CiteULike: jfr's library [1584 articles]

The need for standards, not guidelines, in biological data reporting and sharing

Lyle Burgoon — 2006-11-09T05:18:57-00:00

Nature Biotechnology, Vol. 24, No. 11., pp. 1369-1373.

Standardizing the standards.

J Quackenbush — 2007-04-02T06:29:34-00:00

Mol Syst Biol, Vol. 2 (2006)

Are we stuck in the standards?

Catherine Ball — 2006-11-09T05:18:57-00:00

Nature Biotechnology, Vol. 24, No. 11., pp. 1374-1376.

ArrayExpress service for reviewers/editors of DNA microarray papers

Alvis Brazma — 2006-11-09T05:19:00-00:00

Nature Biotechnology, Vol. 24, No. 11., pp. 1321-1322.

Lack of correct data format and comparability limits future integrative microarray research

Ola Larsson — 2006-11-09T05:19:00-00:00

Nature Biotechnology, Vol. 24, No. 11., pp. 1322-1323.

Impact of microarray data quality on genomic data submissions to the FDA

Felix Frueh — 2006-09-09T05:29:44-00:00

Nature Biotechnology, Vol. 24, No. 9., pp. 1105-1107.

Molecular signatures predict outcomes of breast cancer.

JA O'Shaughnessy — 2008-07-23T09:00:57-00:00

The New England journal of medicine, Vol. 355, No. 6. (10 August 2006), pp. 615-617.

Sizing up miRNAs as cancer genes

Carlos Caldas — 2005-07-06T22:13:41-00:00

Nature Medicine, Vol. 11, No. 7., pp. 712-714.

Open software for biologists: from famine to feast

Dawn Field — 2006-07-16T22:10:39-00:00

Nature Biotechnology, Vol. 24, No. 7., pp. 801-803.

Linking oncogenic pathways with therapeutic opportunities

Andrea Bild — 2006-08-25T04:21:31-00:00

Nature Reviews Cancer, Vol. 6, No. 9. (17 August 2006), pp. 735-741.

Gene-expression profiling in breast cancer.

TK Jenssen — 2008-07-21T17:17:30-00:00

Lancet, Vol. 365, No. 9460. (5 2005), pp. 634-635.

MIAME, we have a problem.

Robert Shields — 2006-01-25T14:08:54-00:00

Trends Genet (24 December 2005)

Microarrays and molecular research: noise discovery?

JP Ioannidis — 2005-06-28T16:05:25-00:00

Lancet, Vol. 365, No. 9458. (5 February 2005), pp. 454-455.

Individualized care for patients with cancer - a work in progress.

RC Bast — 2008-07-21T17:10:59-00:00

The New England journal of medicine, Vol. 351, No. 27. (30 December 2004), pp. 2865-2867.

Technology insight: Emerging techniques to predict response to preoperative chemotherapy in breast cancer.

L Pusztai — 2008-07-21T17:08:22-00:00

Nature clinical practice. Oncology, Vol. 1, No. 1. (November 2004), pp. 44-50.

During the past decade, several high-throughput analytical methods have been developed, and most of these are being explored as potential diagnostic tools. Gene expression profiling with DNA microarrays or with multiplex polymerase chain reaction are the methods closest to being of clinical use. Prediction of clinically meaningful response to particular chemotherapy regimens or drugs remains a persistent challenge. There are established clinical and histopathologic predictors of prognosis for breast cancer, but there is no test to assist in selecting the optimal chemotherapy regimen for patients. Here we review recent advances in the application of gene expression profiling to chemotherapy response prediction.

Getting the Noise Out of Gene Arrays

Eliot Marshall — 2006-04-12T19:11:20-00:00

Science, Vol. 306, No. 5696. (22 October 2004), pp. 630-631.

10.1126/science.306.5696.630

Facts from text-is text mining ready to deliver?

D Rebholz-Schuhmann — 2005-03-02T09:32:23-00:00

PLoS Biol, Vol. 3, No. 2. (February 2005)

Trawling for genes that predict response to breast cancer adjuvant therapy.

M Ellis — 2008-07-21T17:04:25-00:00

Journal of clinical oncology : official journal of the American Society of Clinical Oncology, Vol. 22, No. 12. (15 June 2004), pp. 2267-2269.

Clinical proteomics: translating benchside promise into bedside reality.

EF Petricoin — 2008-07-21T14:18:50-00:00

Nature reviews. Drug discovery, Vol. 1, No. 9. (September 2002), pp. 683-695.

The ultimate goal of proteomics is to characterize the information flow through protein networks. This information can be a cause, or a consequence, of disease processes. Clinical proteomics is an exciting new subdiscipline of proteomics that involves the application of proteomic technologies at the bedside, and cancer, in particular, is a model disease for studying such applications. Here, we describe proteomic technologies that are being developed to detect cancer earlier, to discover the next generation of targets and imaging biomarkers, and finally to tailor the therapy to the patient.

DNA microarrays in clinical cancer research.

R Wadlow — 2008-07-21T13:36:16-00:00

Current molecular medicine, Vol. 5, No. 1. (February 2005), pp. 111-120.

The recent sequencing of the human genome, coupled with advances in biotechnology, is enabling the comprehensive molecular "profiling" of human tissues. In particular, DNA microarrays are powerful tools for obtaining global views of human tumor gene expression. Complex information from tumor "expression profiling" studies can, in turn, be used to create novel molecular cancer diagnostics. We discuss the utility of DNA microarray-based tumor profiling in clinical cancer research, highlight some important recent studies, and identify future avenues of research in this evolving field.

In the pursuit of complexity: systems medicine in cancer biology.

ET Liu — 2008-07-21T13:32:34-00:00

Cancer cell, Vol. 9, No. 4. (April 2006), pp. 245-247.

Adler et al., in a paper appearing in Nature Genetics, exploited the intersect of genetic information from expression profiles with that from array comparative genomic hybridization in human breast cancers to identify genes that may induce the transcription of the prognostic "wound response" expression signature. The amplification of two genes, MYC and CSN5, appeared to be correlated with the wound response cassette. In vitro validation showed that the wound signature could be induced in MCF10A cells only when MYC and CSN5 were coexpressed. This work shows that the intersect analysis of gene amplification and transcriptional expression on a genome-wide scale can uncover complex conditional interactions embedded in the systems map of transcriptional regulation.

Clinical trial design for microarray predictive marker discovery and assessment.

L Pusztai — 2006-12-04T20:58:32-00:00

Ann Oncol, Vol. 15, No. 12. (December 2004), pp. 1731-1737.

Transcriptional profiling technologies that simultaneously measure the expression of thousands of mRNA species represent a powerful new clinical research tool. Similar to previous laboratory analytical methods including immunohistochemistry, PCR and in situ hybridization, this new technology may also find its niche in routine diagnostics. Outcome predictors discovered by these methods may be quite different from previous single-gene markers. These novel tests will probably combine the information embedded in the expression of multiple genes with mathematical prediction algorithms to formulate classification rules and predict outcome. The performance of machine learning-algorithm-based diagnostic tests may improve as they are trained on larger and larger sets of samples, and several generations of tests with improving accuracy may be introduced sequentially. Several gene-expression profiling-technology platforms are mature enough for clinical testing. The most important next step that is needed for further progress is the development and validation of multigene predictors in prospectively designed clinical trials to determine the true accuracy and clinical value of this new technology. This manuscript reviews methodological and statistical issues relevant to clinical trial design to discover and validate multigene predictors of response to therapy.

Targeted cancer therapy

Charles Sawyers — 2007-01-03T18:11:24-00:00

Nature, Vol. 432, No. 7015. (18 November 2004), pp. 294-297.

Chipping away at the chip bias: RNA degradation in microarray analysis.

H Auer — 2007-09-07T01:00:59-00:00

Nat Genet, Vol. 35, No. 4. (December 2003), pp. 292-293.

Microarray reality checks in the context of a complex disease.

GL Miklos — 2005-08-30T18:24:35-00:00

Nat Biotechnol, Vol. 22, No. 5. (May 2004), pp. 615-621.

A problem in analyzing microarray-based gene expression data is the separation of genes causally involved in a disease from innocent bystander genes, whose expression levels have been secondarily altered by primary changes elsewhere. To investigate this issue systematically in the context of a class of complex human diseases, we have compared microarray-based gene expression data with non-microarray-based clinical and biological data about the schizophrenias to ask whether these two approaches prioritize the same genes. We find that genes whose expression changes are deemed to be of importance from microarrays are rarely those classified as of importance from clinical, in situ, molecular, single-nucleotide polymorphism (SNP) association, knockout and drug perturbation data. This disparity is not limited to the schizophrenias but characterizes other human disease data sets. It also extends to biological validation of microarray data in model organisms, in which genome-wide phenotypic data have been systematically compared with microarray data. In addition, different bioinformatic protocols applied to the same microarray data yield quite different gene sets and thus make clinical decisions less straightforward. We discuss how progress may be improved in the clinical area by the assignment of high-quality phenotypic values to each member of a microarray-assigned gene set.

Identification of gene interactions associated with disease from gene expression data using synergy networks

John Watkinson — 2008-01-31T10:05:47-00:00

BMC Systems Biology, Vol. 2, No. 1. (2008)

BACKGROUND:Analysis of microarray data has been used for the inference of gene-gene interactions. If, however, the aim is the discovery of disease-related biological mechanisms, then the criterion for defining such interactions must be specifically linked to disease.RESULTS:Here we present a computational methodology that jointly analyzes two sets of microarray data, one in the presence and one in the absence of a disease, identifying gene pairs whose correlation with disease is due to cooperative, rather than independent, contributions of genes, using the recently developed information theoretic measure of synergy. High levels of synergy in gene pairs indicates possible membership of the two genes in a shared pathway and leads to a graphical representation of inferred gene-gene interactions associated with disease, in the form of a "synergy network." We apply this technique on a set of publicly available prostate cancer expression data and successfully validate our results, confirming that they cannot be due to pure chance and providing a biological explanation for gene pairs with exceptionally high synergy.CONCLUSIONS:Thus, synergy networks provide a computational methodology helpful for deriving "disease interactomes" from biological data. When coupled with additional biological knowledge, they can also be helpful for deciphering biological mechanisms responsible for disease.

Sharing and reusing gene expression profiling data in neuroscience.

X Wan — 2008-04-23T18:04:10-00:00

Neuroinformatics, Vol. 5, No. 3. (2007), pp. 161-175.

As public availability of gene expression profiling data increases, it is natural to ask how these data can be used by neuroscientists. Here we review the public availability of high-throughput expression data in neuroscience and how it has been reused, and tools that have been developed to facilitate reuse. There is increasing interest in making expression data reuse a routine part of the neuroscience tool-kit, but there are a number of challenges. Data must become more readily available in public databases; efforts to encourage investigators to make data available are important, as is education on the benefits of public data release. Once released, data must be better-annotated. Techniques and tools for data reuse are also in need of improvement. Integration of expression profiling data with neuroscience-specific resources such as anatomical atlases will further increase the value of expression data.

The use of logic relationships to model colon cancer gene expression networks with mRNA microarray data.

X Ruan — 2008-07-17T13:35:49-00:00

Journal of biomedical informatics, Vol. 41, No. 4. (August 2008), pp. 530-543.

The ultimate goal of genomics research is to describe the network of molecules and interactions that govern all biological functions and disease processes in cells. Nonlinear interactions among genes in terms of their logic relationships play a key role for deciphering the networks of molecules that underlie cellular function. We present a method based on a graph coloring scheme and information theory to identify the gene expression network with lower and higher order logic interactions of genes. The analysis of oncogenes and suppressor genes from a colon cancer mRNA microarray dataset identifies a gene expression network with directionality and weights that reflects intracellular communication pathways. The success of the proposed method in mining hidden, complicated gene interactions and reliably interpreting experimental results suggests that the proposed method is a useful tool for understanding cancer systems. Extension of this method holds the potential to be fruitful for understanding other complex, nonsymmetric systems.

ITALICS: an algorithm for normalization and DNA copy number calling for Affymetrix SNP arrays

Guillem Rigaill — 2008-03-13T16:40:36-00:00

Bioinformatics, Vol. 24, No. 6. (15 March 2008), pp. 768-774.

Motivation: Affymetrix SNP arrays can be used to determine the DNA copy number measurement of 11 000500 000 SNPs along the genome. Their high density facilitates the precise localization of genomic alterations and makes them a powerful tool for studies of cancers and copy number polymorphism. Like other microarray technologies it is influenced by non-relevant sources of variation, requiring correction. Moreover, the amplitude of variation induced by non-relevant effects is similar or greater than the biologically relevant effect (i.e. true copy number), making it difficult to estimate non-relevant effects accurately without including the biologically relevant effect. Results: We addressed this problem by developing ITALICS, a normalization method that estimates both biological and non-relevant effects in an alternate, iterative manner, accurately eliminating irrelevant effects. We compared our normalization method with other existing and available methods, and found that ITALICS outperformed these methods for several in-house datasets and one public dataset. These results were validated biologically by quantitative PCR. Availability: The R package ITALICS (ITerative and Alternative normaLIzation and Copy number calling for affymetrix Snp arrays) has been submitted to Bioconductor. Contact: italics@curie.fr Supplementary information: Supplementary data are available at Bioinformatics online. 10.1093/bioinformatics/btn048

An improved method for detecting and delineating genomic regions with altered gene expression in cancer

Bjorn Nilsson — 2008-01-22T05:23:31-00:00

Genome Biology, Vol. 9, No. 1. (2008)

Genomic regions with altered gene expression are a characteristic feature of cancer cells. We present a novel method for identifying such regions in gene expression maps. This method is based on total variation (TV) minimization, a classical signal restoration technique. In systematic evaluations, we show that our method combines top-notch detection performance with an ability to delineate relevant regions without excessive over-segmentation, making it a significant advance over existing methods. Software -- Rendersome -- is provided.

Identification and validation of colorectal neoplasia-specific methylation markers for accurate classification of disease.

F Model — 2008-07-17T13:10:08-00:00

Molecular cancer research : MCR, Vol. 5, No. 2. (February 2007), pp. 153-163.

Aberrant DNA methylation occurs early in oncogenesis, is stable, and can be assayed in tissues and body fluids. Therefore, genes with aberrant methylation can provide clues for understanding tumor pathways and are attractive candidates for detection of early neoplastic events. Identification of sequences that optimally discriminate cancer from other diseased and healthy tissues is needed to advance both approaches. Using well-characterized specimens, genome-wide methylation techniques were used to identify candidate markers specific for colorectal neoplasia. To further validate 30 of these candidates from genome-wide analysis and 13 literature-derived genes, including genes involved in cancer and others with unknown functions, a high-throughput methylation-specific oligonucleotide microarray was used. The arrays were probed with bisulfite-converted DNA from 89 colorectal adenocarcinomas, 55 colorectal polyps, 31 inflammatory bowel disease, 115 extracolonic cancers, and 67 healthy tissues. The 20 most discriminating markers were highly methylated in colorectal neoplasia (area under the receiver operating characteristic curve > 0.8; P < 0.0001). Normal epithelium and extracolonic cancers revealed significantly lower methylation. Real-time PCR assays developed for 11 markers were tested on an independent set of 149 samples from colorectal adenocarcinomas, other diseases, and healthy tissues. Microarray results could be reproduced for 10 of 11 marker assays, including eight of the most discriminating markers (area under the receiver operating characteristic curve > 0.72; P < 0.009). The markers with high specificity for colorectal cancer have potential as blood-based screening markers whereas markers that are specific for multiple cancers could potentially be used as prognostic indicators, as biomarkers for therapeutic response monitoring or other diagnostic applications, compelling further investigation into their use in clinical testing and overall roles in tumorigenesis.

Spatial preferences of microRNA targets in 3' untranslated regions

William Majoros — 2007-06-07T20:23:08-00:00

BMC Genomics, Vol. 8 (07 June 2007), 152.

Cross-generation and cross-laboratory predictions of Affymetrix microarrays by rank-based methods.

HC Liu — 2008-07-17T13:05:15-00:00

Journal of biomedical informatics, Vol. 41, No. 4. (August 2008), pp. 570-579.

Past experiments of the popular Affymetrix (Affy) microarrays have accumulated a huge amount of public data sets. To apply them for more wide studies, the comparability across generations and experimental environments is an important research topic. This paper particularly investigates the issue of cross-generation/laboratory predictions. That is, whether models built upon data of one generation (laboratory) can differentiate data of another. We consider eight public sets of three cancers. They are from different laboratories and are across various generations of Affy human microarrays. Each cancer has certain subtypes, and we investigate if a model trained from one set correctly differentiates another. We propose a simple rank-based approach to make data from different sources more comparable. Results show that it leads to higher prediction accuracy than using expression values. We further investigate normalization issues in preparing training/testing data. In addition, we discuss some pitfalls in evaluating cross-generation/laboratory predictions. To use data from various sources one must be cautious on some important but easily neglected steps.

Regulation of microRNA expression: the hypoxic component.

R Kulshreshtha — 2008-07-10T16:25:21-00:00

Cell cycle (Georgetown, Tex.), Vol. 6, No. 12. (15 June 2007), pp. 1426-1431.

microRNAs are involved in a wide variety of normal and pathological cellular processes, including tumorigenic transformation. Despite significant progress made towards understanding their mechanisms of action, much less is known about the regulation of expression of specific microRNAs. Recent reports have established a link between hypoxia, a key feature of the tumor microenvironment, and a group of microRNAs. Select members of this group seem to affect apoptotic signaling in a hypoxic environment and are also predicted to target genes of critical importance for tumor biology. Interestingly, most hypoxia-induced microRNAs are also overexpressed in human cancers, suggesting a role in tumorigenesis. We hereby discuss the known and predicted regulators of microRNA expression and approaches for expanding this fledgling research area.

A susceptibility gene set for early onset colorectal cancer that integrates diverse signaling pathways: implication for tumorigenesis.

Y Hong — 2008-07-10T16:19:24-00:00

Clinical cancer research : an official journal of the American Association for Cancer Research, Vol. 13, No. 4. (15 February 2007), pp. 1107-1114.

PURPOSE: The causative genes for autosomal dominantly inherited familial adenomatous polyposis (FAP) and hereditary nonpolyposis colorectal cancer have been well characterized. There is, however, another 10% to 15% of early onset colorectal cancers (CRC) in which the genetic components are unclear. In this study, we used microarray technology to systematically search for differentially expressed genes in early onset CRC. EXPERIMENTAL DESIGN: Young patients with non-FAP or non-hereditary nonpolyposis colorectal cancer, and healthy controls were age- (<or=50 years old), ethnicity- (Chinese), and tissue-matched. RNAs extracted from colonic mucosa specimens were analyzed using GeneChip U133-Plus 2.0 Array. RESULTS: Seven genes, CYR61, UCHL1, FOS, FOS B, EGR1, VIP, and KRT24, were consistently up-regulated in the mucosa of all six patients compared with the mucosa from four healthy controls. The overexpression of these genes was independently validated with a testing set of six patients and six healthy controls. Principal component analysis clustered the healthy control specimens separately from the patient specimens. Real-time PCR quantification with SYBR-Green on nine other patient specimens not previously used in microarray assays confirmed the up-regulation of these seven genes. These genes function in a multitude of biological processes ranging from transcription, angiogenesis, adhesion, and inflammatory regulation to protein catabolism in various cellular compartments, from extracellular to the nucleus. They integrate known tumorigenesis (Wnt, PI3K, MAP kinase, hypoxia, G protein-coupled receptor), neurologic, insulin-signaling, and NFAT-immune pathways into an intricate biological network. CONCLUSIONS: The data suggest that the patient's mucosa is primed for tumorigenesis when cellular homeostasis is disrupted, and that the seven overexpressed genes could potentially predict early onset CRC.

A segmental maximum a posteriori approach to genome-wide copy number profiling.

R Andersson — 2008-07-10T16:14:42-00:00

Bioinformatics (Oxford, England), Vol. 24, No. 6. (15 March 2008), pp. 751-758.

MOTIVATION: Copy number profiling methods aim at assigning DNA copy numbers to chromosomal regions using measurements from microarray-based comparative genomic hybridizations. Among the proposed methods to this end, Hidden Markov Model (HMM)-based approaches seem promising since DNA copy number transitions are naturally captured in the model. Current discrete-index HMM-based approaches do not, however, take into account heterogeneous information regarding the genomic overlap between clones. Moreover, the majority of existing methods are restricted to chromosome-wise analysis. RESULTS: We introduce a novel Segmental Maximum A Posteriori approach, SMAP, for DNA copy number profiling. Our method is based on discrete-index Hidden Markov Modeling and incorporates genomic distance and overlap between clones. We exploit a priori information through user-controllable parameterization that enables the identification of copy number deviations of various lengths and amplitudes. The model parameters may be inferred at a genome-wide scale to avoid overfitting of model parameters often resulting from chromosome-wise model inference. We report superior performances of SMAP on synthetic data when compared with two recent methods. When applied on our new experimental data, SMAP readily recognizes already known genetic aberrations including both large-scale regions with aberrant DNA copy number and changes affecting only single features on the array. We highlight the differences between the prediction of SMAP and the compared methods and show that SMAP accurately determines copy number changes and benefits from overlap consideration.

Multimarker phenotype predicts adverse survival in patients with lymph node-negative colorectal cancer.

I Zlobec — 2008-07-10T16:08:01-00:00

Cancer, Vol. 112, No. 3. (1 February 2008), pp. 495-502.

BACKGROUND: The heterogeneity of stage II colon cancer underlines the need for identifying high-risk, lymph node-negative patients. The objective of this study was to define a multimarker prognostic model of 5-year survival in patients with lymph node-negative, mismatch repair (MMR)-proficient colorectal cancer (CRC). METHODS: Immunohistochemistry for 13 tumor markers was performed on 587 lymph node-negative, MMR-proficient CRC samples by using a tissue microarray. Immunoreactivity was evaluated semiquantitatively. A receiver-operating characteristic-based approach was used to detect clinically relevant tumor markers and to determine cutoff scores for tumor positivity. Univariate and multivariate analyses stratified by pathologic T3 (pT3) or pT4 tumor classification were performed. RESULTS: In univariate analysis, the absence of CD8+ tumor infiltrating lymphocytes (TILs) (P < .001), loss of p27 (P = .006), positive urokinase-type plasminogen activator (uPA) expression (P = .002), and positive uPA receptor (uPAR) expression (P = .037) were associated with an adverse prognosis. In multivariate analysis, CD8 (P = .001), p27 (P = .031), and uPA (P = .014) were independent prognostic factors. The multimarker phenotype of negative CD8, loss of p27, and positive uPA expression led to significantly worse survival compared with all other combinations of these features. Stratified by pT3 or pT4 stage, CD8 (P = .006) and uPA (P = .011) had independent prognostic value. Combined CD8 negativity and uPA positivity led to a more adverse prognosis in both patients with pT3 tumors and patients with pT4 tumors (P < .001). No difference was observed in the length of survival between patients with pT3 tumors who had CD8 negativity and uPA positivity and patients with pT4 tumors (P = .267). CONCLUSIONS: The multimarker phenotype of the absence of CD8+ TILs, loss of p27, and positive uPA expression was predictive of an adverse prognosis in patients with lymph node-negative, MMR-proficient CRC. The current findings suggested that a subgroup of patients with high-risk, lymph node-negative pT3 tumors should be considered for adjuvant therapy.

A genome-wide map of aberrantly expressed chromosomal islands in colorectal cancer

Eike Staub — 2006-09-19T06:44:22-00:00

Molecular Cancer, Vol. 5 (18 September 2006), 37.

Identification of cancer genes using a statistical framework for multiexperiment analysis of nondiscretized array CGH data.

C Klijn — 2008-07-10T16:03:01-00:00

Nucleic acids research, Vol. 36, No. 2. (February 2008)

Tumor formation is in part driven by DNA copy number alterations (CNAs), which can be measured using microarray-based Comparative Genomic Hybridization (aCGH). Multiexperiment analysis of aCGH data from tumors allows discovery of recurrent CNAs that are potentially causal to cancer development. Until now, multiexperiment aCGH data analysis has been dependent on discretization of measurement data to a gain, loss or no-change state. Valuable biological information is lost when a heterogeneous system such as a solid tumor is reduced to these states. We have developed a new approach which inputs nondiscretized aCGH data to identify regions that are significantly aberrant across an entire tumor set. Our method is based on kernel regression and accounts for the strength of a probe's signal, its local genomic environment and the signal distribution across multiple tumors. In an analysis of 89 human breast tumors, our method showed enrichment for known cancer genes in the detected regions and identified aberrations that are strongly associated with breast cancer subtypes and clinical parameters. Furthermore, we identified 18 recurrent aberrant regions in a new dataset of 19 p53-deficient mouse mammary tumors. These regions, combined with gene expression microarray data, point to known cancer genes and novel candidate cancer genes.

Single nucleotide polymorphism array analysis of chromosomal instability patterns discriminates rectal adenomas from carcinomas

EH Lips — 2007-05-14T08:56:55-00:00

The Journal of Pathology, Vol. 9999, No. 9999. (2007), n/a.

Total mesorectal excision (TME) is the standard treatment for rectal cancer, while transanal endoscopic microsurgery (TEM) is a recently introduced surgical approach for the treatment of rectal adenomas. Incorrect preoperative staging before TEM is a problem. To identify genetic changes that might correlate with tumour stage and could lead to optimized treatment selection we performed a genome-wide chromosomal instability search in a homogeneous, clinical cohort of rectal tumours. 78 rectal tumours during different clinical stages were analysed with 10K single nucleotide polymorphism (SNP) arrays. Logistic regression was performed to build a quantitative model of specific chromosomal aberrations. Overall, most cases (95%) had one or more chromosomal aberrations. We observed a clear correlation between the total number of aberrations and the different tumour stages. Specifically, the chromosomal events: gain of 8q22-24, 13q and 20q, and loss of 17p and 18q12-22, were far more abundant in carcinoma than in adenoma. In adenoma fractions from cases with a carcinoma (infiltrating at least in the submucosa), twice the amount of such ?malignant aberrations? was observed, compared to pure adenomas. Furthermore, combined aberrations such as gain of 13q and loss of 18q were only found in adenomatous fractions of carcinomas and not in benign lesions. Based on these five genomic events associated with carcinoma, a clear distinction between adenoma and carcinoma tissue could be made. These data should be validated further in order that they may be used in preoperative staging of rectal tumours. Copyright © 2007 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Combined array-comparative genomic hybridization and single-nucleotide polymorphism-loss of heterozygosity analysis reveals complex genetic alterations in cervical cancer

Judith Kloth — 2007-02-21T09:43:20-00:00

BMC Genomics, Vol. 8 (20 February 2007), 53.

Array CGH identifies distinct DNA copy number profiles of oncogenes and tumor suppressor genes in chromosomal- and microsatellite-unstable sporadic colorectal carcinomas

Lassmann — 2007-03-15T20:07:36-00:00

Journal of Molecular Medicine, Vol. 85, No. 3. (March 2007), pp. 289-300.

Common and distinct genomic events in sporadic colorectal cancer and diverse cancer types.

ES Martin — 2008-07-10T15:38:28-00:00

Cancer research, Vol. 67, No. 22. (15 November 2007), pp. 10736-10743.

Colorectal cancer (CRC) is a major cause of cancer morbidity and mortality, and elucidation of its underlying genetics has advanced diagnostic screening, early detection, and treatment. Because CRC genomes are characterized by numerous non-random chromosomal structural alterations, we sought to delimit regions of recurrent amplifications and deletions in a collection of 42 primary specimens and 37 tumor cell lines derived from chromosomal instability neoplasia and microsatellite instability neoplasia CRC subtypes and to compare the pattern of genomic aberrations in CRC with those in other cancers. Application of oligomer-based array-comparative genome hybridization and custom analytic tools identified 50 minimal common regions (MCRs) of copy number alterations, 28 amplifications, and 22 deletions. Fifteen were highly recurrent and focal (<12 genes) MCRs, five of them harboring known CRC genes including EGFR and MYC with the remaining 10 containing a total of 65 resident genes with established links to cancer. Furthermore, comparisons of these delimited genomic profiles revealed that 22 of the 50 CRC MCRs are also present in lung cancer, glioblastoma, and/or multiple myeloma. Among 22 shared MCRs, nine do not contain genes previously shown genetically altered in cancer, whereas the remaining 13 harbor 35 known cancer genes, of which only 14 have been linked to CRC pathogenesis. Together, these observations point to the existence of many yet-to-be discovered cancer genes driving CRC development, as well as other human cancers, and show the utility of high-resolution copy number analysis in the identification of genetic events common and specific to the development of various tumor types.

Computational Methods for the Analysis of Array Comparative Genomic Hybridization.

Raj Chari — 2008-07-10T15:34:18-00:00

Cancer informatics, Vol. 2 (2006), pp. 48-58.

Array comparative genomic hybridization (array CGH) is a technique for assaying the copy number status of cancer genomes. The widespread use of this technology has lead to a rapid accumulation of high throughput data, which in turn has prompted the development of computational strategies for the analysis of array CGH data. Here we explain the principles behind array image processing, data visualization and genomic profile analysis, review currently available software packages, and raise considerations for future software development.

Comprehensive analysis of loss of heterozygosity events in glioblastoma using the 100K SNP mapping arrays and comparison with copy number abnormalities defined by BAC array comparative genomic hybridization.

KC Lo — 2008-07-10T15:28:47-00:00

Genes, chromosomes & cancer, Vol. 47, No. 3. (March 2008), pp. 221-237.

We have undertaken an extensive high-resolution analysis of loss of heterozygosity (LOH) in 30 high grade gliomas using the Affymetrix 100K SNP mapping array. Only 70% of LOH events were accompanied by a copy number loss (CNA(loss)), and of the other 30%, the distal region of 17p preferentially showed copy number neutral (CNN)-associated LOH. Combined analysis of CNA(loss) and LOH using MergeLevels analysis software predicts whether the observed losses occurred on a diploid or tetraploid background. In a side-by-side comparison between SNP and bacterial artificial chromosome (BAC) arrays, the overall identification of CNAs was similar on both platforms. The resolution provided by the SNP arrays, however, allowed a considerably more accurate definition of breakpoints as well as defining small events within the cancer genomes, which could not be detected on BAC arrays. CNN LOH was only detected by the SNP arrays, as was ploidy prediction. From our analysis, therefore, it is clear that simultaneously defining CNAs and CNN-LOH using the SNP platform provides a higher resolution and more complete analysis of the genetic events that have occurred within tumor cells. Our extensive analysis of SNP array data has also allowed an objective assessment of threshold LOH scores that can accurately predict LOH. This capability has important implications for interpretation of LOH events since they have consistently been used to localize potential tumor suppressor genes within the cancer genome.

wuHMM: a robust algorithm to detect DNA copy number variation using long oligonucleotide microarray data.

P Cahan — 2008-06-27T16:07:08-00:00

Nucleic acids research, Vol. 36, No. 7. (April 2008)

Copy number variants (CNVs) are currently defined as genomic sequences that are polymorphic in copy number and range in length from 1000 to several million base pairs. Among current array-based CNV detection platforms, long-oligonucleotide arrays promise the highest resolution. However, the performance of currently available analytical tools suffers when applied to these data because of the lower signal:noise ratio inherent in oligonucleotide-based hybridization assays. We have developed wuHMM, an algorithm for mapping CNVs from array comparative genomic hybridization (aCGH) platforms comprised of 385 000 to more than 3 million probes. wuHMM is unique in that it can utilize sequence divergence information to reduce the false positive rate (FPR). We apply wuHMM to 385K-aCGH, 2.1M-aCGH and 3.1M-aCGH experiments comparing the 129X1/SvJ and C57BL/6J inbred mouse genomes. We assess wuHMM's performance on the 385K platform by comparison to the higher resolution platforms and we independently validate 10 CNVs. The method requires no training data and is robust with respect to changes in algorithm parameters. At a FPR of <10%, the algorithm can detect CNVs with five probes on the 385K platform and three on the 2.1M and 3.1M platforms, resulting in effective resolutions of 24 kb, 2-5 kb and 1 kb, respectively.

Argonaute--a database for gene regulation by mammalian microRNAs.

P Shahi — 2006-01-04T15:52:37-00:00

Nucleic Acids Res, Vol. 34, No. Database issue. (1 January 2006)

MicroRNAs (miRNAs) constitute a recently discovered class of small non-coding RNAs that regulate expression of target genes either by decreasing the stability of the target mRNA or by translational inhibition. They are involved in diverse processes, including cellular differentiation, proliferation and apoptosis. Recent evidence also suggests their importance for cancerogenesis. By far the most important model systems in cancer research are mammalian organisms. Thus, we decided to compile comprehensive information on mammalian miRNAs, their origin and regulated target genes in an exhaustive, curated database called Argonaute (http://www.ma.uni-heidelberg.de/apps/zmf/argonaute/interface). Argonaute collects latest information from both literature and other databases. In contrast to current databases on miRNAs like miRBase::Sequences, NONCODE or RNAdb, Argonaute hosts additional information on the origin of an miRNA, i.e. in which host gene it is encoded, its expression in different tissues and its known or proposed function, its potential target genes including Gene Ontology annotation, as well as miRNA families and proteins known to be involved in miRNA processing. Additionally, target genes are linked to an information retrieval system that provides comprehensive information from sequence databases and a simultaneous search of MEDLINE with all synonyms of a given gene. The web interface allows the user to get information for a single or multiple miRNAs, either selected or uploaded through a text file. Argonaute currently has information on 839 miRNAs from human, mouse and rat.

Genomic analysis of epithelial ovarian cancer.

J Farley — 2008-06-27T13:27:40-00:00

Cell research, Vol. 18, No. 5. (May 2008), pp. 538-548.

Ovarian cancer is a major health problem for women in the United States. Despite evidence of considerable heterogeneity, most cases of ovarian cancer are treated in a similar fashion. The molecular basis for the clinicopathologic characteristics of these tumors remains poorly defined. Whole genome expression profiling is a genomic tool, which can identify dysregulated genes and uncover unique sub-classes of tumors. The application of this technology to ovarian cancer has provided a solid molecular basis for differences in histology and grade of ovarian tumors. Differentially expressed genes identified pathways implicated in cell proliferation, invasion, motility, chromosomal instability, and gene silencing and provided new insights into the origin and potential treatment of these cancers. The added knowledge provided by global gene expression profiling should allow for a more rational treatment of ovarian cancers. These techniques are leading to a paradigm shift from empirical treatment to an individually tailored approach. This review summarizes the new genomic data on epithelial ovarian cancers of different histology and grade and the impact it will have on our understanding and treatment of this disease.

Positional gene enrichment analysis of gene sets for high-resolution identification of overrepresented chromosomal regions.

Katleen De Preter — 2008-03-20T08:18:07-00:00

Nucleic Acids Res (16 March 2008)

The search for feature enrichment is a widely used method to characterize a set of genes. While several tools have been designed for nominal features such as Gene Ontology annotations or KEGG Pathways, very little has been proposed to tackle numerical features such as the chromosomal positions of genes. For instance, microarray studies typically generate gene lists that are differentially expressed in the sample subgroups under investigation, and when studying diseases caused by genome alterations, it is of great interest to delineate the chromosomal regions that are significantly enriched in these lists. In this article, we present a positional gene enrichment analysis method (PGE) for the identification of chromosomal regions that are significantly enriched in a given set of genes. The strength of our method relies on an original query optimization approach that allows to virtually consider all the possible chromosomal regions for enrichment, and on the multiple testing correction which discriminates truly enriched regions versus those that can occur by chance. We have developed a Web tool implementing this method applied to the human genome (http://www.esat.kuleuven.be/ approximately bioiuser/pge). We validated PGE on published lists of differentially expressed genes. These analyses showed significant overrepresentation of known aberrant chromosomal regions.

Real-time quantification of microRNAs by stem-loop RT-PCR.

C Chen — 2006-03-19T08:58:11-00:00

Nucleic Acids Res, Vol. 33, No. 20. (2005)

A novel microRNA (miRNA) quantification method has been developed using stem-loop RT followed by TaqMan PCR analysis. Stem-loop RT primers are better than conventional ones in terms of RT efficiency and specificity. TaqMan miRNA assays are specific for mature miRNAs and discriminate among related miRNAs that differ by as little as one nucleotide. Furthermore, they are not affected by genomic DNA contamination. Precise quantification is achieved routinely with as little as 25 pg of total RNA for most miRNAs. In fact, the high sensitivity, specificity and precision of this method allows for direct analysis of a single cell without nucleic acid purification. Like standard TaqMan gene expression assays, TaqMan miRNA assays exhibit a dynamic range of seven orders of magnitude. Quantification of five miRNAs in seven mouse tissues showed variation from less than 10 to more than 30,000 copies per cell. This method enables fast, accurate and sensitive miRNA expression profiling and can identify and monitor potential biomarkers specific to tissues or diseases. Stem-loop RT-PCR can be used for the quantification of other small RNA molecules such as short interfering RNAs (siRNAs). Furthermore, the concept of stem-loop RT primer design could be applied in small RNA cloning and multiplex assays for better specificity and efficiency.