Citations

Abstract Motivation Interpreting genetic variation in noncoding regions of the genome is an important challenge for personal genome analysis. One mechanism by which noncoding single nucleotide variants (SNVs) influence downstream phenotypes is through the regulation of gene expression. Methods to predict whether or not individual SNVs are likely to regulate gene expression would aid interpretation of variants of unknown significance identified in whole-genome sequencing studies. Results We developed FIRE (Functional Inference of Regulators of Expression), a tool to score both noncoding and coding SNVs based on their potential to regulate the expression levels of nearby genes. FIRE consists of 23 random forests trained to recognize SNVs in cis-expression quantitative trait loci (cis-eQTLs) using a set of 92 genomic annotations as predictive features. FIRE scores discriminate cis-eQTL SNVs from non-eQTL SNVs in the training set with a cross-validated area under the receiver operating characteristic curve (AUC) of 0.807, and discriminate cis-eQTL SNVs shared across six populations of different ancestry from non-eQTL SNVs with an AUC of 0.939. FIRE scores are also predictive of cis-eQTL SNVs across a variety of tissue types. Availability and implementation FIRE scores for genome-wide SNVs in hg19/GRCh37 are available for download at https://sites.google.com/site/fireregulatoryvariation/. Supplementary information Supplementary data are available at Bioinformatics online.

Abstract Introduction: In females, X-chromosome inactivation (XCI) epigenetically silences transcription of one copy of the X chromosome. Which chromosome is silenced is randomly selected, and is tissue- and cell-specific. While some genes are known to escape XCI under normal conditions, aberrant XCI patterns are thought to occur in female-specific cancers, although the role of XCI in ovarian tumorigenesis and progression is largely unknown. The process of XCI is complex, and integration of gene expression, DNA methylation, and copy number data can inform the XCI status of individual genes and chromosome-wide XCI patterns for individual patients. Methods: We evaluated gene- and chromosome-level patterns of XCI by integrating RNA sequence, copy number alteration, genotype, and DNA methylation data to study XCI escape patterns in tumor samples from 99 ovarian cancer patients. We measured allele-specific expression (ASE) for 397 X-linked genes to identify the active alleles for each tumor. Combining ASE data with knowledge of copy number status, we used a Bayesian beta-binomial mixture model to estimate which genes escaped XCI for each patient, and validated our findings using DNA methylation data. To assess global XCI patterns, we performed cluster analyses on the ASE and methylation data, after adjusting for loss of heterozygosity. We examined the relationship between the clusters and clinical factors, including overall survival and time to recurrence. Results: DNA promoter methylation demonstrated inverse regional correlations with ASE. Cluster analyses using ASE and methylation data demonstrated evidence of two tumor clusters, representing normal XCI and global XCI dysregulation. The dysregulated XCI cluster (N=52) was associated with lower X-inactive specific transcript expression as expected (p<0.01). Patients with XCI dysregulated tumors were higher grade, stage, serous histology and were sub-optimally debulked (p<0.05). These patients also had shorter overall survival time (HR=1.87, p=0.02) and time to recurrence (HR=2.34, p<0.01), although associations were attenuated after covariate adjustment. In 45 tumor samples with sufficient data, we observed escape patterns largely consistent with previous reports of multiple tissue types. When comparing tumor to normal ovarian tissue, eight genes (CXorf23, CXorf36, BRWD3, ELF4, SLITRK4, GABRE, CLCN4, SH3BGRL) showed putative escape in the tumor and two genes (RBBP7, OFD1) showed discrepant tumor inactivation. Conclusions: We identified discrepant gene-level XCI tumor classifications compared to normal tissue and identified a group of patients with chromosome-wide XCI dysregulation associated with worse clinical prognosis. This provides evidence of the role of XCI in ovarian cancer and highlights the need to integrate multiple genomic data types to study XCI. Citation Format: Stacey J. Winham, Nicholas B. Larson, Sebastian M. Armasu, Zachary C. Fogarty, Melissa C. Larson, Kimberly R. Kalli, Kate Lawrenson, Simon Gayther, Brooke L. Fridley, Ellen L. Goode. Integrative analyses of gene expression, DNA methylation, genotype and copy number alterations characterize X-chromosome inactivation in ovarian cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2420. doi:10.1158/1538-7445.AM2017-2420

Objective To determine if hepatocyte growth factor (HGF), a promising biomarker of coronary heart disease (CHD) given its release into circulation in response to endothelial damage, is associated with subclinical and clinical CHD in a racial/ethnic diverse population. Methods HGF was measured in 6738 participants of the Multi-Ethnic Study of Atherosclerosis (MESA). Highest mean HGF values (pg/mL) were observed in Hispanic, followed by African, non-Hispanic white, then Chinese Americans. Results In all races/ethnicities, HGF levels were associated with older age, higher systolic blood pressure (SBP) and body mass index, lower high-density lipoprotein, diabetes and current smoking. In fully adjusted models, each SD higher HGF was associated with an average increase in coronary artery calcium (CAC) of 55 Agatston units for non-Hispanic whites (p<0.001) and 51 Agatston units for African-Americans (p=0.007) but was not in the other race/ethnic groups (interaction p=0.02). There were 529 incident CHD events, and CHD risk was 41% higher in African (p<0.001), 17% in non-Hispanic white (p=0.026) and Chinese (p=0.36), and 6% in Hispanic Americans (p=0.56) per SD increase in HGF. Conclusion In a large and diverse population-based cohort, we report that HGF is associated with subclinical and incident CHD. We demonstrate evidence of racial/ethnic heterogeneity within these associations, as the results are most compelling in African-Americans and non-Hispanic white Americans. We provide evidence that HGF is a biomarker of atherosclerotic disease that is independent of traditional risk factors.

ABSTRACTNext‐generation sequencing technologies have afforded unprecedented characterization of low‐frequency and rare genetic variation. Due to low power for single‐variant testing, aggregative methods are commonly used to combine observed rare variation within a single gene. Causal variation may also aggregate across multiple genes within relevant biomolecular pathways. Kernel‐machine regression and adaptive testing methods for aggregative rare‐variant association testing have been demonstrated to be powerful approaches for pathway‐level analysis, although these methods tend to be computationally intensive at high‐variant dimensionality and require access to complete data. An additional analytical issue in scans of large pathway definition sets is multiple testing correction. Gene set definitions may exhibit substantial genic overlap, and the impact of the resultant correlation in test statistics on Type I error rate control for large agnostic gene set scans has not been fully explored. Herein, we first outline a statistical strategy for aggregative rare‐variant analysis using component gene‐level linear kernel score test summary statistics as well as derive simple estimators of the effective number of tests for family‐wise error rate control. We then conduct extensive simulation studies to characterize the behavior of our approach relative to direct application of kernel and adaptive methods under a variety of conditions. We also apply our method to two case‐control studies, respectively, evaluating rare variation in hereditary prostate cancer and schizophrenia. Finally, we provide open‐source R code for public use to facilitate easy application of our methods to existing rare‐variant analysis results.

Background and Purpose— Hepatocyte growth factor (HGF) is positively associated with ischemic and hemorrhagic stroke risk factors. However, understanding the relation between HGF and stroke is in its infancy. Therefore, we sought to examine the association of circulating HGF with incident stroke using data from the MESA (Multi-Ethnic Study of Atherosclerosis). We hypothesized that circulating HGF would be positively associated with an increased risk of stroke. Methods— Participants aged 45 to 84 years (n=6711) had HGF measured between 2000 and 2002 and were followed for incident stroke through 2013 (n=233). Cox proportional hazards regression was used to calculate hazard ratios and 95% confidence intervals for incident stroke. A secondary analysis stratified results by adjudicated stroke type (n=183 ischemic; n=39 hemorrhagic; n=11 other). Results— After adjustment for potential confounding variables, risk of stroke was 17% higher with each standard deviation increase in HGF (hazard ratio, 1.17; 95% confidence interval, 1.03–1.34). This association was mainly driven by ischemic stroke and did not change on exclusion of cardioembolic strokes, although the number of excluded cases was small. The few hemorrhagic and other types of stroke were not associated with HGF. Conclusions— Circulating HGF was positively associated with the incidence of stroke in a diverse, population-based cohort of men and women from the United States. Our findings support the hypothesis that circulating HGF is a marker of endothelial damage and suggest that HGF may have utility as a prognostic marker of stroke risk.

Abstract Individual tumors, including the aggressive and difficult to treat triple-negative (ER-/PR-/HER2-) breast cancers (TNBCs) are heterogeneous collections of cells with multiple subclonal populations each contributing to the tumor. While subclonal heterogeneity is likely responsible for the development of drug resistance, identification of how tumor cell populations change over time has been difficult, largely because of the challenges in resampling tumor tissue at close time points. Here we quantify tumor evolution in human patient-derived xenografts implanted into NSG mice, which we use to test subclonal heterogeneity as a function of location within a tumor, propagation time, and drug treatment. We used high-depth (∼400x) sequencing of a targeted panel of 358 genes to quantify somatic mutation allele frequencies from 6 spatially-separated and 8 temporally-propagated xenograft samples derived from the same TNBC patient tumors. Samples ranged in age from 2-4 months post-engraftment. Although we observed a few low frequency mutations distinguishing samples, overall we found that allele frequencies of somatic mutations were well-preserved on this time scale. We then generated replicate xenografts from the same patient tumor and treated them respectively with cisplatin, doxorubicin, cyclophosphamide, docetaxel, or vehicle control for 25 days. Although again somatic mutations showed few differences in allele frequency across samples, substantial variations were seen when data were analyzed for copy number alterations. To confirm these effects we repeated the treatments for xenografts derived from two additional TNBC patients. Again we observed strong changes in tumor heterogeneity at the copy number level. This effect was particularly, but not exclusively, apparent in tumors with the greatest response to therapy. We further verified these measurements through Sanger and digital PCR sequencing on the treated mice and other mice in the same cohorts. Using a multi-sample xenograft propagation, dissection, sequencing, and computational analysis protocol, we have shown that tumor subpopulation changes in response to treatment can be quantified and distinguished from spatial or temporal effects, even for treatment time courses as short as 1 month. In triple negative breast cancer these variations are most apparent at the level of copy number variation. Our study demonstrates how patient-derived xenografts can provide detailed resolution of tumor population evolution during the manifestation of resistance. Citation Format: Hyunsoo Kim, Pooja Kumar, Francesca Menghi, Joshy George, Guru Ananda, Susan Mockus, Chengsheng Zhang, Nicholas Larson, Henry C. Chen, Yan Yang, James Keck, R. Krishnamurthy Karuturi, Charles Lee, Carol Bult, Edison Liu, Jeffrey H. Chuang. Evolution during propagation and treatment of patient-derived triple negative breast cancer xenografts. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2411.

Inflammation plays a pivotal role in peripheral artery disease (PAD). Cellular adhesion proteins mediate the interaction of leukocytes with endothelial cells during inflammation. To determine the association of cellular adhesion molecules with ankle–brachial index (ABI) and ABI category (≤1.0 vs >1.0) in a diverse population, 15 adhesion proteins were measured in the Multi-Ethnic Study of Atherosclerosis (MESA). To assess multivariable associations of each protein with ABI and ABI category, linear and logistic regression was used, respectively. Among 2364 participants, 23 presented with poorly compressible arteries (ABI > 1.4) and were excluded and 261 had ABI ≤ 1.0. Adjusting for traditional risk factors, elevated levels of soluble P-selectin, hepatocyte growth factor, and secretory leukocyte protease inhibitor were associated with lower ABI ( P = .0004, .001, and .002, respectively). Per each standard deviation of protein, we found 26%, 20%, and 19% greater odds of lower ABI category ( P = .001, .01, and .02, respectively). Further investigation into the adhesion pathway may shed new light on biological mechanisms implicated in PAD.

ABSTRACTRare variants (RVs) have been shown to be significant contributors to complex disease risk. By definition, these variants have very low minor allele frequencies and traditional single‐marker methods for statistical analysis are underpowered for typical sequencing study sample sizes. Multimarker burden‐type approaches attempt to identify aggregation of RVs across case‐control status by analyzing relatively small partitions of the genome, such as genes. However, it is generally the case that the aggregative measure would be a mixture of causal and neutral variants, and these omnibus tests do not directly provide any indication of which RVs may be driving a given association. Recently, Bayesian variable selection approaches have been proposed to identify RV associations from a large set of RVs under consideration. Although these approaches have been shown to be powerful at detecting associations at the RV level, there are often computational limitations on the total quantity of RVs under consideration and compromises are necessary for large‐scale application. Here, we propose a computationally efficient alternative formulation of this method using a probit regression approach specifically capable of simultaneously analyzing hundreds to thousands of RVs. We evaluate our approach to detect causal variation on simulated data and examine sensitivity and specificity in instances of high RV dimensionality as well as apply it to pathway‐level RV analysis results from a prostate cancer (PC) risk case‐control sequencing study. Finally, we discuss potential extensions and future directions of this work.

AbstractAimsTo test the hypothesis that soluble cellular adhesion molecules would be positively and independently associated with risk of diabetes.MethodsSoluble levels of six cellular adhesion molecules (ICAM‐1, E‐selectin, VCAM‐1, E‐cadherin, L‐selectin and P‐selectin) were measured in participants in the Multi‐Ethnic Study of Atherosclerosis, a prospective cohort study. Participants were then followed for up to 10 years to ascertain incident diabetes.ResultsSample sizes ranged from 826 to 2185. After adjusting for age, sex, race/ethnicity, BMI and fasting glucose or HbA1c, four cellular adhesion molecules (ICAM‐1, E‐selectin, VCAM‐1 and E‐cadherin) were positively associated with incident diabetes and there was a statistically significant trend across quartiles. Comparing the incidence of diabetes in the highest and lowest quartiles of each cellular adhesion molecule, the magnitude of association was largest for E‐selectin (hazard ratio 2.49; 95% CI 1.26–4.93) and ICAM‐1 (hazard ratio 1.76; 95% CI 1.22–2.55) in fully adjusted models. Tests of effect modification by racial/ethnic group and sex were not statistically significant for any of the cellular adhesion molecules (P > 0.05).ConclusionsThe finding of significant associations between multiple cellular adhesion molecules and incident diabetes may lend further support to the hypothesis that microvascular endothelial dysfunction contributes to risk of diabetes.