure to Pandemrix® similarly increased the risk of islet autoimmunity or type 1 diabetes. Methods: In The Environmental Determinants of Diabetes in the Young (TEDDY) study, children are followed prospectively for the development of islet autoimmunity and type 1 diabetes. In October 2009, when the mass vaccination began, 3401 children at risk for islet autoimmunity and type 1 diabetes were followed in Sweden and Finland. Vaccinations were recorded and autoantibodies against insulin, GAD65 and insulinoma-associated protein 2 were ascertained quarterly before the age of 4 years and semi-annually thereafter. Results: By 5 August 2010, 2413 of the 3401 (71%) children observed as at risk for an islet autoantibody or type 1 diabetes on 1 October 2009 had been vaccinated with Pandemrix®. By 31 July 2016, 232 children had at least one islet autoantibody before 10 years of age, 148 had multiple islet autoantibodies and 96 had developed type 1 diabetes. The risk of islet autoimmunity was not increased among vaccinated children. The HR (95% CI) for the appearance of at least one islet autoantibody was 0.75 (0.55, 1.03), at least two autoantibodies was 0.85 (0.57, 1.26) and type 1 diabetes was 0.67 (0.42, 1.07). In Finland, but not in Sweden, vaccinated children had a lower risk of islet autoimmunity (0.47 [0.29, 0.75]), multiple autoantibodies (0.50 [0.28, 0.90] ) and type 1 diabetes (0.38 [0.20, 0.72]) compared with those who did not receive Pandemrix®. The analyses were adjusted for confounding factors. Conclusions/interpretation: Children with an increased genetic risk for type 1 diabetes who received the Pandemrix® vaccine during the A/H1N1 2009 pandemic had no increased risk of islet autoimmunity, multiple islet autoantibodies or type 1 diabetes. In Finland, the vaccine was associated with a reduced risk of islet autoimmunity and type 1 diabetes.

We examined the association between plasma 25-hydroxyvitamin D [25(OH)D] concentration and islet autoimmunity (IA) and whether vitamin D gene polymorphisms modify the effect of 25(OH)D on IA risk. We followed 8,676 children at increased genetic risk of type 1 diabetes at six sites in the U.S. and Europe. We defined IA as positivity for at least one autoantibody (GADA, IAA, or IA-2A) on two or more visits. We conducted a risk set sampled nested case-control study of 376 IA case subjects and up to 3 control subjects per case subject. 25(OH)D concentration was measured on all samples prior to, and including, the first IA positive visit. Nine polymorphisms in VDR, CYP24A, CYP27B1, GC, and RXRA were analyzed as effect modifiers of 25(OH)D. Adjusting for HLA-DR-DQ and ancestry, higher childhood 25(OH)D was associated with lower IA risk (odds ratio = 0.93 for a 5 nmol/L difference; 95% CI 0.89, 0.97). Moreover, this association was modified by VDR rs7975232 (interaction P = 0.0072), where increased childhood 25(OH)D was associated with a decreasing IA risk based upon number of minor alleles: 0 (1.00; 0.93, 1.07), 1 (0.92; 0.89, 0.96), and 2 (0.86; 0.80, 0.92). Vitamin D and VDR may have a combined role in IA development in children at increased genetic risk for type 1 diabetes.