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Appraisal of progenitor markers in the context of molecular classification of breast cancers
Izhak Haviv
Breast Cancer Research , 2011, DOI: 10.1186/bcr2792
Abstract: The Heisenberg uncertainty principle has a great impact on medical research by drawing our attention to the bias introduced by our experimental tools. In a recent issue of Breast Cancer Research, Keller and colleagues [1] report an example of this principle: sustained propagation of large numbers of cells, through the establishment of cell lines, disrupts the normal balance between differentiated cells and their progenitors, as observed in fresh biological specimens. The work of these authors contributes another piece in a contentious field that combines tissue morphology and immunohistochemical phenotypes [2,3], molecular classification of breast cancer tissues [4], and cell biological assays aimed at the tumor-initiating cell (TIC) phenotype [5]. Sorting cells according to their respective cell surface markers, CD44+/CD24-/low, results in the enrichment of TIC activities, including mammospheres [6] and transplantation efficiency in mouse xenografts [7]. Establishing xenograft growth could be the product of several system-specific selections other than breast progenitor phenotypes. However, further molecular profiling of these cell populations - in which CD44+/CD24-/low-sorted cells expressed low levels of luminal differentiation markers (such as MUC1, CD24, or CDH1) and elevated levels of epithelial-mesenchymal transition markers (such as VIM, collagens, TWIST1, SNAI1/2, and Zeb1/2) - indicated a link between epithelial-mesenchymal transition, TIC, and basal-like [6,8] or claudin-low [9,10]-specific breast cancer molecular subtypes. More recently, however, a more comprehensive interrogation of pluripotent self-renewal identified a population high for CD24, or luminal progenitors [9,11-13], capable of giving rise to mesenchymal or basal-like tumors, at least in the context of BrCa1 mutations. Given the variability of single markers within single individuals, the different sensitivities each cell biological assay presents with, and the consistency across other genes
Prediction of breast cancer prognosis using gene set statistics provides signature stability and biological context
Gad Abraham, Adam Kowalczyk, Sherene Loi, Izhak Haviv, Justin Zobel
BMC Bioinformatics , 2010, DOI: 10.1186/1471-2105-11-277
Abstract: We sought to examine the stability of prognostic signatures based on gene sets rather than individual genes. We classified breast cancer cases from five microarray studies according to the risk of metastasis, using features derived from predefined gene sets. The expression levels of genes in the sets are aggregated, using what we call a set statistic. The resulting prognostic gene sets were as predictive as the lists of individual genes, but displayed more consistent rankings via bootstrap replications within datasets, produced more stable classifiers across different datasets, and are potentially more interpretable in the biological context since they examine gene expression in the context of their neighbouring genes in the pathway. In addition, we performed this analysis in each breast cancer molecular subtype, based on ER/HER2 status. The prognostic gene sets found in each subtype were consistent with the biology based on previous analysis of individual genes.To date, most analyses of gene expression data have focused at the level of the individual genes. We show that a complementary approach of examining the data using predefined gene sets can reduce the noise and could provide increased insight into the underlying biological pathways.Much attention has been given to predicting patient survival from microarray data. In breast cancer, van 't Veer et al. [1,2] set out to find genes that could be used to predict whether breast cancer patients would experience a metastasis five years after surgery (a binary variable). Their list of 70 genes (NKI70) performed well in predicting the clinical outcome (area under receiver-operating characteristic curve, AUC ≈ 0.7) and is currently commercially available as a prognostic test for breast cancer patients. However, Ein-Dor et al. [3] used the stratified bootstrap to show that many other gene lists of similar predictive ability could be found from the same data. Moreover, the overlap between the gene lists was low. Similarly,
Meta-analysis of gene expression microarrays with missing replicates
Fan Shi, Gad Abraham, Christopher Leckie, Izhak Haviv, Adam Kowalczyk
BMC Bioinformatics , 2011, DOI: 10.1186/1471-2105-12-84
Abstract: We propose a meta-analysis framework, called "Incomplete Gene Meta-analysis", which can include incomplete genes by imputing the significance of missing replicates, and computing a meta-score for every gene across all datasets. We demonstrate that the incomplete genes are worthy of being included and our method is able to appropriately estimate their significance in two groups of experiments. We first apply the Incomplete Gene Meta-analysis and several comparable methods to five breast cancer datasets with an identical set of probes. We simulate incomplete genes by randomly removing a subset of probes from each dataset and demonstrate that our method consistently outperforms two other methods in terms of their false discovery rate. We also apply the methods to three gastric cancer datasets for the purpose of discriminating diffuse and intestinal subtypes.Meta-analysis is an effective approach that identifies more robust sets of differentially expressed genes from multiple studies. The incomplete genes that mainly arise from the use of different platforms may also have statistical and biological importance but are ignored or are not appropriately involved by previous studies. Our Incomplete Gene Meta-analysis is able to incorporate the incomplete genes by estimating their significance. The results on both breast and gastric cancer datasets suggest that the highly ranked genes and associated GO terms produced by our method are more significant and biologically meaningful according to the previous literature.Gene expression microarrays are a high throughput technique for measuring gene expression levels in thousands of genes simultaneously, and have been widely used in the study of cancer genomics. An important application of gene expression microarrays is detecting differentially expressed genes by statistical analysis. For example, the classical t-test can be used to assess the statistical significance of genes in terms of their ability to discriminate samples from t
A bi-ordering approach to linking gene expression with clinical annotations in gastric cancer
Fan Shi, Christopher Leckie, Geoff MacIntyre, Izhak Haviv, Alex Boussioutas, Adam Kowalczyk
BMC Bioinformatics , 2010, DOI: 10.1186/1471-2105-11-477
Abstract: In this paper, we develop a robust and efficient method for exploratory analysis of microarray data, which produces a number of different orderings (rankings) of both genes and samples (reflecting correlation among those genes and samples). The core algorithm is closely related to biclustering, and so we first compare its performance with several existing biclustering algorithms on two real datasets - gastric cancer and lymphoma datasets. We then show on the gastric cancer data that the sample orderings generated by our method are highly statistically significant with respect to the histological classification of samples by using the Jonckheere trend test, while the gene modules are biologically significant with respect to biological processes (from the Gene Ontology). In particular, some of the gene modules associated with biclusters are closely linked to gastric cancer tumorigenesis reported in previous literature, while others are potentially novel discoveries.In conclusion, we have developed an effective and efficient method, Bi-Ordering Analysis, to detect informative patterns in gene expression microarrays by ranking genes and samples. In addition, a number of evaluation metrics were applied to assess both the statistical and biological significance of the resulting bi-orderings. The methodology was validated on gastric cancer and lymphoma datasets.A typical aim of exploratory analysis of genomics data is to identify potentially interesting genes and pathways that warrant further investigation. There is a critical need to streamline the analysis in order to support continuing advances in high throughput genomics methods such as gene expression microarrays, which measure thousands of genes in a single assay and are the focus of this paper. However, such assays provide noisy and incomplete measurements, which require sophisticated bioinformatics techniques to identify statistically and biologically significant associations between genes and relevant phenotypes o
Combining target enrichment with barcode multiplexing for high throughput SNP discovery
Nik Cummings, Rob King, Andre Rickers, Antony Kaspi, Sebastian Lunke, Izhak Haviv, Jeremy BM Jowett
BMC Genomics , 2010, DOI: 10.1186/1471-2164-11-641
Abstract: We developed a method that combines current multiplexing methodologies with a solution-based target enrichment method to further reduce the cost of resequencing where region-specific sequencing is required. Our multiplex/enrichment strategy produced high quality data with nominal reduction of sequencing depth. We undertook a genotyping study and were successful in the discovery of novel SNP alleles in all samples at uniplex, duplex and pentaplex levels.Our work describes the successful combination of a targeted enrichment method and index barcode multiplexing to reduce costs, time and labour associated with processing large sample sets. Furthermore, we have shown that the sequencing depth obtained is adequate for credible SNP genotyping analysis at uniplex, duplex and pentaplex levels.The development of massively parallel sequencing or next generation sequencing (NGS) platforms provide the capacity for high-throughput sequencing of whole genomes at low cost. However, while those platforms improve the capacity to find novel variations that are not covered by existing genotyping arrays, they do not make use of the existing data, composed of thousands of relatively small genomic regions that have been associated with diseases through the use of genome wide association and linkage studies, where isolation of causative genetic variants has been problematic.The efficiency of NGS-mediated genotyping has recently been improved through employing amplicon libraries of long-range PCR, which encompass discrete genomic intervals [1]. However, this method of library construction remains time-consuming, costly and limited to very small genomic regions (5 kbp-1 Mbp) and is impractical for genetic dissection of disease linked loci which can span 10 Mb or more. The development of molecular inversion probes (MIPs) and the use of chip-based technologies for massively parallel capture of specific genomic targets is limited by representational and allelic bias and remains costly and time
Enhanced RAD21 cohesin expression confers poor prognosis in BRCA2 and BRCAX, but not BRCA1 familial breast cancers
Max Yan, Huiling Xu, Nic Waddell, Kristy Shield-Artin, Izhak Haviv, kConFab authors, Michael J McKay, Stephen B Fox
Breast Cancer Research , 2012, DOI: 10.1186/bcr3176
Abstract: We performed an immunohistochemical analysis of RAD21 expression in a cohort of 94 familial breast cancers (28 BRCA1, 27 BRCA2, and 39 BRCAX) and correlated these data with genotype and clinicopathologic parameters, including survival. In these cancers, we also correlated RAD21 expression with genomic expression profiling and gene copy-number changes and miRNAs predicted to target RAD21.No significant differences in nuclear RAD21 expression were observed between BRCA1 (12 (43%) of 28), BRCA2 (12 (44%) of 27), and BRCAX cancers (12 (33%) of 39 (p = 0.598). No correlation was found between RAD21 expression and grade, size, or lymph node, ER, or HER2 status (all P > 0.05). As for sporadic breast cancers, RAD21 expression correlated with shorter survival in grade 3 (P = 0.009) and but not in grade 1 (P = 0.065) or 2 cancers (P = 0.090). Expression of RAD21 correlated with poorer survival in patients treated with chemotherapy (P = 0.036) but not with hormonal therapy (P = 0.881). RAD21 expression correlated with shorter survival in BRCA2 (P = 0.006) and BRCAX (P = 0.008), but not BRCA1 cancers (P = 0.713). Changes in RAD21 mRNA were reflected by genomic changes in DNA copy number (P < 0.001) and by RAD21 protein expression, as assessed with immunohistochemistry (P = 0.047). High RAD21 expression was associated with genomic instability, as assessed by the total number of base pairs affected by genomic change (P = 0.048). Of 15 miRNAs predicted to target RAD21, mir-299-5p inversely correlated with RAD21 expression (P = 0.002).Potential use of RAD21 as a predictive and prognostic marker in familial breast cancers is hence feasible and may therefore take into account the patient's BRCA1/2 mutation status.It is estimated that 5% to 10% of all breast cancers are attributable to inherited mutations, of which the two most important and highly penetrant are BRCA1 and BRCA2 [1]. Studies have demonstrated key differences in spontaneous BRCA-associated tumors [2,3]. BRCA1 cancers ar
Successful In Vitro Expansion and Differentiation of Cord Blood Derived CD34+ Cells into Early Endothelial Progenitor Cells Reveals Highly Differential Gene Expression
Ingo Ahrens, Helena Domeij, Denijal Topcic, Izhak Haviv, Ruusu-Maaria Merivirta, Alexander Agrotis, Ephraem Leitner, Jeremy B. Jowett, Christoph Bode, Martha Lappas, Karlheinz Peter
PLOS ONE , 2011, DOI: 10.1371/journal.pone.0023210
Abstract: Endothelial progenitor cells (EPCs) can be purified from peripheral blood, bone marrow or cord blood and are typically defined by a limited number of cell surface markers and a few functional tests. A detailed in vitro characterization is often restricted by the low cell numbers of circulating EPCs. Therefore in vitro culturing and expansion methods are applied, which allow at least distinguishing two different types of EPCs, early and late EPCs. Herein, we describe an in vitro culture technique with the aim to generate high numbers of phenotypically, functionally and genetically defined early EPCs from human cord blood. Characterization of EPCs was done by flow cytometry, immunofluorescence microscopy, colony forming unit (CFU) assay and endothelial tube formation assay. There was an average 48-fold increase in EPC numbers. EPCs expressed VEGFR-2, CD144, CD18, and CD61, and were positive for acetylated LDL uptake and ulex lectin binding. The cells stimulated endothelial tube formation only in co-cultures with mature endothelial cells and formed CFUs. Microarray analysis revealed highly up-regulated genes, including LL-37 (CAMP), PDK4, and alpha-2-macroglobulin. In addition, genes known to be associated with cardioprotective (GDF15) or pro-angiogenic (galectin-3) properties were also significantly up-regulated after a 72 h differentiation period on fibronectin. We present a novel method that allows to generate high numbers of phenotypically, functionally and genetically characterized early EPCs. Furthermore, we identified several genes newly linked to EPC differentiation, among them LL-37 (CAMP) was the most up-regulated gene.
Widespread FRA1-Dependent Control of Mesenchymal Transdifferentiation Programs in Colorectal Cancer Cells
Jeannine Diesch, Elaine Sanij, Omer Gilan, Christopher Love, Hoanh Tran, Nicholas I. Fleming, Jason Ellul, Marcia Amalia, Izhak Haviv, Richard B. Pearson, Eugene Tulchinsky, John M. Mariadason, Oliver M. Sieber, Ross D. Hannan, Amardeep S. Dhillon
PLOS ONE , 2014, DOI: 10.1371/journal.pone.0088950
Abstract: Tumor invasion and metastasis involves complex remodeling of gene expression programs governing epithelial homeostasis. Mutational activation of the RAS-ERK is a frequent occurrence in many cancers and has been shown to drive overexpression of the AP-1 family transcription factor FRA1, a potent regulator of migration and invasion in a variety of tumor cell types. However, the nature of FRA1 transcriptional targets and the molecular pathways through which they promote tumor progression remain poorly understood. We found that FRA1 was strongly expressed in tumor cells at the invasive front of human colorectal cancers (CRCs), and that its depletion suppressed mesenchymal-like features in CRC cells in vitro. Genome-wide analysis of FRA1 chromatin occupancy and transcriptional regulation identified epithelial-mesenchymal transition (EMT)-related genes as a major class of direct FRA1 targets in CRC cells. Expression of the pro-mesenchymal subset of these genes predicted adverse outcomes in CRC patients, and involved FRA-1-dependent regulation and cooperation with TGFβ signaling pathway. Our findings reveal an unexpectedly widespread and direct role for FRA1 in control of epithelial-mesenchymal plasticity in CRC cells, and suggest that FRA1 plays an important role in mediating cross talk between oncogenic RAS-ERK and TGFβ signaling networks during tumor progression.
Towards a Diagrammatic Analogue of the Reshetikhin-Turaev Link Invariants
Ami Haviv
Mathematics , 2002,
Abstract: By considering spaces of directed Jacobi diagrams, we construct a diagrammatic version of the Etingof-Kazhdan quantization of complex semisimple Lie algebras. This diagrammatic quantization is used to provide a construction of a directed version of the Kontsevich integral, denoted $Z_EK$, in a way which is analogous to the construction of the Reshetikhin-Turaev invariants from the R-matrices of the Drinfel'd-Jimbo quantum groups. Based on this analogy, we conjecture (and prove in a restricted sense) a formula for the value of the invariant $Z_EK$ on the unknot. This formula is simpler than the Wheels formula of [BGRT], but the precise relationship between the two is yet unknown.
The Duistermaat-Heckman Measure for the Coadjoint Orbits of Compact Semisimple Lie Groups
Ami Haviv
Mathematics , 1998,
Abstract: We apply the Guillemin-Lerman-Sternberg theorem to reprove a formula of Heckman for the Duistermaat-Heckman measure associated to the coadjoint action of $T$, a maximal torus of a compact semisimple Lie group $G$, on a regular coadjoint $G$-orbit in the dual space of the Lie algebra of $G$. This formula is, in an appropriate sense, a limiting case of the Kostant multiplicity formula.
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