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- All Subjects: Breast Cancer
Description
Background: Breast cancer is the most frequently diagnosed cancer and the leading cause of cancer deaths in females worldwide, accounting for 23% of all new cancer cases and 14% of all total cancer deaths in 2008. Five tumor-normal pairs of primary breast epithelial cells were treated for infinite proliferation by using a ROCK inhibitor and mouse feeder cells. Methods: Raw paired-end, 100x coverage RNA-Seq data was aligned to the Human Reference Genome Version 19 using BWA and Tophat. Gene differential expression analysis was completed using Cufflinks and Cuffdiff. Interactive Genome Viewer was used for data visualization. Results: 15 genes were found to be down-regulated by at least one log-fold change in 4/5 of tumor samples. 75 genes were found to be down-regulated in 3/5 of our tumor samples by at least one log-fold change. 11 genes were found to be up-regulated in 4/5 of our tumor samples, and 68 genes were identified to be up-regulated in 3/5 of the tumor samples by at least one-fold change. Conclusion: Expression changes in genes such as AZGP1, AGER, ALG11, and S1007 suggest a disruption in the glycosylation pathway. No correlation was found between Cufflink's Her2 gene-expression and DAKO score classification.
ContributorsHernandez, Fernando (Author) / Anderson, Karen (Thesis director) / Mangone, Marco (Committee member) / Park, Jin (Committee member) / Barrett, The Honors College (Contributor) / Department of Information Systems (Contributor)
Created2013-05
Description
Extracellular vesicles (EVs) represent a heterogeneous population of small vesicles, consisting of a phospholipidic bilayer surrounding a soluble interior cargo. These vesicles play an important role in cellular communication by virtue of their protein, RNA, and lipid content, which can be transferred among cells. Peripheral blood is a rich source of circulating EVs. An analysis of EVs in peripheral blood could provide access to unparalleled amounts of biomarkers of great diagnostic, prognostic as well as therapeutic value. In the current study, a plasma EV enrichment method based on pluronic co-polymer was first established and characterized. Plasma EVs from breast cancer patients were then enriched, profiled and compared to non-cancer controls. Proteins signatures that contributed to the prediction of cancer samples from non-cancer controls were created by a random-forest based cross-validation approach. We found that a large portion of these signatures were related to breast cancer aggression. To verify such findings, KIAA0100, one of the features identified, was chosen for in vitro molecular and cellular studies in the breast cancer cell line MDA-MB-231. We found that KIAA0100 regulates cancer cell aggression in MDA-MB-231 in an anchorage-independent manner and is particularly associated with anoikis resistance through its interaction with HSPA1A. Lastly, plasma EVs contain not only individual proteins, but also numerous molecular complexes. In order to measure millions of proteins, isoforms, and complexes simultaneously, Adaptive Dynamic Artificial Poly-ligand Targeting (ADAPT) platform was applied. ADAPT employs an enriched library of single-stranded oligodeoxynucleotides to profile complex biological samples, thus achieving a deep coverage of system-wide, native biomolecules. Profiling of EVs from breast cancer patients was able to obtain a prediction AUC performance of 0.73 when compared biopsy-positive cancer patient to healthy controls and 0.64 compared to biopsy-negative controls and such performance was not associated with the physical breast condition indicated by BIRAD scores. Taken together, current research demonstrated the potential of profiling plasma EVs in searching for therapeutic targets as well as diagnostic signatures.
ContributorsZhong, Zhenyu (Author) / Spetzler, David (Thesis advisor) / Yan, Hao (Thesis advisor) / Lake, Douglas (Committee member) / Mangone, Marco (Committee member) / Arizona State University (Publisher)
Created2018