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.

Linking oncogenic pathways with therapeutic opportunities.

The accumulation of multiple mutations and alterations in the cancer genome underlies the complexity of cancer phenotypes. A consequence of these alterations is the deregulation of various cell-signalling pathways that control cell function. Molecular-profiling studies, particularly DNA microarray analyses, have the potential to describe this complexity. These studies also provide an opportunity to link pathway deregulation with potential therapeutic strategies. This approach, when coupled with other methods for identifying pathway activation, provides an opportunity to both match individual patients with the most appropriate therapeutic strategy and identify potential options for combination therapy.

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.

Genomic signatures to guide the use of chemotherapeutics.

Using in vitro drug sensitivity data coupled with Affymetrix microarray data, we developed gene expression signatures that predict sensitivity to individual chemotherapeutic drugs. Each signature was validated with response data from an independent set of cell line studies. We further show that many of these signatures can accurately predict clinical response in individuals treated with these drugs. Notably, signatures developed to predict response to individual agents, when combined, could also predict response to multidrug regimens. Finally, we integrated the chemotherapy response signatures with signatures of oncogenic pathway deregulation to identify new therapeutic strategies that make use of all available drugs. The development of gene expression profiles that can predict response to commonly used cytotoxic agents provides opportunities to better use these drugs, including using them in combination with existing targeted therapies.

A pathway-based classification of human breast cancer.

The hallmark of human cancer is heterogeneity, reflecting the complexity and variability of the vast array of somatic mutations acquired during oncogenesis. An ability to dissect this heterogeneity, to identify subgroups that represent common mechanisms of disease, will be critical to understanding the complexities of genetic alterations and to provide a framework to develop rational therapeutic strategies. Here, we describe a classification scheme for human breast cancer making use of patterns of pathway activity to build on previous subtype characterizations using intrinsic gene expression signatures, to provide a functional interpretation of the gene expression data that can be linked to therapeutic options. We show that the identified subgroups provide a robust mechanism for classifying independent samples, identifying tumors that share patterns of pathway activity and exhibit similar clinical and biological properties, including distinct patterns of chromosomal alterations that were not evident in the heterogeneous total population of tumors. We propose that this classification scheme provides a basis for understanding the complex mechanisms of oncogenesis that give rise to these tumors and to identify rational opportunities for combination therapies.

A pathway-based approach to identify molecular biomarkers in cancer.

Many examples highlight the power of gene expression profiles, or signatures, to provide an understanding of biological phenotypes. This is best seen in the context of cancer, where expression signatures have tremendous power to identify new cancer subtypes and to predict clinical outcomes. Gene expression profiles have been developed to personalize medicine, accurately predicting disease recurrence and tumor response to therapy. The use of these signatures as surrogate phenotypes allows us to link diverse experimental systems, which dissect the complexity of biological systems, with the in vivo setting in a way that was not previously feasible. Taken together, these new genomic tools provide the opportunity to develop rational strategies for treating the individual cancer patient.

Utilization of genomic signatures to direct use of primary chemotherapy.

The success of treatment of cancer patients depends on matching the most effective therapeutic regimen with the characteristics of the individual patient, balancing benefit against risk of adverse events. The primary challenge in achieving this goal is the heterogeneity of the disease, recognizing that breast, lung, colon and other cancers are not single diseases but rather an array of disorders with distinct molecular mechanisms. Genomic analyses, and in particular gene expression profiling, has been shown to have the capacity to dissect this heterogeneity and afford opportunities to match therapies with the characteristics of the individual patient's tumor. Here we review the success in developing gene expression signatures that have the capability of predicting response to various commonly used and newly developing cancer therapeutics. We further discuss the challenges and the opportunities in utilizing these tools in present-day clinical practice.

Oncogenic pathway signatures in human cancers as a guide to targeted therapies.

The development of an oncogenic state is a complex process involving the accumulation of multiple independent mutations that lead to deregulation of cell signalling pathways central to the control of cell growth and cell fate. The ability to define cancer subtypes, recurrence of disease and response to specific therapies using DNA microarray-based gene expression signatures has been demonstrated in multiple studies. Various studies have also demonstrated the potential for using gene expression profiles for the analysis of oncogenic pathways. Here we show that gene expression signatures can be identified that reflect the activation status of several oncogenic pathways. When evaluated in several large collections of human cancers, these gene expression signatures identify patterns of pathway deregulation in tumours and clinically relevant associations with disease outcomes. Combining signature-based predictions across several pathways identifies coordinated patterns of pathway deregulation that distinguish between specific cancers and tumour subtypes. Clustering tumours based on pathway signatures further defines prognosis in respective patient subsets, demonstrating that patterns of oncogenic pathway deregulation underlie the development of the oncogenic phenotype and reflect the biology and outcome of specific cancers. Predictions of pathway deregulation in cancer cell lines are also shown to predict the sensitivity to therapeutic agents that target components of the pathway. Linking pathway deregulation with sensitivity to therapeutics that target components of the pathway provides an opportunity to make use of these oncogenic pathway signatures to guide the use of targeted therapeutics.

Genetic heterogeneity of Myc-induced mammary tumors reflecting diverse phenotypes including metastatic potential.

Human cancers result from a complex series of genetic alterations, resulting in heterogeneous disease states. Dissecting this heterogeneity is critical for understanding underlying mechanisms and providing opportunities for therapeutics matching the complexity. Mouse models of cancer have generally been used to reduce this complexity and focus on the role of single genes. Nevertheless, our analysis of tumors arising in the MMTV-Myc model of mammary carcinogenesis reveals substantial heterogeneity, seen in both histological and expression phenotypes. One contribution to this heterogeneity is the substantial frequency of activating Ras mutations. Additionally, we show that these Myc-induced mammary tumors exhibit even greater heterogeneity, revealed by distinct histological subtypes as well as distinct patterns of gene expression, than many other mouse models of tumorigenesis. Two of the major histological subtypes are characterized by differential patterns of cellular signaling pathways, including beta-catenin and Stat3 activities. We also demonstrate that one of the MMTV-Myc mammary tumor subgroups exhibits metastatic capacity and that the signature derived from the subgroup can predict metastatic potential of human breast cancer. Together, these data reveal that a combination of histological and genomic analyses can uncover substantial heterogeneity in mammary tumor formation and therefore highlight aspects of tumor phenotype not evident in the population as a whole.

Characterizing the developmental pathways TTF-1, NKX2-8, and PAX9 in lung cancer.

We investigated the clinical implications of lung developmental transcription factors (TTF-1, NKX2-8, and PAX9) that we recently discovered as cooperating oncogenes activated by way of gene amplification at chromosome 14q13 in lung cancer. Using stable transfectants of human bronchial epithelial cells, RNA expression profiles (signatures) representing activation of the biological pathways defined by each of the 3 genes were determined and used to risk stratify a non-small-cell lung cancer (NSCLC) clinical data set consisting of 91 early stage tumors. Coactivation of the TTF-1 and NKX2-8 pathways identified a cluster of patients with poor survival, representing approximately 20% of patients with early stage NSCLC, whereas activation of individual pathways did not reveal significant prognostic power. Importantly, the poor prognosis associated with coactivation of TTF-1 and NKX2-8 was validated in 2 other independent clinical data sets. Furthermore, lung cancer cell lines showing coactivation of the TTF-1 and NKX2-8 pathways were shown to exhibit resistance to cisplatin, the standard of care for the treatment of NSCLC. This suggests that the cohort of patients with coactivation of TTF-1 and NKX2-8 pathways appears to be resistant to standard cisplatin therapy, suggesting the need for alternative therapies in this cohort of high-risk patients.

A genomic approach to colon cancer risk stratification yields biologic insights into therapeutic opportunities.

Gene expression profiles provide an opportunity to dissect the heterogeneity of solid tumors, including colon cancer, to improve prognosis and predict response to therapies. Bayesian binary regression methods were used to generate a signature of disease recurrence in patients with resected early stage colon cancer validated in an independent cohort. A 50-gene signature was developed that effectively distinguished early stage colon cancer patients with a low or high risk of disease recurrence. RT-PCR analysis of the 50-gene signature validated 9 of the top 10 differentially expressed genes. When applied to two independent validation cohorts of 55 and 73 patients, the 50-gene model accurately predicted recurrence. Standard Kaplan-Meier survival analysis confirmed the prognostic accuracy (P < 0.01, log rank), as did multivariate Cox proportional hazard models. We tested potential targeted therapeutic options for patients at high risk for disease recurrence and found a clinically important relationship between sensitivity to celecoxib, LY-294002 (PI3kinase inhibitor), retinol, and sulindac in colon cancer cell lines expressing the poor prognostic phenotype (P < 0.01, t test), which performed better than standard chemotherapy (5-FU and oxaliplatin). We present a genomic strategy in early stage colon cancer to identify patients at highest risk of recurrence. An ability to move beyond current staging by refining the estimation of prognosis in early stage colon cancer also has implications for individualized therapy.

The lung cancer exercise training study: a randomized trial of aerobic training, resistance training, or both in postsurgical lung cancer patients: rationale and design.

BACKGROUND: The Lung Cancer Exercise Training Study (LUNGEVITY) is a randomized trial to investigate the efficacy of different types of exercise training on cardiorespiratory fitness (VO2peak), patient-reported outcomes, and the organ components that govern VO2peak in post-operative non-small cell lung cancer (NSCLC) patients. METHODS/DESIGN: Using a single-center, randomized design, 160 subjects (40 patients/study arm) with histologically confirmed stage I-IIIA NSCLC following curative-intent complete surgical resection at Duke University Medical Center (DUMC) will be potentially eligible for this trial. Following baseline assessments, eligible participants will be randomly assigned to one of four conditions: (1) aerobic training alone, (2) resistance training alone, (3) the combination of aerobic and resistance training, or (4) attention-control (progressive stretching). The ultimate goal for all exercise training groups will be 3 supervised exercise sessions per week an intensity above 70% of the individually determined VO2peak for aerobic training and an intensity between 60 and 80% of one-repetition maximum for resistance training, for 30-45 minutes/session. Progressive stretching will be matched to the exercise groups in terms of program length (i.e., 16 weeks), social interaction (participants will receive one-on-one instruction), and duration (30-45 mins/session). The primary study endpoint is VO2peak. Secondary endpoints include: patient-reported outcomes (PROs) (e.g., quality of life, fatigue, depression, etc.) and organ components of the oxygen cascade (i.e., pulmonary function, cardiac function, skeletal muscle function). All endpoints will be assessed at baseline and postintervention (16 weeks). Substudies will include genetic studies regarding individual responses to an exercise stimulus, theoretical determinants of exercise adherence, examination of the psychological mediators of the exercise - PRO relationship, and exercise-induced changes in gene expression. DISCUSSION: VO2peak is becoming increasingly recognized as an outcome of major importance in NSCLC. LUNGEVITY will identify the optimal form of exercise training for NSCLC survivors as well as provide insight into the physiological mechanisms underlying this effect. Overall, this study will contribute to the establishment of clinical exercise therapy rehabilitation guidelines for patients across the entire NSCLC continuum. TRIAL REGISTRATION: NCT00018255.

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

Age- and sex-specific genomic profiles in non-small cell lung cancer.

CONTEXT: Gene expression profiling may be useful in examining differences underlying age- and sex-specific outcomes in non-small cell lung cancer (NSCLC). OBJECTIVE: To describe clinically relevant differences in the underlying biology of NSCLC based on patient age and sex. DESIGN, SETTING, AND PATIENTS: Retrospective analysis of 787 patients with predominantly early stage NSCLC performed at Duke University, Durham, North Carolina, from July 2008 to June 2009. Lung tumor samples with corresponding microarray and clinical data were used. All patients were divided into subgroups based on age (< 70 vs > or = 70 years old) or sex. Gene expression signatures representing oncogenic pathway activation and tumor biology/microenvironment status were applied to these samples to obtain patterns of activation/deregulation. MAIN OUTCOME MEASURES: Patterns of oncogenic and molecular signaling pathway activation that are reproducible and correlate with 5-year recurrence-free patient survival. RESULTS: Low- and high-risk patient clusters/cohorts were identified with the longest and shortest 5-year recurrence-free survival, respectively, within the age and sex NSCLC subgroups. These cohorts of NSCLC demonstrate similar patterns of pathway activation. In patients younger than 70 years, high-risk patients, with the shortest recurrence-free survival, demonstrated increased activation of the Src (25% vs 6%; P<.001) and tumor necrosis factor (76% vs 42%; P<.001) pathways compared with low-risk patients. High-risk patients aged 70 years or older demonstrated increased activation of the wound healing (40% vs 24%; P = .02) and invasiveness (64% vs 20%; P<.001) pathways compared with low-risk patients. In women, high-risk patients demonstrated increased activation of the invasiveness (99% vs 2%; P<.001) and STAT3 (72% vs 35%; P<.001) pathways while high-risk men demonstrated increased activation of the STAT3 (87% vs 18%; P<.001), tumor necrosis factor (90% vs 46%; P<.001), EGFR (13% vs 2%; P = .003), and wound healing (50% vs 22%; P<.001) pathways. Multivariate analyses confirmed the independent clinical relevance of the pathway-based subphenotypes in women (hazard ratio [HR], 2.02; 95% confidence interval [CI], 1.34-3.03; P<.001) and patients younger than 70 years (HR, 1.83; 95% CI, 1.24-2.71; P = .003). All observations were reproducible in split sample analyses. CONCLUSIONS: Among a cohort of patients with NSCLC, subgroups defined by oncogenic pathway activation profiles were associated with recurrence-free survival. These findings require validation in independent patient data sets.

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.

A genomic strategy to refine prognosis in early-stage non-small-cell lung cancer.

BACKGROUND: Clinical trials have indicated a benefit of adjuvant chemotherapy for patients with stage IB, II, or IIIA--but not stage IA--non-small-cell lung cancer (NSCLC). This classification scheme is probably an imprecise predictor of the prognosis of an individual patient. Indeed, approximately 25 percent of patients with stage IA disease have a recurrence after surgery, suggesting the need to identify patients in this subgroup for more effective therapy. METHODS: We identified gene-expression profiles that predicted the risk of recurrence in a cohort of 89 patients with early-stage NSCLC (the lung metagene model). We evaluated the predictor in two independent groups of 25 patients from the American College of Surgeons Oncology Group (ACOSOG) Z0030 study and 84 patients from the Cancer and Leukemia Group B (CALGB) 9761 study. RESULTS: The lung metagene model predicted recurrence for individual patients significantly better than did clinical prognostic factors and was consistent across all early stages of NSCLC. Applied to the cohorts from the ACOSOG Z0030 trial and the CALGB 9761 trial, the lung metagene model had an overall predictive accuracy of 72 percent and 79 percent, respectively. The predictor also identified a subgroup of patients with stage IA disease who were at high risk for recurrence and who might be best treated by adjuvant chemotherapy. CONCLUSIONS: The lung metagene model provides a potential mechanism to refine the estimation of a patient's risk of disease recurrence and, in principle, to alter decisions regarding the use of adjuvant chemotherapy in early-stage NSCLC.

Translating genomics into clinical practice: applications in lung cancer.

Non-small cell lung cancer (NSCLC) is the leading cause of death from cancer worldwide and has a poor overall survival across all stages of disease. The recent advancement of gene expression technology addresses the phenotypic complexity of many diseases, including NSCLC. These genomic approaches have shown great promise in NSCLC in helping to improve risk stratification, prognosis, and the clinician's ability to match the right therapy to an individual patient. Large prospective clinical trials are under way to evaluate the application and clinical impact of the use of genomics-based predictors of prognosis and therapy compared with current standard-of-care methods in patients with NSCLC. Several challenges of genomics-based therapy must be addressed before widespread application of these techniques becomes a reality. Genomic approaches in NSCLC have the potential to advance our understanding of underlying disease biology, to improve current prognostic and treatment paradigms, and to identify new targets for treatment, ultimately improving survival in patients with NSCLC and providing an opportunity for "personalized medicine."

A genomic approach to identify molecular pathways associated with chemotherapy resistance.

Resistance to chemotherapy in cancer is common. As gene expression profiling has been shown to anticipate chemotherapeutic resistance, we sought to identify cellular pathways associated with resistance to facilitate effective combination therapy. Gene set enrichment analysis was used to associate pathways with resistance in two data sets: the NCI-60 cancer cell lines deemed sensitive and resistant to specific chemotherapeutic agents (Adriamycin, cyclophosphamide, docetaxel, etoposide, 5-fluorouracil, paclitaxel, and topotecan) and a series of 40 lung cancer cell lines for which sensitivity to cisplatin and docetaxel was determined. Candidate pathways were further screened in silico using the Connectivity Map. The lead candidate pathway was functionally validated in vitro. Gene set enrichment analysis associated the matrix metalloproteinase, p53, methionine metabolism, and free pathways with cytotoxic resistance in the NCI-60 cell lines across multiple agents, but no gene set was common to all drugs. Analysis of the lung cancer cell lines identified the bcl-2 pathway to be associated with cisplatin resistance and the AKT pathway enriched in cisplatin- and docetaxel-resistant cell lines. Results from Connectivity Map supported an association between phosphatidylinositol 3-kinase/AKT and docetaxel resistance but did not support the association with cisplatin. Targeted inhibition of the phosphatidylinositol 3-kinase/AKT pathway with LY294002, in combination with docetaxel, resulted in a synergistic effect in previously docetaxel-resistant cell lines but not with cisplatin. These results support the use of a genomic approach to identify drug-specific targets associated with the development of chemotherapy resistance and underscore the importance of disease context in identifying these pathways.

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.