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.

Gene Expression Patterns That Characterize Advanced Stage Serous Ovarian Cancers

OBJECTIVE

To identify gene expression patterns that characterize advanced stage serous ovarian cancers by using microarray expression analysis.

METHODS

Using genome-wide expression analysis, we compared a series of 31 advanced stage (III or IV) serous ovarian cancers from patients who survived either less than 2 years or more than 7 years with three normal ovarian epithelial samples. Array findings were validated by analysis of expression of the insulin-like growth factor binding protein 2 (IGFBP2) and tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) genes using quantitative real-time polymerase chain reaction (QRT-PCR).

RESULTS

Hierarchical clustering identified patterns of gene expression that distinguished cancer from normal ovarian epithelium. We also identified gene expression patterns that distinguish cancers on the basis of patient survival. These genes include many that are associated with immune function. Expression of IGFBP2 and TRAIL genes measured by array and QRT-PCR analysis demonstrated correlation coefficients of 0.63 and 0.78, respectively.

CONCLUSION

Global expression analysis can identify expression patterns and individual genes that contribute to ovarian cancer development and outcome. Many of the genes that determine ovarian cancer survival are associated with the immune response, suggesting that immune function influences ovarian cancer virulence. With the generation of newer arrays with more transcripts, larger studies are possible to fully characterize genetic signatures that predict survival that may ultimately be used to guide therapeutic decision-making.

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.

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.

Abstract 5123: An integrated genomics approach to identify novel drivers of oncogenic pathway activity in human cancer

Human cancers are defined by molecular and clinical heterogeneity. The molecular diversity of human tumors is a significant contributing factor to the inefficiency of current therapeutic regimens and the high failure rate of developing new anti-cancer therapies. One of the major contributing factors to this variability is the enormous diversity and complexity of genome alterations. In order to understand the molecular complexity driving tumor development and to identify novel therapeutic targets to enhance treatment efficacy and overcome therapeutic resistance, we have developed an integrative genomics approach to identify novel drivers of oncogenic pathway activity and applied this strategy to investigate serous ovarian cancer. Since it has been demonstrated that mutations in many different genes can result in the activation of an oncogenic signaling pathway resulting in enhanced cell proliferation and transformation, our analysis focused on regulation of pathway activity rather than the mutation of a single gene. As such, we utilized 18 previously developed and validated gene expression signatures of oncogenic pathway activity as a framework to integrate multiple, disparate forms of genomic data. We first identified 1,020 high-grade, late stage serous ovarian tumors with Affymetrix U133 expression data from multiple published studies and the predicted oncogenic pathway activity was determined for each tumor. Of these tumors, >500 had matched aCGH data which were used to identify statistically significant chromosomal alterations directly and indirectly associated with each pathway. This strategy was validated by identifying associations between pathway activity and copy number alterations of known pathway drivers. We next compiled a dataset of ovarian cancer cell lines with U133 expression data and for which genome-wide shRNA proliferation data was available. These data were used to identify essential genes required for cell viability in pathway-dependent manner. The resulting genes were analyzed by DAVID and GATHER to validate that this strategy identified key pathway components. Finally, by integrating results obtained from the aCGH and shRNA analyses, we identified known and candidate genes associated with oncogenic pathway activity that are required for cell viability and are amplified in human tumors. These analyses have identified alterations of putative and known regulators of pathway activity that often exist independent of each other in human tumors suggesting that examining the mutational status of a single gene is an incomplete measure of pathway activity and that this strategy is able to identify novel regulators, and therefore potential therapeutic targets, of oncogenic signaling pathways required for tumor proliferation. Additional studies are underway to investigate the role identified candidate genes play in pathway activity and tumor proliferation.

Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 5123. doi:1538-7445.AM2012-5123

Discovering pathways in the tumor microenvironment important for recurrence-free survival in patients with colorectal liver metastasis

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Background: Colorectal cancer (CRC) is the third leading cause of cancer-related deaths in the United States. Curative treatment for CRC liver metastasis can be achieved by surgical resection, but 5 year survival ranges between 20-40%. Current treatment strategies to prevent recurrences after resection of CRC liver metastases have focused on the use of chemotherapy cytotoxic to malignant epithelial cells of the tumor. However, stromal components within the tumor microenvironment have recently been implicated as contributors to the aggressiveness of the malignancy. We hypothesized that genomic based signatures associated with tumor stroma would be prognostic for recurrence after CRC hepatic metastasectomy.

Methods: Thirteen independent CRC liver metastatic samples were subjected to laser-capture microdissection to isolate the tumor epithelium and tumor-associated stroma separately. Microarray analysis using class neighbors and gene set enrichment analysis (GSEA) was applied to the stromal mRNA to identify differentially expressed genes and pathways between samples from patients with early recurrence (<1yr) after resection (Group R) and those who remain disease free (>2yrs) (Group NoR).

Results: Class neighbor analysis revealed a greater number of genes associated with immunity and cell to cell adhesion in Group R compared to Group NoR (p<0.05). Using GSEA, gene sets associated with T-cell immunity, Wnt pathway, and homeostasis were enriched in stroma Group R when compared to stroma Group NoR (p<0.05).

Conclusions: These findings suggest several pathways are prognostic for recurrence of CRC liver metastasis. Development of stroma-directed therapy combined with tumor-directed therapy in preclinical models could hold the promise of major advances in the treatment of colorectal cancer liver metastasis in order to improve the clinical outcomes of these patients.

Predictive and prognostic markers of recurrence after resection of primary or metastatic colorectal cancer

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Background: Current biomarkers for colorectal cancer sub-classify tumors based on single mutations, such as KRAS; however, studies of single mutations belie the molecular complexity of colorectal cancer in which an average of 14 key genes per tumor is dysregulated. We hypothesize that colorectal cancer may be molecularly sub-classified based on an oncogenic pathway prediction model in which tumors are grouped based on patterns of oncogenic pathway dysregulation/expression.

Methods: Affymetrix microarray data from 850 patients with primary colorectal cancer from publically available datasets were combined using Bayesian factor regression modeling normalization. The activity of 19 separate oncogenic pathways was predicted among tumors to generate patterns of pathway activity for each sample. Mixture modeling was applied to these samples to identify subgroups of tumors with unique patterns of pathway dysregulation. Validation of subclasses was performed on a dataset of 133 primary and metastatic colorectal cancer samples of patients undergoing curative surgical resection at our institution. Tumors were subgrouped according to our previous model and recurrence free survival was calculated. In vivo validation was performed by treating NOD/SCID mice bearing patient derived tumors with everolimus with changes in tumor size calculated between day 0 and day 21.

Results: Mixture modeling resulted in 6 individual subgroups of colorectal cancer based on pathway dysregulation. Kaplan Meier curves revealed that patients in subclass 4 had the poorest prognosis while patients in subclass 6 had the best prognosis (p=0.05). Further, tumors in subclass 4 were generally enriched for high mTOR pathway activation and patient derived explants from subclass 4 with high predicted mTOR activity were found to be sensitive to the MTOR pathway inhibitor everolimus.

Conclusions: Prediction of oncogenic signaling pathway activity is a powerful tool that may be used to molecularly sub-classify colorectal cancer into biologically relevant subgroups. These subgroups have prognostic and predictive implications for recurrence following surgical resection and responsiveness to targeted therapy.

High-Dimensional Sparse Factor Modeling: Applications in Gene Expression Genomics

We describe studies in molecular profiling and biological pathway analysis that use sparse latent factor and regression models for microarray gene expression data. We discuss breast cancer applications and key aspects of the modeling and computational methodology. Our case studies aim to investigate and characterize heterogeneity of structure related to specific oncogenic pathways, as well as links between aggregate patterns in gene expression profiles and clinical biomarkers. Based on the metaphor of statistically derived "factors" as representing biological "subpathway" structure, we explore the decomposition of fitted sparse factor models into pathway subcomponents and investigate how these components overlay multiple aspects of known biological activity. Our methodology is based on sparsity modeling of multivariate regression, ANOVA, and latent factor models, as well as a class of models that combines all components. Hierarchical sparsity priors address questions of dimension reduction and multiple comparisons, as well as scalability of the methodology. The models include practically relevant non-Gaussian/nonparametric components for latent structure, underlying often quite complex non-Gaussianity in multivariate expression patterns. Model search and fitting are addressed through stochastic simulation and evolutionary stochastic search methods that are exemplified in the oncogenic pathway studies. Supplementary supporting material provides more details of the applications, as well as examples of the use of freely available software tools for implementing the methodology.

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/.

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.

Heterogeneity in MYC-induced mammary tumors contributes to escape from oncogene dependence

A hallmark of human cancer is heterogeneity, reflecting the complex series of changes resulting in the activation of oncogenes coupled with inactivation of tumor suppressor genes. Breast cancer is no exception and indeed, many studies have revealed considerable complexity and heterogeneity in the population of primary breast tumors and substantial changes in a recurrent breast tumor that has acquired metastatic properties and drug resistance. We have made use of a Myc-inducible transgenic mouse model of breast cancer in which elimination of Myc activity following tumor development initially leads to a regression of a subset of tumors generally followed by de novo Myc-independent growth. We have observed that tumors that grow independent of Myc expression have gene profiles that are distinct from the primary tumors with characteristics indicative of an epithelial-mesenchymal transition (EMT) phenotype. Phenotypic analyses of Myc-independent tumors confirm the acquisition of an EMT phenotype suggested to be associated with invasive and migratory properties in human cancer cells. Further genomic analyses reveal mouse mammary tumors growing independent of myc have a higher probability of exhibiting a gene signature similar to that observed for human 'tumor-initiating' cells. Collectively, the data reveal genetic alterations that underlie tumor progression and an escape from Myc-dependent growth in a transgenic mouse model that can provide insights to what occurs in human cancers as they acquire drug resistance and metastatic properties.

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.

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.

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.

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.

Viral-mediated noisy gene expression reveals biphasic E2f1 response to MYC

Gene expression mediated by viral vectors is subject to cell-to-cell variability, which limits the accuracy of gene delivery. When coupled with single-cell measurements, however, such variability provides an efficient means to quantify signaling dynamics in mammalian cells. Here, we illustrate the utility of this approach by mapping the E2f1 response to MYC, serum stimulation, or both. Our results revealed an underappreciated mode of gene regulation: E2f1 expression first increased, then decreased as MYC input increased. This biphasic pattern was also reflected in other nodes of the network, including the miR-17-92 microRNA cluster and p19Arf. A mathematical model of the network successfully predicted modulation of the biphasic E2F response by serum and a CDK inhibitor. In addition to demonstrating how noise can be exploited to probe signaling dynamics, our results reveal how coordination of the MYC/RB/E2F pathway enables dynamic discrimination of aberrant and normal levels of growth stimulation.

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.

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.

Using a stem cell-based signature to guide therapeutic selection in cancer

Given the very substantial heterogeneity of most human cancers, it is likely that most cancer therapeutics will be active in only a small fraction of any population of patients. As such, the development of new therapeutics, coupled with methods to match a therapy with the individual patient, will be critical to achieving significant gains in disease outcome. One such opportunity is the use of expression signatures to identify key oncogenic phenotypes that can serve not only as biomarkers but also as a means of identifying therapeutic compounds that might specifically target these phenotypes. Given the potential importance of targeting tumors exhibiting a stem-like phenotype, we have developed an expression signature that reflects common biological aspects of various stem-like characteristics. The consensus stemness ranking (CSR) signature is upregulated in cancer stem cell-enriched samples at advanced tumor stages and is associated with poor prognosis in multiple cancer types. Using two independent computational approaches we utilized the CSR signature to identify clinically useful compounds that could target the CSR phenotype. In vitro assays confirmed selectivity of several predicted compounds including topoisomerase inhibitors and resveratrol towards breast cancer cell lines that exhibit a high-CSR phenotype. Importantly, the CSR signature could predict clinical response of breast cancer patients to a neoadjuvant regimen that included a CSR-specific agent. Collectively, these results suggest therapeutic opportunities to target the CSR phenotype in a relevant cohort of cancer patients.

Stochastic E2F activation and reconciliation of phenomenological cell-cycle models

A new, stochastic model of entry into the mammalian cell cycle provides a mechanistic understanding of the temporal variability observed across populations of cells and reconciles previously proposed phenomenological cell-cycle models.

The transition of the mammalian cell from quiescence to proliferation is a highly variable process. Over the last four decades, two lines of apparently contradictory, phenomenological models have been proposed to account for such temporal variability. These include various forms of the transition probability (TP) model and the growth control (GC) model, which lack mechanistic details. The GC model was further proposed as an alternative explanation for the concept of the restriction point, which we recently demonstrated as being controlled by a bistable Rb-E2F switch. Here, through a combination of modeling and experiments, we show that these different lines of models in essence reflect different aspects of stochastic dynamics in cell cycle entry. In particular, we show that the variable activation of E2F can be described by stochastic activation of the bistable Rb-E2F switch, which in turn may account for the temporal variability in cell cycle entry. Moreover, we show that temporal dynamics of E2F activation can be recast into the frameworks of both the TP model and the GC model via parameter mapping. This mapping suggests that the two lines of phenomenological models can be reconciled through the stochastic dynamics of the Rb-E2F switch. It also suggests a potential utility of the TP or GC models in defining concise, quantitative phenotypes of cell physiology. This may have implications in classifying cell types or states.

Mammalian cells enter the division cycle in response to appropriate growth signals. For each cell, the decision to do so is critically dependent on the interplay between environmental cues and the internal state of the cell and is influenced by random fluctuations in cellular processes. Indeed, experimental evidence indicates that cell cycle entry is highly variable from cell to cell, even within a clonal population. To account for such variability, a number of phenomenological models have been previously proposed. These models primarily fall into two types depending on their fundamental assumptions on the origin of the variability. “Transition probability” models presume that variability in cell cycle entry originates from the fact that entry in each individual cell is random but also governed by a fixed probability. In contrast, “growth-controlled” models assume that the growth rates across a population are variable and result in cells that are out of phase developmentally. While both kinds of models provide a good fit to experimental data, their lack of mechanistic details limits their predictive power and has led to unresolved debate between their practitioners. In this study, we developed a mechanistically based stochastic model of the temporal dynamics of activation of the E2F transcription factor, which is used here as a marker of the transition of cells from quiescence to active cell cycling. Using this model, we show that “transition probability” and “growth-controlled” models can be reconciled by incorporation of a small number of basic cellular parameters related to protein synthesis and turnover, protein modification, stochasticity, and the like. Essentially our work shows that each kind of phenomenological model holds true for describing a particular aspect of the cell cycle transition. We suggest that incorporation of basic cellular parameters in this manner into phenomenological models may constitute a broadly applicable approach to defining concise, quantitative phenotypes of cell physiology.

Abstract IA3-2: Utilization of genomic signatures to guide therapeutic decisions

The heterogeneity of human cancers presents major challenges in understanding disease mechanisms as well as in developing an effective therapeutic strategy for the individual patient. The ability to tailor cancer therapy to characteristics of the individual patient, both with a focus on better utilization of existing standard-of-care regimens as well as new investigational drugs. Towards these goals, we have developed gene expression signatures that have the capacity to predict sensitivity to cytotoxic chemotherapeutics, and utilized these in prospective trials to determine their capacity to guide choice amongst multiple options. In addition, we have also made use of expression profiling to develop signatures of oncogenic pathway deregulation that can then be used to profile the state of these pathways within populations of tumors. These pathway signatures have been useful in dissecting disease heterogeneity, identifying subgroups of tumors with again distinct outcomes, and providing a link in pathway activation with therapeutics. We have extended this concept to develop more refined signatures that can dissect the complexities of many of the known signaling pathways, providing a more precise capacity to probe the activity or deregulation of the pathway and linking to a broader array of therapeutics. This approach, when combined with the capacity to predict chemotherapy use, has the potential to identify therapeutic strategies that make use of all available drugs, matched to the characteristics of the individual patient and thus an approach towards the development of personalized treatment options for the individual patient.

Citation Information: Clin Cancer Res 2010;16(14 Suppl):IA3-2.

Diagnosis of partial body radiation exposure in mice using peripheral blood gene expression profiles

In the event of a terrorist-mediated attack in the United States using radiological or improvised nuclear weapons, it is expected that hundreds of thousands of people could be exposed to life-threatening levels of ionizing radiation. We have recently shown that genome-wide expression analysis of the peripheral blood (PB) can generate gene expression profiles that can predict radiation exposure and distinguish the dose level of exposure following total body irradiation (TBI). However, in the event a radiation-mass casualty scenario, many victims will have heterogeneous exposure due to partial shielding and it is unknown whether PB gene expression profiles would be useful in predicting the status of partially irradiated individuals. Here, we identified gene expression profiles in the PB that were characteristic of anterior hemibody-, posterior hemibody- and single limb-irradiation at 0.5 Gy, 2 Gy and 10 Gy in C57Bl6 mice. These PB signatures predicted the radiation status of partially irradiated mice with a high level of accuracy (range 79-100%) compared to non-irradiated mice. Interestingly, PB signatures of partial body irradiation were poorly predictive of radiation status by site of injury (range 16-43%), suggesting that the PB molecular response to partial body irradiation was anatomic site specific. Importantly, PB gene signatures generated from TBI-treated mice failed completely to predict the radiation status of partially irradiated animals or non-irradiated controls. These data demonstrate that partial body irradiation, even to a single limb, generates a characteristic PB signature of radiation injury and thus may necessitate the use of multiple signatures, both partial body and total body, to accurately assess the status of an individual exposed to radiation.

Myc-induced microRNAs integrate Myc-mediated cell proliferation and cell fate

The Myc pathway, often deregulated in cancer, is critical in determining cell fate by coordinating a gene expression program that links the control of cell proliferation with cell fate decisions. As such, precise control of the Myc pathway activity must be achieved to ensure faithful execution of appropriate cellular response and to prevent progressing toward a malignant state. With recent highlighted roles of microRNAs (miRNA) as critical components of gene control, we sought to evaluate the extent to which miRNAs may contribute in the execution of Myc function. Combined analysis of mRNA and miRNA expression profiles reveals an integration whereby the Myc-mediated induction of miRNAs leads to the repression of various mRNAs encoding tumor suppressors that block cell proliferation including p21, p27, and Rb. In addition, the proapoptotic PTEN tumor suppressor gene is also repressed by Myc-induced miRNAs, suggesting that Myc-induced miRNAs contribute to the precise control of a transcriptional program that coordinates the balance of cell proliferation and cell death.

Intratumor heterogeneity and precision of microarray-based predictors of breast cancer biology and clinical outcome

PURPOSE

Identifying sources of variation in expression microarray data and the effect of variance in gene expression measurements on complex predictive and diagnostic models is essential when translating microarray-based experimental approaches into clinical assays. The technical reproducibility of microarray platforms is well established. Here, we investigate the additional impact of intratumor heterogeneity, a largely unstudied component of variance, on the performance of several microarray-based assays in breast cancer.

PATIENTS AND METHODS

Genome-wide expression profiling was performed on 50 core needle biopsies from 18 breast cancer patients using Affymetrix GeneChip Human Genome U133 Plus 2.0 arrays. Global profiles of expression were characterized using unsupervised clustering methods and variance components models. Array-based measures of estrogen receptor (ER) and progesterone receptor (PR) status were compared with immunohistochemistry. The precision of genomic predictors of ER pathway status, recurrence risk, and sensitivity to chemotherapeutics was evaluated by interclass correlation.

RESULTS

Global patterns of gene expression demonstrated that intratumor variation was substantially less than the total variation observed across the patient population. Nevertheless, a fraction of genes exhibited significant intratumor heterogeneity in expression. A high degree of reproducibility was observed in single-gene predictors of ER (intraclass correlation coefficient [ICC] = 0.94) and PR expression (ICC = 0.90), and in a multigene predictor of ER pathway activation (ICC = 0.98) with high concordance with immunohistochemistry. Substantial agreement was also observed for multigene signatures of cancer recurrence (ICC = 0.71) and chemotherapeutic sensitivity (ICC = 0.72 and 0.64).

CONCLUSION

Intratumor heterogeneity, although present at the level of individual gene expression, does not preclude precise microarray-based predictions of tumor behavior or clinical outcome in breast cancer patients.

A genomic approach to improve prognosis and predict therapeutic response in chronic lymphocytic leukemia

PURPOSE

Chronic lymphocytic leukemia (CLL) is a B-cell malignancy characterized by a variable clinical course. Several parameters have prognostic capabilities but are associated with altered response to therapy in only a small subset of patients.

EXPERIMENTAL DESIGN

We used gene expression profiling methods to generate predictors of therapy response and prognosis. Genomic signatures that reflect progressive disease and responses to chemotherapy or chemoimmunotherapy were created using cancer cell lines and patient leukemia cell samples. We validated and applied these three signatures to independent clinical data from four cohorts, representing a total of 301 CLL patients.

RESULTS

A genomic signature of prognosis created from patient leukemic cell gene expression data coupled with clinical parameters significantly differentiated patients with stable disease from those with progressive disease in the training data set. The progression signature was validated in two independent data sets, showing a capacity to accurately identify patients at risk for progressive disease. In addition, genomic signatures that predict response to chlorambucil or pentostatin, cyclophosphamide, and rituximab were generated and could accurately distinguish responding and nonresponding CLL patients.

CONCLUSIONS

Thus, microarray analysis of CLL lymphocytes can be used to refine prognosis and predict response to different therapies. These results have implications for standard and investigational therapeutics in CLL patients.

Utilization of genomic signatures to identify phenotype-specific drugs

Genetic and genomic studies highlight the substantial complexity and heterogeneity of human cancers and emphasize the general lack of therapeutics that can match this complexity. With the goal of expanding opportunities for drug discovery, we describe an approach that makes use of a phenotype-based screen combined with the use of multiple cancer cell lines. In particular, we have used the NCI-60 cancer cell line panel that includes drug sensitivity measures for over 40,000 compounds assayed on 59 independent cells lines. Targets are cancer-relevant phenotypes represented as gene expression signatures that are used to identify cells within the NCI-60 panel reflecting the signature phenotype and then connect to compounds that are selectively active against those cells. As a proof-of-concept, we show that this strategy effectively identifies compounds with selectivity to the RAS or PI3K pathways. We have then extended this strategy to identify compounds that have activity towards cells exhibiting the basal phenotype of breast cancer, a clinically-important breast cancer characterized as ER-, PR-, and Her2- that lacks viable therapeutic options. One of these compounds, Simvastatin, has previously been shown to inhibit breast cancer cell growth in vitro and importantly, has been associated with a reduction in ER-, PR- breast cancer in a clinical study. We suggest that this approach provides a novel strategy towards identification of therapeutic agents based on clinically relevant phenotypes that can augment the conventional strategies of target-based screens.

Anchorage-independent cell growth signature identifies tumors with metastatic potential

The oncogenic phenotype is complex, resulting from the accumulation of multiple somatic mutations that lead to the deregulation of growth regulatory and cell fate controlling activities and pathways. The ability to dissect this complexity, so as to reveal discrete aspects of the biology underlying the oncogenic phenotype, is critical to understanding the various mechanisms of disease as well as to reveal opportunities for novel therapeutic strategies. Previous work has characterized the process of anchorage-independent growth of cancer cells in vitro as a key aspect of the tumor phenotype, particularly with respect to metastatic potential. Nevertheless, it remains a major challenge to translate these cell biology findings into the context of human tumors. We previously used DNA microarray assays to develop expression signatures, which have the capacity to identify subtle distinctions in biological states and can be used to connect in vitro and in vivo states. Here we describe the development of a signature of anchorage-independent growth, show that the signature exhibits characteristics of deregulated mitochondrial function and then demonstrate that the signature identifies human tumors with the potential for metastasis.

An integration of complementary strategies for gene-expression analysis to reveal novel therapeutic opportunities for breast cancer

Introduction

Perhaps the major challenge in developing more effective therapeutic strategies for the treatment of breast cancer patients is confronting the heterogeneity of the disease, recognizing that breast cancer is not one disease but multiple disorders with distinct underlying mechanisms. Gene-expression profiling studies have been used to dissect this complexity, and our previous studies identified a series of intrinsic subtypes of breast cancer that define distinct populations of patients with respect to survival. Additional work has also used signatures of oncogenic pathway deregulation to dissect breast cancer heterogeneity as well as to suggest therapeutic opportunities linked to pathway activation.

Methods

We used genomic analyses to identify relations between breast cancer subtypes, pathway deregulation, and drug sensitivity. For these studies, we use three independent breast cancer gene-expression data sets to measure an individual tumor phenotype. Correlation between pathway status and subtype are examined and linked to predictions for response to conventional chemotherapies.

Results

We reveal patterns of pathway activation characteristic of each molecular breast cancer subtype, including within the more aggressive subtypes in which novel therapeutic opportunities are critically needed. Whereas some oncogenic pathways have high correlations to breast cancer subtype (RAS, CTNNB1, p53, HER1), others have high variability of activity within a specific subtype (MYC, E2F3, SRC), reflecting biology independent of common clinical factors. Additionally, we combined these analyses with predictions of sensitivity to commonly used cytotoxic chemotherapies to provide additional opportunities for therapeutics specific to the intrinsic subtype that might be better aligned with the characteristics of the individual patient.

Conclusions

Genomic analyses can be used to dissect the heterogeneity of breast cancer. We use an integrated analysis of breast cancer that combines independent methods of genomic analyses to highlight the complexity of signaling pathways underlying different breast cancer phenotypes and to identify optimal therapeutic opportunities.

Genomic and molecular profiling predicts response to temozolomide in melanoma

PURPOSE

Despite objective response rates of only approximately 13%, temozolomide remains one of the most effective single chemotherapy agents against metastatic melanoma, second only to dacarbazine, the current standard of care for systemic treatment of melanoma. The goal of this study was to identify molecular and/or genetic markers that correlate with, and could be used to predict, response to temozolomide-based treatment regimens and that reflect the intrinsic properties of a patient's tumor.

EXPERIMENTAL DESIGN

Using a panel of 26 human melanoma-derived cell lines, we determined in vitro temozolomide sensitivity, O(6)-methylguanine-DNA methyltransferase (MGMT) activity, MGMT protein expression and promoter methylation status, and mismatch repair proficiency, as well as the expression profile of 38,000 genes using an oligonucleotide-based microarray platform.

RESULTS

The results showed a broad spectrum of temozolomide sensitivity across the panel of cell lines, with IC(50) values ranging from 100 micromol/L to 1 mmol/L. There was a significant correlation between measured temozolomide sensitivity and a gene expression signature-derived prediction of temozolomide sensitivity (P < 0.005). Notably, MGMT alone showed a significant correlation with temozolomide sensitivity (MGMT activity, P < 0.0001; MGMT expression, P <or= 0.0001). The promoter methylation status of the MGMT gene, however, was not consistent with MGMT gene expression or temozolomide sensitivity.

CONCLUSIONS

These results show that melanoma resistance to temozolomide is conferred predominantly by MGMT activity and suggest that MGMT expression could potentially be a useful tool for predicting the response of melanoma patients to temozolomide therapy.

A genomic strategy to elucidate modules of oncogenic pathway signaling networks

Recent studies have emphasized the importance of pathway-specific interpretations for understanding the functional relevance of gene alterations in human cancers. Although signaling activities are often conceptualized as linear events, in reality, they reflect the activity of complex functional networks assembled from modules that each respond to input signals. To acquire a deeper understanding of this network structure, we developed an approach to deconstruct pathways into modules represented by gene expression signatures. Our studies confirm that they represent units of underlying biological activity linked to known biochemical pathway structures. Importantly, we show that these signaling modules provide tools to dissect the complexity of oncogenic states that define disease outcomes as well as response to pathway-specific therapeutics. We propose that this model of pathway structure constitutes a framework to study the processes by which information propogates through cellular networks and to elucidate the relationships of fundamental modules to cellular and clinical phenotypes.

Novel tumor sampling strategies to enable microarray gene expression signatures in breast cancer: a study to determine feasibility and reproducibility in the context of clinical care

Feasibility and reproducibility of microarray biomarkers in clinical settings are doubted because of reliance on fresh frozen tissue. We sought to develop and validate a paradigm of frozen tissue collection from early breast tumors to enable use of microarray in oncology practice. Frozen core needle biopsies (CNBx) were collected from 150 clinical stage I patients during image-guided diagnostic biopsy and/or surgery. Histology and tumor content from frozen cores were compared to diagnostic specimens. Twenty-eight patients had microarray analysis to examine accuracy and reproducibility of predictive gene signatures developed for estrogen receptor (ER) and HER2. One hundred twenty-seven (85%) of 150 patients had at least one frozen core containing cancer suitable for microarray analysis. Larger tumor size, ex vivo biopsy, and use of a new specimen device increased the likelihood of obtaining adequate specimens. Sufficient quality RNA was obtained from 90% of tumor cores. Microarray signatures predicting ER and HER2 expression were developed in training sets of up to 363 surgical samples and were applied to microarray data obtained from core samples collected in clinical settings. In these samples, prediction of ER and HER2 expression achieved a sensitivity/specificity of 94%/100%, and 82%/72%, respectively. Predictions were reproducible in 83-100% of paired samples. Frozen CNBx can be readily obtained from most breast cancers without interfering with pathologic evaluation in routine clinical settings. Collection of tumor tissue at diagnostic biopsy and/or at surgery from lumpectomy specimens using image guidance resulted in sufficient samples for array analysis from over 90% of patients. Sampling of breast cancer for microarray data is reproducible and feasible in clinical practice and can yield signatures predictive of multiple breast cancer phenotypes.

Balancing the decision of cell proliferation and cell fate

The control of cellular proliferation is key in the proper development of a complex organism, the maintenance of tissue homeostasis and the ability to respond to various hormonal and other inducers. Key in the control of proliferation is the retinoblastoma (Rb) protein which regulates the activity of a family of transcription factors known as E2Fs. The E2F proteins are now recognized to regulate the expression of a large number of genes associated with cell proliferation including genes encoding DNA replication as well as mitotic activities. What has also become clear over the past several years is the intimate relationship between the control of cell proliferation and the control of cell fate, particularly the activation of apoptotic pathways. Central in this connection is the Rb/E2F pathway that not only provides the primary signals for proliferation but at the same time, connects with the p53-dependent apoptotic pathway. This review addresses this inter-connection and the molecular mechanisms that control the decision between proliferation and cell death.

Utilization of pathway signatures to reveal distinct types of B lymphoma in the Emicro-myc model and human diffuse large B-cell lymphoma

The Emu-myc transgenic mouse has provided a valuable model for the study of B-cell lymphoma. Making use of gene expression analysis and, in particular, expression signatures of cell signaling pathway activation, we now show that several forms of B lymphoma can be identified in the Emu-myc mice associated with time of tumor onset. Furthermore, one form of Emu-myc tumor with pre-B character is shown to resemble human Burkitt lymphoma, whereas others exhibit more differentiated B-cell characteristics and show similarity with human diffuse large B-cell lymphoma in the pattern of gene expression, as well as oncogenic pathway activation. Importantly, we show that signatures of oncogenic pathway activity provide further dissection of the spectrum of diffuse large B-cell lymphoma, identifying a subset of patients who have very poor prognosis and could benefit from more aggressive or novel therapeutic strategies. Taken together, these studies provide insight into the complexity of the oncogenic process and a novel strategy for dissecting the heterogeneity of B lymphoma.

Young Age at Diagnosis Correlates With Worse Prognosis and Defines a Subset of Breast Cancers With Shared Patterns of Gene Expression

Purpose

Breast cancer arising in young women is correlated with inferior survival and higher incidence of negative clinicopathologic features. The biology driving this aggressive disease has yet to be defined.

Patients and Methods

Clinically annotated, microarray data from 784 early-stage breast cancers were identified, and prospectively defined, age-specific cohorts (young: ≤ 45 years, n = 200; older: ≥ 65 years, n = 211) were compared by prognosis, clinicopathologic variables, mRNA expression values, single-gene analysis, and gene set enrichment analysis (GSEA). Univariate and multivariate analyses were performed.

Results

Using clinicopathologic variables, young women illustrated lower estrogen receptor (ER) positivity (immunohistochemistry [IHC], P = .027), larger tumors (P = .012), higher human epidermal growth factor receptor 2 (HER-2) overexpression (IHC, P = .075), lymph node positivity (P = .008), higher grade tumors (P < .0001), and trends toward inferior disease-free survival (DFS; hazard ratio = 1.32; P = .094). Using genomic expression analysis, tumors arising in young women had significantly lower ERα mRNA (P < .0001), ERβ (P = .02), and progesterone receptor (PR) expression (P < .0001), but higher HER-2 (P < .0001) and epidermal growth factor receptor (EGFR) expression (P < .0001). Exploratory analysis (GSEA) revealed 367 biologically relevant gene sets significantly distinguishing breast tumors arising in young women. Combining clinicopathologic and genomic variables among tumors arising in young women demonstrated that younger age and lower ERβ and higher EGFR mRNA expression were significant predictors of inferior DFS.

Conclusion

This large-scale genomic analysis illustrates that breast cancer arising in young women is a unique biologic entity driven by unifying oncogenic signaling pathways, is characterized by less hormone sensitivity and higher HER-2/EGFR expression, and warrants further study to offer this poor-prognosis group of women better preventative and therapeutic options.

Patterns of cell signaling pathway activation that characterize mammary development

Previous work has detailed the histological and biochemical changes associated with mammary development and remodeling. We have now made use of gene expression profiling, and in particular of the previously described signatures of cell signaling pathway activation, to explore the events associated with mammary gland development. We find that there is elevated E2F-specific pathway activity prior to lactation and relatively low levels of other important signaling pathways, such as RAS, MYC and SRC. Upon lactation and continuing into the involution phase, these patterns reverse with a dramatic increase in RAS, SRC and MYC pathway activity and a decline in E2F activity. At the end of involution, these patterns return to that of the adult non-lactating mammary gland. The importance of the changes in E2F pathway activity, particularly during the proliferative phase of mammary development, was confirmed through the analysis of mice deficient for various E2F proteins. Taken together, these results reveal a complex pattern of pathway activity in relation to the various phases of mammary gland development.

Phylogenetic simulation of promoter evolution: estimation and modeling of binding site turnover events and assessment of their impact on alignment tools

BACKGROUND

The phenomenon of functional site turnover has important implications for the study of regulatory region evolution, such as for promoter sequence alignments and transcription factor binding site (TFBS) identification. At present, it remains difficult to estimate TFBS turnover rates on real genomic sequences, as reliable mappings of functional sites across related species are often not available. As an alternative, we introduce a flexible new simulation system, Phylogenetic Simulation of Promoter Evolution (PSPE), designed to study functional site turnovers in regulatory sequences.

RESULTS

Using PSPE, we study replacement turnover rates of different individual TFBSs and simple modules of two sites under neutral evolutionary functional constraints. We find that TFBS replacement turnover can happen rapidly in promoters, and turnover rates vary significantly among different TFBSs and modules. We assess the influence of different constraints such as insertion/deletion rate and translocation distances. Complementing the simulations, we give simple but effective mathematical models for TFBS turnover rate prediction. As one important application of PSPE, we also present a first systematic evaluation of multiple sequence aligners regarding their capability of detecting TFBSs in promoters with site turnovers.

CONCLUSION

PSPE allows researchers for the first time to investigate TFBS replacement turnovers in promoters systematically. The assessment of alignment tools points out the limitations of current approaches to identify TFBSs in non-coding sequences, where turnover events of functional sites may happen frequently, and where we are interested in assessing the similarity on the functional level. PSPE is freely available at the authors' website.

Gene expression signatures of radiation response are specific, durable and accurate in mice and humans

BACKGROUND

Previous work has demonstrated the potential for peripheral blood (PB) gene expression profiling for the detection of disease or environmental exposures.

METHODS AND FINDINGS

We have sought to determine the impact of several variables on the PB gene expression profile of an environmental exposure, ionizing radiation, and to determine the specificity of the PB signature of radiation versus other genotoxic stresses. Neither genotype differences nor the time of PB sampling caused any lessening of the accuracy of PB signatures to predict radiation exposure, but sex difference did influence the accuracy of the prediction of radiation exposure at the lowest level (50 cGy). A PB signature of sepsis was also generated and both the PB signature of radiation and the PB signature of sepsis were found to be 100% specific at distinguishing irradiated from septic animals. We also identified human PB signatures of radiation exposure and chemotherapy treatment which distinguished irradiated patients and chemotherapy-treated individuals within a heterogeneous population with accuracies of 90% and 81%, respectively.

CONCLUSIONS

We conclude that PB gene expression profiles can be identified in mice and humans that are accurate in predicting medical conditions, are specific to each condition and remain highly accurate over time.

An integrated approach to the prediction of chemotherapeutic response in patients with breast cancer

Background

A major challenge in oncology is the selection of the most effective chemotherapeutic agents for individual patients, while the administration of ineffective chemotherapy increases mortality and decreases quality of life in cancer patients. This emphasizes the need to evaluate every patient's probability of responding to each chemotherapeutic agent and limiting the agents used to those most likely to be effective.

Methods and Results

Using gene expression data on the NCI-60 and corresponding drug sensitivity, mRNA and microRNA profiles were developed representing sensitivity to individual chemotherapeutic agents. The mRNA signatures were tested in an independent cohort of 133 breast cancer patients treated with the TFAC (paclitaxel, 5-fluorouracil, adriamycin, and cyclophosphamide) chemotherapy regimen. To further dissect the biology of resistance, we applied signatures of oncogenic pathway activation and performed hierarchical clustering. We then used mRNA signatures of chemotherapy sensitivity to identify alternative therapeutics for patients resistant to TFAC. Profiles from mRNA and microRNA expression data represent distinct biologic mechanisms of resistance to common cytotoxic agents. The individual mRNA signatures were validated in an independent dataset of breast tumors (P = 0.002, NPV = 82%). When the accuracy of the signatures was analyzed based on molecular variables, the predictive ability was found to be greater in basal-like than non basal-like patients (P = 0.03 and P = 0.06). Samples from patients with co-activated Myc and E2F represented the cohort with the lowest percentage (8%) of responders. Using mRNA signatures of sensitivity to other cytotoxic agents, we predict that TFAC non-responders are more likely to be sensitive to docetaxel (P = 0.04), representing a viable alternative therapy.

Conclusions

Our results suggest that the optimal strategy for chemotherapy sensitivity prediction integrates molecular variables such as ER and HER2 status with corresponding microRNA and mRNA expression profiles. Importantly, we also present evidence to support the concept that analysis of molecular variables can present a rational strategy to identifying alternative therapeutic opportunities.

Gene expression signatures, clinicopathological features, and individualized therapy in breast cancer

CONTEXT

Gene expression profiling may be useful for prognostic and therapeutic strategies in breast carcinoma.

OBJECTIVES

To demonstrate the value in integrating genomic information with clinical and pathological risk factors, to refine prognosis, and to improve therapeutic strategies for early stage breast cancer.

DESIGN, SETTING, AND PATIENTS

Retrospective study of patients with early stage breast carcinoma who were candidates for adjuvant chemotherapy; 964 clinically annotated breast tumor samples (573 in the initial discovery set and 391 in the validation cohort) with corresponding microarray data were used. All patients were assigned relapse risk scores based on their respective clinicopathological features. Signatures representing oncogenic pathway activation and tumor biology/microenvironment status were applied to these samples to obtain patterns of deregulation that correspond with relapse risk scores to refine prognosis with the clinicopathological prognostic model alone. Predictors of chemotherapeutic response were also applied to further characterize clinically relevant heterogeneity in early stage breast cancer.

MAIN OUTCOME MEASURES

Gene expression signatures and clinicopathological variables in early stage breast cancer to determine a refined estimation of relapse-free survival and sensitivity to chemotherapy.

RESULTS

In the initial data set of 573 patients, prognostically significant clusters representing patterns of oncogenic pathway activation and tumor biology/microenvironment states were identified within the low-risk (log-rank P = .004), intermediate-risk (log-rank P = .01), and high-risk (log-rank P = .003) model cohorts, representing clinically important genomic subphenotypes of breast cancer. As an example, in the low-risk cohort, of 6 prognostically significant clusters, patients in cluster 4 had an inferior relapse-free survival vs patients in cluster 1 (log-rank P = .004) and cluster 5 (log-rank P = .03). Median relapse-free survival for patients in cluster 4 was 16 months less than for patients in cluster 1 (95% CI, 7.5-24.5 months) and 19 months less than for patients in cluster 5 (95% CI, 10.5-27.5 months). Multivariate analyses confirmed the independent prognostic value of the genomic clusters (low risk, P = .05; high risk, P = .02). The reproducibility and validity of these patterns of pathway deregulation in predicting relapse risk was established using related but not identical clusters in the independent validation cohort. The prognostic clinicogenomic clusters also have unique sensitivity patterns to commonly used cytotoxic therapies.

CONCLUSIONS

These results provide preliminary evidence that incorporation of gene expression signatures into clinical risk stratification can refine prognosis. Prospective studies are needed to determine the value of this approach for individualizing therapeutic strategies.

A role for Myc in facilitating transcription activation by E2F1

Previous work has demonstrated that E2F proteins regulate the expression of various genes encoding proteins essential for DNA replication and cell-cycle progression. E2F1 in particular is required for the initial entry to the cell cycle from a quiescent state and is required for the activation of other E2F genes. Other work has demonstrated a role for the Myc transcription factor in the activation of a large number of genes associated with cell growth, including E2F genes. We now show that Myc is required to allow the interaction of the E2F1 protein with the E2F gene promoters. As such, Myc thus provides a link between the development of a growth-competent state during the initial stage of G(1) and the activation of genes essential for DNA replication at G(1)/S.