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Richard Simon

Publications and source records attributed to Richard Simon.

At least 55 records · Page 3Linked to original sources
PubMed2003

Application of support vector machines for T-cell epitopes prediction.

MOTIVATION: The T-cell receptor, a major histocompatibility complex (MHC) molecule, and a bound antigenic peptide, play major roles in the process of antigen-specific T-cell activation. T-cell recognition was long considered exquisitely specific. Recent data also indicate that it is highly flexible, and one receptor may recognize thousands of different peptides. Deciphering the patterns of peptides that elicit a MHC restricted T-cell response is critical for vaccine development. RESULTS: For the first time we develop a support vector machine (SVM) for T-cell epitope prediction with an MHC type I restricted T-cell clone. Using cross-validation, we demonstrate that SVMs can be trained on relatively small data sets to provide prediction more accurate than those based on previously published methods or on MHC binding. SUPPLEMENTARY INFORMATION: Data for 203 synthesized peptides is available at http://linus.nci.nih.gov/Data/LAU203_Peptide.pdf

Algorithms↗
PubMed2003

Molecular diagnosis of primary mediastinal B cell lymphoma identifies a clinically favorable subgroup of diffuse large B cell lymphoma related to Hodgkin lymphoma.

Using current diagnostic criteria, primary mediastinal B cell lymphoma (PMBL) cannot be distinguished from other types of diffuse large B cell lymphoma (DLBCL) reliably. We used gene expression profiling to develop a more precise molecular diagnosis of PMBL. PMBL patients were considerably younger than other DLBCL patients, and their lymphomas frequently involved other thoracic structures but not extrathoracic sites typical of other DLBCLs. PMBL patients had a relatively favorable clinical outcome, with a 5-yr survival rate of 64% compared with 46% for other DLBCL patients. Gene expression profiling strongly supported a relationship between PMBL and Hodgkin lymphoma: over one third of the genes that were more highly expressed in PMBL than in other DLBCLs were also characteristically expressed in Hodgkin lymphoma cells. PDL2, which encodes a regulator of T cell activation, was the gene that best discriminated PMBL from other DLBCLs and was also highly expressed in Hodgkin lymphoma cells. The genomic loci for PDL2 and several neighboring genes were amplified in over half of the PMBLs and in Hodgkin lymphoma cell lines. The molecular diagnosis of PMBL should significantly aid in the development of therapies tailored to this clinically and pathogenetically distinctive subgroup of DLBCL.

Adult↗
PubMed2003

Evaluation of normalization methods for microarray data.

BACKGROUND: Microarray technology allows the monitoring of expression levels for thousands of genes simultaneously. This novel technique helps us to understand gene regulation as well as gene by gene interactions more systematically. In the microarray experiment, however, many undesirable systematic variations are observed. Even in replicated experiment, some variations are commonly observed. Normalization is the process of removing some sources of variation which affect the measured gene expression levels. Although a number of normalization methods have been proposed, it has been difficult to decide which methods perform best. Normalization plays an important role in the earlier stage of microarray data analysis. The subsequent analysis results are highly dependent on normalization. RESULTS: In this paper, we use the variability among the replicated slides to compare performance of normalization methods. We also compare normalization methods with regard to bias and mean square error using simulated data. CONCLUSIONS: Our results show that intensity-dependent normalization often performs better than global normalization methods, and that linear and nonlinear normalization methods perform similarly. These conclusions are based on analysis of 36 cDNA microarrays of 3,840 genes obtained in an experiment to search for changes in gene expression profiles during neuronal differentiation of cortical stem cells. Simulation studies confirm our findings.

Analysis of Variance↗
PubMed2003

A strategy for detection of known and unknown SNP using a minimum number of oligonucleotides applicable in the clinical settings.

Detection of unknown single nucleotide polymorphism (SNP) relies on large scale sequencing expeditions of genomic fragments or complex high-throughput chip technology. We describe a simplified strategy for fluorimetric detection of known and unknown SNP by proportional hybridization to oligonucleotide arrays based on optimization of the established principle of signal loss or gain that requires a drastically reduced number of matched or mismatched probes. The array consists of two sets of 18-mer oligonucleotide probes. One set includes overlapping oligos with 4-nucleotide tiling representing an arbitrarily selected "consensus" sequence (consensus-oligos), the other includes oligos specific for known SNP within the same genomic region (variant-oligos). Fluorescence-labeled DNA amplified from a homozygous source identical to the consensus represents the reference target and is co-hybridized with a differentially-labeled test sample. Lack of hybridization of the test sample to consensus- with simultaneous hybridization to variant-oligos designates a known allele. Lack of hybridization to consensus- and variant-oligos indicates a new allele. Detection of unknown variants in heterozygous samples depends upon fluorimetric analysis of signal intensity based on the principle that homozygous samples generate twice the amount of signal. This method can identify unknown SNP in heterozygous conditions with a sensitivity of 82% and specificity of 90%. This strategy should dramatically increase the efficiency of SNP detection throughout the human genome and will decrease the cost and complexity of applying genomic wide analysis in the context of clinical trials.

Journal Article↗
PubMed2003

Predicting survival in patients with metastatic kidney cancer by gene-expression profiling in the primary tumor.

To identify potential molecular determinants of tumor biology and possible clinical outcomes, global gene-expression patterns were analyzed in the primary tumors of patients with metastatic renal cell cancer by using cDNA microarrays. We used grossly dissected tumor masses that included tumor, blood vessels, connective tissue, and infiltrating immune cells to obtain a gene-expression "profile" from each primary tumor. Two patterns of gene expression were found within this uniformly staged patient population, which correlated with a significant difference in overall survival between the two patient groups. Subsets of genes most significantly associated with survival were defined, and vascular cell adhesion molecule-1 (VCAM-1) was the gene most predictive for survival. Therefore, despite the complex biological nature of metastatic cancer, basic clinical behavior as defined by survival may be determined by the gene-expression patterns expressed within the compilation of primary gross tumor cells. We conclude that survival in patients with metastatic renal cell cancer can be correlated with the expression of various genes based solely on the expression profile in the primary kidney tumor.

Adult↗
PubMed2003

Predicting hepatitis B virus-positive metastatic hepatocellular carcinomas using gene expression profiling and supervised machine learning.

Hepatocellular carcinoma (HCC) is one of the most common and aggressive human malignancies. Its high mortality rate is mainly a result of intra-hepatic metastases. We analyzed the expression profiles of HCC samples without or with intra-hepatic metastases. Using a supervised machine-learning algorithm, we generated for the first time a molecular signature that can classify metastatic HCC patients and identified genes that were relevant to metastasis and patient survival. We found that the gene expression signature of primary HCCs with accompanying metastasis was very similar to that of their corresponding metastases, implying that genes favoring metastasis progression were initiated in the primary tumors. Osteopontin, which was identified as a lead gene in the signature, was over-expressed in metastatic HCC; an osteopontin-specific antibody effectively blocked HCC cell invasion in vitro and inhibited pulmonary metastasis of HCC cells in nude mice. Thus, osteopontin acts as both a diagnostic marker and a potential therapeutic target for metastatic HCC.

Algorithms↗
PubMed2003

The proliferation gene expression signature is a quantitative integrator of oncogenic events that predicts survival in mantle cell lymphoma.

We used gene expression profiling to establish a molecular diagnosis of mantle cell lymphoma (MCL), to elucidate its pathogenesis, and to predict the length of survival of these patients. An MCL gene expression signature defined a large subset of MCLs that expressed cyclin D1 and a novel subset that lacked cyclin D1 expression. A precise measurement of tumor cell proliferation, provided by the expression of proliferation signature genes, identified patient subsets that differed by more than 5 years in median survival. Differences in cyclin D1 mRNA abundance synergized with INK4a/ARF locus deletions to dictate tumor proliferation rate and survival. We propose a quantitative model of the aberrant cell cycle regulation in MCL that provides a rationale for the design of cell cycle inhibitor therapy in this malignancy.

ADP-Ribosylation Factors↗
PubMed2003

Molecular differentiation of high- and moderate-grade human prostate cancer by cDNA microarray analysis.

The prognosis of men with moderate-grade prostate cancer is uncertain. At present, there are few if any reliable molecular markers that can distinguish moderate-grade tumors from those that behave more aggressively. To better understand the molecular basis of human prostate cancer and potentially provide information toward more accurate prognosis, we measured and analyzed gene expression profiles of 13 high- and moderate-grade human prostate tumors using cDNA microarrays. The expression of 136 genes was observed to differ significantly (P < 0.001) between normal prostate and tumors using one-sample t testing and Wilcoxon ranking. Hierarchical clustering of genes demonstrated a relatively similar pattern of differential expression across the tumors. However, importantly, permutation t tests (two-tailed P < 0.001) revealed 21 genes whose expression profiles segregated moderate- and high-grade tumors from each other, which was significantly (P < 0.03) greater than what was expected by chance. These results were compared in silico with prostate cancer profiling efforts performed by other groups, including a meta-analysis of four data sets, which validated many of the dysregulated genes. We suggest that these data provide insight into the molecular nature of clinically aggressive prostate cancer.

Adenocarcinoma↗
PubMed2003

Using DNA microarrays for diagnostic and prognostic prediction.

DNA microarrays are a potentially powerful technology for improving diagnostic classification, treatment selection and prognostic assessment. There are, however, many potential pitfalls in the use of microarrays that result in false leads and erroneous conclusions. Effective use of this technology requires new levels of interdisciplinary collaboration with statistical and computational scientists. This paper provides a review of the key features to be observed in developing diagnostic and prognostic classification systems based upon gene expression profiling. It also attempts to outline some of the steps needed to develop initial microarray research findings into classification systems suitable for broad clinical application.

Algorithms↗
PubMed2003

Distinguishing right from left colon by the pattern of gene expression.

Distinct epidemiological and clinicopathological characteristics of colorectal carcinomas (CRCs) based on their anatomical location suggest different risk factors and pathways of transformation associated with proximal and distal colon carcinogenesis. These differences may reflect distinct biological characteristics of proximal and distal colonic mucosa, acquired in embryonic or postnatal development, that determine a differential response to uniformly distributed environmental factors. Alternatively, the differences in the epidemiology of proximal and distal CRCs could result from the presence of different procarcinogenic factors in the ascending versus descending colon, acting on cells with either similar or distinct biological characteristics. We applied cDNA microarray technology to explore the possibility that mucosal epithelium from adult proximal and distal colon can be distinguished by their pattern of gene expression. In addition, gene expression was studied in fetal (17-24 weeks gestation) proximal and distal colon. More than 1000 genes were expressed differentially in adult ascending versus descending colon, with 165 genes showing >2-fold and 49 genes showing >3-fold differences in expression. With almost complete concordance, biopsies of adult colonic epithelium can be correctly classified as proximal or distal by gene expression profile. Only 87 genes were expressed differently in ascending and descending fetal colon, indicating that, although anatomically relevant differences are already established in embryonic colon, additional changes in gene expression occur in postnatal development.

Adult↗
PubMed2002

Positional scanning-synthetic peptide library-based analysis of self- and pathogen-derived peptide cross-reactivity with tumor-reactive Melan-A-specific CTL.

Synthetic combinatorial peptide libraries in positional scanning format (PS-SCL) have recently emerged as a useful tool for the analysis of T cell recognition. This includes identification of potentially cross-reactive sequences of self or pathogen origin that could be relevant for the understanding of TCR repertoire selection and maintenance, as well as of the cross-reactive potential of Ag-specific immune responses. In this study, we have analyzed the recognition of sequences retrieved by using a biometric analysis of the data generated by screening a PS-SCL with a tumor-reactive CTL clone specific for an immunodominant peptide from the melanocyte differentiation and tumor-associated Ag Melan-A. We found that 39% of the retrieved peptides were recognized by the CTL clone used for PS-SCL screening. The proportion of peptides recognized was higher among those with both high predicted affinity for the HLA-A2 molecule and high predicted stimulatory score. Interestingly, up to 94% of the retrieved peptides were cross-recognized by other Melan-A-specific CTL. Cross-recognition was at least partially focused, as some peptides were cross-recognized by the majority of CTL. Importantly, stimulation of PBMC from melanoma patients with the most frequently recognized peptides elicited the expansion of heterogeneous CD8(+) T cell populations, one fraction of which cross-recognized Melan-A. Together, these results underline the high predictive value of PS-SCL for the identification of sequences cross-recognized by Ag-specific T cells.

Antigens, Bacterial↗
PubMed2002

Bayesian subset analysis: application to studying treatment-by-gender interactions.

Evaluating treatment effects within subsets of patients plays a major part of the analysis of many major clinical trials. Clinicians are often impressed by the heterogeneity of patient populations in clinical trials and hence are interested in examining subset effects. Statisticians generally discourage subset analysis or suggest that clinicians 'do subset analysis but do not believe it'. This advice, however, is a sign of the inadequacy of the analytic methods generally used for subset analysis. Separate analysis of many subsets, and basing conclusions on whether the observed treatment difference achieves significance at the 0.05 level, is likely to yield erroneous conclusions. Making the separate analysis of subsets dependent on demonstration of a statistically significant treatment-by-subset interaction is also not an effective analytic strategy because of the limited power of interaction tests. This paper describes a Bayesian approach to subset analysis developed by Simon, Dixon and Freidlin. The method avoids many of the problems of subset analysis because it is not 'separate' analysis of subsets. Instead, subset-specific treatment effects are estimated as an average of observed within-subset differences and overall differences; the two components are weighted by the a priori estimate of the likelihood of qualitative treatment by subset interactions. Hence, the Bayesian method proposed permits subset analyses incorporating the assumption that qualitative interactions are unlikely. The methodology is applied to the problem of designing and analysing clinical trials to estimate treatment effects for males and females.

Bayes Theorem↗
PubMed2002

Findings on T cell specificity revealed by synthetic combinatorial libraries.

Combinatorial libraries and in particular positional scanning synthetic combinatorial libraries (PS-SCL) allow the study of T cell specificity. This is a systematic and unbiased approach that does not require any previous knowledge about the clones to be studied, neither their specificity nor they major histocompatibility complex (MHC) restriction. Two different types of T cell clone ligands can be identified: (1) peptides that do not necessarily correspond to proteins described in the databases, and (2) peptides that are fragments of natural proteins. In this paper, relevant examples of the application of PS-SCL and the deconvolution strategies followed to identify T cell epitopes for clones of known and unknown specificity will be reviewed. Also, important issues like the immunogenicity of such T cell ligands will be discussed.

Animals↗
PubMed2002

Prospective molecular profiling of melanoma metastases suggests classifiers of immune responsiveness.

We amplified RNAs from 63 fine needle aspiration (FNA) samples from 37 s.c. melanoma metastases from 25 patients undergoing immunotherapy for hybridization to a 6108-gene human cDNA chip. By prospectively following the history of the lesions, we could correlate transcript patterns with clinical outcome. Cluster analysis revealed a tight relationship among autologous synchronously sampled tumors compared with unrelated lesions (average Pearson's r = 0.83 and 0.7, respectively, P < 0.0003). As reported previously, two subgroups of metastatic melanoma lesions were identified that, however, had no predictive correlation with clinical outcome. Ranking of gene expression data from pretreatment samples identified approximately 30 genes predictive of clinical response (P < 0.001). Analysis of their annotations denoted that approximately half of them were related to T-cell regulation, suggesting that immune responsiveness might be predetermined by a tumor microenvironment conducive to immune recognition.

Adult↗
PubMed2002

The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma.

BACKGROUND: The survival of patients with diffuse large-B-cell lymphoma after chemotherapy is influenced by molecular features of the tumors. We used the gene-expression profiles of these lymphomas to develop a molecular predictor of survival. METHODS: Biopsy samples of diffuse large-B-cell lymphoma from 240 patients were examined for gene expression with the use of DNA microarrays and analyzed for genomic abnormalities. Subgroups with distinctive gene-expression profiles were defined on the basis of hierarchical clustering. A molecular predictor of risk was constructed with the use of genes with expression patterns that were associated with survival in a preliminary group of 160 patients and was then tested in a validation group of 80 patients. The accuracy of this predictor was compared with that of the international prognostic index. RESULTS: Three gene-expression subgroups--germinal-center B-cell-like, activated B-cell-like, and type 3 diffuse large-B-cell lymphoma--were identified. Two common oncogenic events in diffuse large-B-cell lymphoma, bcl-2 translocation and c-rel amplification, were detected only in the germinal-center B-cell-like subgroup. Patients in this subgroup had the highest five-year survival rate. To identify other molecular determinants of outcome, we searched for individual genes with expression patterns that correlated with survival in the preliminary group of patients. Most of these genes fell within four gene-expression signatures characteristic of germinal-center B cells, proliferating cells, reactive stromal and immune cells in the lymph node, or major-histocompatibility-complex class II complex. We used 17 genes to construct a predictor of overall survival after chemotherapy. This gene-based predictor and the international prognostic index were independent prognostic indicators. CONCLUSIONS: DNA microarrays can be used to formulate a molecular predictor of survival after chemotherapy for diffuse large-B-cell lymphoma.

Antibiotics, Antineoplastic↗
PubMed2002

Laser capture microdissection and microarray expression analysis of lung adenocarcinoma reveals tobacco smoking- and prognosis-related molecular profiles.

Recent expression profile analyses revealed that lung adenocarcinomas can be divided into several subgroups with diverse pathological features. Because cellular heterogeneity of tumors can confound these analyses, we used laser capture microdissection and microarray expression analysis to characterize the molecular profiles of lung adenocarcinomas. We found 45 genes delineating smokers and nonsmokers that were located at chromosomal loci frequently altered in non-small cell lung cancers, and 27 genes, which were differentially expressed between survivors and nonsurvivors 5 years after surgery. These results are consistent with the hypothesis that the abnormal expression of genes involved in maintaining the mitotic spindle checkpoint and genomic stability, e.g., hBUB3, hZW10, and APC2, contribute to the molecular pathogenesis and tumor progression of tobacco smoke-induced adenocarcinoma of the lung.

Adenocarcinoma↗