26311749
A robust tool for discriminative analysis and feature selection in paired samples impacts the identification of the genes essential for reprogramming lung tissue to adenocarcinoma
BackgroundLung cancer is the leading cause of cancer deaths in the world. The most common type of lung cancer is lung adenocarcinoma (AC). The genetic mechanisms of the early stages and lung AC progression steps are poorly understood. There is currently no clinically applicable gene test for the early diagnosis and AC aggressiveness. Among the major reasons for the lack of reliable diagnostic biomarkers are the extraordinary heterogeneity of the cancer cells, complex and poorly understudied interactions of the AC cells with adjacent tissue and immune system, gene variation across patient cohorts, measurement variability, small sample sizes and sub-optimal analytical methods. We suggest that gene expression profiling of the primary tumours and adjacent tissues (PT-AT) handled with a rational statistical and bioinformatics strategy of biomarker prediction and validation could provide significant progress in the identification of clinical biomarkers of AC. To minimise sample-to-sample variability, repeated multivariate measurements in the same object (organ or tissue, e.g. PT-AT in lung) across patients should be designed, but prediction and validation on the genome scale with small sample size is a great methodical challenge.ResultsTo analyse PT-AT relationships efficiently in the statistical modelling, we propose an Extreme Class Discrimination (ECD) feature selection method that identifies a sub-set of the most discriminative variables (e.g. expressed genes). Our method consists of a paired Cross-normalization (CN) step followed by a modified sign Wilcoxon test with multivariate adjustment carried out for each variable. Using an Affymetrix U133A microarray paired dataset of 27 AC patients, we reviewed the global reprogramming of the transcriptome in human lung AC tissue versus normal lung tissue, which is associated with about 2,300 genes discriminating the tissues with 100% accuracy. Cluster analysis applied to these genes resulted in four distinct gene groups which we classified as associated with (i) up-regulated genes in the mitotic cell cycle lung AC, (ii) silenced/suppressed gene specific for normal lung tissue, (iii) cell communication and cell motility and (iv) the immune system features. The genes related to mutagenesis, specific lung cancers, early stage of AC development, tumour aggressiveness and metabolic pathway alterations and adaptations of cancer cells are strongly enriched in the AC PT-AT discriminative gene set. Two AC diagnostic biomarkers SPP1 and CENPA were successfully validated on RT-RCR tissue array. ECD method was systematically compared to several alternative methods and proved to be of better performance and as well as it was validated by comparison of the predicted gene set with literature meta-signature.ConclusionsWe developed a method that identifies and selects highly discriminative variables from high dimensional data spaces of potential biomarkers based on a statistical analysis of paired samples when the number of samples is small. This method provides superior selection in comparison to conventional methods and can be widely used in different applications. Our method revealed at least 23 hundreds patho-biologically essential genes associated with the global transcriptional reprogramming of human lung epithelium cells and lung AC aggressiveness. This gene set includes many previously published AC biomarkers reflecting inherent disease complexity and specifies the mechanisms of carcinogenesis in the lung AC. SPP1, CENPA and many other PT-AT discriminative genes could be considered as the prospective diagnostic and prognostic biomarkers of lung AC.
23908877
Identification of genes whose expression is upregulated in lung adenocarcinoma cells in comparison with type II alveolar cells and bronchiolar epithelial cells in vivo
To identify genes whose expression is upregulated in lung adenocarcinoma (AdC) cells in comparison with noncancerous peripheral lung epithelial cells, type II alveolar cells and bronchiolar epithelial cells, as well as AdC cells, were isolated by laser capture microdissection, and subjected to cDNA microarray analysis of 637 human cancer-related genes. Each of the component cells was obtained from several different individuals and analysed independently. As a comparison, two lung AdC cell lines and two primarily cultured normal lung epithelial cell lines were also subjected to cDNA microarray analysis. Four genes, TOP2A, MMP15, MX2 and KOC1, were commonly upregulated in microdissected AdC cells in comparison with microdissected epithelial cells. Hierarchical clustering analysis revealed that differences in gene-expression profiles were more evident between cultured and uncultured cells than between cancerous and noncancerous cells. To further identify the common molecular targets of AdC cells in vivo, quantitative real-time RT–PCR was performed against the four genes upregulated by cDNA microarray analysis. The TOP2A, MMP15, MX2 and KOC1 genes were overexpressed in 10/10 (100%), 8/10 (80%), 5/10 (50%) and 3/10 (30%) microdissected AdC cell samples, respectively, in comparison with any of nine independently microdissected noncancerous epithelial cell samples. The TOP2A gene was commonly overexpressed in lung AdC cells, as previously reported. In addition, the MMP15 and MX2 genes were identified, for the first time, as being commonly overexpressed in lung AdC cells. These results strongly indicate that the MMP15 and MX2 genes could be novel markers for molecular diagnosis and therapy of lung AdC.
24535026
Identification of tumor markers and differentiation markers for molecular diagnosis of lung adenocarcinoma
To identify tumor markers and differentiation markers for lung adenocarcinoma (AdC), we analysed expression profiles of 14 500 genes against three cases of type II alveolar epithelial cells, bronchiolar epithelial cells, and bronchial epithelial cells, respectively, and 10 cases of AdC cells isolated by laser capture microdissection. Hierarchical clustering analysis indicated that AdC cells and noncancerous lung epithelial cells are significantly different in their expression profiles, and that different sets of differentiation markers are expressed among alveolar, bronchiolar and bronchial epithelial cells. Nine genes were identified as being highly expressed in AdC cells, but not expressed in noncancerous lung epithelial cells. Sixteen genes were identified as differentiation markers for lung epithelial cells. Real-time RT–PCR analysis of 45 lung AdC cases further revealed that expression of four tumor markers in AdC cells was significantly higher than that in noncancerous lung cells and that expression of ten differentiation markers was retained in a considerable fraction of lung AdC cases. Five tumor markers and seven differentiation markers were not expressed in peripheral blood cells. Similarities and differences in expression profiles between normal epithelial cells from different lung respiratory compartments and AdC cells demonstrated in this study will be informative for the molecular diagnosis of lung AdC.
13965188
Data-driven Networking Reveals 5-Genes Signature for Early Detection of Lung Cancer
A new strategy is developed for a gene signature search proceeding from a biological basis in analyzing microarray databases. The procedure involves a combination of known and original methods for correlation, statistical, and network analysis. The application of the strategy to lung adenocarcinoma resulted in a 5-gene signature, which included four genes not associated earlier with lung adenocarcinoma. 93-96% accuracy of classification of cancer vs. normal was achieved. The final stage of our procedure included expanding of the gene signature network to a 43 gene/protein network, which showed that the five genes are in the cross-talk of 24 pathways, providing thus information for mechanistic analysis.
5516596
Gene expression-based prognostic signatures in lung cancer: ready for clinical use?
A substantial number of studies have reported the development of gene expression-based prognostic signatures for lung cancer. The ultimate aim of such studies should be the development of well-validated clinically useful prognostic signatures that improve therapeutic decision making beyond current practice standards. We critically reviewed published studies reporting the development of gene expression-based prognostic signatures for non-small cell lung cancer to assess the progress made toward this objective. Studies published between January 1, 2002, and February 28, 2009, were identified through a PubMed search. Following hand-screening of abstracts of the identified articles, 16 were selected as relevant. Those publications were evaluated in detail for appropriateness of the study design, statistical validation of the prognostic signature on independent datasets, presentation of results in an unbiased manner, and demonstration of medical utility for the new signature beyond that obtained using existing treatment guidelines. Based on this review, we found little evidence that any of the reported gene expression signatures are ready for clinical application. We also found serious problems in the design and analysis of many of the studies. We suggest a set of guidelines to aid the design, analysis, and evaluation of prognostic signature studies. These guidelines emphasize the importance of focused study planning to address specific medically important questions and the use of unbiased analysis methods to evaluate whether the resulting signatures provide evidence of medical utility beyond standard of care-based prognostic factors.
2757962
Gene Expression-Based Classification of Non-Small Cell Lung Carcinomas and Survival Prediction
Background Current clinical therapy of non-small cell lung cancer depends on histo-pathological classification. This approach poorly predicts clinical outcome for individual patients. Gene expression profiling holds promise to improve clinical stratification, thus paving the way for individualized therapy. Methodology and Principal Findings A genome-wide gene expression analysis was performed on a cohort of 91 patients. We used 91 tumor- and 65 adjacent normal lung tissue samples. We defined sets of predictor genes (probe sets) with the expression profiles. The power of predictor genes was evaluated using an independent cohort of 96 non-small cell lung cancer- and 6 normal lung samples. We identified a tumor signature of 5 genes that aggregates the 156 tumor and normal samples into the expected groups. We also identified a histology signature of 75 genes, which classifies the samples in the major histological subtypes of non-small cell lung cancer. Correlation analysis identified 17 genes which showed the best association with post-surgery survival time. This signature was used for stratification of all patients in two risk groups. Kaplan-Meier survival curves show that the two groups display a significant difference in post-surgery survival time (p = 5.6E-6). The performance of the signatures was validated using a patient cohort of similar size (Duke University, n = 96). Compared to previously published prognostic signatures for NSCLC, the 17 gene signature performed well on these two cohorts. Conclusions The gene signatures identified are promising tools for histo-pathological classification of non-small cell lung cancer, and may improve the prediction of clinical outcome.
33335594
Confirmation of Gene Expression–Based Prediction of Survival in Non–Small Cell Lung Cancer
Purpose: It is a critical challenge to determine the risk of recurrence in early stage non–small cell lung cancer (NSCLC) patients. Accurate gene expression signatures are needed to classify patients into high- and low-risk groups to improve the selection of patients for adjuvant therapy. Experimental Design: Multiple published microarray data sets were used to evaluate our previously identified lung cancer prognostic gene signature. Expression of the signature genes was further validated with real-time reverse transcription-PCR and Western blot assays of snap-frozen lung cancer tumor tissues. Results: Our previously identified 35-gene signature stratified 264 patients with NSCLC into high- and low-risk groups with distinct overall survival rates (P < 0.05, Kaplan-Meier analysis, log-rank tests). The 35-gene signature further stratified patients with clinical stage 1A diseases into poor prognostic and good prognostic subgroups (P = 0.0007, Kaplan-Meier analysis, log-rank tests). This signature is independent of other prognostic factors for NSCLC, including age, sex, tumor differentiation, tumor grade, and tumor stage. The expression of the signature genes was validated with real-time reverse transcription-PCR analysis of lung cancer tumor specimens. Protein expression of two signature genes, TAL2 and ILF3, was confirmed in lung adenocarcinoma tumors by using Western blot analysis. These two biomarkers showed correlated mRNA and protein overexpression in lung cancer development and progression. Conclusions: The results indicate that the identified 35-gene signature is an accurate predictor of survival in NSCLC. It provides independent prognostic information in addition to traditional clinicopathologic criteria.
21176485
Frequent genetic differences between matched primary and metastatic breast cancer provide an approach to identification of biomarkers for disease progression
Breast cancer is a major cause of morbidity and mortality in women and its metastatic spread is the principal reason behind the fatal outcome. Metastasis-related research of breast cancer is however underdeveloped when compared with the abundant literature on primary tumors. We applied an unexplored approach comparing at high resolution the genomic profiles of primary tumors and synchronous axillary lymph node metastases from 13 patients with breast cancer. Overall, primary tumors displayed 20% higher number of aberrations than metastases. In all but two patients, we detected in total 157 statistically significant differences between primary lesions and matched metastases. We further observed differences that can be linked to metastatic disease and there was also an overlapping pattern of changes between different patients. Many of the differences described here have been previously linked to poor patient survival, suggesting that this is a viable approach toward finding biomarkers for disease progression and definition of new targets useful for development of anticancer drugs. Frequent genetic differences between primary tumors and metastases in breast cancer also question, at least to some extent, the role of primary tumors as a surrogate subject of study for the systemic disease.
18195512
The properties of high-dimensional data spaces: implications for exploring gene and protein expression data
High-throughput genomic and proteomic technologies are widely used in cancer research to build better predictive models of diagnosis, prognosis and therapy, to identify and characterize key signalling networks and to find new targets for drug development. These technologies present investigators with the task of extracting meaningful statistical and biological information from high-dimensional data spaces, wherein each sample is defined by hundreds or thousands of measurements, usually concurrently obtained. The properties of high dimensionality are often poorly understood or overlooked in data modelling and analysis. From the perspective of translational science, this Review discusses the properties of high-dimensional data spaces that arise in genomic and proteomic studies and the challenges they can pose for data analysis and interpretation.
206747436
Global gene expression analysis reveals specific patterns of cell junctions in non-small cell lung cancer subtypes.
Non-small cell lung cancer (NSCLC) can be classified into the major subtypes adenocarcinoma (AC) and squamous cell carcinoma (SCC). Although explicit molecular, histological and clinical characteristics have been reported for both subtypes, no specific therapy exists so far. However, the characterization of suitable molecular targets holds great promises to develop novel therapies in NSCLC. In the present study, global gene expression profiling of 58 human NSCLC specimens revealed large transcriptomic differences between AC and SCC subtypes: more than 1700 genes were found to be differentially expressed. The assignment of these genes to biological processes pointed to the deregulation of distinct sets of genes coding for cell junctions in both tumor subtypes. We focused on 17 cell adhesion genes and 11 reported marker genes for epithelial-mesenchymal transition (EMT), and investigated their expression in matched tumor-normal specimens by quantitative real-time PCR. The majority of the cell adhesion genes was significantly up-regulated in at least one tumor subtype compared to normal tissue, predominantly desmosomes and gap junctions in SCC, and tight junctions in AC. The higher expression of EMT marker transcripts in tumor specimens suggested a large potential for invasion and migration processes in NSCLC. Our results indicate that AC and SCC in the lung are characterized by the expression of distinct sets of cell adhesion molecules which may represent promising targets for novel specific therapies.
32598249
Use of biomarkers and surrogate endpoints in drug development and regulatory decision making: criteria, validation, strategies.
In the future, biomarkers will play an increasingly important role in all phases of drug development, including regulatory review. However, only a few of these biomarkers will become established well enough to serve in regulatory decision making as surrogate endpoints, thereby substituting for traditional clinical endpoints. Even generally accepted surrogate endpoints are unlikely to capture all the therapeutic benefits and potential adverse effects a drug will have in a diverse patient population. Accordingly, combinations of biomarkers probably will be needed to provide a more complete characterization of the spectrum of pharmacologic response. In the future, pharmacogenomic approaches, including those based on differential expression of gene arrays, will provide panels of relevant biomarkers that can be expected to transform the drug development process.
39679276
Gene expression profiling of non-small cell lung cancer.
Functional genomics has emerged over the past 10 years as a novel technology to study genetic alterations. Gene expression arrays are one genomic technique employed to discover changes in the DNA expression that occur in neoplastic transformation. Microarrays have been applied to investigating lung cancer. Specific applications include discovering novel genetic changes that occur in lung tumors. Microarrays can also be applied to improve diagnosis, staging and discover prognostic markers. The eventual goal of this technology is to discover new markers for therapy and to customize therapy based on an individual tumor genetic composition. In this review, we present the current state of gene expression array technology in its application to lung cancer.
30838793
Lower osteopontin plasma levels are associated with superior outcomes in advanced non-small-cell lung cancer patients receiving platinum-based chemotherapy: SWOG Study S0003.
PURPOSE
S0003 was a phase III trial of carboplatin/paclitaxel with or without the hypoxic cytotoxin tirapazamine in patients with advanced or metastatic non-small-cell lung cancer (NSCLC). We investigated the relationship between clinical outcomes and plasma levels of the hypoxia-associated protein osteopontin (OPN) in patients on this protocol.


PATIENTS AND METHODS
Baseline plasma was obtained from 172 patients. In 56 patients, sequential plasma was obtained after one or two cycles. Concentrations of OPN, as well as plasminogen activator inhibitor-1 (PAI-1) and vascular endothelial growth factor (VEGF), were measured using enzyme-linked immunosorbent assay. Tumor expression of OPN was assessed by immunohistochemistry in 61 matched archival specimens.


RESULTS
Patients with lower OPN levels (below the median) had a significantly superior overall survival compared with patients with higher levels, regardless of treatment arm (hazard ratio [HR] = 0.60, P = .002). A similar correlation was observed for progression-free survival (HR = 0.69, P = .02). When examined as a continuous variable, OPN maintained its significant association with both progression-free (HR = 1.05, P = .01) and overall survival (HR = 1.09, P < .0001). Patients with lower plasma OPN levels were significantly more likely to have tumor response (P = .03). No differences were observed between treatment arms. Tumor OPN levels did not correlate with patient outcomes or with plasma levels. No associations were observed between patient outcomes and VEGF or PAI-1 levels; however, plasma concentrations of these markers were significantly interrelated (P < .0001) and significantly decreased after treatment (P = .0002 and P = .03, respectively).


CONCLUSION
Pretreatment plasma levels of OPN are significantly associated with patient response, progression-free survival, and overall survival in chemotherapy-treated patients with advanced NSCLC.
7718676
A five-gene signature and clinical outcome in non-small-cell lung cancer.
BACKGROUND
Current staging methods are inadequate for predicting the outcome of treatment of non-small-cell lung cancer (NSCLC). We developed a five-gene signature that is closely associated with survival of patients with NSCLC.


METHODS
We used computer-generated random numbers to assign 185 frozen specimens for microarray analysis, real-time reverse-transcriptase polymerase chain reaction (RT-PCR) analysis, or both. We studied gene expression in frozen specimens of lung-cancer tissue from 125 randomly selected patients who had undergone surgical resection of NSCLC and evaluated the association between the level of expression and survival. We used risk scores and decision-tree analysis to develop a gene-expression model for the prediction of the outcome of treatment of NSCLC. For validation, we used randomly assigned specimens from 60 other patients.


RESULTS
Sixteen genes that correlated with survival among patients with NSCLC were identified by analyzing microarray data and risk scores. We selected five genes (DUSP6, MMD, STAT1, ERBB3, and LCK) for RT-PCR and decision-tree analysis. The five-gene signature was an independent predictor of relapse-free and overall survival. We validated the model with data from an independent cohort of 60 patients with NSCLC and with a set of published microarray data from 86 patients with NSCLC.


CONCLUSIONS
Our five-gene signature is closely associated with relapse-free and overall survival among patients with NSCLC.
34070842
MALAT-1, a novel noncoding RNA, and thymosin β4 predict metastasis and survival in early-stage non-small cell lung cancer
Early-stage non-small cell lung cancer (NSCLC) can be cured by surgical resection, but a substantial fraction of patients ultimately dies due to distant metastasis. In this study, we used subtractive hybridization to identify gene expression differences in stage I NSCLC tumors that either did or did not metastasize in the course of disease. Individual clones (n=225) were sequenced and quantitative RT–PCR verified overexpression in metastasizing samples. Several of the identified genes (eIF4A1, thymosin β4 and a novel transcript named MALAT-1) were demonstrated to be significantly associated with metastasis in NSCLC patients (n=70). The genes’ association with metastasis was stage- and histology specific. The Kaplan–Meier analyses identified MALAT-1 and thymosin β4 as prognostic parameters for patient survival in stage I NSCLC. The novel MALAT-1 transcript is a noncoding RNA of more than 8000 nt expressed from chromosome 11q13. It is highly expressed in lung, pancreas and other healthy organs as well as in NSCLC. MALAT-1 expressed sequences are conserved across several species indicating its potentially important function. Taken together, these data contribute to the identification of early-stage NSCLC patients that are at high risk to develop metastasis. The identification of MALAT-1 emphasizes the potential role of noncoding RNAs in human cancer.
8811615
Expression profiling of primary non-small cell lung cancer for target identification
Using a panel of cDNA microarrays comprising 47 650 transcript elements, we have carried out a dual-channel analysis of gene expression in 39 resected primary human non-small cell lung tumours versus normal lung tissue. Whilst ∼11 000 elements were scored as differentially expressed at least twofold in at least one sample, 96 transcripts were scored as over-represented fourfold or more in at least seven out of 39 tumours and 30 sequences 16-fold in at least two out of 39 tumours, including 24 transcripts in common. Transcripts (178) were found under-represented fourfold in at least seven out of 39 tumours, 31 of which are under-represented 16-fold in at least two out of 39 lesions. The relative expression levels of representative genes from these lists were analysed by comparative multiplex RT–PCR and found to be broadly consistent with the microarray data. Two dramatically over-represented genes, previously designated as potential tumour suppressors in breast (maspin) and lung and breast (S100A2) cancers, were analysed more extensively and demonstrate the effectiveness of this approach in identifying potential lung cancer diagnostic or therapeutic targets. Whilst it has been reported that S100A2 is downregulated in NSCLC at an early stage, our microarray, cmRT–PCR, Western and immunohistochemistry data indicate that it is strongly expressed in the majority of tumours.
1913209
S100A2 is strongly expressed in airway basal cells, preneoplastic bronchial lesions and primary non-small cell lung carcinomas
S100A2 gene products were shown to be frequently and dramatically over-represented in non-small cell lung cancer (NSCLC) lesions over normal tissue by microarray analysis. We have now analysed an independent series of NSCLC tumours and multiple matched normal bronchial epithelial sites by RT–PCR and immunohistochemistry to investigate: whether this expression pattern can be confirmed and whether elevated expression is associated with tumour histology, clinical outcome or preneoplasia. In this second series, S100A2 was strongly expressed in 76% (35 out of 46) of tumours, more frequently in squamous cell than adenocarcinomas (P<0.002). This strong expression was not related to high-level gene amplification, but was associated in one of five informative cases with an allele-specific imbalance in transcript levels. Most tumours strongly expressed the ΔNp63 transcript, the product of which is a putative regulator of S100A2 transcription and while all but one of the tumours positive for ΔNp63 expressed S100A2, others negative for this regulator also expressed the gene. Contrary to the hypothesis that S100A2 is a tumour suppressor, no somatic mutations were identified in the coding sequence in 44 tumours. Furthermore, an examination of multiple tumour-free epithelial sites from 20 patients showed that strong expression was often associated with increasing levels of disorder in preinvasive bronchial lesions (P<0.0001).
30476718
Overexpression of S100A2 protein as a prognostic marker for patients with stage I non small cell lung cancer
S100A2, a calcium‐binding protein, recently became of major interest because of its differential expression during transformation and metastasis in various tumors. The purpose of this study was to investigate the prognostic significance of S100A2 expression in the early‐stage non small lung cancer (NSCLC). Immunohistochemical analysis to determine the percentage of cells staining positive for S100A2 was performed on 11 NSCLC tissue microarray slides containing samples from 113 patients with pathologic stage I NSCLC who had undergone curative surgery. S100A2 was expressed in samples from 79 patients (69.9%). Kaplan‐Meier analysis showed that patients whose tumors had positive S100A2 expression had a significantly lower overall survival and disease‐specific survival rate at 5 years after surgery than did patients with negative S100A2 expression (p < 0.001 and p < 0.001, respectively). Age at diagnosis, histologic type of cancer, degree of differentiation and smoking history did not have a statistically significant effect on survival. Multivariate analysis confirmed that S100A2 expression is a better predictor for disease‐specific survival than were other clinical and histologic variables tested. Our results suggested that the expression of the S100A2 protein in stage I NSCLC indicates poor prognosis and may be used to identify patients with early‐stage NSCLC who might benefit from adjuvant treatment. © 2005 Wiley‐Liss, Inc.
25322963
Tumor expression of S100A6 correlates with survival of patients with stage I non-small-cell lung cancer.
BACKGROUND
In a previously published in vitro study based on top-down proteomics we found that the calcium-binding proteins S100A6 and S100A4 were affected by exposure to ionizing radiation in a p53-dependent fashion. Both proteins showed post-translational modification changes, and S100A6 also showed increased expression and translocation in response to irradiation. Aim of the present study was to evaluate the expression of S100A6 and S100A4 in non-small-cell lung cancer (NSCLC).


METHODS
S100A6 expression on archival tumor cell lysates from 39 patients with radically resected NSCLC was assessed with SELDI-TOF-MS. S100A6 identity was confirmed using a SELDI-based antibody-capture method on lysates from the A549 lung cancer cell line, cell lysates from two freshly prepared NSCLC samples, four plasma samples and one pleural effusion sample. Immunostainings for S100A6, S100A4 and p53 were performed on tissue microarrays containing 103 stage I surgically resected NSCLC cases and 14 normal lung parenchyma specimens.


RESULTS
The presence of post-translationally modified S100A6 forms was confirmed with SELDI-MS on enriched tumor cell lysates, as well as in plasma and pleural effusion samples. In addition, high S100A6 peak intensity was associated with longer median survival (35 months vs. 18 months for high and low peak intensity, respectively; p=n.s.). The immunohistochemical analysis showed that 25% of tumors were S100A6 positive. S100A6 expression correlated directly with non-squamous histology (p<0.0001) and S100A4 expression (p=0.005), and inversely with p53 expression (p=0.01). S100A6-positive cases showed a trend of longer survival compared with S100A6-negative cases (p=0.07). This difference became significant when the analysis was restricted to p53-negative cases (n=72). In this subgroup of patients, whose tumors likely exhibit a functional p53, S100A6 was an independent prognostic factor of improved survival at multivariate analysis (HR 0.49, 95% CI 0.27-0.81, p=0.017).


CONCLUSIONS
In this study we have validated on clinical material our previous findings on cell lines in terms of S100A6 expression and post-translational modifications pattern in NSCLC. Moreover, the survival results obtained in p53-negative stage I NSCLC cases support the proposed pro-apoptotic function of S100A6 and suggest the hypothesis of a cross regulation between these two proteins.
25392852
Differential Methylation of a Short CpG-Rich Sequence within Exon 1 of TCF21 Gene: A Promising Cancer Biomarker Assay
Detection of cancer cells at early stages could potentially increase survival rates in cancer patients. Aberrant promoter hypermethylation is a major mechanism for silencing tumor suppressor genes in many kinds of human cancers. A recent report from our laboratory described the use of quantitative methylation-specific PCR assays for discriminating patients with lung cancer from those without lung cancer using lung biopsies as well as sputum samples. TCF21 is known to be essential for differentiation of epithelial cells adjacent to mesenchyme. Using restriction landmark genomic scanning, a recent study identified TCF21 as candidate tumor suppressor at 6q23-q24 that is epigenetically inactivated in lung and head and neck cancers. Using DNA sequencing technique, we narrowed down a short CpG-rich segment (eight specific CpG sites in the CpG island within exon 1) of the TCF21 gene, which was unmethylated in normal lung epithelial cells but predominantly methylated in lung cancer cell lines. We specifically targeted this short CpG-rich sequence and developed a quantitative methylation-specific PCR assay suitable for high-throughput analysis. We showed the usefulness of this assay in discriminating patients with lung cancer from those without lung cancer using biopsies and sputum samples. We further showed similar applications with multiple other malignancies. Our assay might have important implications in early detection and surveillance of multiple malignancies. (Cancer Epidemiol Biomarkers Prev 2008;17(4):995–1000)
24360537
Epigenetic regulation of the tumor suppressor gene TCF21 on 6q23-q24 in lung and head and neck cancer.
The identification of tumor suppressor genes has classically depended on their localization within recurrent regions of loss of heterozygosity. According to Knudson's two-hit hypothesis, the remaining allele is lost, either genetically or, more recently identified, through epigenetic events. To date, retrospective analyses have determined promoter methylation as a common alternative alteration in cancer cells to silence cancer-related genes. Here we report an application of restriction landmark genomic scanning that allows for DNA methylation profiling along a region of recurrent loss of heterozygosity at chromosome 6q23-q24. This approach resulted in the identification of a tumor suppressor gene, TCF21, which is frequently lost in human malignancies. We demonstrate that TCF21 is expressed in normal lung airway epithelial cells and aberrantly methylated and silenced in the majority of head and neck squamous cell carcinomas and non-small-cell lung cancers analyzed. TCF21 is known to regulate mesenchymal cell transition into epithelial cells, a property that has been shown to be deficient in carcinomas. We further demonstrate that exogenous expression of TCF21 in cells that have silenced the endogenous TCF21 locus resulted in a reduction of tumor properties in vitro and in vivo.
29033062
The eln3 gene involved in fruiting body morphogenesis of Coprinus cinereus encodes a putative membrane protein with a general glycosyltransferase domain.
We identified and characterized elongationless3 (eln3-1), a restriction enzyme-mediated integration (REMI) mutation affecting fruiting body morphogenesis in Coprinus cinereus. The mutant produces an aberrant fruiting body in which the stipe hardly elongates during fruiting body maturation. In the wild type, cylindrical stipe cells, elongation growth of which is responsible for stipe elongation, make side-by-side contact with one another and run parallel to the stipe axis, whereas in the mutant, the organization of the stipe tissue is disturbed and much space is produced between stipe cells. This disorganization of the stipe tissue, together with reduced elongation of the stipe cells, causes the mutant stipe short and bulgy. After a plasmid rescue, the eln3 gene was identified as a DNA fragment that complements the eln3-1 mutation. The eln3 ORF is predicted to encode a protein of 927 amino acids with a general glycosyltransferase domain and to be located in the plasma membrane. Transcription of the eln3 gene is specifically activated in rapidly elongating stipes. Possible involvement of the putative Eln3 enzyme in cell-to-cell connection is discussed.
12851986
Gene expression profiling in clinically localized prostate cancer: a four-gene expression model predicts clinical behavior.
PURPOSE
New diagnostic and prognostic molecular markers are required for prostate cancer, one of the most common male malignancies in Western countries. Gene expression profiling may help to identify genes involved in prostate carcinogenesis, yield clinical biomarkers, and improve tumor classification.


EXPERIMENTAL DESIGN
To identify fundamental differences between normal and neoplastic prostate tissue, we used real-time quantitative RT-PCR assays to quantify the mRNA expression of 291 selected genes in samples of normal prostate and of well-documented primary, clinically localized prostate tumors.


RESULTS
Forty-six genes showed significantly different expression in tumors relative to normal prostate. The dysregulated genes belong notably to the extracellular membrane and extracellular membrane remodeling categories and are involved in angiogenesis. Furthermore, we obtained a four-gene (XLKD1/LYVE1, CGA, F2R/PAR1, and BCL-G) model that discriminated between the seven patients with and the seven patients without relapse, independently of stage and grade.


CONCLUSIONS
Some dysregulated genes are good candidates for use as molecular markers and/or therapeutic targets. Furthermore, differential gene expression profiling of clinically localized prostate tumors from relapsing and nonrelapsing patients identified a set of four genes with a pattern of expression that defines a molecular signature that could predict the clinical behavior of this disease.
2822396
Androgen receptor gene amplification and protein expression in hormone refractory prostate cancer
This study examined androgen receptor (AR) gene amplification and protein expression in 102 matched paired hormone sensitive and resistant tumours from 51 patients. AR gene amplification and X chromosome copy number were assessed by fluorescent in situ hybridisation, and protein expression was assessed by immunohistochemistry. All tumours were stained for PSA protein expression. Significantly more tumours exhibited AR amplification following the development of hormone resistance (20%, 10 out of 49) compared to matched hormone-sensitive tumours from the same patient (2%, one out of 48) (P=0.0085). The level of AR expression was significantly higher in hormone-resistant tumours compared to matched hormone-sensitive tumours from the same patient (130, interquartile range, 55–167 vs 94.5 interquartile range, 55–120, P=0.019). AR expression levels in hormone-resistant tumours with and without AR amplification were not significantly different. However, an increase in AR expression was seen with the development of AR amplification in paired tumours. The rate of AR gene amplification and/or an increase in AR protein expression during androgen resistant is too low to wholly explain the development of androgen resistance. Alternative mechanisms for modulating the function of the AR, or other signalling pathways, must be considered as key factors in the development of hormone-resistant prostate.
33185138
Benefits of using paired controls for analyzing gene expression of prostate cancer
The genetics of prostate cancer are highly variable and not well understood. No consistent pattern of mutations across prostate cancer samples has yet been found. Due to this inherent heterogeneity it is natural to wonder whether or not using paired controls in gene expression studies might be useful. Although other studies have previously analyzed the use of paired controls for the expression of a handful of genes at a time no study has yet been performed to assess the benefits of using paired controls rather than independent controls on a large scale. By using a prostate cancer microarray data set that consisted of 58 pairs of paired cancer and control samples as well as 18 independent controls we found that searches for differentially expressed genes and for upregulated genes were significantly enhanced by using paired controls instead of independent controls.
208046901
Feature Selection for Predicting Tumor Metastases in Microarray Experiments using Paired Design
Among the major issues in gene expression profile classification, feature selection is an important and necessary step in achieving and creating good classification rules given the high dimensionality of microarray data. Although different feature selection methods have been reported, there has been no method specifically proposed for paired microarray experiments. In this paper, we introduce a simple procedure based on a modified t-statistic for feature selection to microarray experiments using the popular matched case-control design and apply to our recent study on tumor metastasis in a low-malignant group of breast cancer patients for selecting genes that best predict metastases. Gene or feature selection is optimized by thresholding in a leaving one-pair out cross-validation. Model comparison through empirical application has shown that our method manifests improved efficiency with high sensitivity and specificity.
1257754
A novel SNP analysis method to detect copy number alterations with an unbiased reference signal directly from tumor samples
BackgroundGenomic instability in cancer leads to abnormal genome copy number alterations (CNA) as a mechanism underlying tumorigenesis. Using microarrays and other technologies, tumor CNA are detected by comparing tumor sample CN to normal reference sample CN. While advances in microarray technology have improved detection of copy number alterations, the increase in the number of measured signals, noise from array probes, variations in signal-to-noise ratio across batches and disparity across laboratories leads to significant limitations for the accurate identification of CNA regions when comparing tumor and normal samples.MethodsTo address these limitations, we designed a novel "Virtual Normal" algorithm (VN), which allowed for construction of an unbiased reference signal directly from test samples within an experiment using any publicly available normal reference set as a baseline thus eliminating the need for an in-lab normal reference set.ResultsThe algorithm was tested using an optimal, paired tumor/normal data set as well as previously uncharacterized pediatric malignant gliomas for which a normal reference set was not available. Using Affymetrix 250K Sty microarrays, we demonstrated improved signal-to-noise ratio and detected significant copy number alterations using the VN algorithm that were validated by independent PCR analysis of the target CNA regions.ConclusionsWe developed and validated an algorithm to provide a virtual normal reference signal directly from tumor samples and minimize noise in the derivation of the raw CN signal. The algorithm reduces the variability of assays performed across different reagent and array batches, methods of sample preservation, multiple personnel, and among different laboratories. This approach may be valuable when matched normal samples are unavailable or the paired normal specimens have been subjected to variations in methods of preservation.
15034149
Conditional random pattern algorithm for LOH inference and segmentation
MOTIVATION
Loss of heterozygosity (LOH) is one of the most important mechanisms in the tumor evolution. LOH can be detected from the genotypes of the tumor samples with or without paired normal samples. In paired sample cases, LOH detection for informative single nucleotide polymorphisms (SNPs) is straightforward if there is no genotyping error. But genotyping errors are always unavoidable, and there are about 70% non-informative SNPs whose LOH status can only be inferred from the neighboring informative SNPs.


RESULTS
This article presents a novel LOH inference and segmentation algorithm based on the conditional random pattern (CRP) model. The new model explicitly considers the distance between two neighboring SNPs, as well as the genotyping error rate and the heterozygous rate. This new method is tested on the simulated and real data of the Affymetrix Human Mapping 500K SNP arrays. The experimental results show that the CRP method outperforms the conventional methods based on the hidden Markov model (HMM).


AVAILABILITY
Software is available upon request.
205017102
Computational genetics: Computational analysis of microarray data
Microarray experiments are providing unprecedented quantities of genome-wide data on gene-expression patterns. Although this technique has been enthusiastically developed and applied in many biological contexts, the management and analysis of the millions of data points that result from these experiments has received less attention. Sophisticated computational tools are available, but the methods that are used to analyse the data can have a profound influence on the interpretation of the results. A basic understanding of these computational tools is therefore required for optimal experimental design and meaningful data analysis.
17093270
Significance analysis of time course microarray experiments.
Characterizing the genome-wide dynamic regulation of gene expression is important and will be of much interest in the future. However, there is currently no established method for identifying differentially expressed genes in a time course study. Here we propose a significance method for analyzing time course microarray studies that can be applied to the typical types of comparisons and sampling schemes. This method is applied to two studies on humans. In one study, genes are identified that show differential expression over time in response to in vivo endotoxin administration. By using our method, 7,409 genes are called significant at a 1% false-discovery rate level, whereas several existing approaches fail to identify any genes. In another study, 417 genes are identified at a 10% false-discovery rate level that show expression changing with age in the kidney cortex. Here it is also shown that as many as 47% of the genes change with age in a manner more complex than simple exponential growth or decay. The methodology proposed here has been implemented in the freely distributed and open-source edge software package.
564309
Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments
The problem of identifying differentially expressed genes in designed microarray experiments is considered. Lonnstedt and Speed (2002) derived an expression for the posterior odds of differential expression in a replicated two-color experiment using a simple hierarchical parametric model. The purpose of this paper is to develop the hierarchical model of Lonnstedt and Speed (2002) into a practical approach for general microarray experiments with arbitrary numbers of treatments and RNA samples. The model is reset in the context of general linear models with arbitrary coefficients and contrasts of interest. The approach applies equally well to both single channel and two color microarray experiments. Consistent, closed form estimators are derived for the hyperparameters in the model. The estimators proposed have robust behavior even for small numbers of arrays and allow for incomplete data arising from spot filtering or spot quality weights. The posterior odds statistic is reformulated in terms of a moderated t-statistic in which posterior residual standard deviations are used in place of ordinary standard deviations. The empirical Bayes approach is equivalent to shrinkage of the estimated sample variances towards a pooled estimate, resulting in far more stable inference when the number of arrays is small. The use of moderated t-statistics has the advantage over the posterior odds that the number of hyperparameters which need to estimated is reduced; in particular, knowledge of the non-null prior for the fold changes are not required. The moderated t-statistic is shown to follow a t-distribution with augmented degrees of freedom. The moderated t inferential approach extends to accommodate tests of composite null hypotheses through the use of moderated F-statistics. The performance of the methods is demonstrated in a simulation study. Results are presented for two publicly available data sets.
5540354
Significance analysis of microarrays applied to the ionizing radiation response
Microarrays can measure the expression of thousands of genes to identify changes in expression between different biological states. Methods are needed to determine the significance of these changes while accounting for the enormous number of genes. We describe a method, Significance Analysis of Microarrays (SAM), that assigns a score to each gene on the basis of change in gene expression relative to the standard deviation of repeated measurements. For genes with scores greater than an adjustable threshold, SAM uses permutations of the repeated measurements to estimate the percentage of genes identified by chance, the false discovery rate (FDR). When the transcriptional response of human cells to ionizing radiation was measured by microarrays, SAM identified 34 genes that changed at least 1.5-fold with an estimated FDR of 12%, compared with FDRs of 60 and 84% by using conventional methods of analysis. Of the 34 genes, 19 were involved in cell cycle regulation and 3 in apoptosis. Surprisingly, four nucleotide excision repair genes were induced, suggesting that this repair pathway for UV-damaged DNA might play a previously unrecognized role in repairing DNA damaged by ionizing radiation.
20016803
Immunohistochemical markers of prognosis in non-small cell lung cancer: a review and proposal for a multiphase approach to marker evaluation
Characteristics of the tumour that affect and predict the survival outcome of patients with cancer are prognostic markers for cancer. In non-small cell lung carcinoma (NSCLC), stage is the main determinant of prognosis and the basis for deciding options for treatment. Patients with early-stage tumour are treated by complete surgical resection, which is curative in 40–70% of patients. That there are other factors important in determining the biology of these tumours, especially genes that have a role in metastasis, is indicated. Such factors could potentially be used to further classify patients into groups according to substages that may be treated differently. During the past decade, a large number of proteins that are putatively important in carcinogenesis and cancer biology have been studied for their prognostic value in NSCLC, but none of them have been proved to be sufficiently useful in clinical diagnosis. Several markers (epidermal growth factor receptor, human epidermal growth factor receptor 2, Ki-67, p53 and Bcl-2) have been studied exhaustively. Ki-67, p53 and Bcl-2 are suggested to be important but weak prognostic markers, by meta-analyses of the results. Cyclin E, vascular endothelial growth factor A, p16INK4A, p27kip1 and β-catenin are promising candidates, but require further study in large randomised clinical trial samples by using standardised assays and scoring systems. Some issues and inconsistencies in the reported studies to date are highlighted and discussed. A guideline for a multi-phase approach for conducting future studies on prognostic immunohistochemistry markers is proposed here.
16709968
Src as a potential therapeutic target in non-small-cell lung cancer.
Lung cancer is the most common cause of cancer-related death, with non-small-cell lung cancer (NSCLC) accounting for 80%-85% of all cases. Although survival rates are reasonably good for patients diagnosed with very early disease, the majority of patients present with advanced disease. For these patients, palliation and improvements in quality of life are the primary goals of therapy. Although chemotherapeutic agents remain the cornerstone of first-line therapy, these agents have limited use in patients who have relapsed and have metastatic disease. Therefore, new strategies are required to improve survival and quality of life in this setting. With the substantial advances in our understanding of tumour biology, it has been possible to identify signalling pathways involved in mediating tumour growth and progression. These pathways offer targets for new biological agents such as small molecule inhibitors and monoclonal antibodies. One such target is Src, a tyrosine kinase that is involved in multiple aspects of tumorigenesis including proliferation, migration and angiogenesis. Increased levels of Src expression have been found in a range of cancers, especially breast, colorectal, prostate and lung. Preliminary preclinical data and pharmacodynamic data suggest that Src inhibition is a viable therapeutic option in the treatment of advanced NSCLC.
29995377
Epidermal growth factor receptor mutations in plasma DNA samples predict tumor response in Chinese patients with stages IIIB to IV non-small-cell lung cancer.
PURPOSE
Mutations in the epidermal growth factor receptor (EGFR) kinase domain can predict tumor response to tyrosine kinase inhibitors (TKIs) in non-small-cell lung cancer (NSCLC). However, obtaining tumor tissues for mutation analysis is challenging. We hypothesized that plasma-based EGFR mutation analysis is feasible and has value in predicting tumor response in patients with NSCLC.


PATIENTS AND METHODS
Plasma DNA samples and matched tumors from 230 patients with stages IIIB to IV NSCLC were analyzed for EGFR mutations in exons 19 and 21 by using denaturing high-performance liquid chromatography. We compared the mutations in the plasma samples and the matched tumors and determined an association between EGFR mutation status and the patients' clinical outcomes prospectively.


RESULTS
In 230 patients, we detected 81 EGFR mutations in 79 (34.3%) of the patients' plasma samples. We detected the same mutations in 63 (79.7%) of the matched tumors. Sixteen plasma (7.0%) and fourteen tumor (6.1%) samples showed unique mutations. The mutation frequencies were significantly higher in never-smokers and in patients with adenocarcinomas (P = .012 and P = .009, respectively). In the 102 patients who failed platinum-based treatment and who were treated with gefitinib, 22 (59.5%) of the 37 with EGFR mutations in the plasma samples, whereas only 15 (23.1%) of the 65 without EGFR mutations, achieved an objective response (P = .002). Patients with EGFR mutations had a significantly longer progression-free survival time than those without mutations (P = .044) in plasma.


CONCLUSION
EGFR mutations can be reliably detected in plasma DNA of patients with stages IIIB to IV NSCLC and can be used as a biomarker to predict tumor response to TKIs.
24407871
Trying to compose the puzzle with all the pieces: Epidermal growth factor receptor tyrosine kinase inhibitors in non‐small cell lung cancer
Small molecule epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) have shown promising activity in patients with non‐small cell lung cancer (NSCLC). Gefitinib has been the first of these drugs to be licensed for third‐line treatment of advanced NSCLC patients. More recently, erlotinib has been shown to be more effective than placebo in increasing overall survival (OAS) and has been approved for NSCLC patients after failure of chemotherapy. However, a large body of clinical and experimental evidence suggests that the benefit from these drugs is limited to a subgroup of patients. The availability of clinical or molecular criteria for predicting sensitivity to EGFR‐TKIs is the most relevant issue for their correct use and for planning future research. Determination of EGFR expression is not sufficient to predict sensitivity to EGFR‐TKIs. However, several clinical features (female gender, adenocarcinoma/bronchioloalveolar histotype, never‐smoking status, Oriental Asian origin) are associated with major clinical responses. The identification of somatic mutations in the tyrosine kinase domain of the EGFR gene represents the most important molecular marker of sensitivity to EGFR‐TKIs. These “activating” mutations can be found in a high proportion of gefitinib‐ or erlotinib‐responding patients. However, clinical effectiveness might not be limited to patients carrying EGFR mutations, in which the objective response is probably the detectable effect of apoptosis induction in cancer cells. In fact, clinical efficacy with gefitinib or erlotinib is also observed in another subgroup of patients, in which a tumor growth delay, determined by a block in cancer cell proliferation, could induce a prolonged and clinically relevant disease stabilization. © 2005 Wiley‐Liss, Inc.
6516572
High coexpression of both insulin-like growth factor receptor-1 (IGFR-1) and epidermal growth factor receptor (EGFR) is associated with shorter disease-free survival in resected non-small-cell lung cancer patients.
BACKGROUND
Insulin-like growth factor receptor-1 (IGFR-1) represents a novel molecular target in non-small-cell lung cancer (NSCLC). IGFR-1 and epidermal growth factor receptor (EGFR) activation is essential to mediate tumor cell survival, proliferation and invasion. We explored the correlation between IGFR-1 and EGFR, their relationship with clinicopathological parameters and their impact on outcome in resected stage I-III NSCLC patients.


PATIENTS AND METHODS
Tumors from 125 surgical NSCLC patients were evaluated for IGFR-1 and EGFR expression by immunohistochemistry. Kaplan-Meier estimates of survival and time to recurrence were calculated for clinical variables and biologic markers using the Cox model for multivariate analysis.


RESULTS
IGFR-1 protein overexpression was detected in 36.0% of NSCLC patients and was associated with larger tumor size (P = 0.04) but not with other clinical or biological characteristics. EGFR protein overexpression was observed in 55.2% of NSCLC, more frequently in squamous cell carcinoma (SCC) than non-SCC (63.7% versus 36.3%, chi(2) = 9.8, P = 0.001). IGFR-1 protein expression was associated with EGFR protein expression (P = 0.03). At the multivariate analysis, high coexpression of both IGFR-1 and EGFR was a significant prognostic factor of worse disease-free survival (DFS) (hazard ratio 2.51, P = 0.01).


CONCLUSION
A statistically significant association was observed between high coexpression of both IGFR-1 and EGFR and worse DFS in early NSCLC patients.
19704658
Immunogenic Targets in Non–Small Cell Lung Cancer: More Is More
Reports of human clinical trials aiming at antigen-specific induction of T cell and antibody responses against non–small cell lung cancers (NSCLC) have started to surface within the last 5 years ([1][1]–[11][2]). In a large proportion of trials, the nature of the antigen(s) has not been
10957787
Identification of XAGE-1 isoforms: predominant expression of XAGE-1b in testis and tumors.
XAGE-1 is a cancer/testis (CT) antigen and has been shown to be immunogenic in lung cancer patients. Among 4 alternative splicing variants, XAGE-1a, b, c and d, XAGE-1b mRNA was dominantly expressed in cancer. In this study, we generated a XAGE-1b mAb, USO9-13. The B cell epitope recognized by the USO9-13 mAb was in the C-terminal region of the XAGE-1b protein and is also recognized by sera from patients with lung adenocarcinoma. Using USO9-13 and an anti-Flag mAb, we examined the translation products of the 4 transcripts. The XAGE-1a and b transcripts translated to the XAGE-1b protein. The XAGE-1c transcript possibly translated to 9- and 17-aa polypeptides. The XAGE-1d transcript translated to a protein consisting of 69 amino acids. Immunofluorescence analysis using USO9-13 mAb showed that the XAGE-1b protein is located in the nuclei of cells. Immunohistochemically, nuclear staining was heterogeneously observed in 25/47 lung adenocarcinomas, 1/12 hepatocellular carcinomas and 1/11 gastric cancers, but not in adjacent normal tissues. These findings suggested that XAGE-1b is a promising target molecule for a cancer vaccine against lung cancer.
8121862
Clinical validity of the lung cancer biomarkers identified by bioinformatics analysis of public expression data.
Identification of molecular markers often leads to important clinical applications such as early diagnosis, prognosis, and drug targeting. Lung cancer, the leading cause of cancer-related deaths, still lacks reliable molecular markers. We have combined the bioinformatics analysis of the public gene expression data and clinical validation to identify biomarker genes for non-small-cell lung cancer. The serial analysis of gene expression and the expressed sequence tag data were meta-analyzed to produce a list of the differentially expressed genes in lung cancer. Through careful inspection of the predicted genes, we selected 20 genes for experimental validation using semiquantitative reverse transcriptase-PCR. The microdissected clinical specimens used in the study consisted of three groups: lung tissues from benign diseases and the paired (cancer and pathologic normal) tissues from non-small-cell lung cancer patients. After extensive statistical analyses, seven genes (CBLC, CYP24A1, ALDH3A1, AKR1B10, S100P, PLUNC, and LOC147166) were identified as potential diagnostic markers. Quantitative real-time PCR was carried out to additionally assess the value of the seven identified genes leading to the confirmation of at least two genes (CBLC and CYP24A1) as highly probable novel biomarkers. The gene properties of the identified markers, especially their relationship to lung cancer and cell signaling pathway regulation, further suggest their potential value as drug targets as well.
86730427
Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses
We have generated a molecular taxonomy of lung carcinoma, the leading cause of cancer death in the United States and worldwide. Using oligonucleotide microarrays, we analyzed mRNA expression levels corresponding to 12,600 transcript sequences in 186 lung tumor samples, including 139 adenocarcinomas resected from the lung. Hierarchical and probabilistic clustering of expression data defined distinct subclasses of lung adenocarcinoma. Among these were tumors with high relative expression of neuroendocrine genes and of type II pneumocyte genes, respectively. Retrospective analysis revealed a less favorable outcome for the adenocarcinomas with neuroendocrine gene expression. The diagnostic potential of expression profiling is emphasized by its ability to discriminate primary lung adenocarcinomas from metastases of extra-pulmonary origin. These results suggest that integration of expression profile data with clinical parameters could aid in diagnosis of lung cancer patients.
2478905
Diagnosis of multiple cancer types by shrunken centroids of gene expression
We have devised an approach to cancer class prediction from gene expression profiling, based on an enhancement of the simple nearest prototype (centroid) classifier. We shrink the prototypes and hence obtain a classifier that is often more accurate than competing methods. Our method of “nearest shrunken centroids” identifies subsets of genes that best characterize each class. The technique is general and can be used in many other classification problems. To demonstrate its effectiveness, we show that the method was highly efficient in finding genes for classifying small round blue cell tumors and leukemias.
2492980
Metabolic transformation in cancer.
In 2000, Douglas Hanahan and Robert Weinberg published a review detailing the six hallmarks of cancer. These are six phenotypes that a tumour requires in order to become a fully fledged malignancy: persistent growth signals, evasion of apoptosis, insensitivity to anti-growth signals, unlimited replicative potential, angiogenesis and invasion and metastasis. However, it is becoming increasingly clear that these phenotypes do not portray the whole story and that other hallmarks are necessary: one of which is a shift in cellular metabolism. The tumour environment creates a unique collection of stresses to which cells must adapt in order to survive. This environment is formed by the uncontrolled proliferation of cells, which ignore the cues that would create normal tissue architecture. As a result, the cells forming the tumour are exposed to low oxygen and nutrient levels, as well as high levels of toxic cellular waste products, which is thought to propel cells towards a more transformed phenotype, resistant to cell death and pro-metastatic.
146667
Targeting metabolic transformation for cancer therapy
Cancer therapy has long relied on the rapid proliferation of tumour cells for effective treatment. However, the lack of specificity in this approach often leads to undesirable side effects. Many reports have described various 'metabolic transformation' events that enable cancer cells to survive, suggesting that metabolic pathways might be good targets. There are currently several drugs under development or in clinical trials that are based on specifically targeting the altered metabolic pathways of tumours. This Review highlights pathways against which there are already drugs in different stages of development and also discusses additional druggable targets.
5877307
CENPA a Genomic Marker for Centromere Activity and Human Diseases
Inheritance of genetic material requires that chromosomes segregate faithfully during cell division. Failure in this process can drive to aneuploidy phenomenon. Kinetochores are unique centromere macromolecular protein structures that attach chromosomes to the spindle for a proper movement and segregation. A unique type of nucleosomes of centromeric chromatin provides the base for kinetochore formation. A specific histone H3 variant, CENPA, replaces conventional histone H3 and together with centromere-specific-DNA-binding factors directs the assembly of active kinetochores. Recent studies on CENPA nucleosomal structure, epigenetic inheritance of centromeric chromatin and transcription of pericentric heterochromatin provide new clues to our understanding of centromere structure and function. This review highlights the role and dynamics of CENPA assembly into centromeres and the potential contribution of this kinetochore protein to autoimmune and cancer diseases in humans.
7848619
Comparison and evaluation of methods for generating differentially expressed gene lists from microarray data
BackgroundNumerous feature selection methods have been applied to the identification of differentially expressed genes in microarray data. These include simple fold change, classical t-statistic and moderated t-statistics. Even though these methods return gene lists that are often dissimilar, few direct comparisons of these exist. We present an empirical study in which we compare some of the most commonly used feature selection methods. We apply these to 9 publicly available datasets, and compare, both the gene lists produced and how these perform in class prediction of test datasets.ResultsIn this study, we compared the efficiency of the feature selection methods; significance analysis of microarrays (SAM), analysis of variance (ANOVA), empirical bayes t-statistic, template matching, maxT, between group analysis (BGA), Area under the receiver operating characteristic (ROC) curve, the Welch t-statistic, fold change, rank products, and sets of randomly selected genes. In each case these methods were applied to 9 different binary (two class) microarray datasets. Firstly we found little agreement in gene lists produced by the different methods. Only 8 to 21% of genes were in common across all 10 feature selection methods. Secondly, we evaluated the class prediction efficiency of each gene list in training and test cross-validation using four supervised classifiers.ConclusionWe report that the choice of feature selection method, the number of genes in the genelist, the number of cases (samples) and the noise in the dataset, substantially influence classification success. Recommendations are made for choice of feature selection. Area under a ROC curve performed well with datasets that had low levels of noise and large sample size. Rank products performs well when datasets had low numbers of samples or high levels of noise. The Empirical bayes t-statistic performed well across a range of sample sizes.
23444566
Functional analysis of ABCA8, a new drug transporter.
We examined the transport capacity in Xenopus laevis oocytes of human EST KIAA0822/ABCA8, a member of the ABC superfamily. Substrates of ABCC2/MRP-2 such as [14C]estradiol-beta-glucuronide, taurocholate, and LTC4, and of organic anion transporter (OAT), such as para-aminohippuric acid, ochratoxin-A, were significantly accumulated while tetraethylammonium and doxorubicin were not. The transport of [14C]estradiol-beta-glucuronide was ATP-dependent and K(m) and V(max) values of 30.4microM and 66.9pmol/h/egg, respectively, were estimated. The transport of [14C]estradiol-beta-glucuronide was inhibited by substrates/inhibitors of ABCC2/MRP-2, but not by those of the organic cation transporter and multidrug resistance protein (MDR)-1. KIAA0822/ABCA8 possesses two ATP-binding sites and fourteen transmembrane domains. Northern blot analysis revealed expression in most organs, especially in heart, skeletal muscle, and liver. Thus, ABCA8 is a new member of the xenobiotic transporter ABC-subfamily.
206747436
Global gene expression analysis reveals specific patterns of cell junctions in non-small cell lung cancer subtypes.
Non-small cell lung cancer (NSCLC) can be classified into the major subtypes adenocarcinoma (AC) and squamous cell carcinoma (SCC). Although explicit molecular, histological and clinical characteristics have been reported for both subtypes, no specific therapy exists so far. However, the characterization of suitable molecular targets holds great promises to develop novel therapies in NSCLC. In the present study, global gene expression profiling of 58 human NSCLC specimens revealed large transcriptomic differences between AC and SCC subtypes: more than 1700 genes were found to be differentially expressed. The assignment of these genes to biological processes pointed to the deregulation of distinct sets of genes coding for cell junctions in both tumor subtypes. We focused on 17 cell adhesion genes and 11 reported marker genes for epithelial-mesenchymal transition (EMT), and investigated their expression in matched tumor-normal specimens by quantitative real-time PCR. The majority of the cell adhesion genes was significantly up-regulated in at least one tumor subtype compared to normal tissue, predominantly desmosomes and gap junctions in SCC, and tight junctions in AC. The higher expression of EMT marker transcripts in tumor specimens suggested a large potential for invasion and migration processes in NSCLC. Our results indicate that AC and SCC in the lung are characterized by the expression of distinct sets of cell adhesion molecules which may represent promising targets for novel specific therapies.
6473570
Ensemble classification of paired data
In many medical applications, data are taken from paired organs or from repeated measurements of the same organ or subject. Subject based as opposed to observation based evaluation of these data results in increased efficiency of the estimation of the misclassification rate. A subject based approach for classification in the generation of bootstrap samples of bagging and bundling methods is analyzed. A simulation model is used to compare the performance of different strategies to create the bootstrap samples which are used to grow individual trees. The proposed approach is compared to linear discriminant analysis, logistic regression, random forests and gradient boosting. Finally, the simulation results are applied to glaucoma diagnosis using both eyes of glaucoma patients and healthy controls. It is demonstrated that the proposed subject based resampling reduces the misclassification rate.
15963277
A Bootstrap Test of Stochastic Equality of Two Populations
When comparing two variables with nonnormal distributions, application of the Wilcoxon-Mann-Whitney test (WMW) is a common choice. However, it is only valid to test the null hypothesis stating equality of the distributions. Sometimes the hypothesis of interest is H0 : P(X < Y) = P(X > Y) against P(X < Y) ≠ P(X > Y), called stochastic equality and in-equality. Here we propose a bootstrap test for this problem. Results of an extensive simulation study basedon empirical distributions suggest that the new test is valid for a wide range of problems in parasitology and psychology, and the loss of power as compared to WMW is rather small in those cases when both tests are applicable.
122987911
A direct approach to false discovery rates
Multiple-hypothesis testing involves guarding against much more complicated errors than single-hypothesis testing. Whereas we typically control the type I error rate for a single-hypothesis test, a compound error rate is controlled for multiple-hypothesis tests. For example, controlling the false discovery rate FDR traditionally involves intricate sequential "p"-value rejection methods based on the observed data. Whereas a sequential "p"-value method fixes the error rate and "estimates" its corresponding rejection region, we propose the opposite approach-we "fix" the rejection region and then estimate its corresponding error rate. This new approach offers increased applicability, accuracy and power. We apply the methodology to both the positive false discovery rate pFDR and FDR, and provide evidence for its benefits. It is shown that pFDR is probably the quantity of interest over FDR. Also discussed is the calculation of the "q"-value, the pFDR analogue of the "p"-value, which eliminates the need to set the error rate beforehand as is traditionally done. Some simple numerical examples are presented that show that this new approach can yield an increase of over eight times in power compared with the Benjamini-Hochberg FDR method. Copyright 2002 Royal Statistical Society.
206339921
Java Treeview - extensible visualization of microarray data
Open source software encourages innovation by allowing users to extend the functionality of existing applications. Treeview is a popular application for the visualization of microarray data, but is closed-source and platform-specific, which limits both its current utility and suitability as a platform for further development. Java Treeview is an open-source, cross-platform rewrite that handles very large datasets well, and supports extensions to the file format that allow the results of additional analysis to be visualized and compared. The combination of a general file format and open source makes Java Treeview an attractive choice for solving a class of visualization problems. An applet version is also available that can be used on any website with no special server-side setup.
61481
DAVID: Database for Annotation, Visualization, and Integrated Discovery
The distributed nature of biological knowledge poses a major challenge to the interpretation of genome-scale datasets, including those derived from microarray and proteomic studies. This report describes DAVID, a web-accessible program that integrates functional genomic annotations with intuitive graphical summaries. Lists of gene or protein identifiers are rapidly annotated and summarized according to shared categorical data for Gene Ontology, protein domain, and biochemical pathway membership. DAVID assists in the interpretation of genome-scale datasets by facilitating the transition from data collection to biological meaning.
16214239
ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks
Summary: We have developed ClueGO, an easy to use Cytoscape plug-in that strongly improves biological interpretation of large lists of genes. ClueGO integrates Gene Ontology (GO) terms as well as KEGG/BioCarta pathways and creates a functionally organized GO/pathway term network. It can analyze one or compare two lists of genes and comprehensively visualizes functionally grouped terms. A one-click update option allows ClueGO to automatically download the most recent GO/KEGG release at any time. ClueGO provides an intuitive representation of the analysis results and can be optionally used in conjunction with the GOlorize plug-in. Availability: http://www.ici.upmc.fr/cluego/cluegoDownload.shtml Contact: jerome.galon@crc.jussieu.fr Supplementary information: Supplementary data are available at Bioinformatics online.