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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Telomere lengths in women treated for breast cancer show associations with chemotherapy, pain symptoms, and cognitive domain measures: a longitudinal study.

BACKGROUND: Survival rates for breast cancer (BC) have improved, but quality of life post-diagnosis/treatment can be adversely affected, with survivors reporting a constellation of psychoneurological symptoms (PNS) including stress, anxiety, depression, pain, fatigue, sleep disturbance, and cognitive dysfunction. METHODS: To assess a potential relationship between telomere length (TL) and the development/persistence of PNS, we longitudinally studied 70 women (ages 23-71) with early stage BC (I-IIIA) at 5 time-points: prior to treatment (baseline), the mid-point of their chemotherapy cycle, 6 months, 1 year, and 2 years following the initiation of chemotherapy. Measures quantified included assessments of each of the PNS noted above and TL [using both a multiplex qPCR assay and a chromosome-specific fluorescence in situ hybridization (FISH) assay]. RESULTS: Variables associated with qPCR mean TLs were age (p = 0.004) and race (T/S ratios higher in Blacks than Whites; p = 0.019). Significant differences (mostly decreases) in chromosome-specific TLs were identified for 32 of the 46 chromosomal arms at the mid-chemo time-point (p = 0.004 to 0.049). Unexpectedly, the sequential administration of doxorubicin [Adriamycin], cyclophosphamide [Cytoxan], and docetaxel [Taxotere] (TAC regimen) was consistently associated with higher TLs, when compared to TLs in women receiving a docetaxel [Taxotere], Carboplatin [Paraplatin], and trastuzumab [Herceptin] [TCH] chemotherapy regimen [association was shown with both the qPCR and FISH assays (p = 0.036)]. Of the PNS, pain was significantly negatively associated with TL (higher pain; shorter telomeres) for a subset of chromosomal arms (5q, 8p, 13p, 20p, 22p, Xp, Xq) (p = 0.014-0.047). Chromosomal TLs were also associated with 7 of the 8 cognitive domains evaluated, with the strongest relationship being noted for chromosome 17 and the visual memory domain (shorter telomeres; lower scores). CONCLUSIONS: We showed that race and age were significantly associated with telomere length in women treated for early stage BC and that acquired telomere alterations differed based on the woman's treatment regimen. Our study also demonstrated that pain and cognitive domain measures were significantly related to telomere values in this study cohort. Expanding upon the knowledge gained from this longitudinal study could provide insight about the biological cascade of events that contribute to PNS related to BC and/or its treatment.

Mutations associated with a 17-gene leukemia stem cell score and the score's prognostic relevance in the context of the European LeukemiaNet classification of acute myeloid leukemia.

Leukemia stem cells (LSC) are more resistant to standard chemotherapy and their persistence during remission can cause relapse, which is still one of the major clinical challenges in the treatment of acute myeloid leukemia (AML). A better understanding of the mutational patterns and the prognostic impact of molecular markers associated with stemness could lead to better clinical management and improve patients' outcomes. We applied a previously described 17-gene expression score comprising genes differently expressed between LSC and leukemic bulk blasts, for 934 adult patients with de novo AML, and studied associations of the 17-gene LSC score with clinical data and mutation status of 81 genes recurrently mutated in cancer and leukemia. We found that patients with a high 17-gene score were older and had more mutations. The 17-gene score was found to have a prognostic impact in both younger (aged <60 years) and older (aged ≥60 years) patients with AML. We also analyzed the 17-gene LSC score in the context of the 2017 European LeukemiaNet genetic-risk classification and found that for younger patients the score refined the classification, and identified patients currently classified in the European LeukemiaNet Favorable-risk category who had a worse outcome.

Classical and Bayesian random-effects meta-analysis models with sample quality weights in gene expression studies.

BACKGROUND: Random-effects (RE) models are commonly applied to account for heterogeneity in effect sizes in gene expression meta-analysis. The degree of heterogeneity may differ due to inconsistencies in sample quality. High heterogeneity can arise in meta-analyses containing poor quality samples. We applied sample-quality weights to adjust the study heterogeneity in the DerSimonian and Laird (DSL) and two-step DSL (DSLR2) RE models and the Bayesian random-effects (BRE) models with unweighted and weighted data, Gibbs and Metropolis-Hasting (MH) sampling algorithms, weighted common effect, and weighted between-study variance. We evaluated the performance of the models through simulations and illustrated application of the methods using Alzheimer's gene expression datasets. RESULTS: Sample quality adjusting within study variance (wP6) models provided an appropriate reduction of differentially expressed (DE) genes compared to other weighted functions in classical RE models. The BRE model with a uniform(0,1) prior was appropriate for detecting DE genes as compared to the models with other prior distributions. The precision of DE gene detection in the heterogeneous data was increased with the DSLR2wP6 weighted model compared to the DSLwP6 weighted model. Among the BRE weighted models, the wP6weighted- and unweighted-data models and both Gibbs- and MH-based models performed similarly. The wP6 weighted common-effect model performed similarly to the unweighted model in the homogeneous data, but performed worse in the heterogeneous data. The wP6weighted data were appropriate for detecting DE genes with high precision, while the wP6weighted between-study variance models were appropriate for detecting DE genes with high overall accuracy. Without the weight, when the number of genes in microarray increased, the DSLR2 performed stably, while the overall accuracy of the BRE model was reduced. When applying the weighted models in the Alzheimer's gene expression data, the number of DE genes decreased in all metadata sets with the DSLR2wP6weighted and the wP6weighted between study variance models. Four hundred and forty-six DE genes identified by the wP6weighted between study variance model could be potentially down-regulated genes that may contribute to good classification of Alzheimer's samples. CONCLUSIONS: The application of sample quality weights can increase precision and accuracy of the classical RE and BRE models; however, the performance of the models varied depending on data features, levels of sample quality, and adjustment of parameter estimates.

Estimation of random effects and identifying heterogeneous genes in meta-analysis of gene expression studies.

Combining effect sizes from individual studies using random-effects meta-analysis models are commonly applied in high-dimensional gene expression data. However, unknown study heterogeneity can arise from inconsistencies in sample quality and experimental conditions. High heterogeneity of effect sizes can reduce statistical power of the models. In this study, we describe three hypothesis-testing frameworks for meta-analysis of microarray data, and review several existing meta-analytic techniques that have been used in the genomic setting. These include P-value-based methods, rank-based methods and effect-size-based methods. We then discuss limitations of some of these methods and describe random-effects-based methods in detail. We introduce two methods for estimating the inter-study variance in random-effects meta-analytic models and another method for identifying heterogeneous genes for gene expression data. We compared various methods with the standard and existing meta-analytic techniques in the genomic framework. We demonstrate our results through a series of simulations and application in Alzheimer's gene expression data.

A MEIG1/PACRG complex in the manchette is essential for building the sperm flagella.

A key event in the process of spermiogenesis is the formation of the flagella, which enables sperm to reach eggs for fertilization. Yeast two-hybrid studies revealed that meiosis-expressed gene 1 (MEIG1) and Parkin co-regulated gene (PACRG) interact, and that sperm-associated antigen 16, which encodes an axoneme central apparatus protein, is also a binding partner of MEIG1. In spermatocytes of wild-type mice, MEIG1 is expressed in the whole germ cell bodies, but the protein migrates to the manchette, a unique structure at the base of elongating spermatid that directs formation of the flagella. In the elongating spermatids of wild-type mice, PACRG colocalizes with α-tubulin, a marker for the manchette, whereas this localization was not changed in the few remaining elongating spermatids of Meig1-deficient mice. In addition, MEIG1 no longer localizes to the manchette in the remaining elongating spermatids of Pacrg-deficient mice, indicating that PACRG recruits MEIG1 to the manchette. PACRG is not stable in mammalian cells, but can be stabilized by MEIG1 or by inhibition of proteasome function. SPAG16L is present in the spermatocyte cytoplasm of wild-type mice, and in the manchette of elongating spermatids, but in the Meig1 or Pacrg-deficient mice, SPAG16L no longer localizes to the manchette. By contrast, MEIG1 and PACRG are still present in the manchette of Spag16L-deficient mice, indicating that SPAG16L is a downstream partner of these two proteins. Together, our studies demonstrate that MEIG1/PACRG forms a complex in the manchette and that this complex is necessary to transport cargos, such as SPAG16L, to build the sperm flagella.

Papillary lesions of the breast: To excise or observe?

Papillary lesions of the breast range from benign to atypical to malignant. Although papillomas without frank cancer are benign, their management remains controversial. When a core needle biopsy of a lesion yields a diagnosis of intraductal papilloma with atypia, excision is generally recommended to rule out a concurrent malignant neoplasm. For intraductal papillomas without atypia, however, recommendations for excision versus observation are variable. The aims of this study are to evaluate the rate of concurrent malignancies for intraductal papilloma diagnosed on core needle biopsy and to assess the long-term risk of developing cancer after the diagnosis of a papillary lesion. This single institution retrospective study analyzed 259 patients that were diagnosed with intraductal papilloma (IDP) by core needle biopsy from 1995 to 2010. Patients were grouped by initial diagnosis into three groups (papilloma without atypia, papilloma with atypia, and papilloma with atypical duct hyperplasia or atypical lobular hyperplasia (ADH/ALH) and followed up for long-term outcomes. After a core needle biopsy showing IDP with atypia or IDP + ADH/ALH, surgical excision yielded a diagnosis of concomitant invasive or ductal in situ cancer in greater that 30% of cases. For intraductal papilloma without atypia, the likelihood of cancer was much lower. Moreover, even with excision, the finding of intraductal papilloma with atypia carries a significant risk of developing cancer long-term, and such patients should be followed carefully and perhaps should be considered for chemoprevention.

Depletion of the chromatin remodeler CHD4 sensitizes AML blasts to genotoxic agents and reduces tumor formation.

Chromodomain helicase DNA-binding protein 4 (CHD4) is an ATPase that alters the phasing of nucleosomes on DNA and has recently been implicated in DNA double-stranded break (DSB) repair. Here, we show that depletion of CHD4 in acute myeloid leukemia (AML) blasts induces a global relaxation of chromatin that renders cells more susceptible to DSB formation, while concurrently impeding their repair. Furthermore, CHD4 depletion renders AML blasts more sensitive both in vitro and in vivo to genotoxic agents used in clinical therapy: daunorubicin (DNR) and cytarabine (ara-C). Sensitization to DNR and ara-C is mediated in part by activation of the ataxia-telangiectasia mutated pathway, which is preliminarily activated by a Tip60-dependent mechanism in response to chromatin relaxation and further activated by genotoxic agent-induced DSBs. This sensitization preferentially affects AML cells, as CHD4 depletion in normal CD34(+) hematopoietic progenitors does not increase their susceptibility to DNR or ara-C. Unexpectedly, we found that CHD4 is necessary for maintaining the tumor-forming behavior of AML cells, as CHD4 depletion severely restricted the ability of AML cells to form xenografts in mice and colonies in soft agar. Taken together, these results provide evidence for CHD4 as a novel therapeutic target whose inhibition has the potential to enhance the effectiveness of genotoxic agents used in AML therapy.

The Alternative Splicing of Cytoplasmic Polyadenylation Element Binding Protein 2 Drives Anoikis Resistance and the Metastasis of Triple Negative Breast Cancer.

Triple negative breast cancer (TNBC) represents an anomalous subset of breast cancer with a greatly reduced (30%) 5-year survival rate. The enhanced mortality and morbidity of TNBC arises from the high metastatic rate, which requires the acquisition of AnR, a process whereby anchorage-dependent cells become resistant to cell death induced by detachment. In this study TNBC cell lines were selected for AnR, and these cell lines demonstrated dramatic enhancement in the formation of lung metastases as compared with parental cells. Genetic analysis of the AnR subclones versus parental cells via next generation sequencing and analysis of global alternative RNA splicing identified that the mRNA splicing of cytoplasmic polyadenylation element binding 2 (CPEB2), a translational regulator, was altered in AnR TNBC cells. Specifically, increased inclusion of exon 4 into the mature mRNA to produce the CPEB2B isoform was observed in AnR cell lines. Molecular manipulations of CPEB2 splice variants demonstrated a key role for this RNA splicing event in the resistance of cells to anoikis. Specifically, down-regulation of the CPEB2B isoform using siRNA re-sensitized the AnR cell lines to detachment-induced cell death. The ectopic expression of CPEB2B in parental TNBC cell lines induced AnR and dramatically increased metastatic potential. Importantly, alterations in the alternative splicing of CPEB2 were also observed in human TNBC and additional subtypes of human breast cancer tumors linked to a high metastatic rate. Our findings demonstrate that the regulation of CPEB2 mRNA splicing is a key mechanism in AnR and a driving force in TNBC metastasis.

Regularization method for predicting an ordinal response using longitudinal high-dimensional genomic data.

Abstract An ordinal scale is commonly used to measure health status and disease related outcomes in hospital settings as well as in translational medical research. In addition, repeated measurements are common in clinical practice for tracking and monitoring the progression of complex diseases. Classical methodology based on statistical inference, in particular, ordinal modeling has contributed to the analysis of data in which the response categories are ordered and the number of covariates (p) remains smaller than the sample size (n). With the emergence of genomic technologies being increasingly applied for more accurate diagnosis and prognosis, high-dimensional data where the number of covariates (p) is much larger than the number of samples (n), are generated. To meet the emerging needs, we introduce our proposed model which is a two-stage algorithm: Extend the generalized monotone incremental forward stagewise (GMIFS) method to the cumulative logit ordinal model; and combine the GMIFS procedure with the classical mixed-effects model for classifying disease status in disease progression along with time. We demonstrate the efficiency and accuracy of the proposed models in classification using a time-course microarray dataset collected from the Inflammation and the Host Response to Injury study.