Analysis of mass spectrometry profiles of the serum proteome.
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Humans↗
Biomedical subjects
Kevin R Coombes
Publications and source records attributed to Kevin R Coombes.
Validation of oligonucleotide microarray data using microfluidic low-density arrays: a new statistical method to normalize real-time RT-PCR data.
Profiling studies using microarrays to measure messenger RNA (mRNA) expression frequently identify long lists of differentially expressed genes. Differential expression is often validated using real-time reverse transcription PCR (RT-PCR) assays. In conventional real-time RT-PCR assays, expression is normalized to a control, or housekeeping gene. However, no single housekeeping gene can be used for all studies. We used TaqMan Low-Density Arrays, a medium-throughput method for real-time RT-PCR using microfluidics to simultaneously assay the expression of 96 genes in nine samples of chronic lymphocytic leukemia (CLL). We developed a novel statistical method, based on linear mixed-effects models, to analyze the data. This method automatically identifies the genes whose expression does not vary significantly over the samples, allowing them to be used to normalize the remaining genes. We compared the normalized real-time RT-PCR values with results obtained from Affymetrix Hu133A GeneChip oligonucleotide microarrays. We found that real-time RT-PCR using TaqMan Low-Density Arrays yielded reproducible measurements over seven orders of magnitude. Our model identified numerous genes that were expressed at nearly constant levels, including the housekeeping genes PGK1, GAPD, GUSB, TFRC, and 18S rRNA. After normalizing to the geometric mean of the unvarying genes, the correlation between real-time RT-PCR and microarrays was high for genes that were moderately expressed and varied across samples.
Algorithms↗
Inter-gene correlation on oligonucleotide arrays: how much does normalization matter?
BACKGROUND AND OBJECTIVE: Normalization is a standard low-level preprocessing procedure in microarray data analysis to minimize the systematic technological variations and produce more reliable results. A variety of normalization approaches have been introduced and are widely applied. Normalization, however, remains controversial. The sensitivity of array results to normalization is an open question. No clear standard for comparing or judging normalization methods has yet emerged, and the effects of normalization on gene-to-gene co-expression are unclear. METHODS: In this investigation, we applied 1-, 2-, and N-quantile normalization to several publicly available microarray datasets quantified with either MAS 5.0 or dCHIP and evaluated the effect on gene-to-gene co-expression. We introduced a graphical method to explore trends in gene correlation. RESULTS: We found clear differences in the distributions of gene dependencies by normalization method. Increasing the number of standardized quantiles in the normalization reduced trends in correlation by signal intensity in MAS 5.0 quantifications but not dCHIP. Increasing the number of standardized quantiles did not markedly reduce the correlation of known overlapping targets with MAS 5.0. CONCLUSIONS: Normalization plays a very important role in the estimation of inter-gene dependency. Caution should be used when making inferences concerning gene-wise dependencies with microarrays until this source of variation is better understood.
Algorithms↗
Serum amyloid A as a tumor marker in sera of nude mice with orthotopic human pancreatic cancer and in plasma of patients with pancreatic cancer.
We screened an orthotopic nude mouse model of human pancreatic cancer for candidate serum biomarkers and examined their presence in the plasma of pancreatic cancer patients. Nude mice were injected in the pancreas with L3.9pl human pancreatic cancer cells. One week later, the mice were randomized into 4 treatment groups: i) control, saline; ii) oral STI 571; iii) intraperitoneal gemcitabine; and iv) STI 571 and gemcitabine. After 1, 2, and 3 weeks of treatment, sera and tumors were collected from mice in each group as well as uninjected mice. All sera were analyzed by surface enhanced laser desorption ionization mass spectrometry using ProteinChip technology. Protein profiles were analyzed with the Biomarker Wizard software package. The concentration of candidate proteins was evaluated in mouse sera and plasma from 135 pancreatic cancer patients, 7 pancreatitis patients, and 113 healthy volunteers. The combination therapy inhibited tumor growth. A 11.7-kDa protein peak correlating with tumor weight was purified by gel filtration, separated by SDS-PAGE, and identified as mouse serum amyloid A (SAA) by amino acid sequencing and public database searches. The expression of SAA in mouse sera was confirmed by Western blotting and correlated with tumor weight. The level of SAA in plasma of pancreatic cancer patients correlated with clinical stage and was significantly higher than in normal volunteers (mean value: 180.1 microg/ml vs 27.9 microg/ml: P<0.01) or pancreatitis patients. For SAA used as a single tumor marker with a cut-off of 75 microg/ml, the sensitivity for pancreatic cancer was 96.5% and specificity was 31.9%. Our search for specific marker proteins to identify pancreatic cancer was unsuccessful. Although SAA is not specific for pancreatic cancer and not sensitive enough to detect stage I patients, it may be a candidate biomarker for detecting and monitoring the progressive growth of pancreatic cancer.
Animals↗
A comprehensive assessment of p53-responsive genes following adenoviral-p53 gene transfer in Bcl-2-expressing prostate cancer cells.
The p53 protein can induce cell cycle arrest or apoptosis following activation in response to DNA damage. The function of p53 is largely mediated by regulating the expression of downstream target genes. Adenoviral-p53 gene transfer (Ad-p53) is currently being evaluated in clinical trials as a therapeutic intervention. Tumor response is likely to be influenced by context-dependent variables, such as expression of bcl-2. Bcl-2 is upregulated in a variety of neoplasms, and can inhibit p53-dependent apoptosis. It was therefore of interest to use a global genomic strategy to assess gene expression following Ad-p53 gene transfer and to determine if the expression of specific Ad-p53-responsive genes could be modulated in the context of bcl-2 gene deregulation. cDNA arrays were used to identify p53-responsive genes following Ad-p53 gene transfer in control and bcl-2-overexpressing PC3 prostate cancer cells. A total of 40 transcripts were significantly upregulated by Ad-p53 in both control and bcl-2-transfectant PC3 cells. Conversely, 19 transcripts were significantly repressed in both cell lines. These Ad-p53-responsive transcripts included previously identified p53 targets, known genes representing candidate p53 targets, and transcripts identified as expressed sequence tags. A subset of 15 transcripts was differentially modulated by Ad-p53 in the context of bcl-2. Some of these genes were also differentially modulated in LNCaP (wt p53) cells following DNA damage. These results document a number of potential p53 targets and mediators of therapeutically relevant genotoxic stress. The findings further suggest that bcl-2 may inhibit cell death at multiple points downstream of p53 activation.
Adenoviridae↗
Reproducibility of SELDI-TOF protein patterns in serum: comparing datasets from different experiments.
MOTIVATION: There has been much interest in using patterns derived from surface-enhanced laser desorption and ionization (SELDI) protein mass spectra from serum to differentiate samples from patients both with and without disease. Such patterns have been used without identification of the underlying proteins responsible. However, there are questions as to the stability of this procedure over multiple experiments. RESULTS: We compared SELDI proteomic spectra from serum from three experiments by the same group on separating ovarian cancer from normal tissue. These spectra are available on the web at http://clinicalproteomics.steem.com. In general, the results were not reproducible across experiments. Baseline correction prevents reproduction of the results for two of the experiments. In one experiment, there is evidence of a major shift in protocol mid-experiment which could bias the results. In another, structure in the noise regions of the spectra allows us to distinguish normal from cancer, suggesting that the normals and cancers were processed differently. Sets of features found to discriminate well in one experiment do not generalize to other experiments. Finally, the mass calibration in all three experiments appears suspect. Taken together, these and other concerns suggest that much of the structure uncovered in these experiments could be due to artifacts of sample processing, not to the underlying biology of cancer. We provide some guidelines for design and analysis in experiments like these to ensure better reproducible, biologically meaningfully results. AVAILABILITY: The MATLAB and Perl code used in our analyses is available at http://bioinformatics.mdanderson.org
Algorithms↗
Association between ductal fluid proteomic expression profiles and the presence of lymph node metastases in women with breast cancer.
BACKGROUND: Proteomic analysis of nipple aspiration fluid (NAF) is a noninvasive method for studying the local biologic microenvironment of the breast ducts where carcinoma originates. METHODS: NAF samples from each breast of 23 women with stage I or II unilateral invasive breast carcinoma were collected, and protein expression was analyzed comprehensively by using protein arrays and time-of-flight mass spectrometry. Blinded hierarchical clustering analysis was performed to identify potential associations between protein expression patterns and clinicopathologic factors. RESULTS: After analysis of all spectra, 463 distinct peaks in the mass range of 7 to 30 kD were identified in NAF samples. Blinded hierarchical clustering analysis of protein expression patterns demonstrated a conservation of these patterns between the breasts of individual patients (P=.0003 x 10(-12)). Hierarchical clustering revealed an association between protein expression patterns, and the presence and absolute number of axillary lymph nodes containing metastases (P=.038). CONCLUSIONS: Protein expression patterns are highly conserved between cancerous and noncancerous breasts in women with unilateral invasive breast cancer; unique expression patterns may be associated with extent of disease. High-throughput proteomic methods may reveal biologically relevant proteins involved in carcinogenesis and progression of disease.
Adult↗
Response to Preprocessing of oligonucleotide array data.
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Algorithms↗
High expression of activation-induced cytidine deaminase (AID) and splice variants is a distinctive feature of poor-prognosis chronic lymphocytic leukemia.
In chronic lymphocytic leukemia (CLL), analysis of immunoglobulin heavy chain variable regions for somatic hypermutation identifies 2 prognostic subsets, mutated and unmutated. Investigators have postulated that unmutated and mutated CLL arises from malignant transformation of pre- and post-germinal center (GC) B cells, respectively. Alternatively, unmutated cases may arise from B cells stimulated by T-cell-independent antigens or from GC B cells with inactive somatic hypermutation. Activation-induced cytidine deaminase (AID), a protein essential for somatic hypermutation, is expressed by GC B cells in which this process occurs. We investigated AID mRNA expression in 20 CLL cases. In 8 cases we detected high expression of wild-type AID mRNA and 2 splice variants; in 12 cases and 5 normal peripheral blood B-cell samples we detected no expression using standard conditions. Of 8 CLL cases that highly expressed AID, 7 were unmutated, suggesting that this subset may arise from GC-experienced B cells with inactive somatic hypermutation, and may predict prognosis.
Alternative Splicing↗
A comprehensive approach to the analysis of matrix-assisted laser desorption/ionization-time of flight proteomics spectra from serum samples.
For our analysis of the data from the First Annual Proteomics Data Mining Conference, we attempted to discriminate between 24 disease spectra (group A) and 17 normal spectra (group B). First, we processed the raw spectra by (i) correcting for additive sinusoidal noise (periodic on the time scale) affecting most spectra, (ii) correcting for the overall baseline level, (iii) normalizing, (iv) recombining fractions, and (v) using variable-width windows for data reduction. Also, we identified a set of polymeric peaks (at multiples of 180.6 Da) that is present in several normal spectra (B1-B8). After data processing, we found the intensities at the following mass to charge (m/z) values to be useful discriminators: 3077, 12 886 and 74 263. Using these values, we were able to achieve an overall classification accuracy of 38/41 (92.6%). Perfect classification could be achieved by adding two additional peaks, at 2476 and 6955. We identified these values by applying a genetic algorithm to a filtered list of m/z values using Mahalanobis distance between the group means as a fitness function.
Blood Proteins↗
Establishment and characterization by gene expression profiling of a new diffuse large B-cell lymphoma cell line, EJ-1, carrying t(14;18) and t(8;14) translocations.
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Cell Culture Techniques↗
Bayesian shrinkage estimation of the relative abundance of mRNA transcripts using SAGE.
Serial analysis of gene expression (SAGE) is a technology for quantifying gene expression in biological tissue that yields count data that can be modeled by a multinomial distribution with two characteristics: skewness in the relative frequencies and small sample size relative to the dimension. As a result of these characteristics, a given SAGE sample may fail to capture a large number of expressed mRNA species present in the tissue. Empirical estimators of mRNA species' relative abundance effectively ignore these missing species, and as a result tend to overestimate the abundance of the scarce observed species comprising a vast majority of the total. We have developed a new Bayesian estimation procedure that quantifies our prior information about these characteristics, yielding a nonlinear shrinkage estimator with efficiency advantages over the MLE. Our prior is mixture of Dirichlets, whereby species are stochastically partitioned into abundant and scarce classes, each with its own multivariate prior. Simulation studies reveal our estimator has lower integrated mean squared error (IMSE) than the MLE for the SAGE scenarios simulated, and yields relative abundance profiles closer in Euclidean distance to the truth for all samples simulated. We apply our method to a SAGE library of normal colon tissue, and discuss its implications for assessing differential expression.
Bayes Theorem↗
Quality control and peak finding for proteomics data collected from nipple aspirate fluid by surface-enhanced laser desorption and ionization.
BACKGROUND: Recently, researchers have been using mass spectroscopy to study cancer. For use of proteomics spectra in a clinical setting, stringent quality-control procedures will be needed. METHODS: We pooled samples of nipple aspirate fluid from healthy breasts and breasts with cancer to prepare a control sample. Aliquots of the control sample were used on two spots on each of three IMAC ProteinChip arrays (Ciphergen Biosystems, Inc.) on 4 successive days to generate 24 SELDI spectra. In 36 subsequent experiments, the control sample was applied to two spots of each ProteinChip array, and the resulting spectra were analyzed to determine how closely they agreed with the original 24 spectra. RESULTS: We describe novel algorithms that (a) locate peaks in unprocessed proteomics spectra and (b) iteratively combine peak detection with baseline correction. These algorithms detected approximately 200 peaks per spectrum, 68 of which are detected in all 24 original spectra. The peaks were highly correlated across samples. Moreover, we could explain 80% of the variance, using only six principal components. Using a criterion that rejects a chip if the Mahalanobis distance from both control spectra to the center of the six-dimensional principal component space exceeds the 95% confidence limit threshold, we rejected 5 of the 36 chips. CONCLUSIONS: Mahalanobis distance in principal component space provides a method for assessing the reproducibility of proteomics spectra that is robust, effective, easily computed, and statistically sound.
Algorithms↗
Obtaining reliable information from minute amounts of RNA using cDNA microarrays.
BACKGROUND: High density cDNA microarray technology provides a powerful tool to survey the activity of thousands of genes in normal and diseased cells, which helps us both to understand the molecular basis of the disease and to identify potential targets for therapeutic intervention. The promise of this technology has been hampered by the large amount of biological material required for the experiments (more than 50 microg of total RNA per array). We have modified an amplification procedure that requires only 1 microg of total RNA. Analyses of the results showed that most genes that were detected as expressed or differentially expressed using the regular protocol were also detected using the amplification protocol. In addition, many genes that were undetected or weakly detected using the regular protocol were clearly detected using the amplification protocol. We have carried out a series of confirmation studies by northern blotting, western blotting, and immunohistochemistry assays. RESULTS: Our results showed that most of the new information revealed by the amplification protocol represents real gene activity in the cells. CONCLUSION: We have confirmed a powerful and consistent cDNA microarray procedure that can be used to study minute amounts of biological tissue.
Journal Article↗
Identifying and quantifying sources of variation in microarray data using high-density cDNA membrane arrays.
Microarray experiments involve many steps, including spotting cDNA, extracting RNA, labeling targets, hybridizing, scanning, and analyzing images. Each step introduces variability, confounding our ability to obtain accurate estimates of the biological differences between samples. We ran repeated experiments using high-density cDNA microarray membranes (Research Genetics Human GeneFilters Microarrays Version I) and 33P-labeled targets. Total RNA was extracted from a Burkitt lymphoma cell line (GA-10). We estimated the components of variation coming from: (1) image analysis, (2) exposure time to PhosphorImager screens, (3) differences in membranes, (4) reuse of membranes, and (5) differences in targets prepared from two independent RNA extractions. Variation was assessed qualitatively using a clustering algorithm and quantitatively using a version of ANOVA adapted to multivariate microarray data. The largest contribution to variation came from reusing membranes, which contributed 38% of the total variation. Differences in membranes and in exposure time each contributed about 10%. Differences in target preparations contributed less than 5%. The effect of image quantification was negligible. Much of the effect from reusing membranes was attributable to increasing levels of background radiation and can be reduced by using membranes at most four times. The effects of exposure time, which were partly attributable to variation in the scanning process, can be minimized by using the same exposure time for all experiments.
Algorithms↗
TAD: a web interface and database for tissue microarrays.
Tissue microarrays are increasingly important tools that bring high-throughput technology to traditional pathology laboratories. In many cases, each spot on a tissue microarray is scored by a skilled pathologist and recorded manually. TAD consists of an Active Server Page web interface to a relational database that automates recording scores and linking them with clinical data for future interpretation. TAD is an open source application that can be installed locally.
Computational Biology↗
Direct tandem mass spectrometry reveals limitations in protein profiling experiments for plasma biomarker discovery.
The low molecular weight plasma proteome and its biological relevance are not well defined; therefore, experiments were conducted to directly sequence and identify peptides observed in plasma and serum protein profiles. Protein fractionation, matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS) profiling, and liquid-chromatography coupled to MALDI tandem mass spectrometry (MS/MS) sequencing were used to analyze the low molecular weight proteome of heparinized plasma. Four fractionation techniques using functionally derivatized 96-well plates were used to extract peptides from plasma. Tandem TOF was successful for identifying peptides up to m/z 5500 with no prior knowledge of the sequence and was also used to verify the sequence assignments for larger ion signals. The peptides (n>250) sequenced in these profiles came from a surprisingly small number of proteins (n approximately 20), which were all common to plasma, including fibrinogen, complement components, antiproteases, and carrier proteins. The cleavage patterns were consistent with those of known plasma proteases, including initial cleavages by thrombin, plasmin and complement proteins, followed by aminopeptidase and carboxypeptidase activity. On the basis of these data, we discuss limitations in biomarker discovery in the low molecular weight plasma or serum proteome using crude fractionation coupled to MALDI-MS profiling.
Biomarkers↗