Koichi Nagasaki

Reduced Levels of ATF-2 Predispose Mice to Mammary Tumors

ABSTRACT Transcription factor ATF-2 is a nuclear target of stress-activated protein kinases, such as p38, which are activated by various extracellular stresses, including UV light. Here, we show that ATF-2 plays a critical role in hypoxia- and high-cell-density-induced apoptosis and the development of mammary tumors. Compared to wild-type cells, Atf-2−/− mouse embryonic fibroblasts (MEFs) were more resistant to hypoxia- and anisomycin-induced apoptosis but remained equally susceptible to other stresses, including UV. Atf-2−/− and Atf-2+/− MEFs could not express a group of genes, such as Gadd45α, whose overexpression can induce apoptosis, in response to hypoxia. Atf-2−/− MEFs also had a higher saturation density than wild-type cells and expressed lower levels of Maspin, the breast cancer tumor suppressor, which is also known to enhance cellular sensitivity to apoptotic stimuli. Atf-2−/− MEFs underwent a lower degree of apoptosis at high cell density than wild-type cells. Atf-2+/− mice were highly prone to mammary tumors that expressed reduced levels of Gadd45α and Maspin. The ATF-2 mRNA levels in human breast cancers were lower than those in normal breast tissue. Thus, ATF-2 acts as a tumor susceptibility gene of mammary tumors, at least partly, by activating a group of target genes, including Maspin and Gadd45α.

Whole-exome sequencing of human pancreatic cancers and characterization of genomic instability caused by MLH1 haploinsufficiency and complete deficiency

Whole-exome sequencing (Exome-seq) has been successfully applied in several recent studies. We here sequenced the exomes of 15 pancreatic tumor cell lines and their matched normal samples. We captured 162,073 exons of 16,954 genes and sequenced the targeted regions to a mean coverage of 56-fold. This study identified a total of 1517 somatic mutations and validated 934 mutations by transcriptome sequencing. We detected recurrent mutations in 56 genes. Among them, 41 have not been described. The mutation rates varied widely among cell lines. The diversity of the mutation rates was significantly correlated with the distinct MLH1 copy-number status. Exome-seq revealed intensive genomic instability in a cell line with MLH1 homozygous deletion, indicated by a dramatically elevated rate of somatic substitutions, small insertions/deletions (indels), as well as indels in microsatellites. Notably, we found that MLH1 expression was decreased by nearly half in cell lines with an allelic loss of MLH1. While these cell lines were negative in conventional microsatellite instability assay, they showed a 10.5-fold increase in the rate of somatic indels, e.g., truncating indels in TP53 and TGFBR2, indicating MLH1 haploinsufficiency in the correction of DNA indel errors. We further analyzed the exomes of 15 renal cell carcinomas and confirmed MLH1 haploinsufficiency. We observed a much higher rate of indel mutations in the affected cases and identified recurrent truncating indels in several cancer genes such as VHL, PBRM1, and JARID1C. Together, our data suggest that MLH1 hemizygous deletion, through increasing the rate of indel mutations, could drive the development and progression of sporadic cancers.

SKI and MEL1 Cooperate to Inhibit Transforming Growth Factor-β Signal in Gastric Cancer Cells

Chromosomal amplification occurs frequently in solid tumors and is associated with poor prognosis. Several reports demonstrated the cooperative effects of oncogenic factors in the same amplicon during cancer development. However, the functional correlation between the factors remains unclear. Transforming growth factor (TGF)-β signaling plays important roles in cytostasis and normal epithelium differentiation, and alterations in TGF-β signaling have been identified in many malignancies. Here, we demonstrated that transcriptional co-repressors of TGF-β signaling, SKI and MDS1/EVI1-like gene 1 (MEL1), were aberrantly expressed in MKN28 gastric cancer cells by chromosomal co-amplification of 1p36.32. SKI and MEL1 knockdown synergistically restored TGF-β responsiveness in MKN28 cells and reduced tumor growth in vivo. MEL1 interacted with SKI and inhibited TGF-β signaling by stabilizing the inactive Smad3-SKI complex on the promoter of TGF-β target genes. These findings reveal a novel mechanism where distinct transcriptional co-repressors are co-amplified and functionally interact, and provide molecular targets for gastric cancer treatment.

Functional pathway characterized by gene expression analysis of supraclavicular lymph node metastasis-positive breast cancer

AbstractThe outcome of breast cancer patients with supraclavicular lymph node metastasis is generally poor, but some patients do survive for a long time. Consequently, the ability to predict the outcome is important in terms of choosing the appropriate therapy for breast cancer patients with supraclavicular lymph node metastasis. In this study, we attempted to identify functional pathways that determine the outcome of breast cancer patients with supraclavicular lymph node metastasis by profiling cDNA microarrays. Thirty-one breast cancer patients with supraclavicular lymph node metastasis without distant metastasis comprised the study cohort; these were divided into three groups based on prognosis - poor, intermediate, and good. Two functional pathways, the Wnt signaling pathway and the mitochondrial apoptosis pathway, were constructed using six genes (DVL1, VDAC2, BIRC5, Stathmin1, PARP1, and RAD21) that were differently expressed between the good and poor outcome groups. Our results indicate that these two functional pathways may play an important role in determining the outcome of breast cancer patients with supraclavicular lymph node metastasis. We also determined that immunohistostaining for the Stathmin1 gene product is a potential tool for predicting the outcome of breast cancer patients with supraclavicular lymph node metastasis.

Molecular prediction of the therapeutic response to neoadjuvant chemotherapy in breast cancer.

Breast cancer is considered to be relatively sensitive to chemotherapy, and multiple combinations of cytotoxic agents are used as standard therapy. Chemotherapy is applied empirically despite the observation that not all regimens are equally effective across the population of patients. Up to date clinical tests for predicting cancer chemotherapy response are not available, and individual markers have shown little predictive value. A number of microarray studies have demonstrated the use of genomic data, particularly gene expression signatures, as clinical prognostic factors in breast cancer. The identification of patient subpopulations most likely to respond to therapy is a central goal of recent personalized medicine. We have designed experiments to identify gene sets that will predict treatment-specific response in breast cancer. Taken together with our recent trial about the construction of a high-throughput functional screening system for chemo-sensitivity related genes, studies for drug sensitivity will provide rational strategies for establishment of the prediction system with high accuracy, and identification of ideal targets for drug intervention.

Prognostic value of matrix Gla protein in breast cancer

To assess the prognostic value of matrix Gla protein (MGP) expression in cases of breast cancer, 9 samples from patients diagnosed with breast cancer who were followed up for more than 10 years were microdissected and then analyzed using Affymetrix U133 Plus 2.0 Arrays. Genes that exhibited significant differences in expression between patients with a good prognosis and those with a poor prognosis were identified. The MGP gene was among the genes up-regulated in cases where the prognosis was poor, indicating that the mRNA levels of MGP are a potential prognostic indicator of breast cancer. However, immunohistostaining of breast tissue microarrays (n=207) did not reveal a correlation between the protein expression of MGP and overall survival, neither was there a correlation between the protein expression of MGP and ER status or bone metastasis. In breast cancer cases, the mRNA level of MGP may be a marker indicating poor prognosis; however, protein expression determined by immunohistostaining is not.

Function of EWS-POU5F1 in Sarcomagenesis and Tumor Cell Maintenance

POU5F1 is a transcription factor essential for the self-renewal activity and pluripotency of embryonic stem cells and germ cells. We have previously reported that POU5F1 is fused to EWSR1 in a case of undifferentiated sarcoma with chromosomal translocation t(6;22)(p21;q12). In addition, the EWS-POU5F1 chimeras have been recently identified in human neoplasms of the skin and salivary glands. To clarify the roles of the EWS-POU5F1 chimera in tumorigenesis and tumor cell maintenance, we used small-interfering RNA-mediated gene silencing. Knockdown of EWS-POU5F1 in the t(6;22) sarcoma-derived GBS6 cell line resulted in a significant decrease of cell proliferation because of G1 cell cycle arrest associated with p27(Kip1) up-regulation. Moreover, senescence-like morphological changes accompanied by actin polymerization were observed. In contrast, EWS-POU5F1 down-regulation markedly increased the cell migration and invasion as well as activation of metalloproteinase 2 and metalloproteinase 14. The results indicate that the proliferative activity of cancer cells and cell motility are discrete processes in multistep carcinogenesis. These findings reveal the functional role of the sarcoma-related chimeric protein as well as POU5F1 in the development and progression of human neoplasms.

Development of detection method for novel fusion gene using GeneChip exon array

BackgroundFusion genes have been recognized to play key roles in oncogenesis. Though, many techniques have been developed for genome-wide analysis of fusion genes, a more efficient method is desired.ResultsWe introduced a new method of detecting the novel fusion gene by using GeneChip Exon Array that enables exon expression analysis on a whole-genome scale and TAIL-PCR. To screen genes with abnormal exon expression profiles, we developed computational program, and confirmed that the program was able to search the fusion partner gene using Exon Array data of T-cell acute lymphocytic leukemia (T-ALL) cell lines. It was reported that the T-ALL cell lines, ALL-SIL, BE13 and LOUCY, harbored the fusion gene NUP214-ABL1, NUP214-ABL1 and SET-NUP214, respectively. The program extracted the candidate genes with abnormal exon expression profiles: 1 gene in ALL-SIL, 1 gene in BE13, and 2 genes in LOUCY. The known fusion partner gene NUP214 was included in the genes in ALL-SIL and LOUCY. Thus, we applied the proposed program to the detection of fusion partner genes in other tumors. To discover novel fusion genes, we examined 24 breast cancer cell lines and 20 pancreatic cancer cell lines by using the program. As a result, 20 and 23 candidate genes were obtained for the breast and pancreatic cancer cell lines respectively, and seven genes were selected as the final candidate gene based on information of the EST data base, comparison with normal cell samples and visual inspection of Exon expression profile. Finding of fusion partners for the final candidate genes was tried by TAIL-PCR, and three novel fusion genes were identified.ConclusionsThe usefulness of our detection method was confirmed. Using this method for more samples, it is thought that fusion genes can be identified.

Common peak approach using mass spectrometry data sets for predicting the effects of anticancer drugs on breast cancer.

Background Although a majority of studies in cancer biomarker discovery claim to use proportional hazards regression (PHREG) to the study the ability of a biomarker to predict survival, few studies use the predicted probabilities obtained from the model to test the quality of the model. In this paper, we compared the quality of predictions by a PHREG model to that of a linear discriminant analysis (LDA) in both training and test set settings. Methods The PHREG and LDA models were built on a 491 colorectal cancer (CRC) patient dataset comprised of demographic and clinicopathologic variables, and phenotypic expression of p53 and Bcl-2. Two variable selection methods, stepwise discriminant analysis and the backward selection, were used to identify the final models. The endpoint of prediction in these models was five-year post-surgery survival. We also used linear regression model to examine the effect of bin size in the training set on the accuracy of prediction in the test set. Results The two variable selection techniques resulted in different models when stage was included in the list of variables available for selection. However, the proportion of survivors and non-survivors correctly identified was identical in both of these models. When stage was excluded from the variable list, the error rate for the LDA model was 42% as compared to an error rate of 34% for the PHREG model. Conclusions This study suggests that a PHREG model can perform as well or better than a traditional classifier such as LDA to classify patients into prognostic classes. Also, this study suggests that in the absence of the tumor stage as a variable, Bcl-2 expression is a strong prognostic molecular marker of CRC.Integrative cancer biology research relies on a variety of data-driven computational modeling and simulation methods and techniques geared towards gaining new insights into the complexity of biological processes that are of critical importance for cancer research. These include the dynamics of gene-protein interaction networks, the percolation of sub-cellular perturbations across scales and the impact they may have on tumorigenesis in both experiments and clinics. Such innovative ‘systems’ research will greatly benefit from enabling Information Technology that is currently under development, including an online collaborative environment, a Semantic Web based computing platform that hosts data and model repositories as well as high-performance computing access. Here, we present one of the National Cancer Institute’s recently established Integrative Cancer Biology Programs, i.e. the Center for the Development of a Virtual Tumor, CViT, which is charged with building a cancer modeling community, developing the aforementioned enabling technologies and fostering multi-scale cancer modeling and simulation.The issue of wide feature-set variability has recently been raised in the context of expression-based classification using microarray data. This paper addresses this concern by demonstrating the natural manner in which many feature sets of a certain size chosen from a large collection of potential features can be so close to being optimal that they are statistically indistinguishable. Feature-set optimality is inherently related to sample size because it only arises on account of the tendency for diminished classifier accuracy as the number of features grows too large for satisfactory design from the sample data. The paper considers optimal feature sets in the framework of a model in which the features are grouped in such a way that intra-group correlation is substantial whereas inter-group correlation is minimal, the intent being to model the situation in which there are groups of highly correlated co-regulated genes and there is little correlation between the co-regulated groups. This is accomplished by using a block model for the covariance matrix that reflects these conditions. Focusing on linear discriminant analysis, we demonstrate how these assumptions can lead to very large numbers of close-to-optimal feature sets.The use of MALDI-TOF mass spectrometry as a means of analyzing the proteome has been evaluated extensively in recent years. One of the limitations of this technique that has impeded the development of robust data analysis algorithms is the variability in the location of protein ion signals along the x-axis. We studied technical variations of MALDI-TOF measurements in the context of proteomics profiling. By acquiring a benchmark data set with five replicates, we estimated 76% to 85% of the total variance is due to phase variation. We devised a lobster plot, so named because of the resemblance to a lobster claw, to help detect the phase variation in replicates. We also investigated a peak alignment algorithm to remove the phase variation. This operation is analogous to the normalization step in microarray data analysis. Only after this critical step can features of biological interest be clearly revealed. With the help of principal component analysis, we demonstrated that after peak alignment, the differences among replicates are reduced. We compared this approach to peak alignment with a model-based calibration approach in which there was known information about peaks in common among all spectra. Finally, we examined the potential value at each point in an analysis pipeline of having a set of methods available that includes parametric, semiparametric and nonparametric methods; among such methods are those that benefit from the use of prior information.Array comparative genomic hybridization (aCGH) is a high-throughput lab technique to measure genome-wide chromosomal copy numbers. Data from aCGH experiments require extensive pre-processing, which consists of three steps: normalization, segmentation and calling. Each of these pre-processing steps yields a different data set: normalized data, segmented data, and called data. Publications using aCGH base their findings on data from all stages of the pre-processing. Hence, there is no consensus on which should be used for further down-stream analysis. This consensus is however important for correct reporting of findings, and comparison of results from different studies. We discuss several issues that should be taken into account when deciding on which data are to be used. We express the believe that called data are best used, but would welcome opposing views.We propose a method for biomarker discovery from mass spectrometry data, improving the common peak approach developed by Fushiki et al. (BMC Bioinformatics, 7:358, 2006). The common peak method is a simple way to select the sensible peaks that are shared with many subjects among all detected peaks by combining a standard spectrum alignment and kernel density estimates. The key idea of our proposed method is to apply the common peak approach to each class label separately. Hence, the proposed method gains more informative peaks for predicting class labels, while minor peaks associated with specific subjects are deleted correctly. We used a SELDI-TOF MS data set from laser microdissected cancer tissues for predicting the treatment effects of neoadjuvant therapy using an anticancer drug on breast cancer patients. The AdaBoost algorithm is adopted for pattern recognition, based on the set of candidate peaks selected by the proposed method. The analysis gives good performance in the sense of test errors for classifying the class labels for a given feature vector of selected peak values.Motivation Our goal was to understand why the PLIER algorithm performs so well given its derivation is based on a biologically implausible assumption. Results In spite of a non-intuitive assumption regarding the PM and MM errors made as part of the derivation for PLIER, the resulting probe level error function does capture the key characteristics of the ideal error function, assuming MM probes only measure non-specific binding and no signal.In this paper we develop a Bayesian analysis to estimate the disease prevalence, the sensitivity and specificity of three cervical cancer screening tests (cervical cytology, visual inspection with acetic acid and Hybrid Capture II) in the presence of a covariate and in the absence of a gold standard. We use Metropolis-Hastings algorithm to obtain the posterior summaries of interest. The estimated prevalence of cervical lesions was 6.4% (a 95% credible interval [95% CI] was 3.9, 9.3). The sensitivity of cervical cytology (with a result of ≥ ASC-US) was 53.6% (95% CI: 42.1, 65.0) compared with 52.9% (95% CI: 43.5, 62.5) for visual inspection with acetic acid and 90.3% (95% CI: 76.2, 98.7) for Hybrid Capture II (with result of >1 relative light units). The specificity of cervical cytology was 97.0% (95% CI: 95.5, 98.4) and the specificities for visual inspection with acetic acid and Hybrid Capture II were 93.0% (95% CI: 91.0, 94.7) and 88.7% (95% CI: 85.9, 91.4), respectively. The Bayesian model with covariates suggests that the sensitivity and the specificity of the visual inspection with acetic acid tend to increase as the age of the women increases. The Bayesian method proposed here is an useful alternative to estimate measures of performance of diagnostic tests in the presence of covariates and when a gold standard is not available. An advantage of the method is the fact that the number of parameters to be estimated is not limited by the number of observations, as it happens with several frequentist approaches. However, it is important to point out that the Bayesian analysis requires informative priors in order for the parameters to be identifiable. The method can be easily extended for the analysis of other medical data sets.The biological interpretation of gene expression microarray results is a daunting challenge. For complex diseases such as cancer, wherein the body of published research is extensive, the incorporation of expert knowledge provides a useful analytical framework. We have previously developed the Exploratory Visual Analysis (EVA) software for exploring data analysis results in the context of annotation information about each gene, as well as biologically relevant groups of genes. We present EVA as a flexible combination of statistics and biological annotation that provides a straightforward visual interface for the interpretation of microarray analyses of gene expression in the most commonly occuring class of brain tumors, glioma. We demonstrate the utility of EVA for the biological interpretation of statistical results by analyzing publicly available gene expression profiles of two important glial tumors. The results of a statistical comparison between 21 malignant, high-grade glioblastoma multiforme (GBM) tumors and 19 indolent, low-grade pilocytic astrocytomas were analyzed using EVA. By using EVA to examine the results of a relatively simple statistical analysis, we were able to identify tumor class-specific gene expression patterns having both statistical and biological significance. Our interactive analysis highlighted the potential importance of genes involved in cell cycle progression, proliferation, signaling, adhesion, migration, motility, and structure, as well as candidate gene loci on a region of Chromosome 7 that has been implicated in glioma. Because EVA does not require statistical or computational expertise and has the flexibility to accommodate any type of statistical analysis, we anticipate EVA will prove a useful addition to the repertoire of computational methods used for microarray data analysis. EVA is available at no charge to academic users and can be found at http://www.epistasis.org.Consider a gene expression array study comparing two groups of subjects where the goal is to explore a large number of genes in order to select for further investigation a subset that appear to be differently expressed. There has been much statistical research into the development of formal methods for designating genes as differentially expressed. These procedures control error rates such as the false detection rate or family wise error rate. We contend however that other statistical considerations are also relevant to the task of gene selection. These include the extent of differential expression and the strength of evidence for differential expression at a gene. Using real and simulated data we first demonstrate that a proper exploratory analysis should evaluate these aspects as well as decision rules that control error rates. We propose a new measure called the mp-value that quantifies strength of evidence for differential expression. The mp-values are calculated with a resampling based algorithm taking into account the multiplicity and dependence encountered in microarray data. In contrast to traditional p-values our mp-values do not depend on specification of a decision rule for their definition. They are simply descriptive in nature. We contrast the mp-values with multiple testing p-values in the context of data from a breast cancer prognosis study and from a simulation model.Sound data analysis is critical to the success of modern molecular medicine research that involves collection and interpretation of mass-throughput data. The novel nature and high-dimensionality in such datasets pose a series of nontrivial data analysis problems. This technical commentary discusses the problems of over-fitting, error estimation, curse of dimensionality, causal versus predictive modeling, integration of heterogeneous types of data, and lack of standard protocols for data analysis. We attempt to shed light on the nature and causes of these problems and to outline viable methodological approaches to overcome them.The arrival of high-throughput technologies in cancer science and medicine has made the possibility for knowledge generation greater than ever before. However, this has brought with it real challenges as researchers struggle to analyse the avalanche of information available to them. A unique U.K.-based initiative has been established to promote data sharing in cancer science and medicine and to address the technical and cultural issues needed to support this.Searching PubMed for citations related to a specific cancer center or group of authors can be labor-intensive. We have created a tool, PubMed QUEST, to aid in the rapid searching of PubMed for publications of interest. It was designed by taking into account the needs of entire cancer centers as well as individual investigators. The experience of using the tool by our institution’s cancer center administration and investigators has been favorable and we believe it could easily be adapted to other institutions. Use of the tool has identified limitations of automated searches for publications based on an author’s name, especially for common names. These limitations could likely be solved if the PubMed database assigned a unique identifier to each author.In this paper, a model of signaling pathways involving G proteins is investigated. The model incorporates reaction-diffusion mechanisms in which various reactants participate inside and on the extra-cellular surface membrane. The messenger molecules may diffuse over the surface of the cell membrane and signal transduction across the cell membrane is mediated by membrane receptor bound proteins which connect the genetically controlled biochemical intra-cellular reactions to the production of the second messenger, leading to desired functional responses. Dynamic and steady-state properties of the model are then investigated through weakly nonlinear stability analysis. Turing-type patterns are shown to form robustly under different delineating conditions on the system parameters. The theoretical predictions are then discussed in the context of some recently reported experimental evidence.Introduction: As an alternative to DNA microarrays, mass spectrometry based analysis of proteomic patterns has shown great potential in cancer diagnosis. The ultimate application of this technique in clinical settings relies on the advancement of the technology itself and the maturity of the computational tools used to analyze the data. A number of computational algorithms constructed on different principles are available for the classification of disease status based on proteomic patterns. Nevertheless, few studies have addressed the difference in the performance of these approaches. In this report, we describe a comparative case study on the classification accuracy of hepatocellular carcinoma based on the serum proteomic pattern generated from a Surface Enhanced Laser Desorption/Ionization (SELDI) mass spectrometer. Methods: Nine supervised classification algorithms are implemented in R software and compared for the classification accuracy. Results: We found that the support vector machine with radial function is preferable as a tool for classification of hepatocellular carcinoma using features in SELDI mass spectra. Among the rest of the methods, random forest and prediction analysis of microarrays have better performance. A permutation-based technique reveals that the support vector machine with a radial function seems intrinsically superior in learning from the training data since it has a lower prediction error than others when there is essentially no differential signal. On the other hand, the performance of the random forest and prediction analysis of microarrays rely on their capability of capturing the signals with substantial differentiation between groups. Conclusions: Our finding is similar to a previous study, where classification methods based on the Matrix Assisted Laser Desorption/Ionization (MALDI) mass spectrometry are compared for the prediction accuracy of ovarian cancer. The support vector machine, random forest and prediction analysis of microarrays provide better prediction accuracy for hepatocellular carcinoma using SELDI proteomic data than six other approaches.Summary In our previous study [1], we have compared the performance of a number of widely used discrimination methods for classifying ovarian cancer using Matrix Assisted Laser Desorption Ionization (MALDI) mass spectrometry data on serum samples obtained from Reflectron mode. Our results demonstrate good performance with a random forest classifier. In this follow-up study, to improve the molecular classification power of the MALDI platform for ovarian cancer disease, we expanded the mass range of the MS data by adding data acquired in Linear mode and evaluated the resultant decrease in classification error. A general statistical framework is proposed to obtain unbiased classification error estimates and to analyze the effects of sample size and number of selected m/z features on classification errors. We also emphasize the importance of combining biological knowledge and statistical analysis to obtain both biologically and statistically sound results. Our study shows improvement in classification accuracy upon expanding the mass range of the analysis. In order to obtain the best classification accuracies possible, we found that a relatively large training sample size is needed to obviate the sample variations. For the ovarian MS dataset that is the focus of the current study, our results show that approximately 20–40 m/z features are needed to achieve the best classification accuracy from MALDI-MS analysis of sera. Supplementary information can be found at http://bioinformatics.med.yale.edu/proteomics/BioSupp2.html.In vitro experimentation provides a convenient controlled environment for testing biological hypotheses of functional genomics in cancer induction and progression. However, it is necessary to validate resulting gene signatures from these in vitro experiments in human tumor samples (i.e. in vivo). We discuss the several methods for integrating data from these two sources paying particular attention to formulating statistical tests and corresponding null hypotheses. We propose a classification null hypothesis that can be simply modeled via permutation testing. A classification method is proposed based upon the Tissue Similarity Index of Sandberg and Ernberg (PNAS, 2005) that uses the classification null hypothesis. This method is demonstrated using the in vitro signature of Core Serum Response developed by Chang et al. (PLoS Biology, 2004).Multiple studies have reported that surface enhanced laser desorption/ionization time of flight mass spectroscopy (SELDI-TOF-MS) is useful in the early detection of disease based on the analysis of bodily fluids. Use of any multiplex mass spectroscopy based approach as in the analysis of bodily fluids to detect disease must be analyzed with great care due to the susceptibility of multiplex and mass spectroscopy methods to biases introduced via experimental design, patient samples, and/or methodology. Specific biases include those related to experimental design, patients, samples, protein chips, chip reader and spectral analysis. Contributions to biases based on patients include demographics (e.g., age, race, ethnicity, sex), homeostasis (e.g., fasting, medications, stress, time of sampling), and site of analysis (hospital, clinic, other). Biases in samples include conditions of sampling (type of sample container, time of processing, time to storage), conditions of storage, (time and temperature of storage), and prior sample manipulation (freeze thaw cycles). Also, there are many potential biases in methodology which can be avoided by careful experimental design including ensuring that cases and controls are analyzed randomly. All the above forms of biases affect any system based on analyzing multiple analytes and especially all mass spectroscopy based methods, not just SELDI-TOF-MS. Also, all current mass spectroscopy systems have relatively low sensitivity compared with immunoassays (e.g., ELISA). There are several problems which may be unique to the SELDI-TOF-MS system marketed by Ciphergen®. Of these, the most important is a relatively low resolution (±0.2%) of the bundled mass spectrometer which may cause problems with analysis of data. Foremost, this low resolution results in difficulties in determining what constitutes a “peak” if a peak matching approach is used in analysis. Also, once peaks are selected, the peaks may represent multiple proteins. In addition, because peaks may vary slightly in location due to instrumental drift, long term identification of the same peaks may prove to be a challenge. Finally, the Ciphergen® system has some “noise” of the baseline which results from the accumulation of charge in the detector system. Thus, we must be very aware of the factors that may affect the use of proteomics in the early detection of disease, in determining aggressive subsets of cancers, in risk assessment and in monitoring the effectiveness of novel therapies.Summary: A key challenge in clinical proteomics of cancer is the identification of biomarkers that could allow detection, diagnosis and prognosis of the diseases. Recent advances in mass spectrometry and proteomic instrumentations offer unique chance to rapidly identify these markers. These advances pose considerable challenges, similar to those created by microarray-based investigation, for the discovery of pattern of markers from high-dimensional data, specific to each pathologic state (e.g. normal vs cancer). We propose a three-step strategy to select important markers from high-dimensional mass spectrometry data using surface enhanced laser desorption/ionization (SELDI) technology. The first two steps are the selection of the most discriminating biomarkers with a construction of different classifiers. Finally, we compare and validate their performance and robustness using different supervised classification methods such as Support Vector Machine, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Neural Networks, Classification Trees and Boosting Trees. We show that the proposed method is suitable for analysing high-throughput proteomics data and that the combination of logistic regression and Linear Discriminant Analysis outperform other methods tested.Proteins involved in the regulation of the cell cycle are highly conserved across all eukaryotes, and so a relatively simple eukaryote such as yeast can provide insight into a variety of cell cycle perturbations including those that occur in human cancer. To date, the budding yeast Saccharomyces cerevisiae has provided the largest amount of experimental and modeling data on the progression of the cell cycle, making it a logical choice for in-depth studies of this process. Moreover, the advent of methods for collection of high-throughput genome, transcriptome, and proteome data has provided a means to collect and precisely quantify simultaneous cell cycle gene transcript and protein levels, permitting modeling of the cell cycle on the systems level. With the appropriate mathematical framework and sufficient and accurate data on cell cycle components, it should be possible to create a model of the cell cycle that not only effectively describes its operation, but can also predict responses to perturbations such as variation in protein levels and responses to external stimuli including targeted inhibition by drugs. In this review, we summarize existing data on the yeast cell cycle, proteomics technologies for quantifying cell cycle proteins, and the mathematical frameworks that can integrate this data into representative and effective models. Systems level modeling of the cell cycle will require the integration of high-quality data with the appropriate mathematical framework, which can currently be attained through the combination of dynamic modeling based on proteomics data and using yeast as a model organism.Proteomic patterns derived from mass spectrometry have recently been put forth as potential biomarkers for the early diagnosis of cancer. This approach has generated much excitement, particularly as initial results reported on SELDI profiling of serum suggested that near perfect sensitivity and specificity could be achieved in diagnosing ovarian cancer. However, more recent reports have suggested that much of the observed structure could be due to the presence of experimental bias. A rebuttal to the findings of bias, subtitled “Producers and Consumers”, lists several objections. In this paper, we attempt to address these objections. While we continue to find evidence of experimental bias, we emphasize that the problems found are associated with experimental design and processing, and can be avoided in future studies.Microarray technologies have been an increasingly important tool in cancer research in the last decade, and a number of initiatives have sought to stress the importance of the provision and sharing of raw microarray data. Illumina BeadArrays provide a particular problem in this regard, as their random construction simultaneously adds value to analysis of the raw data and obstructs the sharing of those data. We present a compression scheme for raw Illumina BeadArray data, designed to ease the burdens of sharing and storing such data, that is implemented in the BeadDataPackR BioConductor package (http://bioconductor.org/packages/release/bioc/html/BeadDataPackR.html). It offers two key advantages over off-the-peg compression tools. First it uses knowledge of the data formats to achieve greater compression than other approaches, and second it does not need to be decompressed for analysis, but rather the values held within can be directly accessed.An important issue in current medical science research is to find the genes that are strongly related to an inherited disease. A particular focus is placed on cancer-gene relations, since some types of cancers are inherited. As biomedical databases have grown speedily in recent years, an informatics approach to predict such relations from currently available databases should be developed. Our objective is to find implicit associated cancer-genes from biomedical databases including the literature database. Co-occurrence of biological entities has been shown to be a popular and efficient technique in biomedical text mining. We have applied a new probabilistic model, called mixture aspect model (MAM) [48], to combine different types of co-occurrences of genes and cancer derived from Medline and OMIM (Online Mendelian Inheritance in Man). We trained the probability parameters of MAM using a learning method based on an EM (Expectation and Maximization) algorithm. We examined the performance of MAM by predicting associated cancer gene pairs. Through cross-validation, prediction accuracy was shown to be improved by adding gene-gene co-occurrences from Medline to cancer-gene cooccurrences in OMIM. Further experiments showed that MAM found new cancer-gene relations which are unknown in the literature. Supplementary information can be found at http://www.bic.kyotou.ac.jp/pathway/zhusf/CancerInformatics/Supplemental2006.htmlConstructing pathways of tumor progression and discovering the biomarkers associated with cancer is critical for understanding the molecular basis of the disease and for the establishment of novel chemotherapeutic approaches and in turn improving the clinical efficiency of the drugs. It has recently received a lot of attention from bioinformatics researchers. However, relatively few methods are available for constructing pathways. This article develops a novel entropy kernel based kernel clustering and fuzzy kernel clustering algorithms to construct the tumor progression pathways using CpG island methylation data. The methylation data which come from tumor tissues diagnosed at different stages can be used to distinguish epigenotype and phenotypes the describe the molecular events of different phases. Using kernel and fuzzy kernel kmeans, we built tumor progression trees to describe the pathways of tumor progression and find the possible biomarkers associated with cancer. Our results indicate that the proposed algorithms together with methylation profiles can predict the tumor progression stages and discover the biomarkers efficiently. Software is available upon request.Whole genome microarray investigations (e.g. differential expression, differential methylation, ChIP-Chip) provide opportunities to test millions of features in a genome. Traditional multiple comparison procedures such as familywise error rate (FWER) controlling procedures are too conservative. Although false discovery rate (FDR) procedures have been suggested as having greater power, the control itself is not exact and depends on the proportion of true null hypotheses. Because this proportion is unknown, it has to be accurately (small bias, small variance) estimated, preferably using a simple calculation that can be made accessible to the general scientific community. We propose an easy-to-implement method and make the R code available, for estimating the proportion of true null hypotheses. This estimate has relatively small bias and small variance as demonstrated by (simulated and real data) comparing it with four existing procedures. Although presented here in the context of microarrays, this estimate is applicable for many multiple comparison situations.Summary: Clinical covariates such as age, gender, tumor grade, and smoking history have been extensively used in prediction of disease occurrence and progression. On the other hand, genomic biomarkers selected from microarray measurements may provide an alternative, satisfactory way of disease prediction. Recent studies show that better prediction can be achieved by using both clinical and genomic biomarkers. However, due to different characteristics of clinical and genomic measurements, combining those covariates in disease prediction is very challenging. We propose a new regularization method, Covariate-Adjusted Threshold Gradient Directed Regularization (Cov-TGDR), for combining different type of covariates in disease prediction. The proposed approach is capable of simultaneous biomarker selection and predictive model building. It allows different degrees of regularization for different type of covariates. We consider biomedical studies with binary outcomes and right censored survival outcomes as examples. Logistic model and Cox model are assumed, respectively. Analysis of the Breast Cancer data and the Follicular lymphoma data show that the proposed approach can have better prediction performance than using clinical or genomic covariates alone.In this review, we take a survey of bioinformatics databases and quantitative structure-activity relationship studies reported in published literature. Databases from the most general to special cancer-related ones have been included. Most commonly used methods of structure-based analysis of molecules have been reviewed, along with some case studies where they have been used in cancer research. This article is expected to be of use for general bioinformatics researchers interested in cancer and will also provide an update to those who have been actively pursuing this field of research.Dedication by Dr James Lyons-Weiler, University of Pittsburgh Cancer Institute, Pittsburgh, PA, USA.We are experiencing a time of great growth in knowledge about human disease. However, translation of the knowledge into clinical practice has not kept pace. Clinical trials are an important part of the drug development process. The cost of conducting clinical trials has become greater because: 1) regulations on how the trial must be conducted have become more complex; 2) proposed therapies must be compared against standard therapies; and 3) if the end point is survival—it may take longer to reach that end-point as therapies and non-specific supportive measures become more effective. Moreover, therapies administered prior to or subsequent to the experimental intervention may confound the interpretation of survival as an endpoint. Finding valid alternative outcome measures that can be observed soon after the therapy is given could reduce the cost of drug trials, and make effective therapies available to the public more quickly. Imaging can assess therapeutic efficacy for cancers and may be a part of the solution to reduce costs and improve timeliness of clinical trials. (Fig 1). n n n nFigure 1 n nNumber of submissions of new molecular entities (NMEs) and biologics license application (BLA) to FDA over the past 10 years. (U.S. Department of Health and Human Services-Food and Drug Administration 2004) n n n n nThe Challenges of Clinical Trials nProblem 1: Clinical trials are too expensive nClinical trials are an essential part of the process of documenting the effectiveness of a new therapy. While laboratory experiments attempt to simulate the human situation, validating efficacy and safety in the population of interest remains a necessary step. But the cost of performing a clinical trial large enough to document a treatment effect and monitor for side effects is usually quite expensive. The FDA estimates that the cost to develop a new drug can be as high as

Prospective randomized phase II study determines the clinical usefulness of genetic biomarkers for sensitivity to primary chemotherapy with paclitaxel in breast cancer.

1.7 billion (Fig 2), with others estimating that the median cost at ‘only’