Purpose: The HDM2 gene represents one of the central nodes in the p53 pathway. A recent study reported the association of a single nucleotide polymorphism (SNP309) in the HDM2 promoter region with accelerated tumor formation in both hereditary and sporadic cancers. In this study, we aim to evaluate the SNP309 in bladder cancer and to link it to TP53 status. Experimental Design: SNP309 genotyping and TP53 mutation status were done on 141 bladder tumors and 8 bladder cancer cell lines using a RFLP strategy andTP53 genotyping arrays, respectively. Transcript profiling of a subset of cases (n = 41) was done using oligonucleotide arrays to identify genes differentially expressed regarding their SNP309 status. Results: Of 141 bladder tumors analyzed, 36.9% displayed the SNP309 wild-type (WT; T/T) genotype, whereas 11.3% were homozygous (G/G) and 51.8% were heterozygous (T/G) cases. Patients with superficial disease and the G/G genotype had an earlier age on onset than those with the T/G or T/T genotypes (P = 0.029). Tumors with SNP309 WT genotype significantly displayed TP53 mutations when compared with tumors harboring G/G or T/G genotypes (P < 0.05). SNP309 WT cases had a poorer overall survival than cases with G/G and T/G genotypes (P < 0.05).TP53 mutation status provided enhanced prognostic value (P < 0.001).Transcript profiling identified TP53 targets among those differentially expressed between tumors displaying G/G orT/G SNP309 versus WTcases. Conclusions: SNP309 is a frequent event in bladder cancer, related to earlier onset of superficial disease and TP53 mutation status. SNP309 genotypes were found to be associated with clinical outcome.The HDM2 gene is a transcriptional target of p53. It encodes a negative feedback regulator (p90-Hdm2) that binds to p53 and acts as an ubiquitin-ligase, targeting p53 to proteasomal degradation (1). The association of a single nucleotide polymorphism (SNP309) in the HDM2 promoter region with accelerated tumor formation in both hereditary and sporadic cancers has been studied by several groups (2 -24). As Hdm2 expression levels seem to be critical to a well-regulated p53 response, naturally occurring sequence variations in the HDM2 promoter may result in altered expression of the Hdm2, affecting p53 tumor suppression activity (2). In addition, the association of SNP309 to an early age of onset in cancer predisposition syndromes has also been reported (2, 3), although it has also been reported lack of evidence that the HDM2 SNP309 accelerates tumor development in carriers of known pathogenic germ-line mutations, such of BRCA1 (4). Several case-control studies of squamous head and neck tumors, breast, ovarian, lung, and colon carcinomas did not found either such correlation (6 -10, 12, 16).Bladder tumors are known to commonly harbor TP53 mutations and less frequently HDM2 amplifications (25 -31). Although the abrogation of normal p53 function may be one of the permissive events promoting proto-oncogene amplification, predisposition to gene amplification of HDM2 in uroth...

Competition may influence the weights of animals confined to litters or pens, if conditions occur that limit the space and the feed provided, by inducing a negative correlation among the weights within the groups. An example of the phenomenon appeared in the birthweight of pigs where the intra-class correlation declined in a linear manner with increasing litter size. The data consisted of records on 33,165 pigs from 3282 litters raised on a single farm.

Selecting the number of pace categories using cross validation Conclusion 3 ESTIMATION AND PREDICTION OF LABOR HOURS USING LOCALLY WEIGHTED REGRESSION 23 Application to the truck data 27 Computation of rh{t) 33 Diagnostics 34 Bootstrap estimates for standaxd error of m(f) and Ay{ta,U) 35 Incorporating pace into the loess analysis Conclusion 43 4 GROWTH CURVE ESTIMATION AND PREDICTION USING LINEAR MIXED MODELS Using the model for prediction of future values 58 Interpretation of 7,and y-p 60 Choosing a model via cross validation 61 Checking model assumptions 62 Incorporating pace into the mixed model analysis 64 Discussion and conclusion 67 5 A COMPARISON OF METHODS USING 1994 DATA 69 c triangular factorization of the covaxiance matrix, c NR, maximum number of nonlineax parameters, c MODEL(1)+MODEL(2)+MODEL(3)+NOD c NCV, number of covaariates or grouping variables, constant for each c subj ect. c NTVCV, number of covauriates that vary within each subject's data c NLP, number of linear pairameters c ND must be at least NLP+1 c NS, number of subjects c NMAX, msLximum number of observations c

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