Does Survival Vary for Breast Cancer Patients in the United States? A Study from Six Randomly Selected States.

BACKGROUND: Breast cancer is the most common cancer in women. Disparities in some characteristics of breast cancer patients and their survival data for six randomly selected states in the US were examined. MATERIALS AND METHODS: A probability random sampling method was used to select the records of 2,000 patients from each of six randomly selected states. Demographic and disease characteristics were extracted from the Surveillance Epidemiology and End Results (SEER) database. To evaluate relationships between variables, we employed a Cox Proportional Regression to compare survival times in the different states. RESULTS: Iowa had the highest mean age of diagnosis at 64.14 years (SE = 0.324) and Georgia had the lowest at 57.97 years (SE = 0.313). New Mexico had the longest mean survival time of 189.09 months (SE = 20.414) and Hawaii the shortest at 119.01 (SE = 5.394) months, a 70.08-month difference (5.84 years). Analysis of stage of diagnosis showed that the highest survival times for Whites and American Indians/Alaska Natives were for stage I cancers. The highest survival times for Blacks varied. Stage IV cancer consistently showed the lowest survival times. CONCLUSIONS: Differences in breast cancer characteristics across states highlight the need to understand differences between the states that result in variances in breast cancer survival.

Does Breast Cancer Drive the Building of Survival Probability Models among States? An Assessment of Goodness of Fit for Patient Data from SEER Registries

Background: Breast cancer is a worldwide public health concern and is the most prevalent type of cancer in women in the United States. This study concerned the best fit of statistical probability models on the basis of survival times for nine state cancer registries: California, Connecticut, Georgia, Hawaii, Iowa, Michigan, New Mexico, Utah, and Washington. Materials and Methods: A probability random sampling method was applied to select and extract records of 2,000 breast cancer patients from the Surveillance Epidemiology and End Results (SEER) database for each of the nine state cancer registries used in this study. EasyFit software was utilized to identify the best probability models by using goodness of fit tests, and to estimate parameters for various statistical probability distributions that fit survival data. Results: Statistical analysis for the summary of statistics is reported for each of the states for the years 1973 to 2012. Kolmogorov-Smirnov, Anderson-Darling, and Chi-squared goodness of fit test values were used for survival data, the highest values of goodness of fit statistics being considered indicative of the best fit survival model for each state. Conclusions: It was found that California, Connecticut, Georgia, Iowa, New Mexico, and Washington followed the Burr probability distribution, while the Dagum probability distribution gave the best fit for Michigan and Utah, and Hawaii followed the Gamma probability distribution. These findings highlight differences between states through selected sociodemographic variables and also demonstrate probability modeling differences in breast cancer survival times. The results of this study can be used to guide healthcare providers and researchers for further investigations into social and environmental factors in order to reduce the occurrence of and mortality due to breast cancer.

Whole-exome sequencing reveals TopBP1 as a novel gene in idiopathic pulmonary arterial hypertension.

RATIONALE: Idiopathic pulmonary arterial hypertension (IPAH) is a life-threatening disorder characterized by progressive loss of pulmonary microvessels. Although mutations in the bone morphogenetic receptor 2 (BMPR2) are found in 80% of heritable and ∼15% of patients with IPAH, their low penetrance (∼20%) suggests that other unidentified genetic modifiers are required for manifestation of the disease phenotype. Use of whole-exome sequencing (WES) has recently led to the discovery of novel susceptibility genes in heritable PAH, but whether WES can also accelerate gene discovery in IPAH remains unknown. OBJECTIVES: To determine whether WES can help identify novel gene modifiers in patients with IPAH. METHODS: Exome capture and sequencing was performed on genomic DNA isolated from 12 unrelated patients with IPAH lacking BMPR2 mutations. Observed genetic variants were prioritized according to their pathogenic potential using ANNOVAR. MEASUREMENTS AND MAIN RESULTS: A total of nine genes were identified as high-priority candidates. Our top hit was topoisomerase DNA binding II binding protein 1 (TopBP1), a gene involved in the response to DNA damage and replication stress. We found that TopBP1 expression was reduced in vascular lesions and pulmonary endothelial cells isolated from patients with IPAH. Although TopBP1 deficiency made endothelial cells susceptible to DNA damage and apoptosis in response to hydroxyurea, its restoration resulted in less DNA damage and improved cell survival. CONCLUSIONS: WES led to the discovery of TopBP1, a gene whose deficiency may increase susceptibility to small vessel loss in IPAH. We predict that use of WES will help identify gene modifiers that influence an individual's risk of developing IPAH.