CD8+ Cytotoxic Immune Infiltrate in Non-Muscle Invasive Bladder Cancer: A Standardized Methodology to Study Association with Clinico-Pathological Features and Prognosis.

Background: Major interest lies in the evaluation of immune infiltrate in bladder cancer. CD8+ cytotoxic lymphocytes are key effectors of adaptive immune response. Objectives: The aims of the study were to set up a standardized methodology for CD8+ lymphocytes estimation in NMIBC and investigate how intra-tumoral heterogeneity influences CD8+ immune infiltrate. Methods: We considered 995 NMIBC included in the Spanish Bladder Cancer (SBC)/EPICURO Study. Duplicate 0.6mm TMA spots and paired full sections (FS) for 50 selected cases were double stained with anti-pan cytokeratin antibody and anti-CD8 antibody. Slides were digitalized and CD8+ cells were automatically counted after tissue recognition (tumor vs stroma). Spatial heterogeneity was assessed and a resampling strategy was applied to estimate the proper number of 0.6mm TMA spots providing an adequate CD8+ cell estimate. Association between CD8+ count and expression of urothelial differentiation markers was estimated. Cox regression models were performed to assess association between CD8+ cell count and risk of recurrence and progression. Results: Microscopic examination of full sections showed spatial heterogeneity for CD8+ infiltrates. Simulation analyses demonstrated that 5 TMA regions provided a correct sampling of tumor and stromal compartments in Ta while 2 and 6 TMA regions were necessary in T1, respectively. CD8+ cells infiltration was associated with stage, regardless of the histological compartment analyzed (median CD8+ /mm2 were 25/mm2 and 129/mm2 in tumor and stroma respectively in Ta and 111/mm2 and 344/mm2 in T1; p-value = 0.006). CD8+ infiltration in tumor compartment was significantly associated with low FGFR3 expression. CD8+/mm2 count in the tumor compartment was not associated with prognosis. Conclusion: Differences identified between Ta and T1 tumours supported the hypothesis that rigorous efforts should be placed in proper study design. These results provide a new framework to investigate microenvironment complexity in bladder cancer.

Bladder Cancer Genetic Susceptibility. A Systematic Review.

BACKGROUND: The variant/gene candidate approach to explore bladder cancer (BC) genetic susceptibility has been applied in many studies with significant findings reported. However, results are not always conclusive due to the lack of replication by subsequent studies. OBJECTIVES: To identify all epidemiological investigations on the genetic associations with BC risk, to quantify the likely magnitude of the associations by applying metaanalysis methodology and to assess whether there is a potential for publication/reporting bias. METHODS: To address our aims, we have catalogued all genetic association studies published in the field of BC risk since 2000. Furthermore, we metaanalysed all polymorphisms with data available from at least three independent case-control studies with subjects of Caucasian origin analyzed under the same mode of inheritance. RESULTS: The characterization of the genetic susceptibility of BC is composed of 28 variants, GWAS contributing most of them. Most of the significant variants associated with BC risk are located in genes belonging to chemical carcinogenesis, DNA repair, and cell cycle pathways. Causal relationship was also provided by functional analysis for GSTM1-null, NAT2-slow, APOBEC-rs1014971, CCNE1-rs8102137, SLC14A1-rs10775480, PSCA-rs2294008, UGT1A-rs1189203, and TP63-rs35592567. CONCLUSIONS: Genetic susceptibility of BC is still poorly defined, with GWAS contributing most of the strongest evidence. The systematic review did not provide evidence of further genetic associations. The potential public health translation of the existing knowledge on genetic susceptibility on BC is still limited.

Whole genome prediction of bladder cancer risk with the Bayesian LASSO.

To build a predictive model for urothelial carcinoma of the bladder (UCB) risk combining both genomic and nongenomic data, 1,127 cases and 1,090 controls from the Spanish Bladder Cancer/EPICURO study were genotyped using the HumanHap 1M SNP array. After quality control filters, genotypes from 475,290 variants were available. Nongenomic information comprised age, gender, region, and smoking status. Three Bayesian threshold models were implemented including: (1) only genomic information, (2) only nongenomic data, and (3) both sources of information. The three models were applied to the whole population, to only nonsmokers, to male smokers, and to extreme phenotypes to potentiate the UCB genetic component. The area under the ROC curve allowed evaluating the predictive ability of each model in a 10-fold cross-validation scenario. Smoking status showed the highest predictive ability of UCB risk (AUCtest = 0.62). On the other hand, the AUC of all genetic variants was poorer (0.53). When the extreme phenotype approach was applied, the predictive ability of the genomic model improved 15%. This study represents a first attempt to build a predictive model for UCB risk combining both genomic and nongenomic data and applying state-of-the-art statistical approaches. However, the lack of genetic relatedness among individuals, the complexity of UCB etiology, as well as a relatively small statistical power, may explain the low predictive ability for UCB risk. The study confirms the difficulty of predicting complex diseases using genetic data, and suggests the limited translational potential of findings from this type of data into public health interventions.

Application of multi-SNP approaches Bayesian LASSO and AUC-RF to detect main effects of inflammatory-gene variants associated with bladder cancer risk.

The relationship between inflammation and cancer is well established in several tumor types, including bladder cancer. We performed an association study between 886 inflammatory-gene variants and bladder cancer risk in 1,047 cases and 988 controls from the Spanish Bladder Cancer (SBC)/EPICURO Study. A preliminary exploration with the widely used univariate logistic regression approach did not identify any significant SNP after correcting for multiple testing. We further applied two more comprehensive methods to capture the complexity of bladder cancer genetic susceptibility: Bayesian Threshold LASSO (BTL), a regularized regression method, and AUC-Random Forest, a machine-learning algorithm. Both approaches explore the joint effect of markers. BTL analysis identified a signature of 37 SNPs in 34 genes showing an association with bladder cancer. AUC-RF detected an optimal predictive subset of 56 SNPs. 13 SNPs were identified by both methods in the total population. Using resources from the Texas Bladder Cancer study we were able to replicate 30% of the SNPs assessed. The associations between inflammatory SNPs and bladder cancer were reexamined among non-smokers to eliminate the effect of tobacco, one of the strongest and most prevalent environmental risk factor for this tumor. A 9 SNP-signature was detected by BTL. Here we report, for the first time, a set of SNP in inflammatory genes jointly associated with bladder cancer risk. These results highlight the importance of the complex structure of genetic susceptibility associated with cancer risk.

Modeling relationships between calving traits: a comparison between standard and recursive mixed models.

BACKGROUND: The use of structural equation models for the analysis of recursive and simultaneous relationships between phenotypes has become more popular recently. The aim of this paper is to illustrate how these models can be applied in animal breeding to achieve parameterizations of different levels of complexity and, more specifically, to model phenotypic recursion between three calving traits: gestation length (GL), calving difficulty (CD) and stillbirth (SB). All recursive models considered here postulate heterogeneous recursive relationships between GL and liabilities to CD and SB, and between liability to CD and liability to SB, depending on categories of GL phenotype. METHODS: Four models were compared in terms of goodness of fit and predictive ability: 1) standard mixed model (SMM), a model with unstructured (co)variance matrices; 2) recursive mixed model 1 (RMM1), assuming that residual correlations are due to the recursive relationships between phenotypes; 3) RMM2, assuming that correlations between residuals and contemporary groups are due to recursive relationships between phenotypes; and 4) RMM3, postulating that the correlations between genetic effects, contemporary groups and residuals are due to recursive relationships between phenotypes. RESULTS: For all the RMM considered, the estimates of the structural coefficients were similar. Results revealed a nonlinear relationship between GL and the liabilities both to CD and to SB, and a linear relationship between the liabilities to CD and SB.Differences in terms of goodness of fit and predictive ability of the models considered were negligible, suggesting that RMM3 is plausible. CONCLUSIONS: The applications examined in this study suggest the plausibility of a nonlinear recursive effect from GL onto CD and SB. Also, the fact that the most restrictive model RMM3, which assumes that the only cause of correlation is phenotypic recursion, performs as well as the others indicates that the phenotypic recursion may be an important cause of the observed patterns of genetic and environmental correlations.

Detecting single-nucleotide polymorphism by single-nucleotide polymorphism interactions in rheumatoid arthritis using a two-step approach with machine learning and a Bayesian threshold least absolute shrinkage and selection operator (LASSO) model.

The objective of this study was to detect interactions between relevant single-nucleotide polymorphisms (SNPs) associated with rheumatoid arthritis (RA). Data from Problem 1 of the Genetic Analysis Workshop 16 were used. These data consisted of 868 cases and 1,194 controls genotyped with the 500 k Illumina chip. First, machine learning methods were applied for preselecting SNPs. One hundred SNPs outside the HLA region and 1,500 SNPs in the HLA region were preselected using information-gain theory. The software weka was used to reduce colinearity and redundancy in the HLA region, resulting in a subset of 6 SNPs out of 1,500. In a second step, a parametric approach to account for interactions between SNPs in the HLA region, as well as HLA-nonHLA interactions was conducted using a Bayesian threshold least absolute shrinkage and selection operator (LASSO) model incorporating 2,560 covariates. This approach detected some main and interaction effects for SNPs in genes that have previously been associated with RA (e.g., rs2395175, rs660895, rs10484560, and rs2476601). Further, some other SNPs detected in this study may be considered in candidate gene studies.

Exploring biological relationships between calving traits in primiparous cattle with a Bayesian recursive model.

Structural equation models (SEMs) of a recursive type with heterogeneous structural coefficients were used to explore biological relationships between gestation length (GL), calving difficulty (CD), and perinatal mortality, also known as stillbirth (SB), in cattle, with the last two traits having categorical expression. An acyclic model was assumed, where recursive effects existed from the GL phenotype to the liabilities (latent variables) to CD and SB and from the liability to CD to that of SB considering four periods regarding GL. The data contained GL, CD, and SB records from 90,393 primiparous cows, sired by 1122 bulls, distributed over 935 herd-calving year classes. Low genetic correlations between GL and the other calving traits were found, whereas the liabilities to CD and SB were high and positively correlated, genetically. The model indicated that gestations of approximately 274 days of length (3 days shorter than the average) would lead to the lowest CD and SB and confirmed the existence of an intermediate optimum of GL with respect to these traits.