Format

Send to

Choose Destination
Lancet. 2003 Nov 1;362(9394):1439-44.

Predictive ability of DNA microarrays for cancer outcomes and correlates: an empirical assessment.

Author information

1
Clinical and Molecular Epidemiology Unit, Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece.

Abstract

BACKGROUND:

DNA microarrays are being used for many applications, including the prediction of cancer outcomes by simultaneous analysis of the expression of thousands of genes. We systematically assessed the predictive performance of this method for major clinical outcomes (death, metastasis, recurrence, response to therapy) and the correlation of gene profiling with other clinicopathological correlates of malignant disorders.

METHODS:

Eligible reports retrieved from MEDLINE (1995 to April, 2003) were assessed for features of study design, reported predictive performance, and consideration of other prognostic factors. We searched for study variables that increased the chances that a significant association with a clinical outcome or correlate would be found.

FINDINGS:

84 eligible studies were identified, of which 30 addressed major clinical outcomes. A median of 25 (IQR 15-45) patients with cancer were included. Among the studies of major clinical outcomes, nine did cross-validation but it was complete in only two of them; six studies used independent validation of supervised predictive models. Smaller studies showed better sensitivity and specificity for clinical outcomes than larger studies. Only 11 studies addressing major clinical outcomes did subgroup or adjusted analyses for other prognostic factors. Across all 84 studies, significant associations were 3.5 (95% CI 1.5-8.0) times more likely per doubling of sample size and 9.7 (2.0-47.0) times more likely per ten-fold increase in microarray probes.

INTERPRETATION:

DNA microarrays addressing cancer outcomes show variable prognostic performance. Larger studies with appropriate clinical design, adjustment for known predictors, and proper validation are essential for this highly promising technology.

PMID:
14602436
DOI:
10.1016/S0140-6736(03)14686-7
[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for Elsevier Science
Loading ...
Support Center