Showing papers by "Lance D. Miller published in 2005"

An expression signature for p53 status in human breast cancer predicts mutation status, transcriptional effects, and patient survival

Abstract: Perturbations of the p53 pathway are associated with more aggressive and therapeutically refractory tumors. However, molecular assessment of p53 status, by using sequence analysis and immunohistochemistry, are incomplete assessors of p53 functional effects. We posited that the transcriptional fingerprint is a more definitive downstream indicator of p53 function. Herein, we analyzed transcript profiles of 251 p53-sequenced primary breast tumors and identified a clinically embedded 32-gene expression signature that distinguishes p53-mutant and wild-type tumors of different histologies and outperforms sequence-based assessments of p53 in predicting prognosis and therapeutic response. Moreover, the p53 signature identified a subset of aggressive tumors absent of sequence mutations in p53 yet exhibiting expression characteristics consistent with p53 deficiency because of attenuated p53 transcript levels. Our results show the primary importance of p53 functional status in predicting clinical breast cancer behavior.

Gene expression profiling spares early breast cancer patients from adjuvant therapy: derived and validated in two population-based cohorts.

Abstract: Adjuvant breast cancer therapy significantly improves survival, but overtreatment and undertreatment are major problems. Breast cancer expression profiling has so far mainly been used to identify women with a poor prognosis as candidates for adjuvant therapy but without demonstrated value for therapy prediction. We obtained the gene expression profiles of 159 population-derived breast cancer patients, and used hierarchical clustering to identify the signature associated with prognosis and impact of adjuvant therapies, defined as distant metastasis or death within 5 years. Independent datasets of 76 treated population-derived Swedish patients, 135 untreated population-derived Swedish patients and 78 Dutch patients were used for validation. The inclusion and exclusion criteria for the studies of population-derived Swedish patients were defined. Among the 159 patients, a subset of 64 genes was found to give an optimal separation of patients with good and poor outcomes. Hierarchical clustering revealed three subgroups: patients who did well with therapy, patients who did well without therapy, and patients that failed to benefit from given therapy. The expression profile gave significantly better prognostication (odds ratio, 4.19; P = 0.007) (breast cancer end-points odds ratio, 10.64) compared with the Elston–Ellis histological grading (odds ratio of grade 2 vs 1 and grade 3 vs 1, 2.81 and 3.32 respectively; P = 0.24 and 0.16), tumor stage (odds ratio of stage 2 vs 1 and stage 3 vs 1, 1.11 and 1.28; P = 0.83 and 0.68) and age (odds ratio, 0.11; P = 0.55). The risk groups were consistent and validated in the independent Swedish and Dutch data sets used with 211 and 78 patients, respectively. We have identified discriminatory gene expression signatures working both on untreated and systematically treated primary breast cancer patients with the potential to spare them from adjuvant therapy.

Whole-Genome Cartography of Estrogen Receptor α Binding Sites

Abstract: Using a chromatin immunoprecipitation-paired end diTag cloning and sequencing strategy, we mapped estrogen receptor alpha (ERalpha) binding sites in MCF-7 breast cancer cells. We identified 1,234 high confidence binding clusters of which 94% are projected to be bona fide ERalpha binding regions. Only 5% of the mapped estrogen receptor binding sites are located within 5 kb upstream of the transcriptional start sites of adjacent genes, regions containing the proximal promoters, whereas vast majority of the sites are mapped to intronic or distal locations (>5 kb from 5' and 3' ends of adjacent transcript), suggesting transcriptional regulatory mechanisms over significant physical distances. Of all the identified sites, 71% harbored putative full estrogen response elements (EREs), 25% bore ERE half sites, and only 4% had no recognizable ERE sequences. Genes in the vicinity of ERalpha binding sites were enriched for regulation by estradiol in MCF-7 cells, and their expression profiles in patient samples segregate ERalpha-positive from ERalpha-negative breast tumors. The expression dynamics of the genes adjacent to ERalpha binding sites suggest a direct induction of gene expression through binding to ERE-like sequences, whereas transcriptional repression by ERalpha appears to be through indirect mechanisms. Our analysis also indicates a number of candidate transcription factor binding sites adjacent to occupied EREs at frequencies much greater than by chance, including the previously reported FOXA1 sites, and demonstrate the potential involvement of one such putative adjacent factor, Sp1, in the global regulation of ERalpha target genes. Unexpectedly, we found that only 22%-24% of the bona fide human ERalpha binding sites were overlapping conserved regions in whole genome vertebrate alignments, which suggest limited conservation of functional binding sites. Taken together, this genome-scale analysis suggests complex but definable rules governing ERalpha binding and gene regulation.

Transcriptome analysis of zebrafish embryogenesis using microarrays.

Abstract: Zebrafish (Danio rerio) is a well-recognized model for the study of vertebrate developmental genetics, yet at the same time little is known about the transcriptional events that underlie zebrafish embryogenesis. Here we have employed microarray analysis to study the temporal activity of developmentally regulated genes during zebrafish embryogenesis. Transcriptome analysis at 12 different embryonic time points covering five different developmental stages (maternal, blastula, gastrula, segmentation, and pharyngula) revealed a highly dynamic transcriptional profile. Hierarchical clustering, stage-specific clustering, and algorithms to detect onset and peak of gene expression revealed clearly demarcated transcript clusters with maximum gene activity at distinct developmental stages as well as co-regulated expression of gene groups involved in dedicated functions such as organogenesis. Our study also revealed a previously unidentified cohort of genes that are transcribed prior to the mid-blastula transition, a time point earlier than when the zygotic genome was traditionally thought to become active. Here we provide, for the first time to our knowledge, a comprehensive list of developmentally regulated zebrafish genes and their expression profiles during embryogenesis, including novel information on the temporal expression of several thousand previously uncharacterized genes. The expression data generated from this study are accessible to all interested scientists from our institute resource database (http://giscompute.gis.a-star.edu.sg/~govind/zebrafish/data_download.html).

Identification of cell cycle-regulated genes in fission yeast.

Abstract: Cell cycle progression is both regulated and accompanied by periodic changes in the expression levels of a large number of genes. To investigate cell cycle-regulated transcriptional programs in the fission yeast Schizosaccharomyces pombe, we developed a whole-genome oligonucleotide-based DNA microarray. Microarray analysis of both wild-type and cdc25 mutant cell cultures was performed to identify transcripts whose levels oscillated during the cell cycle. Using an unsupervised algorithm, we identified 747 genes that met the criteria for cell cycle-regulated expression. Peaks of gene expression were found to be distributed throughout the entire cell cycle. Furthermore, we found that four promoter motifs exhibited strong association with cell cycle phase-specific expression. Examination of the regulation of MCB motif-containing genes through the perturbation of DNA synthesis control/MCB-binding factor (DSC/MBF)-mediated transcription in arrested synchronous cdc10 mutant cell cultures revealed a subset of functional targets of the DSC/MBF transcription factor complex, as well as certain gene promoter requirements. Finally, we compared our data with those for the budding yeast Saccharomyces cerevisiae and found approximately 140 genes that are cell cycle regulated in both yeasts, suggesting that these genes may play an evolutionarily conserved role in regulation of cell cycle-specific processes. Our complete data sets are available at http://giscompute.gis.a-star.edu.sg/~gisljh/CDC.

THY1 is a candidate tumour suppressor gene with decreased expression in metastatic nasopharyngeal carcinoma

Abstract: Using oligonucleotide microarray analysis, THY1, mapping close to a previously defined 11q22-23 nasopharyngeal carcinoma (NPC) critical region was identified as showing consistent downregulated expression in the tumour segregants, as compared to their parental tumour-suppressing microcell hybrids (MCHs). Gene expression and protein analyses show that THY1 was not expressed in the NPC HONE1 recipient cells, tumour segregants, and other NPC cell lines; THY1 was exclusively expressed in the non-tumourigenic MCHs. The mechanism of THY1 gene inactivation in these cell lines was attributed to hypermethylation. Clinical study showed that in 65% of NPC specimens there was either downregulation or loss of THY1 gene expression. Using a tissue microarray and immunohistochemical staining, 44% of the NPC cases showed downregulated expression of THY1 and 9% lost THY1 expression. The frequency of THY1 downregulated expression in lymph node metastatic NPC was 63%, which was significantly higher than in the primary tumour (33%). After transfection of THY1 gene into HONE1 cells, a dramatic reduction of colony formation ability was observed. These findings suggest that THY1 is a good candidate tumour suppressor gene in NPC, which is significantly associated with lymph node metastases.

Correlation test to assess low-level processing of high-density oligonucleotide microarray data

Abstract: There are currently a number of competing techniques for low-level processing of oligonucleotide array data. The choice of technique has a profound effect on subsequent statistical analyses, but there is no method to assess whether a particular technique is appropriate for a specific data set, without reference to external data. We analyzed coregulation between genes in order to detect insufficient normalization between arrays, where coregulation is measured in terms of statistical correlation. In a large collection of genes, a random pair of genes should have on average zero correlation, hence allowing a correlation test. For all data sets that we evaluated, and the three most commonly used low-level processing procedures including MAS5, RMA and MBEI, the housekeeping-gene normalization failed the test. For a real clinical data set, RMA and MBEI showed significant correlation for absent genes. We also found that a second round of normalization on the probe set level improved normalization significantly throughout. Previous evaluation of low-level processing in the literature has been limited to artificial spike-in and mixture data sets. In the absence of a known gold-standard, the correlation criterion allows us to assess the appropriateness of low-level processing of a specific data set and the success of normalization for subsets of genes.

Prediction of early distant relapses on tamoxifen in early-stage breast cancer (BC): A potential tool for adjuvant aromatase inhibitor (AI) tailoring

Abstract: 509 Background: The majority of early-stage BC express estrogen receptors (ER) and receive tamoxifen in the adjuvant setting. Yet up to 40% of these patients will relapse on tamoxifen and develop incurable metastatic disease. Recent evidence from three large randomised controlled trials exploring the role of AI in the adjuvant setting shows a benefit from the novel strategy, however the optimal sequence and duration of AI/tamoxifen treatment is unknown. Therefore, it is vital that we learn to identify those women at higher risk of tamoxifen resistance. Our aim was to identify genes that could predict for this subset of women. Methods: We determined the gene expression profiles from 105 tamoxifen-only treated ER positive early stage BC (training set) using Affymetrix U133 A and B chips. Within this group, 30 (29%) patients developed distant recurrence at a median time to relapse of 3.8 years (yrs) and 75 (71%) remained disease free at a median of 5.7 yrs of follow-up. The independent validation set consist...

Methods, systems, and arrays based on correlating p53 status with gene expression profiles, for classification, prognosis, and diagnosis of cancers

Abstract: The present invention provides methods, systems and compositions for predicting disease susceptibility in a patient based on correlating p53 mutational status and gene expression profiles for a set of predetermined genes. In some embodiments, methods for the classification, prognosis, and diagnosis of cancers are provided. In other embodiments, the present invention provides statistical methods for building a gene-expression-based classifier that may be employe for predicting disease susceptibility in a patient, for classifying carcinomas, and for the prognosis of clinical outcomes

Method of probe design and/or of nucleic acids detection

Abstract: It is provided a method of designing oligonucleotide probe(s) for nucleic acid detection comprising the following steps in any order: (i) identifying and selecting region(s) of a target nucleic acid to be amplified, the region(s) having an efficiency of amplification (AE) higher than the average AE; and (ii) designing oligonucleotide probe(s) capable of hybridizing to the selected region(s). It is also provided a method of detecting at least one target nucleic acid comprising the steps of: (i) providing a biological sample; (ii) amplifying the nucleic acid(s) of the biological sample; (iii) providing at least an oligonucleotide probe capable of hybridizing to at least a target nucleic acid, if present in the biological sample; and (iv) contacting the probe(s) with the amplified nucleic acids and detecting the probe(s) hybridized to the target nucleic acid(s). In particular, the method indicates the presence of at least a pathogen, for example a virus, in a human biological sample. The probes may be placed on a support, for example a microarray or a biochip.

Multimodality as a criterion for feature selection in unsupervised analysis of gene expression data

Abstract: One important way that gene expression data are often analysed in an unsupervised way is to cluster the samples without reference to any annotations about them. Before clustering, the data are often subjected to a feature selection preprocessing step, in which a subset of genes are chosen for further analysis. We examine the use of multimodality as a criterion for choosing genes in feature selection, and also propose a novel measure of pairwise dissimilarity to cluster the genes that have survived the preprocessing step. The resulting multiple gene subsets usually contain those that are more strongly correlated with the sample annotations of interest than those obtained through variance-based feature selection. Class discovery may be facilitated when gene expression data are analysed using the proposed method.