Current Status of Gene Expression Profiling to Assist Decision Making in Stage II Colon Cancer

Purpose This study mainly aimed to identify and assess the performance of a microarray-based prognosis predictor (PP) for stage II colon cancer. A previously suggested 23-gene prognosis signature (PS) was also evaluated. Patients and Methods Tumor mRNA samples from 50 patients were profiled using oligonucleotide microarrays. PPs were built and assessed by random divisions of patients into training and validation sets (TSs and VSs, respectively). For each TS/VS split, a 30-gene PP, identified on the TS by selecting the 30 most differentially expressed genes and applying diagonal linear discriminant analysis, was used to predict the prognoses of VS patients. Two schemes were considered: single-split validation, based on a single random split of patients into two groups of equal size (group 1 and group 2), and Monte Carlo cross validation (MCCV), whereby patients were repeatedly and randomly divided into TS and VS of various sizes. Results The 30-gene PP, identified from group 1 patients, yielded an 80% prognosis prediction accuracy on group 2 patients. MCCV yielded the following average prognosis prediction performance measures: 76.3% accuracy, 85.1% sensitivity, and 67.5% specificity. Improvements in prognosis prediction were observed with increasing TS size. The 30-gene PS were found to be highly-variable across TS/VS splits. Assessed on the same random splits of patients, the previously suggested 23-gene PS yielded a 67.7% mean prognosis prediction accuracy. Conclusion Microarray gene expression profiling is able to predict the prognosis of stage II colon cancer patients. The present study also illustrates the usefulness of resampling techniques for honest performance assessment of microarray-based PPs.

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Prognosis prediction of stage II colon cancer by gene expression profiling

Journal of Clinical Oncology ◽

10.1200/jco.2006.24.18_suppl.3565 ◽

2006 ◽

Vol 24 (18_suppl) ◽

pp. 3565-3565

Author(s):

A. Barrier ◽

D. Brault ◽

S. Houry ◽

S. Dudoit ◽

A. Lemoine ◽

...

Keyword(s):

Gene Expression ◽

Colon Cancer ◽

Expression Profiling ◽

Prediction Accuracy ◽

Stage Ii ◽

Microarray Gene Expression ◽

Prognosis Prediction ◽

Learning Set ◽

Stage Ii Colon Cancer ◽

High Instability

3565 Background: The aims of the present study were: 1) to identify a prognosis signature (PS), based on microarray gene expression measures, in stage II colon cancer patients and to assess its accuracy with resampling techniques ; 2) to assess the accuracy, also with resampling techniques, of a previously proposed 23-gene PS. Methods: Colon tumor mRNA samples from 50 patients were profiled using the Affymetrix HGU133A GeneChip (22283 sequences). In a first part, the 50 patients were randomly divided into 2 groups (G1 and G2) of equal size that were considered alternately as training and validation sets. In a second part, the 50 patients were randomly divided into 1600 training (size=n) and validation (size=50-n) sets. Informative genes were selected on the training set by taking the 30 most differentially expressed genes between patients who recurred and those who remained disease-free; the accuracy of this PS was assessed by comparing the predicted prognosis (using a diagonal linear discriminant analysis (DLDA)) and the actual evolution for all the validation set patients. Using the same random splits, the accuracy of the 23-gene PS was assessed with a DLDA that used learning set patients as reference samples. Results: The 30-gene PS that was identified from G1 (G2) patients yielded a 80% (84%) prognosis prediction accuracy when applied on G2 (G1) patients. With resampling techniques, the prediction accuracy regularly increased with the learning set (LS) size: 65.5% (range=52.5–75%) with LS of size 10, and 82.7% (range=60–100%) with LS of size 40. Comparisons of compositions of the 100 PS for a given value of n suggested a high instability of informative genes; with LS of size 10, 7 genes were part of at least 10% of signatures; with LS of size 40, 7 genes were part of all the 100 signatures. The accuracy of the previously proposed 23-gene PS also increased with the learning set size. Conclusion: Microarray gene expression profiling represents a promising technique to predict the prognosis of stage II colon cancer patients. The present study also outlines the high instability of informative gene selection and suggests the usefulness of resampling techniques to obtain an honest assessment of prognosis prediction accuracy. No significant financial relationships to disclose.

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