Literature DB >> 15152942

Microarrays in cancer research.

Geraldine M Grant1, Amanda Fortney, Francesco Gorreta, Michael Estep, Luca Del Giacco, Amy Van Meter, Alan Christensen, Lakshmi Appalla, Chahla Naouar, Curtis Jamison, Ali Al-Timimi, Jean Donovan, James Cooper, Carleton Garrett, Vikas Chandhoke.   

Abstract

Microarray technology has presented the scientific community with a compelling approach that allows for simultaneous evaluation of all cellular processes at once. Cancer, being one of the most challenging diseases due to its polygenic nature, presents itself as a perfect candidate for evaluation by this approach. Several recent articles have provided significant insight into the strengths and limitations of microarrays. Nevertheless, there are strong indications that this approach will provide new molecular markers that could be used in diagnosis and prognosis of cancers. To achieve these goals it is essential that there is a seamless integration of clinical and molecular biological data that allows us to elucidate genes and pathways involved in various cancers. To this effect we are currently evaluating gene expression profiles in human brain, ovarian, breast and hematopoetic, lung, colorectal, head and neck and biliary tract cancers. To address the issues we have a joint team of scientists, doctors and computer scientists from two Virginia Universities and a major healthcare provider. The study has been divided into several focus groups that include; Tissue Bank Clinical & Pathology Laboratory Data, Chip Fabrication, QA/QC, Tissue Devitalization, Database Design and Data Analysis, using multiple microarray platforms. Currently over 300 consenting patients have been enrolled in the study with the largest number being that of breast cancer patients. Clinical data on each patient is being compiled into a secure and interactive relational database and integration of these data elements will be accomplished by a common programming interface. This clinical database contains several key parameters on each patient including demographic (risk factors, nutrition, co-morbidity, familial history), histopathology (non genetic predictors), tumor, treatment and follow-up information. Gene expression data derived from the tissue samples will be linked to this database, which allows us to query the data at multiple levels. The challenge of tissue acquisition and processing is of paramount importance to the success of this venture. A tissue devitalization timeline protocol was devised to ensure sample and RNA integrity. Stringent protocols are being employed to ascertain accurate tumor homogeneity, by serial dissection of each tumor sample at 10 microM frozen sections followed by histopathological evaluation. The multiple platforms being utilized in this study include Affimetrix, Oligo-Chips and custom-designed cDNA arrays. Selected RNA samples will be evaluated on each platform between the groups. Analysis steps will involve normalization and standardization of gene expression data followed by hierarchical clustering to determine co-regulation profiles. The aim of this conjoint effort is to elucidate pathways and genes involved in various cancers, resistance mechanisms, molecular markers for diagnosis and prognosis.

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Mesh:

Year:  2004        PMID: 15152942

Source DB:  PubMed          Journal:  Anticancer Res        ISSN: 0250-7005            Impact factor:   2.480


  3 in total

1.  Krüppel-like zinc finger proteins in end-stage COPD lungs with and without severe alpha1-antitrypsin deficiency.

Authors:  A-Rembert Koczulla; Danny Jonigk; Thomas Wolf; Christian Herr; Sarah Noeske; Walter Klepetko; Claus Vogelmeier; Nils von Neuhoff; Johanna Rische; Sabine Wrenger; Heiko Golpon; Robert Voswinckel; Maurizio Luisetti; Ilaria Ferrarotti; Tobias Welte; Sabina Janciauskiene
Journal:  Orphanet J Rare Dis       Date:  2012-05-23       Impact factor: 4.123

Review 2.  Computational characterisation of cancer molecular profiles derived using next generation sequencing.

Authors:  Urszula Oleksiewicz; Katarzyna Tomczak; Jakub Woropaj; Monika Markowska; Piotr Stępniak; Parantu K Shah
Journal:  Contemp Oncol (Pozn)       Date:  2015

3.  Application of a correlation correction factor in a microarray cross-platform reproducibility study.

Authors:  Kellie J Archer; Catherine I Dumur; G Scott Taylor; Michael D Chaplin; Anthony Guiseppi-Elie; Geraldine Grant; Andrea Ferreira-Gonzalez; Carleton T Garrett
Journal:  BMC Bioinformatics       Date:  2007-11-15       Impact factor: 3.169

  3 in total

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