Pretreatment Gene Expression Profiles Can Be Used to Predict Response to Neoadjuvant Chemoradiotherapy in Esophageal Cancer View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2007-12

AUTHORS

Cuong Duong, Danielle M. Greenawalt, Adam Kowalczyk, Marianne L. Ciavarella, Garvesh Raskutti, William K. Murray, Wayne A. Phillips, Robert J. S. Thomas

ABSTRACT

BACKGROUND: The use of neoadjuvant therapy, in particular chemoradiotherapy (CRT), in the treatment of esophageal cancer (EC) remains controversial. The ability to predict treatment response in an individual EC patient would greatly aid therapeutic planning. Gene expression profiles of EC were measured and relationship to therapeutic response assessed. METHODS: Tumor biopsy samples taken from 46 EC patients before neoadjuvant CRT were analyzed on 10.5K cDNA microarrays. Response to treatment was assessed and correlated to gene expression patterns by using a support vector machine learning algorithm. RESULTS: Complete clinical response at conclusion of CRT was achieved in 6 of 21 squamous cell carcinoma (SCC) and 11 of 25 adenocarcinoma (AC) patients. CRT response was an independent prognostic factor for survival (P < .001). A range of support vector machine models incorporating 10 to 1000 genes produced a predictive performance of tumor response to CRT peaking at 87% in SCC, but a distinct positive prediction profile was unobtainable for AC. A 32-gene classifier was produced, and by means of this classifier, 10 of 21 SCC patients could be accurately identified as having disease with an incomplete response to therapy, and thus unlikely to benefit from neoadjuvant CRT. CONCLUSIONS: Our study identifies a 32-gene classifier that can be used to predict response to neoadjuvant CRT in SCC. However, because of the molecular diversity between the two histological subtypes of EC, when considering the AC and SCC samples as a single cohort, a predictive profile could not be resolved, and a negative predictive profile was observed for AC. More... »

PAGES

3602-3609

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1245/s10434-007-9550-1

DOI

http://dx.doi.org/10.1245/s10434-007-9550-1

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1050524686

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/17896157


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Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1245/s10434-007-9550-1'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1245/s10434-007-9550-1'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1245/s10434-007-9550-1'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1245/s10434-007-9550-1'


 

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