Showing papers by "Lance D. Miller published in 2006"

Genetic Reclassification of Histologic Grade Delineates New Clinical Subtypes of Breast Cancer

Abstract: Histologic grading of breast cancer defines morphologic subtypes informative of metastatic potential, although not without considerable interobserver disagreement and clinical heterogeneity particularly among the moderately differentiated grade 2 (G2) tumors. We posited that a gene expression signature capable of discerning tumors of grade 1 (G1) and grade 3 (G3) histology might provide a more objective measure of grade with prognostic benefit for patients with G2 disease. To this end, we studied the expression profiles of 347 primary invasive breast tumors analyzed on Affymetrix microarrays. Using class prediction algorithms, we identified 264 robust grade-associated markers, six of which could accurately classify G1 and G3 tumors, and separate G2 tumors into two highly discriminant classes (termed G2a and G2b genetic grades) with patient survival outcomes highly similar to those with G1 and G3 histology, respectively. Statistical analysis of conventional clinical variables further distinguished G2a and G2b subtypes from each other, but also from histologic G1 and G3 tumors. In multivariate analyses, genetic grade was consistently found to be an independent prognostic indicator of disease recurrence comparable with that of lymph node status and tumor size. When incorporated into the Nottingham prognostic index, genetic grade enhanced detection of patients with less harmful tumors, likely to benefit little from adjuvant therapy. Our findings show that a genetic grade signature can improve prognosis and therapeutic planning for breast cancer patients, and support the view that low- and high-grade disease, as defined genetically, reflect independent pathobiological entities rather than a continuum of cancer progression.

Conservation of gene expression signatures between zebrafish and human liver tumors and tumor progression

Abstract: The zebrafish (Danio rerio) has been long advocated as a model for cancer research, but little is known about the real molecular similarities between zebrafish and human tumors. Comparative analysis of microarray data from zebrafish liver tumors with those from four human tumor types revealed molecular conservation at various levels between fish and human tumors. This approach provides a useful strategy for identifying an expression signature that is strongly associated with a disease phenotype.

Intrinsic molecular signature of breast cancer in a population-based cohort of 412 patients.

Abstract: Molecular markers and the rich biological information they contain have great potential for cancer diagnosis, prognostication and therapy prediction. So far, however, they have not superseded routine histopathology and staging criteria, partly because the few studies performed on molecular subtyping have had little validation and limited clinical characterization. We obtained gene expression and clinical data for 412 breast cancers obtained from population-based cohorts of patients from Stockholm and Uppsala, Sweden. Using the intrinsic set of approximately 500 genes derived in the Norway/Stanford breast cancer data, we validated the existence of five molecular subtypes – basal-like, ERBB2, luminal A/B and normal-like – and characterized these subtypes extensively with the use of conventional clinical variables. We found an overall 77.5% concordance between the centroid prediction of the Swedish cohort by using the Norway/Stanford signature and the k-means clustering performed internally within the Swedish cohort. The highest rate of discordant assignments occurred between the luminal A and luminal B subtypes and between the luminal B and ERBB2 subtypes. The subtypes varied significantly in terms of grade (p < 0.001), p53 mutation (p < 0.001) and genomic instability (p = 0.01), but surprisingly there was little difference in lymph-node metastasis (p = 0.31). Furthermore, current users of hormone-replacement therapy were strikingly over-represented in the normal-like subgroup (p < 0.001). Separate analyses of the patients who received endocrine therapy and those who did not receive any adjuvant therapy supported the previous hypothesis that the basal-like subtype responded to adjuvant treatment, whereas the ERBB2 and luminal B subtypes were poor responders. We found that the intrinsic molecular subtypes of breast cancer are broadly present in a diverse collection of patients from a population-based cohort in Sweden. The intrinsic gene set, originally selected to reveal stable tumor characteristics, was shown to have a strong correlation with progression-related properties such as grade, p53 mutation and genomic instability.

Gene Expression Preferentially Regulated by Tamoxifen in Breast Cancer Cells and Correlations with Clinical Outcome

Abstract: The beneficial effect of the selective estrogen receptor (ER) modulator tamoxifen in the treatment and prevention of breast cancer is assumed to be through its ability to antagonize the stimulatory actions of estrogen, although tamoxifen can also have some estrogen-like agonist effects. Here, we report that, in addition to these mixed agonist/antagonist actions, tamoxifen can also selectively regulate a unique set of >60 genes, which are minimally regulated by estradiol (E2) or raloxifene in ERalpha-positive MCF-7 human breast cancer cells. This gene regulation by tamoxifen is mediated by ERalpha and reversed by E2 or ICI 182,780. Introduction of ERbeta into MCF-7 cells reverses tamoxifen action on approximately 75% of these genes. To examine whether these genes might serve as markers of tamoxifen sensitivity and/or the development of resistance, their expression level was examined in breast cancers of women who had received adjuvant therapy with tamoxifen. High expression of two of the tamoxifen-stimulated genes, YWHAZ/14-3-3z and LOC441453, was found to correlate significantly with disease recurrence following tamoxifen treatment in women with ER-positive cancers and hence seem to be markers of a poor prognosis. Our data indicate a new dimension in tamoxifen action, involving gene expression regulation that is tamoxifen preferential, and identify genes that might serve as markers of tumor responsiveness or resistance to tamoxifen therapy. This may have a potential effect on the choice of tamoxifen versus aromatase inhibitors as adjuvant endocrine therapy.

Transcriptome kinetics of arsenic-induced adaptive response in zebrafish liver

Abstract: Arsenic is a prominent environmental toxicant and carcinogen; however, its molecular mechanism of toxicity and carcinogenicity remains poorly understood. In this study, we performed microarray-base...

Hormone-replacement therapy influences gene expression profiles and is associated with breast-cancer prognosis: a cohort study

Abstract: Postmenopausal hormone-replacement therapy (HRT) increases breast-cancer risk. The influence of HRT on the biology of the primary tumor, however, is not well understood. We obtained breast-cancer gene expression profiles using Affymetrix human genome U133A arrays. We examined the relationship between HRT-regulated gene profiles, tumor characteristics, and recurrence-free survival in 72 postmenopausal women. HRT use in patients with estrogen receptor (ER) protein positive tumors (n = 72) was associated with an altered regulation of 276 genes. Expression profiles based on these genes clustered ER-positive tumors into two molecular subclasses, one of which was associated with HRT use and had significantly better recurrence free survival despite lower ER levels. A comparison with external data suggested that gene regulation in tumors associated with HRT was negatively correlated with gene regulation induced by short-term estrogen exposure, but positively correlated with the effect of tamoxifen. Our findings suggest that post-menopausal HRT use is associated with a distinct gene expression profile related to better recurrence-free survival and lower ER protein levels. Tentatively, HRT-associated gene expression in tumors resembles the effect of tamoxifen exposure on MCF-7 cells.

A modular analysis of breast cancer reveals a novel low-grade molecular signature in estrogen receptor-positive tumors.

Abstract: Purpose: Previous reports using genome-wide gene expression data to classify breast tumors have typically used standard unsupervised or supervised techniques, both of which have known limitations. We hypothesized that novel clinically relevant information could be revealed in these data sets by an alternative analytic approach. Using a recently described algorithm, signature analysis (SA), we identified “modules,” comprising groups of tightly coexpressed genes that are conditionally linked to particular tumors, in a series of breast tumor gene expression profiles. Experimental Design and Results: The SA successfully identified multiple breast cancer modules specifically linked to distinct biological functions. We identified a novel module, TuM1, whose presence was not readily discernible by conventional clustering techniques. The TuM1 module is expressed in a subset of estrogen receptor (ER)–positive tumors and is significantly enriched with genes involved in apoptosis and cell death. Clinically, TuM1-expressing tumors are associated with low histopathologic grade, and this association is independent of the inherent ER status of a tumor. We confirmed the robustness and general applicability of TuM1 module by demonstrating its association with low tumor grade in multiple independent breast cancer data sets generated using different array technologies. In vitro , the TuM1 module is down-regulated in ER+ MCF7 cells upon treatment with tamoxifen, suggesting that TuM1 expression may be dependent on active signaling by ER. Initial data is also suggestive that TuM1 expression may be clinically associated with a patient9s response to antihormonal therapy. Conclusion: Our results suggest that modular-based approaches toward gene expression data can prove useful in identifying novel, robust, and biologically relevant signatures even from data sets that have been the subject of substantial prior analysis.

Identifying gene expression changes in breast cancer that distinguish early and late relapse among uncured patients

Abstract: Motivation: In recent years, microarray technology has revealed many tumor-expressed genes prognostic of clinical outcomes in early-stage breast cancer patients. However, in the presence of cured patients, evaluating gene effect on time to relapse is quite complex since it may affect either the probability of never experiencing a relapse (cure effect) or the time to relapse among the uncured patients (disease progression effect) or both. In this context, we propose a simple and an efficient method for identifying gene expression changes that characterize early and late recurrence for uncured patients. Results: Simulation results show the good performance of the proposed statistic for detecting a disease progression effect. In a study of early-stage breast cancer, our results show that the proposed statistic provides a more powerful basis for gene selection than the classical Cox model-based statistic. From a biological perspective, many of the genes identified here as associated with the speed of disease recurrence have known roles in tumorigenesis. Contact:broet@vjf.inserm.fr; kuznetsov@gis.a-star.edu.sg

Statistically Weighted Voting Analysis of Microarrays for Molecular Pattern Selection and Discovery Cancer Genotypes

Abstract: Summary We developed a methodological approach to genetic class discovery using gene expression microarray data, which is based a on statistically-oriented class-prediction method called Statistically Weighted Voting (SWV) analysis integrating with clinical risk factor and survival analyses, and statistics of Gene Ontology annotation terms which we use to validate candidate biomarker selection. Our approach provides a "voting" class prediction function constructed using the most informative and robust discrete segments (sub-regions) of all covariate ranges and their gradated pairs, which thus allows to model the interactions of variables (genes). We show here that the SWV-based methodology can be adapted for microarray data and profitably used to biomarker selection and discovered two genetic classes associated with essentially improvement of classical histological grade II of human breast cancer. Our findings show that small and reliable genetic grade signatures could improve an individual prognosis for patients with histologic grated II and, thus after further biomedical validation, be used in therapeutic planning for breast cancer patients.

Differential Friendly Neighbors Algorithm for Differential Relationships Based Gene Selection and Classification using Microarray Data.

Abstract: Identifying biologically relevant genes in a given tumor classification problem is as important as accurately classifying the samples. Differential expression may not always achieve this task. Differential friendly neighbors (DiffFNs) algorithm is proposed to select the classification relevant and biologically interesting genes that can discriminate two classes of tumors. DiffFNs achieves it by selecting genes based on their differential relationships from one class to the other. DiffFNs has been applied to select genes that can discriminate patients based on their tumor p53 status and Grade status. The results show that DiffFNs identify the p53 activity associated genes which could be submerged in the differentially expressed genes if we used differential expression analysis alone. This result proves the effectiveness of DiffFNs approach in selecting genes that are biologically relevant to the sample classification.

Method and/or apparatus of oligonucleotide design and/or nucleic acid detection

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