Using Gene Expression Profiling to Predict Response and Prognosis in Gastrointestinal Cancers—The Promise and the Perils View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2011-05

AUTHORS

Kate H. Brettingham-Moore, Cuong P. Duong, Alexander G. Heriot, Robert J. S. Thomas, Wayne A. Phillips

ABSTRACT

Cancer treatment is now moving toward a personalized approach, promising improved rates of response and survival. A number of studies have employed the use of microarrays to investigate the predictive potential of expression profiling in gastrointestinal (GI) cancer patients. However while many robust predictive classifiers relating to response and prognosis have been generated for GI cancer patients, these have yet to make the transition to the clinic. The main obstacle is the limited cross validation between predictive gene lists identified for the same tumor type and outcome. Differences in the experimental design, analysis, and interpretation of results all contribute to this variation, with numerous factors influencing which genes are highlighted as predictive. While predictive genomics shows immense potential, it is still a relatively new field and the validation of predictive gene lists derived from microarray data remains a challenge. Future studies must carefully consider all aspects of experimental design to ensure a clinically applicable predictive test can be developed. With this in mind, more extensive and collaborative research must be undertaken before microarray-based platforms can be used routinely in tailoring GI cancer treatment and change clinical practice. Larger cohorts and consistency in methodology will enable the findings from this research to make the transition to the clinic. More... »

PAGES

1484-1491

References to SciGraph publications

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1245/s10434-010-1433-1

    DOI

    http://dx.doi.org/10.1245/s10434-010-1433-1

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1045878032

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/21104326


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    RDF/XML is a standard XML format for linked data.

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