Biomedical subjects

Philip S Bernard

Publications and source records attributed to Philip S Bernard.

10 recordsLinked to original sources

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↗

PubMed2005

Molecular classification of melanoma using real-time quantitative reverse transcriptase-polymerase chain reaction.

BACKGROUND: The early detection and characterization of metastatic melanoma are important for prognosis and management of the disease. Molecular methods are more sensitive in detecting occult lymph node metastases compared with standard histopathology and are reported to have utility in clinical diagnostics. METHODS: Using real-time quantitative reverse transcriptase-polymerase chain reaction ([q]RT-PCR), the authors examined 36 samples (30 melanomas, 4 benign nevi, and 2 reactive lymph nodes) for the expression of 20 melanoma-related genes that function in cell growth and differentiation (epidermal growth factor receptor [EGFR], WNT5A, BRAF, FOS, JUN, MATP, and TMP1), cell proliferation (KI-67, TOP2A, BUB1, BIRC5, and STK6), melanoma progression (CD63, MAGEA3, and GALGT), and melanin synthesis (TYR, MLANA, SILV, PAX3, and MITF). In addition, samples were tested for mutations in BRAF (exons 11 and 15) and NRAS (exons 2 and 3). RESULTS: Hierarchical clustering analysis of the expression data was able to distinguish between the melanoma and nonmelanoma samples and further stratified the melanoma samples into two groups differentiated by high expression of the genes involved in beta-catenin activation (EGFR and WNT5A) and the MAPK/ERK pathway (BRAF, FOS, and JUN). Eighteen of the 28 patients (64%) were found to have mutations in either exon 15 of BRAF (V599 substitution) or codon 61 of NRAS. The mutations were mutually exclusive and did not appear to be associated with the different expression subtypes. CONCLUSIONS: The results of the current study demonstrate that real-time qRT-PCR can be analyzed using hierarchical clustering to identify expression patterns that differentiate between melanomas and other tissue types. Using a supervised analysis of the data, the authors found that the best discriminators for molecularly distinguishing between melanoma, benign nevi, and lymph nodes were MLANA, CD63, and BUB1. These markers could have diagnostic utility for the detection of melanoma micrometastasis in sentinel lymph nodes.

Biomarkers, Tumor↗

PubMed2004

Discriminate gene lists derived from cDNA microarray profiles of limited samples permit distinguishing mesenchymal neoplasia ex vivo.

BACKGROUND: Mesenchymal neoplasia comprises a heterogeneous group of tumors with over 200 benign neoplasms and 100 sarcomas. Currently, tumors are classified using histologic and immunocytologic characteristics, with diagnostic error rates reported as high as 40% of cases. As a feasibility study, our goal was to generate a preliminary discriminatory gene list for selected mesenchymal tumors, including sarcomas. This technique may enable an eventual molecular classification schema based on expression profiles that can complement current clinical and pathologic diagnostic procedures in mesenchymal tumors. METHODS: cDNA microarray analyses were preformed on connective tissue tumors obtained at time of surgical resection or biopsy. Messenger RNA (mRNA) from four general tumor classes was competitively hybridized against a human dermal fibroblast cell line comparator and the resulting gene expression profiles processed by ANOVA and linear discriminate analysis. RESULTS: The tissue classification involved 18 patients with malignant peripheral nerve sheath tumors, giant cell containing tumors, benign spindle cell lesions, or Ewing's family of tumors. Lymph nodes from two patients served comparative purposes. Twenty-five differentially regulated genes considered most variable among the five tissue classes were identified. The tissues were segregated into five classes by linear discriminate analysis. CONCLUSIONS: Linear discriminate analysis of cDNA gene expression profiles partitioned mesenchymal tumor classes, even when constrained by limited sample sizes.

Analysis of Variance↗

PubMed2004

Statistical modeling for selecting housekeeper genes.

There is a need for statistical methods to identify genes that have minimal variation in expression across a variety of experimental conditions. These 'housekeeper' genes are widely employed as controls for quantification of test genes using gel analysis and real-time RT-PCR. Using real-time quantitative RT-PCR, we analyzed 80 primary breast tumors for variation in expression of six putative housekeeper genes (MRPL19 (mitochondrial ribosomal protein L19), PSMC4 (proteasome (prosome, macropain) 26S subunit, ATPase, 4), SF3A1 (splicing factor 3a, subunit 1, 120 kDa), PUM1 (pumilio homolog 1 (Drosophila)), ACTB (actin, beta) and GAPD (glyceraldehyde-3-phosphate dehydrogenase)). We present appropriate models for selecting the best housekeepers to normalize quantitative data within a given tissue type (for example, breast cancer) and across different types of tissue samples.

ATPases Associated with Diverse Cellular Activitie↗

PubMed2004

State of the science: molecular classifications of breast cancer for clinical diagnostics.

Over the past few years, the study of genomics has embarked on developing gene expression-based classifications for tumors-an initiative that promises to revolutionize cancer medicine. High-throughput genomic platforms, such as microarray and SAGE, have found gene expression signatures that correlate to important clinical parameters used in current staging and are providing additional information that will improve standard of care. Although implementing a molecular taxonomy for prognosis and treatment would likely benefit cancer patients, there remain significant obstacles to using these assays within the current diagnostic framework. Since most genomic assays are being performed from fresh tissue, there is a need to either change the practice of formalin-fixing and paraffin-embedding tissue or adapting the assays for use on degraded RNA specimens. To date, even the most mature data sets, such as molecular classifications for breast cancer, still fall short of the number of patients needed to generalize the results to treating large populations. To implement these assays in large scale, there will need to be standardization of sample procurement, preparation, and analysis. Certainly, the greatest improvements in patient care will come through tailored therapies as genomics is coupled with clinical trials that randomize cohorts to different treatments. This manuscript reviews the current standards of care, presents progress that is being made in the development of genomic assays for breast cancer and discusses options for implementing these new tests into the clinical setting.

Biomarkers, Tumor↗

PubMed2003

Color multiplex polymerase chain reaction for quantitative analysis of epidermal growth factor receptor genes in colorectal adenocarcinoma.

BACKGROUND AND OBJECTIVES: Epidermal growth factor receptor (EGFR) is a 170-kDa transmembrane cell surface receptor, which belongs to the c-erbB family of tyrosine kinases. Chimeric anti-EGFR monoclonal antibody is an investigational therapy for advanced adenocarcinoma of the colon. Anti-EGFR is believed to be most effective against those neoplasms with elevated EGFR levels. Possible mechanisms for over expression of EGFR include gene amplification, transcriptional upregulation, or decreased degredation of the EGFR protein. METHODS: We analyzed a series of 36 specimens of colonic adenocarcinoma with known levels of EGFR protein expression for amplification of the gene sequence of EGFR. Carcinomas were considered positive for EGFR expression when greater than 10% of the neoplastic cells stained at a level of 2+ or 3+. Foci of adenocarcinoma were microdissected from paraffin sections and quantitative real-time PCR (polymerase chain reaction) performed using a thermal cycler with real time fluorescence capability (Light cycler(TM), Roche Diagnostics, Indianapolis, IN). A relative quantitation assay comparing the EGFR gene to the control albumin gene was performed by 2-color multiplexing. RESULTS: Usable data on gene amplification status were obtained in 31 of the 36 samples. The average EGFR/albumin gene copy number ratio for the 31 samples of colon adenocarcinoma in which PCR results were obtained was 1.13 +/- 0.55 with a range of 0.26-2.29. The average EGFR/albumin gene copy number ratio obtained for 16 normal DNA leukocyte samples used to establish the efficiency curves was 1.03 +/- 0.31 with a range of 0.49-1.51. CONCLUSIONS: EGFR protein is overexpressed in a significant percentage of colonic adenocarcinomas. As with Her-2/neu overexpression, overexpression of EGFR serves as a basis for specific antibody therapy in a subset of carcinomas. Unlike Her-2/neu, where most overexpression is secondary to gene amplification, overexpression of EGFR appears to be unrelated to gene amplification.

Adenocarcinoma↗

PubMed2002

Molecular portraits and the family tree of cancer.

The twenty-first century heralds a new era for the biological sciences and medicine. The tools of our time are allowing us to analyze complex genomes more comprehensively than ever before. A principal technology contributing to this explosion of information is the DNA microarray, which enables us to study genome-wide expression patterns in complex biological systems. Although the potential of microarrays is yet to be fully realized, these tools have shown great promise in deciphering complex diseases such as cancer. The early results are painting a detailed portrait of cancer that illustrates the individuality of each tumor and allows familial relationships to be recognized through the identification of cell types sharing common expression patterns.

DNA, Neoplasm↗

PubMed2002

Real-time PCR technology for cancer diagnostics.

BACKGROUND: Advances in the biological sciences and technology are providing molecular targets for diagnosing and treating cancer. Current classifications in surgical pathology for staging malignancies are based primarily on anatomic features (e.g., tumor-node-metastasis) and histopathology (e.g., grade). Microarrays together with clustering algorithms are revealing a molecular diversity among cancers that promises to form a new taxonomy with prognostic and, more importantly, therapeutic significance. The challenge for pathology will be the development and implementation of these molecular classifications for routine clinical practice. APPROACH: This article discusses the benefits, challenges, and possibilities for solid-tumor profiling in the clinical laboratory with an emphasis on DNA-based PCR techniques. CONTENT: Molecular markers can be used to provide accurate prognosis and to predict response, resistance, or toxicity to therapy. The diversity of genomic alterations involved in malignancy necessitates a variety of assays for complete tumor profiling. Some new molecular classifications of tumors are based on gene expression, requiring a paradigm shift in specimen processing to preserve the integrity of RNA for analysis. More stable markers (i.e., DNA and protein) are readily handled in the clinical laboratory. Quantitative real-time PCR can determine gene duplications or deletions. Furthermore, melting curve analysis immediately after PCR can identify small mutations, down to single base changes. These techniques are becoming easier and faster and can be multiplexed. Real-time PCR methods are a favorable option for the analysis of cancer markers. SUMMARY: There is a need to translate recent discoveries in oncology research into clinical practice. This requires objective, robust, and cost-effective molecular techniques for clinical trials and, eventually, routine use. Real-time PCR has attractive features for tumor profiling in the clinical laboratory.

Biomarkers, Tumor↗

PubMed2002

Homogeneous amplification and mutation scanning of the p53 gene using fluorescent melting curves.

BACKGROUND: In malignancy, gene mutations frequently occur in tumor suppressor genes such as p53 and are sporadically located. We describe a homogeneous method for amplification and mutation scanning, and apply the method to the p53 gene. METHODS: Using a series of overlapping fluorescein-labeled oligonucleotides complementary to a wild-type p53 sequence, we detected somatic mutations in colorectal cancers by aberrant probe:target melting temperatures (T(m)). The probes were designed so that fluorescence decreased on target annealing as a result of deoxyguanosine quenching. Probes were walked along the sequence to be scanned, using two to three probes per cuvette and placing overlapping probes in separate reactions. After amplification, the reaction was cooled to anneal probes and then slowly heated (0.1 degrees C/s) while fluorescence was continuously monitored. Somatic mutations in tumor tissue were detected by changes from a characteristic wild-type melting curve profile using leukocyte DNA. RESULTS: A complete scanning of the DNA binding domain (exons 5-8) of the p53 gene was completed in a single run ( approximately 30 min) starting from genomic leukocyte DNA. To show proof-of-principle, p53 exons 6-8 from 63 colon cancers were probe-scanned and showed 100% agreement with direct sequencing for detecting alterations from wild-type DNA. CONCLUSIONS: p53 mutation scanning by single-labeled hybridization probes is a homogeneous, rapid, and sensitive method with application in both research and clinical diagnostics.

Colorectal Neoplasms↗