The treatment of acute lymphoblastic leukemia (ALL) and osteosarcoma (OSC) is very effective: the vast majority of patients recover and survive for decades. However, they still need to face serious adverse effects of chemotherapy. One of these is cardiotoxicity which may lead to progressive heart failure in the long term. Cardiotoxicity is contributed mainly to the use of anthracyclines and might have genetic risk factors. Our goal was to test the association between left ventricular function and genetic variations of candidate genes.Echocardiography data from medical records of 622 pediatric ALL and 39 OSC patients were collected from the period 1989-2015. Fractional shortening (FS) and ejection fraction (EF) were determined, 70 single nucleotide polymorphisms (SNPs) in 26 genes were genotyped. Multivariate logistic regression and multi-adjusted general linear model were performed to investigate the influence of genetic polymorphisms on the left ventricular parameters. Bayesian network based Bayesian multilevel analysis of relevance (BN-BMLA) method was applied to test for the potential interaction of the studied cofactors and SNPs.Our results indicate that variations in ABCC2, CYP3A5, NQO1, SLC22A6 and SLC28A3 genes might influence the left ventricular parameters. CYP3A5 rs4646450 TT was 17% among ALL cases with FS lower than 28, and 3% in ALL patients without pathological FS (p = 5.60E-03; OR = 6.94 (1.76-27.39)). SLC28A3 rs7853758 AA was 12% in ALL cases population, while only 1% among controls (p = 6.50E-03; OR = 11.56 (1.98-67.45)). Patients with ABCC2 rs3740066 GG genotype had lower FS during the acute phase of therapy and 5-10 years after treatment (p = 7.38E-03, p = 7.11E-04, respectively). NQO1 rs1043470 rare T allele was associated with lower left ventricular function in the acute phase and 5-10 years after the diagnosis (p = 4.28E-03 and 5.82E-03, respectively), and SLC22A6 gene rs6591722 AA genotype was associated with lower mean FS (p = 1.71E-03), 5-10 years after the diagnosis.Genetic variants in transporters and metabolic enzymes might modulate the individual risk to cardiac toxicity after chemotherapy.
Genetic studies indicate high number of potential factors related to asthma. Based on earlier linkage analyses we selected the 11q13 and 14q22 asthma susceptibility regions, for which we designed a partial genome screening study using 145 SNPs in 1201 individuals (436 asthmatic children and 765 controls). The results were evaluated with traditional frequentist methods and we applied a new statistical method, called Bayesian network based Bayesian multilevel analysis of relevance (BN-BMLA). This method uses Bayesian network representation to provide detailed characterization of the relevance of factors, such as joint significance, the type of dependency, and multi-target aspects. We estimated posteriors for these relations within the Bayesian statistical framework, in order to estimate the posteriors whether a variable is directly relevant or its association is only mediated. With frequentist methods one SNP (rs3751464 in the FRMD6 gene) provided evidence for an association with asthma (OR = 1.43(1.2–1.8); p = 3×10−4). The possible role of the FRMD6 gene in asthma was also confirmed in an animal model and human asthmatics. In the BN-BMLA analysis altogether 5 SNPs in 4 genes were found relevant in connection with asthma phenotype: PRPF19 on chromosome 11, and FRMD6, PTGER2 and PTGDR on chromosome 14. In a subsequent step a partial dataset containing rhinitis and further clinical parameters was used, which allowed the analysis of relevance of SNPs for asthma and multiple targets. These analyses suggested that SNPs in the AHNAK and MS4A2 genes were indirectly associated with asthma. This paper indicates that BN-BMLA explores the relevant factors more comprehensively than traditional statistical methods and extends the scope of strong relevance based methods to include partial relevance, global characterization of relevance and multi-target relevance.
Accurate risk prediction of acute graft versus host disease (aGvHD) is currently an unmet clinical need. This study sought to analyze whether three plasma proteins expressed in a largely skin- and gut-restricted manner would be affected by the development of acute cutaneous and gastrointestinal aGvHD. The diagnostic sensitivity, specificity, and prognostic value of plasma cytokeratin-15 (KRT15) cytokeratin-20 (KRT20), and occludin (OCLN) were evaluated in a discovery and a validation cohort using ELISA in comparison with elafin (PI3) and regenerating family member 3 alpha (REG3A), two established markers of skin- and gut aGvHD. The discovery cohort (n = 39) revealed that at the time of diagnosis, plasma KRT20 showed a progressive decrease from unaffected individuals to patients with single-, and patients with multi-organ aGvHD. KRT20 was affected by cutaneous (p = 0.0263) and gastrointestinal aGvHD (p = 0.0242) independently and in an additive manner. Sensitivity and specificity of KRT20 for aGvHD involving both target organs (AUC = 0.852) were comparable to that of PI3 for skin-aGvHD (AUC = 0.708) or that of REG3A for gut-aGvHD (AUC = 0.855). Patient follow-up in the validation cohort (n = 67) corroborated these observations (p < 0.001), and linked low KRT20 to grade 2+ disease (p < 0.001), but failed to confirm low KRT20 as an independent risk factor. These data established a link between low plasma KRT20 levels and moderate to severe aGvHD involving multiple target organs.
Abstract The relative scarcity of the results reported by genetic association studies (GAS) prompted many research directions. Despite the centrality of the concept of association in GASs, refined concepts of association are missing; meanwhile, various feature subset selection methods became de facto standards for defining multivariate relevance. On the other hand, probabilistic graphical models, including Bayesian networks (BNs) are more and more popular, as they can learn nontransitive, multivariate, nonlinear relations between complex phenotypic descriptors and heterogeneous explanatory variables. To integrate the advantages of Bayesian statistics and BNs, the Bayesian network based Bayesian multilevel analysis of relevance (BN-BMLA) was proposed. This approach allows the processing of multiple target variables, while ensuring scalability and providing a multilevel view of the results of multivariate analysis. This chapter discusses the use of Bayesian BN-based analysis of relevance in exploratory data analysis, optimal decision and study design, and knowledge fusion, in the context of GASs.
// Marta Hegyi 1 , Adam Arany 2, 3 , Agnes F. Semsei 4 , Katalin Csordas 1 , Oliver Eipel 1 , Andras Gezsi 2, 4 , Nora Kutszegi 1, 4 , Monika Csoka 1 , Judit Muller 1 , Daniel J. Erdelyi 1 , Peter Antal 2 , Csaba Szalai 4 , Gabor T. Kovacs 1 1 Second Department of Pediatrics, Semmelweis University, H-1094 Budapest, Tűzoltó utca 7-9, Hungary 2 Department of Measurement and Information Systems, University of Technology and Economics, H-1111 Budapest, Műegyetem rkp. 3, Hungary 3 Department of Organic Chemistry, Semmelweis University, H-1092 Budapest, Hőgyes Endre u. 7, Hungary 4 Department of Genetics, Cell and Immunobiology, Semmelweis University, H-1089 Budapest, Nagyvárad tér 4, Hungary Correspondence to: Gabor T. Kovacs, email: kovacs.gabor1@med.semmelweis-univ.hu Keywords: osteosarcoma, methotrexate, toxicity, SNP, Bayesian network-based Bayesian multilevel analysis of relevance (BN-BMLA) Received: February 18, 2016 Accepted: August 09, 2016 Published: August 23, 2016 ABSTRACT Inter-individual differences in toxic symptoms and pharmacokinetics of high-dose methotrexate (MTX) treatment may be caused by genetic variants in the MTX pathway. Correlations between polymorphisms and pharmacokinetic parameters and the occurrence of hepato- and myelotoxicity were studied. Single nucleotide polymorphisms (SNPs) of the ABCB1 , ABCC1 , ABCC2 , ABCC3 , ABCC10 , ABCG2 , GGH , SLC19A1 and NR1I2 genes were analyzed in 59 patients with osteosarcoma. Univariate association analysis and Bayesian network-based Bayesian univariate and multilevel analysis of relevance (BN-BMLA) were applied. Rare alleles of 10 SNPs of ABCB1 , ABCC2 , ABCC3 , ABCG2 and NR1I2 genes showed a correlation with the pharmacokinetic values and univariate association analysis. The risk of toxicity was associated with five SNPs in the ABCC2 and NR1I2 genes. Pharmacokinetic parameters were associated with four SNPs of the ABCB1, ABCC3, NR1I2 , and GGH genes, and toxicity was shown to be associated with ABCC1 rs246219 and ABCC2 rs717620 using the univariate and BN-BMLA method. BN-BMLA analysis detected relevant effects on the AUC 0-48 in the following SNPs: ABCB1 rs928256, ABCC3 rs4793665, and GGH rs3758149. In both univariate and multivariate analyses the SNPs ABCB1 rs928256, ABCC3 rs4793665, GGH rs3758149, and NR1I2 rs3814058 SNPs were relevant. These SNPs should be considered in future dose individualization during treatment.
Additional file 1. Associations between miR expression changes and MRD parameters or risk factors. In these analyses, the dependent variable is the normalized miR expression change between the time points compared, all measured in PB PFP. Fold change indicates change in the normalized miR expression between the two time points, the median of these values are provided. Pearson’s r indicates the correlation between dependent and independent variables. Adjusted p refers to the statistical significance of the Pearson’s correlation. *Normal karyotype: no alteration on FISH, DNA index diploid and cytogenetics normal or unsuccessful.
The frequency of brain metastasis (BM) is up to 45-50% in patients with advanced melanoma. Our aim was to identify the risk factors for the early occurrence of BM.A total of 333 patients with BM were identified from our database of 2,972 patients with melanoma between 2003-2015.The median elapsed time to BM (TTBM) was significantly associated with Breslow thickness, ulceration, location, and patient age. Head and neck location was the strongest predictor for early BM development [hazard ratio (HR)=1.81, 95% confidence interval (CI)=1.05-3.12; p=0.031) followed by Breslow thickness >2 mm (HR=1.53, 95% CI=1.04-2.23; p=0.027). Body part-specific median TTBM was 51.5, 43, 38.5, 32, 35, 36.5, 35.5 and 19 months in leg-foot, thigh, abdomen-pelvic, chest-back, lower arm-hand, upper arm-shoulder, face-neck and scalp regions, respectively.We suggest brain magnetic resonance imaging follow-up in the high-risk patient group of patients with melanoma in the head and neck region, especially for those with primary melanoma over Breslow 2 mm located in the scalp.
Health(span)-related gene clusters/modules were recently identified based on knowledge about the cross-species genetic basis of health, to interpret transcriptomic datasets describing health-related interventions. However, the cross-species comparison of health-related observations reveals a lot of heterogeneity, not least due to widely varying health(span) definitions and study designs, posing a challenge for the exploration of conserved healthspan modules and, specifically, their transfer across species. To improve the identification and exploration of conserved/transferable healthspan modules, here we apply an established workflow based on gene co-expression network analyses employing GEO/ArrayExpress data for human and animal models, and perform a comprehensive meta-study of the resulting modules related to health(span), yielding a small set of literature backed health(span) candidate genes. For each experiment, WGCNA (weighted gene correlation network analysis) was used to infer modules of genes which correlate in their expression with a 'health phenotype score' and to determine the most-connected (hub) genes (and their interactions) for each such module. After mapping these hub genes to their human orthologs, 12 health(span) genes were identified in at least two species (ACTN3, ANK1, MRPL18, MYL1, PAXIP1, PPP1CA, SCN3B, SDCBP, SKIV2L, TUBG1, TYROBP, WIPF1), for which enrichment analysis by g:profiler found an association with actin filament-based movement and associated organelles, as well as muscular structures. We conclude that a meta-study of hub genes from co-expression network analyses for the complex phenotype health(span), across multiple species, can yield molecular-mechanistic insights and can direct experimentalists to further investigate the contribution of individual genes and their interactions to health(span).
Refractory central nervous system (CNS) involvement is among the major causes of therapy failure in childhood acute leukemia. Applying contemporary diagnostic methods, CNS disease is often underdiagnosed. To explore more sensitive and less invasive CNS status indicators, we examined microRNA (miR) expressions and extracellular vesicle (EV) characteristics.In an acute lymphoblastic leukemia (ALL) discovery cohort, 47 miRs were screened using Custom TaqMan Advanced Low-Density Array gene expression cards. As a validation step, a candidate miR family was further scrutinized with TaqMan Advanced miRNA Assays on serial cerebrospinal fluid (CSF), bone marrow (BM) and peripheral blood samples with different acute leukemia subtypes. Furthermore, small EV-rich fractions were isolated from CSF and the samples were processed for immunoelectron microscopy with anti-CD63 and anti-CD81 antibodies, simultaneously.Regarding the discovery study, principal component analysis identified the role of miR-181-family (miR-181a-5p, miR-181b-5p, miR-181c-5p) in clustering CNS-positive (CNS+) and CNS-negative (CNS‒) CSF samples. We were able to validate miR-181a expression differences: it was about 52 times higher in CSF samples of CNS+ ALL patients compared to CNS‒ cases (n = 8 vs. n = 10, ΔFC = 52.30, p = 1.5E-4), and CNS+ precursor B cell subgroup also had ninefold higher miR-181a levels in their BM (p = 0.04). The sensitivity of CSF miR-181a measurement in ALL highly exceeded those of conventional cytospin in the initial diagnosis of CNS leukemia (90% vs. 54.5%). Pellet resulting from ultracentrifugation of CNS+ CSF samples of ALL patients showed atypical CD63-/CD81- small EVs in high density by immunoelectron microscopy.After validating in extensive cohorts, quantification of miR-181a or a specific EV subtype might provide novel tools to monitor CNS disease course and further adjust CNS-directed therapy in pediatric ALL.
