Universität zu Köln

Zhao, Chen, Shi, Leming ORCID: 0000-0002-7143-2033, Tong, Weida, Shaughnessy, John D., Jr., Oberthuer, Andre, Pusztai, Lajos, Deng, Youping, Symmans, W. Fraser and Shi, Tieliu (2011). Maximum predictive power of the microarray-based models for clinical outcomes is limited by correlation between endpoint and gene expression profile. BMC Genomics, 12. LONDON: BIOMED CENTRAL LTD. ISSN 1471-2164

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Abstract

Background: Microarray data have been used for gene signature selection to predict clinical outcomes. Many studies have attempted to identify factors that affect models' performance with only little success. Fine-tuning of model parameters and optimizing each step of the modeling process often results in over-fitting problems without improving performance. Results: We propose a quantitative measurement, termed consistency degree, to detect the correlation between disease endpoint and gene expression profile. Different endpoints were shown to have different consistency degrees to gene expression profiles. The validity of this measurement to estimate the consistency was tested with significance at a p-value less than 2.2e-16 for all of the studied endpoints. According to the consistency degree score, overall survival milestone outcome of multiple myeloma was proposed to extend from 730 days to 1561 days, which is more consistent with gene expression profile. Conclusion: For various clinical endpoints, the maximum predictive powers of different microarray-based models are limited by the correlation between endpoint and gene expression profile of disease samples as indicated by the consistency degree score. In addition, previous defined clinical outcomes can also be reassessed and refined more coherent according to related disease gene expression profile. Our findings point to an entirely new direction for assessing the microarray-based predictive models and provide important information to gene signature based clinical applications.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Zhao, Chen UNSPECIFIED UNSPECIFIED UNSPECIFIED Shi, Leming UNSPECIFIED orcid.org/0000-0002-7143-2033 UNSPECIFIED Tong, Weida UNSPECIFIED UNSPECIFIED UNSPECIFIED Shaughnessy, John D., Jr. UNSPECIFIED UNSPECIFIED UNSPECIFIED Oberthuer, Andre UNSPECIFIED UNSPECIFIED UNSPECIFIED Pusztai, Lajos UNSPECIFIED UNSPECIFIED UNSPECIFIED Deng, Youping UNSPECIFIED UNSPECIFIED UNSPECIFIED Symmans, W. Fraser UNSPECIFIED UNSPECIFIED UNSPECIFIED Shi, Tieliu UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-483275
DOI:	10.1186/1471-2164-12-S5-S3
Journal or Publication Title:	BMC Genomics
Volume:	12
Date:	2011
Publisher:	BIOMED CENTRAL LTD
Place of Publication:	LONDON
ISSN:	1471-2164
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language BREAST-CANCER; HEPATOCELLULAR-CARCINOMA; BAYESIAN-ANALYSIS; STAGING SYSTEMS Multiple languages Biotechnology & Applied Microbiology; Genetics & Heredity Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/48327