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PubMed2006

Customized oligonucleotide microarray gene expression-based classification of neuroblastoma patients outperforms current clinical risk stratification.

PURPOSE: To develop a gene expression-based classifier for neuroblastoma patients that reliably predicts courses of the disease. PATIENTS AND METHODS: Two hundred fifty-one neuroblastoma specimens were analyzed using a customized oligonucleotide microarray comprising 10,163 probes for transcripts with differential expression in clinical subgroups of the disease. Subsequently, the prediction analysis for microarrays (PAM) was applied to a first set of patients with maximally divergent clinical courses (n = 77). The classification accuracy was estimated by a complete 10-times-repeated 10-fold cross validation, and a 144-gene predictor was constructed from this set. This classifier's predictive power was evaluated in an independent second set (n = 174) by comparing results of the gene expression-based classification with those of risk stratification systems of current trials from Germany, Japan, and the United States. RESULTS: The first set of patients was accurately predicted by PAM (cross-validated accuracy, 99%). Within the second set, the PAM classifier significantly separated cohorts with distinct courses (3-year event-free survival [EFS] 0.86 +/- 0.03 [favorable; n = 115] v 0.52 +/- 0.07 [unfavorable; n = 59] and 3-year overall survival 0.99 +/- 0.01 v 0.84 +/- 0.05; both P < .0001) and separated risk groups of current neuroblastoma trials into subgroups with divergent outcome (NB2004: low-risk 3-year EFS 0.86 +/- 0.04 v 0.25 +/- 0.15, P < .0001; intermediate-risk 1.00 v 0.57 +/- 0.19, P = .018; high-risk 0.81 +/- 0.10 v 0.56 +/- 0.08, P = .06). In a multivariate Cox regression model, the PAM predictor classified patients of the second set more accurately than risk stratification of current trials from Germany, Japan, and the United States (P < .001; hazard ratio, 4.756 [95% CI, 2.544 to 8.893]). CONCLUSION: Integration of gene expression-based class prediction of neuroblastoma patients may improve risk estimation of current neuroblastoma trials.

Biomarkers, Tumor↗
PubMed2006

Serum proteomic fingerprints of adult patients with severe acute respiratory syndrome.

BACKGROUND: Severe acute respiratory syndrome (SARS) is an emerging infectious disease caused by a new coronavirus strain, SARS-CoV. Specific proteomic patterns might be present in serum in response to the infection and could be useful for early detection of the disease. METHODS: Using surface-enhanced laser desorption/ionization (SELDI) ProteinChip technology, we profiled and compared serum proteins of 39 patients with early-stage SARS infection and 39 non-SARS patients who were suspected cases during the SARS outbreak period. Proteomic patterns associated with SARS were identified by bioinformatic and biostatistical analyses. Features of interest were then purified and identified by tandem mass spectrometry. RESULTS: Twenty proteomic features were significantly different between the 2 groups. Fifteen were increased in the SARS group, and 5 were decreased. Their concentrations were correlated with 2 or more clinical and/or biochemical variables. Two were correlated with the SARS-CoV viral load. Hierarchical clustering analysis showed that a majority of the SARS patients (95%) had similar serum proteomic profiles and identified 2 subgroups with poor prognosis. ROC curve analysis identified individual features as potential biomarkers for SARS diagnosis (areas under ROC curves, 0.733-0.995). ROC curve areas were largest for an N-terminal fragment of complement C3c alpha chain (m/z 28 119) and an internal fragment of fibrinogen alpha-E chain (m/z 5908). Immunoglobulin kappa light chain (m/z 24 505) positively correlated with viral load. CONCLUSIONS: Specific proteomic fingerprints in the sera of adult SARS patients could be used to identify SARS cases early during onset with high specificity and sensitivity.

Adult↗
PubMed2006

Identification of novel brain biomarkers.

BACKGROUND: The diagnosis of diseases leading to brain injury, such as stroke, Alzheimer disease, and Parkinson disease, can often be problematic. In this study, we pursued the discovery of biomarkers that might be specific and sensitive to brain injury. METHODS: We performed gene array analyses on a mouse model to look for biomarkers that are both preferentially and abundantly produced in the brain. Via bioinformatics databases, we identified the human homologs of genes that appeared abundant in brain but not in other tissues. We then confirmed protein production of the genes via Western blot of various tissue homogenates and assayed for one of the markers, visinin-like protein 1 (VLP-1), in plasma from patients after ischemic stroke. RESULTS: Twenty-nine genes that were preferentially and abundantly expressed in the mouse brain were identified; of these 29 genes, 26 had human homologs. We focused on 17 of these genes and their protein products on the basis of their molecular characteristics, novelty, and/or availability of antibodies. Western blot showed strong signals in brain homogenates for 13 of these proteins. Tissue specificity was tested by Western blot on a human tissue array, and a sensitive and quantitative sandwich immunoassay was developed for the most abundant gene product observed in our search, VLP-1. VLP-1 was detected in plasma of patients after stroke and in cerebrospinal fluid of a rat model of stroke. CONCLUSIONS: The use of relative mRNA production appears to be a valid method of identifying possible biomarkers of tissue injury. The tissue specificity suggested by gene expression was confirmed by Western blot. One of the biomarkers identified, VLP-1, was increased in a rat model of stroke and in plasma of patients after stroke. More extensive, prospective studies of the candidate biomarkers identified appear warranted.

Animals↗
PubMed2005

Survivin overexpression in hepatocellular carcinoma is associated with p53 dysregulation.

BACKGROUND: Survivin is a recently described anti-apoptotic protein that is suppressed by wild-type p53 and is overexpressed in 41-70% of hepatocellular carcinomas from Asia. Two alternatively spliced transcripts have also been described: anti-apoptotic survivin-DeltaEx3 and non-anti-apoptotic survivin-2B. Survivin splice variant expression has not been studied in HCC, and little is known about survivin expression in hepatocellular carcinomas arising in other parts of the world, where risk factors are often different than they are in Asia. AIM OF THE STUDY: We studied survivin mRNA and protein expression in a United States cohort of hepatocellular carcinomas and correlated the findings with p53 immunopositivity. METHODS: RT-PCR was performed for survivin, survivin-2B, and survivin-DeltaEx3 in 20 HCCs and one intrahepatic cholangiocarcinoma. Expression levels of total survivin were evaluated with real-time PCR. Protein expression was examined by immunohistochemistry. RESULTS: Survivin was the major transcript, and all transcripts were present in all normal and neoplastic tissues; 11/20 (55%) HCCs and the one cholangiocarcinoma showed twofold or greater overexpression of survivin. Next, we examined survivin and p53 protein expression by immunohistochemistry on a separate series of 79 HCC, 13 fibrolamellar carcinomas, and 15 hepatic adenomas; 14/79 (17%) HCC, but none of the fibrolamellar carcinomas or hepatic adenomas, showed survivin protein overexpression, and 25/79 HCC (32%) showed abnormal nuclear accumulation of p53, which correlated with increased survivin expression. CONCLUSIONS: All three survivin transcripts are present in normal liver and HCC. Survivin is the dominant transcript in HCC and is overexpressed in 55% of cases. Survivin protein overexpression is associated with aberrant p53 nuclear positivity.

Adult↗
PubMed2003

Fetal hemoglobin synthesis determined by gamma-mRNA/gamma-mRNA + beta-mRNA quantitation in infants at risk for sudden infant death syndrome being monitored at home for apnea.

OBJECTIVE: Fetal hemoglobin (HbF) levels in the hemolysates obtained from infants who died from sudden infant death syndrome (SIDS) are reported to be markedly increased compared with controls. This finding could have been explained by increased HbF synthesis caused by episodes of hypoxemia in the SIDS infants. A prospective study in a group of infants being monitored at home after an apparent life-threatening event (ALTE) and considered at increased risk for SIDS was conducted with an improved ribonuclease protection assay. The ribonuclease protection assay allowed for the quantitation of [gamma/(gamma+beta)]-globin mRNAs, which has a highly significant correlation with the levels of HbF synthesis. METHODS: Thirty-five infants who were admitted for an ALTE were included in the study. All infants were at home under surveillance with a cardiorespiratory monitor and followed in an apnea clinic with monthly appointments. Seventy-three blood samples were obtained between 38 and 61 weeks of postconceptional age. For control purposes, a similar group of 37 normal infants (99 samples) whose HbF synthesis was previously determined were included. RESULTS: Mean [gamma/(gamma+beta)]-globin mRNAs were increased in the ALTE group at 42 to 45 and 46 to 49 weeks of postconceptional age (mean: 55.2 +/- 17.4% and 33.9 +/- 14%) in comparison with HbF synthesis in controls (mean: 42.6 +/- 13.7% and 23.6 +/- 9.8%). CONCLUSIONS: The data obtained in this report from infants who were considered at risk for SIDS show that HbF synthesis is increased between 42 and 49 weeks of postconceptional age. Determining HbF synthesis as described in this study may have value as a marker for episodes of hypoxemia for certain infants who are at risk for SIDS.

Apnea↗
PubMed2006

Perspectives of proteomics in acute myeloid leukemia.

Acute myeloid leukemia (AML) is a frequent hematological malignancy. Despite enormous therapeutic efforts that range from various cytotoxic agents to allogeneic stem cell transplantation, overall survival of patients with AML remains unsatisfying. The poor survival rates are mainly due to therapy-related mortality, failure of induction chemotherapy and early relapses. Therefore, novel therapeutic agents that are more efficient and better tolerated are eagerly sought after. For existing therapeutic strategies, there is a lack of markers that are capable of reliably predicting prognosis or the therapeutic response prior to treatment. There is hope that elucidation of the AML-specific proteome will prompt the discovery of novel therapeutic targets and biomarkers in AML. Modern mass-spectrometry instrumentation has achieved excellent performance in terms of sensitivity, resolution and mass accuracy; however, so far, the contribution of proteomics to the care of patients with AML is virtually zero. This might be partly because mass spectrometry instrumentation and protein fractionation still lack true high-throughput capabilities with highest levels of reproducibility, thus hampering large-scale translational studies with clinical samples. Since mass-spectrometry instruments are very intricate devices, their successful operation will hinge on the willingness and ability of mass-spectrometry experts and clinical researchers to adopt new views, learn from each other and cooperate in order to ultimately benefit the patient suffering from AML. This review highlights some clinical problems circumventing the treatment of patients with AML. Furthermore, it provides a brief overview of the technical background of standard proteomics approaches and describes opportunities, challenges and pitfalls of proteomic studies with regards to AML.

Biomarkers, Tumor↗
PubMed2003

Prostate cancer biomarkers: a current perspective.

Genomic- and proteomic-based studies have led to the identification of a large number of candidate biomarkers, as well as signature patterns of multiple markers for prostate cancer diagnosis, disease progression and prediction of survival. While these candidates include the usual suspects, including oncogenes, proliferation markers and cytoskeletal proteins, there are many additional unexpected molecules such as those involved in processes such as transcriptional repression and fatty acid metabolism. Patterns of expression serving as useful biomarkers is a new and, as yet, clinically untested concept which promises to permit a consideration of the complex milieu of cancer. Exciting as these developments are, clinical application will have to await careful validation of these candidates by independent biochemical approaches over large and diverse samples.

Biomarkers, Tumor↗
PubMed2004

Prognostic and predictive factors in early-stage breast cancer.

Breast cancer is the most common malignancy among American women. Due to increased screening, the majority of patients present with early-stage breast cancer. The Oxford Overview Analysis demonstrates that adjuvant hormonal therapy and polychemotherapy reduce the risk of recurrence and death from breast cancer. Adjuvant systemic therapy, however, has associated risks and it would be useful to be able to optimally select patients most likely to benefit. The purpose of adjuvant systemic therapy is to eradicate distant micrometastatic deposits. It is essential therefore to be able to estimate an individual patient's risk of harboring clinically silent micrometastatic disease using established prognostic factors. It is also beneficial to be able to select the optimal adjuvant therapy for an individual patient based on established predictive factors. It is standard practice to administer systemic therapy to all patients with lymph node-positive disease. However, there are clearly differences among node-positive women that may warrant a more aggressive therapeutic approach. Furthermore, there are many node-negative women who would also benefit from adjuvant systemic therapy. Prognostic factors therefore must be differentiated from predictive factors. A prognostic factor is any measurement available at the time of surgery that correlates with disease-free or overall survival in the absence of systemic adjuvant therapy and, as a result, is able to correlate with the natural history of the disease. In contrast, a predictive factor is any measurement associated with response to a given therapy. Some factors, such as hormone receptors and HER2/neu overexpression, are both prognostic and predictive.

Age Factors↗
PubMed2006

Robust Selection of Predictive Genes via a Simple Classifier.

Identifying genes that direct the mechanism of a disease from expression data is extremely useful in understanding how that mechanism works. This in turn may lead to better diagnoses and potentially could lead to a cure for that disease. This task becomes extremely challenging when the data are characterised by only a small number of samples and a high number of dimensions, as is often the case with gene expression data. Motivated by this challenge, we present a general framework that focuses on simplicity and data perturbation. These are the keys for robust identification of the most predictive features in such data. Within this framework, we propose a simple selective naive Bayes classifier discovered using a global search technique, and combine it with data perturbation to increase its robustness for small sample sizes. An extensive validation of the method was carried out using two applied datasets from the field of microarrays and a simulated dataset, all confounded by small sample sizes and high dimensionality. The method has been shown to be capable of selecting genes known to be associated with prostate cancer and viral infections.

Artificial Intelligence↗
PubMed2006

A nonparametric likelihood ratio test to identify differentially expressed genes from microarray data.

Microarray experiments contribute significantly to the progress in disease treatment by enabling a precise and early diagnosis. One of the major objectives of microarray experiments is to identify differentially expressed genes under various conditions. The statistical methods currently used to analyse microarray data are inadequate, mainly due to the lack of understanding of the distribution of microarray data. We present a nonparametric likelihood ratio (NPLR) test to identify differentially expressed genes using microarray data. The NPLR test is highly robust against extreme values and does not assume the distribution of the parent population. Simulation studies show that the NPLR test is more powerful than some of the commonly used methods, such as the two-sample t-test, the Mann-Whitney U-test and significance analysis of microarrays (SAM). When applied to microarray data, we found that the NPLR test identifies more differentially expressed genes than its competitors. The asymptotic distribution of the NPLR test statistic and the p-value function is presented. The application of the NPLR method is shown, using both synthetic and real-life data. The biological significance of some of the genes detected only by the NPLR method is discussed.

Algorithms↗
PubMed2006

An efficient strategy to identify early TPA-responsive genes during differentiation of HL-60 cells.

We have adopted a special experimental strategy to identify early responsive genes during 12-O-tetradecanoylphorbol-13-acetate (TPA)-induced macrophage-like differentiation of human myeloid leukemia cells (HL-60). This was performed in cells that were synchronized by nocodazole and treated with TPA in the presence of a protein synthesis inhibitor, cycloheximide, to prevent activation of secondary targets and therefore increase the probability of early transcripts in total RNA pool. The expression alteration was analyzed by microarray and the selection criteria of candidate genes were adjusted by real-time PCR validation to increase its reliability. Finally, 56 genes were identified as early TPA-responsive genes in this multiscreening step approach. Furthermore, upregulation of three candidate genes (NFIL3, SKIL, and JMJD3) was shown to be dosage and time dependent with TPA treatment and was found to be directly regulated by TPA through PKC-dependent signaling. These results revealed that our screenings provide a useful and efficient approach to identify early TPA-responsive genes and these genes might involve the regulation of TPA-induced differentiation program of HL-60 cells as primary

Antineoplastic Agents↗
PubMed2004

Gene regulation by phorbol 12-myristate 13-acetate in MCF-7 and MDA-MB-231, two breast cancer cell lines exhibiting highly different phenotypes.

We have examined the effects of the protein kinase C (PKC)-activator phorbol 12-myristate 13-acetate (PMA) on gene expression in two breast cancer cell (BCC) lines exhibiting highly different phenotypes. These are the estrogen receptor alpha (ERalpha)-positive, weakly invasive, luminal epithelial-like MCF-7 and the ERalpha-negative, highly invasive, fibroblast-like MDA-MB-231. They express constitutively low and high PKC activities, respectively. After a 24-h exposition to 100 nM PMA, the number of genes showing an altered expression at the 2-fold change level was much higher in MCF-7 (n=435) than in MDA-MB-231 (n=18) BCC. Four of these genes, namely CDC2, CENPA, NR4A1 and MMP10, were altered in the same way in both cell lines. Two genes were regulated in an opposite way: ID1 and EVA1. Many of the genes down-regulated in MCF-7 BCC appeared to be preferentially expressed in the G1, S, and/or G2 phases of the cell cycle. The ERalpha gene, ESR1, and other genes associated to the ERalpha-positive, luminal epithelial-like BCC phenotype were down-regulated, while a series of genes related to a more aggressive, fibroblast-like BCC phenotype were up-regulated. Other altered genes were notably linked to cell architecture, supporting profound effects of PMA on cell morphology and motility, as well as on the interactions between BCC and their neighboring proteins. Of note, all the modulated genes involved in proteolysis and its control were up-regulated. In summary, PMA effects suggest that PKC activation may induce, to some extent, a more fibroblast-like phenotype in the ERalpha-positive, luminal epithelial-like MCF-7 BCC, and significantly modulate the interactions of these cells with their environment.

Biomarkers, Tumor↗
PubMed2004

An inhibitory Ig superfamily protein expressed by lymphocytes and APCs is also an early marker of thymocyte positive selection.

Positive selection of developing thymocytes is associated with changes in cell function, at least in part caused by alterations in expression of cell surface proteins. Surprisingly, however, few such proteins have been identified. We have analyzed the pattern of gene expression during the early stages of murine thymocyte differentiation. These studies led to identification of a cell surface protein that is a useful marker of positive selection and is a likely regulator of mature lymphocyte and APC function. The protein is a member of the Ig superfamily and contains conserved tyrosine-based signaling motifs. The gene encoding this protein was independently isolated recently and termed B and T lymphocyte attenuator (Btla). We describe in this study anti-BTLA mAbs that demonstrate that the protein is expressed in the bone marrow and thymus on developing B and T cells, respectively. BTLA is also expressed by all mature lymphocytes, splenic macrophages, and mature, but not immature bone marrow-derived dendritic cells. Although mice deficient in BTLA do not show lymphocyte developmental defects, T cells from these animals are hyperresponsive to anti-CD3 Ab stimulation. Conversely, anti-BTLA Ab can inhibit T cell activation. These results implicate BTLA as a negative regulator of the activation and/or function of various hemopoietic cell types.

Alternative Splicing↗
PubMed2001

Analysis of gene expression identifies candidate markers and pharmacological targets in prostate cancer.

Detection, treatment, and prediction of outcome for men with prostate cancer increasingly depend on a molecular understanding of tumor development and behavior. We characterized primary prostate cancer by monitoring expression levels of more than 8900 genes in normal and malignant tissues. Patterns of gene expression across tissues revealed a precise distinction between normal and tumor samples, and revealed a striking group of about 400 genes that were overexpressed in tumor tissues. We ranked these genes according to their differential expression in normal and cancer tissues by selecting for highly and specifically overexpressed genes in the majority of cancers with correspondingly low or absent expression in normal tissues. Several such genes were identified that act within a variety of biochemical pathways and encode secreted molecules with diagnostic potential, such as the secreted macrophage inhibitory cytokine, MIC-1. Other genes, such as fatty acid synthase, encode enzymes known as drug targets in other contexts, which suggests new therapeutic approaches.

Adenocarcinoma↗
PubMed2002

Recent advances in oncoproteomics.

Advances in proteomics are contributing to the understanding of pathophysiology of cancer, cancer diagnosis and anticancer drug discovery. Laser capture microdissection (LCM) provides an ideal method for extraction of cells from specimens in which the exact morphologies of both the captured cells and the surrounding tissue are preserved. Differentially expressed proteins in tumor tissue are found by comparing the protein expression patterns generated using SELDI (surface-enhanced laser desorption/ionization)-based protein chip technology. Proteomic technologies have been used for the study of cancer of various organs. Continued refinement of techniques and methods to determine the abundance and status of proteins in vivo holds great promise for future study of cancer and development of personalized cancer therapies.

Biomarkers, Tumor↗
PubMedDate not supplied

Investigations for a multi-marker RT-PCR to improve sensitivity of disseminated tumor cell detection.

BACKGROUND: In order to develop a multi-marker RT-PCR, which as such may be more sensitive than a single marker assay for the detection of disseminated tumor cells, we evaluated six RT-PCR markers: cytokeratin 20 (CK20), carcinoembryonic antigen (CEA), guanylyl cyclase C (GCC), epidermal growth factor receptor (EGFR), matrilysin (MMP-7) and HeLa metastatic gene (HLM). MATERIALS AND METHODS: The expression was studied in human colon tumor cell lines, in colon cancer tissues, and in blood and/or bone marrow samples of colorectal cancer patients and control subjects. RESULTS: The cell lines showed a differential expression pattern. The expression of all markers was detected in control blood samples with the lowest frequency for CK20 and EGFR. Semiquantitative analysis, which was performed to study threshold setting, demonstrated that GCC expression was elevated in patient compared to control samples. However, the reproducibility was questionable. CONCLUSION: The results presented in this study suggest an enhanced sensitivity for a combination of RT-PCR markers. Due to limited specificity however, the development of a multi-marker RT-PCR by using conventional PCR does not seem feasible. Future studies should focus on the potential of quantitative RT-PCR.

Biomarkers, Tumor↗
PubMed2004

Pharmacogenomics in breast cancer: current trends and future directions.

Pharmacogenomics is the study of genetic variations between individuals to predict the risk of toxic side effects and the probability that a patient will respond to single- or multidrug chemotherapy. Breast cancer remains one of the most common cancers among women worldwide and is second only to lung cancer in cancer-related death. A better understanding of the mechanisms of initiation and progression of breast cancer is needed for early diagnosis and development of better therapeutic methodologies. Differences in cancer patients' responses to chemotherapy have often been attributed to pathogenesis and severity of the disease, drug interactions, patient's age, gender, nutritional status, organ functions and tumor biology. It is now well recognized that genetic variations in drug target genes, disease pathway genes and drug metabolizing enzymes can have greater influence on drug efficacy and toxicity. In addition, germline variants can be used to study breast cancer susceptibility, as well as the variable response to both drug and radiation therapy used in the treatment of breast cancer. This review discusses clinically relevant individual gene variations that influence breast cancer susceptibility and cancer therapy, as well as the microarray-based expression profiling studies that have great potential in cancer pharmacogenomics in terms of tumor classification, drug and biomarker discovery and drug efficacy testing.

Age Factors↗