The Effects of miR-20a on p21: Two Mechanisms Blocking Growth Arrest in TGF-ß-Responsive Colon Carcinoma.

Loss of response to TGF-ß 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-ß 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-ß cytostatic response in CRC. Using the poorly tumorigenic and TGF-ß-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-ß by preventing its delay of G1/S transition and promoting progression into cell cycle. Moreover, besides modulating CDKN1A, miR-20a blocked TGF-ß-induced transactivation of its promoter without affecting the post-receptor activation of Smad3/4 effectors directly. Finally, miR-20a abrogated the TGF-ß-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-ß, which is essential for its malignant transformation.

A functional in vitro model of heterotypic interactions reveals a role for interferon-positive carcinoma associated fibroblasts in breast cancer.

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-ß, 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.

Chromosome band 17q21 in breast cancer: significant association between beclin 1 loss and HER2/NEU amplification.

Treatment success of breast cancer patients with trastuzumab alone or in combination depends not only on HER2/NEU amplification but also on PTEN and PI3K status and efficient cell death programs. In this pilot study, we found a significant association between loss of beclin 1 and HER2/NEU amplification (both on 17q21) in breast cancers. This finding was confirmed in two public copy number microarray datasets. Furthermore, there is a trend associating beclin 1 loss with TP53 mutations, PI3KCA gene gain, and PTEN mutations. Finally, the observation that beclin 1 gene loss predicted a response to trastuzumab alone or in combination with other drugs is worthy of further confirmation in larger cohorts. Our results suggest that, beclin 1 loss may contribute to genome instability and to a defective autophagy that may lead to tumoral cell death in presence of competent apoptosis or senescence pathways.

Integrative approach for prioritizing cancer genes in sporadic colon cancer.

The current multistep carcinogenesis models of colon cancer do not fully capture the genetic heterogeneity of the disease, which is additionally complicated by the presence of passenger and driver genetic alterations. The aim of this study was to select in the context of this significant heterogeneity additional genes functionally related to colon cancer development. High-throughput copy number and gene expression data of 36 microsatellite stable sporadic colon cancers resected from patients of a single institution characterized for mutations in APC, KRAS, TP53 and loss of 18q were analyzed. Genes whose expression correlated with the underlying copy number pattern were selected, and their association with the above listed mutations and overall survival was evaluated. Gain of 20q was strongly associated with TP53 mutation, and overall survival with alterations on 7p, 8p, 13q, 18q, and 20q. An association with 18q loss and gain of 8q24 was also observed. New candidate genes with a potential role in colon cancer are PLCG1 on 20q, DBC1 on 8q21, and NDGR1 on 8p24. In addition, an unexpected pattern of loss and mutability was found in the region upstream of the KRAS gene. By integrating copy number alterations with gene expression and mutations in colon cancer associated genes, we have developed a strategy that identifies previously known molecular features and additional players in the molecular landscape of colon cancer.

Complex Loci in human and mouse genomes.

Mammalian genomes harbor a larger than expected number of complex loci, in which multiple genes are coupled by shared transcribed regions in antisense orientation and/or by bidirectional core promoters. To determine the incidence, functional significance, and evolutionary context of mammalian complex loci, we identified and characterized 5,248 cis-antisense pairs, 1,638 bidirectional promoters, and 1,153 chains of multiple cis-antisense and/or bidirectionally promoted pairs from 36,606 mouse transcriptional units (TUs), along with 6,141 cis-antisense pairs, 2,113 bidirectional promoters, and 1,480 chains from 42,887 human TUs. In both human and mouse, 25% of TUs resided in cis-antisense pairs, only 17% of which were conserved between the two organisms, indicating frequent species specificity of antisense gene arrangements. A sampling approach indicated that over 40% of all TUs might actually be in cis-antisense pairs, and that only a minority of these arrangements are likely to be conserved between human and mouse. Bidirectional promoters were characterized by variable transcriptional start sites and an identifiable midpoint at which overall sequence composition changed strand and the direction of transcriptional initiation switched. In microarray data covering a wide range of mouse tissues, genes in cis-antisense and bidirectionally promoted arrangement showed a higher probability of being coordinately expressed than random pairs of genes. In a case study on homeotic loci, we observed extensive transcription of nonconserved sequences on the noncoding strand, implying that the presence rather than the sequence of these transcripts is of functional importance. Complex loci are ubiquitous, host numerous nonconserved gene structures and lineage-specific exonification events, and may have a cis-regulatory impact on the member genes.

Patterns and changes in gene expression following neo-adjuvant anti-estrogen treatment in estrogen receptor-positive breast cancer.

This study aimed to define a gene expression profile associated with response to anti-estrogen treatment in estrogen receptor alpha (ERalpha)-positive breast cancer from elderly patients and to identify possible candidate genes associated with resistance by detecting those modulated by treatment. Using cDNA microarrays containing 16 702 unique clones, 21 pre-treatment and 11 paired post-treatment samples collected in a neo-adjuvant toremifene trial on elderly patients with operable and locally advanced ERalpha-positive breast cancer were profiled. Gene expression profiles generated from pre-treatment samples were correlated with treatment-induced tumor shrinkage and compared with those obtained from post-treatment paired samples to define genes differentially modulated following anti-estrogen treatment. Correlation analysis on 21 pre-treatment samples highlighted 53 genes significantly related to treatment response (P<0.001). Genes involved in cell cycle and proliferation were more frequently upregulated in responders compared with non-responders. Class comparison analysis identified 101 genes significantly modulated independently of treatment response; 82 genes were modulated in non-responders, whereas only 8 genes were differently expressed after treatment in responders. Gene expression profiles appear to be more frequently modulated by anti-estrogen treatment in non-responding patients and may harbor interesting genes possibly involved in anti-estrogen resistance, including clusterin, MAPK6, and MMP2. This concept was corroborated by in vitro studies showing that silencing of CLU restored toremifene sensitivity in the ER anti-estrogen-resistant breast cancer cell line T47D. Integration between neo-adjuvant therapy and transcriptional profiling has therefore the potential to identify therapeutic targets to be challenged for overcoming treatment resistance.

Predicting prognosis using molecular profiling in estrogen receptor-positive breast cancer treated with tamoxifen.

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.

New potential ligand-receptor signaling loops in ovarian cancer identified in multiple gene expression studies.

Based on the hypothesis that gene products involved in the same biological process would be coupled at transcriptional level, a previous study analyzed the correlation of the gene expression patterns of ligand-receptor (L-R) pairs to discover potential autocrine/paracrine signaling loops in different cancers (Graeber and Eisenberg. Nat Genet 2001; 29:295). By refining the starting database, a list of 511 L-R pairs was compiled, combined to eight data sets from a single pathology, epithelial ovarian cancer, and examined as a proof-of-principle of the statistical and biological validity of the correlation of the L-R gene expression patterns in cancer. Analysis revealed a Bonferroni-corrected significant correlation of 105 L-R pairs in at least one data set and, by systematic analysis, identified 39 more frequently correlated L-R pairs, 7 of which were already biologically confirmed. In four data sets examined for an L-R correlation associated with patient survival time, 15 L-R pairs were significantly correlated in short surviving patients in two of the data sets. Immunohistochemical analysis of one of the newly identified correlated L-R pairs (i.e., EFNB3-EPHB4) revealed the correlated expression of ephrin-B3 and EphB4 proteins in 45 of 55 epithelial ovarian tumor samples (P < 0.0001). Together, these data not only support the validity of cross-comparison analysis of gene expression data because known and expected correlations were confirmed but also point to the promise of such analysis in identifying new L-R signaling loops in cancer.

The meaning of it all: web-based resources for large-scale functional annotation and visualization of DNA microarray data.

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.

Molecular predictors of response and outcome in ovarian cancer.

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.

Gene expression profiling of advanced ovarian cancer: characterization of a molecular signature involving fibroblast growth factor 2.

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.

The meaning of it all: web-based resources for large-scale functional annotation and visualization of DNA microarray data.

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.

Automated DNA chip annotation tables at IFOM: the importance of synchronisation and cross-referencing of sequence databases.

The increasing popularity of DNA chip technology for the study of gene expression is producing, for each experiment, a sizable quantity of numerical data to analyse and an accompanying large number of gene identifiers that should be associated with the relevant biological annotation. We describe here a website at IFOM (FIRC Institute of Molecular Oncology) where we release regularly updated annotation tables for the most used Affymetrix oligonucleotide DNA chips and for the whole Research Genetics 46K clone collection for cDNA arrays. These tables are synchronised with every new release of the mouse and human UniGene databases (NCBI; National Center for Biotechnology Information), allowing fast and easy preliminary annotation of DNA array experiments. We also report some comparative evidence about the importance of biological database synchronisation and cross-references in the process of generating annotation tables for DNA chips.

LNCIB human full-length cDNAs collection: towards a better comprehension of the human transcriptome.

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 23000 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.

miRNA profiling in colorectal cancer highlights miR-1 involvement in MET-dependent proliferation.

Altered expression of miRNAs is associated with development and progression of various human cancers by regulating the translation of oncogenes and tumor suppressor genes. In colorectal cancer, these regulators complement the Vogelstein multistep model of pathogenesis and have the potential of becoming a novel class of tumor biomarkers and therapeutic targets. Using quantitative real-time PCR, we measured the expression of 621 mature miRNAs in 40 colorectal cancers and their paired normal tissues and identified 23 significantly deregulated miRNAs. We subsequently evaluated their association with clinical characteristics of the samples and presence of alterations in the molecular markers of colorectal cancer progression. Expression levels of miR-31 were correlated with CA19-9 and miR-18a, miR-21, and miR-31 were associated with mutations in APC gene. To investigate the downstream regulation of the differentially expressed miRNAs identified, we integrated putative mRNA target predictions with the results of a meta-analysis of seven public gene expression datasets of normal and tumor samples of colorectal cancer patients. Many of the colorectal cancer deregulated miRNAs computationally mapped to targets involved in pathways related to progression. Here one promising candidate pair (miR-1 and MET) was studied and functionally validated. We show that miR-1 can have a tumor suppressor function in colorectal cancer by directly downregulating MET oncogene both at RNA and protein level and that reexpression of miR-1 leads to MET-driven reduction of cell proliferation and motility, identifying the miR-1 downmodulation as one of the events that could enhance colorectal cancer progression.

Challenges in projecting clustering results across gene expression-profiling datasets.

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.

Regulation of lipocalin-2 gene by the cancer chemopreventive retinoid 4-HPR.

N-(4-Hydroxyphenyl)retinamide (4-HPR) is a nonclassical retinoid with cancer preventive effects in vivo and antiproliferative and apoptotic activities in vitro. Examining the transcriptional profile of human breast cancer cell lines, MCF7 and T47D, treated with 4-HPR, we identified the lipocalin member LCN2 (NGAL or 24p3) as a gene, markedly induced by the retinoid. Because of its presumed function in apoptosis, LCN2 was examined more thoroughly in response to 4-HPR. Like mRNA, the expression of LCN2 protein in MCF7 and T47D cells was highly induced in a time-dependent manner by 4-HPR, but not by its inactive metabolite 4-MPR and, to some extent, this event was linked to the free radicals normally generated by 4-HPR. All-trans retinoic acid also induced LCN2 protein, particularly in T47D cells. Ectopic LCN2 compromised cell viability, and the few MCF7 clones that survived LCN2 overexpression were less sensitive than do mock cells to 4HPR, indicating that selective pressure for survival to LCN2 confers cross-resistance to 4-HPR. Significantly, ablation of LCN2 induction by siRNA did not modify the response to 4-HPR, implying that LCN2 is not critical for apoptosis by 4-HPR. Our results indicate that 4-HPR markedly induces LCN2 expression, but this event may not represent an apoptotic response.

Identification of tumor-specific molecular signatures in intracranial ependymoma and association with clinical characteristics.

PURPOSE: To delineate clinically relevant molecular signatures of intracranial ependymoma. MATERIALS AND METHODS: We analyzed 24 primary intracranial ependymomas. For genomic profiling, microarray-based comparative genomic hybridization (CGH) was used and results were validated by fluorescent in situ hybridization and loss of heterozygosity mapping. We performed gene expression profiling using microarrays, real-time quantitative reverse transcriptase polymerase chain reaction, and methylation analysis of selected genes. We applied class comparison analyses to compare both genomic and expression profiling data with clinical characteristics. RESULTS: A variable number of genomic imbalances were detected by array CGH, revealing multiple regions of recurrent gain (including 2q23, 7p21, 12p, 13q21.1, and 20p12) and loss (including 5q31, 6q26, 7q36, 15q21.1, 16q24, 17p13.3, 19p13.2, and 22q13.3). An ependymoma-specific gene expression signature was characterized by the concurrent abnormal expression of developmental and differentiation pathways, including NOTCH and sonic hedgehog signaling. We identified specific differentially imbalanced genomic clones and gene expression signatures significantly associated with tumor location, patient age at disease onset, and retrospective risk for relapse. Integrated genomic and expression profiling allowed us to identify genes of which the expression is deregulated in intracranial ependymoma, such as overexpression of the putative proto-oncogene YAP1 (located at 11q22) and downregulation of the SULT4A1 gene (at 22q13.3). CONCLUSION: The present exploratory molecular profiling study allowed us to refine previously reported intervals of genomic imbalance, to identify novel restricted regions of gain and loss, and to identify molecular signatures correlating with various clinical variables. Validation of these results on independent data sets represents the next step before translation into the clinical setting.

Transcriptional network dynamics in macrophage activation.

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.

Limits of predictive models using microarray data for breast cancer clinical treatment outcome.

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.