Predictive ability of DNA microarrays for cancer outcomes and correlates: an empirical assessment - PubMed (original) (raw)
Review
Predictive ability of DNA microarrays for cancer outcomes and correlates: an empirical assessment
Evangelia E Ntzani et al. Lancet. 2003.
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.
Comment in
- Microarrays in cancer: moving from hype to clinical reality.
Winegarden N. Winegarden N. Lancet. 2003 Nov 1;362(9394):1428. doi: 10.1016/S0140-6736(03)14724-1. Lancet. 2003. PMID: 14602430 No abstract available.
Similar articles
- National Oncology Forum: perspectives for the year 2000.
DeVita VT Jr, Bleickardt EW. DeVita VT Jr, et al. Cancer J. 2001 Jul-Aug;7 Suppl 1:S2-13. Cancer J. 2001. PMID: 11504281 Review. - Using microarray analysis as a prognostic and predictive tool in oncology: focus on breast cancer and normal tissue toxicity.
Nuyten DS, van de Vijver MJ. Nuyten DS, et al. Semin Radiat Oncol. 2008 Apr;18(2):105-14. doi: 10.1016/j.semradonc.2007.10.007. Semin Radiat Oncol. 2008. PMID: 18314065 Review. - Clinical outcome prediction by microRNAs in human cancer: a systematic review.
Nair VS, Maeda LS, Ioannidis JP. Nair VS, et al. J Natl Cancer Inst. 2012 Apr 4;104(7):528-40. doi: 10.1093/jnci/djs027. Epub 2012 Mar 6. J Natl Cancer Inst. 2012. PMID: 22395642 Free PMC article. Review. - Multiple gene expression classifiers from different array platforms predict poor prognosis of colorectal cancer.
Lin YH, Friederichs J, Black MA, Mages J, Rosenberg R, Guilford PJ, Phillips V, Thompson-Fawcett M, Kasabov N, Toro T, Merrie AE, van Rij A, Yoon HS, McCall JL, Siewert JR, Holzmann B, Reeve AE. Lin YH, et al. Clin Cancer Res. 2007 Jan 15;13(2 Pt 1):498-507. doi: 10.1158/1078-0432.CCR-05-2734. Clin Cancer Res. 2007. PMID: 17255271 - DNA microarrays are predictive of cancer prognosis: a re-evaluation.
Fan X, Shi L, Fang H, Cheng Y, Perkins R, Tong W. Fan X, et al. Clin Cancer Res. 2010 Jan 15;16(2):629-36. doi: 10.1158/1078-0432.CCR-09-1815. Epub 2010 Jan 12. Clin Cancer Res. 2010. PMID: 20068095 Review.
Cited by
- A comparison of RNA-Seq data preprocessing pipelines for transcriptomic predictions across independent studies.
Van R, Alvarez D, Mize T, Gannavarapu S, Chintham Reddy L, Nasoz F, Han MV. Van R, et al. BMC Bioinformatics. 2024 May 8;25(1):181. doi: 10.1186/s12859-024-05801-x. BMC Bioinformatics. 2024. PMID: 38720247 Free PMC article. - Prevalence and predictors of data and code sharing in the medical and health sciences: systematic review with meta-analysis of individual participant data.
Hamilton DG, Hong K, Fraser H, Rowhani-Farid A, Fidler F, Page MJ. Hamilton DG, et al. BMJ. 2023 Jul 11;382:e075767. doi: 10.1136/bmj-2023-075767. BMJ. 2023. PMID: 37433624 Free PMC article. - High-plex immunofluorescence imaging and traditional histology of the same tissue section for discovering image-based biomarkers.
Lin JR, Chen YA, Campton D, Cooper J, Coy S, Yapp C, Tefft JB, McCarty E, Ligon KL, Rodig SJ, Reese S, George T, Santagata S, Sorger PK. Lin JR, et al. Nat Cancer. 2023 Jul;4(7):1036-1052. doi: 10.1038/s43018-023-00576-1. Epub 2023 Jun 22. Nat Cancer. 2023. PMID: 37349501 Free PMC article. - The systematic comparison between Gaussian mirror and Model-X knockoff models.
Chen S, Li Z, Liu L, Wen Y. Chen S, et al. Sci Rep. 2023 Apr 4;13(1):5478. doi: 10.1038/s41598-023-32605-5. Sci Rep. 2023. PMID: 37015993 Free PMC article. - Identification of Prognostic Biomarkers for Breast Cancer Metastasis Using Penalized Additive Hazards Regression Model.
Tapak L, Hamidi O, Amini P, Afshar S, Salimy S, Dinu I. Tapak L, et al. Cancer Inform. 2023 Mar 21;22:11769351231157942. doi: 10.1177/11769351231157942. eCollection 2023. Cancer Inform. 2023. PMID: 36968522 Free PMC article.
Publication types
MeSH terms
Substances
LinkOut - more resources
Full Text Sources
Other Literature Sources
Miscellaneous