posted on 2023-03-08, 10:23authored byJohn Hogan, Conor Judge, Michael O'Callaghan, Amir Aziz, Cormac O'Connor, John Burke, Colum P. DunneColum P. Dunne, Stewart R. Walsh, Matthew Kalady, Calvin J. Coffey
Objective: This study aims to harness the potential of public gene expression
repositories, to develop gene expression profiles that could accurately
determine nodal status in colorectal cancer.
Background: Currently, techniques that determine lymph node positivity (before
resection) have poor sensitivity and specificity. The ability to determine
lymph node status, based on preoperative biopsies, would greatly assist in
planning treatment in colorectal cancer. This is particularly relevant in polypdetected
cancers.
Methods: Public gene expression repositories were screened for experiments
comparing metastatic and nonmetastatic colorectal cancer. A customized
graphic user interface was developed to extract genes dysregulated across
most identified studies (ie, consensus profiles). The utility of consensus profiles
was tested by determining whether classifiers could be derived that determined
nodal positivity or negativity. Consensus profiles-derived classifiers
were tested on separate Affymetrix- and Illumina-based experiments, and
collated outputs were compiled in summary receiver operator curve characteristic
format, with area under the curve (AUC) reflecting accuracy. The
association between classification and oncologic outcome was determined using
an additional, independent data set. Final validation was conducted using
the Ingenuity network-linkage environment.
Results: Four consensus profiles were generated from which classifiers were
derived that accurately determined node positive and negative status (pooled
AUC were 0.79 ± 0.04 and 0.8 ± 0.03 for nodal positivity and negativity,
respectively). Overall AUC ranged from 0.73 to 0.86, demonstrating high
accuracy across consensus profile type, classification technique, and array
platform used. As consensus profile enabled classification of nodal status,
survival outcomes could be compared for those predicted node negative or
positive. Patterns of disease-free and overall survival were identical to those
observed for standard histopathologic nodal status. Genes contained within
consensus profiles were strongly linked to the metastatic process and included
(among others) FYN,WNT5A, COL8A1, BMP, and SMAD family members.
Conclusions: Microarray expression data available in public gene expression
repositories can be harnessed to generate consensus profiles. The latter are a
source of classifiers that have prognostic and predictive properties.
History
Publication
Annals of Surgery;260 (1), pp, 94-103
Publisher
Lippincott Williams & Wilkins
Note
peer-reviewed
Rights
This is the author’s version of a work that was accepted for publication in Annals of Surgery. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Annals of Surgery, 2014 260 (1), pp. 94-103, http://dx.doi.org/10.1097/SLA.0000000000000289