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O John Semmes

Publications and source records attributed to O John Semmes.

At least 37 records · Page 2Linked to original sources
PubMed2004

Pharmacoproteomic analysis of prechemotherapy and postchemotherapy plasma samples from patients receiving neoadjuvant or adjuvant chemotherapy for breast carcinoma.

BACKGROUND: In this study, proteomic changes were examined in response to paclitaxel chemotherapy or 5-fluorouracil, doxorubicin, and cyclophosphamide (FAC) chemotherapy in plasma from patients with Stage I-III breast carcinoma. The authors also compared the plasma profiles of patients with cancer with the plasma profiles of healthy women to identify breast carcinoma-associated protein markers. METHODS: Sixty-nine patients and 15 healthy volunteers participated in the study. Plasma was sampled on Day 0 before chemotherapy and on Day 3 posttreatment in the 69 patients or 3 days apart in the 15 healthy women. Twenty-nine patients received preoperative chemotherapy, and 40 received postoperative chemotherapy. Surface-enhanced laser desorption/ionization mass spectrometry was used to generate protein mass profiles. RESULTS: Few changes were observed in plasma during treatment. Only 1 protein peak was identified (mass/charge ratio [m/z], 2790) that was induced by paclitaxel and, to a lesser extent, by FAC chemotherapy. This proteomic response was detectable in 80% of patients who were treated preoperatively but also was present with lesser intensity in approximately 40% of patients treated postoperatively. There was no clear correlation between induction of m/z 2790 during a single course of treatment and final tumor response to preoperative chemotherapy. Five other peaks also were identified that discriminated between plasma from patients with breast carcinoma and plasma from normal women. These same peaks also were detectable in a subset of patients who already had undergone surgery to remove their tumors. CONCLUSIONS: A single chemotherapy-inducible SELDI-MS peak and five other peaks that distinguished plasma obtained from patients with breast carcinoma from plasma obtained from normal, healthy women were identified. The (as yet unsequenced) proteins represented by these peaks are candidate markers of micrometastatic disease after surgery.

Adult↗
PubMed2004

Computational protein biomarker prediction: a case study for prostate cancer.

BACKGROUND: Recent technological advances in mass spectrometry pose challenges in computational mathematics and statistics to process the mass spectral data into predictive models with clinical and biological significance. We discuss several classification-based approaches to finding protein biomarker candidates using protein profiles obtained via mass spectrometry, and we assess their statistical significance. Our overall goal is to implicate peaks that have a high likelihood of being biologically linked to a given disease state, and thus to narrow the search for biomarker candidates. RESULTS: Thorough cross-validation studies and randomization tests are performed on a prostate cancer dataset with over 300 patients, obtained at the Eastern Virginia Medical School using SELDI-TOF mass spectrometry. We obtain average classification accuracies of 87% on a four-group classification problem using a two-stage linear SVM-based procedure and just 13 peaks, with other methods performing comparably. CONCLUSIONS: Modern feature selection and classification methods are powerful techniques for both the identification of biomarker candidates and the related problem of building predictive models from protein mass spectrometric profiles. Cross-validation and randomization are essential tools that must be performed carefully in order not to bias the results unfairly. However, only a biological validation and identification of the underlying proteins will ultimately confirm the actual value and power of any computational predictions.

Biomarkers, Tumor↗
PubMed2004

Serum protein profiles to identify head and neck cancer.

PURPOSE: New and more consistent biomarkers of head and neck squamous cell carcinoma (HNSCC) are needed to improve early detection of disease and to monitor successful patient management. The purpose of this study was to determine whether a new proteomic technology could correctly identify protein expression profiles for cancer in patient serum samples. EXPERIMENTAL DESIGN: Surface-enhanced laser desorption/ionization-time of flight-mass spectrometry ProteinChip system was used to screen for differentially expressed proteins in serum from 99 patients with HNSCC and 102 normal controls. Protein peak clustering and classification analyses of the surface-enhanced laser desorption/ionization spectral data were performed using the Biomarker Wizard and Biomarker Patterns software (version 3.0), respectively (Ciphergen Biosystems, Fremont, CA). RESULTS: Several proteins, with masses ranging from 2778 to 20800 Da, were differentially expressed between HNSCC and the healthy controls. The serum protein expression profiles were used to develop and train a classification and regression tree algorithm, which reliably achieved a sensitivity of 83.3% and a specificity of 100% in discriminating HNSCC from normal controls. CONCLUSIONS: We propose that this technique has potential for the development of a screening test for the detection of HNSCC.

Adult↗
PubMed2004

Identification of patients with head and neck cancer using serum protein profiles.

BACKGROUND: New and more consistent biomarkers of head and neck squamous cell carcinoma (HNSCC) are needed to improve early detection of disease and to monitor successful patient management. OBJECTIVE: To determine if a new proteomic technology can correctly identify protein expression profiles for cancer in patient serum samples as well as detect the presence of a known tumor marker. DESIGN: Direct proteomic analysis and comparison. METHODS: The surface-enhanced laser desorption/ionization time of flight mass spectrometry (SELDI-TOF) ProteinChip system was used to screen for differentially expressed proteins in serum samples from 99 patients with HNSCC, 25 "healthy" smokers, and 102 healthy (normal) controls. Protein peak clustering and classification analyses of the SELDI spectral data were performed. RESULTS: Several proteins, with masses ranging from 2778 to 20,800 Da, were differentially expressed between patients with HNSCC and the normal controls. The serum protein expression profiles were used to develop a classification tree algorithm, which achieved a sensitivity of 83.3% and a specificity of 90% in discriminating HNSCC from normal and healthy smoker controls. The positive and negative predictive values were 80% and 92%, respectively. A peak with an average mass of 10,068 Da was detected in sera from HNSCC patients and identified as the known biomarker metallopanstimulin-1 (MPS-1), based on mass. Peak relative intensity of the 10,068-Da protein correlated consistently with MPS-1 levels detected by radioimmunoassay in serum samples of HNSCC patients and controls. The 10,068-Da peak was provisionally identified as MPS-1 by SELDI immunoassay. CONCLUSION: We propose that this technique may allow for the development of a reliable screening test for the early detection and diagnosis of HNSCC, as well as the potential identification of tumor biomarkers.

Biomarkers, Tumor↗
PubMed2004

Comparative proteomic analysis of de-N-glycosylated serum from hepatitis B carriers reveals polypeptides that correlate with disease status.

Analysis of the polypeptide profile in tissues, cells, and sera by high-resolution two-dimensional (2-D) gel electrophoresis offers promise in the identification of biomarkers that correlate with disease. However, sera contain many polypeptides bearing N-linked glycosylation that can complicate interpretation. Therefore, we tested the possibility that de-N-glycosylation of the polypeptides present in human serum would result in a simplification of serum proteome profiles. Briefly, polypeptides present in human serum were left untreated or subjected to de-N-glycosylation by incubation with PNGase F and resolved by high-resolution 2-D gel electrophoresis. De-N-glycosylation reduced the number of glycoform variants, enhanced the resolution of many polypeptides and allowed other polypeptides to become visible. As an initial test of concept, clinically relevant serum samples from individuals with or without diagnosis of hepatocellular carcinoma were compared. Several polypeptides, apparent only after de-N-glycosylation, were shown to correlate with disease. Although the results are preliminary and the identities of all the putative biomarkers not yet known, the data suggest that de-N-glycosylation offers a method to enhance the resolution of serum polypeptide profiles and has value in comparative proteomic studies.

Amyloid↗
PubMed2004

Expression of genes and proteins specific for prostate cancer.

PURPOSE: We reviewed the pertinent literature and discuss high throughput approaches to the identification of molecular markers characteristic of prostate cancer. MATERIALS AND METHODS: A PubMed search was performed for expression array studies comparing prostate cancer tissues with nonmalignant tissues and serum proteomic studies comparing patients with prostate cancer and controls. RESULTS: The expression of several genes may help distinguish prostate cancer cells from nonmalignant prostatic epithelial cells. A few genes, such as those coding for hepsin, LIM protein and alpha-methylacyl-coenzyme A racemase, have consistently been identified as being over expressed in cancer cells compared with nonmalignant cells. Proteomic analysis using mass spectroscopy has identified spectral patterns that have a significant correlation with the presence of prostate cancer. The optimal presentation of the spectra may depend highly on technical factors and experimental protocols have yet to be standardized among laboratories. Moreover, to our knowledge the serum proteins that comprise the mass spectral patterns of prostate cancer have yet to be identified. CONCLUSIONS: High throughput techniques have the potential to aid in prostate cancer diagnosis, early detection and developmental therapies. Markers with the potential to aid in diagnosis have been identified and targets for imaging or therapeutic approaches appear to be at hand. Mass spectral analysis of serum is in the early stages and it requires standardization of technical parameters among laboratories. In addition, the identification of target proteins will contribute to ease of detection and cross-laboratory verification.

Biomarkers, Tumor↗
PubMed2004

Measurement reproducibility in the early stages of biomarker development.

Biomarker discovery and development requires measurement reproducibility studies in addition to case-control studies. Parallel pursuit of reproducibility studies is especially important for emerging technologies such as protein biomarkers based on time-of-flight mass spectrometry, the case considered in this paper. For parallel studies, a way to improve reproducibility prior to identification of protein species is necessary. One approach is use of functional principal components analysis (PCA) as the basis for assessing measurement reproducibility. Reproducibility studies involve repeated measurement of a reference material such as a human serum standard. Measurement in our example is by SELDI-TOF (surface-enhanced laser desorption and ionization time-of-flight) mass spectrometry. Reproducibility is defined in reference to a source of variation, which in our example is associated with a type of commercially available protein biochip. We obtained spectra for 8 spots on each 11 chips. Two spectra are generally more alike when obtained from the same chip rather than different chips. Thus, our experiment indicates potential improvements from reducing variation in chip manufacture and chip handling during measurement. Our analysis involves careful registration of the spectra and characterization of the spectral differences. As shown by our example, a metrological analysis may enhance case-control studies by guiding optimization of the measurements underlying the biomarker.

Biomarkers↗
PubMed2004

Retrotransposition-Competent Human LINE-1 Induces Apoptosis in Cancer Cells With Intact p53.

Retrotransposition of human LINE-1 (L1) element, a major representative non-LTR retrotransposon in the human genome, is known to be a source of insertional mutagenesis. However, nothing is known about effects of L1 retrotransposition on cell growth and differentiation. To investigate the potential for such biological effects and the impact that human L1 retrotransposition has upon cancer cell growth, we examined a panel of human L1 transformed cell lines following a complete retrotransposition process. The results demonstrated that transposition of L1 leads to the activation of the p53-mediated apoptotic pathway in human cancer cells that possess a wild-type p53. In addition, we found that inactivation of p53 in cells, where L1 was undergoing retrotransposition, inhibited the induction of apoptosis. This suggests an association between active retrotransposition and a competent p53 response in which induction of apoptosis is a major outcome. These data are consistent with a model in which human retrotransposition is sensed by the cell as a "genetic damaging event" and that massive retrotransposition triggers signaling pathways resulting in apoptosis.

Journal Article↗
PubMed2003

Interaction of retroviral Tax oncoproteins with tristetraprolin and regulation of tumor necrosis factor-alpha expression.

BACKGROUND: The Tax oncoproteins are transcriptional regulators of viral expression involved in pathogenesis induced by complex leukemogenic retroviruses (or delta-retroviruses, i.e., primate T-cell leukemia viruses and bovine leukemia virus). To better understand the molecular pathways leading to cell transformation, we aimed to identify cellular proteins interacting with Tax. METHODS: We used a yeast two-hybrid system to identify interacting cellular proteins. Interactions between Tax and candidate interacting cellular proteins were confirmed by glutathione S-transferase (GST) pulldown assays, co-immunoprecipitation, and confocal microscopy. Functional interactions between Tax and one interacting protein, tristetraprolin (TTP), were assessed by analyzing the expression of tumor necrosis factor-alpha (TNF-alpha), which is regulated by TTP, in mammalian cells (HeLa, D17, HEK 293, and RAW 264.7) transiently transfected with combinations of intact and mutant Tax and TTP. RESULTS: We obtained seven interacting cellular proteins, of which one, TTP, was further characterized. Tax and TTP were found to interact specifically through their respective carboxyl-terminal domains. The proteins colocalized in the cytoplasm in a region surrounding the nucleus of HeLa cells. Furthermore, coexpression of Tax was associated with nuclear accumulation of TTP. TTP is an immediate-early protein that inhibits expression of TNF-alpha at the post-transcriptional level. Expression of Tax reverted this inhibition, both in transient transfection experiments and in stably transfected macrophage cell lines. CONCLUSION: Tax, through its interactions with the TTP repressor, indirectly increases TNF-alpha expression. This observation is of importance for the cell transformation process induced by leukemogenic retroviruses, because TNF-alpha overexpression plays a central role in pathogenesis.

Animals↗
PubMed2003

Human T-cell leukemia virus-I tax oncoprotein functionally targets a subnuclear complex involved in cellular DNA damage-response.

The virally encoded oncoprotein Tax has been implicated in HTLV-1-mediated cellular transformation. The exact mechanism by which this protein contributes to the oncogenic process is not known. However, it has been hypothesized that Tax induces genomic instability via repression of cellular DNA repair. We examined the effect of de novo Tax expression upon the cell cycle, because appropriate activation of cell cycle checkpoints is essential to a robust damage-repair response. Upon induction of tax expression, Jurkat T-cells displayed a pronounced accumulation in G2/M that was reversible by caffeine. We examined the G2-specific checkpoint signaling response in these cells and found activation of the ATM/chk2-mediated pathway, whereas the ATR/chk1-mediated response was unaffected. Immunoprecipitation with anti-chk2 antibody results in co-precipitation of Tax demonstrating a direct interaction of Tax with a chk2-containing complex. We also show that Tax targets a discrete nuclear site and co-localizes with chk2 and not chk1. This nuclear site, previously identified as Tax Speckled Structures (TSS), also contains the early damage response factor 53BP1. The recruitment of 53BP1 to TSS is dependent upon ATM signaling and requires expression of Tax. Specifically, Tax expression induces redistribution of diffuse nuclear 53BP1 to the TSS foci. Taken together these data suggest that the TSS describe a unique nuclear site involved in DNA damage recognition, repair response, and cell cycle checkpoint activation. We suggest that association of Tax with this multifunctional subnuclear site results in disruption of a subset of the site-specific activities and contributes to cellular genomic instability.

Carrier Proteins↗
PubMed2003

The HTLV-1 tax oncoprotein attenuates DNA damage induced G1 arrest and enhances apoptosis in p53 null cells.

Transformation of cells by the human T cell leukemia virus type 1 occurs via mechanisms unique among oncogenic retroviruses. A prevailing hypothesis for HTLV-1-mediated cellular transformation is that expression of the viral transactivator, Tax, induces genomic instability. Tax-mediated failure in the cellular repair response is one possible mechanism for loss in genomic integrity. Here we have examined the in vivo repair response of Tax-expressing cells to determine the underlying defects that contribute to loss of genomic integrity. In these studies we examined the effects of de novo Tax-expression in naive "pre-neoplastic" REF52 cells. DNA-damage-induced p53 stabilization and concomitant transient stabilization of p21 were clearly evident in Tax-expressing cells. Likewise, the damage-induced apoptotic response of Tax-expressing cells was normal. However, the damage-induced G1 checkpoint was abrogated in either p53+ or p53- cellular backgrounds. Although nucleotide excision repair (NER) of asynchronous Tax-expressing cells was impaired, cell-cycle-independent assessment of NER in the global excision repair assay demonstrated comparable NER activity in Tax-expressing cells, suggesting that the failure of G1 checkpoint contributes to NER deficiency. Interestingly, we observed a dramatic increase in apoptosis and UV sensitivity of Tax-expressing p53-/- cells when compared to Tax-expressing p53+/+ cells. These data demonstrate that Tax-mediated cellular genomic instability arises from attenuation of cell-cycle checkpoint and imply a clonal dependence on p53 status separate from genomic integrity.

Apoptosis↗
PubMed2003

A novel approach toward development of a rapid blood test for breast cancer.

Mammography remains the diagnostic test of choice for breast cancer, but 20% of cancers still go undetected. Many serum biomarkers have been reported for breast cancer but none have proven to represent effective diagnostic strategies. ProteinChip mass spectrometry is an innovative technology that searches the proteome for differentially expressed proteins, allowing for the creation of a panel or profile of biomarkers. The objective of this study was to construct unique cancer-associated serum profiles that, combined with a classification algorithm, would enhance the detection of breast cancer Pretreatment serum samples from 134 female patients (45 with cancer, 42 with benign disease, 47 normal) were procured prospectively following institutional review board-approved protocols. Proteins were denatured, applied onto ProteinChip affinity surfaces, and subjected to surface enhanced laser desorption/ionization (SELDI) time-of-flight mass spectrometry. The SELDI output was analyzed using Biomarker Pattern Software to develop a classification tree based on group-specific protein profiles. The cross-validation analysis of cancer versus normal revealed sensitivity and specificity rates of 80% and 79%, and for cancer versus benign disease, 78% and 83%, respectively. When 2 different chip surfaces were combined the sensitivity and specificity increased to 90% and 93%, respectively. The sensitivity and specificity of this technique are comparable to those of mammography and, if confirmed in a larger study, this technique could provide the means toward development of a simple blood test to aid in the early detection of breast cancer. The combination of SELDI ProteinChip mass spectrometry and a classification- and regression-tree algorithm has the potential to use serum protein expression profiles for detection and diagnosis of breast cancer.

Adult↗
PubMed2002

Serum protein fingerprinting coupled with a pattern-matching algorithm distinguishes prostate cancer from benign prostate hyperplasia and healthy men.

The prostate-specific antigen test has been a major factor in increasing awareness and better patient management of prostate cancer (PCA), but its lack of specificity limits its use in diagnosis and makes for poor early detection of PCA. The objective of our studies is to identify better biomarkers for early detection of PCA using protein profiling technologies that can simultaneously resolve and analyze multiple proteins. Evaluating multiple proteins will be essential to establishing signature proteomic patterns that distinguish cancer from noncancer as well as identify all genetic subtypes of the cancer and their biological activity. In this study, we used a protein biochip surface enhanced laser desorption/ionization mass spectrometry approach coupled with an artificial intelligence learning algorithm to differentiate PCA from noncancer cohorts. Surface enhanced laser desorption/ionization mass spectrometry protein profiles of serum from 167 PCA patients, 77 patients with benign prostate hyperplasia, and 82 age-matched unaffected healthy men were used to train and develop a decision tree classification algorithm that used a nine-protein mass pattern that correctly classified 96% of the samples. A blinded test set, separated from the training set by a stratified random sampling before the analysis, was used to determine the sensitivity and specificity of the classification system. A sensitivity of 83%, a specificity of 97%, and a positive predictive value of 96% for the study population and 91% for the general population were obtained when comparing the PCA versus noncancer (benign prostate hyperplasia/healthy men) groups. This high-throughput proteomic classification system will provide a highly accurate and innovative approach for the early detection/diagnosis of PCA.

Aged↗
PubMed2002

Normal, benign, preneoplastic, and malignant prostate cells have distinct protein expression profiles resolved by surface enhanced laser desorption/ionization mass spectrometry.

PURPOSE: The objective of this study was to discover protein biomarkers that differentiate malignant from nonmalignant cell populations, especially early protein alterations that signal the initiation of a developing cancer. We hypothesized that Surface Enhanced Laser Desorption/Ionization-time of flight-mass spectrometry-assisted protein profiling could detect these protein alterations. EXPERIMENTAL DESIGN: Epithelial cell populations [benign prostatic hyperplasia (BPH), prostate intraepithelial neoplasia (PIN), and prostate cancer (PCA)] were procured from nine prostatectomy specimens using laser capture microdissection. Surface Enhanced Laser Desorption/Ionization-time of flight-mass spectrometry analysis was performed on cell lysates, and the relative intensity levels of each protein or peptide in the mass spectra was calculated and compared for each cell type. RESULTS: Several small molecular mass peptides or proteins (3000-5000 Da) were found in greater abundance in PIN and PCA cell lysates. Another peak, with an average mass of 5666 Da, was observed to be up-regulated in 86% of the BPH cell lysates. Higher levels of this same peak were found in only 22% of the PIN lysates and none of the PCA lysates. Expression differences were also found for intracellular levels of prostate-specific antigen, which were reduced in PIN and PCA cells when compared with matched normals. Although no single protein alteration was observed in all PIN/PCA samples, combining two or more of the markers was effective in distinguishing the benign cell types (normal/BPH) from diseased cell types (PIN/PCA). Logistic regression analysis using seven differentially expressed proteins resulted in a predictive equation that correctly distinguished the diseased lysates with a sensitivity and specificity of 93.3 and 93.8%, respectively. CONCLUSIONS: We have shown that the protein profiles from prostate cells with different disease states have discriminating differences. These differentially regulated proteins are potential markers for early detection and/or risk factors for development of prostate cancer. Studies are under way to identify these protein/peptides, with the goal of developing a diagnostic test for the early detection of prostate cancer.

Adult↗
PubMed2002

Boosted decision tree analysis of surface-enhanced laser desorption/ionization mass spectral serum profiles discriminates prostate cancer from noncancer patients.

BACKGROUND: The low specificity of the prostate-specific antigen (PSA) test makes it a poor biomarker for early detection of prostate cancer (PCA). Because single biomarkers most likely will not be found that are expressed by all genetic forms of PCA, we evaluated and developed a proteomic approach for the simultaneous detection and analysis of multiple proteins for the differentiation of PCA from noncancer patients. METHODS: Serum samples from 386 men [197 with PCA, 92 with benign prostatic hyperplasia (BPH), and 96 healthy individuals], randomly divided into training (n = 326) and test (n = 60) sets, were analyzed by surface-enhanced laser desorption/ionization (SELDI) mass spectrometry. The 124 peaks detected by computer analyses were analyzed in the training set by a boosting tree algorithm to develop a classifier for separating PCA from the noncancer groups. The classifier was then challenged with the test set (30 PCA samples, 15 BPH samples, 15 samples from healthy men) to determine the validity and accuracy of the classification system. RESULTS: Two classifiers were developed. The AdaBoost classifier completely separated the PCA from the noncancer samples, achieving 100% sensitivity and specificity. The second classifier, the Boosted Decision Stump Feature Selection classifier, was easier to interpret and used only 21 (compared with 74) peaks and a combination of 21 (vs 500) base classifiers to achieve a sensitivity and specificity of 97% for the test set. CONCLUSIONS: The high sensitivity and specificity achieved in this study provides support of the potential for SELDI, coupled with a bioinformatics learning algorithm, to improve the early detection/diagnosis of PCA.

Algorithms↗
PubMedDate not supplied

The early detection research network surface-enhanced laser desorption and ionization prostate cancer detection study: A study in biomarker validation in genitourinary oncology.

Prostate-specific antigen (PSA) screening has led to a dramatic increase in prostate cancer detection with a concurrent stage migration. Although the test has revolutionized prostate cancer detection by identifying disease that is potentially curable in the majority of men, only 25% of men receiving test results of PSA > 4 ng/ml will have prostate cancer and many men receiving a normal PSA will have disease, including high-grade disease. There is a need for improved biomarkers for detecting prostate cancer. One such method of cancer detection is surface-enhanced laser desorption and ionization (SELDI). The Early Detection Research Network (EDRN) validation study for SELDI for prostate cancer is described. In a three-stage study, the portability and reproducibility of the technique will be determined; the predictive algorithm will be refined in a multi-institutional case-control population; followed by ultimate validation in the context of a prospective trial with complete disease ascertainment. The unique aspect of the EDRN SELDI validation study is the novel use of two groups of cancer cases: those cases with higher-risk disease (Gleason > or = 7) and those cases with lower-risk disease (Gleason < or = 6). This study will allow the first evaluation of a predictive algorithm that includes prognosis in disease screening. The EDRN SELDI prostate cancer biomarker validation study is a rigorous evaluation of a new detection method for prostate cancer. The methodologies used for this evaluation will prove useful for guiding future biomarker studies in this challenging disease.

Biomarkers, Tumor↗