Biomedical subjects

Andrew E Teschendorff

Publications and source records attributed to Andrew E Teschendorff.

6 recordsLinked to original sources

PubMed2006

A consensus prognostic gene expression classifier for ER positive breast cancer.

BACKGROUND: A consensus prognostic gene expression classifier is still elusive in heterogeneous diseases such as breast cancer. RESULTS: Here we perform a combined analysis of three major breast cancer microarray data sets to hone in on a universally valid prognostic molecular classifier in estrogen receptor (ER) positive tumors. Using a recently developed robust measure of prognostic separation, we further validate the prognostic classifier in three external independent cohorts, confirming the validity of our molecular classifier in a total of 877 ER positive samples. Furthermore, we find that molecular classifiers may not outperform classical prognostic indices but that they can be used in hybrid molecular-pathological classification schemes to improve prognostic separation. CONCLUSION: The prognostic molecular classifier presented here is the first to be valid in over 877 ER positive breast cancer samples and across three different microarray platforms. Larger multi-institutional studies will be needed to fully determine the added prognostic value of molecular classifiers when combined with standard prognostic factors.

Breast Neoplasms↗

PubMed2006

Interferon-beta treatment of cervical keratinocytes naturally infected with human papillomavirus 16 episomes promotes rapid reduction in episome numbers and emergence of latent integrants.

Following integration of human papillomavirus (HPV) into the host genome, overexpression of the viral oncogenes E6 and E7 requires loss of the transcriptional repressor functions of E2. A key step in HPV-related carcinogenesis is therefore clearance of residual viral episomes, which encode E2. As spontaneous loss of HPV-16 episomes in vitro is associated with increased expression of antiviral genes inducible by type I interferon (IFN), we used the W12 model to examine the effects of exogenous IFN-beta on cervical keratinocytes containing HPV-16 episomes as a result of 'natural' infection in vivo. In contrast to studies of cells transfected with HPV-31 or bovine papillomavirus, IFN-beta caused rapid reduction in numbers of HPV-16 episomes. This was associated with the emergence of cells bearing previously latent integrants, in which there was increased expression of E6 and E7. Our data indicate that integrated HPV-16 can exist in a minority of cells in a mixed population without exerting a selective advantage until episome numbers are reduced. The kinetics of cell death and changes in viral transcription and translation that we observed support a model where integrants are initially present in cells also containing episomes, with generalized episome clearance by IFN-beta resulting in integrant de-repression. We conclude that IFN-beta can hasten the transition from episomal to integrated HPV-16 in naturally infected cervical keratinocytes. Greater emphasis should be placed on episome loss in models of HPV-related carcinogenesis. We provide the strongest evidence to date that treating HPV-16 lesions by inducing an IFN response may cause clinical progression.

Animals↗

PubMed2006

PACK: Profile Analysis using Clustering and Kurtosis to find molecular classifiers in cancer.

MOTIVATION: Elucidating the molecular taxonomy of cancers and finding biological and clinical markers from microarray experiments is problematic due to the large number of variables being measured. Feature selection methods that can identify relevant classifiers or that can remove likely false positives prior to supervised analysis are therefore desirable. RESULTS: We present a novel feature selection procedure based on a mixture model and a non-gaussianity measure of a gene's expression profile. The method can be used to find genes that define either small outlier subgroups or major subdivisions, depending on the sign of kurtosis. The method can also be used as a filtering step, prior to supervised analysis, in order to reduce the false discovery rate. We validate our methodology using six independent datasets by rediscovering major classifiers in ER negative and ER positive breast cancer and in prostate cancer. Furthermore, our method finds two novel subtypes within the basal subgroup of ER negative breast tumours, associated with apoptotic and immune response functions respectively, and with statistically different clinical outcome. AVAILABILITY: An R-function pack that implements the methods used here has been added to vabayelMix, available from (www.cran.r-project.org). CONTACT: aet21@cam.ac.uk SUPPLEMENTARY INFORMATION: Supplementary information is available at Bioinformatics online.

Algorithms↗

PubMed2006

Differential expression of selected histone modifier genes in human solid cancers.

BACKGROUND: Post-translational modification of histones resulting in chromatin remodelling plays a key role in the regulation of gene expression. Here we report characteristic patterns of expression of 12 members of 3 classes of chromatin modifier genes in 6 different cancer types: histone acetyltransferases (HATs)- EP300, CREBBP, and PCAF; histone deacetylases (HDACs)- HDAC1, HDAC2, HDAC4, HDAC5, HDAC7A, and SIRT1; and histone methyltransferases (HMTs)- SUV39H1and SUV39H2. Expression of each gene in 225 samples (135 primary tumours, 47 cancer cell lines, and 43 normal tissues) was analysedby QRT-PCR, normalized with 8 housekeeping genes, and given as a ratio by comparison with a universal reference RNA. RESULTS: This involved a total of 13,000 PCR assays allowing for rigorous analysis by fitting a linear regression model to the data. Mutation analysis of HDAC1, HDAC2, SUV39H1, and SUV39H2 revealed only two out of 181 cancer samples (both cell lines) with significant coding-sequence alterations. Supervised analysis and Independent Component Analysis showed that expression of many of these genes was able to discriminate tumour samples from their normal counterparts. Clustering based on the normalized expression ratios of the 12 genes also showed that most samples were grouped according to tissue type. Using a linear discriminant classifier and internal cross-validation revealed that with as few as 5 of the 12 genes, SIRT1, CREBBP, HDAC7A, HDAC5 and PCAF, most samples were correctly assigned. CONCLUSION: The expression patterns of HATs, HDACs, and HMTs suggest these genes are important in neoplastic transformation and have characteristic patterns of expression depending on tissue of origin, with implications for potential clinical application.

Breast Neoplasms↗

PubMed2006

Distribution of breakpoints on chromosome 18 in breast, colorectal, and pancreatic carcinoma cell lines.

Chromosome 18 is frequently rearranged in carcinomas. We explored the distribution of breakpoints affecting chromosome 18 by mapping 56 breakpoints in 26 carcinoma cell lines by fluorescence in situ hybridization (FISH) using bacterial artificial chromosomes (BACs) and band paints. The distribution of breaks among 18 intervals of chromosome 18 was significantly nonrandom. The interval spanning the centromere contained the greatest number of breaks and had the highest average copy number of any interval. There was a high density of breaks close to the centromere as well as actually within the centromere. A cluster of breaks encompassing SMAD4 was associated with the minimum average copy number, consistent with SMAD4 being a tumor suppressor gene. There may be another cluster of breaks around 18q12. We offer two interpretations of the concentration of breaks near the centromere. It may reflect selection for an oncogene near the centromere, or there may be an underlying bias of breakage toward the centromere. We show that the latter is predicted by a simple model that invokes random breakage following anchorage of some random point on the chromosome, or selection of breaks proximal to one of several tumor suppressor genes.

Breast Neoplasms↗

PubMed2005

A variational Bayesian mixture modelling framework for cluster analysis of gene-expression data.

MOTIVATION: Accurate subcategorization of tumour types through gene-expression profiling requires analytical techniques that estimate the number of categories or clusters rigorously and reliably. Parametric mixture modelling provides a natural setting to address this problem. RESULTS: We compare a criterion for model selection that is derived from a variational Bayesian framework with a popular alternative based on the Bayesian information criterion. Using simulated data, we show that the variational Bayesian method is more accurate in finding the true number of clusters in situations that are relevant to current and future microarray studies. We also compare the two criteria using freely available tumour microarray datasets and show that the variational Bayesian method is more sensitive to capturing biologically relevant structure.

Algorithms↗