Identifying long-term survivors and those at higher or lower risk of relapse among patients with cytogenetically normal acute myeloid leukemia using a high-dimensional mixture cure model.

Patients with cytogenetically normal acute myeloid leukemia (CN-AML) may harbor prognostically relevant gene mutations and thus be categorized into one of the three 2022 European LeukemiaNet (ELN) genetic-risk groups. Nevertheless, there remains heterogeneity with respect to relapse-free survival (RFS) within these genetic-risk groups. Our training set included 306 adults on Alliance for Clinical Trials in Oncology studies with de novo CN-AML aged < 60 years who achieved a complete remission and for whom centrally reviewed cytogenetics, RNA-sequencing, and gene mutation data from diagnostic samples were available (Alliance trial A152010). To overcome deficiencies of the Cox proportional hazards model when long-term survivors are present, we developed a penalized semi-parametric mixture cure model (MCM) to predict RFS where RNA-sequencing data comprised the predictor space. To validate model performance, we employed an independent test set from the German Acute Myeloid Leukemia Cooperative Group (AMLCG) consisting of 40 de novo CN-AML patients aged < 60 years who achieved a complete remission and had RNA-sequencing of their pre-treatment sample. For the training set, there was a significant non-zero cure fraction (p = 0.019) with 28.5% of patients estimated to be cured. Our MCM included 112 genes associated with cure, or long-term RFS, and 87 genes associated with latency, or shorter-term time-to-relapse. The area under the curve and C-statistic were respectively, 0.947 and 0.783 for our training set and 0.837 and 0.718 for our test set. We identified a novel, prognostically relevant molecular signature in CN-AML, which allows identification of patient subgroups independent of 2022 ELN genetic-risk groups.Trial registration Data from companion studies CALGB 8461, 9665 and 20202 (trials registered at www.clinicaltrials.gov as, respectively, NCT00048958, NCT00899223, and NCT00900224) were obtained from Alliance for Clinical Trials in Oncology under data sharing study A152010. Data from the AMLCG 2008 trial was registered at www.clinicaltrials.gov as NCT01382147.

Penalized Bayesian forward continuation ratio model with application to high-dimensional data with discrete survival outcomes.

While time-to-event data are often continuous, there are several instances where discrete survival data, which are inherently ordinal, may be available or are more appropriate or useful. Several discrete survival models exist, but the forward continuation ratio model with a complementary log-log link has a survival interpretation and is closely related to the Cox proportional hazards model, despite being an ordinal model. This model has previously been implemented in the high-dimensional setting using the ordinal generalized monotone incremental forward stagewise algorithm. Here, we propose a Bayesian penalized forward continuation ratio model with a complementary log-log link and explore different priors to perform variable selection and regularization. Through simulations, we show that our Bayesian model outperformed the existing frequentist method in terms of variable selection performance, and that a 10% prior inclusion probability performed better than 1% or 50%. We also illustrate our model on a publicly available acute myeloid leukemia dataset to identify genomic features associated with discrete survival. We identified nine features that map to ten unique genes, five of which have been previously associated with leukemia in the literature. In conclusion, our proposed Bayesian model is flexible, allows simultaneous variable selection and uncertainty quantification, and performed well in simulation studies and application to real data.

Preliminary assessment of three quantitative approaches for estimating time-since-deposition from autofluorescence and morphological profiles of cell populations from forensic biological samples.

Determining when DNA recovered from a crime scene transferred from its biological source, i.e., a sample's 'time-since-deposition' (TSD), can provide critical context for biological evidence. Yet, there remains no analytical techniques for TSD that are validated for forensic casework. In this study, we investigate whether morphological and autofluorescence measurements of forensically-relevant cell populations generated with Imaging Flow Cytometry (IFC) can be used to predict the TSD of 'touch' or trace biological samples. To this end, three different prediction frameworks for estimating the number of day(s) for TSD were evaluated: the elastic net, gradient boosting machines (GBM), and generalized linear mixed model (GLMM) LASSO. Additionally, we transformed these continuous predictions into a series of binary classifiers to evaluate the potential utility for forensic casework. Results showed that GBM and GLMM-LASSO showed the highest accuracy, with mean absolute error estimates in a hold-out test set of 29 and 21 days, respectively. Binary classifiers for these models correctly binned 94-96% and 98-99% of the age estimates as over/under 7 or 180 days, respectively. This suggests that predicted TSD using IFC measurements coupled to one or, possibly, a combination binary classification decision rules, may provide probative information for trace biological samples encountered during forensic casework.

Sex-specific lung inflammation and mitochondrial damage in a model of electronic cigarette exposure in asthma.

The prevalence of electronic cigarette (EC) use among adult with asthma has continued to increase over time, in part due to the belief of being less harmful than smoking. However, the extent of their toxicity and the involved mechanisms contributing to the deleterious impact of EC exposure on patients with preexisting asthma have not been delineated. In the present project, we tested the hypothesis that EC use contributes to respiratory damage and worsening inflammation in the lungs of patients with asthma. To define the consequences of EC exposure in established asthma, we used a mouse model with/without preexisting asthma for short-term exposure to EC aerosols. C57/BL6J mice were sensitized and challenged with a DRA (dust mite, ragweed, Aspergillus fumigates, 200 µg/mL) mixture and exposed daily to EC with nicotine (2% nicotine in 30:70 propylene glycol: vegetable glycerin) or filtered air for 2 wk. The mice were evaluated at 24 h after the final EC exposure. After EC exposure in asthmatic mice, lung inflammatory cell infiltration and goblet cell hyperplasia were increased, whereas EC alone did not cause airway inflammation. Our data also show that mitochondrial DNA (mtDNA) content and a key mtDNA regulator, mitochondrial transcription factor A (TFAM), are reduced in asthmatic EC-exposed mice in a sex-dependent manner. Together, these results indicate that TFAM loss in lung epithelium following EC contributes to male-predominant sex pathological differences, including mitochondrial damage, inflammation, and remodeling in asthmatic airways.NEW & NOTEWORTHY Respiratory immunity is dysregulated in preexisting asthma, and further perturbations by EC use could exacerbate asthma severity. However, the extent of their toxicity and the involved mechanisms contributing to the deleterious impact of EC exposure on patients with preexisting asthma have not been delineated. We found that EC has unique biological impacts in lungs and potential sex differences with loss of TFAM, a key mtDNA regulator, in lung epithelial region from our animal EC study.

Long-Term Impact of Daily E-cigarette Exposure on the Lungs of Asthmatic Mice.

INTRODUCTION: Although the greater popularity of electronic cigarettes (EC) among asthmatics is alarming, there is limited knowledge of the long-term consequences of EC exposure in asthmatics. AIMS AND METHODS: Mild asthmatic C57/BL6J adult male and female mice were established by intranasal insufflation with three combined allergens. The asthmatic and age and sex-matched' naïve mice were exposed to air, nicotine-free (propylene glycol [PG]/vegetable glycerin [VG]-only), or PG/VG+Nicotine, 4 hours daily for 3 months. The effects of EC exposure were accessed by measuring cytokines in bronchoalveolar lavage, periodic acid-schiff (PAS) staining, mitochondrial DNA copy numbers (mtCN), and the transcriptome in the lung. Significance was false discovery rate <0.2 for transcriptome and 0.05 for the others. RESULTS: In asthmatic mice, PG/VG+Nicotine increased PAS-positive cells and IL-13 compared to mice exposed to air and PG/VG-only. In naïve mice exposed to PG/VG+Nicotine and PG/VG-only, higher INF-γ was observed compared to mice exposed only to air. PG/VG-only and PG/VG+Nicotine had significantly higher mtCN compared to air exposure in asthmatic mice, while the opposite pattern was observed in non-asthmatic naïve mice. Different gene expression patterns were profoundly found for asthmatic mice exposed to PG/VG+Nicotine compared to PG/VG-only, including genes involved in mitochondrial dysfunction, oxidative phosphorylation, and p21-activated kinase (PAK) signaling. CONCLUSIONS: This study provides experimental evidence of the potential impact of nicotine enhancement on the long-term effects of EC in asthmatics compared to non-asthmatics. IMPLICATIONS: The findings from this study indicate the potential impact of EC in asthmatics by addressing multiple biological markers. The long-term health outcomes of EC in the susceptible group can be instrumental in supporting policymaking and educational campaigns and informing the public, healthcare providers, and EC users about the underlying risks of EC use.

Comparison of serum metabolomics in women with breast Cancer Prior to Chemotherapy and at 1 year: cardiometabolic implications.

OBJECTIVE: Early-stage breast cancer (BC) is the second most common malignancy in women, worldwide. Early-detection and treatment advances have led to 5-year survival rates of 90% for early-stage breast cancer. However, the long-term morbidity of breast cancer remains high, with a majority of survivors facing increased risk of cardiometabolic conditions as well as secondary cancers. In particular, African American women with breast cancer experience higher morbidity and mortality than other women. Metabolomics is the comprehensive study of metabolites in biological samples to elucidate the role of monosaccharides, amino acids, and their respective metabolic pathways. Although some studies have found differential metabolites in women with breast cancer compared to normal controls, there has been little study of women with breast cancer across time and the active treatment trajectory. This study examines and compares the serum metabolomic profile of women with BC, prior to initial chemotherapy and at 1 year after inception of chemotherapy. METHODS: This study examined serum metabolites through a secondary analysis of a longitudinal parent study (EPIGEN) of women diagnosed with early-stage BC. Participants were evaluated across 5 time points: prior to their receipt of chemotherapy (T1), at the time of their 4th chemotherapy treatment (T2), 6 months after the initiation of chemotherapy (T3), one year after the initiation of chemotherapy (T4) and two years after the initiation of chemotherapy (T5). This analysis focused on the metabolomic data from 70 participants from T1 to T4. Using ultra high-pressure liquid chromatography high resolution mass spectrometry (UHPLC-HRMS), we performed Friedman Rank Sum Test followed by Nemenyi post-hoc pairwise tests to identify which metabolite levels differed between time points, focusing on metabolites with a Benjamini-Hochberg false discovery rate (FDR) from the overall Friedman test < 0.05 and then specifically examined the p-values from the T1 vs. T4 pairwise comparison. RESULTS: The untargeted serum metabolomics yielded a total of 2,395 metabolites identified on the basis of the accurate mass and MS/MS fragmentation, 1,264 of which were significant after Friedman's test (FDR < 0.05). The analysis then focused on the levels of 124 metabolites from the T1 vs. T4 post-hoc comparison that had a combined FDR < 0.05 and fold change (FC) > 2.0. Metabolite set enrichment analysis (MSEA) as part of Metaboanalyst 3.0 was performed to identify pathways that were significantly altered. The known metabolites identified from the functional analysis were used to evaluate the up and down regulated pathways. The 40metabolites from the Functional Analysis were mainly attributed to amino acids (specifically lysine regulation), fatty acids (particularly unsaturated) and steroid hormone synthesis (lysophosphatidic acid). CONCLUSION: There were multiple significant changes in the serum metabolomic profile of women with breast cancer at one-year post inception of chemotherapy compared to pre-chemotherapy, most notably associated with lysine degradation, branched-chain amino acid synthesis, linoleic acid metabolism, tyrosine metabolism and biosynthesis of unsaturated fatty acids as the top 5 metabolic pathways. Some of these changes could be associated with metabolic perturbations that are consistent with heightened risk of cardiometabolic morbidity. Our results provide new insights into the mechanisms underlying potential heightened cardiovascular health risks in this population.

Bioactive lipid mediators in plasma are predictors of preeclampsia irrespective of aspirin therapy.

There are few early biomarkers to identify pregnancies at risk of preeclampsia (PE) and abnormal placental function. In this cross-sectional study, we utilized targeted ultra-performance liquid chromatography-ESI MS/MS and a linear regression model to identify specific bioactive lipids that serve as early predictors of PE. Plasma samples were collected from 57 pregnant women prior to 24-weeks of gestation with outcomes of either PE (n = 26) or uncomplicated term pregnancies (n = 31), and the profiles of eicosanoids and sphingolipids were evaluated. Significant differences were revealed in the eicosanoid, (±)11,12 DHET, as well as multiple classes of sphingolipids; ceramides, ceramide-1-phosphate, sphingomyelin, and monohexosylceramides; all of which were associated with the subsequent development of PE regardless of aspirin therapy. Profiles of these bioactive lipids were found to vary based on self-designated race. Additional analyses demonstrated that PE patients can be stratified based on the lipid profile as to PE with a preterm birth linked to significant differences in the levels of 12-HETE, 15-HETE, and resolvin D1. Furthermore, subjects referred to a high-risk OB/GYN clinic had higher levels of 20-HETE, arachidonic acid, and Resolvin D1 versus subjects recruited from a routine, general OB/GYN clinic. Overall, this study shows that quantitative changes in plasma bioactive lipids detected by ultra-performance liquid chromatography-ESI-MS/MS can serve as an early predictor of PE and stratify pregnant people for PE type and risk.

Outcome prediction by the 2022 European LeukemiaNet genetic-risk classification for adults with acute myeloid leukemia: an Alliance study.

Recently, the European LeukemiaNet (ELN) revised its genetic-risk classification of acute myeloid leukemia (AML). We categorized 1637 adults with AML treated with cytarabine/anthracycline regimens according to the 2022 and 2017 ELN classifications. Compared with the 2017 ELN classification, 2022 favorable group decreased from 40% to 35% and adverse group increased from 37% to 41% of patients. The 2022 genetic-risk groups seemed to accurately reflect treatment outcomes in all patients and patients aged <60 years, but in patients aged ≥60 years, relapse rates, disease-free (DFS) and overall (OS) survival were not significantly different between intermediate and adverse groups. In younger African-American patients, DFS and OS did not differ between intermediate-risk and adverse-risk patients nor did DFS between favorable and intermediate groups. In Hispanic patients, DFS and OS did not differ between favorable and intermediate groups. Outcome prediction abilities of 2022 and 2017 ELN classifications were similar. Among favorable-risk patients, myelodysplasia-related mutations did not affect patients with CEBPAbZIP mutations or core-binding factor AML, but changed risk assignment of NPM1-mutated/FLT3-ITD-negative patients to intermediate. NPM1-mutated patients with adverse-risk cytogenetic abnormalities were closer prognostically to the intermediate than adverse group. Our analyses both confirm and challenge prognostic significance of some of the newly added markers.

Loci on chromosome 12q13.2 encompassing ERBB3, PA2G4 and RAB5B are associated with polycystic ovary syndrome.

Polycystic ovary syndrome (PCOS) is characterized by hyperandrogenemia of ovarian theca cell origin. We report significant association of androgen production with 15 single nucleotide variants (SNVs) identified by exome sequencing of theca cells from women with PCOS and normal ovulatory women. Ten SNVs are located within a 150 kbp region on 12q13.2 which encompasses loci identified in PCOS genome-wide association studies (GWAS) and contains PCOS candidate genes ERBB3 and RAB5B. The region also contains PA2G4 which encodes a transcriptional corepressor of androgen receptor and androgen receptor-regulated genes. PA2G4 has not previously been recognized as related to PCOS in published GWAS studies. Two of the SNVs are predicted to have functional consequences (ERBB3 missense SNV, PA2G4 promoter SNV). PA2G4 interacts with the ERBB3 cytoplasmic domain containing the missense variant, suggesting a potential signaling pathway disruption that could lead to the PCOS ovarian phenotype. Single cell RNA sequencing of theca cells showed significantly less expression of PA2G4 after forskolin treatment in PCOS cells compared to normal cells (padj = 3.82E-30) and in cells heterozygous for the PA2G4 promoter SNV compared to those without the SNV (padj = 2.16E-11). This is consistent with a functional effect of the PA2G4 promoter SNV. No individual SNV was significantly associated with PCOS in an independent family cohort, but a haplotype with minor alleles of three SNVs was found preferentially in women with PCOS. These findings suggest a functional role for 12q13.2 variants in PCOS and implicate variants in ERBB3 and PA2G4 in the pathophysiology of PCOS.

High-dimensional genomic feature selection with the ordered stereotype logit model.

For many high-dimensional genomic and epigenomic datasets, the outcome of interest is ordinal. While these ordinal outcomes are often thought of as the observed cutpoints of some latent continuous variable, some ordinal outcomes are truly discrete and are comprised of the subjective combination of several factors. The nonlinear stereotype logistic model, which does not assume proportional odds, was developed for these 'assessed' ordinal variables. It has previously been extended to the frequentist high-dimensional feature selection setting, but the Bayesian framework provides some distinct advantages in terms of simultaneous uncertainty quantification and variable selection. Here, we review the stereotype model and Bayesian variable selection methods and demonstrate how to combine them to select genomic features associated with discrete ordinal outcomes. We compared the Bayesian and frequentist methods in terms of variable selection performance. We additionally applied the Bayesian stereotype method to an acute myeloid leukemia RNA-sequencing dataset to further demonstrate its variable selection abilities by identifying features associated with the European LeukemiaNet prognostic risk score.

Controlled variable selection in Weibull mixture cure models for high-dimensional data.

Medical breakthroughs in recent years have led to cures for many diseases. The mixture cure model (MCM) is a type of survival model that is often used when a cured fraction exists. Many have sought to identify genomic features associated with a time-to-event outcome which requires variable selection strategies for high-dimensional spaces. Unfortunately, currently few variable selection methods exist for MCMs especially when there are more predictors than samples. This study develops high-dimensional penalized Weibull MCMs, which allow for identification of prognostic factors associated with both cure status and/or survival. We demonstrated how such models may be estimated using two different iterative algorithms. The model-X knockoffs method was combined with these algorithms to control the false discovery rate (FDR) in variable selection. Through extensive simulation studies, our penalized MCMs have been shown to outperform alternative methods on multiple metrics and achieve high statistical power with FDR being controlled. In an acute myeloid leukemia (AML) application with gene expression data, our proposed approach identified 14 genes associated with potential cure and 12 genes with time-to-relapse, which may help inform treatment decisions for AML patients.

Pretransplant kidney transcriptome captures intrinsic donor organ quality and predicts 24-month outcomes.

With the development of novel prognostic tools derived from omics technologies, transplant medicine is entering the era of precision medicine. Currently, there are no established predictive biomarkers for posttransplant kidney function. A total of 270 deceased donor pretransplant kidney biopsies were collected and posttransplant function was prospectively monitored. This study first assessed the utility of pretransplant gene expression profiles in predicting 24-month outcomes in a training set (n = 174). Nearly 600 differentially expressed genes were associated with 24-month graft function. Grafts that progressed to low function at 24 months exhibited upregulated immune responses and downregulated metabolic processes at pretransplantation. Using penalized logistic regression modeling, a 55 gene model area under the receiver operating curve (AUROC) for 24-month graft function was 0.994. Gene expression for a subset of candidate genes was then measured in an independent set of pretransplant biopsies (n = 96) using quantitative polymerase chain reaction. The AUROC when using 13 genes with three donor characteristics (age, race, body mass index) was 0.821. Subsequently, a risk score was calculated using this combination for each patient in the validation cohort, demonstrating the translational feasibility of using gene markers as prognostic tools. These findings support the potential of pretransplant transcriptomic biomarkers as novel instruments for improving posttransplant outcome predictions and associated management.

ordinalbayes: Fitting Ordinal Bayesian Regression Models to High-Dimensional Data Using R.

The stage of cancer is a discrete ordinal response that indicates the aggressiveness of disease and is often used by physicians to determine the type and intensity of treatment to be administered. For example, the FIGO stage in cervical cancer is based on the size and depth of the tumor as well as the level of spread. It may be of clinical relevance to identify molecular features from high-throughput genomic assays that are associated with the stage of cervical cancer to elucidate pathways related to tumor aggressiveness, identify improved molecular features that may be useful for staging, and identify therapeutic targets. High-throughput RNA-Seq data and corresponding clinical data (including stage) for cervical cancer patients have been made available through The Cancer Genome Atlas Project (TCGA). We recently described penalized Bayesian ordinal response models that can be used for variable selection for over-parameterized datasets, such as the TCGA-CESC dataset. Herein, we describe our ordinalbayes R package, available from the Comprehensive R Archive Network (CRAN), which enhances the runjags R package by enabling users to easily fit cumulative logit models when the outcome is ordinal and the number of predictors exceeds the sample size, P > N, such as for TCGA and other high-throughput genomic data. We demonstrate the use of this package by applying it to the TCGA cervical cancer dataset. Our ordinalbayes package can be used to fit models to high-dimensional datasets, and it effectively performs variable selection.

Epigenetic Regulation of Interleukin-17-Related Genes and Their Potential Roles in Neutrophil Vascular Infiltration in Preeclampsia.

DNA methylation is an epigenetic mechanism controlling gene expression, and reduced methylation is associated with increased gene expression. We hypothesized that IL-17 cytokines are regulated by DNA methylation, are elevated in the circulation of preeclamptic women, and stimulate vascular neutrophil chemokine expression, which could account for vascular infiltration of neutrophils in preeclampsia. We found significantly reduced DNA methylation of IL17A, IL17E, and IL17F genes in omental arteries of preeclamptic women, significantly reduced methylation of IL2, which regulates IL-17-producing T-lymphocytes, and significantly reduced methylation of genes encoding neutrophil chemokines and TNFα receptors related to lymphocyte function. Maternal plasma levels of IL-17A were significantly elevated in the second trimester of preeclamptic pregnancy as compared to normal pregnancy. To test if methylation regulates IL-17 cytokines, a lymphocyte cell line (Jurkat) was cultured with a hypomethylating agent. Hypomethylation increased expression of IL17E (aka IL25), IL17F, and IL2. IL17A was not expressed by Jurkat cells. To test the potential role of IL-17 cytokines in vascular neutrophil infiltration associated with preeclampsia, human vascular smooth muscle cells were cultured with IL-17 cytokines. IL-17A, but not IL-17E or IL-17F, increased gene expression of neutrophil chemokines (IL-8, CXCL5, and CXCL6) that are increased in vascular smooth muscle of preeclamptic women. The monocyte chemokine, CCL-2, was not increased. TNFα also increased neutrophil chemokines. IL-17 cytokines are regulated by DNA methylation; IL-17A is elevated in preeclampsia and stimulates expression of neutrophil chemokines in vascular smooth muscle. IL-17A could be responsible for vascular infiltration of neutrophils in preeclampsia.

Patterns of Maternal Neutrophil Gene Expression at 30 Weeks of Gestation, but Not DNA Methylation, Distinguish Mild from Severe Preeclampsia.

Neutrophils are activated and extensively infiltrate blood vessels in preeclamptic women. To identify genes that contribute to neutrophil activation and infiltration, we analyzed the transcriptomes of circulating neutrophils from normal pregnant and preeclamptic women. Neutrophils were collected at 30 weeks' gestation and RNA and DNA were isolated for RNA sequencing and 5-hydroxy-methylcytosine (5-hmC) sequencing as an index of dynamic changes in neutrophil DNA methylation. Women with normal pregnancy who went on to develop mild preeclampsia at term had the most uniquely expressed genes (697) with 325 gene ontology pathways upregulated, many related to neutrophil activation and function. Women with severe preeclampsia who delivered prematurely had few pathways up- or downregulated. Cluster analysis revealed that gene expression in women with severe preeclampsia was an inverse mirror image of gene expression in normal pregnancy, while gene expression in women who developed mild preeclampsia was remarkably different from both. DNA methylation marks, key regulators of gene expression, are removed by the action of ten-eleven translocation (TET) enzymes, which oxidize 5-methylcytosines (5mCs), resulting in locus-specific reversal of DNA methylation. DNA sequencing for 5-hmC revealed no differences among the three groups. Genome-wide DNA methylation revealed extremely low levels in circulating neutrophils suggesting they are de-methylated. Collectively, these data demonstrate that neutrophil gene expression profiles can distinguish different preeclampsia phenotypes, and in the case of mild preeclampsia, alterations in gene expression occur well before clinical symptoms emerge. These findings serve as a foundation for further evaluation of neutrophil transcriptomes as biomarkers of preeclampsia phenotypes. Changes in DNA methylation in circulating neutrophils do not appear to mediate differential patterns of gene expression in either mild or severe preeclampsia.

Bayesian variable selection for high-dimensional data with an ordinal response: identifying genes associated with prognostic risk group in acute myeloid leukemia.

BACKGROUND: Acute myeloid leukemia (AML) is a heterogeneous cancer of the blood, though specific recurring cytogenetic abnormalities in AML are strongly associated with attaining complete response after induction chemotherapy, remission duration, and survival. Therefore recurring cytogenetic abnormalities have been used to segregate patients into favorable, intermediate, and adverse prognostic risk groups. However, it is unclear how expression of genes is associated with these prognostic risk groups. We postulate that expression of genes monotonically associated with these prognostic risk groups may yield important insights into leukemogenesis. Therefore, in this paper we propose penalized Bayesian ordinal response models to predict prognostic risk group using gene expression data. We consider a double exponential prior, a spike-and-slab normal prior, a spike-and-slab double exponential prior, and a regression-based approach with variable inclusion indicators for modeling our high-dimensional ordinal response, prognostic risk group, and identify genes through hypothesis tests using Bayes factor. RESULTS: Gene expression was ascertained using Affymetrix HG-U133Plus2.0 GeneChips for 97 favorable, 259 intermediate, and 97 adverse risk AML patients. When applying our penalized Bayesian ordinal response models, genes identified for model inclusion were consistent among the four different models. Additionally, the genes included in the models were biologically plausible, as most have been previously associated with either AML or other types of cancer. CONCLUSION: These findings demonstrate that our proposed penalized Bayesian ordinal response models are useful for performing variable selection for high-dimensional genomic data and have the potential to identify genes relevantly associated with an ordinal phenotype.

Race/ethnicity-associated blood DNA methylation differences between Japanese and European American women: an exploratory study.

BACKGROUND: Racial/ethnic disparities in health reflect a combination of genetic and environmental causes, and DNA methylation may be an important mediator. We compared in an exploratory manner the blood DNA methylome of Japanese Americans (JPA) versus European Americans (EUA). METHODS: Genome-wide buffy coat DNA methylation was profiled among healthy Multiethnic Cohort participant women who were Japanese (JPA; n = 30) or European (EUA; n = 28) Americans aged 60-65. Differentially methylated CpGs by race/ethnicity (DM-CpGs) were identified by linear regression (Bonferroni-corrected P < 0.1) and analyzed in relation to corresponding gene expression, a priori selected single nucleotide polymorphisms (SNPs), and blood biomarkers of inflammation and metabolism using Pearson or Spearman correlations (FDR < 0.1). RESULTS: We identified 174 DM-CpGs with the majority of hypermethylated in JPA compared to EUA (n = 133), often in promoter regions (n = 48). Half (51%) of the genes corresponding to the DM-CpGs were involved in liver function and liver disease, and the methylation in nine genes was significantly correlated with gene expression for DM-CpGs. A total of 156 DM-CpGs were associated with rs7489665 (SH2B1). Methylation of DM-CpGs was correlated with blood levels of the cytokine MIP1B (n = 146). We confirmed some of the DM-CpGs in the TCGA adjacent non-tumor liver tissue of Asians versus EUA. CONCLUSION: We found a number of differentially methylated CpGs in blood DNA between JPA and EUA women with a potential link to liver disease, specific SNPs, and systemic inflammation. These findings may support further research on the role of DNA methylation in mediating some of the higher risk of liver disease among JPA.

Trisomy 21-associated increases in chromosomal instability are unmasked by comparing isogenic trisomic/disomic leukocytes from people with mosaic Down syndrome.

Down syndrome, which results from a trisomic imbalance for chromosome 21, has been associated with 80+ phenotypic traits. However, the cellular changes that arise in somatic cells due to this aneuploid condition are not fully understood. The primary aim of this study was to determine if germline trisomy 21 is associated with an increase in spontaneous somatic cell chromosomal instability frequencies (SCINF). To achieve this aim, we quantified SCINF in people with mosaic Down syndrome using a cytokinesis-blocked micronucleus assay. By comparing values in their isogenic trisomic/disomic cells, we obtained a measure of differences in SCINF that are directly attributable to a trisomy 21 imbalance, since differential effects attributable to "background" genetic factors and environmental exposures could be eliminated. A cross-sectional assessment of 69 people with mosaic Down syndrome (ages 1 to 44; mean age of 12.84 years) showed a significantly higher frequency of micronuclei in their trisomic (0.37 ± 0.35 [mean ± standard deviation]) compared to disomic cells (0.18 ± 0.11)(P <0.0001). The daughter binucleates also showed significantly higher levels of abnormal patterns in the trisomic (1.68 ± 1.21) compared to disomic (0.35 ± 0.45) cells (P <0.0001). Moreover, a significant Age x Cell Type interaction was noted (P = 0.0113), indicating the relationship between age and SCINF differed between the trisomic and disomic cells. Similarly, a longitudinal assessment (mean time interval of 3.9 years; range of 2 to 6 years) of 18 participants showed a mean 1.63-fold increase in SCINF within individuals over time for their trisomic cells (P = 0.0186), compared to a 1.13-fold change in their disomic cells (P = 0.0464). In summary, these results showed a trisomy 21-associated, age-related increase in SCINF. They also underscore the strength of the isogenic mosaic Down syndrome model system for "unmasking" cellular changes arising from a trisomy 21 imbalance.

High-dimensional variable selection for ordinal outcomes with error control.

Many high-throughput genomic applications involve a large set of potential covariates and a response which is frequently measured on an ordinal scale, and it is crucial to identify which variables are truly associated with the response. Effectively controlling the false discovery rate (FDR) without sacrificing power has been a major challenge in variable selection research. This study reviews two existing variable selection frameworks, model-X knockoffs and a modified version of reference distribution variable selection (RDVS), both of which utilize artificial variables as benchmarks for decision making. Model-X knockoffs constructs a 'knockoff' variable for each covariate to mimic the covariance structure, while RDVS generates only one null variable and forms a reference distribution by performing multiple runs of model fitting. Herein, we describe how different importance measures for ordinal responses can be constructed that fit into these two selection frameworks, using either penalized regression or machine learning techniques. We compared these measures in terms of the FDR and power using simulated data. Moreover, we applied these two frameworks to high-throughput methylation data for identifying features associated with the progression from normal liver tissue to hepatocellular carcinoma to further compare and contrast their performances.

Bayesian penalized cumulative logit model for high-dimensional data with an ordinal response.

Many previous studies have identified associations between gene expression, measured using high-throughput genomic platforms, and quantitative or dichotomous traits. However, we note that health outcome and disease status measurements frequently appear on an ordinal scale, that is, the outcome is categorical but has inherent ordering. Identification of important genes may be useful for developing novel diagnostic and prognostic tools to predict or classify stage of disease. Gene expression data are usually high-dimensional, meaning that the number of genes is much larger than the sample size or number of patients. Herein we describe some existing frequentist methods for modeling an ordinal response in a high-dimensional predictor space. Following Tibshirani (1996), who described the LASSO estimate as the Bayesian posterior mode when the regression coefficients have independent Laplace priors, we propose a new approach for high-dimensional data with an ordinal response that is rooted in the Bayesian paradigm. We show that our proposed Bayesian approach outperforms existing frequentist methods through simulation studies. We then compare the performance of frequentist and Bayesian approaches using a study evaluating progression to hepatocellular carcinoma in hepatitis C infected patients.