The cofactor-dependent folding mechanism of Drosophila cryptochrome revealed by single-molecule pulling experiments

The link between cofactor binding and protein activity is well-established. However, how cofactor interactions modulate folding of large proteins remains unknown. We use optical tweezers, clustering and global fitting to dissect the folding mechanism of Drosophila cryptochrome (dCRY), a 542-residue protein that binds FAD, one of the most chemically and structurally complex cofactors in nature. We show that the first dCRY parts to fold are independent of FAD, but later steps are FAD-driven as the remaining polypeptide folds around the cofactor. FAD binds to largely unfolded intermediates, yet with association kinetics above the diffusion-limit. Interestingly, not all FAD moieties are required for folding: whereas the isoalloxazine ring linked to ribitol and one phosphate is sufficient to drive complete folding, the adenosine ring with phosphates only leads to partial folding. Lastly, we propose a dCRY folding model where regions that undergo conformational transitions during signal transduction are the last to fold.

A Bayesian two-step integrative procedure incorporating prior knowledge for the identification of miRNA-mRNAs involved in hepatocellular carcinoma

Recent studies have confirmed the role of miRNA regulation of gene expression in oncogenesis for various cancers. In parallel, prior knowledge about relationships between miRNA and mRNA have been accumulated from biological experiments or statistical analyses. Improved identification of disease-associated miRNA-mRNA pairs may be achieved by incorporating prior knowledge into integrative genomic analyses. In this study we focus on 39 patients with hepatocellular carcinoma (HCC) and 25 patients with liver cirrhosis and use a flexible Bayesian two-step integrative method. We found 66 significant miRNA-mRNA pairs, several of which contain molecules that have previously been identified as potential biomarkers. These results demonstrate the utility of the proposed approach in providing a better understanding of relationships between different biological levels, thereby giving insights into the biological mechanisms underlying the diseases, while providing a better selection of biomarkers that may serve as diagnostic, prognostic, or therapeutic biomarker candidates.

Integrative Analysis of DNA Methylation and microRNA Expression Reveals Mechanisms of Racial Heterogeneity in Hepatocellular Carcinoma

Pathologic alterations in epigenetic regulation have long been considered a hallmark of many cancers, including hepatocellular carcinoma (HCC). In a healthy individual, the relationship between DNA methylation and microRNA (miRNA) expression maintains a fine balance; however, disruptions in this harmony can aid in the genesis of cancer or the propagation of existing cancers. The balance between DNA methylation and microRNA expression and its potential disturbance in HCC can vary by race. There is emerging evidence linking epigenetic events including DNA methylation and miRNA expression to cancer disparities. In this paper, we evaluate the epigenetic mechanisms of racial heterogenity in HCC through an integrated analysis of DNA methylation, miRNA, and combined regulation of gene expression. Specifically, we generated DNA methylation, mRNA-seq, and miRNA-seq data through the analysis of tumor and adjacent non-tumor liver tissues from African Americans (AA) and European Americans (EA) with HCC. Using mixed ANOVA, we identified cytosine-phosphate-guanine (CpG) sites, mRNAs, and miRNAs that are significantly altered in HCC vs. adjacent non-tumor tissue in a race-specific manner. We observed that the methylome was drastically changed in EA with a significantly larger number of differentially methylated and differentially expressed genes than in AA. On the other hand, the miRNA expression was altered to a larger extent in AA than in EA. Pathway analysis functionally linked epigenetic regulation in EA to processes involved in immune cell maturation, inflammation, and vascular remodeling. In contrast, cellular proliferation, metabolism, and growth pathways are found to predominate in AA as a result of this epigenetic analysis. Furthermore, through integrative analysis, we identified significantly differentially expressed genes in HCC with disparate epigenetic regulation, associated with changes in miRNA expression for AA and DNA methylation for EA.

Race and Patient-reported Symptoms in Adherence to Adjuvant Endocrine Therapy: A Report from the Women's Hormonal Initiation and Persistence Study

BACKGROUND

Adjuvant endocrine therapy (AET) improves outcomes in women with hormone receptor-positive (HR+) breast cancer. Suboptimal AET adherence is common, but data are lacking about symptoms and adherence in racial/ethnic minorities. We evaluated adherence by race and the relationship between symptoms and adherence.

METHODS

The Women's Hormonal Initiation and Persistence study included women diagnosed with nonrecurrent HR+ breast cancer who initiated AET. AET adherence was captured using validated items. Data regarding patient (e.g., race), medication-related (e.g., symptoms), cancer care delivery (e.g., communication), and clinicopathologic factors (e.g., chemotherapy) were collected via surveys and medical charts. Multivariable logistic regression models were employed to calculate odds ratios and 95% confidence intervals (CIs) associated with adherence.

RESULTS

Of the 570 participants, 92% were privately insured and nearly one of three were Black. Thirty-six percent reported nonadherent behaviors. In multivariable analysis, women less likely to report adherent behaviors were Black (vs. White; OR, 0.43; 95% CI, 0.27-0.67; P < 0.001) and with greater symptom burden (OR, 0.98; 95% CI, 0.96-1.00; P < 0.05). Participants more likely to be adherent were overweight (vs. normal weight) (OR, 1.58; 95% CI, 1.04-2.43; P < 0.05), sat ≤ 6 hours a day (vs. ≥6 hours; OR, 1.83; 95% CI, 1.25-2.70; P < 0.01), and were taking aromatase inhibitors (vs. tamoxifen; OR, 1.91; 95% CI, 1.28-2.87; P < 0.01).

CONCLUSIONS

Racial differences in AET adherence were observed. Longitudinal assessments of symptom burden are needed to better understand this dynamic process and factors that may explain differences in survivor subgroups.

IMPACT

Future interventions should prioritize Black survivors and women with greater symptom burden.

Maternal-fetal genetic interactions, imprinting, and risk of placental abruption

BACKGROUND

Maternal genetic variations, including variations in mitochondrial biogenesis (MB) and oxidative phosphorylation (OP), are associated with placental abruption (PA). However, the role of maternal-fetal genetic interactions (MFGI) and parent-of-origin (imprinting) effects in PA remain unknown.

OBJECTIVE

To investigate MFGI in MB-OP, and imprinting effects in relation to risk of PA.

METHODS

Among Peruvian mother-infant pairs (503 PA cases and 1052 controls), independent single nucleotide polymorphisms (SNPs), with linkage-disequilibrium coefficient <0.80, were selected to characterize genetic variations in MB-OP (78 SNPs in 24 genes) and imprinted genes (2713 SNPs in 73 genes). For each MB-OP SNP, four multinomial models corresponding to fetal allele effect, maternal allele effect, maternal and fetal allele additive effect, and maternal-fetal allele interaction effect were fit under Hardy-Weinberg equilibrium, random mating, and rare disease assumptions. The Bayesian information criterion (BIC) was used for model selection. For each SNP in imprinted genes, imprinting effect was tested using a likelihood ratio test. Bonferroni corrections were used to determine statistical significance (p-value < 6.4e-4 for MFGI and p-value < 1.8e-5 for imprinting).

RESULTS

Abruption cases were more likely to experience preeclampsia, have shorter gestational age, and deliver infants with lower birthweight compared with controls. Models with MFGI effects provided improved fit than models with only maternal and fetal genotype main effects for SNP rs12530904 (p-value = 1.2e-04) in calcium/calmodulin-dependent protein kinase [CaM kinase] II beta (CAMK2B), and, SNP rs73136795 (p-value = 1.9e-04) in peroxisome proliferator-activated receptor-gamma (PPARG), both MB genes. We identified 320 SNPs in 45 maternally-imprinted genes (including potassium voltage-gated channel subfamily Q member 1 [KCNQ1], neurotrimin [NTM], and, ATPase phospholipid transporting 10 A [ATP10A]) associated with abruption. Top hits included rs2012323 (p-value = 1.6E-16) and rs12221520 (p-value1.3e-13) in KCNQ1, rs8036892 (p-value = 9.3E-17) and rs188497582 in ATP10A, rs12589854 (p-value = 2.9E-11) and rs80203467 (p-value = 4.6e-11) in maternally expressed 8, small nucleolar RNA host (MEG8), and rs138281088 in solute carrier family 22 member 2 (SLC22A2) (p-value = 6.8e-9).

CONCLUSIONS

We identified novel PA-related maternal-fetal MB gene interactions and imprinting effects that highlight the role of the fetus in PA risk development. Findings can inform mechanistic investigations to understand the pathogenesis of PA.

Bayesian variable selection for high dimensional predictors and self-reported outcomes

Background

The onset of silent diseases such as type 2 diabetes is often registered through self-report in large prospective cohorts. Self-reported outcomes are cost-effective; however, they are subject to error. Diagnosis of silent events may also occur through the use of imperfect laboratory-based diagnostic tests. In this paper, we describe an approach for variable selection in high dimensional datasets for settings in which the outcome is observed with error.

Methods

We adapt the spike and slab Bayesian Variable Selection approach in the context of error-prone, self-reported outcomes. The performance of the proposed approach is studied through simulation studies. An illustrative application is included using data from the Women’s Health Initiative SNP Health Association Resource, which includes extensive genotypic (>900,000 SNPs) and phenotypic data on 9,873 African American and Hispanic American women.

Results

Simulation studies show improved sensitivity of our proposed method when compared to a naive approach that ignores error in the self-reported outcomes. Application of the proposed method resulted in discovery of several single nucleotide polymorphisms (SNPs) that are associated with risk of type 2 diabetes in a dataset of 9,873 African American and Hispanic participants in the Women’s Health Initiative. There was little overlap among the top ranking SNPs associated with type 2 diabetes risk between the racial groups, adding support to previous observations in the literature of disease associated genetic loci that are often not generalizable across race/ethnicity populations. The adapted Bayesian variable selection algorithm is implemented in R. The source code for the simulations are available in the Supplement.

Conclusions

Variable selection accuracy is reduced when the outcome is ascertained by error-prone self-reports. For this setting, our proposed algorithm has improved variable selection performance when compared to approaches that neglect to account for the error-prone nature of self-reports.

Abstract A076: Adherence to adjuvant endocrine therapy: Do racial disparities persist among the insured?

Purpose: Adjuvant endocrine therapy (AET) improves survival in women with hormone receptor-positive (HR+) breast cancer (BC). Yet medication adherence is suboptimal. The aim of this study was to assess adherence to AET among insured women using innovative statistical approaches.

Methods: Black and White women diagnosed with HR+ BC were identified from two health maintenance organizations. Automated pharmacy records captured oral AET prescriptions and refill dates. Logistic regression identified predictors of adherence defined in terms of proportion of days covered (PDC) (&gt;=80%) and medication gap of ≤10 days. A zero-inflated negative binominal (ZINB) regression model identified variables associated with the total number of days of medication gaps.

Results: A total of 1,925 women met inclusion criteria. Eighty percent of women were adherent per the PDC measure; 44% had a medication gap of ≤10 days; and 24% of women had zero days without any medication gaps. Race and age were significant predictors of adherence in all multivariable models. Black women were less likely to have PDC &gt;=80% than Whites (OR=0.72; 95%CI: 0.57-0.90; p&lt;0.01), and they were less likely to have a medication gap of ≤10 days (OR=0.65; 95%CI: 0.54-0.79; p&lt;0.001). Women 25-49 years old were less likely to have PDC &gt;=80% than women 65-93 years old (OR=0.65; 95%CI: 0.48-0.87; p&lt;0.001), and they also were less likely to have a medication gap of ≤10 days (OR=0.73; 95%CI: 0.57-0.93; p&lt;0.01). In the zero-inflated negative binominal model, Black women were less likely to having no medication gaps compared to Whites (OR=0.46; 95%CI: 0.54-0.79; p&lt;0.001), and women 25-49 years old were less likely to have no medication gaps compared to women 65-93 years old (OR=0.61; 95%CI: 0.42-0.88; p&lt;0.01).

Conclusions: Disparities in adherence to AET persist among insured women, particularly in Black and young women, highlighting a need for interventions among this population. Novel statistical approaches to study adherence, such as the ZINB approach, appear to constitute a useful alternative to the dichotomous PDC variable to tailor analysis to adherence patterns.

Citation Format: Dennis Tolsma, Mahlet G. Tadesse, Arnethea Sutton, Lee Cromwell, Georges Adunlin, Teresa M. Salgado, Jun He, Martha Trout, Brandi E. Robinson, Megan C. Edmonds, Hayden B. Bosworth, Vanessa B. Sheppard. Adherence to adjuvant endocrine therapy: Do racial disparities persist among the insured? [abstract]. In: Proceedings of the Eleventh AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2018 Nov 2-5; New Orleans, LA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(6 Suppl):Abstract nr A076.

Identification of miRNA-mRNA associations in hepatocellular carcinoma using hierarchical integrative model

Background

The established role miRNA-mRNA regulation of gene expression has in oncogenesis highlights the importance of integrating miRNA with downstream mRNA targets. These findings call for investigations aimed at identifying disease-associated miRNA-mRNA pairs. Hierarchical integrative models (HIM) offer the opportunity to uncover the relationships between disease and the levels of different molecules measured in multiple omic studies.

Methods

The HIM model we formulated for analysis of mRNA-seq and miRNA-seq data can be specified with two levels: (1) a mechanistic submodel relating mRNAs to miRNAs, and (2) a clinical submodel relating disease status to mRNA and miRNA, while accounting for the mechanistic relationships in the first level.

Results

mRNA-seq and miRNA-seq data were acquired by analysis of tumor and normal liver tissues from 30 patients with hepatocellular carcinoma (HCC). We analyzed the data using HIM and identified 157 significant miRNA-mRNA pairs in HCC. The majority of these molecules have already been independently identified as being either diagnostic, prognostic, or therapeutic biomarker candidates for HCC. These pairs appear to be involved in processes contributing to the pathogenesis of HCC involving inflammation, regulation of cell cycle, apoptosis, and metabolism. For further evaluation of our method, we analyzed miRNA-seq and mRNA-seq data from TCGA network. While some of the miRNA-mRNA pairs we identified by analyzing both our and TCGA data are previously reported in the literature and overlap in regulation and function, new pairs have been identified that may contribute to the discovery of novel targets.

Conclusion

The results strongly support the hypothesis that miRNAs are important regulators of mRNAs in HCC. Furthermore, these results emphasize the biological relevance of studying miRNA-mRNA pairs.

Metabolomic Analysis of Liver Tissues for Characterization of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) causes more than half a million annual deaths worldwide. Understanding the mechanisms contributing to HCC development is highly desirable for improved surveillance, diagnosis, and treatment. Liver tissue metabolomics has the potential to reflect the physiological changes behind HCC development. Also, it allows identification of biomarker candidates for future evaluation in biofluids and investigation of racial disparities in HCC. Tumor and nontumor tissues from 40 patients were analyzed by both gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) platforms to increase the metabolome coverage. The levels of the metabolites extracted from solid liver tissue of the HCC area and adjacent non-HCC area were compared. Among the analytes detected by GC-MS and LC-MS with significant alterations, 18 were selected based on biological relevance and confirmed metabolite identification. These metabolites belong to TCA cycle, glycolysis, purines, and lipid metabolism and have been previously reported in liver metabolomic studies where high correlation with HCC progression is implied. We demonstrated that metabolites related to HCC pathogenesis can be identified through liver tissue metabolomic analysis. Additionally, this study has enabled us to identify race-specific metabolites associated with HCC.

Adherence to Adjuvant Endocrine Therapy in Insured Black and White Breast Cancer Survivors: Exploring Adherence Measures in Patient Data

BACKGROUND

Adjuvant endocrine therapy (AET) is a critical therapy in that it improves survival in women with hormone receptor-positive (HR+) breast cancer (BC), but adherence to AET is suboptimal. The purpose of this study was to fill scientific gaps about predictors of adherence to AET among black and white women diagnosed with BC.

OBJECTIVE

To assess AET adherence in black and white insured women using multiple measures, including one that uses an innovative statistical approach.

METHODS

Black and white women newly diagnosed with HR+ BC were identified from 2 health maintenance organizations. Pharmacy records captured the type of oral AET prescriptions and all fill dates. Multivariable logistic regression was used to identify predictors of adherence defined in terms of proportion of days covered (PDC; ≥ 80%) and medication gap of ≤ 10 days. A zero-inflated negative binomial (ZINB) regression model was used to identify variables associated with the total number of days of medication gaps.

RESULTS

1,925 women met inclusion criteria. 80% were PDC adherent (> 80%); 44% had a medication gap of ≤ 10 days; and 24% had no medication gap days. Race and age were significant in all multivariable models. Black women were less likely to be adherent based on PDC than white women (OR = 0.72, 95% CI = 0.57-0.90, P < 0.01), and they were less likely to have a medication gap of ≤ 10 days (OR = 0.65, 95% CI = 0.54-0.79, P < 0.001). Women aged 25-49 years were less likely to be PDC adherent than women aged 65-93 years (OR = 0.65, 95% CI = 0.48-0.87, P < 0.001). In the ZINB model, women were without their medication for an average of 37 days (SD = 50.5).

CONCLUSIONS

Racial disparities in adherence to AET in the study highlight a need for interventions among insured women. Using various measures of adherence may help better understand this multidimensional concept. There might be benefits from using both more common dichotomous measures (e.g., PDC) and integrating novel statistical approaches to allow tailoring adherence to patterns within a specific sample.

DISCLOSURES

This research was funded by the National Institutes of Health (R01CA154848). It was also supported in part by the NIH-NCI Cancer Center Support Grant P30 CA016059, the Laboratory of Telomere Health P30 CA51008, and the TSA Award No. UL1TR002649 from the National Center for Advancing Translational Sciences. The contents of this study are solely the responsibility of the authors and do not necessarily represent official views of the National Center for Advancing Translational Sciences or the National Institutes of Health. Bosworth reports grants from Sanofi, Otsuka, Johnson & Johnson, and Blue Cross/Blue Shield of NC and consulting fees from Sanofi and Otsuka. The other authors have nothing to disclose. The datasets generated during and/or analyzed during the current study are not publicly available due to privacy reasons but are available from the corresponding author on reasonable request. The author does not own these data. Data use was granted to the author as part of a data use agreement between specific agencies and organizations.

Physical activity and epigenetic biomarkers in maternal blood during pregnancy

AIM

Investigate associations of leisure time physical activity (LTPA) with DNA methylation and miRNAs during pregnancy. Patients & methods: LTPA, candidate DNA methylation and circulating miRNAs were measured (average 15 weeks gestation) in pregnant women (n = 92).

RESULTS

Each additional hour of prepregnancy LTPA duration was associated with hypermethylation in C1orf212 (β = 0.137, 95% CI: 0.004-0.270) and higher circulating miR-146b-5p (β = 0.084, 95% CI: 0.017-0.151). Each additional metabolic equivalent hour of early-pregnancy LTPA energy expenditure was associated with higher circulating miR-21-3p (β = 0.431, 95% CI: 0.089-0.772) in women carrying female offspring, and lower circulating miR-146b-5p (β = -0.285, 95% CI: -0.528 to -0.043) and miR-517-5p (β = -0.406, 95% CI: -0.736 to -0.076) in women carrying male offspring.

CONCLUSION

Our findings suggest that LTPA may influence maternal epigenetic biomarkers, possibly in an offspring sex-specific manner.

A Modified Random Survival Forests Algorithm for High Dimensional Predictors and Self-Reported Outcomes

We present an ensemble tree-based algorithm for variable selection in high dimensional datasets, in settings where a time-to-event outcome is observed with error. The proposed methods are motivated by self-reported outcomes collected in large-scale epidemiologic studies, such as the Women's Health Initiative. The proposed methods equally apply to imperfect outcomes that arise in other settings such as data extracted from electronic medical records. To evaluate the performance of our proposed algorithm, we present results from simulation studies, considering both continuous and categorical covariates. We illustrate this approach to discover single nucleotide polymorphisms that are associated with incident Type II diabetes in the Women's Health Initiative. A freely available R package icRSF (R Core Team, 2018; Xu et al., 2018) has been developed to implement the proposed methods.

Initiation of Adjuvant Endocrine Therapy in Black and White Women With Breast Cancer

BACKGROUND

Adjuvant endocrine therapy reduces risk of recurrence and mortality in women with hormone receptor-positive breast cancer, yet many women never initiate it. We examined the influence of race, sociocultural factors, and process-of-care factors on initiation of adjuvant endocrine therapy in a racially diverse sample.

PATIENTS AND METHODS

Eligible women were originally recruited for the Narrowing the Gaps in Adjuvant Therapy Study (2006-2011). Sociocultural and process-of-care factors were collected via telephone surveys before adjuvant therapy. Clinical factors were abstracted from charts. Penalized LASSO (least absolute shrinkage and selection operator) logistic regression model was used to identify variables associated with initiation.

RESULTS

Of the 270 women, 55.6% were black and the rest were white. Most women (74.8%) initiated therapy. A significant interaction (P = .008) was found between race and age. Black women aged ≤ 50 years had the lowest initiation (59.7%) compared to black women > 50 years (87.1%), white women ≤ 50 years (73.7%), or white women > 50 years (72.0%). Multivariate analysis found that younger black women exhibited a marginally higher risk of noninitiation compared to older black women. Additionally, ratings of financial access, presence of comorbidities, and levels of communication were all associated with endocrine therapy initiation.

CONCLUSION

Black women ≤ 50 years of age and women with financial constraints may be important subgroups for interventions. Patient-provider communication appears to be an important leverage point to foster therapy uptake.

Identifying Biomarkers from Mass Spectrometry Data with Ordinal Outcome

In recent years, there has been an increased interest in using protein mass spectroscopy to identify molecular markers that discriminate diseased from healthy individuals. Existing methods are tailored towards classifying observations into nominal categories. Sometimes, however, the outcome of interest may be measured on an ordered scale. Ignoring this natural ordering results in some loss of information. In this paper, we propose a Bayesian model for the analysis of mass spectrometry data with ordered outcome. The method provides a unified approach for identifying relevant markers and predicting class membership. This is accomplished by building a stochastic search variable selection method within an ordinal outcome model. We apply the methodology to mass spectrometry data on ovarian cancer cases and healthy individuals. We also utilize wavelet-based techniques to remove noise from the mass spectra prior to analysis. We identify protein markers associated with being healthy, having low grade ovarian cancer, or being a high grade case. For comparison, we repeated the analysis using conventional classification procedures and found improved predictive accuracy with our method.

Circulating early- and mid-pregnancy microRNAs and risk of gestational diabetes

AIMS

Epigenetic regulators, including microRNAs (miRNAs), are implicated in type 2 diabetes, but evidence linking circulating miRNAs in pregnancy and risk of gestational diabetes (GDM) is sparse. Potential modifiers, including pre-pregnancy overweight/obesity and offspring sex, are unexamined. We hypothesized that circulating levels of early-mid-pregnancy (range 7-23weeks of gestation) candidate miRNAs are related to subsequent development of GDM. We also hypothesized that miRNA-GDM associations might vary by pre-pregnancy body-mass index (ppBMI) or offspring sex.

METHODS

In a case-control analysis (36GDM cases/80 controls) from the Omega study, a prospective cohort study of pregnancy complications, we measured early-mid-pregnancy plasma levels of 10miRNAs chosen for potential roles in pregnancy course and complications (miR-126-3p, -155-5p, -21-3p, -146b-5p, -210-3p, -222-3p, -223-3p, -517-5p, -518a-3p, and 29a-3p) using qRT-PCR. Logistic regression models adjusted for gestational age at blood draw (GA) were fit to compare circulating miRNAs between cases and controls. We repeated analyses among overweight/obese (ppBMI≥25kg/m²) or lean (ppBMI<25kg/m²) women, and women with male or female offspring separately.

RESULTS

Mean age was 34.3years (cases) and 32.9years (controls). GA-adjusted miR-155-5p (β=0.260/p=0.028) and -21-3p (β=0.316/p=0.005) levels were positively associated with GDM. MiR-146b-5p (β=0.266/p=0.068) and miR-517-5p (β=0.196/p=0.074) were borderline. Associations of miR-21-3p and miR-210-3p with GDM were observed among overweight/obese but not lean women. Associations of six miRNAs (miR-155-5p, -21-3p, -146b-5p, -223-3p, -517-5p, and -29a-3p) with GDM were present only among women carrying male fetuses (all p<0.05).

CONCLUSIONS

Circulating early-mid-pregnancy miRNAs are associated with GDM, particularly among women who are overweight/obese pre-pregnancy or pregnant with male offspring. This area has potential to clarify mechanisms underlying GDM pathogenesis and identify at-risk mothers earlier in pregnancy.

Multi-omic approaches for characterization of hepatocellular carcinoma

Multi-omic approaches offer the opportunity to characterize complex diseases such as cancer at various molecular levels. In this paper, we present transcriptomic, proteomic/glycoproteomic, glycomic, and metabolomic (TPGM) data we acquired by analysis of liver tissues from hepatocellular carcinoma (HCC) cases and patients with liver cirrhosis. We evaluated changes in the levels of transcripts, proteins, glycans, and metabolites between tumor and cirrhotic tissues by statistical methods. We demonstrated the potential of multi-omic approaches and network analysis to investigate the interactions among these biomolecules in the progression of liver cirrhosis to HCC. Also, we showed the significance of multi-omic approaches to identify pathways altered in HCC.

Genetic variations related to maternal whole blood mitochondrial DNA copy number: a genome-wide and candidate gene study

We conducted genome-wide (GWAS) and candidate gene association studies of maternal mitochondrial DNA copy number. Maternal peripheral blood was collected during labor and delivery admission from 471 participants of a placental abruption case-control study conducted in Lima, Peru. Single nucleotide polymorphism (SNP) genotyping was performed using the Illumina Cardio-Metabo Chip. Whole blood mitochondrial DNA (mtDNA) copy number was measured using qRT-PCR techniques. We evaluated 119,629 SNPs in the GWAS and 161 SNPs (in 29 mitochondrial biogenesis and oxidative phosphorylation genes) in the candidate association study. Top hits from GWAS and the candidate gene study were selected to compute weighted genetic risk scores (wGRS). Linear regression models were used to calculate effect size estimates and related nominal p values. The top hit in our GWAS was chr19:51063065 in FOXA3 (empirical p values = 2.20e - 6). A total of 134 SNPs had p values < 0.001 including rs17111633 in CNNM1 (p values = 6.32e - 6) and chr19:51083059 in MYPOP (p values = 3.23e - 5). In the candidate association study, several SNPs in PPARG, PRKCA, SP1 and THRB were associated with mtDNA copy number (p values < 0.05). mtDNA copy number was significantly associated with wGRS based on top GWAS hits (β = 0.49, 95% CI:0.38-0.60, p < 0.001). Variations in nuclear DNA are potentially associated with maternal mtDNA copy number.

Metabolomic Characterization of Hepatocellular Carcinoma in Patients with Liver Cirrhosis for Biomarker Discovery

Background: Metabolomics plays an important role in providing insight into the etiology and mechanisms of hepatocellular carcinoma (HCC). This is accomplished by a comprehensive analysis of patterns involved in metabolic alterations in human specimens. This study compares the levels of plasma metabolites in HCC cases versus cirrhotic patients and evaluates the ability of candidate metabolites in distinguishing the two groups. Also, it investigates the combined use of metabolites and clinical covariates for detection of HCC in patients with liver cirrhosis.Methods: Untargeted analysis of metabolites in plasma from 128 subjects (63 HCC cases and 65 cirrhotic controls) was conducted using gas chromatography coupled to mass spectrometry (GC-MS). This was followed by targeted evaluation of selected metabolites. LASSO regression was used to select a set of metabolites and clinical covariates that are associated with HCC. The performance of candidate biomarkers in distinguishing HCC from cirrhosis was evaluated through a leave-one-out cross-validation based on area under the receiver operating characteristics (ROC) curve.Results: We identified 11 metabolites and three clinical covariates that differentiated HCC cases from cirrhotic controls. Combining these features in a panel for disease classification using support vector machines (SVM) yielded better area under the ROC curve compared with alpha-fetoprotein (AFP).Conclusions: This study demonstrates the combination of metabolites and clinical covariates as an effective approach for early detection of HCC in patients with liver cirrhosis.Impact: Further investigation of these findings may improve understanding of HCC pathophysiology and possible implication of the metabolites in HCC prevention and diagnosis. Cancer Epidemiol Biomarkers Prev; 26(5); 675-83. ©2016 AACR.

Maternal pre-pregnancy body mass index and circulating microRNAs in pregnancy

BACKGROUND

Maternal pre-pregnancy overweight and obese status has been associated with a number of pregnancy complications and adverse offspring outcomes. Mechanisms for observed associations, however, are largely unknown. We investigated associations of pre-pregnancy body mass index with early-mid pregnancy epigenetic biomarkers, circulating microRNAs.

METHODS

Peripheral blood was collected from participants (16-27 weeks gestation) of two multi-racial pregnancy cohorts, the Omega Study and the Pregnancy Outcomes and Community Health Study. Plasma miRNA expression was characterised using epigenome-wide (319 miRNAs) profiling among 20 pregnant women in each cohort. Cohort-specific linear regression models that included the predictor (pre-pregnancy body mass index), the outcome (microRNA expression), and adjustment factors (maternal age, gestational age at blood collection, and race) were fit.

RESULTS

Expression of 27 miRNAs was positively associated with pre-pregnancy body mass index in both cohorts (p-values <0.05). A number of these differentially expressed miRNAs have previously been associated with adipogenesis (e.g. let-7d*, miR-103-2*, -130b, -146b-5-p, -29c, and -26b). Identified miRNAs as well as their experimentally validated targets participate in pathways that involve organismal injury, reproductive system disease, connective tissue disorders, cancer, cellular development, growth and proliferation.

CONCLUSION

Pre-pregnancy body mass index is associated with circulating miRNAs in early-mid pregnancy.

Multivariate Bayesian variable selection exploiting dependence structure among outcomes: Application to air pollution effects on DNA methylation

The analysis of multiple outcomes is becoming increasingly common in modern biomedical studies. It is well-known that joint statistical models for multiple outcomes are more flexible and more powerful than fitting a separate model for each outcome; they yield more powerful tests of exposure or treatment effects by taking into account the dependence among outcomes and pooling evidence across outcomes. It is, however, unlikely that all outcomes are related to the same subset of covariates. Therefore, there is interest in identifying exposures or treatments associated with particular outcomes, which we term outcome-specific variable selection. In this work, we propose a variable selection approach for multivariate normal responses that incorporates not only information on the mean model, but also information on the variance-covariance structure of the outcomes. The approach effectively leverages evidence from all correlated outcomes to estimate the effect of a particular covariate on a given outcome. To implement this strategy, we develop a Bayesian method that builds a multivariate prior for the variable selection indicators based on the variance-covariance of the outcomes. We show via simulation that the proposed variable selection strategy can boost power to detect subtle effects without increasing the probability of false discoveries. We apply the approach to the Normative Aging Study (NAS) epigenetic data and identify a subset of five genes in the asthma pathway for which gene-specific DNA methylations are associated with exposures to either black carbon, a marker of traffic pollution, or sulfate, a marker of particles generated by power plants.

Placental genetic variations in circadian clock-related genes increase the risk of placental abruption

The genetic architecture of placental abruption (PA) remains poorly understood. We examined variations in SNPs of circadian clock-related genes in placenta with PA risk. We also explored placental and maternal genomic contributions to PA risk. Placental genomic DNA samples were isolated from 280 PA cases and 244 controls. Genotyping was performed using the Illumina Cardio-MetaboChip. We examined 116 SNPs in 13 genes known to moderate circadian rhythms. Logistic regression models were fit to estimate odds ratios (ORs). The combined effect of multiple SNPs on PA risk was estimated using a weighted genetic risk score. We examined independent and joint associations of wGRS derived from placental and maternal genomes with PA. Seven SNPs in five genes (ARNTL2, CRY2, DEC1, PER3 and RORA), in the placental genome, were associated with PA risk. Each copy of the minor allele (G) of a SNP in the RORA gene (rs2899663) was associated with a 30% reduced odds of PA (95% CI 0.52-0.95). The odds of PA increased with increasing placental-wGRS (Ptrend<0.001). The ORs were 1.00, 2.16, 3.24 and 4.48 across quartiles. Associations persisted after the maternal-wGRS was included in the model. There was evidence of an additive contribution of placental and maternal genetic contributions to PA risk. Participants with placental- and maternal-wGRS in the highest quartile, compared with those in the lowest quartile, had a 15.57-fold (95% CI 3.34-72.60) increased odds of PA. Placental variants in circadian clock-related genes are associated with PA risk; and the association persists after control of genetic variants in the maternal genome.

Risk of Common Mental Disorders in Relation to Symptoms of Obstructive Sleep Apnea Syndrome among Ethiopian College Students

BACKGROUND

The Berlin and Epworth Sleepiness Scale (ESS) are simple, validated, and widely used questionnaires designed to assess symptoms of obstructive sleep apnea syndrome (OSAS) a common but often unrecognized cause of morbidity and mortality.

METHODS

A cross-sectional study was conducted among 2,639 college students to examine the extent to which symptoms of OSAS are associated with the odds of common mental disorders (CMDs). The General Health Questionnaire (GHQ-12) was used to evaluate the presence of CMDs while the Berlin and ESS were used to assess high-risk for obstructive sleep apnea (OSA) and excessive daytime sleepiness, respectively. Logistic regression procedures were used to derive odds ratios (OR) and 95% confidence intervals (CI) assessing the independent and joint associations of high-risk for OSA and excessive daytime sleepiness with odds of CMDs.

RESULTS

Approximately 19% of students had high-risk for OSA while 26.4% had excessive daytime sleepiness. Compared to students without high-risk for OSA and without excessive daytime sleepiness (referent group), students with excessive daytime sleepiness only (OR=2.01; 95%CI: 1.60-2.52) had increased odds of CMDs. The odds of CMDs for students with high-risk OSA only was 1.26 (OR=1.26; 95%CI 0.94-1.68). Students with both high-risk for OSA and excessive daytime sleepiness, compared to the referent group, had the highest odds of CMDs (OR=2.45; 95%CI: 1.69-3.56).

CONCLUSION

Our findings indicate that symptoms of OSAS are associated with increased risk of CMDs. These findings emphasize the comorbidity of sleep disorders and CMDs and suggest that there may be benefits to investing in educational programs that extend the knowledge of sleep disorders in young adults.

Assessment of GC-MS in Detecting Changes in the Levels of Metabolites Using a Spike-in Experiment in Human Plasma

Gas Chromatography coupled with Mass Spectrometry (GC-MS) has been broadly used for the detection of changes in metabolite levels in complex samples. Internal Standards (IS) spiked into a complex background at different concentrations help assess the capability of GC-MS in detecting changes in metabolite levels. This study uses a Latin square design to evaluate the ability of GC-MS in full scan and Single Ion Monitoring (SIM) modes to detect changes among IS spiked into human plasma samples at varying concentrations. Statistical analysis of the data demonstrates the potential of GC-MS to detect true differences over a wide range of concentration levels.

Circadian clock-related genetic risk scores and risk of placental abruption

INTRODUCTION

The circadian clock plays an important role in several aspects of female reproductive biology. Evidence linking circadian clock-related genes to pregnancy outcomes has been inconsistent. We sought to examine whether variations in single nucleotide polymorphisms (SNPs) of circadian clock genes are associated with PA risk.

METHODS

Maternal blood samples were collected from 470 PA case and 473 controls. Genotyping was performed using the Illumina Cardio-MetaboChip platform. We examined 119 SNPs in 13 candidate genes known to control circadian rhythms (e.g., CRY2, ARNTL, and RORA). Univariate and penalized logistic regression models were fit to estimate odds ratios (ORs); and the combined effect of multiple SNPs on PA risk was estimated using a weighted genetic risk score (wGRS).

RESULTS

A common SNP in the RORA gene (rs2899663) was associated with a 21% reduced odds of PA (P < 0.05). The odds of PA increased with increasing wGRS (Ptrend < 0.001). The corresponding ORs were 1.00, 1.83, 2.81 and 5.13 across wGRS quartiles. Participants in the highest wGRS quartile had a 5.13-fold (95% confidence interval: 3.21-8.21) higher odds of PA compared to those in the lowest quartile. Although the test for interaction was not significant, the odds of PA was substantially elevated for preeclamptics with the highest wGRS quartile (OR = 14.44, 95%CI: 6.62-31.53) compared to normotensive women in the lowest wGRS quartile.

DISCUSSION

Genetic variants in circadian rhythm genes may be associated with PA risk. Larger studies are needed to corroborate these findings and to further elucidate the pathogenesis of this important obstetrical complication.

Psychometric properties and factor structure of the General Health Questionnaire as a screening tool for anxiety and depressive symptoms in a multi-national study of young adults

BACKGROUND

Globally, common psychiatric disorders such as depression and anxiety are among the leading causes of morbidity and mortality. The 12-item General Health Questionnaire (GHQ-12) is a widely used questionnaire for screening or detecting common psychiatric disorders. The purpose of this study was to examine the reliability, construct validity and factor structure of the GHQ-12 in a large sample of African, Asian and South American young adults.

METHODS

A cross-sectional study was conducted among 9077 undergraduate students from Chile, Ethiopia, Peru and Thailand. Students aged 18-35 years were invited to complete a self-administered questionnaire that collected information about lifestyle, demographics, and GHQ-12. In each country, the construct validity and factorial structures of the GHQ-12 questionnaire were tested through exploratory and confirmatory factor analyses (EFA and CFA).

RESULTS

Overall the GHQ-12 items showed good internal consistency across all countries as reflected by the Cronbach's alpha: Chile (0.86), Ethiopia (0.83), Peru (0.85), and Thailand (0.82). Results from EFA showed that the GHQ-12 had a two-factor solution in Chile, Ethiopia and Thailand, although a three-factor solution was found in Peru. These findings were corroborated by CFA. Indicators of goodness of fit, comparative fit index (CFI), root mean square error of approximation (RMSEA), and standardized root mean squared residual, were all in acceptable ranges across study sites. The CFI values for Chile, Ethiopia, Peru and Thailand were 0.964, 0.951, 0.949, and 0.931, respectively. The corresponding RMSEA values were 0.051, 0.050, 0.059, and 0.059.

CONCLUSION

Overall, we documented cross-cultural comparability of the GHQ-12 for assessing common psychiatric disorders such as symptoms of depressive and anxiety disorders among young adults. Although the GHQ-12 is typically used as single-factor questionnaire, the results of our EFA and CFA revealed the multi- dimensionality of the scale. Future studies are needed to further evaluate the specific cut points for assessing each component within the multiple factors.

Evaluation of hierarchical models for integrative genomic analyses

MOTIVATION

Advances in high-throughput technologies have led to the acquisition of various types of -omic data on the same biological samples. Each data type gives independent and complementary information that can explain the biological mechanisms of interest. While several studies performing independent analyses of each dataset have led to significant results, a better understanding of complex biological mechanisms requires an integrative analysis of different sources of data.

RESULTS

Flexible modeling approaches, based on penalized likelihood methods and expectation-maximization (EM) algorithms, are studied and tested under various biological relationship scenarios between the different molecular features and their effects on a clinical outcome. The models are applied to genomic datasets from two cancer types in the Cancer Genome Atlas project: glioblastoma multiforme and ovarian serous cystadenocarcinoma. The integrative models lead to improved model fit and predictive performance. They also provide a better understanding of the biological mechanisms underlying patients' survival.

AVAILABILITY AND IMPLEMENTATION

Source code implementing the integrative models is freely available at https://github.com/mgt000/IntegrativeAnalysis along with example datasets and sample R script applying the models to these data. The TCGA datasets used for analysis are publicly available at https://tcga-data.nci.nih.gov/tcga/tcgaDownload.jsp

CONTACT

marie.denis@cirad.fr or mgt26@georgetown.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Maternal Early Pregnancy Serum Metabolites and Risk of Gestational Diabetes Mellitus

CONTEXT

Significant gaps remain in the understanding of genetic and environmental risk factors, as well as related mechanisms that contribute to gestational diabetes mellitus (GDM).

OBJECTIVES

This study aimed to investigate early pregnancy maternal serum metabolites and subsequent risk of GDM.

DESIGN

Information on participant characteristics and GDM diagnosis was collected using in-person interviews and medical record abstraction, respectively. Early pregnancy serum samples were used for nontargeted metabolite profiling using a gas chromatography-mass spectrometry platform. Lasso regression was used to select a set of metabolites that are jointly associated with GDM case-control status. We evaluated the predictive performance of the set of selected metabolites using a receiver operating characteristics curve and area under the curve.

PARTICIPANTS

A total of 178 GDM cases and 180 controls participated in a pregnancy cohort study.

RESULTS

A set of 17 metabolites (linoleic acid, oleic acid, myristic acid, d-galactose, d-sorbitol, o-phosphocolamine, l-alanine, l-valine, 5-hydroxy-l-tryptophan, l-serine, sarcosine, l-pyroglutamic acid, l-mimosine, l-lactic acid, glycolic acid, fumaric acid, and urea) differentiated GDM cases from controls. Fold changes of relative abundance of these metabolites among GDM cases compared with controls ranged from 1.47 (linoleic acid) to 0.78 (5-hydroxy-l-tryptophan). Addition of these selected metabolites to a set of well-known GDM risk factors improved the area under the curve significantly from 0.71 to 0.87 (P = 3.97E-07).

CONCLUSIONS

We identified combinations of metabolites in early pregnancy that are associated with subsequent risk of GDM. Replication of findings may improve understanding of GDM pathogenesis and may have implications for the design of GDM prevention and early diagnosis protocols.

Candidate Gene and MicroRNA Expression in Fetal Membranes and Preterm Delivery Risk

We investigated candidate gene and microRNA (miRNA) expression in amnion and chorion in relation to risk of preterm delivery (PTD). Amnion and chorion were separated from placenta and collected at delivery from participants who delivered at term (N = 10) and from participants who delivered preterm following spontaneous labor (sPTL-PTD; N = 10), premature rupture of membranes (PPROM-PTD; N = 10), and preeclampsia (PE-PTD; N = 10). Expression of genes (metalloproteinase [MMP] 2, MMP-9, and tissue inhibitors of MMP-1) and miRNAs (miR-199a*, -202*, -210, -214, -223, and -338) was profiled using quantitative real-time polymerase chain reaction approaches. Adjusted multinomial logistic regression models were used to calculate relative risk ratios (RRR), 95% confidence intervals, and P values. Among controls, the expression of miR-199a*, -202*, and -214 was lower in the amnion compared with their expression in the chorion, whereas the expression of miR-210 was higher in the amnion compared with its expression in the chorion (all P values < .05). In the amnion, MMP-9 expression was associated with PTD risk (overall P value = .0092), and MMP-9 expression was positively associated with the risk of PPROM-PTD (RRR: 31.10) and inversely associated with the risk of PE-PTD (RRR:6.55e-6), although individual associations were not statistically significant. In addition, in the amnion, the expression of miR-210 (RRR: 0.45; overall P value = .0039) was inversely associated with the risk of PE-PTD, and miR-223 was inversely associated with all subtypes of PTD (overall P value = .0400). The amnion and chorion differ in their miRNA expression. The expression of MMP-9, miR-210, and -223 in the amnion is associated with PTD risk.

Bayesian Normalization Model for Label-Free Quantitative Analysis by LC-MS

We introduce a new method for normalization of data acquired by liquid chromatography coupled with mass spectrometry (LC-MS) in label-free differential expression analysis. Normalization of LC-MS data is desired prior to subsequent statistical analysis to adjust variabilities in ion intensities that are not caused by biological differences but experimental bias. There are different sources of bias including variabilities during sample collection and sample storage, poor experimental design, noise, etc. In addition, instrument variability in experiments involving a large number of LC-MS runs leads to a significant drift in intensity measurements. Although various methods have been proposed for normalization of LC-MS data, there is no universally applicable approach. In this paper, we propose a Bayesian normalization model (BNM) that utilizes scan-level information from LC-MS data. Specifically, the proposed method uses peak shapes to model the scan-level data acquired from extracted ion chromatograms (EIC) with parameters considered as a linear mixed effects model. We extended the model into BNM with drift (BNMD) to compensate for the variability in intensity measurements due to long LC-MS runs. We evaluated the performance of our method using synthetic and experimental data. In comparison with several existing methods, the proposed BNM and BNMD yielded significant improvement.

Circadian rhythm characteristics, poor sleep quality, daytime sleepiness and common psychiatric disorders among Thai college students

INTRODUCTION

To investigate the relationship between common psychiatric disorders (CPDs) and sleep characteristics (evening chronotype, poor sleep quality and daytime sleepiness) among Thai college students.

METHODS

A cross-sectional study was conducted among 2,970 undergraduate students in Thailand. Students were asked to complete a self-administered questionnaire that collected information about lifestyle and demographic characteristics. The Horne and Ostberg Morningness-Eveningness Questionnaire (MEQ), Pittsburgh Sleep Quality Index (PSQI) and Epworth Sleepiness Scale (ESS) were used to evaluate circadian preference, sleep quality and daytime sleepiness, respectively. The General Health Questionnaire-12 (GHQ-12) was used to evaluate presence of CPDs. Logistic regression models were used to estimate adjusted odds ratios (ORs) and 95% confidence intervals (95% CIs) of CPDs in relation to the covariates of interest.

RESULTS

A total of 337 students were classified as having CPDs (11.2%; 95% CI 10.1-12.3%). Evening chronotype (OR = 3.35; 95% CI 2.09-5.37), poor sleep quality (OR = 4.89; 95% CI 3.66-6.54) and excessive daytime sleepiness (OR = 1.95; 95% CI 1.54-2.47) were statistically significantly associated with CPDs.

DISCUSSION

Our study demonstrated that CPDs are common among Thai college students. Further, evening chronotype, poor sleep quality and excessive daytime sleepiness were strongly associated with increased risk of CPDs. These findings highlight the importance of educating students and school administrators about the importance of sleep and their impact on mental health.

GC-MS Based Plasma Metabolomics for Identification of Candidate Biomarkers for Hepatocellular Carcinoma in Egyptian Cohort

This study evaluates changes in metabolite levels in hepatocellular carcinoma (HCC) cases vs. patients with liver cirrhosis by analysis of human blood plasma using gas chromatography coupled with mass spectrometry (GC-MS). Untargeted metabolomic analysis of plasma samples from participants recruited in Egypt was performed using two GC-MS platforms: a GC coupled to single quadruple mass spectrometer (GC-qMS) and a GC coupled to a time-of-flight mass spectrometer (GC-TOFMS). Analytes that showed statistically significant changes in ion intensities were selected using ANOVA models. These analytes and other candidates selected from related studies were further evaluated by targeted analysis in plasma samples from the same participants as in the untargeted metabolomic analysis. The targeted analysis was performed using the GC-qMS in selected ion monitoring (SIM) mode. The method confirmed significant changes in the levels of glutamic acid, citric acid, lactic acid, valine, isoleucine, leucine, alpha tocopherol, cholesterol, and sorbose in HCC cases vs. patients with liver cirrhosis. Specifically, our findings indicate up-regulation of metabolites involved in branched-chain amino acid (BCAA) metabolism. Although BCAAs are increasingly used as a treatment for cancer cachexia, others have shown that BCAA supplementation caused significant enhancement of tumor growth via activation of mTOR/AKT pathway, which is consistent with our results that BCAAs are up-regulated in HCC.

LC-MS/MS-based serum proteomics for identification of candidate biomarkers for hepatocellular carcinoma

Associating changes in protein levels with the onset of cancer has been widely investigated to identify clinically relevant diagnostic biomarkers. In the present study, we analyzed sera from 205 patients recruited in the United States and Egypt for biomarker discovery using label-free proteomic analysis by LC-MS/MS. We performed untargeted proteomic analysis of sera to identify candidate proteins with statistically significant differences between hepatocellular carcinoma (HCC) and patients with liver cirrhosis. We further evaluated the significance of 101 proteins in sera from the same 205 patients through targeted quantitation by MRM on a triple quadrupole mass spectrometer. This led to the identification of 21 candidate protein biomarkers that were significantly altered in both the United States and Egyptian cohorts. Among the 21 candidates, ten were previously reported as HCC-associated proteins (eight exhibiting consistent trends with our observation), whereas 11 are new candidates discovered by this study. Pathway analysis based on the significant proteins reveals upregulation of the complement and coagulation cascades pathway and downregulation of the antigen processing and presentation pathway in HCC cases versus patients with liver cirrhosis. The results of this study demonstrate the power of combining untargeted and targeted quantitation methods for a comprehensive serum proteomic analysis, to evaluate changes in protein levels and discover novel diagnostic biomarkers. All MS data have been deposited in the ProteomeXchange with identifier PXD001171 (http://proteomecentral.proteomexchange.org/dataset/PXD001171).

Identification of functional modules by integration of multiple data sources using a Bayesian network classifier

BACKGROUND

Prediction of functional modules is indispensable for detecting protein deregulation in human complex diseases such as cancer. Bayesian network is one of the most commonly used models to integrate heterogeneous data from multiple sources such as protein domain, interactome, functional annotation, genome-wide gene expression, and the literature.

METHODS AND RESULTS

In this article, we present a Bayesian network classifier that is customized to (1) increase the ability to integrate diverse information from different sources, (2) effectively predict protein-protein interactions, (3) infer aberrant networks with scale-free and small-world properties, and (4) group molecules into functional modules or pathways based on the primary function and biological features. Application of this model in discovering protein biomarkers of hepatocellular carcinoma leads to the identification of functional modules that provide insights into the mechanism of the development and progression of hepatocellular carcinoma. These functional modules include cell cycle deregulation, increased angiogenesis (eg, vascular endothelial growth factor, blood vessel morphogenesis), oxidative metabolic alterations, and aberrant activation of signaling pathways involved in cellular proliferation, survival, and differentiation.

CONCLUSIONS

The discoveries and conclusions derived from our customized Bayesian network classifier are consistent with previously published results. The proposed approach for determining Bayesian network structure facilitates the integration of heterogeneous data from multiple sources to elucidate the mechanisms of complex diseases.

Sleep disturbances and quality of life in Sub-Saharan African migraineurs

BACKGROUND

Although in the past decade occidental countries have increasingly recognized the personal and societal burden of migraine, it remains poorly understood in Africa. No study has evaluated the impact of sleep disturbances and the quality of life (QOL) in sub-Saharan Africans with migraine.

METHODS

This was a cross-sectional study evaluating adults, ≥ 18 years of age, attending outpatient clinics in Ethiopia. Standardized questionnaires were utilized to collect demographic, headache, sleep, lifestyle, and QOL characteristics in all participants. Migraine classification was based on International Classification of Headache Disorders (ICHD)-II criteria. The Pittsburgh Sleep Quality Index (PSQI) and the World Health Organization Quality of Life (WHOQOL-BREF) questionnaires were utilized to assess sleep quality and QOL characteristics, respectively. Multivariable logistic regression models were fit to estimate adjusted odds ratio (OR) and 95% confidence intervals (95% CI).

RESULTS

Of 1,060 participants, 145 (14%) met ICHD-II criteria for migraine. Approximately three-fifth of the study participants (60.5%) were found to have poor sleep quality. After adjustments, migraineurs had over a two-fold increased odds (OR = 2.24, 95% CI 1.49-3.38) of overall poor sleep quality (PSQI global score >5) as compared with non-migraineurs. Compared with non-migraineurs, migraineurs were also more likely to experience short sleep duration (≤7 hours) (OR = 2.07, 95% CI 1.43-3.00), long sleep latency (≥30 min) (OR = 1.97, 95% CI 1.36-2.85), daytime dysfunction due to sleepiness (OR = 1.51, 95% CI 1.12-2.02), and poor sleep efficiency (<85%) (OR = 1.93, 95% CI 1.31-2.88). Similar to occidental countries, Ethiopian migraineurs reported a reduced QOL as compared to non-migraineurs. Specifically Ethiopian migraineurs were more likely to experience poor physical (OR = 1.56, 95% CI 1.08-2.25) and psychological health (OR = 1.75, 95% CI 1.20-2.56), as well as poor social relationships (OR = 1.56, 95% CI 1.08-2.25), and living environments (OR = 1.41, 95% CI 0.97-2.05) as compared to those without migraine.

CONCLUSION

Similar to occidental countries, migraine is highly prevalent among Ethiopians and is associated with poor sleep quality and a lower QOL. These findings support the need for physicians and policy makers to take action to improve the quality of headache care and access to treatment in Ethiopia.

Early Pregnancy Maternal Blood DNA Methylation in Repeat Pregnancies and Change in Gestational Diabetes Mellitus Status—A Pilot Study

Repeat pregnancies with different perinatal outcomes minimize underlying maternal genetic diversity and provide unique opportunities to investigate nongenetic risk factors and epigenetic mechanisms of pregnancy complications. We investigated gestational diabetes mellitus (GDM)-related differential DNA methylation in early pregnancy peripheral blood samples collected from women who had a change in GDM status in repeat pregnancies. Six study participants were randomly selected from among women who had 2 consecutive pregnancies, only 1 of which was complicated by GDM (case pregnancy) and the other was not (control pregnancy). Epigenome-wide DNA methylation was profiled using Illumina HumanMethylation 27 BeadChips. Differential Identification using Mixture Ensemble and false discovery rate (<10%) cutoffs were used to identify differentially methylated targets between the 2 pregnancies of each participant. Overall, 27 target sites, 17 hypomethylated (fold change [FC] range: 0.77-0.99) and 10 hypermethylated (FC range: 1.01-1.09), were differentially methylated between GDM and control pregnancies among 5 or more study participants. Novel genes were related to identified hypomethylated (such as NDUFC1, HAPLN3, HHLA3, and RHOG) or hypermethylated sites (such as SEP11, ZAR1, and DDR). Genes related to identified sites participated in cell morphology, cellular assembly, cellular organization, cellular compromise, and cell cycle. Our findings support early pregnancy peripheral blood DNA methylation differences in repeat pregnancies with change in GDM status. Similar, larger, and repeat pregnancy studies can enhance biomarker discovery and mechanistic studies of GDM.

Pathway and network approaches for identification of cancer signature markers from omics data

The advancement of high throughput omic technologies during the past few years has made it possible to perform many complex assays in a much shorter time than the traditional approaches. The rapid accumulation and wide availability of omic data generated by these technologies offer great opportunities to unravel disease mechanisms, but also presents significant challenges to extract knowledge from such massive data and to evaluate the findings. To address these challenges, a number of pathway and network based approaches have been introduced. This review article evaluates these methods and discusses their application in cancer biomarker discovery using hepatocellular carcinoma (HCC) as an example.

Construct validity and factor structure of the pittsburgh sleep quality index and epworth sleepiness scale in a multi-national study of African, South East Asian and South American college students

BACKGROUND

The Pittsburgh Sleep Quality Index (PSQI) and the Epworth Sleepiness Scale (ESS) are questionnaires used to assess sleep quality and excessive daytime sleepiness in clinical and population-based studies. The present study aimed to evaluate the construct validity and factor structure of the PSQI and ESS questionnaires among young adults in four countries (Chile, Ethiopia, Peru and Thailand).

METHODS

A cross-sectional study was conducted among 8,481 undergraduate students. Students were invited to complete a self-administered questionnaire that collected information about lifestyle, demographic, and sleep characteristics. In each country, the construct validity and factorial structures of PSQI and ESS questionnaires were tested through exploratory and confirmatory factor analyses (EFA and CFA).

RESULTS

The largest component-total correlation coefficient for sleep quality as assessed using PSQI was noted in Chile (r = 0.71) while the smallest component-total correlation coefficient was noted for sleep medication use in Peru (r = 0.28). The largest component-total correlation coefficient for excessive daytime sleepiness as assessed using ESS was found for item 1 (sitting/reading) in Chile (r = 0.65) while the lowest item-total correlation was observed for item 6 (sitting and talking to someone) in Thailand (r = 0.35). Using both EFA and CFA a two-factor model was found for PSQI questionnaire in Chile, Ethiopia and Thailand while a three-factor model was found for Peru. For the ESS questionnaire, we noted two factors for all four countries.

CONCLUSION

Overall, we documented cross-cultural comparability of sleep quality and excessive daytime sleepiness measures using the PSQI and ESS questionnaires among Asian, South American and African young adults. Although both the PSQI and ESS were originally developed as single-factor questionnaires, the results of our EFA and CFA revealed the multi- dimensionality of the scales suggesting limited usefulness of the global PSQI and ESS scores to assess sleep quality and excessive daytime sleepiness.

Placental genome and maternal-placental genetic interactions: a genome-wide and candidate gene association study of placental abruption

While available evidence supports the role of genetics in the pathogenesis of placental abruption (PA), PA-related placental genome variations and maternal-placental genetic interactions have not been investigated. Maternal blood and placental samples collected from participants in the Peruvian Abruptio Placentae Epidemiology study were genotyped using Illumina's Cardio-Metabochip platform. We examined 118,782 genome-wide SNPs and 333 SNPs in 32 candidate genes from mitochondrial biogenesis and oxidative phosphorylation pathways in placental DNA from 280 PA cases and 244 controls. We assessed maternal-placental interactions in the candidate gene SNPS and two imprinted regions (IGF2/H19 and C19MC). Univariate and penalized logistic regression models were fit to estimate odds ratios. We examined the combined effect of multiple SNPs on PA risk using weighted genetic risk scores (WGRS) with repeated ten-fold cross-validations. A multinomial model was used to investigate maternal-placental genetic interactions. In placental genome-wide and candidate gene analyses, no SNP was significant after false discovery rate correction. The top genome-wide association study (GWAS) hits were rs544201, rs1484464 (CTNNA2), rs4149570 (TNFRSF1A) and rs13055470 (ZNRF3) (p-values: 1.11e-05 to 3.54e-05). The top 200 SNPs of the GWAS overrepresented genes involved in cell cycle, growth and proliferation. The top candidate gene hits were rs16949118 (COX10) and rs7609948 (THRB) (p-values: 6.00e-03 and 8.19e-03). Participants in the highest quartile of WGRS based on cross-validations using SNPs selected from the GWAS and candidate gene analyses had a 8.40-fold (95% CI: 5.8-12.56) and a 4.46-fold (95% CI: 2.94-6.72) higher odds of PA compared to participants in the lowest quartile. We found maternal-placental genetic interactions on PA risk for two SNPs in PPARG (chr3:12313450 and chr3:12412978) and maternal imprinting effects for multiple SNPs in the C19MC and IGF2/H19 regions. Variations in the placental genome and interactions between maternal-placental genetic variations may contribute to PA risk. Larger studies may help advance our understanding of PA pathogenesis.

LC-MS profiling of N-Glycans derived from human serum samples for biomarker discovery in hepatocellular carcinoma

Defining clinically relevant biomarkers for early stage hepatocellular carcinoma (HCC) in a high-risk population of cirrhotic patients has potentially far-reaching implications for disease management and patient health. Changes in glycan levels have been associated with the onset of numerous diseases including cancer. In the present study, we used liquid chromatography coupled with electrospray ionization mass spectrometry (LC–ESI-MS) to analyze N-glycans in sera from 183 participants recruited in Egypt and the U.S. and identified candidate biomarkers that distinguish HCC cases from cirrhotic controls. N-Glycans were released from serum proteins and permethylated prior to the LC–ESI-MS analysis. Through two complementary LC–ESI-MS quantitation approaches, global profiling and targeted quantitation, we identified 11 N-glycans with statistically significant differences between HCC cases and cirrhotic controls. These glycans can further be categorized into four structurally related clusters, matching closely with the implications of important glycosyltransferases in cancer progression and metastasis. The results of this study illustrate the power of the integrative approach combining complementary LC–ESI-MS based quantitation approaches to investigate changes in N-glycan levels between HCC cases and patients with liver cirrhosis.

Daytime Sleepiness, Circadian Preference, Caffeine Consumption and Use of Other Stimulants among Thai College Students

We conducted this study to evaluate the prevalence of daytime sleepiness and evening chronotype, and to assess the extent to which both are associated with the use of caffeinated stimulants among 3,000 Thai college students. Demographic and behavioral characteristics were collected using a self-administered questionnaire. The Epworth Sleepiness Scale and the Horne and Ostberg Morningness-Eveningness Questionnaire were used to evaluate prevalence of daytime sleepiness and circadian preference. Multivariable logistic regression models were used to evaluate the association between sleep disorders and consumption of caffeinated beverages. Overall, the prevalence of daytime sleepiness was 27.9 % (95% CI: 26.2-29.5%) while the prevalence of evening chronotype was 13% (95% CI: 11.8-14.2%). Students who use energy drinks were more likely to be evening types. For instance, the use of M100/M150 energy drinks was associated with a more than 3-fold increased odds of evening chronotype (OR 3.50; 95% CI 1.90-6.44), while Red Bull users were more than twice as likely to have evening chronotype (OR 2.39; 95% CI 1.02-5.58). Additionally, those who consumed any energy drinks were more likely to be daytime sleepers. For example, Red Bull (OR 1.72; 95% CI 1.08-2.75) or M100/M150 (OR 1.52; 95% CI 1.10-2.11) consumption was associated with increased odds of daytime sleepiness. Our findings emphasize the importance of implementing educational and prevention programs targeted toward improving sleep hygiene and reducing the consumption of energy drinks among young adults.

Assessing validity of a depression screening instrument in the absence of a gold standard

PURPOSE

We evaluated the extent to which use of a hypothesized imperfect gold standard, the Composite International Diagnostic Interview (CIDI), biases the estimates of diagnostic accuracy of the Patient Health Questionnaire-9 (PHQ-9). We also evaluate how statistical correction can be used to address this bias.

METHODS

The study was conducted among 926 adults where structured interviews were conducted to collect information about participants' current major depressive disorder using PHQ-9 and CIDI instruments. First, we evaluated the relative psychometric properties of PHQ-9 using CIDI as a gold standard. Next, we used a Bayesian latent class model to correct for the bias.

RESULTS

In comparison with CIDI, the relative sensitivity and specificity of the PHQ-9 for detecting major depressive disorder at a cut point of 10 or more were 53.1% (95% confidence interval: 45.4%-60.8%) and 77.5% (95% confidence interval, 74.5%-80.5%), respectively. Using a Bayesian latent class model to correct for the bias arising from the use of an imperfect gold standard increased the sensitivity and specificity of PHQ-9 to 79.8% (95% Bayesian credible interval, 64.9%-90.8%) and 79.1% (95% Bayesian credible interval, 74.7%-83.7%), respectively.

CONCLUSIONS

Our results provided evidence that assessing diagnostic validity of mental health screening instrument, where application of a gold standard might not be available, can be accomplished by using appropriate statistical methods.

A HIERARCHICAL BAYESIAN MODEL FOR INFERENCE OF COPY NUMBER VARIANTS AND THEIR ASSOCIATION TO GENE EXPRESSION

A number of statistical models have been successfully developed for the analysis of high-throughput data from a single source, but few methods are available for integrating data from different sources. Here we focus on integrating gene expression levels with comparative genomic hybridization (CGH) array measurements collected on the same subjects. We specify a measurement error model that relates the gene expression levels to latent copy number states which, in turn, are related to the observed surrogate CGH measurements via a hidden Markov model. We employ selection priors that exploit the dependencies across adjacent copy number states and investigate MCMC stochastic search techniques for posterior inference. Our approach results in a unified modeling framework for simultaneously inferring copy number variants (CNV) and identifying their significant associations with mRNA transcripts abundance. We show performance on simulated data and illustrate an application to data from a genomic study on human cancer cell lines.

Daytime Sleepiness, Circadian Preference, Caffeine Consumption and Khat Use among College Students in Ethiopia

OBJECTIVES

To estimate the prevalence of daytime sleepiness and circadian preferences, and to examine the extent to which caffeine consumption and Khat (a herbal stimulant) use are associated with daytime sleepiness and evening chronotype among Ethiopian college students.

METHODS

A cross-sectional study was conducted among 2,410 college students. A self-administered questionnaire was used to collect information about sleep, behavioral risk factors such as caffeinated beverages, tobacco, alcohol, and Khat consumption. Daytime sleepiness and chronotype were assessed using the Epworth Sleepiness Scale (ESS) and the Horne & Ostberg Morningness /Eveningness Questionnaire (MEQ), respectively. Linear and logistic regression models were used to evaluate associations.

RESULTS

Daytime sleepiness (ESS≥10) was present in 26% of the students (95% CI: 24.4-27.8%) with 25.9% in males and 25.5% in females. A total of 30 (0.8%) students were classified as evening chronotypes (0.7% in females and 0.9% in males). Overall, Overall, Khat consumption, excessive alcohol use and cigarette smoking status were associated with evening chronotype. Use of any caffeinated beverages (OR=2.18; 95%CI: 0.82-5.77) and Khat consumption (OR=7.43; 95%CI: 3.28-16.98) increased the odds of evening chronotype.

CONCLUSION

The prevalence of daytime sleepiness among our study population was high while few were classified as evening chronotypes. We also found increased odds of evening chronotype with caffeine consumption and Khat use amongst Ethiopian college students. Prospective cohort studies that examine the effects of caffeinated beverages and Khat use on sleep disorders among young adults are needed.

Abstract B19: Improving initiation of adjuvant hormonal therapy in black women under 50 may contribute towards narrowing racial gaps in breast cancer

Background: The advent of adjuvant hormonal therapy has greatly improved breast cancer survival in women with hormonal receptor (HR) positive breast cancer. Unfortunately, racial disparities have been noted in both the uptake of adjuvant hormonal therapy and in women with HR positive breast cancer.

Purpose: In a prospective study, we examined the influence of race, socio-cultural factors, and process of care factors on initiation of adjuvant hormonal therapy.

Methods: Eligible women were newly diagnosed with invasive, non-metastatic hormonal receptor positive (HR) breast cancer. Socio-cultural and healthcare factors were collected via telephone surveys; clinical data were abstracted from charts. Stepwise logistic regression models examined associations between hormonal initiation and study factors.

Results: The sample was 56% Black (n=150) and 44% White (n=120). Overall 70% initiated therapy with Black women &lt;50 having the lowest rate of initiation (51%) among all Black and White age groups. A significant interaction (p= .002) was found between race and age so a stratified race – age composite variable was created. In multivariate models, Black women &lt; 50 years were less likely to initiate therapy compared to Whites ≥50 (OR: .28; 95% CI: .10 to .82), whites &lt;50 (OR: 0.19; 95% CI: 0.05 to 0.68), or compared to blacks ≥50 (OR: .34; 95% CI: 0.12 to 0.96). Women reporting having higher ratings of financial access were more likely to initiate therapy as were those with higher ratings of communication with their providers.

Conclusion: Attention to financial aspects of cancer care and patient-provider communication may be useful points of intervention to improve initiation in patients, particularly Black patients &lt;50 years of age.

Citation Format: Vanessa B. Sheppard, Claudine Isaacs, Mahlet G. Tadesse, Melissa Butler, Felicity Harper, Fikru Hirpa. Improving initiation of adjuvant hormonal therapy in black women under 50 may contribute towards narrowing racial gaps in breast cancer. [abstract]. In: Proceedings of the Twelfth Annual AACR International Conference on Frontiers in Cancer Prevention Research; 2013 Oct 27-30; National Harbor, MD. Philadelphia (PA): AACR; Can Prev Res 2013;6(11 Suppl): Abstract nr B19.

Evaluation of metabolite biomarkers for hepatocellular carcinoma through stratified analysis by gender, race, and alcoholic cirrhosis

BACKGROUND

The effects of hepatocellular carcinoma on liver metabolism and circulating metabolites have been subjected to continuing investigation. This study compares the levels of selected metabolites in sera of hepatocellular carcinoma cases versus patients with liver cirrhosis and evaluates the influence of gender, race, and alcoholic cirrhosis on the performance of the metabolites as candidate biomarkers for hepatocellular carcinoma.

METHODS

Targeted quantitation of 15 metabolites is performed by selected research monitoring in sera from 89 Egyptian subjects (40 hepatocellular carcinoma cases and 49 cirrhotic controls) and 110 U.S. subjects (56 hepatocellular carcinoma cases and 54 cirrhotic controls). Logistic regression models are used to evaluate the ability of these metabolites in distinguishing hepatocellular carcinoma cases from cirrhotic controls. The influences of gender, race, and alcoholic cirrhosis on the performance of the metabolites are analyzed by stratified logistic regression.

RESULTS

Two metabolites are selected on the basis of their significance to both cohorts. Although both metabolites discriminate hepatocellular carcinoma cases from cirrhotic controls in males and Caucasians, they are insignificant in females and African Americans. One metabolite is significant in patients with alcoholic cirrhosis and the other in nonalcoholic cirrhosis.

CONCLUSIONS

The study demonstrates the potential of two metabolites as candidate biomarkers for hepatocellular carcinoma by combining them with α-fetoprotein (AFP) and gender. Stratified statistical analyses reveal that gender, race, and alcoholic cirrhosis affect the relative levels of small molecules in serum.

IMPACT

The findings of this study contribute to a better understanding of the influence of gender, race, and alcoholic cirrhosis in investigating small molecules as biomarkers for hepatocellular carcinoma.

Multi-profile Bayesian alignment model for LC-MS data analysis with integration of internal standards

MOTIVATION

Liquid chromatography-mass spectrometry (LC-MS) has been widely used for profiling expression levels of biomolecules in various '-omic' studies including proteomics, metabolomics and glycomics. Appropriate LC-MS data preprocessing steps are needed to detect true differences between biological groups. Retention time (RT) alignment, which is required to ensure that ion intensity measurements among multiple LC-MS runs are comparable, is one of the most important yet challenging preprocessing steps. Current alignment approaches estimate RT variability using either single chromatograms or detected peaks, but do not simultaneously take into account the complementary information embedded in the entire LC-MS data.

RESULTS

We propose a Bayesian alignment model for LC-MS data analysis. The alignment model provides estimates of the RT variability along with uncertainty measures. The model enables integration of multiple sources of information including internal standards and clustered chromatograms in a mathematically rigorous framework. We apply the model to LC-MS metabolomic, proteomic and glycomic data. The performance of the model is evaluated based on ground-truth data, by measuring correlation of variation, RT difference across runs and peak-matching performance. We demonstrate that Bayesian alignment model improves significantly the RT alignment performance through appropriate integration of relevant information.

AVAILABILITY AND IMPLEMENTATION

MATLAB code, raw and preprocessed LC-MS data are available at http://omics.georgetown.edu/alignLCMS.html.

CONTACT

hwr@georgetown.edu.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Sleep quality and sleep patterns in relation to consumption of energy drinks, caffeinated beverages, and other stimulants among Thai college students

PURPOSE

Poor sleep and heavy use of caffeinated beverages have been implicated as risk factors for a number of adverse health outcomes. Caffeine consumption and use of other stimulants are common among college students globally. However, to our knowledge, no studies have examined the influence of caffeinated beverages on the sleep quality of college students in Southeast Asian populations. We conducted this study to evaluate the patterns of sleep quality and to examine the extent to which poor sleep quality is associated with consumption of energy drinks, caffeinated beverages, and other stimulants among 2,854 Thai college students.

METHODS

A questionnaire was administered to ascertain demographic and behavioral characteristics. The Pittsburgh Sleep Quality Index was used to assess sleep habits and quality. Chi-square tests and multivariate logistic regression models were used to identify statistically significant associations.

RESULTS

Overall, the prevalence of poor sleep quality was found to be 48.1 %. A significant percent of students used stimulant beverages (58.0 %). Stimulant use (odds ratios (OR) 1.50; 95 % confidence intervals (95 % CI) 1.28-1.77) was found to be statistically significant and positively associated with poor sleep quality. Alcohol consumption (OR 3.10; 95 % CI 1.72-5.59) and cigarette smoking (OR 1.43; 95 % CI 1.02-1.98) also had a statistically significant association with increased daytime dysfunction due to sleepiness. In conclusion, stimulant use is common among Thai college students and is associated with several indices of poor sleep quality.

CONCLUSION

Our findings underscore the need to educate students on the importance of sleep and the influences of dietary and lifestyle choices on their sleep quality and overall health.

Abstract 2492: Targeted quantitation of candidate metabolic biomarkers for hepatocellular carcinoma

The aim of this study is to compare metabolite levels in sera of hepatocellular carcinoma (HCC) patients versus cirrhotic controls. This is accomplished through targeted quantitative analysis of candidate metabolic biomarkers of HCC by using an ultra-performance liquid chromatography coupled to an electrospray ionization-quadrupole linear ion trap mass spectrometry (UPLC-ESI-QLIT MS). The targets were selected from our previous untargeted analysis, where we observed association of HCC to dysregulated levels of bile acids, lysophosphocholines (LPCs), lysophosphoethanolamines (LPEs), sphingophoslipid, long chain carnitines and amino acids. In this study, we performed quantitative analysis of three metabolites (glycodeoxycholic acid, 3beta 6beta-dihydroxy-5beta-cholan-24-oic acid, 3 sulfo-glycodeoxycholic acid) in sera of 199 participants from two cohorts (US and Egypt). The participants consist of 110 subjects (55 HCC cases and 55 cirrhotic controls) from the US and 89 subjects (40 HCC cases and 49 cirrhotic controls) from Egypt. Through a logistic regression model, we analyzed the quantitative results obtained by the UPLC-ESI-QLIT MS to evaluate the ability of the three candidate markers as a panel in distinguishing HCC cases from patients with liver cirrhosis. Receiver operating characteristic (ROC) curve obtained through the regression model yielded an area under the curve (AUC) of 0.754 in distinguishing the HCC cases from cirrhotic controls for the US cohort and an AUC of 0.709 for the Egyptian cohort. The model resulted in an AUC of 0.717 for the combined US and Egyptian cohort. We compared the diagnostic capability of these metabolites versus α-fetoprotein (AFP) in the US cohort, for which AFP measurements were available for both the HCC cases and cirrhotic controls. While AFP alone resulted in an AUC of 0.681, the combination of AFP with the three metabolites increased the AUC to 0.806. These results indicate the potential of these candidate biomarkers in combination with AFP for more accurate diagnosis of HCC in high risk population of cirrhotic patients.

Citation Format: Jun Feng Xiao, Yi Zhao, Rency Varghese, Bin Zhou, Cristina Di Poto, Lihua Zhang, Mahlet G. Tadesse, Dina Hazem Ziada, Kirti Shetty, Habtom W. Ressom. Targeted quantitation of candidate metabolic biomarkers for hepatocellular carcinoma. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 2492. doi:10.1158/1538-7445.AM2013-2492

Note: This abstract was not presented at the AACR Annual Meeting 2013 because the presenter was unable to attend.

Profile-Based LC-MS data alignment--a Bayesian approach

A Bayesian alignment model (BAM) is proposed for alignment of liquid chromatography-mass spectrometry (LC-MS) data. BAM belongs to the category of profile-based approaches, which are composed of two major components: a prototype function and a set of mapping functions. Appropriate estimation of these functions is crucial for good alignment results. BAM uses Markov chain Monte Carlo (MCMC) methods to draw inference on the model parameters and improves on existing MCMC-based alignment methods through 1) the implementation of an efficient MCMC sampler and 2) an adaptive selection of knots. A block Metropolis-Hastings algorithm that mitigates the problem of the MCMC sampler getting stuck at local modes of the posterior distribution is used for the update of the mapping function coefficients. In addition, a stochastic search variable selection (SSVS) methodology is used to determine the number and positions of knots. We applied BAM to a simulated data set, an LC-MS proteomic data set, and two LC-MS metabolomic data sets, and compared its performance with the Bayesian hierarchical curve registration (BHCR) model, the dynamic time-warping (DTW) model, and the continuous profile model (CPM). The advantage of applying appropriate profile-based retention time correction prior to performing a feature-based approach is also demonstrated through the metabolomic data sets.

A regularization corrected score method for nonlinear regression models with covariate error

Many regression analyses involve explanatory variables that are measured with error, and failing to account for this error is well known to lead to biased point and interval estimates of the regression coefficients. We present here a new general method for adjusting for covariate error. Our method consists of an approximate version of the Stefanski-Nakamura corrected score approach, using the method of regularization to obtain an approximate solution of the relevant integral equation. We develop the theory in the setting of classical likelihood models; this setting covers, for example, linear regression, nonlinear regression, logistic regression, and Poisson regression. The method is extremely general in terms of the types of measurement error models covered, and is a functional method in the sense of not involving assumptions on the distribution of the true covariate. We discuss the theoretical properties of the method and present simulation results in the logistic regression setting (univariate and multivariate). For illustration, we apply the method to data from the Harvard Nurses' Health Study concerning the relationship between physical activity and breast cancer mortality in the period following a diagnosis of breast cancer.