Biomedical subjects

J M Satagopan

Publications and source records attributed to J M Satagopan.

6 recordsLinked to original sources

PubMed2006

Assessing interactions between mdm-2, p53, and bcl-2 as prognostic variables in muscle-invasive bladder cancer treated with neo-adjuvant chemotherapy followed by locoregional surgical treatment.

BACKGROUND: Tumor proliferation and apoptosis may be influenced by the mdm-2 gene product, which can block the antiproliferative effects of p53. bcl-2, one of a family of related genes that regulates the apoptotic pathway, exhibits a negative influence. Both individual and cooperative effects of these gene products may affect the biological behavior of primary bladder cancers and long-term outcome to standard therapy. METHODS: This study retrospectively evaluated the association with survival of mdm-2, p53, and bcl-2 expression in 59 patients with muscle-invasive, node-negative transitional cell carcinoma (TCC) treated with neo-adjuvant chemotherapy followed by locoregional surgery. Each marker was defined as an altered phenotype if >or=20% malignant cells in the primary tumor exhibited staining; normal or minimal expression was defined as <20% cells exhibiting staining. RESULTS: Altered mdm-2, p53, and bcl-2 expression was observed in 37%, 54%, and 46% of patients, respectively. In single marker analysis, altered p53 expression correlated with long-term survival (P = 0.05) but mdm-2 (P = 0.42) or bcl-2 (P = 0.17) did not. In the multiple-marker analysis, a prognostic index simultaneously assessing mdm-2, p53, and bcl-2 correlated with survival (P = 0.01). The 5-year survival for patients in which all markers were normally expressed was 54% compared with 25% in those with all three markers aberrantly expressed. Patients with aberrant expression of either one or two markers had an intermediate 5-year survival (49%). There was no association of molecular markers either alone or in combination with pathologic downstaging after neo-adjuvant chemotherapy. CONCLUSION: The cooperative effects of phenotypes determined by mdm-2, p53, and bcl-2 expression may predict survival in patients with muscle-invasive TCC of the bladder.

Adult↗

PubMed2004

A note on competing risks in survival data analysis.

Survival analysis encompasses investigation of time to event data. In most clinical studies, estimating the cumulative incidence function (or the probability of experiencing an event by a given time) is of primary interest. When the data consist of patients who experience an event and censored individuals, a nonparametric estimate of the cumulative incidence can be obtained using the Kaplan-Meier method. Under this approach, the censoring mechanism is assumed to be noninformative. In other words, the survival time of an individual (or the time at which a subject experiences an event) is assumed to be independent of a mechanism that would cause the patient to be censored. Often times, a patient may experience an event other than the one of interest which alters the probability of experiencing the event of interest. Such events are known as competing risk events. In this setting, it would often be of interest to calculate the cumulative incidence of a specific event of interest. Any subject who does not experience the event of interest can be treated as censored. However, a patient experiencing a competing risk event is censored in an informative manner. Hence, the Kaplan-Meier estimation procedure may not be directly applicable. The cumulative incidence function for an event of interest must be calculated by appropriately accounting for the presence of competing risk events. In this paper, we illustrate nonparametric estimation of the cumulative incidence function for an event of interest in the presence of competing risk events using two published data sets. We compare the resulting estimates with those obtained using the Kaplan-Meier approach to demonstrate the importance of appropriately estimating the cumulative incidence of an event of interest in the presence of competing risk events.

Humans↗

PubMed2001

The lifetime risks of breast cancer in Ashkenazi Jewish carriers of BRCA1 and BRCA2 mutations.

Several studies using families with multiple occurrences of breast cancer have provided evidence for a very high lifetime penetrance in carriers of BRCA1 or BRCA2 mutations. However, there are reasons to suspect that the estimates of penetrance from studies of cancer families may be inflated. Access to the genotypes of incident cases of breast cancer in three hospitals and from a large series of unaffected survey participants provided the basis for direct estimation of the age-specific relative risks attributable to these mutations, and the resulting lifetime penetrance, without any reference to familial aggregation of cancer. Cases were identified from incident series of Jewish patients treated for primary breast cancer at the three hospitals. Control data were obtained from the large series of Jewish women recruited in the Washington, D.C., area by investigators at the National Cancer Institute and limited to 3434 women with no previous history of breast or ovarian cancer. All subjects were genotyped for the three mutations that are relatively common in Ashkenazi Jews, namely 185delAG and 5382 insC in BRCA1 and 6174delT in BRCA2. For BRCA1, the relative risks of breast cancer were estimated to be 21.6 in women under 40 years of age, 9.6 in women 40-49 years of age, and 7.6 in women > or = 50 years of age. On the basis of these estimates, the penetrance of breast cancer at age 70 among BRCA1 mutation carriers is estimated to be 46% (95% confidence, 31%-80%) rising to 59% (95% confidence, 40%-93%) at age 80. For BRCA2, the relative risks in the same three age categories were estimated to be 3.3, 3.3, and 4.6, respectively, resulting in a penetrance at age 70 of 26% (95% confidence, 14%-50%) rising to 38% (95% confidence, 20%-68%) at age 80. The lifetime risk of breast cancer in Jewish women who are mutation carriers estimated via this approach is substantially lower than the reported lifetime risks estimated using multiple-case families. The risks appear to be different for carriers of BRCA1 and BRCA2 mutations.

Adult↗

PubMed1999

Association between molecular detection of GAGE and survival in patients with malignant melanoma: a retrospective cohort study.

We used GAGE as a molecular marker to identify melanoma cells with metastatic potential in the peripheral blood and the bone marrow. One hundred thirty-three patients with malignant melanoma (21 clinical stage II, 74 stage III, and 38 stage IV) had a single marrow and/or blood sample drawn immediately prior to surgical resection. Simultaneous bone marrow and blood samples (85 patients), marrow-only samples (35 patients), and blood-only samples (13 patients) were examined for the presence of GAGE expression using reverse transcription-PCR. GAGE expression was associated with adverse overall patient survival, measured from the time of sampling (P = 0.01). When data were stratified for clinical stage, median survival was statistically longer among GAGE-negative patients in the stage III cohort only (P = 0.01). In a multivariate model, only GAGE positivity in blood and/or marrow and clinical stage were significant prognostic variables. It was the detection of GAGE in blood but not marrow that was associated with poor survival. The detection of blood GAGE by reverse transcription-PCR has significant adverse implications for overall survival of patients with malignant melanoma in this cohort, and it warrants further investigation.

Adult↗

PubMed1998

Type I transforming growth factor beta receptor maps to 9q22 and exhibits a polymorphism and a rare variant within a polyalanine tract.

In a search for mutations of the type I transforming growth factor beta receptor (TbetaR-I), we mapped the gene to 9q22 and found a common polymorphism [TbetaR-I(6A)] and a rare variant [TbetaR-I(10A)] of TbetaR-I, causing an in-frame deletion of three alanines and an in-frame insertion of one alanine, respectively, in the receptor's extracellular domain. The biological relevance of the polymorphism TbetaR-I(6A) was investigated. When TbetaR-I(6A) was transiently transfected into TbetaR-I-deficient cells, the growth-inhibitory effects of transforming growth factor beta were restored. TbetaR-I(6A) and TbetaR-I(10A) frequency were assessed in 108 tumor samples and 80 nontumor samples from patients with a diagnosis of cancer, as well as in 118 normal blood donors of comparable ethnic composition. The frequency of TbetaR-I(6A) heterozygotes was fairly similar in normal blood donors (8%), in nontumor DNA of patients with a diagnosis of cancer (10%), and in tumor samples (14%). However, the frequency of TbetaR-I(6A) homozygotes among nontumor (4%) and tumor (8%) samples obtained from patients with a diagnosis of cancer was higher than that predicted by the Hardy-Weinberg law. The clinical and biological significance of TbetaR-I(6A) homozygosity needs to be further investigated.

Acute Disease↗

PubMed1996

A bayesian approach to detect quantitative trait loci using Markov chain Monte Carlo.

Markov chain Monte Carlo (MCMC) techniques are applied to simultaneously identify multiple quantitative trait loci (QTL) and the magnitude of their effects. Using a Bayesian approach a multi-locus model is fit to quantitative trait and molecular marker data, instead of fitting one locus at a time. The phenotypic trait is modeled as a linear function of the additive and dominance effects of the unknown QTL genotypes. Inference summaries for the locations of the QTL and their effects are derived from the corresponding marginal posterior densities obtained by integrating the likelihood, rather than by optimizing the joint likelihood surface. This is done using MCMC by treating the unknown QTL, genotypes, and any missing marker genotypes, as augmented data and then by including these unknowns in the Markov chain cycle alone with the unknown parameters. Parameter estimates are obtained as means of the corresponding marginal posterior densities. High posterior density regions of the marginal densities are obtained as confidence regions. We examine flowering time data from double haploid progeny of Brassica napus to illustrate the proposed method.

Bayes Theorem↗