Larson, Nicholas B.

Background: Accurate identification of stroke cases from electronic health record (EHR) is needed for efficient and valid clinical and epidemiology research. A number of studies have evaluated the validity of using ICD-9 and ICD-10 codes for stroke identification. Previous research results show that sensitivity and positive predictive value (PPV) for stroke identification is the lowest among all other cerebrovascular diseases, with notable differences observed by stroke pathological subtypes. While most prevalent cerebrovascular disease cases can be detected using 430-438/I60-I69 collectively, more accurate and comprehensive stroke phenotyping algorithm is needed to identify incident stroke cases from EHR. In our work, we compared case identification results using ICD-codes exclusively with ICD plus a natural language processing (NLP) algorithm. Methods: We developed the NLP part of our stroke algorithm using a list of expert provided stroke-related keywords, which covers transient ischemic attack (TIA), ischemic stroke, and hemorrhagic stroke. We validated the hybrid (ICD+NLP) algorithm and compared it with ICD-exclusive algorithm in a previously established atrial fibrillation cohort (n=5,062). Clinical notes and ICD codes of all patients after the incident AF event were reviewed by two nurse abstracters to confirm new stroke incidences. All past clinical notes and ICD codes of a subset of patients (n=402) were reviewed to confirm lifetime (prior and current) stroke cases. Manual abstraction results were considered the gold standard for evaluation of ICD only and ICD+NLP automatic extraction. Sensitivity and positive predictive values (PPV) of both algorithms were calculated. Results: Among 5,062 patients, 593 patients were confirmed to have suffered a stroke after atrial fibrillation while 4,469 patients were confirmed with no EHR evidence of stroke after AF. The ICD-exclusive algorithm had a sensitivity of 47.2% and a PPV of 47.8% for detecting new stroke incidences. The hybrid stroke algorithm achieved a sensitivity of 92.4% and a PPV of 60.6% for extraction of incident stroke. For extraction of lifetime stroke cases, the hybrid approach achieved a sensitivity of 93.9% and a PPV of 88.7%. Performance of new stroke incidence extraction is limited because past/current stroke mentions in clinical notes are difficult to distinguish by our NLP algorithm (may be a future direction). Conclusions: We developed and validated a stroke algorithm that performed well for identifying incident and lifetime stroke cases. The addition of NLP into the stroke algorithm improved the sensitivity and PPV of compared to an ICD-exclusive algorithm.

ABSTRACT Evaluating the association of multiple genetic variants with a trait of interest by use of kernel‐based methods has made a significant impact on how genetic association analyses are conducted. An advantage of kernel methods is that they tend to be robust when the genetic variants have effects that are a mixture of positive and negative effects, as well as when there is a small fraction of causal variants. Another advantage is that kernel methods fit within the framework of mixed models, providing flexible ways to adjust for additional covariates that influence traits. Herein, we review the basic ideas behind the use of kernel methods for genetic association analysis as well as recent methodological advancements for different types of traits, multivariate traits, pedigree data, and longitudinal data. Finally, we discuss opportunities for future research.

AbstractX chromosome inactivation (XCI) is a key epigenetic gene expression regulatory process, which may play a role in women’s cancer. In particular tissues, some genes are known to escape XCI, yet patterns of XCI in ovarian cancer (OC) and their clinical associations are largely unknown. To examine XCI in OC, we integrated germline genotype with tumor copy number, gene expression and DNA methylation information from 99 OC patients. Approximately 10% of genes showed different XCI status (either escaping or being subject to XCI) compared with the studies of other tissues. Many of these genes are known oncogenes or tumor suppressors (e.g. DDX3X, TRAPPC2 and TCEANC). We also observed strong association between cis promoter DNA methylation and allele-specific expression imbalance (P = 2.0 × 10−10). Cluster analyses of the integrated data identified two molecular subgroups of OC patients representing those with regulated (N = 47) and dysregulated (N = 52) XCI. This XCI cluster membership was associated with expression of X inactive specific transcript (P = 0.002), a known driver of XCI, as well as age, grade, stage, tumor histology and extent of rl disease following surgical debulking. Patients with dysregulated XCI (N = 52) had shorter time to recurrence (HR = 2.34, P = 0.001) and overall survival time (HR = 1.87, P = 0.02) than those with regulated XCI, although results were attenuated after covariate adjustment. Similar findings were observed when restricted to high-grade serous tumors. We found evidence of a unique OC XCI profile, suggesting that XCI may play an important role in OC biology. Additional studies to examine somatic changes with paired tumor-normal tissue are needed.

Background: Pathogenic RYR2 variants account for ≈60% of clinically definite cases of catecholaminergic polymorphic ventricular tachycardia. However, the rate of rare benign RYR2 variants identified in the general population remains a challenge for genetic test interpretation. Therefore, we examined the results of the RYR2 genetic test among patients referred for commercial genetic testing and examined factors impacting variant interpretability. Methods: Frequency and location comparisons were made for RYR2 variants identified among 1355 total patients of varying clinical certainty and 60 706 Exome Aggregation Consortium controls. The impact of the clinical phenotype on the yield of RYR2 variants was examined. Six in silico tools were assessed using patient- and control-derived variants. Results: A total of 18.2% (218/1200) of patients referred for commercial testing hosted rare RYR2 variants, statistically less than the 59% (46/78) yield among clinically definite cases, resulting in a much higher potential genetic false discovery rate among referrals considering the 3.2% background rate of rare, benign RYR2 variants. Exclusion of clearly putative pathogenic variants further complicates the interpretation of the next novel RYR2 variant. Exonic/topologic analyses revealed overrepresentation of patient variants in exons covering only one third of the protein. In silico tools largely failed to show evidence toward enhancement of variant interpretation. Conclusions: Current expert recommendations have resulted in increased use of RYR2 genetic testing in patients with questionable clinical phenotypes. Using the largest to date catecholaminergic polymorphic ventricular tachycardia patient versus control comparison, this study highlights important variables in the interpretation of variants to overcome the 3.2% background rate that confounds RYR2 variant interpretation.

Abstract Introduction: Mitochondria are intracellular organelles that play an important role in energy metabolism, reactive oxygen species generation, and apoptosis. They possess their own haploid genome distinct from nuclear DNA, and mutations in mitochondrial DNA (mtDNA) have previously been linked to cancer risk, disease progression, and treatment response for multiple tumor types. However, the spectrum of mtDNA variation in epithelial ovarian carcinoma (EOC) and associations with clinical characteristics have not been fully explored. Methods: We extracted mtDNA from fresh frozen EOC tumors of women diagnosed with primary invasive epithelial ovarian, fallopian tube, or peritoneal carcinoma, and performed targeted sequencing using the Illumina MiSeq Reporter mtDNA workflow. Homoplasmic and heteroplasmic SNVs and indels were called using an in-house bioinformatics pipeline. We estimated mtDNA haplogroup using Haplogrep and compared to results from available germline mtDNA SNP genotyping. We derived regional weighted variant burden scores based on mtDNA functional domains, accounting for underlying haplogroup, heteroplasmic fraction, and predicted functional impact. To assess potential differences in variant burden by histotype, we compared burden scores using Kruskal-Wallis testing. Among high-grade serous tubo-ovarian cases, we explored associations with overall survival (OS) using Cox proportional hazards regression. After quality control filtering, 389 mitochondrial genomes were analyzed, including those from 320 tubo-ovarian high-grade serous tumors, 42 endometrioid tumors, and 18 clear cell tumors. Results: Accounting for predicted haplogroup, we identified a median count of two global private heteroplasmies per tumor (range: 0-9). Elevated mutation rates were identified in the regulatory control region as well as among the various mt-tRNAs (>3 per 10kb per sample) relative to the remainder of the mtDNA. The majority of recurrent variants across tumors were also identified in the mtDNA control region. We did not observe an association with overall mtDNA variant burden and patient age at diagnosis. Regional mtDNA burden scores revealed a significant association with histotype (P<0.001) for the oxidative phosphorylation complex IV genes, with higher burden in tubo-ovarian high-grade serous and clear-cell tumors relative to endometrioid tumors. For patients with tubo-ovarian high-grade serous tumors, increased variant burden in MT-CO1 (cytochrome C oxidase I) was associated with improved OS (HR = 0.32; 95% CI = [0.14,0.70], LRT P = 0.001) after adjustment for age, stage, and debulking status. Conclusions: We demonstrate a wide range of tumor mtDNA variant patterns in the largest known collection of EOC patients. This work provides the first extensive evaluation of mtDNA variation in these diseases and highlights the potential prognostic relevance of mitochondrial dysfunction. Citation Format: Nicholas B. Larson, Stacey J. Winham, Chen Wang, Jared M. Evans, Sebastian M. Armasu, Bryan M. McCauley, Sean J. Yoder, Lan Min Zhang, Catherine M. Phelan, Ellen L. Goode. Targeted deep sequencing of mitochondrial DNA in epithelial ovarian carcinomas [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 420.

ABSTRACTX‐chromosome inactivation (XCI) epigenetically silences transcription of an X chromosome in females; patterns of XCI are thought to be aberrant in women's cancers, but are understudied due to statistical challenges. We develop a two‐stage statistical framework to assess skewed XCI and evaluate gene‐level patterns of XCI for an individual sample by integration of RNA sequence, copy number alteration, and genotype data. Our method relies on allele‐specific expression (ASE) to directly measure XCI and does not rely on male samples or paired normal tissue for comparison. We model ASE using a two‐component mixture of beta distributions, allowing estimation for a given sample of the degree of skewness (based on a composite likelihood ratio test) and the posterior probability that a given gene escapes XCI (using a Bayesian beta‐binomial mixture model). To illustrate the utility of our approach, we applied these methods to data from tumors of ovarian cancer patients. Among 99 patients, 45 tumors were informative for analysis and showed evidence of XCI skewed toward a particular parental chromosome. For 397 X‐linked genes, we observed tumor XCI patterns largely consistent with previously identified consensus states based on multiple normal tissue types. However, 37 genes differed in XCI state between ovarian tumors and the consensus state; 17 genes aberrantly escaped XCI in ovarian tumors (including many oncogenes), whereas 20 genes were unexpectedly inactivated in ovarian tumors (including many tumor suppressor genes). These results provide evidence of the importance of XCI in ovarian cancer and demonstrate the utility of our two‐stage analysis.

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