Browsing by Author "Culhane, Aedin"

Now showing items 1-18 of 18

    • Analysis of differential gene expression in colorectal cancer and stroma using fluorescence-activated cell sorting purification 

      Smith, M J; Culhane, Aedin; Donovan, M; Coffey, J C; Barry, B D; Kelly, M A; Higgins, D G; Wang, Jeffrey Tze-Yee; Kirwan, W O; Cotter, T G; Redmond, H P (Springer Nature, 2009)
      Tumour stroma gene expression in biopsy specimens may obscure the expression of tumour parenchyma, hampering the predictive power of microarrays. We aimed to assess the utility of fluorescence-activated cell sorting (FACS) ...

    • Angiogenic mRNA and microRNA Gene Expression Signature Predicts a Novel Subtype of Serous Ovarian Cancer 

      Risch, Thomas; Fan, Jian-Bing; Holton, Kristina; Rubio, Renee; April, Craig; Wickham-Garcia, Eliza; Bentink, Stefan; Haibe-Kains, Benjamin; Hirsch, Michelle S.; Chen, Jing; Liu, Joyce; Culhane, Aedin; Drapkin, Ronny I.; Quackenbush, John; Matulonis, Ursula Anne (Public Library of Science, 2012)
      Ovarian cancer is the fifth leading cause of cancer death for women in the U.S. and the seventh most fatal worldwide. Although ovarian cancer is notable for its initial sensitivity to platinum-based therapies, the vast ...

    • Confounding Effects in "A Six-Gene Signature Predicting Breast Cancer Lung Metastasis" 

      Culhane, Aedin; Quackenbush, John (American Association for Cancer Research (AACR), 2009)
      The majority of breast cancer deaths result from metastases rather than from direct effects of the primary tumor itself. Recently, Landemaine and colleagues described a six-gene signature purported to predict lung metastasis ...

    • Epithelial Progeny of Estrogen-Exposed Breast Progenitor Cells Display a Cancer-like Methylome 

      Cheng, A. S.L.; Culhane, Aedin; Chan, M. W.Y.; Venkataramu, C. R.; Ehrich, M.; Nasir, A.; Rodriguez, B. A.T.; Liu, J.; Yan, P. S.; Quackenbush, John; Nephew, K. P.; Yeatman, T. J.; Huang, T. H-M. (American Association for Cancer Research (AACR), 2008)
      Estrogen imprinting is used to describe a phenomenon in which early developmental exposure to endocrine disruptors increases breast cancer risk later in adult life. We propose that long-lived, self-regenerating stem and ...

    • Functional classification analysis of somatically mutated genes in human breast and colorectal cancers 

      Chittenden, Thomas W; Howe, Eleanor A.; Culhane, Aedin; Sultana, Razvan; Taylor, Jennifer M.; Holmes, Chris; Quackenbush, John (Elsevier BV, 2008)
      A recent study published by Sjoblom and colleagues performed comprehensive sequencing of 13,023 human genes and identified mutations in genes specific to breast and colorectal tumors, providing insight into organ-specific ...

    • Identification of Novel Kinase Targets for the Treatment of Estrogen Receptor-Negative Breast Cancer 

      Speers, C.; Tsimelzon, A.; Sexton, K.; Herrick, A. M.; Gutierrez, C.; Culhane, Aedin; Quackenbush, John; Hilsenbeck, S.; Chang, J.; Brown, P. (American Association for Cancer Research (AACR), 2009)
      Purpose—Previous gene expression profiling studies of breast cancer have focused on the entire genome to identify genes differentially expressed between estrogen receptor alpha (ER)-positive and ER-alpha-negative cancers. ...

    • Integrated Analysis of Multiple Microarray Datasets Identifies a Reproducible Survival Predictor in Ovarian Cancer 

      Konstantinopoulos, Panagiotis; Cannistra, Stephen Anthony; Fountzilas, Helen; Culhane, Aedin; Pillay, Kamana; Rueda, Bo Ruben; Cramer, Daniel William; Seiden, Michael; Birrer, Michael James; Coukos, George; Zhang, Lin; Quackenbush, John; Spentzos, Dimitrios (Public Library of Science (PLoS), 2011)
      Background Public data integration may help overcome challenges in clinical implementation of microarray profiles. We integrated several ovarian cancer datasets to identify a reproducible predictor of survival. Met ...

    • MECP2 Is a Frequently Amplified Oncogene with a Novel Epigenetic Mechanism That Mimics the Role of Activated RAS in Malignancy 

      Neupane, Manish; Clark, Allison Paige; Landini, S.; Birkbak, N; Eklund, A. C.; Lim, E.; Culhane, Aedin; Barry, William T.; Schumacher, Sandra Kay; Beroukhim, Rameen; Szallasi, Zoltan; Vidal, Marc; Hill, David E.; Silver, Daniel P. (American Association for Cancer Research (AACR), 2015)
      An unbiased genome-scale screen for unmutated genes that drive cancer growth when overexpressed identified MECP2 as a novel oncogene. MECP2 resides in a region of the Xchromosome that is significantly amplified across 18% ...

    • A multivariate approach to the integration of multi-omics datasets 

      Meng, Chen; Kuster, Bernhard; Culhane, Aedín C; Gholami, Amin Moghaddas (BioMed Central, 2014)
      Background: To leverage the potential of multi-omics studies, exploratory data analysis methods that provide systematic integration and comparison of multiple layers of omics information are required. We describe multiple ...

    • Palb2 synergizes with Trp53 to suppress mammary tumor formation in a model of inherited breast cancer 

      Bowman-Colin, Christian; Xia, B.; Bunting, S.; Klijn, C.; Drost, R.; Bouwman, P.; Fineman, L.; Chen, X.; Culhane, Aedin; Cai, H.; Rodig, Scott J.; Bronson, Roderick Terry; Jonkers, J.; Nussenzweig, A.; Kanellopoulou, C.; Livingston, David Morse (Proceedings of the National Academy of Sciences, 2013)
      Germ-line mutations in PALB2 lead to a familial predisposition to breast and pancreatic cancer or to Fanconi Anemia subtype N. PALB2 performs its tumor suppressor role, at least in part, by supporting homologous ...

    • Profiles of Genomic Instability in High-Grade Serous Ovarian Cancer Predict Treatment Outcome 

      Wang, Z. C.; Birkbak, N; Culhane, Aedin; Drapkin, R; Fatima, Aquila; Tian, R; Schwede, M.; Alsop, K.; Daniels, K. E.; Piao, H.; Liu, Joy; Etemadmoghadam, D.; Miron, A; Salvesen, H. B.; Mitchell, G.; DeFazio, A.; Quackenbush, John; Berkowitz, Ross Stuart; Iglehart, James Dirk; Bowtell, D. D. L.; Matulonis, Ursula Anne (American Association for Cancer Research (AACR), 2012)
      Purpose—High-grade serous cancer (HGSC) is the most common cancer of the ovary and is characterized by chromosomal instability. Defects in homologous recombination repair (HRR) are associated with genomic instability in ...

    • Proliferative genes dominate malignancy-risk gene signature in histologically-normal breast tissue 

      Chen, Dung-Tsa; Nasir, Aejaz; Culhane, Aedin; Venkataramu, Chinnambally; Fulp, William; Rubio, Renee; Wang, Tao; Agrawal, Deepak; McCarthy, Susan M.; Gruidl, Mike; Bloom, Gregory; Anderson, Tove; White, Joe; Quackenbush, John; Yeatman, Timothy (Springer Nature, 2009)
      PURPOSE—Historical data have indicated the potential for the histologically-normal breast to harbor pre-malignant changes at the molecular level. We postulated that a histologically-normal tissue with “tumor-like” gene ...

    • RAP80 Targets BRCA1 to Specific Ubiquitin Structures at DNA Damage Sites 

      Sobhian, B.; Shao, G.; Lilli, D. R.; Culhane, Aedin; Moreau, Lisa; Xia, B.; Livingston, David Morse; Greenberg, R. A. (American Association for the Advancement of Science (AAAS), 2007)
      Mutations affecting the BRCT domains of the breast cancer–associated tumor suppressor BRCA1 disrupt the recruitment of this protein to DNA double-strand breaks (DSBs). The molecular structures at DSBs recognized by BRCA1 ...

    • RelA-Induced Interferon Response Negatively Regulates Proliferation 

      Kochupurakkal, Bose S; Wang, Zhigang C.; Hua, Tony; Culhane, Aedin; Rodig, Scott J.; Rajkovic-Molek, Koraljka; Lazaro, Jean-Bernard; Richardson, Andrea; Biswas, Debajit Kumar; Iglehart, James Dirk (Public Library of Science (PLoS), 2015)
      Both oncogenic and tumor-suppressor activities are attributed to the Nuclear Factor kappa B (NF-kB) pathway. Moreover, NF-kB may positively or negatively regulate proliferation. The molecular determinants of these opposing ...

    • Significance Analysis of Prognostic Signatures 

      Beck, Andrew H; Knoblauch, Nicholas W.; Hefti, Marco; Kaplan, Jennifer; Schnitt, Stuart Jay; Culhane, Aedin; Schroeder, Markus S.; Risch, Thomas; Quackenbush, John; Haibe-Kains, Benjamin (Public Library of Science (PLoS), 2013)
      A major goal in translational cancer research is to identify biological signatures driving cancer progression and metastasis. A common technique applied in genomics research is to cluster patients using gene expression ...

    • Taxonomy of breast cancer based on normal cell phenotype predicts outcome 

      Santagata, Sandro; Thakkar, Ankita; Ergonul, Ayse; Wang, Bin; Woo, Terri; Hu, Rong; Harrell, J. Chuck; McNamara, George; Schwede, Matthew; Culhane, Aedin; Kindelberger, David; Rodig, Scott J.; Richardson, Andrea; Schnitt, Stuart Jay; Tamimi, Rulla May; Ince, Tan A. (American Society for Clinical Investigation, 2014)
      Accurate classification is essential for understanding the pathophysiology of a disease and can inform therapeutic choices. For hematopoietic malignancies, a classification scheme based on the phenotypic similarity between ...

    • Therapeutic Implications of GIPC1 Silencing in Cancer 

      Chittenden, Thomas W.; Pak, Jane; Rubio, Renee; Holton, Kristina; Prendergast, Niall; Glinskii, Vladimir; Schwede, Mathew; Howe, Eleanor A.; Aryee, Martin; Sultana, Razvan; Lanahan, Anthony A.; Holmes, Chris; Cheng, Hailing; Cai, Yi; Culhane, Aedin; Bentink, Stefan; Mar, Jessica Cara; Taylor, Jennifer; Hahn, William C.; Zhao, Jean J.; Iglehart, James Dirk; Quackenbush, John (Public Library of Science, 2010)
      GIPC1 is a cytoplasmic scaffold protein that interacts with numerous receptor signaling complexes, and emerging evidence suggests that it plays a role in tumorigenesis. GIPC1 is highly expressed in a number of human ...

    • A Three-Gene Model to Robustly Identify Breast Cancer Molecular Subtypes 

      Haibe-Kains, B; Desmedt, C.; Loi, S.; Culhane, Aedin; Bontempi, G.; Quackenbush, John; Sotiriou, C. (Oxford University Press (OUP), 2012)
      Background - Single sample predictors (SSPs) and Subtype classification models (SCMs) are gene expression–based classifiers used to identify the four primary molecular subtypes of breast cancer (basal-like, HER2-enriched, ...