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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.

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.

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.