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PubMed2006

Markers improve clustering of CGH data.

MOTIVATION: We consider the problem of clustering a population of Comparative Genomic Hybridization (CGH) data samples using similarity based clustering methods. A key requirement for clustering is to avoid using the noisy aberrations in the CGH samples. RESULTS: We develop a dynamic programming algorithm to identify a small set of important genomic intervals called markers. The advantage of using these markers is that the potentially noisy genomic intervals are excluded during the clustering process. We also develop two clustering strategies using these markers. The first one, prototype-based approach, maximizes the support for the markers. The second one, similarity-based approach, develops a new similarity measure called RSim and refines clusters with the aim of maximizing the RSim measure between the samples in the same cluster. Our results demonstrate that the markers we found represent the aberration patterns of cancer types well and they improve the quality of clustering significantly. AVAILABILITY: All software developed in this paper and all the datasets used are available from the authors upon request.

Biomarkers, Tumor↗
PubMed2006

Pilot study to evaluate MYCN expression as a neuroblastoma cell marker to detect minimal residual disease by RT-PCR.

This pilot study was performed to determine whether MYCN expression warrants further investigation as a tumor marker to detect low levels of residual neuroblastoma (NB). Seven NB cell lines and 30 bone marrow (BM) samples from patients with high-risk NB were analyzed by reverse transcriptase-polymerase chain reaction (RT-PCR) for MYCN expression, and for the established NB marker tyrosine hydroxylase. MYCN was expressed in all 7 NB cell lines, but not in normal peripheral blood, CD34 cells, or BM. In dilution studies using cell lines with or without DNA amplification of MYCN, 1 NB cell in 10 to 10 nucleated blood cells was detectable by RT-PCR. MYCN was identified in all 21 BM samples in which tumor cells were identified by histologic examination, including 4 samples in which tyrosine hydroxylase was not detected. Additionally, expression of both markers was detected in 5 samples that were negative by histology but presumably contained low levels of tumor cells, consistent with the greater sensitivity of RT-PCR compared with morphologic methods. Detection of MYCN RNA was independent of MYCN DNA amplification status. The selective expression of MYCN in tumor cells, and the sensitivity of detection of MYCN by RT-PCR noted in this and other studies, supports further evaluation of MYCN as a NB marker for molecular detection of minimal residual disease.

Biomarkers, Tumor↗
PubMedDate not supplied

Dimension reduction-based penalized logistic regression for cancer classification using microarray data.

The use of penalized logistic regression for cancer classification using microarray expression data is presented. Two dimension reduction methods are respectively combined with the penalized logistic regression so that both the classification accuracy and computational speed are enhanced. Two other machine-learning methods, support vector machines and least-squares regression, have been chosen for comparison. It is shown that our methods have achieved at least equal or better results. They also have the advantage that the output probability can be explicitly given and the regression coefficients are easier to interpret. Several other aspects, such as the selection of penalty parameters and components, pertinent to the application of our methods for cancer classification are also discussed.

Algorithms↗
PubMed2006

TMABoost: an integrated system for comprehensive management of tissue microarray data.

In the last decade, high-throughput technologies such as DNA and tissue microarrays (TMAs) have become a means of large-scale investigation of gene expression, providing a plethora of new biomedical data in a relatively short time. Data collection and organization are critical aspects in this process to ensure the quality and reliability of future data interpretation. In this work, we propose a comprehensive approach to handle TMA data with the aim of supporting and promoting biomarker development. We describe a web-based system for the complete management of tissue microarray data in the field of pathology. The system has been in use since June, 2003. Our approach includes automatic localization and identification of tissue microarray samples, and quantitative image analysis that allows high-throughput screening of TMAs by ensuring nonsubjective measures and novel prognosis associations. In this paper, we present the architecture and the components of this system.

Algorithms↗
PubMed2006

An efficient semi-unsupervised gene selection method via spectral biclustering.

Gene selection is an important issue in microarray data processing. In this paper, we propose an efficient method for selecting relevant genes. First, we use spectral biclustering to obtain the best two eigenvectors for class partition. Then gene combinations are selected based on the similarity between the genes and the best eigenvectors. We demonstrate our semi-unsupervised gene selection method using two microarray cancer data sets, i.e., the lymphoma and the liver cancer data sets, where our method is able to identify a single gene or a two-gene combinations which can lead to predictions with very high accuracy.

Artificial Intelligence↗
PubMedDate not supplied

Estrogen receptor, progesterone receptor, and bcl-2 are markers with prognostic significance in CIN III.

There are no known biological markers or technologies to predict the natural history of an individual CIN III. The probability of progression is considered greater with the persistence of high-risk human papillomavirus (HPV) infection and age. p53 polymorphism has been associated with cervical carcinogenesis. Hormone-induced cervical cancer is mediated by estrogen receptor (ER) and progesterone receptor (PR). In cervical cancer, increased bcl-2 and Bax immunoreactivity is generally associated with a better prognosis. The purpose of this study was to evaluate the value of HPV 16 and HPV 18 typing and p53 codon polymorphism genotyping by polymerase chain reaction and ER, PR, bcl-2, and Bax expression by immunohistochemistry in predicting the CIN III clinical behavior of CIN III lesions. We studied the expression of these prognostic factors in the CIN III adjacent to squamous cell microinvasive carcinomas of the cervix (MIC) from 29 patients with FIGO stage IA1 cervical cancer and in 25 patients with CIN III and no documented focus of invasion. In the MIC group, only the CIN III was considered at least 2 mm away from the microinvasive complex. The ER, PR, bcl-2, and Bax immunoreactivity was scored as positive (>10% staining cells) and negative (<10% staining cells). No significant difference was observed between MIC and CIN III group concerning HPV infection and p53 polymorphism. The ER, PR, bcl-2, and Bax immunohistochemical expression was stronger and more frequent in the CIN III group. After multivariable analysis, coexpression of ER, PR, and bcl-2 was the only independent factor in defining low risk of progression for CIN III. Our study suggests that coexpression of ER, PR, and bcl-2 may be a useful tool in identifying the CIN III lesions with low risk of progression to cervical cancer.

Adult↗
PubMed2005

Clustering gene expression data with kernel principal components.

Kernel principal component analysis (KPCA) has been applied to data clustering and graphic cut in the last couple of years. This paper discusses the application of KPCA to microarray data clustering. A new algorithm based on KPCA and fuzzy C-means is proposed. Experiments with microarray data show that the proposed algorithms is in general superior to traditional algorithms.

Algorithms↗
PubMed2002

Novel approaches to identification of biomarkers for detection of early stage cancer.

Implementation of routine cancer screening accompanied by treatment of early stage disease is paramount to realizing reductions in cancer mortality rates. While morphologic assessment of relevant cells has traditionally been used to identify individuals with cancer, this approach is not feasible to identify cancers at inaccessible sites, such as the ovary or pancreas. For these and many other cancers, cancer mortality remains high, as sensitive and specific screening assays to detect in-situ or early stage disease are as yet unavailable. Identification of novel markers for early identification of cancer is an established priority of the National Cancer Institute. The rapid expansion of genomic-based technologies developed over the last decade, and the development of sensitive and specific blood-based assays for the detection of molecular changes associated with cancer, begin to offer the means for achieving this goal. Below, we review current approaches used in the effort to identify biomarkers appropriate for early cancer detection.

Antibodies, Neoplasm↗
PubMed2005

Decreased alpha-methylacyl CoA racemase expression in localized prostate cancer is associated with an increased rate of biochemical recurrence and cancer-specific death.

Alpha-methylacyl CoA racemase (AMACR) is overexpressed in prostate cancer relative to benign prostatic tissue. AMACR expression is highest in localized prostate cancer and decreases in metastatic prostate cancer. Herein, we explored the use of AMACR as a biomarker for aggressive prostate cancer. AMACR protein expression was determined by immunohistochemistry using an image analysis system on two localized prostate cancer cohorts consisting of 204 men treated by radical prostatectomy and 188 men followed expectantly. The end points for the cohorts were time to prostate-specific antigen (PSA) failure (i.e., elevation >0.2 ng/mL) and time to prostate cancer death in the watchful waiting cohort. Using a regression tree method, optimal AMACR protein expression cut-points were determined to best differentiate prostate cancer outcome in each of the cohorts separately. Cox proportional hazard models were then employed to examine the effect of the AMACR cut-point on prostate cancer outcome, and adjusted for clinical variables. Lower AMACR tissue expression was associated with worse prostate cancer outcome, independent of clinical variables (hazard ratio, 3.7 for PSA failure; P = 0.018; hazard ratio, 4.1 for prostate cancer death, P = 0.0006). Among those with both low AMACR expression and high Gleason score, the risk of prostate cancer death was 18-fold higher (P = 0.006). The AMACR cut-point developed using prostate cancer-specific death as the end point predicted PSA failures independent of Gleason score, PSA, and margin status. This is the first study to show that AMACR expression is significantly associated with prostate cancer progression and suggests that not all surrogate end points may be optimal to define biomarkers of aggressive prostate cancer.

Biomarkers, Tumor↗
PubMed2005

DNA microarrays.

Until recently, diagnostic and prognostic assessment of diseased tissues in a Pathology laboratory relied on histological and immunohistological studies. DNA microarray technology now allows the simultaneous analysis of up to thousands of different genes in histological or cytological specimens. Thus, the microarray techniques offer opportunities for the pathologist to obtain 'molecular signatures' of the state of activity of diseased cells in patient tissue samples, providing new information, such as the biological staging of tumors, a risk assessment of pre-malignant lesions, resistance to, and side effects of, treatment, molecular mechanisms in neurodegeneration and inflammation, and detection of micro-organisms. However, despite the great promise of this revolutionary technology, there are several issues that may undermine the power of the DNA microarray approach. After a short summary of the different forms of microarray and the contribution of this technology to understanding human disease, this short review focuses on the potential pitfalls and the important issues that must be considered when using a DNA microarray. Although this new approach shows great potential, the successful application of gene arrays to diagnostic and prognostic problems requires thoughtful interpretation and a strong correlation with other data, such as clinical, histopathological and immunohistochemical findings.

Animals↗
PubMed2024

Inflammatory and tissue injury marker dynamics in pediatric acute respiratory distress syndrome.

BACKGROUNDThe molecular signature of pediatric acute respiratory distress syndrome (ARDS) is poorly described, and the degree to which hyperinflammation or specific tissue injury contributes to outcomes is unknown. Therefore, we profiled inflammation and tissue injury dynamics over the first 7 days of ARDS, and associated specific biomarkers with mortality, persistent ARDS, and persistent multiple organ dysfunction syndrome (MODS).METHODSIn a single-center prospective cohort of intubated pediatric patients with ARDS, we collected plasma on days 0, 3, and 7. Nineteen biomarkers reflecting inflammation, tissue injury, and damage-associated molecular patterns (DAMPs) were measured. We assessed the relationship between biomarkers and trajectories with mortality, persistent ARDS, or persistent MODS using multivariable mixed effect models.RESULTSIn 279 patients (64 [23%] nonsurvivors), hyperinflammatory cytokines, tissue injury markers, and DAMPs were higher in nonsurvivors. Survivors and nonsurvivors showed different biomarker trajectories. IL-1&#x3b1;, soluble tumor necrosis factor receptor 1, angiopoietin 2 (ANG2), and surfactant protein D increased in nonsurvivors, while DAMPs remained persistently elevated. ANG2 and procollagen type III N-terminal peptide were associated with persistent ARDS, whereas multiple cytokines, tissue injury markers, and DAMPs were associated with persistent MODS. Corticosteroid use did not impact the association of biomarker levels or trajectory with mortality.CONCLUSIONSPediatric ARDS survivors and nonsurvivors had distinct biomarker trajectories, with cytokines, endothelial and alveolar epithelial injury, and DAMPs elevated in nonsurvivors. Mortality markers overlapped with markers associated with persistent MODS, rather than persistent ARDS.FUNDINGNIH (K23HL-136688, R01-HL148054).

Humans↗
PubMed2006

Screening of single-chain variable fragments against TSP50 from a phage display antibody library and their expression as soluble proteins.

A novel gene, testes-specific protease 50 (TSP50), is abnormally activated and differentially expressed in most patients with breast cancer, suggesting it as a novel biomarker for this disease. The possibility that TSP50 may be an oncogene is presently under investigation. In this study, the single-chain variable fragments (scFvs) against TSP50 were panned from a phage display antibody library using TSP50-specific peptide, pep-50, as a target antigen. After 4 rounds of panning, 3 clones (A1, A11, and C8) from the library were verified to show strong binding affinities for TSP50 by enzyme-linked immunosorbent assay (ELISA) and to contain the variable region genes of the light and heavy chains of scFv antibodies but different complementary determining regions by sequencing. The genes of scFv-A1 and scFv-A11 were cloned into expression vector pPELB and successfully expressed as a soluble protein inEscherichia coli Rosetta. The yields of expressions were about 4.0 to 5.0 mg of protein from 1 L of culture. The expressed proteins were purified by a 2-step procedure consisting of ion-exchange chromatography, followed by immobilized metal affinity chromatography. The purified proteins were shown a single band at the position of 31 KDa on sodium dodecyl sulfate-polyacrylamide gel electrophoresis. Sandwich ELISA demonstrated that the expressed scFv proteins were able to specifically react with pep-50, laying a foundation for the investigation of the function of TSP50 in the development and treatment of breast cancer.

Animals↗
PubMed2006

Improved variance estimation of classification performance via reduction of bias caused by small sample size.

BACKGROUND: Supervised learning for classification of cancer employs a set of design examples to learn how to discriminate between tumors. In practice it is crucial to confirm that the classifier is robust with good generalization performance to new examples, or at least that it performs better than random guessing. A suggested alternative is to obtain a confidence interval of the error rate using repeated design and test sets selected from available examples. However, it is known that even in the ideal situation of repeated designs and tests with completely novel samples in each cycle, a small test set size leads to a large bias in the estimate of the true variance between design sets. Therefore different methods for small sample performance estimation such as a recently proposed procedure called Repeated Random Sampling (RSS) is also expected to result in heavily biased estimates, which in turn translates into biased confidence intervals. Here we explore such biases and develop a refined algorithm called Repeated Independent Design and Test (RIDT). RESULTS: Our simulations reveal that repeated designs and tests based on resampling in a fixed bag of samples yield a biased variance estimate. We also demonstrate that it is possible to obtain an improved variance estimate by means of a procedure that explicitly models how this bias depends on the number of samples used for testing. For the special case of repeated designs and tests using new samples for each design and test, we present an exact analytical expression for how the expected value of the bias decreases with the size of the test set. CONCLUSION: We show that via modeling and subsequent reduction of the small sample bias, it is possible to obtain an improved estimate of the variance of classifier performance between design sets. However, the uncertainty of the variance estimate is large in the simulations performed indicating that the method in its present form cannot be directly applied to small data sets.

Analysis of Variance↗
PubMed2006

Empirical study of supervised gene screening.

BACKGROUND: Microarray studies provide a way of linking variations of phenotypes with their genetic causations. Constructing predictive models using high dimensional microarray measurements usually consists of three steps: (1) unsupervised gene screening; (2) supervised gene screening; and (3) statistical model building. Supervised gene screening based on marginal gene ranking is commonly used to reduce the number of genes in the model building. Various simple statistics, such as t-statistic or signal to noise ratio, have been used to rank genes in the supervised screening. Despite of its extensive usage, statistical study of supervised gene screening remains scarce. Our study is partly motivated by the differences in gene discovery results caused by using different supervised gene screening methods. RESULTS: We investigate concordance and reproducibility of supervised gene screening based on eight commonly used marginal statistics. Concordance is assessed by the relative fractions of overlaps between top ranked genes screened using different marginal statistics. We propose a Bootstrap Reproducibility Index, which measures reproducibility of individual genes under the supervised screening. Empirical studies are based on four public microarray data. We consider the cases where the top 20%, 40% and 60% genes are screened. CONCLUSION: From a gene discovery point of view, the effect of supervised gene screening based on different marginal statistics cannot be ignored. Empirical studies show that (1) genes passed different supervised screenings may be considerably different; (2) concordance may vary, depending on the underlying data structure and percentage of selected genes; (3) evaluated with the Bootstrap Reproducibility Index, genes passed supervised screenings are only moderately reproducible; and (4) concordance cannot be improved by supervised screening based on reproducibility.

Artificial Intelligence↗
PubMed2006

A minimally invasive multiple marker approach allows highly efficient detection of meningioma tumors.

BACKGROUND: The development of effective frameworks that permit an accurate diagnosis of tumors, especially in their early stages, remains a grand challenge in the field of bioinformatics. Our approach uses statistical learning techniques applied to multiple antigen tumor antigen markers utilizing the immune system as a very sensitive marker of molecular pathological processes. For validation purposes we choose the intracranial meningioma tumors as model system since they occur very frequently, are mostly benign, and are genetically stable. RESULTS: A total of 183 blood samples from 93 meningioma patients (WHO stages I-III) and 90 healthy controls were screened for seroreactivity with a set of 57 meningioma-associated antigens. We tested several established statistical learning methods on the resulting reactivity patterns using 10-fold cross validation. The best performance was achieved by Naïve Bayes Classifiers. With this classification method, our framework, called Minimally Invasive Multiple Marker (MIMM) approach, yielded a specificity of 96.2%, a sensitivity of 84.5%, and an accuracy of 90.3%, the respective area under the ROC curve was 0.957. Detailed analysis revealed that prediction performs particularly well on low-grade (WHO I) tumors, consistent with our goal of early stage tumor detection. For these tumors the best classification result with a specificity of 97.5%, a sensitivity of 91.3%, an accuracy of 95.6%, and an area under the ROC curve of 0.971 was achieved using a set of 12 antigen markers only. This antigen set was detected by a subset selection method based on Mutual Information. Remarkably, our study proves that the inclusion of non-specific antigens, detected not only in tumor but also in normal sera, increases the performance significantly, since non-specific antigens contribute additional diagnostic information. CONCLUSION: Our approach offers the possibility to screen members of risk groups as a matter of routine such that tumors hopefully can be diagnosed immediately after their genesis. The early detection will finally result in a higher cure- and lower morbidity-rate.

Algorithms↗
PubMed2006

Recursive gene selection based on maximum margin criterion: a comparison with SVM-RFE.

BACKGROUND: In class prediction problems using microarray data, gene selection is essential to improve the prediction accuracy and to identify potential marker genes for a disease. Among numerous existing methods for gene selection, support vector machine-based recursive feature elimination (SVM-RFE) has become one of the leading methods and is being widely used. The SVM-based approach performs gene selection using the weight vector of the hyperplane constructed by the samples on the margin. However, the performance can be easily affected by noise and outliers, when it is applied to noisy, small sample size microarray data. RESULTS: In this paper, we propose a recursive gene selection method using the discriminant vector of the maximum margin criterion (MMC), which is a variant of classical linear discriminant analysis (LDA). To overcome the computational drawback of classical LDA and the problem of high dimensionality, we present efficient and stable algorithms for MMC-based RFE (MMC-RFE). The MMC-RFE algorithms naturally extend to multi-class cases. The performance of MMC-RFE was extensively compared with that of SVM-RFE using nine cancer microarray datasets, including four multi-class datasets. CONCLUSION: Our extensive comparison has demonstrated that for binary-class datasets MMC-RFE tends to show intermediate performance between hard-margin SVM-RFE and SVM-RFE with a properly chosen soft-margin parameter. Notably, MMC-RFE achieves significantly better performance with a smaller number of genes than SVM-RFE for multi-class datasets. The results suggest that MMC-RFE is less sensitive to noise and outliers due to the use of average margin, and thus may be useful for biomarker discovery from noisy data.

Algorithms↗
PubMed2006

Cytokeratin 8/18 expression indicates a poor prognosis in squamous cell carcinomas of the oral cavity.

BACKGROUND: Intermediary filaments are involved in cell motility and cancer progression. In a variety of organs, the expression of distinct intermediary filaments are associated with patient prognosis. In this study, we seeked to define the prognostic potential of cytokeratin and vimentin expression patterns in squamous cell carcinomas (SCC's) of the oral cavity. METHODS: 308 patients with histologically proven and surgically treated squamous cell carcinomas of the oral cavity were investigated for the immunohistochemical expression of a variety of intermediary filaments including high- and low-molecular weight cytokeratins (Ck's), such as Ck 5/6, Ck 8/18, Ck 1, CK 10, Ck 14, Ck 19 and vimentin, using the tissue microarray technique. Correlations between clinical features and the expression of Cytokeratins and vimentin were evaluated statistically by Kaplan-Meier curves and multivariate Cox regression analysis. RESULTS: The expression of Ck 8/18 and Ck 19 were overall significantly correlated with a poor clinical prognosis (Ck 8/18 p = 0.04; Ck19 p < 0.01). These findings could also be reproduced for Ck 8/18 in primary nodal-negative SCC's and held true in multivariate-analysis. No significant correlation with patient prognosis could be found for the expression of the other cytokeratins and for vimentin. CONCLUSION: The expression of Ck 8/18 in SCC's of the oral cavity is an independent prognostic marker and indicates a decreased overall and progression free survival. These results provide an extended knowledge about the role of intermediary filament expression patterns in SCC's.

Adult↗
PubMed2006

Combined immunohistochemistry of beta-catenin, cytokeratin 7, and cytokeratin 20 is useful in discriminating primary lung adenocarcinomas from metastatic colorectal cancer.

BACKGROUND: It is important to discriminate between primary and secondary lung cancer. However, often, the discriminating diagnosis of primary lung acinar adenocarcinoma and lung metastasis of colorectal cancer based on morphological and pathological findings is difficult. The purpose of this study was to evaluate the clinical usefulness of immunohistochemistry of beta-catenin, cytokeratin (CK) 7, and CK20 for the discriminating diagnosis of lung cancer. METHODS: We performed immunohistochemistry of beta-catenin, CK7, and CK20 in 19 lung metastasis of colorectal cancer samples, 10 corresponding primary colorectal cancer samples and 11 primary lung acinar adenocarcinoma samples and compared the levels of accuracy of the discriminating diagnosis by using antibodies against these antigens. RESULTS: Positive staining of beta-catenin was observed in all the lung metastasis of colorectal cancer samples as well as in the primary colorectal cancer samples but in none of the primary lung acinar adenocarcinoma samples. Positive staining of CK7 was observed in 90.9% of the primary lung acinar adenocarcinoma samples and in 5.3% of the lung metastasis of colorectal cancer samples, but in none of the primary colorectal cancer samples. Positive staining of CK20 was observed in all the primary colorectal cancer samples and in 84.2% of the lung metastasis of colorectal cancer samples, but in none of the primary lung acinar adenocarcinoma samples. CONCLUSION: Combined immunohistochemistry of beta-catenin, CK7, and CK20 is useful for making a discriminating diagnosis between lung metastasis of colorectal cancer and primary lung acinar adenocarcinoma. This method will enable accurate diagnosis of a lung tumor and will be useful for selecting appropriate therapeutic strategies, including chemotherapeutic agents and operation methods.

Adenocarcinoma↗