PTMViz: a tool for analyzing and visualizing histone post translational modification data.

BACKGROUND: Histone post-translational modifications (PTMs) play an important role in our system by regulating the structure of chromatin and therefore contribute to the regulation of gene and protein expression. Irregularities in histone PTMs can lead to a variety of different diseases including various forms of cancer. Histone modifications are analyzed using high resolution mass spectrometry, which generate large amounts of data that requires sophisticated bioinformatics tools for analysis and visualization. PTMViz is designed for downstream differential abundance analysis and visualization of both protein and/or histone modifications. RESULTS: PTMViz provides users with data tables and visualization plots of significantly differentiated proteins and histone PTMs between two sample groups. All the data is packaged into interactive data tables and graphs using the Shiny platform to help the user explore the results in a fast and efficient manner to assess if changes in the system are due to protein abundance changes or epigenetic changes. In the example data provided, we identified several proteins differentially regulated in the dopaminergic pathway between mice treated with methamphetamine compared to a saline control. We also identified histone post-translational modifications including histone H3K9me, H3K27me3, H4K16ac, and that were regulated due to drug exposure. CONCLUSIONS: Histone modifications play an integral role in the regulation of gene expression. PTMViz provides an interactive platform for analyzing proteins and histone post-translational modifications from mass spectrometry data in order to quickly identify differentially expressed proteins and PTMs.

Pre-diagnosis neutrophil-to-lymphocyte ratio and mortality in individuals who develop lung cancer.

PURPOSE: The neutrophil-to-lymphocyte ratio (NLR) is a marker of systemic inflammation that has been reported to be associated with survival after chronic disease diagnoses, including lung cancer. We hypothesized that the inflammatory profile reflected by pre-diagnosis NLR, rather than the well-studied pre-treatment NLR at diagnosis, may be associated with increased mortality after lung cancer is diagnosed in high-risk heavy smokers. METHODS: We examined associations between pre-diagnosis methylation-derived NLR (mdNLR) and lung cancer-specific and all-cause mortality in 279 non-small lung cancer (NSCLC) and 81 small cell lung cancer (SCLC) cases from the ß-Carotene and Retinol Efficacy Trial (CARET). Cox proportional hazards models were adjusted for age, sex, smoking status, pack years, and time between blood draw and diagnosis, and stratified by stage of disease. Models were run separately by histotype. RESULTS: Among SCLC cases, those with pre-diagnosis mdNLR in the highest quartile had 2.5-fold increased mortality compared to those in the lowest quartile. For each unit increase in pre-diagnosis mdNLR, we observed 22-23% increased mortality (SCLC-specific hazard ratio [HR] = 1.23, 95% confidence interval [CI]: 1.02, 1.48; all-cause HR = 1.22, 95% CI 1.01, 1.46). SCLC associations were strongest for current smokers at blood draw (Interaction Ps = 0.03). Increasing mdNLR was not associated with mortality among NSCLC overall, nor within adenocarcinoma (N = 148) or squamous cell carcinoma (N = 115) case groups. CONCLUSION: Our findings suggest that increased mdNLR, representing a systemic inflammatory profile on average 4.5 years before a SCLC diagnosis, may be associated with mortality in heavy smokers who go on to develop SCLC but not NSCLC.

O-GlcNAc homeostasis contributes to cell fate decisions during hematopoiesis.

The addition of a single ß-d-GlcNAc sugar (O-GlcNAc) by O-GlcNAc-transferase (OGT) and O-GlcNAc removal by O-GlcNAcase (OGA) maintain homeostatic O-GlcNAc levels on cellular proteins. Changes in protein O-GlcNAcylation regulate cellular differentiation and cell fate decisions, but how these changes affect erythropoiesis, an essential process in blood cell formation, remains unclear. Here, we investigated the role of O-GlcNAcylation in erythropoiesis by using G1E-ER4 cells, which carry the erythroid-specific transcription factor GATA-binding protein 1 (GATA-1) fused to the estrogen receptor (GATA-1-ER) and therefore undergo erythropoiesis after ß-estradiol (E2) addition. We observed that during G1E-ER4 differentiation, overall O-GlcNAc levels decrease, and physical interactions of GATA-1 with both OGT and OGA increase. RNA-Seq-based transcriptome analysis of G1E-ER4 cells differentiated in the presence of the OGA inhibitor Thiamet-G (TMG) revealed changes in expression of 433 GATA-1 target genes. ChIP results indicated that the TMG treatment decreases the occupancy of GATA-1, OGT, and OGA at the GATA-binding site of the lysosomal protein transmembrane 5 (Laptm5) gene promoter. TMG also reduced the expression of genes involved in differentiation of NB4 and HL60 human myeloid leukemia cells, suggesting that O-GlcNAcylation is involved in the regulation of hematopoietic differentiation. Sustained treatment of G1E-ER4 cells with TMG before differentiation reduced hemoglobin-positive cells and increased stem/progenitor cell surface markers. Our results show that alterations in O-GlcNAcylation disrupt transcriptional programs controlling erythropoietic lineage commitment, suggesting a role for O-GlcNAcylation in regulating hematopoietic cell fate.

AHRR methylation in heavy smokers: associations with smoking, lung cancer risk, and lung cancer mortality.

BACKGROUND: A low level of methylation at cg05575921 in the aryl-hydrocarbon receptor repressor (AHRR) gene is robustly associated with smoking, and some studies have observed associations between cg05575921 methylation and increased lung cancer risk and mortality. To prospectively examine whether decreased methylation at cg05575921 may identify high risk subpopulations for lung cancer screening among heavy smokers, and mortality in cases, we evaluated associations between cg05575921 methylation and lung cancer risk and mortality, by histotype, in heavy smokers. METHODS: The ß-Carotene and Retinol Efficacy Trial (CARET) included enrollees ages 45-69 with ≥ 20 pack-year smoking histories and/or occupational asbestos exposure. A subset of CARET participants had cg05575921 methylation available from HumanMethylationEPIC assays of blood collected on average 4.3 years prior to lung cancer diagnosis in cases. Cg05575921 methylation ß-values were treated continuously for a 10% methylation decrease and as quintiles, where quintile 1 (Q1, referent) represents high methylation and Q5, low methylation. We used conditional logistic regression models to examine lung cancer risk overall and by histotype in a nested case-control study including 316 lung cancer cases (diagnosed through 2005) and 316 lung cancer-free controls matched on age (±5 years), sex, race/ethnicity, enrollment year, current/former smoking, asbestos exposure, and follow-up time. Mortality analyses included 372 lung cancer cases diagnosed between 1985 and 2013 with available methylation data. We used Cox proportional hazards models to examine mortality overall and by histotype. RESULTS: Decreased cg05575921 methylation was strongly associated with smoking, even in our population of heavy smokers. We did not observe associations between decreased pre-diagnosis cg05575921 methylation and increased lung cancer risk, overall or by histotype. We observed linear increasing trends for lung cancer-specific mortality across decreasing cg05575921 methylation quintiles for adenocarcinoma and small cell carcinoma (P-trends = 0.01 and 0.04, respectively). CONCLUSIONS: In our study of heavy smokers, decreased cg05575921 methylation was strongly associated with smoking but not increased lung cancer risk. The observed association between cg05575921 methylation and increased mortality in adenocarcinoma and small cell histotypes requires further examination. Our results do not support using decreased cg05575921 methylation as a biomarker for lung cancer screening risk stratification.

pwrEWAS: a user-friendly tool for comprehensive power estimation for epigenome wide association studies (EWAS).

BACKGROUND: When designing an epigenome-wide association study (EWAS) to investigate the relationship between DNA methylation (DNAm) and some exposure(s) or phenotype(s), it is critically important to assess the sample size needed to detect a hypothesized difference with adequate statistical power. However, the complex and nuanced nature of DNAm data makes direct assessment of statistical power challenging. To circumvent these challenges and to address the outstanding need for a user-friendly interface for EWAS power evaluation, we have developed pwrEWAS. RESULTS: The current implementation of pwrEWAS accommodates power estimation for two-group comparisons of DNAm (e.g. case vs control, exposed vs non-exposed, etc.), where methylation assessment is carried out using the Illumina Human Methylation BeadChip technology. Power is calculated using a semi-parametric simulation-based approach in which DNAm data is randomly generated from beta-distributions using CpG-specific means and variances estimated from one of several different existing DNAm data sets, chosen to cover the most common tissue-types used in EWAS. In addition to specifying the tissue type to be used for DNAm profiling, users are required to specify the sample size, number of differentially methylated CpGs, effect size(s) (Δß), target false discovery rate (FDR) and the number of simulated data sets, and have the option of selecting from several different statistical methods to perform differential methylation analyses. pwrEWAS reports the marginal power, marginal type I error rate, marginal FDR, and false discovery cost (FDC). Here, we demonstrate how pwrEWAS can be applied in practice using a hypothetical EWAS. In addition, we report its computational efficiency across a variety of user settings. CONCLUSION: Both under- and overpowered studies unnecessarily deplete resources and even risk failure of a study. With pwrEWAS, we provide a user-friendly tool to help researchers circumvent these risks and to assist in the design and planning of EWAS. AVAILABILITY: The web interface is written in the R statistical programming language using Shiny (RStudio Inc., 2016) and is available at https://biostats-shinyr.kumc.edu/pwrEWAS/ . The R package for pwrEWAS is publicly available at GitHub ( https://github.com/stefangraw/pwrEWAS ).

digit-a tool for detection and identification of genomic interchromosomal translocations.

Structural variations (SVs) in genomic DNA can have profound effects on the evolution of living organisms, on phenotypic variations and on disease processes. A critical step in discovering the full extent of structural variations is the development of tools to characterize these variations accurately in next generation sequencing data. Toward this goal, we developed a software pipeline named digit that implements a novel measure of mapping ambiguity to discover interchromosomal SVs from mate-pair and pair-end sequencing data. The workflow robustly handles the high numbers of artifacts present in mate-pair sequencing and reduces the false positive rate while maintaining sensitivity. In the simulated data set, our workflow recovered 96% of simulated SVs. It generates a self-updating library of common translocations and allows for the investigation of patient- or group-specific events, making it suitable for discovering and cataloging chromosomal translocations associated with specific groups, traits, diseases or population structures.

Sustained O-GlcNAcylation reprograms mitochondrial function to regulate energy metabolism.

Dysfunctional mitochondria and generation of reactive oxygen species (ROS) promote chronic diseases, which have spurred interest in the molecular mechanisms underlying these conditions. Previously, we have demonstrated that disruption of post-translational modification of proteins with ß-linked N-acetylglucosamine (O-GlcNAcylation) via overexpression of the O-GlcNAc-regulating enzymes O-GlcNAc transferase (OGT) or O-GlcNAcase (OGA) impairs mitochondrial function. Here, we report that sustained alterations in O-GlcNAcylation either by pharmacological or genetic manipulation also alter metabolic function. Sustained O-GlcNAc elevation in SH-SY5Y neuroblastoma cells increased OGA expression and reduced cellular respiration and ROS generation. Cells with elevated O-GlcNAc levels had elongated mitochondria and increased mitochondrial membrane potential, and RNA-sequencing analysis indicated transcriptome reprogramming and down-regulation of the NRF2-mediated antioxidant response. Sustained O-GlcNAcylation in mouse brain and liver validated the metabolic phenotypes observed in the cells, and OGT knockdown in the liver elevated ROS levels, impaired respiration, and increased the NRF2 antioxidant response. Moreover, elevated O-GlcNAc levels promoted weight loss and lowered respiration in mice and skewed the mice toward carbohydrate-dependent metabolism as determined by indirect calorimetry. In summary, sustained elevation in O-GlcNAcylation coupled with increased OGA expression reprograms energy metabolism, a finding that has potential implications for the etiology, development, and management of metabolic diseases.

Characterization of methionine dependence in melanoma cells.

Dietary methionine restriction is associated with a reduction in tumor growth in preclinical studies and an increase in lifespan in animal models. The mechanism by which methionine restriction inhibits tumor growth while sparing normal cells is incompletely understood. We do know that normal cells can utilize methionine or homocysteine interchangeably (methionine independence) while most cancer cells are strictly dependent on methionine availability. Here, we compared a typical methionine dependent and a rare methionine independent melanoma cell line. We show that replacing methionine, a methyl donor, with its precursor homocysteine generally induced hypomethylation in gene promoters. This decrease was similar in methionine dependent and methionine independent cells. There was only a low level of pathway enrichment, suggesting that the hypomethylation is generalized rather than gene specific. Whole proteome and transcriptome were also analyzed. This analysis revealed that contrarily to the effect on methylation, the replacement of methionine with homocysteine had a much greater effect on the transcriptome and proteome of methionine dependent cells than methionine independent cells. Interestingly, methionine adenosyltransferase 2A (MAT2A), responsible for the synthesis of S-adenosylmethionine from methionine, was equally strongly upregulated in both cell lines. This suggests that the absence of methionine is equally detected but triggers different outcomes in methionine dependent versus independent cells. Our analysis reveals the importance of cell cycle control, DNA damage repair, translation, nutrient sensing, oxidative stress and immune functions in the cellular response to methionine stress in melanoma.

Characterization of methionine dependence in melanoma cells.

Dietary methionine restriction is associated with a reduction in tumor growth in preclinical studies and an increase in lifespan in animal models. The mechanism by which methionine restriction inhibits tumor growth while sparing normal cells is incompletely understood. We do know that normal cells can utilize methionine or homocysteine interchangeably (methionine independence) while most cancer cells are strictly dependent on methionine availability. Here, we compared a typical methionine dependent and a rare methionine independent melanoma cell line. We show that replacing methionine, a methyl donor, with its precursor homocysteine generally induced hypomethylation in gene promoters. This decrease was similar in methionine dependent and methionine independent cells. There was only a low level of pathway enrichment, suggesting that the hypomethylation is generalized rather than gene specific. Whole proteome and transcriptome were also analyzed. This analysis revealed that contrarily to the effect on methylation, the replacement of methionine with homocysteine had a much greater effect on the transcriptome and proteome of methionine dependent cells than methionine independent cells. Interestingly, methionine adenosyltransferase 2A (MAT2A), responsible for the synthesis of s-adenosylmethionine from methionine, was equally strongly upregulated in both cell lines. This suggests that the absence of methionine is equally detected but triggers different outcomes in methionine dependent versus independent cells. Our analysis reveals the importance of cell cycle control, DNA damage repair, translation, nutrient sensing, oxidative stress and immune functions in the cellular response to methionine stress in melanoma.

Effect of excess weight and insulin resistance on DNA methylation in prepubertal children.

Epigenetic mechanisms, such as DNA methylation, regulate gene expression and play a role in the development of insulin resistance. This study evaluates how the BMI z-score (BMIz) and the homeostatic model assessment of insulin resistance (HOMA-IR), alone or in combination, relate to clinical outcomes and DNA methylation patterns in prepubertal children. DNA methylation in peripheral blood mononuclear cells (PBMCs) and clinical outcomes were measured in a cohort of 41 prepubertal children. Children with higher HOMA-IR had higher blood pressure and plasma lactate levels while children with higher BMIz had higher triglycerides levels. Moreover, the DNA methylation analysis demonstrated that a 1 unit increase in the BMIz was associated with a 0.41 (95% CI: 0.29, 0.53) increase in methylation of a CpG near the PPP6R2 gene. This gene is important in the regulation of NF-kB expression. However, there was no strong evidence that the BMIz and the HOMA-IR were synergistically related to any clinical or DNA methylation outcomes. In summary, the results suggest that obesity and insulin resistance may impact metabolic health both independently in prepubertal children. In addition, obesity also has an impact on the DNA methylation of the PPP6R2 gene. This may be a novel underlying starting point for the systemic inflammation associated with obesity and insulin resistance, in this population.

Multi-omics data integration considerations and study design for biological systems and disease.

With the advancement of next-generation sequencing and mass spectrometry, there is a growing need for the ability to merge biological features in order to study a system as a whole. Features such as the transcriptome, methylome, proteome, histone post-translational modifications and the microbiome all influence the host response to various diseases and cancers. Each of these platforms have technological limitations due to sample preparation steps, amount of material needed for sequencing, and sequencing depth requirements. These features provide a snapshot of one level of regulation in a system. The obvious next step is to integrate this information and learn how genes, proteins, and/or epigenetic factors influence the phenotype of a disease in context of the system. In recent years, there has been a push for the development of data integration methods. Each method specifically integrates a subset of omics data using approaches such as conceptual integration, statistical integration, model-based integration, networks, and pathway data integration. In this review, we discuss considerations of the study design for each data feature, the limitations in gene and protein abundance and their rate of expression, the current data integration methods, and microbiome influences on gene and protein expression. The considerations discussed in this review should be regarded when developing new algorithms for integrating multi-omics data.

Multi-omics data integration reveals correlated regulatory features of triple negative breast cancer.

Triple negative breast cancer (TNBC) is an aggressive type of breast cancer with very little treatment options. TNBC is very heterogeneous with large alterations in the genomic, transcriptomic, and proteomic landscapes leading to various subtypes with differing responses to therapeutic treatments. We applied a multi-omics data integration method to evaluate the correlation of important regulatory features in TNBC BRCA1 wild-type MDA-MB-231 and TNBC BRCA1 5382insC mutated HCC1937 cells compared with non-tumorigenic epithelial breast MCF10A cells. The data includes DNA methylation, RNAseq, protein, phosphoproteomics, and histone post-translational modification. Data integration methods identified regulatory features from each omics method that had greater than 80% positive correlation within each TNBC subtype. Key regulatory features at each omics level were identified distinguishing the three cell lines and were involved in important cancer related pathways such as TGFß signaling, PI3K/AKT/mTOR, and Wnt/beta-catenin signaling. We observed overexpression of PTEN, which antagonizes the PI3K/AKT/mTOR pathway, and MYC, which downregulates the same pathway in the HCC1937 cells relative to the MDA-MB-231 cells. The PI3K/AKT/mTOR and Wnt/beta-catenin pathways are both downregulated in HCC1937 cells relative to MDA-MB-231 cells, which likely explains the divergent sensitivities of these cell lines to inhibitors of downstream signaling pathways. The DNA methylation and RNAseq data is freely available via GEO GSE171958 and the proteomics data is available via the ProteomeXchange PXD025238.

NEOage clocks - epigenetic clocks to estimate post-menstrual and postnatal age in preterm infants.

Epigenetic clocks based on DNA methylation (DNAm) can accurately predict chronological age and are thought to capture biological aging. A variety of epigenetic clocks have been developed for different tissue types and age ranges, but none have focused on postnatal age prediction for preterm infants. Epigenetic estimators of biological age might be especially informative in epidemiologic studies of neonates since DNAm is highly dynamic during the neonatal period and this is a key developmental window. Additionally, markers of biological aging could be particularly important for those born preterm since they are at heightened risk of developmental impairments. We aimed to fill this gap by developing epigenetic clocks for neonatal aging in preterm infants. As part of the Neonatal Neurobehavior and Outcomes in Very Preterm Infants (NOVI) study, buccal cells were collected at NICU discharge to profile DNAm levels in 542 very preterm infants. We applied elastic net regression to identify four epigenetic clocks (NEOage Clocks) predictive of post-menstrual and postnatal age, compatible with the Illumina EPIC and 450K arrays. We observed high correlations between predicted and reported ages (0.93 - 0.94) with root mean squared errors (1.28 - 1.63 weeks). Epigenetic estimators of neonatal aging in preterm infants can be useful tools to evaluate biological maturity and associations with neonatal and long-term morbidities.

Prenatal risk factors and neonatal DNA methylation in very preterm infants.

BACKGROUND: Prenatal risk factors are related to poor health and developmental outcomes for infants, potentially via epigenetic mechanisms. We tested associations between person-centered prenatal risk profiles, cumulative prenatal risk models, and epigenome-wide DNA methylation (DNAm) in very preterm neonates. METHODS: We studied 542 infants from a multi-center study of infants born < 30 weeks postmenstrual age. We assessed 24 prenatal risk factors via maternal report and medical record review. Latent class analysis was used to define prenatal risk profiles. DNAm was quantified from neonatal buccal cells using the Illumina MethylationEPIC Beadarray. RESULTS: We identified three latent profiles of women: a group with few risk factors (61%) and groups with elevated physical (26%) and psychological (13%) risk factors. Neonates born to women in higher risk subgroups had differential DNAm at 2 CpG sites. Higher cumulative prenatal risk was associated with methylation at 15 CpG sites, 12 of which were located in genes previously linked to physical and mental health and neurodevelopment. CONCLUSION: We observed associations between prenatal risk factors and DNAm in very preterm infants using both person-centered and cumulative risk approaches. Epigenetics offers a potential biological indicator of prenatal risk exposure.

proteiNorm - A User-Friendly Tool for Normalization and Analysis of TMT and Label-Free Protein Quantification.

The technological advances in mass spectrometry allow us to collect more comprehensive data with higher quality and increasing speed. With the rapidly increasing amount of data generated, the need for streamlining analyses becomes more apparent. Proteomics data is known to be often affected by systemic bias from unknown sources, and failing to adequately normalize the data can lead to erroneous conclusions. To allow researchers to easily evaluate and compare different normalization methods via a user-friendly interface, we have developed "proteiNorm". The current implementation of proteiNorm accommodates preliminary filters on peptide and sample levels followed by an evaluation of several popular normalization methods and visualization of the missing value. The user then selects an adequate normalization method and one of the several imputation methods used for the subsequent comparison of different differential expression methods and estimation of statistical power. The application of proteiNorm and interpretation of its results are demonstrated on two tandem mass tag multiplex (TMT6plex and TMT10plex) and one label-free spike-in mass spectrometry example data set. The three data sets reveal how the normalization methods perform differently on different experimental designs and the need for evaluation of normalization methods for each mass spectrometry experiment. With proteiNorm, we provide a user-friendly tool to identify an adequate normalization method and to select an appropriate method for differential expression analysis.

Optimizing Sample Size Allocation and Power in a Bayesian Two-Stage Drop-The-Losers Design.

When a researcher desires to test several treatment arms against a control arm, a two-stage adaptive design can be more efficient than a single-stage design where patients are equally allocated to all treatment arms and the control. We see this type of approach in clinical trials as a seamless Phase II - Phase III design. These designs require more statistical support and are less straightforward to plan and analyze than a standard single-stage design. To diminish the barriers associated with a Bayesian two-stage drop-the-losers design, we built a user-friendly point-and-click graphical user interface with R Shiny to aid researchers in planning such designs by allowing them to easily obtain trial operating characteristics, estimate statistical power and sample size, and optimize patient allocation in each stage to maximize power. We assume that endpoints are distributed normally with unknown but common variance between treatments. We recommend this software as an easy way to engage statisticians and researchers in two-stage designs as well as to actively investigate the power of two-stage designs relative to more traditional approaches. The software is freely available at https://github.com/stefangraw/Allocation-Power-Optimizer.

Methylation-derived Neutrophil-to-Lymphocyte Ratio and Lung Cancer Risk in Heavy Smokers.

The neutrophil-to-lymphocyte ratio (NLR) is a biomarker that indicates systemic inflammation and can be estimated using array-based DNA methylation data as methylation-derived NLR (mdNLR). We assessed the relationship between prediagnosis mdNLR and lung cancer risk in a nested case-control study in the ß-Carotene and Retinol Efficacy Trial (CARET) of individuals at high risk for lung cancer due to heavy smoking or substantial occupational asbestos exposure. We matched 319 incident lung cancer cases to controls based on age at blood draw, smoking, sex, race, asbestos, enrollment year, and time at risk. We computed mdNLR using the ratio of predicted granulocyte and lymphocyte proportions derived from DNA methylation signatures in whole blood collected prior to diagnosis (median 4.4 years in cases). Mean mdNLR was higher in cases than controls (2.06 vs. 1.86, P = 0.03). Conditional logistic regression models adjusted for potential confounders revealed a 21% increased risk of lung cancer per unit increase in mdNLR [OR 1.21; 95% confidence interval (CI) 1.01-1.45]. A 30% increased risk of non-small cell lung cancer (NSCLC) was observed for each unit increase in mdNLR (n = 240 pairs; OR 1.30, 95% CI, 1.03-1.63), and there was no statistically significant association between mdNLR and small-cell lung cancer risk. The mdNLR-NSCLC association was most pronounced in those with asbestos exposure (n = 42 male pairs; OR 3.39; 95% CI, 1.32-8.67). A better understanding of the role of mdNLR in lung cancer etiology may improve prevention and detection of lung cancer. Cancer Prev Res; 11(11); 727-34. ©2018 AACR.

Licofelone Enhances the Efficacy of Paclitaxel in Ovarian Cancer by Reversing Drug Resistance and Tumor Stem-like Properties.

Drug development for first-line treatment of epithelial ovarian cancer (EOC) has been stagnant for almost three decades. Traditional cell culture methods for primary drug screening do not always accurately reflect clinical disease. To overcome this barrier, we grew a panel of EOC cell lines in three-dimensional (3D) cell cultures to form multicellular tumor spheroids (MCTS). We characterized these MCTS for molecular and cellular features of EOC and performed a comparative screen with cells grown using two-dimensional (2D) cell culture to identify previously unappreciated anticancer drugs. MCTS exhibited greater resistance to chemotherapeutic agents, showed signs of senescence and hypoxia, and expressed a number of stem cell-associated transcripts including ALDH1A and CD133, also known as PROM1 Using a library of clinically repurposed drugs, we identified candidates with preferential activity in MCTS over 2D cultured cells. One of the lead compounds, the dual COX/LOX inhibitor licofelone, reversed the stem-like properties of ovarian MCTS. Licofelone also synergized with paclitaxel in ovarian MCTS models and in a patient-derived tumor xenograft model. Importantly, the combination of licofelone with paclitaxel prolonged the median survival of mice (>141 days) relative to paclitaxel (115 days), licofelone (37 days), or vehicle (30 days). Increased efficacy was confirmed by Mantel-Haenszel HR compared with vehicle (HR = 0.037) and paclitaxel (HR = 0.017). These results identify for the first time an unappreciated, anti-inflammatory drug that can reverse chemotherapeutic resistance in ovarian cancer, highlighting the need to clinically evaluate licofelone in combination with first-line chemotherapy in primary and chemotherapy-refractory EOC.Significance: This study highlights the use of an in vitro spheroid 3D drug screening model to identify new therapeutic approaches to reverse chemotherapy resistance in ovarian cancer. Cancer Res; 78(15); 4370-85. ©2018 AACR.

Mitochondrial Genomic Backgrounds Affect Nuclear DNA Methylation and Gene Expression.

Mitochondrial DNA (mtDNA) mutations and polymorphisms contribute to many complex diseases, including cancer. Using a unique mouse model that contains nDNA from one mouse strain and homoplasmic mitochondrial haplotypes from different mouse strain(s)-designated Mitochondrial Nuclear Exchange (MNX)-we showed that mtDNA could alter mammary tumor metastasis. Because retrograde and anterograde communication exists between the nuclear and mitochondrial genomes, we hypothesized that there are differential mtDNA-driven changes in nuclear (n)DNA expression and DNA methylation. Genome-wide nDNA methylation and gene expression were measured in harvested brain tissue from paired wild-type and MNX mice. Selective differential DNA methylation and gene expression were observed between strains having identical nDNA, but different mtDNA. These observations provide insights into how mtDNA could be altering epigenetic regulation and thereby contribute to the pathogenesis of metastasis. Cancer Res; 77(22); 6202-14. ©2017 AACR.

An ensemble-based Cox proportional hazards regression framework for predicting survival in metastatic castration-resistant prostate cancer (mCRPC) patients.

From March through August 2015, nearly 60 teams from around the world participated in the Prostate Cancer Dream Challenge (PCDC). Participating teams were faced with the task of developing prediction models for patient survival and treatment discontinuation using baseline clinical variables collected on metastatic castrate-resistant prostate cancer (mCRPC) patients in the comparator arm of four phase III clinical trials. In total, over 2,000 mCRPC patients treated with first-line docetaxel comprised the training and testing data sets used in this challenge. In this paper we describe: (a) the sub-challenges comprising the PCDC, (b) the statistical metrics used to benchmark prediction performance, (c) our analytical approach, and finally (d) our team's overall performance in this challenge. Specifically, we discuss our curated, ad-hoc, feature selection (CAFS) strategy for identifying clinically important risk-predictors, the ensemble-based Cox proportional hazards regression framework used in our final submission, and the adaptation of our modeling framework based on the results from the intermittent leaderboard rounds. Strong predictors of patient survival were successfully identified utilizing our model building approach. Several of the identified predictors were new features created by our team via strategically merging collections of weak predictors. In each of the three intermittent leaderboard rounds, our prediction models scored among the top four models across all participating teams and our final submission ranked 9 th place overall with an integrated area under the curve (iAUC) of 0.7711 computed in an independent test set. While the prediction performance of teams placing between 2 nd- 10 th (iAUC: 0.7710-0.7789) was better than the current gold-standard prediction model for prostate cancer survival, the top-performing team, FIMM-UTU significantly outperformed all other contestants with an iAUC of 0.7915.  In summary, our ensemble-based Cox regression framework with CAFS resulted in strong overall performance for predicting prostate cancer survival and represents a promising approach for future prediction problems.