Citations

AbstractBackgroundFatigue is a consistently reported, severe symptom among patients with gliomas throughout the disease trajectory. Genomic pathways associated with fatigue in glioma patients have yet to be identified.MethodsClinical factors (performance status, tumor details, age, gender) were collected by chart review on glioma patients with fatigue (“I have lack of energy” on Functional Assessment of Cancer Therapy-Brain), as well as available genotyping data. Candidate genes in clock and inflammatory pathways were identified from a literature review, of which 50 single nucleotide polymorphisms (SNPs) in 7 genes were available. Clinical factors and SNPs identified by univariate analyses were included in a multivariate model for moderate-severe fatigue.ResultsThe study included 176 patients (median age = 47 years, 67% males). Moderate-severe fatigue was reported by 43%. Results from multivariate analysis revealed poor performance status and 2 SNPs were associated with fatigue severity. Moderate-severe fatigue was more common in patients with poor performance status (OR = 3.52, P < .01). For each additional copy of the minor allele in rs934945 (PER2) the odds of fatigue decreased (OR = 0.51, P < .05). For each additional copy of the minor allele in rs922270 (ARTNL2) the odds of fatigue increased (OR = 2.38, P < .01). Both of these genes are important in the circadian clock pathway, which has been implicated in diurnal preference, and duration and quality of sleep. No genes in the inflammatory pathway were associated with fatigue in the current study.ConclusionsIdentifying patients at highest risk for fatigue during treatment allows for improved clinical monitoring and enrichment of patient selection for clinical trials.

BACKGROUNDSt. Louis encephalitis virus is a mosquito‐borne flavivirus that infrequently causes epidemic central nervous system infections. In the United States, blood donors are not screened for St. Louis encephalitis virus infection, and transmission through blood transfusion has not been reported. During September 2015, St. Louis encephalitis virus infection was confirmed in an Arizona kidney transplant recipient. An investigation was initiated to determine the infection source.STUDY DESIGN AND METHODSThe patient was interviewed, and medical records were reviewed. To determine the likelihood of mosquito‐borne infection, mosquito surveillance data collected at patient and blood donor residences in timeframes consistent with their possible exposure periods were reviewed. To investigate other routes of exposure, organ and blood donor and recipient specimens were obtained and tested for evidence of St. Louis encephalitis virus infection.RESULTSThe patient presented with symptoms of central nervous system infection. Recent St. Louis encephalitis virus infection was serologically confirmed. The organ donor and three other organ recipients showed no laboratory or clinical evidence of St. Louis encephalitis virus infection. Among four donors of blood products received by the patient via transfusion, one donor had a serologically confirmed, recent St. Louis encephalitis virus infection. Exposure to an infected mosquito was unlikely based on the patient's minimal outdoor exposure. In addition, no St. Louis encephalitis virus‐infected mosquito pools were identified around the patient's residence.CONCLUSIONThis investigation provides evidence of the first reported possible case of St. Louis encephalitis virus transmission through blood product transfusion. Health care providers and public health professionals should maintain heightened awareness for St. Louis encephalitis virus transmission through blood transfusion in settings where outbreaks are identified.

SummaryBackgroundTissue transglutaminase (tTG) immunoglobulin A (IgA) testing is a sensitive adjunct to the diagnosis of coeliac disease. The threshold for positivity was developed for diagnosis, with negative results reported as below the reference value (<4 U/mL).AimTo investigate if an undetectable (tTG IgA<1.2 U/mL) is more predictive of healing compared to patients with negative but detectable serology (1.2‐3.9 U/mL).MethodsWe performed a retrospective study of 402 treated coeliac disease patients seen at the Mayo Clinic with negative tTG IgA values drawn within 1 month of duodenal biopsy between January 2009 and December 2015. The Corazza‐Villanacci score was used to assess mucosal healing. The presence of gastrointestinal symptoms was also collected. Logistic regression was used to assess the relationship of clinical variables with a normal biopsy.ResultsPatients with undetectable titres more frequently had normal duodenal histology compared to patients with detectable tTG IgA levels (117/240 vs. 53/162; OR=1.96; 1.292, 2.961). Asymptomatic patients more frequently had normal duodenum as compared to symptomatic patients (88/163 vs. 82/239; OR=2.25; CI: 1.494, 3.377). Patients with undetectable serology and on a gluten‐free diet for ≥2 years were more likely to have no villous atrophy compared to patients with detectable serology (148/192 vs. 55/88; OR=2.02; CI: 1.17, 3.49).ConclusionIn subjects recovering from coeliac disease with negative tTG IgA serology, an undetectable titre is associated with normal histology on follow‐up biopsy.

Abstract Purpose: Lung cancer is the leading cause of cancer deaths worldwide. Most present symptomatically at late stage with high lethality. Early detection reduces mortality but accurate and readily accessible tools for population screening are limited. By whole methylome sequencing, we have identified novel methylated DNA markers (MDMs) for lung cancer in tissue (Giakoumopoulos et al. ASCO 2016). Using top candidate MDMs in the present study, we now explore their clinical accuracy for lung cancer detection when assayed from plasma. Experimental Procedures: Archival plasmas from two independent study groups were tested in blinded fashion. Lung cancer cases and controls (apparently healthy smokers) for each group were balanced on age and sex (Group 1: 64 cases, 231 controls; Group 2: 23 cases, 80 controls). Using multiplex PCR followed by QuARTS (Quantitative Allele-Specific Real-time Target and Signal amplification) assay, a post-bisulfite quantification of MDMs on DNA extracted from plasma was performed. We selected 31 MDM candidates for initial evaluation in Group 1 (1 ml plasma/patient); top individual MDMs were subsequently tested in Group 2 to identify optimal MDM panels for lung cancer detection (2 ml/patient). Results: From Group 1 analyses, 13 high performance MDMs were selected for further testing (CYP26C1, SOBP, SUCLG2, SHOX2, ZDHHC1, NFIX, FLJ45983, HOXA9, B3GALT6, ZNF781, SP9, BARX1, EMX1) with individual areas under the receiver operator curve (AUCs) ranging from 0.593 to 0.939. Discrimination by individual MDMs was corroborated in Group 2 in which data was analyzed using two methods: a logistic regression fit and a regression partition tree approach. The logistic fit model identified a 4-marker panel (ZNF781, BARX1, EMX1, and SOBP) with an AUC of 0.96 and an overall sensitivity of 91% and 90% specificity. Analysis of the data using a regression partition tree approach identified 4 markers (ZNF781, BARX1, EMX1, and HOXA9) with AUC of 0.93 and an overall sensitivity of 96% and specificity of 94%. For both approaches, B3GALT6 was used as a standardizing marker of total DNA input. Conclusion: A panel of MDMs assayed in plasma achieved high sensitivity and specificity for all types of lung cancer. Further clinical evaluation and validation of this promising panel in larger patient groups are clearly indicated. Citation Format: Hatim T. Allawi, Maria Giakoumopoulos, Evan Flietner, Austin Oliphant, Carla Volkmann, Brian Aizenstein, Tamara Sander, Drew Eckmayer, Ashley Poenitzsch Strong, Melissa Gray, Barry Berger, Tracy Yab, William Taylor, Douglas Mahoney, John B. Kisiel, David E. Midthun, David A. Ahlquist, Graham P. Lidgard. Detection of lung cancer by assay of novel methylated DNA markers in plasma [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 712. doi:10.1158/1538-7445.AM2017-712

Abstract High grade serous ovarian cancer (HGSOC) is a complex disease in which initiation and progression have been associated with gene mutation, DNA methylation changes, genetic variation, and epigenetic processes. Variation in several susceptibility regions and cancer-typical global methylation patterns have been observed in HGSOC; however, this knowledge has not been compelling in understanding HGSOC intiation or progression. As ingetration of genomic, epigenomic, and transcriptomic data has increased mechanistic understanding in other cancers, we hypothesized that tumor methylation alone or in combination with germline genetic variation influences tumor gene expression in HGSOC. We examined three nested models using an Elastnic Net (ENET) penalized regression method while adjusting for somatic copy number (CNV): a) germline genotype and tumor DNA methylation (full model), b) genotype only, and c) DNA methylation only. We included 339 cases from The Cancer Genome Atlas (TCGA), 54 cases from Mayo Clinic, and 78 cases from the Australian Ovarian Cancer Study (AOCS). Genotyping and copy number calls on germline DNA, expression, methylation and copy number on somatic samples were collected and analyzed on different platforms separately at each study site. We excluded genes with low overall expression and thus analyzed a total of 11,922 genes available in three datasets ( Ensembl IDs, 500kb window up- and downstream). In general, combining genomic data in HGSOC did not reveal a role for germline genetic variation in altering gene expression. However, in methylation only models 79 genes were associated with differential expression in the TCGA cases (permutation multiple testing adjusted p-val &lt;0.05), in the Mayo cases (unadjusted p-val &lt;0.05) and AOCS cases (unadjusted p-val &lt;0.05). A known tummor suppressor (FBXW7) was associated with differential expression in the three datasets at p-val &lt;0.01. This work demonstrates the feasibility, utility, and statistical power of ENET gene-level analyses incoporating maximal genomic information. Citation Format: Yanina Natanzon, Madalene Earp, Julie M. Cunningham, Kimberly R. Kalli, Stacey J. Winham, Sebastian M. Armasu, Melissa C. Larson, Chen Wang, David D. Bowtell, Dale W. Garsed, Ellen Goode. Omics data integration analysis in high grade serous ovarian cancer: results from three studies [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 3368. doi:10.1158/1538-7445.AM2017-3368

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&lt;0.01). Patients with XCI dysregulated tumors were higher grade, stage, serous histology and were sub-optimally debulked (p&lt;0.05). These patients also had shorter overall survival time (HR=1.87, p=0.02) and time to recurrence (HR=2.34, p&lt;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

Abstract BACKGROUND: Incidence of glioma varies significantly by sex, and most glioma histologies occur with greater incidence in males. Previous analyses have examined the impact of estrogen exposure as a risk factor for these tumors, but have found results of varying significance and low effect size. There may be differences in effect of previously discovered risk alleles that contribute to sex differences. METHODS: Using data collected for three previous glioma GWAS in European-ancestry populations (MD Anderson Cancer Center, the San Francisco Adult Glioma Study, and the Glioma International Case Control Study) we assessed sex-specific effects for 14 previously identified and 13 newly identified glioma risk SNPs (27 total) overall and for glioblastoma (GBM) and non-GBM tumors separately. There were 3,892 male cases (59% GBM), 4,522 male controls, 2,500 female cases (52% GBM) and 4,940 female controls. Sex-specific odds ratios (ORM and ORF), 95% confidence intervals (95% CI) and p values (pM and pF) were generated using stratified logistic regression models. Data from each study were analyzed separately and combined using inverse variance weighted meta-analysis. Results were considered statistically significant at p&lt;6.2x10-4. RESULTS: In GBM, rs11979158 (7p11.2, pM=1.01x10-10, ORM=1.43 [95% CI: 1.28-1.59]; pF=3.43x10-3, ORF=1.22 [95% CI: 1.07-1.39]) and rs2562152 (16p13.3, pM=5.59x10-4, ORM=1.23 [95% CI: 1.09-1.39]; pF=1.22x10-1, ORF=1.12 [95% CI: 0.97-1.29]) had significant effect in males only. In non-GBM gliomas, rs12076373 (1q44, pM=3.56x10-7, ORM=1.41, 95% CI: 1.23-1.61; pF=1.76x10-2, ORF=1.20, 95% CI: 1.03-1.38), rs11979158 (7p11.2, pM=1.77x10-5, ORM=1.32 [95% CI: 1.16-1.49]; pF=2.73x10-1, ORF=1.08 [95% CI: 0.94-1.24]), and rs3751667 (16p13.3, pM=9.44x10-7, ORM=1.30 [95% CI: 1.17-1.44]; pF=5.18x10-2, ORF=1.13 [95% CI: 1.00-1.28]) had significant effect in males only. Effect size for rs55705857 (8q24.21) varied significantly by sex, with ORM=2.63 (95% CI: 2.24-3.09, pM=4.42x10-32), as compared to ORF=3.95 (95% CI: 3.28-4.76, pF=1.82x10-47). A sensitivity analysis was performed due to allele frequency heterogeneity by study and results did not change. CONCLUSIONS: Sex differences and other demographic differences in cancer susceptibility can provide important clues to etiology, and these differences can be leveraged for discovery in genetic association studies. Significant differences in effect size may suggest variation in genetic effect of risk alleles or in unidentified risk factors that vary in prevalence or effect by sex. There may also be differences in the distribution of molecular subtypes within each histology by sex. Further investigation using an agnostic approach may further elucidate the relationship between effect of risk alleles and sex. Citation Format: Quinn T. Ostrom, Ben Kinnersley, Margaret Wrensch, Jeanette E. Eckel-Passow, Georgina Armstrong, Terri Rice, Yanwen Chen, John Wiencke, Lucie McCoy, Helen Hansen, Christopher Amos, Jonine L. Bernstein, Elizabeth B. Claus, Dora Il'yasova, Christoffer Johansen, Daniel Lachance, Rose Lai, Ching C. Lau, Ryan T. Merrell, Sara H. Olson, Siegal Sadetzki, Joellen Schildkraut, Sanjay Shete, Richard S. Houlston, Robert B. Jenkins, Beatrice Melin, Melissa Bondy, Jill S. Barnholtz-Sloan. Estimating sex-specific effects of genetic loci associated with glioma risk [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 1315. doi:10.1158/1538-7445.AM2017-1315