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.