Burstein M.D.,Structural and Computation Biology and Molecular Biophysics Graduate Program |
Robinson J.O.,Center for Medical Ethics and Health Policy |
Hilsenbeck S.G.,Dan ncan Cancer Center |
McGuire A.L.,Center for Medical Ethics and Health Policy |
And 2 more authors.
OBJECTIVE: In the United States, data from federally funded genomics studies are stored in national databases, which may be accessible to anyone online (public release) or only to qualified researchers (restricted release). The availability of such data exposes participants to privacy risk and limits the ability to withdraw from research. This exposure is especially challenging for pediatric participants, who are enrolled in studies with parental permission. The current study examines genomic research participants' attitudes to explore differences in data sharing (DS) preferences between parents of pediatric patients and adult patients. METHODS: A total of 113 parents of pediatric patients and 196 adult participants from 6 genomics studies were randomly assigned to 3 experimental consent forms. Participants were invited to a follow-up structured interview exploring DS preferences, study understanding, and attitudes. Descriptive analyses and regression models were built on responses. RESULTS: Most parents (73.5%) and adult participants (90.3%) ultimately consented to broad public release. However, parents were significantly more restrictive in their data release decisions, not because of understanding or perceived benefits of participation but rather autonomy and control. Parents want to be more involved in the decision about DS and are significantly more concerned than adult participants about unknown future risks. CONCLUSIONS: Parents have the same altruistic motivations and grasp of genomics studies as adult participants. However, they are more concerned about future risks to their child, which probably motivates them to choose more restrictive DS options, but only when such options are made available. © 2014 by the American Academy of Pediatrics. Source
Ding Y.,University of Texas Health Science Center at Houston |
He D.,Wayne State University |
Florentin D.,Wayne State University |
Frolov A.,Dan ncan Cancer Center |
And 4 more authors.
Clinical Cancer Research
Background: Semaphorin 4F (S4F) has roles in embryologic axon guidance and is expressed in adults. S4F is involved in cancer-induced neurogenesis. Methods: Prostate cells were transfected with S4F retrovirus. Cells and controls were used for a bromodeoxyuridine (BrdUrd) incorporation assay (proliferation) and in vitro scratch and Matrigel Transwell chamber invasion assay (migration). Monoclonal antibodies were developed using baculovirusexpressed recombinant GST-S4F and used to immunostain tissue microarrays. Slides were imaged using deconvolution and analyzed using tissue segmentation. Data were correlated with clinicopathologic parameters, other biomarkers and survival analysis conducted. Heterogeneity of S4F expression was analyzed with unsupervised clustering algorithms. Results: Proliferation rates measured by BrdUrd incorporation were higher in all S4F-transfected cells. S4F overexpression was associated with increased motility of the cancer cells. S4F expression was overexpressed in high-grade prostatic intraepithelial neoplasia/prostate cancer than normal epithelium. S4F expression correlated with seminal vesicle invasion. Patients with high values of S4F in prostate cancer cytoplasm are at significantly higher risk of biochemical recurrence, by univariate and multivariate analyses. S4F cytoplasmic expression in prostate cancer cells also correlates with nerve density in prostate cancer and perineural invasion diameter. Correlations were identified with NF-kB and inversely with apoptosis in perineural invasion. Conclusion: These data show that S4F is significantly involved in human prostate cancer progression. S4F is a key regulator of the interactions between nerves in the tumor microenvironment and cancer cells. Because of the importance of cancer nerve interaction in the biology of cancer and its clinical implication, S4F can be considered a major therapeutic target. © 2013 American Association for Cancer Research. Source
Zhao X.,Texas Childrens Cancer Center |
Liu Z.,Texas Childrens Cancer Center |
Yu L.,Texas Childrens Cancer Center |
Zhang Y.,Texas Childrens Cancer Center |
And 10 more authors.
We previously showed that primary tumor-based orthotopic xenograft mouse models of medulloblastoma replicated the histopathological phenotypes of patients original tumors. Here, we performed global gene expression profiling of 11 patient-specific xenograft models to further determine whether the xenograft tumors were molecularly accurate during serial subtransplantations in mouse brains and whether they represented all the molecular subtypes of medulloblastoma that were recently described. Analysis of the transcriptomes of 9 pairs of matched passage I xenografts and patients tumors revealed high correlation coefficients (r 2 > 0.95 in 5 models, > 0.9 in 3 models, and > 0.85 in 1 model) and only identified 69 genes in which expressions were altered (FDR =0.0023). Subsequent pair-wise comparisons between passage I, III, and V xenografts from the 11 models further showed that no dramatic alterations were introduced (r 2 > 0.9 in 8 models and > 0.8 in 3 models). The genetic abnormalities of each model were then identified through comparison with control RNAs from 5 normal cerebella and 2 fetal brains. Hierarchical clustering using 3 previously published molecular signatures showed that our models span the whole spectrum of molecular subtypes, including SHH (n = 2), WNT (n = 2), and the most recently identified group C (n = 4) and group D (n = 3). In conclusion, we demonstrated that the 11 orthotopic medulloblastoma xenograft models were molecularly faithful to the primary tumors, and our comprehensive collection of molecularly distinct animal models should serve as a valuable resource for the development of new targeted therapies for medulloblastoma. © 2012 The Author(s). Source
Chen K.,Dan ncan Cancer Center |
Xi Y.,Dan ncan Cancer Center |
Pan X.,Dan ncan Cancer Center |
Pan X.,Baylor College of Medicine |
And 5 more authors.
Recent developments in next-generation sequencing have enabled whole-genome profiling of nucleosome organizations. Although several algorithms for inferring nucleosome position from a single experimental condition have been available, it remains a challenge to accurately define dynamic nucleosomes associated with environmental changes. Here, we report a comprehensive bioinformatics pipeline, DANPOS, explicitly designed for dynamic nucleosome analysis at single-nucleotide resolution. Using both simulated and real nucleosome data, we demonstrated that bias correction in preliminary data processing and optimal statistical testing significantly enhances the functional interpretation of dynamic nucleosomes. The single-nucleotide resolution analysis of DANPOS allows us to detect all three categories of nucleosome dynamics, such as position shift, fuzziness change, and occupancy change, using a uniform statistical framework. Pathway analysis indicates that each category is involved in distinct biological functions. We also analyzed the influence of sequencing depth and suggest that even 200-fold coverage is probably not enough to identify all the dynamic nucleosomes. Finally, based on nucleosome data from the human hematopoietic stem cells (HSCs) and mouse embryonic stem cells (ESCs), we demonstrated that DANPOS is also robust in defining functional dynamic nucleosomes, not only in promoters, but also in distal regulatory regions in the mammalian genome. © 2013, Published by Cold Spring Harbor Laboratory Press. Source
Luta G.,Georgetown University |
Ford M.B.,MBFord Consulting |
Bondy M.,Dan ncan Cancer Center |
Bondy M.,Baylor College of Medicine |
And 2 more authors.
Background: Recent research suggests that the Bayesian paradigm may be useful for modeling biases in epidemiological studies, such as those due to misclassification and missing data. We used Bayesian methods to perform sensitivity analyses for assessing the robustness of study findings to the potential effect of these two important sources of bias. Methods: We used data from a study of the joint associations of radiotherapy and smoking with primary lung cancer among breast cancer survivors. We used Bayesian methods to provide an operational way to combine both validation data and expert opinion to account for misclassification of the two risk factors and missing data. For comparative purposes we considered a " full model" that allowed for both misclassification and missing data, along with alternative models that considered only misclassification or missing data, and the naïve model that ignored both sources of bias. Results: We identified noticeable differences between the four models with respect to the posterior distributions of the odds ratios that described the joint associations of radiotherapy and smoking with primary lung cancer. Despite those differences we found that the general conclusions regarding the pattern of associations were the same regardless of the model used. Overall our results indicate a nonsignificantly decreased lung cancer risk due to radiotherapy among nonsmokers, and a mildly increased risk among smokers. Conclusions: We described easy to implement Bayesian methods to perform sensitivity analyses for assessing the robustness of study findings to misclassification and missing data. © 2012 Elsevier Ltd. Source