The Biostatistics, Bioinformatics and Systems Biology (BBSB) Core 1. provides statistics and data analysis required by the Projects and Core to achieve their specific aims; 2. provides bioinformatics and systems biology expertise required for analysis of high-throughput assay data; 3. assists in the design and implementation of trials and studies arising from the ongoing research of the SPORE; 4. provides data management support for the Cores and Projects. The Core assists Project 1 with the development of high dimensional predictive models for early detection, and with the identification of further potential markers (e.g. autoantibodies) for improving panels available. The Core assists Project 2 with the design and analysis of in vitro and in vivo studies interrogating the interaction of DII4-Notch signaling, angiogenesis, and VEGF in ovarian cancer. The Core will provide assistance with the design and analysis of a Phase I clinical trial. The Core assists Project 3 with the characterization low grade tumor data from SNP arrays, expression arrays and mutation data measured on selected genes by direct sequencing. The Core assists with biomarker identification, sample size and power calculations, and with planning for a phase I clinical trial. The Core assists Project 4 with refining definitions for PISKness and BRCAness using data from several high-throughput assays, and with determining the extent to which these signatures are predictive of response in cell line and xenograft models. The Core is also providing assistance with the design of two Phase II trials involving targeted therapies. The Core assists Project 5 with the determination of the optimal dose, gene product, and combination with chemtherapy for treatment with Mesenchymal Stem Cells (MSCs). The Core will also assist with assessing effectiveness of MSC therapy and with the design of a Phase l/ll trial to identify an optimal biological dose with acceptable toxicity.
The BBSB Core provides support to the Projects and Cores of the SPORE. This support includes designing in vivo and in vitro experiments and analyzing results obtained, designing new clinical trials, assisting in the conduct of and analyzing results from ongoing trials, supplying analysis and interpretation of high-throughput assays, combining results across assays, and helping interpret results in terms of component pathways.
|Bottsford-Miller, Justin; Choi, Hyun-Jin; Dalton, Heather J et al. (2015) Differential platelet levels affect response to taxane-based therapy in ovarian cancer. Clin Cancer Res 21:602-10|
|Sun, Yan; Hu, Limei; Zheng, Hong et al. (2015) MiR-506 inhibits multiple targets in the epithelial-to-mesenchymal transition network and is associated with good prognosis in epithelial ovarian cancer. J Pathol 235:25-36|
|Wen, Yunfei; Graybill, Whitney S; Previs, Rebecca A et al. (2015) Immunotherapy targeting folate receptor induces cell death associated with autophagy in ovarian cancer. Clin Cancer Res 21:448-59|
|Previs, Rebecca A; Coleman, Robert L; Harris, Adrian L et al. (2015) Molecular pathways: translational and therapeutic implications of the Notch signaling pathway in cancer. Clin Cancer Res 21:955-61|
|Zhang, Shu; Lu, Zhen; Unruh, Anna K et al. (2015) Clinically relevant microRNAs in ovarian cancer. Mol Cancer Res 13:393-401|
|Zhang, Shiwu; Mercado-Uribe, Imelda; Liu, Jinsong (2014) Tumor stroma and differentiated cancer cells can be originated directly from polyploid giant cancer cells induced by paclitaxel. Int J Cancer 134:508-18|
|Matsuo, Koji; Sheridan, Todd B; Mabuchi, Seiji et al. (2014) Estrogen receptor expression and increased risk of lymphovascular space invasion in high-grade serous ovarian carcinoma. Gynecol Oncol 133:473-9|
|Liu, Guoyan; Sun, Yan; Ji, Ping et al. (2014) MiR-506 suppresses proliferation and induces senescence by directly targeting the CDK4/6-FOXM1 axis in ovarian cancer. J Pathol 233:308-18|
|Rich, Thereasa A; Liu, Mei; Etzel, Carol J et al. (2014) Comparison of attitudes regarding preimplantation genetic diagnosis among patients with hereditary cancer syndromes. Fam Cancer 13:291-9|
|Zhang, S; Mercado-Uribe, I; Xing, Z et al. (2014) Generation of cancer stem-like cells through the formation of polyploid giant cancer cells. Oncogene 33:116-28|
Showing the most recent 10 out of 381 publications