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Kevin R Coombes

Publications and source records attributed to Kevin R Coombes.

At least 19 recordsLinked to original sources
PubMed2024

Extracellular Vesicle- MDM2 -DNA as a Potential Liquid Biopsy Biomarker for Disease Identification in Retroperitoneal Liposarcoma.

OBJECTIVE: We aimed to assess the levels of MDM2 -DNA within extracellular vesicles (EVs) isolated from the serum of retroperitoneal liposarcoma (RLS) patients versus healthy donors, as well as within the same patients at the time of surgery versus postoperative surveillance visits. To determine whether EV- MDM2 may serve as a possible first-ever biomarker of liposarcoma recurrence. BACKGROUND: A hallmark of well-differentiated and dedifferentiated (WD/DD) retroperitoneal liposarcoma is elevated MDM2 due to genome amplification, with recurrence rates of >50% even after complete resection. Imaging technologies frequently cannot resolve recurrent WD/DD-RLS versus postoperative scarring. Early detection of recurrent lesions, for which biomarkers are lacking, would guide surveillance and treatment decisions. METHODS: WD/DD-RLS serum samples were collected both at the time of surgery and during follow-up visits from 42 patients, along with sera from healthy donors (n=14). EVs were isolated, DNA purified, and MDM2 -DNA levels determined through q-PCR analysis. Nonparametric tests were employed to compare EV- MDM2 DNA levels from patients versus the control group, as well as the time of surgery versus postsurgery conditions. RESULTS: EV -MDM2 levels were significantly higher in WD/DD-RLS than controls ( P =0.00085). Moreover, EV- MDM2 levels were remarkably decreased in WD/DD-RLS patients after resection ( P =0.00036), reaching values comparable to control group ( P =0.124). During postoperative surveillance, significant increases of EV- MDM2 were observed in some patients, correlating with computed tomography scan evidence of recurrent or persistent postresection disease. CONCLUSIONS: Serum EV- MDM2 may serve as a potential biomarker of early recurrent or postoperatively persistent WD/DD-RLS, a disease currently lacking such determinants.

Humans↗
PubMed2007

Gene expression profile of metastatic human pancreatic cancer cells depends on the organ microenvironment.

To determine the influence of the microenvironment on changes in gene expression, we did microarray analysis on three variant lines of a human pancreatic cancer (FG, L3.3, and L3.6pl) with different metastatic potentials. The variant lines were grown in tissue culture in the subcutis (ectopic) or pancreas (orthotopic) of nude mice. Compared with tissue culture, the number of genes of which the expression was affected by the microenvironment was up-regulated in tumors growing in the subcutis and pancreas. In addition, highly metastatic L3.6pl cells growing in the pancreas expressed significantly higher levels of 226 genes than did the L3.3 or FG variant cells. Growth of the variant lines in the subcutis did not yield similar results, indicating that the orthotopic microenvironment significantly influences gene expression in pancreatic cancer cells. These data suggest that investigations of the functional consequence of gene expression require accounting for experimental growth conditions.

Animals↗
PubMed2006

PrepMS: TOF MS data graphical preprocessing tool.

UNLABELLED: We introduce a simple-to-use graphical tool that enables researchers to easily prepare time-of-flight mass spectrometry data for analysis. For ease of use, the graphical executable provides default parameter settings, experimentally determined to work well in most situations. These values, if desired, can be changed by the user. PrepMS is a stand-alone application made freely available (open source), and is under the General Public License (GPL). Its graphical user interface, default parameter settings, and display plots allow PrepMS to be used effectively for data preprocessing, peak detection and visual data quality assessment. AVAILABILITY: Stand-alone executable files and Matlab toolbox are available for download at: http://sourceforge.net/projects/prepms

Algorithms↗
PubMed2006

Enrichment analysis in high-throughput genomics - accounting for dependency in the NULL.

Translating the overwhelming amount of data generated in high-throughput genomics experiments into biologically meaningful evidence, which may for example point to a series of biomarkers or hint at a relevant pathway, is a matter of great interest in bioinformatics these days. Genes showing similar experimental profiles, it is hypothesized, share biological mechanisms that if understood could provide clues to the molecular processes leading to pathological events. It is the topic of further study to learn if or how a priori information about the known genes may serve to explain coexpression. One popular method of knowledge discovery in high-throughput genomics experiments, enrichment analysis (EA), seeks to infer if an interesting collection of genes is 'enriched' for a Consortium particular set of a priori Gene Ontology Consortium (GO) classes. For the purposes of statistical testing, the conventional methods offered in EA software implicitly assume independence between the GO classes. Genes may be annotated for more than one biological classification, and therefore the resulting test statistics of enrichment between GO classes can be highly dependent if the overlapping gene sets are relatively large. There is a need to formally determine if conventional EA results are robust to the independence assumption. We derive the exact null distribution for testing enrichment of GO classes by relaxing the independence assumption using well-known statistical theory. In applications with publicly available data sets, our test results are similar to the conventional approach which assumes independence. We argue that the independence assumption is not detrimental.

Data Interpretation, Statistical↗
PubMed2006

Identification of cell cycle regulatory genes as principal targets of p53-mediated transcriptional repression.

Historically, most studies attribute p53 function to the transactivation of target genes. That p53 can selectively repress genes to affect a cellular response is less widely appreciated. Available evidence suggests that repression is important for p53-induced apoptosis and cell cycle arrest. To better establish the scope of p53-repressed target genes and the cellular processes they may affect, a global expression profiling strategy was used to identify p53-responsive genes following adenoviral p53 gene transfer (Ad-p53) in PC3 prostate cancer cells. A total of 111 genes, 0.77% of the 14,500 genes represented on the Affymetrix U133A microarray, were repressed more than 2-fold (p < or = 0.05). Validation of the array data, using reverse transcription-PCR of 20 randomly selected genes, yielded a confirmation rate of >95.5% for the complete data set. Functional over-representation analysis revealed that cell cycle regulatory genes exhibited a highly significant enrichment (p < or = 5 x 10(-28)) within the transrepressed targets. 41% of the repressed targets are cell cycle regulators. A subset of these genes exhibited repression following DNA damage, preceding cell cycle arrest, in LNCaP cells. The use of a p53 small interfering RNA strategy in LNCaP cells and the use of p53-null cell lines demonstrated that this repression is p53-dependent. These findings identify a set of genes not known previously to be down-regulated by p53 and indicate that p53-induced cell cycle arrest is a function of not only the transactivation of cell cycle inhibitors (e.g. p21) but also the repression of targets that regulate proliferation at several distinct phases of the cell cycle.

Adenoviridae↗
PubMed2006

Tissue-specific function of lymph node fibroblastic reticulum cells.

OBJECTIVE: We present the first characterization of the cytokine expression pattern of lymph node fibroblastic reticulum cells (FRC), which are the stromal cells responsible for maintaining the highly structured nodal reticular fiber framework. METHODS: Microarray expression profiles of cultured nodal FRC and dermal fibroblasts (DF) were compared as well as their response to TNF, IL-4, IL-6 and IL-13, cytokines responsible for intranodal stromal activation. RESULTS: Hierarchical clustering of FRC and DF short-term culture samples revealed genes that were differentially expressed in FRC and DF. Identified differently regulated genes were confirmed by RNase protection analysis, PCR or immunohistochemistry. At earlier culture time points, FRC showed higher levels of several chemokines, including CCL2/MCP-1, and cytokines, e.g. IL-6, whereas several genes related to the production of extracellular matrix and angiogenesis were preferentially expressed in early DF cultures. By 60 days in culture, FRC and DF showed similar expression patterns consistent with homogenization of specialized stromal subsets. FRC and DF showed nearly identical transcriptional responses to exogenous TNF stimulation. CONCLUSIONS: Cultured FRC showed an overall transcriptional profile similar to cultured DF, including parallel responsiveness to TNF, but with differences in the expression of chemotactic chemokines, which reflect their biological roles.

Cells, Cultured↗
PubMed2005

Gene sequence signatures revealed by mining the UniGene affiliation network.

BACKGROUND: In the post-genomic era, developing tools to decode biological information from genomic sequences is important. Inspired by affiliation network theory, we investigated gene sequences of two kinds of UniGene clusters (UCs): narrowly expressed transcripts (NETs), whose expression is confined to a few tissues; and prevalently expressed transcripts (PETs) that are expressed in many tissues. RESULTS: We explored the human and the mouse UniGene databases to compare NETs and PETs from different perspectives. We found that NETs were associated with smaller cluster size, shorter sequence length, a lower likelihood of having LocusLink annotations, and lower and more sporadic levels of expression. Significantly, the dinucleotide frequencies of NETs are similar to those of intergenic sequences in the genome, and they differ from those of PETs. We used these differences in dinucleotide frequencies to develop a discriminant analysis model to distinguish PETs from intergenic sequences. CONCLUSIONS: Our results show that most NETs resemble intergenic sequences, casting doubts on the quality of such UniGene clusters. However, we also noted that a fraction of NETs resemble PETs in terms of dinucleotide frequencies and other features. Such NETs may have fewer quality problems. This work may be helpful in the studies of non-coding RNAs and in the validation of gene sequence databases.

Animals↗
PubMed2005

Analysis of dose-response effects on gene expression data with comparison of two microarray platforms.

MOTIVATION: The problems of analyzing dose effects on gene expression are gaining attention in biomedical research. A specific challenge is to detect genes with expression levels that change according to dose levels in a non-random manner, but nonetheless may be considered as potential biomarkers. METHOD: We are among the first to formally apply a tool that uses an isotonic (monotonic) regression approach to this area of study. We introduce a test statistic to select genes with significant dose-response expression in a monotonic fashion based on a permutation procedure. We then compare the results with those achieved from the application of a likelihood ratio-based test. RESULTS: We apply the isotonic regression approach to a study of gene expression in the RKO colon carcinoma cell line in response to varying dosage levels of the chemotherapeutic agent 5-fluorouracil. A feature of both Affymetrix and printed 75mer oligomer cDNA arrays produced from the same samples provides an opportunity to compare the two microarray platforms. AVAILABILITY: Statistical software S-plus Code to implement the method is available from the authors. CONTACT: kcoombes@mdanderson.org

Algorithms↗
PubMed2005

Signal in noise: evaluating reported reproducibility of serum proteomic tests for ovarian cancer.

Proteomic profiling of serum initially appeared to be dramatically effective for diagnosis of early-stage ovarian cancer, but these results have proven difficult to reproduce. A recent publication reported good classification in one dataset using results from training on a much earlier dataset, but the authors have since reported that they did not perform the analysis as described. We examined the reproducibility of the proteomic patterns across datasets in more detail. Our analysis reveals that the pattern that enabled successful classification is biologically implausible and that the method, properly applied, does not classify the data accurately. We show that the method used in previously published studies does not establish reproducibility and performs no better than chance for classifying the second dataset, in part because the second dataset is easy to classify correctly. We conclude that the reproducibility of the proteomic profiling approach has yet to be established.

Female↗
PubMed2005

Applications of beta-mixture models in bioinformatics.

SUMMARY: We propose a beta-mixture model approach to solve a variety of problems related to correlations of gene-expression levels. For example, in meta-analyses of microarray gene-expression datasets, a threshold value of correlation coefficients for gene-expression levels is used to decide whether gene-expression levels are strongly correlated across studies. Ad hoc threshold values such as 0.5 are often used. In this paper, we use a beta-mixture model approach to divide the correlation coefficients into several populations so that the large correlation coefficients can be identified. Another important application of the proposed method is in finding co-expressed genes. Two examples are provided to illustrate both applications. Through our analysis, we also discover that the popular model selection criteria BIC and AIC are not suitable for the beta-mixture model. To determine the number of components in the mixture model, we suggest an alternative criterion, ICL-BIC, which is shown to perform better in selecting the correct mixture model. SUPPLEMENTARY INFORMATION: http://odin.mdacc.tmc.edu/~yuanj/highcorgeneanno.html.

Animals↗
PubMed2005

Plasma protein profiling for diagnosis of pancreatic cancer reveals the presence of host response proteins.

Plasma protein profiling using separations coupled to matrix-assisted laser desorption ionization mass spectrometry (MALDI MS) has great potential in translational research; it can be used for biomarker discovery and contribute to disease diagnosis and therapy. Previously reported biomarker searches have been done solely by MS protein profiling followed by bioinformatics analysis of the data. To add to current methods, we tested an alternative strategy for plasma protein profiling using pancreatic cancer as the model. First, offline solid-phase extraction is done with 96-well plates to fractionate and partially purify the proteins. Then, multiple profiling and identification experiments can be conducted on the same protein fractions because only 5% of the fractions are used for MALDI MS profiling. After MALDI MS analysis, the mass spectra are normalized and subjected to a peak detection algorithm. Over three sets of mass spectra acquired using different instrument variables, approximately 400 unique ion signals were detected. Classification schemes employing as many as eight individual peaks were developed using a training set with 123 members (82 cancer patients) and a blinded validation set with 125 members (57 cancer patients). The sensitivity of the study was 88%, but the specificity was significantly lower, 75%. The reason for the low specificity becomes apparent upon protein identification of the ion signals used for the classification. The identifications reveal only common serum proteins and components of the acute phase response, including serum amyloid A, alpha-1-antitrypsin, alpha-1-antichymotrypsin, and inter-alpha-trypsin inhibitor.

Acute-Phase Proteins↗
PubMed2005

Feature extraction and quantification for mass spectrometry in biomedical applications using the mean spectrum.

MOTIVATION: Mass spectrometry yields complex functional data for which the features of scientific interest are peaks. A common two-step approach to analyzing these data involves first extracting and quantifying the peaks, then analyzing the resulting matrix of peak quantifications. Feature extraction and quantification involves a number of interrelated steps. It is important to perform these steps well, since subsequent analyses condition on these determinations. Also, it is difficult to compare the performance of competing methods for analyzing mass spectrometry data since the true expression levels of the proteins in the population are generally not known. RESULTS: In this paper, we introduce a new method for feature extraction in mass spectrometry data that uses translation-invariant wavelet transforms and performs peak detection using the mean spectrum. We examine the method's performance through examples and simulation, and demonstrate the advantages of using the mean spectrum to detect peaks. We also describe a new physics-based computer model of mass spectrometry and demonstrate how one may design simulation studies based on this tool to systematically compare competing methods. AVAILABILITY: MATLAB scripts to implement the methods described in this paper and R code for the virtual mass spectrometer are available at http://bioinformatics.mdanderson.org/software.html SUPPLEMENTARY INFORMATION: http://bioinformatics.mdanderson.org/supplements.html.

Algorithms↗
PubMed2005

Improved peak detection and quantification of mass spectrometry data acquired from surface-enhanced laser desorption and ionization by denoising spectra with the undecimated discrete wavelet transform.

Mass spectrometry is being used to find disease-related patterns in mixtures of proteins derived from biological fluids. Questions have been raised about the reproducibility and reliability of peak quantifications using this technology. We collected nipple aspirate fluid from breast cancer patients and healthy women, pooled them into a quality control sample, and produced 24 replicate SELDI spectra. We developed a novel algorithm to process the spectra, denoising with the undecimated discrete wavelet transform (UDWT), and evaluated it for consistency and reproducibility. UDWT efficiently decomposes spectra into noise and signal. The noise is consistent and uncorrelated. Baseline correction produces isolated peak clusters separated by flat regions. Our method reproducibly detects more peaks than the method implemented in Ciphergen software. After normalization and log transformation, the mean coefficient of variation of peak heights is 10.6%. Our method to process spectra provides improvements over existing methods. Denoising using the UDWT appears to be an important step toward obtaining results that are more accurate. It improves the reproducibility of quantifications and supplies tools for investigation of the variations in the technology more carefully. Further study will be required, because we do not have a gold standard providing an objective assessment of which peaks are present in the samples.

Algorithms↗
PubMed2005

Significant differences in nipple aspirate fluid protein expression between healthy women and those with breast cancer demonstrated by time-of-flight mass spectrometry.

New approaches are needed for the early detection of breast cancer. Proteomic profiling technologies, such as surface-enhanced laser desorption ionization mass spectrometry (SELDI-MS), may be able to identify tumor markers in biological fluids. The objective of this study was to determine whether there are differences in protein expression patterns in nipple aspirate fluid (NAF) from the cancerous and noncancerous breasts of patients with unilateral breast cancer and the breasts of healthy volunteers. Paired NAF samples were obtained from 23 women with stage I or II unilateral invasive breast carcinoma and five healthy female volunteers. Aliquots of the samples were applied to SELDI Protein-chip arrays (WCX2 and IMAC3-Cu++), and protein expression was analyzed using time-of-flight MS. A total of 463 distinct peaks were detected and analyzed. In breast cancer patients, no differences in protein expression were identified between the breast with the intact primary carcinoma and the contralateral noncancerous breast. Seventeen peaks were overexpressed in cancer-bearing breasts compared to breasts of healthy volunteers (p < 0.0005). When spectra from the nontumor-bearing breasts of breast cancer patients were compared with spectra from breasts of healthy volunteers, two peaks that were overexpressed in breast cancer patients and one peak that was underexpressed in breast cancer patients were detected (p < 0.0027). SELDI-MS was able to identify differences in the phenotypic proteomic profile of NAF samples obtained from patients with early-stage breast cancer and healthy women. Proteomic screening techniques such as SELDI-MS analysis of NAF may be useful for breast cancer screening and diagnosis.

Biopsy, Needle↗
PubMed2005

Biological validation of differentially expressed genes in chronic lymphocytic leukemia identified by applying multiple statistical methods to oligonucleotide microarrays.

Oligonucleotide microarrays are a powerful tool for profiling the expression levels of thousands of genes. Different statistical methods for identifying differentially expressed genes can yield different results. To our knowledge, no experimental test has been performed to decide which method best identifies genes that are truly differentially expressed. We applied three statistical methods (dChip, t-test on log-transformed data, and Wilcoxon test) to identify differentially expressed genes in previously untreated patients with chronic lymphocytic leukemia (CLL). We used a training set of Affymetrix Hu133A microarray data from 11 patients with unmutated immunoglobulin (Ig) heavy chain variable region (VH) genes and 8 patients with mutated Ig VH genes. Differential expression was validated using semiquantitative real-time polymerase chain reaction assays and by validating models to predict the somatic mutation status of an independent test set of nine CLL samples. The methods identified 144 genes that were differentially expressed between cases of CLL with unmutated compared with mutated Ig VH genes. Eighty genes were identified by Wilcoxon test, 60 by t-test, and 65 by dChip, but only 11 were identified by all three methods. Greater agreement was found between the t-test and the Wilcoxon test. Differential expression was validated by semiquantitative real-time polymerase chain reaction assays for 83% of individual genes, regardless of the statistical method. However, the Wilcoxon test gave the most accurate predictions on new samples, and dChip, the least accurate. We found that all three methods were equally good for finding differentially expressed genes, but they found different genes. The genes selected by the nonparametric Wilcoxon test are the most robust for predicting the status of new cases. A comprehensive list of all differentially expressed genes can only be obtained by combining the results of multiple statistical tests.

Algorithms↗
PubMed2005

The importance of experimental design in proteomic mass spectrometry experiments: some cautionary tales.

Proteomic expression patterns derived from mass spectrometry have been put forward as potential biomarkers for the early diagnosis of cancer and other diseases. This approach has generated much excitement and has led to a large number of new experiments and vast amounts of new data. The data, derived at great expense, can have very little value if careful attention is not paid to the experimental design and analysis. Using examples from surface-enhanced laser desorption/ionisation time-of-flight (SELDI-TOF) and matrix-assisted laser desorption-ionisation/time-of-flight (MALDI-TOF) experiments, we describe several experimental design issues that can corrupt a dataset. Fortunately, the problems we identify can be avoided if attention is paid to potential sources of bias before the experiment is run. With an appropriate experimental design, proteomics technology can be a useful tool for discovering important information relating protein expression to disease.

Biomarkers, Tumor↗
PubMed2005

Stem cell-ness: a "magic marker" for cancer.

Transcriptional profiling of patient tumors is a much-heralded advancement in cancer therapy, as it provides the opportunity to identify patients who would benefit from more or less aggressive therapy and thus allows the development of individualized treatment. However, translation of this promise into patient benefit has proven challenging. In this issue of the JCI, Glinsky and colleagues used human and murine models to identify a potential stem cell mRNA signature, based on the hypothesis that tumors with stem cell-like characteristics are likely to have a poor prognosis. Remarkably, an 11-gene "expression signature" associated with "stem cell-ness" separated patients with different cancers into good- and poor-prognosis groups. Such a "magic marker" would, if validated, have a major impact on patient care. However, there remain challenges incumbent with creating and validating such signatures.

Animals↗