Ulf Kressner

Colorectal cancer with and without microsatellite instability involves different genes

There is evidence supporting a multistep genetic model for colorectal tumorigenesis. In familial adenomatosis polyposis (FAP), the inherited defect is a mutation in the APC gene. The vast majority of all sporadic colorectal cancers also show mutations in the APC gene, and the tumorigenesis in sporadic colorectal cancer and FAP is assumed to involve the same genes. Hereditary nonpolyposis colorectal cancer (HNPCC) is associated with germline mutations in DNA mismatch repair genes and, as a result of defective mismatch repair, microsatellite instability (MSI) is frequently seen. Tumorigenesis in HNPCC was first thought to involve mutations in the same genes as in FAP and sporadic colorectal cancer. Recently, however, an alternative pathway to development of colorectal cancer has been suggested in colorectal tumors with MSI, compared to those tumors without the MSI phenotype. We used a consecutive series of 191 sporadic colorectal cancers to find out if there were any differences between the two groups of tumors regarding the prevalence of mutations in the APC, KRAS, TP53, and TGFβR2 genes. As expected, 86% (19/22) of MSI‐positive tumors showed a mutation in TGFβR2, while only one of 164 (0.6%) MSI‐negative tumors did. A highly statistically significant negative association was found between MSI and alterations in APC and TP53. The MSI‐positive tumors were screened for mutations in exon 3 of β‐catenin, which has been suggested to substitute for the APC mutation in the genesis of colorectal cancer, without finding mutations in any of the 22 MSI‐positive tumors. The number of mutations found in KRAS was lower in MSI‐positive than in MSI‐negative tumors but the difference was not statistically significant. Our results strongly support the idea that carcinogenesis in MSI‐positive and MSI‐negative colorectal cancer develops through different pathways. Genes Chromosomes Cancer 26:247–252, 1999.

Microsatellite instability in sporadic colorectal cancer is not an independent prognostic factor

SummaryHereditary non-polyposis colorectal cancer (HNPCC) is linked to an inherited defect in the DNA mismatch repair system. DNA from HNPCC tumours shows microsatellite instability (MSI). It has been reported that HNPCC patients have a better prognosis than patients with sporadic colorectal cancer. We examined whether the presence of MSI in a series of unselected colorectal tumours carries prognostic information. In a series of 181 unselected colorectal tumours, 22 tumours (12%) showed MSI. Survival analysis at 5–10 years follow-up showed no statistically significant difference in prognosis between MSI-positive and -negative tumours. Our results suggest that the MSI phenotype as such is not an independent prognostic factor.

Prognostic value of p53 genetic changes in colorectal cancer.

PURPOSE To explore whether there is a linkage between different mutations in the p53 gene in primary colorectal cancer and the risk of death from colorectal cancer in a large group of patients with long follow-up. We also compared a complementary DNA-based sequencing method and an immunohistochemical (IHC) method for detecting p53 protein overexpression in colorectal cancer. MATERIALS AND METHODS The entire coding region of the p53 gene was sequenced in 191 frozen tumor samples collected from January 1988 to November 1992. RNA was extracted and synthesized to cDNA. p53 was amplified by the polymerase chain reaction, and the DO-7 monoclonal antibody was used in the IHC assessments. RESULTS Mutations were detected in 99 samples (52%) from 189 patients. There was a significant relationship between the p53 mutational status and the cancer-specific survival time, with shorter survival time for patients who had p53 mutations than for those who did not (P = .01, log-rank test). Mutations outside the evolutionarily conserved regions were associated with the worst prognosis. Multivariate analysis showed that the presence of p53 mutations was an independent prognostic factor (relative hazard, 1.7, P = .03). There was no significant relationship between overexpression of p53 protein, as determined by IHC analysis, and cancer-specific survival. CONCLUSION Mutational analyses of the p53 gene, using cDNA sequencing in colorectal cancer, provide useful prognostic information. In addition, cDNA sequencing gives better prognostic information than IHC assessment of p53 protein overexpression.

Septic complications and prognosis after surgery for rectal cancer.

AbstractPURPOSE: The influence of septic complications on long-term prognosis after surgery for rectal cancer is controversial. This study was performed to investigate whether an abdominal or perineal septic complication was associated with rectal cancer recurrence. METHODS: A total of 228 patients who had undergone curative resection for rectal cancer from 1973 to 1992 were reviewed. The patients were divided into groups of those who developed either an intra-abdominal abscess or a perineal infection after surgery (infection group) and those who did not (noninfection group). RESULTS: There was no clear difference in the overall incidence of tumor recurrence between the infection group (19/53, 36 percent) and the noninfection group (46/175, 26 percent; P = 0.25). However, the incidence of local recurrence was higher in the infection group (12/53, 23 percent) than in the noninfection group (16/175, 9 percent; P = 0.02). This increased risk was restricted to patients with a perineal infection (10/30, 33 percent; P = 0.003 vs. the noninfection group), whereas patients with an abdominal infection (3/24, 13 percent) did not differ from the noninfection group. CONCLUSION: Patients with a perineal infection after an abdominoperineal resection have an increased incidence of local recurrence. However, there was no association between abdominal sepsis and prognosis after surgery for rectal cancer.

Mutation analyses of KRAS exon 1 comparing three different techniques : Temporal temperature gradient electrophoresis, constant denaturant capillary electrophoresis and allele specific polymerase chain reaction

Mutations in the KRAS gene is a key event in the carcinogenesis of many human cancers and may serve as a diagnostic marker and a target for therapeutic intervention. In this study we have applied three different techniques for mutation detection of KRAS exon 1 mutations: Allele specific polymerase chain reaction (AS-PCR), temporal temperature gradient electrophoresis (TTGE) and constant denaturant capillary electrophoresis (CDCE). Samples from 191 sporadic colon carcinomas were analyzed. AS-PCR were performed with oligonucleotides specific for know mutations in codon 12 and 13 of the KRAS gene. In TTGE analyses, linear ramping of the temperature were performed during electrophoresis in a constant denaturant gel. CDCE analyses were performed using fluorescin labeled PCR-products. Separation was achieved under constant denaturing conditions using high temperature in a gel-filled capillary followed by laser detection. A mutated KRAS gene was found in 42/191 (22.0%) of the samples using AS-PCR, in 62/191 (32.5%) using TTGE and in 66/191 (34.6%) of the samples using CDCE. In the TTGE and CDCE analyses the sequence of the mutant were determined by comparing the electrophoretic pattern to that of known mutations or by mixing the sample with known mutations prior to reanalysis. In a titration experiment mixing mutant and wild-type alleles prior to PCR, the sensitivity for mutation detection was shown to be 10(-2) for TTGE and under optimized conditions 10(-3) for CDCE.

EP1-4 subtype, COX and PPARγ receptor expression in colorectal cancer in prediction of disease-specific mortality

The importance of prostaglandins in tumor growth and progression is well recognized, including antineoplastic activities by cyclooxygenase (COX) inhibitors. Variation in treatment response to COX inhibition has questioned differences in expression of cell surface and nuclear membrane receptors among tumors with different disease progression. The purpose of this study was to evaluate whether EP1–4 subtype, PPARγ receptor and COX‐1/COX‐2 expression in colorectal cancer are related to tumor‐specific mortality. Reverse transcription–polymerase chain reaction and immunohistochemistry were used to demonstrate expression and protein appearance in tumor tissue compared with normal colon tissue. EP1 and EP2 subtype receptor protein was highly present in tumor cells, EP3 occurred occasionally and EP4 was not visible. PPARγ, EP2 and EP4 mRNA were significantly higher in normal colon tissue compared with tumor tissue, without any distinct relationship to Dukes A–D tumor stage. Multivariate analyses indicated that increased tumor tissue EP2 and COX‐2 expression predicted poor survival (p < 0.001). COX‐1 expression was significantly higher than COX‐2 expression in normal colon tissue. Average COX‐2 mRNA was not increased in tumor tissue compared with normal colon. However, most tumor cells stained positive for COX‐2 protein, which was low or undetectable in normal mucosa cells. COX‐1 protein was preferentially visible in stroma. EP1–4 subtype receptor mRNAs were generally positively correlated to both COX‐1 and COX‐2 in tumor tissue, but not in normal colon. Our results imply that both prostaglandin production (COX‐2) and signaling via EP1–4 subtype receptors, particularly EP2, predict disease‐specific mortality in colorectal cancer

Prostanoid receptor expression in colorectal cancer related to tumor stage, differentiation and progression

Introduction: Alterations in eicosanoid metabolism is well established in a variety of malignant tumors, particularly colorectal carcinoma. Recent studies in our laboratory have emphasized a role for EP subtype receptors in progression of colorectal cancer and disease specific mortality. Therefore, the aim of the present study was to extend our knowledge to include additional receptor expression (DP1, DP2, FP, IP, TP) for prostanoids (PGD2, TXA2, PGF2α, PGI2) in relationship to tumor stage, differentiation and progression of colorectal cancer. Material and methods: Total RNA from 62 tumors and adjacent normal colon tissue (n = 48) was extracted. Quantification of receptor expression was performed by realtime PCR and related to the expression of an appropriate housekeeping gene (GAPDH). Tumors were assessed according to Dukes A-D (stage I-IV). Results: DP1, DP2, FP and IP receptor subtypes displayed significantly reduced overall expression in tumor tissue compared to normal colon tissue, while the TP receptor subtype showed significantly higher expression in tumor tissue. Overall expression of the prostanoid receptors in tumor tissue was not related to clinical indexes as tumor stage and tumor cell differentiation evaluated by multivariate analyses. Cultured colorectal cancer cell lines with low (HT-29) and high (HCA-7) intrinsic PGE2 production at confluent state did not express DP1 and IP receptor subtypes, but displayed low expression of DP2, FP and TP receptor subtypes. Conclusion: The results in the present study indicate imbalanced expression of prostanoid receptors in colorectal cancer compared to normal colon tissue without clear cut relationship to disease progression. Therefore, future studies should be performed on defined cells within the tumor tissue compartment determining whether any prostanoid receptor(s) is useful as a molecular target in treatment or prevention of colorectal cancer.

Definition of candidate low risk APC alleles in a Swedish population

Many families experience an apparently inherited increased risk of colorectal cancer (CRC) similar to the known syndromes familial adenomatous polyposis (FAP) and hereditary nonpolyposis colorectal cancer (HNPCC). Besides these high‐risk syndromes, approximately 10% of all CRC cases come from families with 2 affected 1st‐degree relatives, and even 1st‐degree relatives to a single case of CRC are at increased risk. Risk subjects from these families frequently show polyps at colonoscopy, which suggests the APC gene as a good candidate susceptibility gene for these attenuated polypotic syndromes. We used the sensitive DHPLC technique to search for possible predisposing germline mutations in the entire APC gene in 91 risk subjects from these high‐ and low‐risk syndromes with unknown predisposing genes. Most exons were also screened for mutations in 96 normal controls and 96 colorectal cancer cases. In our study we probably have identified the most common APC variants in a Swedish population. Among 30 germline variants identified, 1 clearly pathogenic nonsense mutation and 11 putative pathogenic variants (10 missense and one 3′ UTR) were found in 20 index patients (22%). Twelve silent as well as 5 intronic variants were considered nonpathogenic. Two of the missense variants found here, E1317Q and D1822V, have previously been related to a difference in risk of colorectal cancer. One variant, 8636C>A, located within the 3′ UTR region of the APC gene, was suggested to constitute an additional low risk allele with a similar relative risk as the Jewish I1307K mutation (OR = 1.8; 95% CI, 0.96–3.40). The question of whether all the other variants confer an increased colorectal cancer risk warrants future large association studies.

Tumor Genome Wide DNA Alterations Assessed by Array CGH in Patients with Poor and Excellent Survival Following Operation for Colorectal 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). Figure 1 Number 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) The Challenges of Clinical Trials Problem 1: Clinical trials are too expensive Clinical 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

Immunohistochemically Detected Thymidylate Synthase in Colorectal Cancer: An Independent Prognostic Factor of Survival

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