Search

Data from microarray studies have been used to develop predictive models for treatment outcome in breast cancer, such as a recently proposed predictive model for antiestrogen response after tamoxifen treatment that was based on the expression ratio of two genes. We attempted to validate this model on an independent cohort of 58 patients with resectable estrogen receptor-positive breast cancer. We measured expression of the genes HOXB13 and IL17BR with real time-quantitative polymerase chain reaction and assessed the association between their expression and outcome by use of univariate logistic regression, area under the receiver-operating-characteristic curve (AUC), a two-sample t test, and a Mann-Whitney test. We also applied standard supervised methods to the original microarray dataset and to another independent dataset from similar patients to estimate the classification accuracy obtainable by using more than two genes in a microarray-based predictive model. We could not validate the performance of the two-gene predictor on our cohort of samples (relation between outcome and the following genes estimated by logistic regression: for HOXB13, odds ratio [OR] = 1.04, 95% confidence interval [CI] = 0.92 to 1.16, P = .54; for IL17BR, OR = 0.69, 95% CI = 0.40 to 1.20, P = .18; and for HOXB13/IL17BR, OR = 1.30, 95% CI = 0.88 to 1.93, P = .18). Similar results were obtained with the AUC, a two-sample two-sided t test, and a Mann-Whitney test. In addition, estimates of classification accuracies applied to two independent microarray datasets highlighted the poor performance of treatment-response predictive models that can be achieved with the sample sizes of patients and informative genes to date. [ABSTRACT FROM AUTHOR]

The vast amount of unstructured data emerging from the various genome projects has led to the development of a number of web-based tools designed to annotate genes with biological information. Here we discuss a selection of these tools with regards to their scope, limitations and ease of use. [ABSTRACT FROM AUTHOR]

A partially occluded scene in an image consists of a number of objects that are partially obstructed by others. By validating a partially occluded image one aims to generate a sequence of concatenated and possibly overlapping objects that corresponds to the input image. This is a theoretical study of partially occluded strings (considered as one-dimensional images) allowing for the presence of errors in each occluded object appearing in the input. Using the unit cost edit distance as our measure of errors, for some small integer k ≥ 0, we present a sequential algorithm for validating a k-approximate onedimensional image x of length n over a dictionary T) of m objects each having equal length τ in O(nd) time where d = mτ is the size of the dictionary. A partially occluded scene in an image consists of a number of objects that are partially obstructed by others. By validating a partially occluded image one aims to generate a sequence of concatenated and possibly overlapping objects that corresponds to the input image. This is a theoretical study of partially occluded strings (considered as one-dimensional images) allowing for the presence of errors in each occluded object appearing in the input. Using the unit cost edit distance as our measure of errors, for some small integer k ≥ 0, we present a sequential algorithm for validating a k-approximate onedimensional image x of length n over a dictionary T) of m objects each having equal length τ in O(nd) time where d = mτ is the size of the dictionary. [ABSTRACT FROM AUTHOR]

The article presents a response to the research on the limits of predictive models using microarray data for breast cancer clinical treatment. The author finds that the criticism and the limited data previously presented do not affect his conclusions that the performance of some multigene biomarker classifiers was unsatisfactory. He suggested that more validation studies are needed.

Background Gene expression microarray studies for several types of cancer have been reported to identify previously unknown subtypes of tumors. For breast cancer, a molecular classification consisting of five subtypes based on gene expression microarray data has been proposed. These subtypes have been reported to exist across several breast cancer microarray studies, and they have demonstrated some association with clinical outcome. A classification rule based on the method of centroids has been proposed for identifying the subtypes in new collections of breast cancer samples; the method is based on the similarity of the new profiles to the mean expression profile of the previously identified subtypes. Methods Previously identified centroids of five breast cancer subtypes were used to assign 99 breast cancer samples, including a subset of 65 estrogen receptor-positive (ER+) samples, to five breast cancer subtypes based on microarray data for the samples. The effect of mean centering the genes (i.e., transforming the expression of each gene so that its mean expression is equal to 0) on subtype assignment by method of centroids was assessed. Further studies of the effect of mean centering and of class prevalence in the test set on the accuracy of method of centroids classifications of ER status were carried out using training and test sets for which ER status had been independently determined by ligand-binding assay and for which the proportion of ER+ and ER- samples were systematically varied. Results When all 99 samples were considered, mean centering before application of the method of centroids appeared to be helpful for correctly assigning samples to subtypes, as evidenced by the expression of genes that had previously been used as markers to identify the subtypes. However, when only the 65 ER+ samples were considered for classification, many samples appeared to be misclassified, as evidenced by an unexpected distribution of ER+ samples among the resultant subtypes. When genes were mean centered before classification of samples for ER status, the accuracy of the ER subgroup assignments was highly dependent on the proportion of ER+ samples in the test set; this effect of subtype prevalence was not seen when gene expression data were not mean centered. Conclusions Simple corrections such as mean centering of genes aimed at microarray platform or batch effect correction can have undesirable consequences because patient population effects can easily be confused with these assay-related effects. Careful thought should be given to the comparability of the patient populations before attempting to force data comparability for purposes of assigning subtypes to independent subjects. [ABSTRACT FROM AUTHOR]

Abstract: A major problem in clinical management of patients with epithelial ovarian cancer (EOC) is the largely unpredictable response to first-line treatment and the occurrence of relapse after complete initial response, associated with broad cross-resistance to even structurally dissimilar drugs. During tumor development and progression, multiple genic alterations take place that might contribute specifically to the treatment response and eventually impact on disease outcome. One area of intense research is the identification of molecular markers to accurately assess the prognosis of EOC patients and to define innovative therapeutic strategies. A large survey of recent published data indicates the need to revisit traditional molecular markers with respect to their contribution to the assessment of overall survival in selected populations. Furthermore, recent technological developments that enable simultaneous measurement of many parameters (“omic” approaches) hold the promise of identifying new molecular prognostic and predictive markers. [Copyright &y& Elsevier]

Loss of response to TGF-β is a central event in the genesis of colorectal cancer (CRC), a disease that, in the majority cases, is refractory to growth inhibition induced by this cytokine. However, inactivating mutations at receptors and transducers from the TGF-β cascade occur only in approximately half of CRCs, suggesting the involvement of additional mechanisms altering the response to the cytokine. We have recently described the amplification of the 13q31 locus, where the miR-17-92 cluster maps, associated with overexpression of its members. In this study, we address the potential role of miR-20a, from the miR-17-92 cluster, in the suppression of TGF-β cytostatic response in CRC. Using the poorly tumorigenic and TGF-β-sensitive FET cell line that expresses low miR-20a levels, we first confirmed that miR-20a downmodulated CDKN1A expression, both at mRNA and protein level, through direct binding to its 3′-UTR. We demonstrated that miR-20a significantly diminished cell response to TGF-β by preventing its delay of G1/S transition and promoting progression into cell cycle. Moreover, besides modulating CDKN1A, miR-20a blocked TGF-β-induced transactivation of its promoter without affecting the post-receptor activation of Smad3/4 effectors directly. Finally, miR-20a abrogated the TGF-β-mediated c-Myc repression, a direct inhibitor of the CDKN1A promoter activation, most likely by reducing the expression of specific MYC-regulating genes from the Smad/E2F-based core repressor complex. Our experiments indicate that miR-20a interferes with the colonic epithelium homeostasis by disrupting the regulation of Myc/p21 by TGF-β, which is essential for its malignant transformation. J. Cell. Physiol. 230: 3105-3114, 2015. © 2015 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Background: Cancer-associated fibroblasts (CAFs) play an important role in breast cancer pathogenesis by paracrine regulation of breast cancer cell biology. Several in vitro and mouse models have characterized the role of cell contact and cytokine molecules mediating this relationship, although few reports have used human CAFs from breast tumors. Methods: Primary breast CAF cultures were established and gene expression profiles analysed in order to guide subsequent co-culture models. We used a combination of colorimetric proliferation assays and gene expression profiling to determine the effect of CAFs on the MCF-7 breast cancer cell in an indirect co-culture system. Results: Using gene expression profiling, we found that a subgroup of breast CAFs are positive for a type one interferon response, confirming previous reports of an activated type one interferon response in whole tumor datasets. Interferon positive breast cancer patients show a poor prognostic outcome in an independent microarray dataset. In addition, CAFs positive for the type one interferon response promoted the growth of the MCF-7 breast cancer cell line in an indirect co-culture model. The addition of a neutralizing antibody against the ligand mediating the type one response in fibroblasts, interferon-β, reverted this co-culture phenotype. CAFs not expressing the interferon response genes also promoted the growth of the MCF-7 breast cancer cell line but this phenotype was independent of the type one fibroblast interferon ligand. Conclusions: Primary breast CAFs show inter-patient molecular heterogeneity as evidenced by interferon response gene elements activated in a subgroup of CAFs, which result in paracrine pro-proliferative effects in a breast cancer cell line co-culture model. [ABSTRACT FROM AUTHOR]

Background: Estrogen receptor positive (ER+) breast cancers (BC) are heterogeneous with regard to their clinical behavior and response to therapies. The ER is currently the best predictor of response to the anti-estrogen agent tamoxifen, yet up to 30-40% of ER+BC will relapse despite tamoxifen treatment. New prognostic biomarkers and further biological understanding of tamoxifen resistance are required. We used gene expression profiling to develop an outcome-based predictor using a training set of 255 ER+ BC samples from women treated with adjuvant tamoxifen monotherapy. We used clusters of highly correlated genes to develop our predictor to facilitate both signature stability and biological interpretation. Independent validation was performed using 362 tamoxifen-treated ER+ BC samples obtained from multiple institutions and treated with tamoxifen only in the adjuvant and metastatic settings. Results: We developed a gene classifier consisting of 181 genes belonging to 13 biological clusters. In the independent set of adjuvantly-treated samples, it was able to define two distinct prognostic groups (HR 2.01 95%CI: 1.29-3.13; p = 0.002). Six of the 13 gene clusters represented pathways involved in cell cycle and proliferation. In 112 metastatic breast cancer patients treated with tamoxifen, one of the classifier components suggesting a cellular inflammatory mechanism was significantly predictive of response. Conclusion: We have developed a gene classifier that can predict clinical outcome in tamoxifen-treated ER+ BC patients. Whilst our study emphasizes the important role of proliferation genes in prognosis, our approach proposes other genes and pathways that may elucidate further mechanisms that influence clinical outcome and prediction of response to tamoxifen. [ABSTRACT FROM AUTHOR]

Abstract: Transcriptional regulatory networks govern cell differentiation and the cellular response to external stimuli. However, mammalian model systems have not yet been accessible for network analysis. Here, we present a genome-wide network analysis of the transcriptional regulation underlying the mouse macrophage response to bacterial lipopolysaccharide (LPS). Key to uncovering the network structure is our combination of time-series cap analysis of gene expression with in silico prediction of transcription factor binding sites. By integrating microarray and qPCR time-series expression data with a promoter analysis, we find dynamic subnetworks that describe how signaling pathways change dynamically during the progress of the macrophage LPS response, thus defining regulatory modules characteristic of the inflammatory response. In particular, our integrative analysis enabled us to suggest novel roles for the transcription factors ATF-3 and NRF-2 during the inflammatory response. We believe that our system approach presented here is applicable to understanding cellular differentiation in higher eukaryotes. [Copyright &y& Elsevier]

Epithelial ovarian cancer (EOC) is the gynecological disease with the highest death rate. We applied an automatic class discovery procedure based on gene expression profiling to stages III-IV tumors to search for molecular signatures associated with the biological properties and progression of EOC. Using a complementary DNA microarray containing 4451 cancer-related, sequence-verified features, we identified a subset of EOC characterized by the expression of numerous genes related to the extracellular matrix (ECM) and its remodeling, along with elements of the fibroblast growth factor 2 (FGF2) signaling pathway. A total of 10 genes were validated by quantitative real-time polymerase chain reaction, and coexpression of FGF2 and fibroblast growth factor receptor 4 in tumor cells was revealed by immunohistochemistry, confirming the reliability of gene expression by cDNA microarray. Since the functional relationships among these genes clearly suggested involvement of the identified molecular signature in processes related to epithelial-stromal interactions and/or epithelial-mesenchymal cellular plasticity, we applied supervised learning analysis on ovarian-derived cell lines showing distinct cellular phenotypes in culture. This procedure enabled construction of a gene classifier able to discriminate mesenchymal-like from epithelial-like cells. Genes overexpressed in mesenchymal-like cells proved to match the FGF2 signaling and ECM molecular signature, as identified by unsupervised class discovery on advanced tumor samples. In vitro functional analysis of the cell plasticity classifier was carried out using two isogenic and immortalized cell lines derived from ovarian surface epithelium and displaying mesenchymal and epithelial morphology, respectively. The results indicated the autocrine, but not intracrine stimulation of mesenchymal conversion and cohort/scatter migration of cells by FGF2, suggesting a central role for FGF2 signaling in the maintenance of cellular plasticity of ovary-derived cells throughout the carcinogenesis process. These findings raise mechanistic hypotheses on EOC pathogenesis and progression that might provide a rational underpinning for new therapeutic modalities. [ABSTRACT FROM AUTHOR]

LNCIB has been producing a variety of human full-length-enriched, normalized and subtracted cDNA libraries from various cell lines and tissues in different developmental stages by using the CAP-Trapper method. By sequencing 23 000 clones of these libraries we identified a pool of about 5800 good quality unique cDNAs. After BLAST analysis on Human RefSeq/Unigene databases, 1717 of these sequences remained with no or poor annotation. We show that cross-species comparative BLAST resulted as a valid tool for the annotation of orthologous genes. To cite this article: E. Dalla et al., C. R. Biologies 326 (2003). [Copyright &y& Elsevier]