Spiridon Tsavachidis

Baylor College of Medicine, Houston, Texas, United States

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Publications (5)17.65 Total impact

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    ABSTRACT: Males have excess advanced liver disease and cirrhosis risk including from chronic hepatitis C virus (HCV) infection though the reasons are unclear.
    International Journal of Molecular Epidemiology and Genetics 05/2014; 5(3):164-76. DOI:10.1016/S0016-5085(14)63524-1
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    ABSTRACT: Chronic hepatitis C infection is the leading cause of hepatocellular carcinoma (HCC), a highly lethal malignancy with rapidly increasing prevalence in the United States. Little is known about genetic variations and HCC risk. This study aimed to determine if genetic variation in Wnt signaling pathway genes are associated with advanced hepatic fibrosis and inflammation risk in a hepatitis C virus (HCV) infected population. We performed a genetic association cross-sectional study evaluating single nucleotide polymorphisms (SNPs) in 58 candidate genes and risk of FibroSURE-Acti Test determined advanced fibrosis (F3/F4-F4 advanced cases vs. F0-F3 mild controls) and inflammation (A2/A3-A3 advanced cases vs. A0-A2 mild controls). We calculated odds ratios (ORs) and 95% confidence intervals (CIs) employing multivariate logistic regression. Haplotypes were inferred by the HAPLO.STAT program, interactions were evaluated using multifactor dimensionality reduction (MDR) analysis. Among 425 chronically HCV-infected male veterans, 155 (37%) had advanced fibrosis and 180 (42%) had advanced inflammation. Of 3016 SNPs evaluated, eight were significantly associated with fibrosis risk (e.g., SFRP2 rs11937424: OR = 2.19, 95% CI 1.48-3.23, P = 0.00004), and seven were significantly associated with inflammation risk (e.g., SFRP1 rs16890282: OR = 2.15, 95% CI 1.39-3.16, P = 0.0004). MDR analysis identified overweight/obese, SOST rs1405952, SFRP2 rs11937424, and FZD4 rs11234870 as the best interaction model for predicting risk of fibrosis; whereas race/ethnicity, FZD1 rs1346665, and TBX3 rs1520177 as the best interaction model for predicting risk of inflammation. Polymorphisms in several genes involved in the Wnt signaling pathway were associated with hepatic fibrosis or inflammation risk in HCV-infected males. Additional studies in other multi-ethnic HCV cohorts are needed to validate our findings in males and to assess if similar associations exist in chronically HCV-infected females.
    PLoS ONE 12/2013; 8(12):e84407. DOI:10.1371/journal.pone.0084407 · 3.53 Impact Factor
  • Cancer Epidemiology Biomarkers & Prevention 08/2013; 21(11_Supplement):04-4. DOI:10.1158/1055-9965.GWAS-04 · 4.32 Impact Factor
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    ABSTRACT: The risk of glioma has consistently been shown to be increased twofold in relatives of patients with primary brain tumors (PBT). A recent genome-wide linkage study of glioma families provided evidence for a disease locus on 17q12-21.32, with the possibility of four additional risk loci at 6p22.3, 12p13.33-12.1, 17q22-23.2, and 18q23. To identify the underlying genetic variants responsible for the linkage signals, we compared the genotype frequencies of 5,122 SNPs mapping to these five regions in 88 glioma cases with and 1,100 cases without a family history of PBT (discovery study). An additional series of 84 familial and 903 non-familial cases were used to replicate associations. In the discovery study, 12 SNPs showed significant associations with family history of PBT (P < 0.001). In the replication study, two of the 12 SNPs were confirmed: 12p13.33-12.1 PRMT8 rs17780102 (P = 0.031) and 17q12-21.32 SPOP rs650461 (P = 0.025). In the combined analysis of discovery and replication studies, the strongest associations were attained at four SNPs: 12p13.33-12.1 PRMT8 rs17780102 (P = 0.0001), SOX5 rs7305773 (P = 0.0001) and STKY1 rs2418087 (P = 0.0003), and 17q12-21.32 SPOP rs6504618 (P = 0.0006). Further, a significant gene-dosage effect was found for increased risk of family history of PBT with these four SNPs in the combined data set (P(trend) <1.0 × 10(-8)). The results support the linkage finding that some loci in the 12p13.33-12.1 and 17q12-q21.32 may contribute to gliomagenesis and suggest potential target genes underscoring linkage signals.
    Human Genetics 06/2012; 131(9):1507-17. DOI:10.1007/s00439-012-1187-x · 4.52 Impact Factor
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    ABSTRACT: Screening mammography results in the increased detection of indolent tumors. We hypothesized that screen- and symptom-detected tumors would show genotypic differences as copy number imbalances (CNI) that, in part, explain differences in the clinical behavior between screen- and symptom-detected breast tumors. We evaluated 850 women aged 40 and above diagnosed with stage I and II breast cancer at the University of Texas MD Anderson Cancer Center between 1985 and 2000 with information available on method of tumor detection (screen vs. symptoms). CNIs in screen- and symptom-detected tumors were identified using high-density molecular inversion probe arrays. Cox proportional modeling was used to estimate the effect of method of tumor detection on disease-free survival after adjusting for age, stage, and the CNIs. The majority of tumors were symptom detected (n = 603) compared with screen detected (n = 247). Copy number gains in chromosomes 2p, 3q, 8q, 11p, and 20q were associated with method of breast cancer detection (P < 0.00001). We estimated that 32% and 63% of the survival advantage of screen detection was accounted for by age, stage, nuclear grade, and Ki67 in women aged 50 to 70 and aged 40 to 87, respectively. In each age category, an additional 20% of the survival advantage was accounted for by CNIs associated with method of detection. Specific CNIs differ between screen- and symptom-detected tumors and explain part of the survival advantage associated with screen-detected tumors. Measurement of tumor genotype has the potential to improve discrimination between indolent and aggressive screen-detected tumors and aids patient and physician decision making about use of surgical and adjuvant treatments.
    Cancer Prevention Research 07/2011; 4(10):1609-16. DOI:10.1158/1940-6207.CAPR-10-0361 · 5.27 Impact Factor