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Culhane, Aedin

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Culhane

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Aedin

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Culhane, Aedin
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Now showing 1 - 10 of 18
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    A multivariate approach to the integration of multi-omics datasets
    (BioMed Central, 2014) Meng, Chen; Kuster, Bernhard; Culhane, Aedin; Gholami, Amin Moghaddas
    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 co-inertia analysis (MCIA), an exploratory data analysis method that identifies co-relationships between multiple high dimensional datasets. Based on a covariance optimization criterion, MCIA simultaneously projects several datasets into the same dimensional space, transforming diverse sets of features onto the same scale, to extract the most variant from each dataset and facilitate biological interpretation and pathway analysis. Results: We demonstrate integration of multiple layers of information using MCIA, applied to two typical “omics” research scenarios. The integration of transcriptome and proteome profiles of cells in the NCI-60 cancer cell line panel revealed distinct, complementary features, which together increased the coverage and power of pathway analysis. Our analysis highlighted the importance of the leukemia extravasation signaling pathway in leukemia that was not highly ranked in the analysis of any individual dataset. Secondly, we compared transcriptome profiles of high grade serous ovarian tumors that were obtained, on two different microarray platforms and next generation RNA-sequencing, to identify the most informative platform and extract robust biomarkers of molecular subtypes. We discovered that the variance of RNA-sequencing data processed using RPKM had greater variance than that with MapSplice and RSEM. We provided novel markers highly associated to tumor molecular subtype combined from four data platforms. MCIA is implemented and available in the R/Bioconductor “omicade4” package. Conclusion: We believe MCIA is an attractive method for data integration and visualization of several datasets of multi-omics features observed on the same set of individuals. The method is not dependent on feature annotation, and thus it can extract important features even when there are not present across all datasets. MCIA provides simple graphical representations for the identification of relationships between large datasets.
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    Epithelial Progeny of Estrogen-Exposed Breast Progenitor Cells Display a Cancer-like Methylome
    (American Association for Cancer Research (AACR), 2008) 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.
    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 progenitor cells are more susceptible to the exposure injury than terminally differentiated epithelial cells in the breast duct. Mammospheres, containing enriched breast progenitors, were used as an exposure system to simulate this imprinting phenomenon in vitro. Using MeDIP-chip, a methylation microarray screening method, we found that 0.5% (120 loci) of human CpG islands were hypermethylated in epithelial cells derived from estrogenexposed progenitors compared with the non–estrogen-exposed control cells. This epigenetic event may lead to progressive silencing of tumor suppressor genes, including RUNX3, in these epithelial cells, which also occurred in primary breast tumors. Furthermore, normal tissue in close proximity to the tumor site also displayed RUNX3 hypermethylation, suggesting that this aberrant event occurs in early breast carcinogenesis. The high prevalence of estrogen-induced epigenetic changes in primary tumors and the surrounding histologically normal tissues provides the first empirical link between estrogen injury of breast stem/progenitor cells and carcinogenesis. This finding also offers a mechanistic explanation as to why a tumor suppressor gene, such as RUNX3, can be heritably silenced by epigenetic mechanisms in breast cancer.
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    Profiles of Genomic Instability in High-Grade Serous Ovarian Cancer Predict Treatment Outcome
    (American Association for Cancer Research (AACR), 2012) Wang, Z. C.; Birkbak, N; Culhane, Aedin; Drapkin, Ronny; 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; Iglehart, James; Bowtell, D. D. L.; Matulonis, Ursula
    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 HGSC, and are exploited by therapy targeting DNA repair. Defective HRR causes uniparental deletions and loss of heterozygosity (LOH). Our purpose is to profile LOH in HGSC and correlate our findings to clinical outcome, and compare HGSC and high-grade breast cancers. Experimental Design—We examined LOH and copy number changes using single nucleotide polymorphism array data from three HGSC cohorts and compared results to a cohort of high-grade breast cancers. The LOH profiles in HGSC were matched to chemotherapy resistance and progression-free survival (PFS). Results—LOH-based clustering divided HGSC into two clusters. The major group displayed extensive LOH and was further divided into two subgroups. The second group contained remarkably less LOH. BRCA1 promoter methylation was associated with the major group. LOH clusters were reproducible when validated in two independent HGSC datasets. LOH burden in the major cluster of HGSC was similar to triple-negative, and distinct from other high-grade breast cancers. Our analysis revealed an LOH cluster with lower treatment resistance and a significant correlation between LOH burden and PFS. Conclusions—Separating HGSC by LOH-based clustering produces remarkably stable subgroups in three different cohorts. Patients in the various LOH clusters differed with respect to chemotherapy resistance, and the extent of LOH correlated with PFS. LOH burden may indicate vulnerability to treatment targeting DNA repair, such as PARP1 inhibitors.
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    Palb2 synergizes with Trp53 to suppress mammary tumor formation in a model of inherited breast cancer
    (Proceedings of the National Academy of Sciences, 2013) Bowman-Colin, Christian; Xia, B.; Bunting, S.; Klijn, C.; Drost, R.; Bouwman, P.; Fineman, L.; Chen, X.; Culhane, Aedin; Cai, H.; Rodig, Scott; Bronson, Roderick; Jonkers, J.; Nussenzweig, A.; Kanellopoulou, C.; Livingston, David
    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 recombination-type double strand break repair (HRDSBR) through physical interactions with BRCA1, BRCA2, and RAD51. To further understand the mechanisms underlying PALB2mediated DNA repair and tumor suppression functions, we targeted Palb2 in the mouse. Palb2-deficient murine ES cells recapitulated DNA damage defects caused by PALB2 depletion in human cells, and germline deletion of Palb2 led to early embryonic lethality. Somatic deletion of Palb2 driven by K14-Cre led to mammary tumor formation with long latency. Codeletion of both Palb2 and Tumor protein 53 (Trp53) accelerated mammary tumor formation. Like BRCA1 and BRCA2 mutant breast cancers, these tumors were defective in RAD51 focus formation, reflecting a defect in Palb2 HR-DSBR function, a strongly suspected contributor to Brca1, Brca2, and Palb2 mammary tumor development. However, unlike the case of Brca1mutant cells, Trp53bp1 deletion failed to rescue the genomic instability of Palb2- or Brca2-mutant primary lymphocytes. Therefore, Palb2-driven DNA damage control is, in part, distinct from that executed by Brca1 and more similar to that of Brca2. The mechanisms underlying Palb2 mammary tumor suppression functions can now be explored genetically in vivo.
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    RAP80 Targets BRCA1 to Specific Ubiquitin Structures at DNA Damage Sites
    (American Association for the Advancement of Science (AAAS), 2007) Sobhian, B.; Shao, G.; Lilli, D. R.; Culhane, Aedin; Moreau, Lisa; Xia, B.; Livingston, David; Greenberg, R. A.
    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 are presently unknown. We report the interaction of the BRCA1 BRCT domain with RAP80, a ubiquitin-binding protein. RAP80 targets a complex containing the BRCA1-BARD1 (BRCA1-associated ring domain protein 1) E3 ligase and the deubiquitinating enzyme (DUB) BRCC36 to MDC1-γH2AX–dependent lysine6 - and lysine63-linked ubiquitin polymers at DSBs. These events are required for cell cycle checkpoint and repair responses to ionizing radiation, implicating ubiquitin chain recognition and turnover in the BRCA1-mediated repair of DSBs.
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    Identification of Novel Kinase Targets for the Treatment of Estrogen Receptor-Negative Breast Cancer
    (American Association for Cancer Research (AACR), 2009) Speers, C.; Tsimelzon, A.; Sexton, K.; Herrick, A. M.; Gutierrez, C.; Culhane, Aedin; Quackenbush, John; Hilsenbeck, S.; Chang, J.; Brown, P.
    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. Experimental Design—Here we used gene expression microarray profiling to identify a distinct kinase gene expression profile that identifies ER-negative breast tumors and subsets ER-negative breast tumors into 4 distinct subtypes. Results—Based upon the types of kinases expressed in these clusters, we identify a cell cycle regulatory subset, a S6 kinase pathway cluster, an immunomodulatory kinase expressing cluster, and a MAPK pathway cluster. Furthermore, we show that this specific kinase profile is validated using independent sets of human tumors, and is also seen in a panel of breast cancer cell lines. Kinase expression knockdown studies show that many of these kinases are essential for the growth of ERnegative, but not ER-positive, breast cancer cell lines. Finally, survival analysis of patients with breast cancer shows that the S6 kinase pathway signature subtype of ER-negative cancers confers an extremely poor prognosis, while patients whose tumors express high levels of immunomodulatory kinases have a significantly better prognosis. Conclusions—This study identifies a list of kinases that are prognostic and may serve as druggable targets for the treatment of ER-negative breast cancer.
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    Proliferative genes dominate malignancy-risk gene signature in histologically-normal breast tissue
    (Springer Nature, 2009) 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
    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 expression pattern might harbor substantial risk for future cancer development. Genes associated with these high-risk tissues were considered to be “malignancy-risk genes”. EXPERIMENTAL DESIGN—From a total of 90 breast cancer patients, we collected a set of 143 histologically-normal breast tissues derived from patients harboring breast cancer who underwent curative mastectomy, as well as a set of 42 invasive ductal carcinomas (IDC) of various histologic grades. All samples were assessed for global gene expression differences using microarray analysis. For the purpose of this study we defined normal breast tissue to include histologically normal and benign lesions. RESULTS—Here we report the discovery of a “malignancy-risk” gene signature that may portend risk of breast cancer development in benign, but molecularly-abnormal, breast tissue. Pathway analysis showed that the malignancy-risk signature had a dramatic enrichment for genes with proliferative function, but appears to be independent of ER, PR, and HER2 status. The signature was validated by RT-PCR, with a high correlation (Pearson correlation=0.95 with p<0.0001) with microarray data. CONCLUSION—These results suggest a predictive role for the malignancy-risk signature in normal breast tissue. Proliferative biology dominates the earliest stages of tumor development
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    A Three-Gene Model to Robustly Identify Breast Cancer Molecular Subtypes
    (Oxford University Press (OUP), 2012) Haibe-Kains, Benjamin; Desmedt, C.; Loi, S.; Culhane, Aedin; Bontempi, G.; Quackenbush, John; Sotiriou, C.
    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, luminal A, and luminal B). SSPs use hierarchical clustering, followed by nearest centroid classification, based on large sets of tumor-intrinsic genes. SCMs use a mixture of Gaussian distributions based on sets of genes with expression specifically correlated with three key breast cancer genes (estrogen receptor [ER], HER2, and aurora kinase A [AURKA]). The aim of this study was to compare the robustness, classification concordance, and prognostic value of these classifiers with those of a simplified three-gene SCM in a large compendium of microarray datasets. Methods - Thirty-six publicly available breast cancer datasets (n = 5715) were subjected to molecular subtyping using five published classifiers (three SSPs and two SCMs) and SCMGENE, the new three-gene (ER, HER2, and AURKA) SCM. We used the prediction strength statistic to estimate robustness of the classification models, defined as the capacity of a classifier to assign the same tumors to the same subtypes independently of the dataset used to fit it. We used Cohen k and Cramer V coefficients to assess concordance between the subtype classifiers and association with clinical variables, respectively. We used Kaplan–Meier survival curves and cross-validated partial likelihood to compare prognostic value of the resulting classifications. All statistical tests were two-sided. Results - SCMs were statistically significantly more robust than SSPs, with SCMGENE being the most robust because of its simplicity. SCMGENE was statistically significantly concordant with published SCMs (k = 0.65–0.70) and SSPs (k = 0.34–0.59), statistically significantly associated with ER (V = 0.64), HER2 (V = 0.52) status, and histological grade (V = 0.55), and yielded similar strong prognostic value. Conclusion - Our results suggest that adequate classification of the major and clinically relevant molecular subtypes of breast cancer can be robustly achieved with quantitative measurements of three key genes.
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    Significance Analysis of Prognostic Signatures
    (Public Library of Science (PLoS), 2013) Beck, Andrew; Knoblauch, Nicholas W.; Hefti, Marco; Kaplan, Jennifer; Schnitt, Stuart; Culhane, Aedin; Schroeder, Markus S.; Risch, Thomas; Quackenbush, John; Haibe-Kains, Benjamin
    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 data from a candidate prognostic gene set, and if the resulting clusters show statistically significant outcome stratification, to associate the gene set with prognosis, suggesting its biological and clinical importance. Recent work has questioned the validity of this approach by showing in several breast cancer data sets that ‘‘random’’ gene sets tend to cluster patients into prognostically variable subgroups. This work suggests that new rigorous statistical methods are needed to identify biologically informative prognostic gene sets. To address this problem, we developed Significance Analysis of Prognostic Signatures (SAPS) which integrates standard prognostic tests with a new prognostic significance test based on stratifying patients into prognostic subtypes with random gene sets. SAPS ensures that a significant gene set is not only able to stratify patients into prognostically variable groups, but is also enriched for genes showing strong univariate associations with patient prognosis, and performs significantly better than random gene sets. We use SAPS to perform a large meta-analysis (the largest completed to date) of prognostic pathways in breast and ovarian cancer and their molecular subtypes. Our analyses show that only a small subset of the gene sets found statistically significant using standard measures achieve significance by SAPS. We identify new prognostic signatures in breast and ovarian cancer and their corresponding molecular subtypes, and we show that prognostic signatures in ER negative breast cancer are more similar to prognostic signatures in ovarian cancer than to prognostic signatures in ER positive breast cancer. SAPS is a powerful new method for deriving robust prognostic biological signatures from clinically annotated genomic datasets.
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    Taxonomy of breast cancer based on normal cell phenotype predicts outcome
    (American Society for Clinical Investigation, 2014) 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; Richardson, Andrea; Schnitt, Stuart; Tamimi, Rulla; Ince, Tan A.
    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 tumor cells and normal cells has been successfully used to define tumor subtypes; however, use of normal cell types as a reference by which to classify solid tumors has not been widely emulated, in part due to more limited understanding of epithelial cell differentiation compared with hematopoiesis. To provide a better definition of the subtypes of epithelial cells comprising the breast epithelium, we performed a systematic analysis of a large set of breast epithelial markers in more than 15,000 normal breast cells, which identified 11 differentiation states for normal luminal cells. We then applied information from this analysis to classify human breast tumors based on normal cell types into 4 major subtypes, HR0–HR3, which were differentiated by vitamin D, androgen, and estrogen hormone receptor (HR) expression. Examination of 3,157 human breast tumors revealed that these HR subtypes were distinct from the current classification scheme, which is based on estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2. Patient outcomes were best when tumors expressed all 3 hormone receptors (subtype HR3) and worst when they expressed none of the receptors (subtype HR0). Together, these data provide an ontological classification scheme associated with patient survival differences and provides actionable insights for treating breast tumors.