Browsing by Author "Bekele, B. Nebiyou"

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    Aberrant promoter hypermethylation of multiple genes in gallbladder carcinoma and chronic cholecystitis
    (American Association Cancer Research, 2004) Takahashi, Takao; Shivapurkar, Narayan; Riquelme Sánchez, Erick Marcelo; Shigematsu, Hisayuki; Reddy, Jyotsna; Suzuki, Makoto; Miyajima, Kuniharu; Zhou, Xian; Bekele, B. Nebiyou; Gazdar, Adi F.; Wistuba Oyarzún, Ignacio
    Purpose: Aberrant methylation of 5' gene promoter regions is an epigenetic phenomenon that is a major mechanism for silencing of tumor suppressor genes in many cancer types. There is limited information about the molecular changes involved in the pathogenesis of gallbladder carcinoma (GBC), including methylation status. Experimental Design: We investigated the aberrant promoter methylation profile of 24 known or suspected tumor suppressor genes in 50 GBCs and compared those results with the findings in 25 chronic cholecystitis (CC) specimens without cancer. The methylation-specific polymerase chain reaction and combined restriction analysis methods were used to detect methylation, and the results were confirmed by sequencing of cloned polymerase chain reaction products. Results: In GBC, gene methylation frequencies varied from 0% to 80%. Ten genes demonstrated relatively high frequencies of aberrant methylation: SHP1 (80%), 3-OST-2 (72%), CDH13 (44%), P-15(INK4B) (44%), CDH1 (38%), RUNX3 (32%), APC (30%), RIZ1 (26%), P16(INK4A) (24%), and HPP1 (20 %). Eight genes (P73, RARbeta2, SOCS-1, DAPK, DcR2, DcR1, HIN1, and CHFR) showed low frequencies (2-14%) of methylation, and no methylation of the remaining six genes (TIMP-3, P57, RASSF1A, CRBP1, SYK, and NORE1) was detected. In CC, methylation was detected for seven genes: SHP1 (88 %), P15(INK4B) (28 %), 3-OST-2 (12%), CDH1 (12 %), CDH13 (8 %), DcR2 (4 %), and P16(INK4A) (4%) Significantly higher frequencies of methylation in GBC compared with CC were detected for eight genes (3-OST-2, CDH13, CDH1, RUNX3, APC, RIZ1, P16(INK4A), and HPP1). Of those, four genes showed frequent methylation (>30%) in GBCs. The mean methylation index, an expression of the amount of methylated genes by case, was significantly higher in GBC (0.196 +/- 0.013) compared with CC (0.065 +/- 0.008; P < 0.001). Conclusions: Our study constitutes the most comprehensive methylation profile report available in GBC and demonstrates that this neoplasm has a distinct pattern of abnormal gene methylation. Whereas gallbladders from healthy individual were not available, our finding of methylation in CC cases without cancer suggests that this phenomenon represents an early event in the pathogenesis of GBC.
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    Borrowing Strength with Nonexchangeable Priors over Subpopulations
    (WILEY-BLACKWELL, 2012) Leon Novelo, L. G.; Bekele, B. Nebiyou; Mueller, P.; Quintana, F.; Wathen, K.
    We introduce a nonparametric Bayesian model for a phase II clinical trial with patients presenting different subtypes of the disease under study. The objective is to estimate the success probability of an experimental therapy for each subtype. We consider the case when small sample sizes require extensive borrowing of information across subtypes, but the subtypes are not a priori exchangeable. The lack of a priori exchangeability hinders the straightforward use of traditional hierarchical models to implement borrowing of strength across disease subtypes. We introduce instead a random partition model for the set of disease subtypes. This is a variation of the product partition model that allows us to model a nonexchangeable prior structure. Like a hierarchical model, the proposed clustering approach considers all observations, across all disease subtypes, to estimate individual success probabilities. But in contrast to standard hierarchical models, the model considers disease subtypes a priori nonexchangeable. This implies that when assessing the success probability for a particular type our model borrows more information from the outcome of the patients sharing the same prognosis than from the others. Our data arise from a phase II clinical trial of patients with sarcoma, a rare type of cancer affecting connective or supportive tissues and soft tissue (e.g., cartilage and fat). Each patient presents one subtype of the disease and subtypes are grouped by good, intermediate, and poor prognosis. The prior model should respect the varying prognosis across disease subtypes. The practical motivation for the proposed approach is that the number of accrued patients within each disease subtype is small. Thus it is not possible to carry out a clinical study of possible new therapies for rare conditions, because it would be impossible to plan for sufficiently large sample size to achieve the desired power. We carry out a simulation study to compare the proposed model with a model that assumes similar success probabilities for all subtypes with the same prognosis, i.e., a fixed partition of subtypes by prognosis. When the assumption is satisfied the two models perform comparably. But the proposed model outperforms the competing model when the assumption is incorrect.