Author Correction: Division of labour between Myc and G1 cyclins in cell cycle commitment and pace control.

This Article contains errors in Supplementary Table 3, which are described in the Author Correction associated with this Article. The simulation results in the Article were based on the correct formula and thus the results are not affected by this correction. The errors have not been fixed in the original Article.

E2F1-Mediated Induction of NFYB Attenuates Apoptosis via Joint Regulation of a Pro-Survival Transcriptional Program.

The E2F1 transcription factor regulates cell proliferation and apoptosis through the control of a considerable variety of target genes. Previous work has detailed the role of other transcription factors in mediating the specificity of E2F function. Here we identify the NF-YB transcription factor as a novel direct E2F1 target. Genome-wide expression analysis of the effects of NFYB knockdown on E2F1-mediated transcription identified a large group of genes that are co-regulated by E2F1 and NFYB. We also provide evidence that knockdown of NFYB enhances E2F1-induced apoptosis, suggesting a pro-survival function of the NFYB/E2F1 joint transcriptional program. Bioinformatic analysis suggests that deregulation of these NFY-dependent E2F1 target genes might play a role in sarcomagenesis as well as drug resistance.

Utilization of the Eµ-Myc mouse to model heterogeneity of therapeutic response.

Human aggressive B-cell non-Hodgkin lymphomas (NHL) encompass the continuum between Burkitt lymphoma and diffuse large B-cell lymphoma (DLBCL), and display considerable clinical and biologic heterogeneity, most notably related to therapy response. We previously showed that lymphomas arising in the Eµ-Myc transgenic mouse are heterogeneous, mirroring genomic differences between Burkitt lymphoma and DLBCL. Given clinical heterogeneity in NHL and the need to develop strategies to match therapeutics with discrete forms of disease, we investigated the extent to which genomic variation in the Eµ-Myc model predicts response to therapy. We used genomic analyses to classify Eµ-Myc lymphomas, link Eµ-Myc lymphomas with NHL subtypes, and identify lymphomas with predicted resistance to conventional and NF-κB-targeted therapies. Experimental evaluation of these predictions links genomic profiles with distinct outcomes to conventional and targeted therapies in the Eµ-Myc model, and establishes a framework to test novel targeted therapies or combination therapies in specific genomically defined lymphoma subgroups. In turn, this will rationally inform the design of new treatment options for aggressive human NHL.

Division of labour between Myc and G1 cyclins in cell cycle commitment and pace control.

A body of evidence has shown that the control of E2F transcription factor activity is critical for determining cell cycle entry and cell proliferation. However, an understanding of the precise determinants of this control, including the role of other cell-cycle regulatory activities, has not been clearly defined. Here, recognizing that the contributions of individual regulatory components could be masked by heterogeneity in populations of cells, we model the potential roles of individual components together with the use of an integrated system to follow E2F dynamics at the single-cell level and in real time. These analyses reveal that crossing a threshold amplitude of E2F accumulation determines cell cycle commitment. Importantly, we find that Myc is critical in modulating the amplitude, whereas cyclin D/E activities have little effect on amplitude but do contribute to the modulation of duration of E2F activation, thereby affecting the pace of cell cycle progression.

FOXO transcription factors control E2F1 transcriptional specificity and apoptotic function.

The transcription factor E2F1 is a key regulator of proliferation and apoptosis but the molecular mechanisms that mediate these cell fate decisions remain unclear. Here, we identify FOXO transcription factors as E2F1 target genes that act in a feed-forward regulatory loop to reinforce gene induction of multiple apoptotic genes. We found that E2F1 forms a complex with FOXO1 and FOXO3. RNAi-mediated silencing of FOXO impaired E2F1 binding to the promoters of cooperative target genes. A FOXO3 mutant insensitive to inactivation by survival kinases rescued the inhibitory effect of growth factor signaling on E2F1-mediated transcription and apoptosis. The E2F1/FOXO axis is frequently blocked in cancer, as evidenced by the specific downregulation of the FOXO-dependent E2F1 transcriptional program in multiple cancer types and by the association of a reduced E2F1/FOXO transcriptional program with poor prognosis. HDAC and phosphoinositide 3-kinase (PI3K) inhibitors were identified as specific activators of E2F1/FOXO transcription, acting to enhance E2F1-induced apoptosis in a FOXO3-dependent manner. Notably, combining the histone deacetylase inhibitor vorinostat with a PI3K inhibitor led to enhanced FOXO-dependent apoptosis. Collectively, our results identify E2F1/FOXO cooperation as a regulatory mechanism that places E2F1 apoptotic activity under the control of survival signaling. Therapeutic reactivation of this tumor suppressive mechanism may offer a novel broad-acting therapy for cancer.

Interaction of E2F7 transcription factor with E2F1 and C-terminal-binding protein (CtBP) provides a mechanism for E2F7-dependent transcription repression.

Previous work has identified distinct functions for E2F proteins during a cellular proliferative response including a role for E2F1-3 in the activation of transcription at G1/S and a role for E2F4-8 in repressing the same group of E2F1-3 target genes as cells progress through S phase. We now find that E2F7 and E2F8, which are induced by E2F1-3 at G1/S, can form a heterodimer with E2F1 through interactions involving the DNA-binding domains of the two proteins. In vitro DNA interaction assays demonstrate the formation of an E2F1-E2F7 complex, as well as an E2F7-E2F7 complex on adjacent E2F-binding sites. We also show that E2F7 recruits the co-repressor C-terminal-binding protein (CtBP) and that CtBP2 is essential for E2F7 to repress E2F1 transcription. Taken together, these findings suggest a mechanism for the repression of transcription by E2F7.

E2F6 associates with BRG1 in transcriptional regulation.

The E2F6 protein functions as an Rb-independent repressor of gene transcription. We have previously provided evidence suggesting a role for E2F6 in repression of E2F-responsive genes at S phase. Here, we have identified BRG1, the ATPase subunit of the SWI/SNF chromatin-remodeling complex, as an E2F6 interacting protein. Immunoprecipitation experiments demonstrate that BRG1 binds specifically to E2F6 and E2F4 but not the activator E2Fs. E2F6 was also able to interact with BAF155, a BRG1-associated factor, in the SWI/SNF complex. Chromatin immunoprecipitation assays demonstrate the binding of BRG1 coincident with E2F6 on G1/S gene promoters during S phase. Collectively, our studies suggest that E2F6 may recruit BRG1 in transcriptional regulation of genes important for G1/S phase transition of the cell cycle.

SIGNATURE: a workbench for gene expression signature analysis.

BACKGROUND: The biological phenotype of a cell, such as a characteristic visual image or behavior, reflects activities derived from the expression of collections of genes. As such, an ability to measure the expression of these genes provides an opportunity to develop more precise and varied sets of phenotypes. However, to use this approach requires computational methods that are difficult to implement and apply, and thus there is a critical need for intelligent software tools that can reduce the technical burden of the analysis. Tools for gene expression analyses are unusually difficult to implement in a user-friendly way because their application requires a combination of biological data curation, statistical computational methods, and database expertise. RESULTS: We have developed SIGNATURE, a web-based resource that simplifies gene expression signature analysis by providing software, data, and protocols to perform the analysis successfully. This resource uses bayesian methods for processing gene expression data coupled with a curated database of gene expression signatures, all carried out within a GenePattern web interface for easy use and access. CONCLUSIONS: SIGNATURE is available for public use at http://genepattern.genome.duke.edu/signature/.

Network calisthenics: control of E2F dynamics in cell cycle entry.

Stimulation of quiescent mammalian cells with mitogens induces an abrupt increase in E2F1-3 expression just prior to the onset of DNA synthesis, followed by a rapid decline as replication ceases. This temporal adaptation in E2F facilitates a transient pattern of gene expression that reflects the ordered nature of DNA replication. The challenge to understand how E2F dynamics coordinate molecular events required for high-fidelity DNA replication has great biological implications. Indeed, precocious, prolonged, elevated or reduced accumulation of E2F can generate replication stress that culminates in either arrest or death. Accordingly, temporal characteristics of E2F are regulated by several network modules that include feedforward and autoregulatory loops. In this review, we discuss how these network modules contribute to "shaping" E2F dynamics in the context of mammalian cell cycle entry.

Peripheral blood signatures of lead exposure.

BACKGROUND: Current evidence indicates that even low-level lead (Pb) exposure can have detrimental effects, especially in children. We tested the hypothesis that Pb exposure alters gene expression patterns in peripheral blood cells and that these changes reflect dose-specific alterations in the activity of particular pathways. METHODOLOGY/PRINCIPAL FINDING: Using Affymetrix Mouse Genome 430 2.0 arrays, we examined gene expression changes in the peripheral blood of female Balb/c mice following exposure to per os lead acetate trihydrate or plain drinking water for two weeks and after a two-week recovery period. Data sets were RMA-normalized and dose-specific signatures were generated using established methods of supervised classification and binary regression. Pathway activity was analyzed using the ScoreSignatures module from GenePattern. CONCLUSIONS/SIGNIFICANCE: The low-level Pb signature was 93% sensitive and 100% specific in classifying samples a leave-one-out crossvalidation. The high-level Pb signature demonstrated 100% sensitivity and specificity in the leave-one-out crossvalidation. These two signatures exhibited dose-specificity in their ability to predict Pb exposure and had little overlap in terms of constituent genes. The signatures also seemed to reflect current levels of Pb exposure rather than past exposure. Finally, the two doses showed differential activation of cellular pathways. Low-level Pb exposure increased activity of the interferon-gamma pathway, whereas high-level Pb exposure increased activity of the E2F1 pathway.

A methodology for utilization of predictive genomic signatures in FFPE samples.

BACKGROUND: Gene expression signatures developed to measure the activity of oncogenic signaling pathways have been used to dissect the heterogeneity of tumor samples and to predict sensitivity to various cancer drugs that target components of the relevant pathways, thus potentially identifying therapeutic options for subgroups of patients. To facilitate broad use, including in a clinical setting, the ability to generate data from formalin-fixed, paraffin-embedded (FFPE) tissues is essential. METHODS: Patterns of pathway activity in matched fresh-frozen and FFPE xenograft tumor samples were generated using the MessageAmp Premier methodology in combination with assays using Affymetrix arrays. Results generated were compared with those obtained from fresh-frozen samples using a standard Affymetrix assay. In addition, gene expression data from patient matched fresh-frozen and FFPE melanomas were also utilized to evaluate the consistency of predictions of oncogenic signaling pathway status. RESULTS: Significant correlation was observed between pathway activity predictions from paired fresh-frozen and FFPE xenograft tumor samples. In addition, significant concordance of pathway activity predictions was also observed between patient matched fresh-frozen and FFPE melanomas. CONCLUSIONS: Reliable and consistent predictions of oncogenic pathway activities can be obtained from FFPE tumor tissue samples. The ability to reliably utilize FFPE patient tumor tissue samples for genomic analyses will lead to a better understanding of the biology of disease progression and, in the clinical setting, will provide tools to guide the choice of therapeutics to those most likely to be effective in treating a patient's disease.

Integrating factor analysis and a transgenic mouse model to reveal a peripheral blood predictor of breast tumors.

BACKGROUND: Transgenic mouse tumor models have the advantage of facilitating controlled in vivo oncogenic perturbations in a common genetic background. This provides an idealized context for generating transcriptome-based diagnostic models while minimizing the inherent noisiness of high-throughput technologies. However, the question remains whether models developed in such a setting are suitable prototypes for useful human diagnostics. We show that latent factor modeling of the peripheral blood transcriptome in a mouse model of breast cancer provides the basis for using computational methods to link a mouse model to a prototype human diagnostic based on a common underlying biological response to the presence of a tumor. METHODS: We used gene expression data from mouse peripheral blood cell (PBC) samples to identify significantly differentially expressed genes using supervised classification and sparse ANOVA. We employed these transcriptome data as the starting point for developing a breast tumor predictor from human peripheral blood mononuclear cells (PBMCs) by using a factor modeling approach. RESULTS: The predictor distinguished breast cancer patients from healthy individuals in a cohort of patients independent from that used to build the factors and train the model with 89% sensitivity, 100% specificity and an area under the curve (AUC) of 0.97 using Youden's J-statistic to objectively select the model's classification threshold. Both permutation testing of the model and evaluating the model strategy by swapping the training and validation sets highlight its stability. CONCLUSIONS: We describe a human breast tumor predictor based on the gene expression of mouse PBCs. This strategy overcomes many of the limitations of earlier studies by using the model system to reduce noise and identify transcripts associated with the presence of a breast tumor over other potentially confounding factors. Our results serve as a proof-of-concept for using an animal model to develop a blood-based diagnostic, and it establishes an experimental framework for identifying predictors of solid tumors, not only in the context of breast cancer, but also in other types of cancer.

Use of gene expression and pathway signatures to characterize the complexity of human melanoma.

A defining characteristic of most human cancers is heterogeneity, resulting from the somatic acquisition of a complex array of genetic and genomic alterations. Dissecting this heterogeneity is critical to developing an understanding of the underlying mechanisms of disease and to paving the way toward personalized treatments of the disease. We used gene expression data sets from the analysis of primary and metastatic melanomas to develop a molecular description of the heterogeneity that characterizes this disease. Unsupervised hierarchical clustering, gene set enrichment analyses, and pathway activity analyses were used to describe the genetic heterogeneity of melanomas. Patterns of gene expression that revealed two distinct classes of primary melanoma, two distinct classes of in-transit melanoma, and at least three subgroups of metastatic melanoma were identified. Expression signatures developed to predict the status of oncogenic signaling pathways were used to explore the biological basis underlying these differential patterns of expression. This analysis of activities revealed unique pathways that distinguished the primary and metastatic subgroups of melanoma. Distinct patterns of gene expression across primary, in-transit, and metastatic melanomas underline the genetic heterogeneity of this disease. This heterogeneity can be described in terms of deregulation of signaling pathways, thus increasing the knowledge of the biological features underlying individual melanomas and potentially directing therapeutic opportunities to individual patients with melanoma.

Using noisy gene expression mediated by engineered adenovirus to probe signaling dynamics in mammalian cells.

Perturbations from environmental, genetic, and pharmacological sources can generate heterogeneous biological responses, even in genetically identical cells. Although these differences have important consequences on cell physiology and survival, they are often subsumed in measurements that average over the population. Here, we describe in detail how variability in adenoviral-mediated gene expression provides an effective means to map dose responses of signaling pathways. Cell-cell variability is inherent in gene delivery methods used in cell biology, which makes this approach adaptable to many existing experimental systems. We also discuss strategies to quantify biologically relevant inputs and outputs.

Pathway-based classification of lung cancer: a strategy to guide therapeutic selection.

The critical challenge in virtually all cancer research is heterogeneity: "Breast cancer" and "lung cancer" are actually collections of disease with distinct molecular mechanisms and clinical characteristics. The challenge is evident in the complexity of most cancers with multiple mutations and alterations generating the cancer phenotype, requiring therapeutic strategies that can match the complexity with equally complex combination regimens. Substantial progress in treatment requires major advances in methods to define refined, "common mechanism" subgroups to allow development of combination therapeutics that target these individual mechanisms. Our work is on the use of genomic signatures of oncogenic signaling pathways that provide an opportunity to dissect the complexity of lung cancer and to serve as tools to direct the use of targeted therapeutic agents.

Origin of bistability underlying mammalian cell cycle entry.

Precise control of cell proliferation is fundamental to tissue homeostasis and differentiation. Mammalian cells commit to proliferation at the restriction point (R-point). It has long been recognized that the R-point is tightly regulated by the Rb-E2F signaling pathway. Our recent work has further demonstrated that this regulation is mediated by a bistable switch mechanism. Nevertheless, the essential regulatory features in the Rb-E2F pathway that create this switching property have not been defined. Here we analyzed a library of gene circuits comprising all possible link combinations in a simplified Rb-E2F network. We identified a minimal circuit that is able to generate robust, resettable bistability. This minimal circuit contains a feed-forward loop coupled with a mutual-inhibition feedback loop, which forms an AND-gate control of the E2F activation. Underscoring its importance, experimental disruption of this circuit abolishes maintenance of the activated E2F state, supporting its importance for the bistability of the Rb-E2F system. Our findings suggested basic design principles for the robust control of the bistable cell cycle entry at the R-point.

Retraction: A genomic strategy to refine prognosis in early-stage non-small-cell lung cancer. N Engl J Med 2006;355:570-80.

To the Editor: We would like to retract our article, "A Genomic Strategy to Refine Prognosis in Early-Stage Non-Small-Cell Lung Cancer,"(1) which was published in the Journal on August 10, 2006. Using a sample set from a study by the American College of Surgeons Oncology Group (ACOSOG) and a collection of samples from a study by the Cancer and Leukemia Group B (CALGB), we have tried and failed to reproduce results supporting the validation of the lung metagene model described in the article. We deeply regret the effect of this action on the work of other investigators.