Medicine and Health Sciences Commons^™

Phylogenetic Tree Construction And “Truncal Loss” Analysis Reveal Hidden Associations Between Loss Of Protein Expression In Swi/Snf Complex Components And Tumor Stage And Survival In Clear Cell Renal Cell Carcinoma (Ccrcc), Wei Jiang, Md, Phd, Essel Dulaimi, Karthik Devarajan, Qiong Wang, Raymond O'Neill, Charalambos C. Solomides, Md, Stephen C Peiper, Phd, Robert Uzzo, Joseph R. Testa, Haifeng Yang, Phd

Department of Pathology, Anatomy, and Cell Biology Faculty Papers

Background

Polybromo-1 (PBRM1), a targeting subunit of the SWI/SNF chromatin remodeling complex, is mutated at a rate of ~40% in clear cell Renal Cell Carcinoma (ccRCC), second only to VHL. Whether its mutation is correlated with tumor stage is controversial. As other components of the SWI/SNF complex were also reported to be mutated in ccRCC, we aim to examine the protein expression patterns of PBRM1, ARID1A, BRG1, and BRM in ccRCC, and to investigate possible association between their loss of expression and tumor stage, as well as survival. We also included a histone modifier, SETD2, which is recently discovered to …

Assessment For Risk Factors Associated With Local Recurrence In Chordoma, John A. Abraham, Md, Wei Jiang, Md, Phd

Department of Pathology, Anatomy, and Cell Biology Faculty Papers

Background

Chordoma is a rare but locally aggressive malignant neoplasm showing notochordal differentiation. The clinical differential diagnoses can be extensive, and definitive diagnosis often relies on histopathologic evaluation. Histologically, chordoma shows dual epithelial and mesenchymal differentiation, with various morphologies. Despite surgical resection and use of adjuvant radiation therapy, the local recurrence rate of chordoma remains high. We aim to establish factors associated with the increased risk of recurrence and help guide treatment decisions.

Department Of Pathology, Thomas Jefferson University, Identification Of Conserved Gene Expression Features Between Murine Mammary Carcinoma Models And Human Breast Tumors., Jason I Herschkowitz, Karl Simin, Victor J Weigman, Igor Mikaelian, Jerry Usary, Zhiyuan Hu, Karen E Rasmussen, Laundette P Jones, Shahin Assefnia, Subhashini Chandrasekharan, Michael G Backlund, Yuzhi Yin, Andrey I Khramtsov, Roy Bastein, John Quackenbush, Robert I Glazer, Powel H Brown, Jeffrey E Green, Levy Kopelovich, Priscilla A Furth, Juan P Palazzo, Olufunmilayo I Olopade, Philip S Bernard, Gary A Churchill, Terry Van Dyke, Charles M Perou

Department of Pathology, Anatomy, and Cell Biology Faculty Papers

BACKGROUND: Although numerous mouse models of breast carcinomas have been developed, we do not know the extent to which any faithfully represent clinically significant human phenotypes. To address this need, we characterized mammary tumor gene expression profiles from 13 different murine models using DNA microarrays and compared the resulting data to those from human breast tumors. RESULTS: Unsupervised hierarchical clustering analysis showed that six models (TgWAP-Myc, TgMMTV-Neu, TgMMTV-PyMT, TgWAP-Int3, TgWAP-Tag, and TgC3(1)-Tag) yielded tumors with distinctive and homogeneous expression patterns within each strain. However, in each of four other models (TgWAP-T121, TgMMTV-Wnt1, Brca1Co/Co;TgMMTV-Cre;p53+/- and DMBA-induced), tumors with a variety of …

Classification And Risk Stratification Of Invasive Breast Carcinomas Using A Real-Time Quantitative Rt-Pcr Assay., Laurent Perreard, Cheng Fan, John F Quackenbush, Michael Mullins, Nicholas P Gauthier, Edward Nelson, Mary Mone, Heidi Hansen, Saundra S Buys, Karen Rasmussen, Alejandra Ruiz Orrico, Donna Dreher, Rhonda Walters, Joel Parker, Zhiyuan Hu, Xiaping He, Juan P Palazzo, Olufunmilayo I Olopade, Aniko Szabo, Charles M Perou, Philip S Bernard

Department of Pathology, Anatomy, and Cell Biology Faculty Papers

INTRODUCTION: Predicting the clinical course of breast cancer is often difficult because it is a diverse disease comprised of many biological subtypes. Gene expression profiling by microarray analysis has identified breast cancer signatures that are important for prognosis and treatment. In the current article, we use microarray analysis and a real-time quantitative reverse-transcription (qRT)-PCR assay to risk-stratify breast cancers based on biological 'intrinsic' subtypes and proliferation. METHODS: Gene sets were selected from microarray data to assess proliferation and to classify breast cancers into four different molecular subtypes, designated Luminal, Normal-like, HER2+/ER-, and Basal-like. One-hundred and twenty-three breast samples (117 invasive …