Biomedical subjects

Joel Parker

Publications and source records attributed to Joel Parker.

8 recordsLinked to original sources

PubMed2006

Increased epidermal growth factor receptor gene copy number is associated with poor prognosis in head and neck squamous cell carcinomas.

PURPOSE: High epidermal growth factor receptor (EGFR) gene copy number is associated with poor prognosis in lung cancer, but such findings have not been reported for HNSCC. A better understanding of the EGFR pathway may improve the use of EGFR inhibitors in HNSCC. PATIENTS AND METHODS: EGFR status was analyzed in 86 tumor samples from 82 HNSCC patients by fluorescent in situ hybridization (FISH) to determine EGFR gene copy number, by polymerase chain reaction and direct sequencing for activating mutations, and by DNA microarray and immunohistochemistry for RNA and protein expression. The results were associated with patient characteristics and clinical end points. RESULTS: Forty-three (58%) of 75 samples with FISH results demonstrated EGFR high polysomy and/or gene amplification (FISH positive). The FISH-positive group did not differ from the FISH-negative group with respect to age, sex, race, tumor grade, subsites and stage, or EGFR expression by analyses of RNA or protein. No activating EGFR mutations were found. However, the FISH-positive group was associated with worse progression-free and overall survival (P < .05 and P < .01, respectively; log-rank test). When microarray data were interrogated using the FISH results as a supervising parameter, ECop (which is known to coamplify with EGFR and regulate nuclear factor-kappa B transcriptional activity) had higher expression in FISH-positive tumors. CONCLUSION: High EGFR gene copy number by FISH is frequent in HNSCC and is a poor prognostic indicator. Additional investigation is indicated to determine the biologic significance and implications for EGFR inhibitor therapies in HNSCC.

Aneuploidy↗

PubMed2006

The molecular portraits of breast tumors are conserved across microarray platforms.

BACKGROUND: Validation of a novel gene expression signature in independent data sets is a critical step in the development of a clinically useful test for cancer patient risk-stratification. However, validation is often unconvincing because the size of the test set is typically small. To overcome this problem we used publicly available breast cancer gene expression data sets and a novel approach to data fusion, in order to validate a new breast tumor intrinsic list. RESULTS: A 105-tumor training set containing 26 sample pairs was used to derive a new breast tumor intrinsic gene list. This intrinsic list contained 1300 genes and a proliferation signature that was not present in previous breast intrinsic gene sets. We tested this list as a survival predictor on a data set of 311 tumors compiled from three independent microarray studies that were fused into a single data set using Distance Weighted Discrimination. When the new intrinsic gene set was used to hierarchically cluster this combined test set, tumors were grouped into LumA, LumB, Basal-like, HER2+/ER-, and Normal Breast-like tumor subtypes that we demonstrated in previous datasets. These subtypes were associated with significant differences in Relapse-Free and Overall Survival. Multivariate Cox analysis of the combined test set showed that the intrinsic subtype classifications added significant prognostic information that was independent of standard clinical predictors. From the combined test set, we developed an objective and unchanging classifier based upon five intrinsic subtype mean expression profiles (i.e. centroids), which is designed for single sample predictions (SSP). The SSP approach was applied to two additional independent data sets and consistently predicted survival in both systemically treated and untreated patient groups. CONCLUSION: This study validates the "breast tumor intrinsic" subtype classification as an objective means of tumor classification that should be translated into a clinical assay for further retrospective and prospective validation. In addition, our method of combining existing data sets can be used to robustly validate the potential clinical value of any new gene expression profile.

Breast Neoplasms↗

PubMed2006

Classification and risk stratification of invasive breast carcinomas using a real-time quantitative RT-PCR assay.

INTRODUCTION: Predicting the clinical course of breast cancer is often difficult because it is a diverse disease comprised of many biological subtypes. Gene expression profiling by microarray analysis has identified breast cancer signatures that are important for prognosis and treatment. In the current article, we use microarray analysis and a real-time quantitative reverse-transcription (qRT)-PCR assay to risk-stratify breast cancers based on biological 'intrinsic' subtypes and proliferation. METHODS: Gene sets were selected from microarray data to assess proliferation and to classify breast cancers into four different molecular subtypes, designated Luminal, Normal-like, HER2+/ER-, and Basal-like. One-hundred and twenty-three breast samples (117 invasive carcinomas, one fibroadenoma and five normal tissues) and three breast cancer cell lines were prospectively analyzed using a microarray (Agilent) and a qRT-PCR assay comprised of 53 genes. Biological subtypes were assigned from the microarray and qRT-PCR data by hierarchical clustering. A proliferation signature was used as a single meta-gene (log2 average of 14 genes) to predict outcome within the context of estrogen receptor status and biological 'intrinsic' subtype. RESULTS: We found that the qRT-PCR assay could determine the intrinsic subtype (93% concordance with microarray-based assignments) and that the intrinsic subtypes were predictive of outcome. The proliferation meta-gene provided additional prognostic information for patients with the Luminal subtype (P = 0.0012), and for patients with estrogen receptor-positive tumors (P = 3.4 x 10-6). High proliferation in the Luminal subtype conferred a 19-fold relative risk of relapse (confidence interval = 95%) compared with Luminal tumors with low proliferation. CONCLUSION: A real-time qRT-PCR assay can recapitulate microarray classifications of breast cancer and can risk-stratify patients using the intrinsic subtype and proliferation. The proliferation meta-gene offers an objective and quantitative measurement for grade and adds significant prognostic information to the biological subtypes.

Breast Neoplasms↗

PubMed2004

A custom microarray platform for analysis of microRNA gene expression.

MicroRNAs are short, noncoding RNA transcripts that post-transcriptionally regulate gene expression. Several hundred microRNA genes have been identified in Caenorhabditis elegans, Drosophila, plants and mammals. MicroRNAs have been linked to developmental processes in C. elegans, plants and humans and to cell growth and apoptosis in Drosophila. A major impediment in the study of microRNA function is the lack of quantitative expression profiling methods. To close this technological gap, we have designed dual-channel microarrays that monitor expression levels of 124 mammalian microRNAs. Using these tools, we observed distinct patterns of expression among adult mouse tissues and embryonic stem cells. Expression profiles of staged embryos demonstrate temporal regulation of a large class of microRNAs, including members of the let-7 family. This microarray technology enables comprehensive investigation of microRNA expression, and furthers our understanding of this class of recently discovered noncoding RNAs.

Animals↗

PubMed2004

Adjustment of systematic microarray data biases.

MOTIVATION: Systematic differences due to experimental features of microarray experiments are present in most large microarray data sets. Many different experimental features can cause biases including different sources of RNA, different production lots of microarrays or different microarray platforms. These systematic effects present a substantial hurdle to the analysis of microarray data. RESULTS: We present here a new method for the identification and adjustment of systematic biases that are present within microarray data sets. Our approach is based on modern statistical discrimination methods and is shown to be very effective in removing systematic biases present in a previously published breast tumor cDNA microarray data set. The new method of 'Distance Weighted Discrimination (DWD)' is shown to be better than Support Vector Machines and Singular Value Decomposition for the adjustment of systematic microarray effects. In addition, it is shown to be of general use as a tool for the discrimination of systematic problems present in microarray data sets, including the merging of two breast tumor data sets completed on different microarray platforms. AVAILABILITY: Matlab software to perform DWD can be retrieved from https://genome.unc.edu/pubsup/dwd/

Algorithms↗

PubMed2004

Profiles of gene expression in human autoimmune disease.

Human autoimmune diseases arise from complex interactions between genetic and environmental factors, result from immune attack upon target tissues, and affect 3-5% of the population. We compared gene expression profiles (>4000 genes) in the peripheral blood mononuclear cells of normal individuals after immunization to individuals with four different autoimmune diseases (rheumatoid arthritis, systemic lupus erythematosus, insulin-dependent diabetes mellitus, and multiple sclerosis). All autoimmune individuals, including unaffected first-degree relatives, share a common gene expression profile that is completely distinct from the immune profile. Therefore, this expression pattern is not simply a recapitulation of the immune response to nonself, is not a result of the disease process, and results, as least in part, from genetic factors. Surprisingly, these genes are clustered in chromosomal domains suggesting there is some genome-wide logic to this unique expression pattern. These data argue that that there is a constant pattern of gene expression in autoimmunity that is independent of the specific autoimmune disease and clinical parameters associated with any individual autoimmune disease.

Autoimmune Diseases↗

PubMed2003

Repeated observation of breast tumor subtypes in independent gene expression data sets.

Characteristic patterns of gene expression measured by DNA microarrays have been used to classify tumors into clinically relevant subgroups. In this study, we have refined the previously defined subtypes of breast tumors that could be distinguished by their distinct patterns of gene expression. A total of 115 malignant breast tumors were analyzed by hierarchical clustering based on patterns of expression of 534 "intrinsic" genes and shown to subdivide into one basal-like, one ERBB2-overexpressing, two luminal-like, and one normal breast tissue-like subgroup. The genes used for classification were selected based on their similar expression levels between pairs of consecutive samples taken from the same tumor separated by 15 weeks of neoadjuvant treatment. Similar cluster analyses of two published, independent data sets representing different patient cohorts from different laboratories, uncovered some of the same breast cancer subtypes. In the one data set that included information on time to development of distant metastasis, subtypes were associated with significant differences in this clinical feature. By including a group of tumors from BRCA1 carriers in the analysis, we found that this genotype predisposes to the basal tumor subtype. Our results strongly support the idea that many of these breast tumor subtypes represent biologically distinct disease entities.

Breast Neoplasms↗

PubMed2002

Cutting edge: molecular portrait of human autoimmune disease.

Autoimmune diseases affect 3-5% of the population, are mediated by the immune response to self-Ags, and are characterized by the site of tissue destruction. We compared expression levels of >4,000 genes in PBMC of control individuals before and after immunization to those of individuals with four distinct autoimmune diseases. The gene expression profile of the normal immune response exhibits coordinate changes in expression of genes with related functions over time. In contrast, each individual from all autoimmune diseases displays a similar gene expression profile unrelated to the pattern of the immunized group. To our surprise, genes with a distinct expression pattern in autoimmunity are not necessarily "immune response" genes, but are genes that encode proteins involved in apoptosis, cell cycle progression, cell differentiation, and cell migration.

Adult↗