Co-expression modules are groups of genes with highly correlated expression patterns. In cancer, differences in module activity potentially represent the heterogeneity of phenotypes important in carcinogenesis, progression, or treatment response. To find gene expression modules active in breast cancer subpopulations, we assembled 72 breast cancer-related gene expression datasets containing ∼5,700 samples altogether. Per dataset, we identified genes with bimodal expression and used mixture-model clustering to ultimately define 11 modules of genes that are consistently co-regulated across multiple datasets. Functionally, these modules reflected estrogen signaling, development/differentiation, immune signaling, histone modification, ERBB2 signaling, the extracellular matrix (ECM) and stroma, and cell proliferation. The Tcell/Bcell immune modules appeared tumor-extrinsic, with coherent expression in tumors but not cell lines; whereas most other modules, interferon and ECM included, appeared intrinsic. Only four of the eleven modules were represented in the PAM50 intrinsic subtype classifier and other well-established prognostic signatures; although the immune modules were highly correlated to previously published immune signatures. As expected, the proliferation module was highly associated with decreased recurrence-free survival (RFS). Interestingly, the immune modules appeared associated with RFS even after adjustment for receptor subtype and proliferation; and in a multivariate analysis, the combination of Tcell/Bcell immune module down-regulation and proliferation module upregulation strongly associated with decreased RFS. Immune modules are unusual in that their upregulation is associated with a good prognosis without chemotherapy and a good response to chemotherapy, suggesting the paradox of high immune patients who respond to chemotherapy but would do well without it. Other findings concern the ECM/stromal modules, which despite common themes were associated with different sites of metastasis, possibly relating to the “seed and soil” hypothesis of cancer dissemination. Overall, co-expression modules provide a high-level functional view of breast cancer that complements the “cancer hallmarks” and may form the basis for improved predictors and treatments.
Gene-expression microarrays allow researchers to characterize biological phenomena in a high-throughput fashion but are subject to technological biases and inevitable variabilities that arise during sample collection and processing. Normalization techniques aim to correct such biases. Most existing methods require multiple samples to be processed in aggregate; consequently, each sample's output is influenced by other samples processed jointly. However, in personalized-medicine workflows, samples may arrive serially, so renormalizing all samples upon each new arrival would be impractical. We have developed Single Channel Array Normalization (SCAN), a single-sample technique that models the effects of probe-nucleotide composition on fluorescence intensity and corrects for such effects, dramatically increasing the signal-to-noise ratio within individual samples while decreasing variation across samples. In various benchmark comparisons, we show that SCAN performs as well as or better than competing methods yet has no dependence on external reference samples and can be applied to any single-channel microarray platform.
Method; normalization; microarray; linear model; mixture model; single-sample technique
Although only a subset of smokers develop lung cancer, we cannot determine which smokers are at highest risk for cancer development, nor do we know the signaling pathways altered early in the process of tumorigenesis in these individuals. On the basis of the concept that cigarette smoke creates a molecular field of injury throughout the respiratory tract, this study explores oncogenic pathway deregulation in cytologically normal proximal airway epithelial cells of smokers at risk for lung cancer. We observed a significant increase in a genomic signature of phosphatidylinositol 3-kinase (PI3K) pathway activation in the cytologically normal bronchial airway of smokers with lung cancer and smokers with dysplastic lesions, suggesting that PI3K is activated in the proximal airway before tumorigenesis. Further, PI3K activity is decreased in the airway of high-risk smokers who had significant regression of dysplasia after treatment with the chemopreventive agent myo-inositol, and myo-inositol inhibits the PI3K pathway in vitro. These results suggest that deregulation of the PI3K pathway in the bronchial airway epithelium of smokers is an early, measurable, and reversible event in the development of lung cancer and that genomic profiling of these relatively accessible airway cells may enable personalized approaches to chemoprevention and therapy. Our work further suggests that additional lung cancer chemoprevention trials either targeting the PI3K pathway or measuring airway PI3K activation as an intermediate endpoint are warranted.
We have previously defined the impact of tobacco smoking on nasal epithelium gene expression using Affymetrix Exon 1.0 ST arrays. In this paper, we compared the performance of the Affymetrix GeneChip Human Gene 1.0 ST array with the Human Exon 1.0 ST array for detecting nasal smoking-related gene expression changes. RNA collected from the nasal epithelium of five current smokers and five never smokers was hybridized to both arrays. While the intersample correlation within each array platform was relatively higher in the Gene array than that in the Exon array, the majority of the genes most changed by smoking were tightly correlated between platforms. Although neither array dataset was powered to detect differentially expressed genes (DEGs) at a false discovery rate (FDR) <0.05, we identified more DEGs than expected by chance using the Gene ST array. These findings suggest that while both platforms show a high degree of correlation for detecting smoking-induced differential gene expression changes, the Gene ST array may be a more cost-effective platform in a clinical setting for gene-level genomewide expression profiling and an effective tool for exploring the host response to cigarette smoking and other inhaled toxins.
Triple-negative breast cancers with elevated MYC are sensitized to CDK inhibition.
Estrogen, progesterone, and HER2 receptor-negative triple-negative breast cancers encompass the most clinically challenging subtype for which targeted therapeutics are lacking. We find that triple-negative tumors exhibit elevated MYC expression, as well as altered expression of MYC regulatory genes, resulting in increased activity of the MYC pathway. In primary breast tumors, MYC signaling did not predict response to neoadjuvant chemotherapy but was associated with poor prognosis. We exploit the increased MYC expression found in triple-negative breast cancers by using a synthetic-lethal approach dependent on cyclin-dependent kinase (CDK) inhibition. CDK inhibition effectively induced tumor regression in triple-negative tumor xenografts. The proapoptotic BCL-2 family member BIM is up-regulated after CDK inhibition and contributes to this synthetic-lethal mechanism. These results indicate that aggressive breast tumors with elevated MYC are uniquely sensitive to CDK inhibitors.
Breast cancers commonly become resistant to EGFR–tyrosine kinase inhibitors (EGFR-TKIs); however, the mechanisms of this resistance remain largely unknown. We hypothesized that resistance may originate, at least in part, from molecular alterations that activate signaling downstream of EGFR. Using a screen to measure reversion of malignant cells into phenotypically nonmalignant cells in 3D gels, we identified FAM83A as a candidate cancer-associated gene capable of conferring resistance to EGFR-TKIs. FAM83A overexpression in cancer cells increased proliferation and invasion and imparted EGFR-TKI resistance both in cultured cells and in animals. Tumor cells that survived EGFR-TKI treatment in vivo had upregulated FAM83A levels. Additionally, FAM83A overexpression dramatically increased the number and size of transformed foci in cultured cells and anchorage-independent growth in soft agar. Conversely, FAM83A depletion in cancer cells caused reversion of the malignant phenotype, delayed tumor growth in mice, and rendered cells more sensitive to EGFR-TKI. Analyses of published clinical data revealed a correlation between high FAM83A expression and breast cancer patients’ poor prognosis. We found that FAM83A interacted with and caused phosphorylation of c-RAF and PI3K p85, upstream of MAPK and downstream of EGFR. These data provide an additional mechanism by which tumor cells can become EGFR-TKI resistant.
The “field of injury” hypothesis proposes that exposure to an inhaled insult such as cigarette smoke elicits a common molecular response throughout the respiratory tract. This response can therefore be quantified in any airway tissue, including readily accessible epithelial cells in the bronchus, nose, and mouth. High-throughput technologies, such as whole-genome gene expression microarrays, can be employed to catalog the physiological consequences of such exposures in the airway epithelium. Pulmonary diseases such as chronic obstructive pulmonary disease, lung cancer, and asthma are also thought to be associated with a field of injury, and in patients with these diseases, airway epithelial cells can be a useful surrogate for diseased tissue that is often difficult to obtain. Global measurement of mRNA and microRNA expression in these cells can provide useful information about the molecular pathogenesis of such diseases and may be useful for diagnosis and for predicting prognosis and response to therapy. In this review, our aim is to summarize the history and state of the art of such “transcriptomic” studies in the human airway epithelium, especially in smoking and smoking-related lung diseases, and to highlight future directions for this field.
epithelium; lung neoplasms; chronic obstructive pulmonary disease; asthma; tobacco
Identifying similarities between patterns of differential gene expression provides an opportunity to identify similarities between the experimental and biological conditions that give rise to these gene expression alterations. The growing volume of gene expression data in open data repositories such as the NCBI Gene Expression Omnibus (GEO) presents an opportunity to identify these gene expression similarities on a large scale across a diverse collection of datasets. We have developed a fast, pattern-based computational approach, named openSESAME (Search of Expression Signatures Across Many Experiments), that identifies datasets enriched in samples that display coordinate differential expression of a query signature. Importantly, openSESAME performs this search without prior knowledge of the phenotypic or experimental groups in the datasets being searched. This allows openSESAME to identify perturbations of gene expression that are due to phenotypic attributes that may not have been described in the sample annotation included in the repository.
To demonstrate the utility of openSESAME, we used gene expression signatures of two biological perturbations to query a set of 75,164 human expression profiles that were generated using Affymetrix microarrays and deposited in GEO. The first query, using a signature of estradiol treatment, identified experiments in which estrogen signaling was perturbed and also identified differences in estrogen signaling between estrogen receptor-positive and -negative breast cancers. The second query, which used a signature of silencing of the transcription factor p63 (a key regulator of epidermal differentiation), identified datasets related to stratified squamous epithelia or epidermal diseases such as melanoma.
openSESAME is a tool for leveraging the growing body of publicly available microarray data to discover relationships between different biological states based on common patterns of differential gene expression. These relationships may serve to generate hypotheses about the causes and consequences of specific patterns of observed differential gene expression. To encourage others to explore the utility of this approach, we have made a website for performing openSESAME queries freely available at http://opensesame.bu.edu.
Smoking is the most important known risk factor for the development of lung cancer. Tobacco exposure results in chronic inflammation, tissue injury and repair. A recent hypothesis argues for a stem/progenitor cell involved in airway epithelial repair that may be a tumor-initiating cell in lung cancer, and which may be associated with recurrence and metastasis. We used immunostaining, quantitative real-time PCR, Western blots and lung cancer tissue microarrays to identify subpopulations of airway epithelial stem/progenitor cells under steady state conditions, normal repair, aberrant repair with premalignant lesions and lung cancer and their correlation with injury and prognosis. We identified a population of keratin 14 (K14)-expressing progenitor epithelial cells that was involved in repair after injury. Dysregulated repair resulted in persistence of K14+ cells in the airway epithelium in premalignant lesions. The presence of K14+ cells in non-small cell lung cancer (NSCLC) samples predicted poorer outcomes. This was especially true in smokers where the presence of K14+ cells in NSCLC was predictive of metastasis. The presence of K14+ progenitor airway epithelial cells in NSCLC predicted a poor prognosis and this predictive value was strongest in smokers, where it also correlated with metastasis. This suggests that reparative K14+ progenitor cells may be tumor-initiating cells in this subgroup of smokers with NSCLC.
Lung carcinogenesis; dysregulated repair; injury
In order to characterize patterns of molecular expression that lead to cartilage formation in vivo in a post-natal setting, mRNA expression profiling was carried out across the timecourse of mechanically induced chondrogenesis.
Retired breeder Sprague-Dawley rats underwent production of a non-critical-size, transverse femoral osteotomy. Experimental animals (n=45) were subjected to bending stimulation (60° cyclic motion in the sagittal plane for 15 minutes/day) of the osteotomy gap beginning on post-operative day (POD) 10. Control animals (n=32) experienced continuous rigid fixation. mRNA isolated on POD 10, 17, 24, and 38 was analyzed using a microarray containing 608 genes involved in skeletal development, tissue differentiation, fracture healing, and mechanotransduction. The glycosaminoglycan (GAG) content of the stimulated tissues was compared to native articular cartilage as a means of assessing the progression of chondrogenic development of the tissues.
The majority of the 100 genes that were differentially expressed were upregulated in response to mechanical stimulation. Many of these genes are associated with articular cartilage development and maintenance, diarthroidal joint development, cell adhesion, extracellular matrix synthesis, signal transduction, and skeletal development. Quantitative real-time PCR results were consistent with the microarray findings. The GAG content of the stimulated tissues increased over time and was no different from that of articular cartilage at POD 38.
The mechanical stimulation caused upregulation of genes principally involved in joint cavity morphogenesis and critical to articular cartilage function. Further study of this type of stimulation may identify key signaling events required for post-natal, hyaline cartilage formation.
mechanobiology; tissue differentiation; bone healing; cartilage matrix; joint development
Fat distribution changes with aging. Inherent changes in fat cell progenitors may contribute because fat cells turn over throughout life. To define mechanisms, gene expression was profiled in preadipocytes cultured from epididymal and perirenal depots of young and old rats. 8.4% of probe sets differed significantly between depots, particularly developmental genes. Only 0.02% differed with aging, despite using less stringent criteria than for comparing depots. Twenty-five genes selected based on fold change with aging were analyzed in preadipocytes from additional young, middle-aged, and old animals by polymerase chain reaction. Thirteen changed significantly with aging, 13 among depots, and 9 with both. Genes involved in inflammation, stress, and differentiation changed with aging, as occurs in fat tissue. Age-related changes were greater in perirenal than epididymal preadipocytes, consistent with larger declines in replication and adipogenesis in perirenal preadipocytes. Thus, age-related changes in preadipocyte gene expression differ among depots, potentially contributing to fat redistribution and dysfunction.
Aging; Preadipocyte; Fat cell progenitor
Although cigarette smoking is the major cause of chronic obstructive pulmonary disease (COPD), only a subset of smokers develops this disease. There is significant clinical, radiographic, and pathologic heterogeneity within smokers who develop COPD that likely reflects multiple molecular mechanisms of disease. It is possible that variations in the individual response to cigarette smoking form the basis for the distinct clinical and molecular phenotypes and variable natural history associated with COPD. Using the biologic premise of a molecular field of airway injury created by cigarette smoking, this response to tobacco exposure can be measured by molecular profiling of the airway epithelium. Noninvasive study of this field effect by profiling airway gene expression in patients with COPD holds important implications for our understanding of disease heterogeneity, early disease detection, and identification of novel disease-modifying therapies.
airway gene expression; chronic obstructive pulmonary disease; bioinformatics
HJURP (Holliday Junction Recognition Protein) is a newly discovered gene reported to function at centromeres and to interact with CENPA. However its role in tumor development remains largely unknown. The goal of this study was to investigate the clinical significance of HJURP in breast cancer and its correlation with radiotherapeutic outcome.
We measured HJURP expression level in human breast cancer cell lines and primary breast cancers by Western blot and/or by Affymetrix Microarray; and determined its associations with clinical variables using standard statistical methods. Validation was performed with the use of published microarray data. We assessed cell growth and apoptosis of breast cancer cells after radiation using high-content image analysis.
HJURP was expressed at higher level in breast cancer than in normal breast tissue. HJURP mRNA levels were significantly associated with estrogen receptor (ER), progesterone receptor (PR), Scarff-Bloom-Richardson (SBR) grade, age and Ki67 proliferation indices, but not with pathologic stage, ERBB2, tumor size, or lymph node status. Higher HJURP mRNA levels significantly decreased disease-free and overall survival. HJURP mRNA levels predicted the prognosis better than Ki67 proliferation indices. In a multivariate Cox proportional-hazard regression, including clinical variables as covariates, HJURP mRNA levels remained an independent prognostic factor for disease-free and overall survival. In addition HJURP mRNA levels were an independent prognostic factor over molecular subtypes (normal like, luminal, Erbb2 and basal). Poor clinical outcomes among patients with high HJURP expression were validated in five additional breast cancer cohorts. Furthermore, the patients with high HJURP levels were much more sensitive to radiotherapy. In vitro studies in breast cancer cell lines showed that cells with high HJURP levels were more sensitive to radiation treatment and had a higher rate of apoptosis than those with low levels. Knock down of HJURP in human breast cancer cells using shRNA reduced the sensitivity to radiation treatment. HJURP mRNA levels were significantly correlated with CENPA mRNA levels.
HJURP mRNA level is a prognostic factor for disease-free and overall survival in patients with breast cancer and is a predictive biomarker for sensitivity to radiotherapy.
The dual bromodomain protein Brd2 is closely related to the basal transcription factor TAFII250, which is essential for cyclin A transactivation and mammalian cell cycle progression. In transgenic mice, constitutive lymphoid expression of Brd2 causes a malignancy most similar to human diffuse large B cell lymphoma. We compare the genome-wide transcriptional expression profiles of these lymphomas with those of proliferating and resting normal B cells. Transgenic tumors reproducibly show differential expression of a large number of genes important for cell cycle control and lymphocyte biology; expression patterns are either tumor-specific or proliferation-specific. Several of their human orthologs have been implicated in human lymphomagenesis. Others correlate with human disease survival time. BRD2 is underexpressed in some subtypes of human lymphoma and these subtypes display a number of similarities to the BRD2-mediated murine tumors. We illustrate with a high degree of detail that cancer is more than rampant cellular proliferation, but involves the additional transcriptional mobilization of many genes, some of them poorly characterized, which show a tumor-specific pattern of gene expression.
Lung cancer is the leading cause of cancer death in the US and the world. The high mortality rate results, in part, from the lack of effective tools for early detection and the inability to identify subsets of patients who would benefit from adjuvant chemotherapy or targeted therapies. The development of high-throughput genome-wide technologies for measuring gene expression, such as microarrays, have the potential to impact the mortality rate of lung cancer patients by improving diagnosis, prognosis, and treatment. This review will highlight recent studies using high-throughput gene expression technologies that have led to clinically relevant insights into lung cancer. The hope is that diagnostic and prognostic biomarkers that have been developed as part of this work will soon be ready for wide-spread clinical application and will have a dramatic impact on the evaluation of patients with suspect lung cancer, leading to effective personalized treatment regimens.
While the role cigarette smoke plays in chronic obstructive pulmonary disease (COPD) is undisputed, the molecular mechanisms by which inhaled smoke contributes to disease pathogenesis remains unclear. One of the major barriers to effective approaches to diagnose and manage COPD is the remarkable heterogeneity displayed by patients with the disease. Whole-genome gene-expression studies of airway and lung tissue from patients with COPD provide an opportunity to gain insights into disease pathogenesis, allowing for both a molecular understanding of the pathogenic processes that contribute to this heterogeneity, and the ability to target therapies to these processes. This review focuses on synthesizing and integrating the limited numbers of high-throughput gene expression studies that have been conducted on lung tissue and airway samples from smokers with COPD. Comparing several lung tissue studies using computational approaches, we find that the results suggest fundamental similarities and identify common biological processes underlying COPD, despite each study having identified largely nonoverlapping lists of differentially expressed genes. Given these similarities, we argue that additional lung tissue and airway gene-expression studies are warranted, and present a roadmap for how such studies could lead to clinically relevant tools that would impact COPD management.
gene expression; microarray analysis; biomarkers; emphysema
Although prior studies have demonstrated a smoking-induced field of molecular injury throughout the lung and airway, the impact of smoking on the airway epithelial proteome and its relationship to smoking-related changes in the airway transcriptome are unclear.
Airway epithelial cells were obtained from never (n = 5) and current (n = 5) smokers by brushing the mainstem bronchus. Proteins were separated by one dimensional polyacrylamide gel electrophoresis (1D-PAGE). After in-gel digestion, tryptic peptides were processed via liquid chromatography/ tandem mass spectrometry (LC-MS/MS) and proteins identified. RNA from the same samples was hybridized to HG-U133A microarrays. Protein detection was compared to RNA expression in the current study and a previously published airway dataset. The functional properties of many of the 197 proteins detected in a majority of never smokers were similar to those observed in the never smoker airway transcriptome. LC-MS/MS identified 23 proteins that differed between never and current smokers. Western blotting confirmed the smoking-related changes of PLUNC, P4HB1, and uteroglobin protein levels. Many of the proteins differentially detected between never and current smokers were also altered at the level of gene expression in this cohort and the prior airway transcriptome study. There was a strong association between protein detection and expression of its corresponding transcript within the same sample, with 86% of the proteins detected by LC-MS/MS having a detectable corresponding probeset by microarray in the same sample. Forty-one proteins identified by LC-MS/MS lacked detectable expression of a corresponding transcript and were detected in ≤5% of airway samples from a previously published dataset.
1D-PAGE coupled with LC-MS/MS effectively profiled the airway epithelium proteome and identified proteins expressed at different levels as a result of cigarette smoke exposure. While there was a strong correlation between protein and transcript detection within the same sample, we also identified proteins whose corresponding transcripts were not detected by microarray. This noninvasive approach to proteomic profiling of airway epithelium may provide additional insights into the field of injury induced by tobacco exposure.
Oligonucleotide microarray analysis revealed 175 genes that are differentially expressed in large airway epithelial cells of people who currently smoke compared with those who never smoked, with 28 classified as irreversible, 6 as slowly reversible, and 139 as rapidly reversible.
Tobacco use remains the leading preventable cause of death in the US. The risk of dying from smoking-related diseases remains elevated for former smokers years after quitting. The identification of irreversible effects of tobacco smoke on airway gene expression may provide insights into the causes of this elevated risk.
Using oligonucleotide microarrays, we measured gene expression in large airway epithelial cells obtained via bronchoscopy from never, current, and former smokers (n = 104). Linear models identified 175 genes differentially expressed between current and never smokers, and classified these as irreversible (n = 28), slowly reversible (n = 6), or rapidly reversible (n = 139) based on their expression in former smokers. A greater percentage of irreversible and slowly reversible genes were down-regulated by smoking, suggesting possible mechanisms for persistent changes, such as allelic loss at 16q13. Similarities with airway epithelium gene expression changes caused by other environmental exposures suggest that common mechanisms are involved in the response to tobacco smoke. Finally, using irreversible genes, we built a biomarker of ever exposure to tobacco smoke capable of classifying an independent set of former and current smokers with 81% and 100% accuracy, respectively.
We have categorized smoking-related changes in airway gene expression by their degree of reversibility upon smoking cessation. Our findings provide insights into the mechanisms leading to reversible and persistent effects of tobacco smoke that may explain former smokers increased risk for developing tobacco-induced lung disease and provide novel targets for chemoprophylaxis. Airway gene expression may also serve as a sensitive biomarker to identify individuals with past exposure to tobacco smoke.
Renal cell carcinoma is a common malignancy that often presents as a metastatic-disease for which there are no effective treatments. To gain insights into the mechanism of renal cell carcinogenesis, a number of genome-wide expression profiling studies have been performed. Surprisingly, there is very poor agreement among these studies as to which genes are differentially regulated. To better understand this lack of agreement we profiled renal cell tumor gene expression using genome-wide microarrays (45,000 probe sets) and compare our analysis to previous microarray studies.
We hybridized total RNA isolated from renal cell tumors and adjacent normal tissue to Affymetrix U133A and U133B arrays. We removed samples with technical defects and removed probesets that failed to exhibit sequence-specific hybridization in any of the samples. We detected differential gene expression in the resulting dataset with parametric methods and identified keywords that are overrepresented in the differentially expressed genes with the Fisher-exact test.
We identify 1,234 genes that are more than three-fold changed in renal tumors by t-test, 800 of which have not been previously reported to be altered in renal cell tumors. Of the only 37 genes that have been identified as being differentially expressed in three or more of five previous microarray studies of renal tumor gene expression, our analysis finds 33 of these genes (89%). A key to the sensitivity and power of our analysis is filtering out defective samples and genes that are not reliably detected.
The widespread use of sample-wise voting schemes for detecting differential expression that do not control for false positives likely account for the poor overlap among previous studies. Among the many genes we identified using parametric methods that were not previously reported as being differentially expressed in renal cell tumors are several oncogenes and tumor suppressor genes that likely play important roles in renal cell carcinogenesis. This highlights the need for rigorous statistical approaches in microarray studies.
A core feature of chronic obstructive pulmonary disease (COPD) is the accelerated decline in forced expiratory volume in one second (FEV1). The recent Groningen and Leiden Universities study of Corticosteroids in Obstructive Lung Disease (GLUCOLD) study suggested that particular phenotypes of COPD benefit from fluticasone±salmeterol by reducing the rate of FEV1 decline, yet the underlying mechanisms are unknown.
Whole-genome gene expression profiling using the Affymetrix Gene ST array (V.1.0) was performed on 221 bronchial biopsies available from 89 COPD patients at baseline and after 6 and 30 months of fluticasone±salmeterol and placebo treatment in GLUCOLD.
Linear mixed effects modelling revealed that the expression of 138 genes decreased, whereas the expression of 140 genes significantly upregulated after both 6 and 30 months of treatment with fluticasone±salmeterol versus placebo. A more pronounced treatment-induced change in the expression of 50 and 55 of these 278 genes was associated with a lower rate of decline in FEV1 and Saint George Respiratory Questionnaire, respectively. Genes decreasing with treatment were involved in pathways related to cell cycle, oxidative phosphorylation, epithelial cell signalling, p53 signalling and T cell signalling. Genes increasing with treatment were involved in pathways related to focal adhesion, gap junction and extracellular matrix deposition. Finally, the fluticasone-induced gene expression changes were enriched among genes that change in the airway epithelium in smokers with versus without COPD in an independent data set.
The present study suggests that gene expression in biological pathways of COPD is dynamic with treatment and reflects disease activity. This study opens the gate to targeted and molecular phenotype-driven therapy of COPD.
COPD ÀÜ Mechanisms
Neoadjuvant chemotherapy for breast cancer allows individual tumor response to be assessed depending on molecular subtype, and to judge the impact of response to therapy on recurrence-free survival (RFS). The multicenter I-SPY 1 TRIAL evaluated patients with ≥3 cm tumors by using early imaging and molecular signatures, with outcomes of pathologic complete response (pCR) and RFS. The current analysis was performed using data from patients who had molecular profiles and did not receive trastuzumab. The various molecular classifiers tested were highly correlated. Categorization of breast cancer by molecular signatures enhanced the ability of pCR to predict improvement in RFS compared to the population as a whole. In multivariate analysis, the molecular signatures that added to the ability of HR and HER2 receptors, clinical stage, and pCR in predicting RFS included 70-gene signature, wound healing signature, p53 mutation signature, and PAM50 risk of recurrence. The low risk signatures were associated with significantly better prognosis, and also identified additional patients with a good prognosis within the no pCR group, primarily in the hormone receptor positive, HER-2 negative subgroup. The I-SPY 1 population is enriched for tumors with a poor prognosis but is still heterogeneous in terms of rates of pCR and RFS. The ability of pCR to predict RFS is better by subset than it is for the whole group. Molecular markers improve prediction of RFS by identifying additional patients with excellent prognosis within the no pCR group.
Electronic supplementary material
The online version of this article (doi:10.1007/s10549-011-1895-2) contains supplementary material, which is available to authorized users.
Breast cancer; Neoadjuvant chemotherapy; Molecular biomarkers; Pathologic complete response; Medicine & Public Health; Oncology
We studied Dicer and Drosha, components of the RNA-interference machinery, in ovarian cancer.
We measured messenger RNA (mRNA) levels of Dicer and Drosha in specimens of invasive epithelial ovarian cancer from 111 patients, using a quantitative reverse-transcriptase–polymerase-chain-reaction assay, and compared the results with clinical outcomes. Validation was performed with the use of published microarray data from cohorts of patients with ovarian, breast, and lung cancer. Mutational analyses of genomic DNA from the Dicer and Drosha genes were performed in a subgroup of ovarian-cancer specimens. Dicer-dependent functional assays were performed by means of in vitro transfection with small interfering RNA (siRNA) and short hairpin RNA (shRNA).
Levels of Dicer and Drosha mRNA correlated with the levels of expression of the corresponding protein and were decreased in 60% and 51% of ovarian-cancer specimens, respectively. Low Dicer expression was significantly associated with advanced tumor stage (P = 0.007), and low Drosha expression with suboptimal surgical cytoreduction (P = 0.02). Cancer specimens with both high Dicer expression and high Drosha expression were associated with increased median survival (>11 years, vs. 2.66 years for other subgroups; P<0.001). We found three independent predictors of reduced disease-specific survival in multivariate analyses: low Dicer expression (hazard ratio, 2.10; P = 0.02), high-grade histologic features (hazard ratio, 2.46; P = 0.03), and poor response to chemotherapy (hazard ratio, 3.95; P<0.001). Poor clinical outcomes among patients with low Dicer expression were validated in additional cohorts of patients. Rare missense mutations were found in the Dicer and Drosha genes, but their presence or absence did not correlate with the level of expression. Functional assays indicated that gene silencing with shRNA, but not siRNA, may be impaired in cells with low Dicer expression.
Our findings indicate that levels of Dicer and Drosha mRNA in ovarian-cancer cells have associations with outcomes in patients with ovarian cancer.
Cigarette smoking is a leading cause of preventable death and a significant cause of lung cancer and chronic obstructive pulmonary disease. Prior studies have demonstrated that smoking creates a field of molecular injury throughout the airway epithelium exposed to cigarette smoke. We have previously characterized gene expression in the bronchial epithelium of never smokers and identified the gene expression changes that occur in the mainstem bronchus in response to smoking. In this study, we explored relationships in whole-genome gene expression between extrathorcic (buccal and nasal) and intrathoracic (bronchial) epithelium in healthy current and never smokers.
Using genes that have been previously defined as being expressed in the bronchial airway of never smokers (the "normal airway transcriptome"), we found that bronchial and nasal epithelium from non-smokers were most similar in gene expression when compared to other epithelial and nonepithelial tissues, with several antioxidant, detoxification, and structural genes being highly expressed in both the bronchus and nose. Principle component analysis of previously defined smoking-induced genes from the bronchus suggested that smoking had a similar effect on gene expression in nasal epithelium. Gene set enrichment analysis demonstrated that this set of genes was also highly enriched among the genes most altered by smoking in both nasal and buccal epithelial samples. The expression of several detoxification genes was commonly altered by smoking in all three respiratory epithelial tissues, suggesting a common airway-wide response to tobacco exposure.
Our findings support a relationship between gene expression in extra- and intrathoracic airway epithelial cells and extend the concept of a smoking-induced field of injury to epithelial cells that line the mouth and nose. This relationship could potentially be utilized to develop a non-invasive biomarker for tobacco exposure as well as a non-invasive screening or diagnostic tool providing information about individual susceptibility to smoking-induced lung diseases.