Study design and data analysis considerations for the discovery of prognostic molecular biomarkers: a case study of progression free survival ... View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2016-12

AUTHORS

Li-Xuan Qin, Douglas A. Levine

ABSTRACT

BACKGROUND: Accurate discovery of molecular biomarkers that are prognostic of a clinical outcome is an important yet challenging task, partly due to the combination of the typically weak genomic signal for a clinical outcome and the frequently strong noise due to microarray handling effects. Effective strategies to resolve this challenge are in dire need. METHODS: We set out to assess the use of careful study design and data normalization for the discovery of prognostic molecular biomarkers. Taking progression free survival in advanced serous ovarian cancer as an example, we conducted empirical analysis on two sets of microRNA arrays for the same set of tumor samples: arrays in one set were collected using careful study design (that is, uniform handling and randomized array-to-sample assignment) and arrays in the other set were not. RESULTS: We found that (1) handling effects can confound the clinical outcome under study as a result of chance even with randomization, (2) the level of confounding handling effects can be reduced by data normalization, and (3) good study design cannot be replaced by post-hoc normalization. In addition, we provided a practical approach to define positive and negative control markers for detecting handling effects and assessing the performance of a normalization method. CONCLUSIONS: Our work showcased the difficulty of finding prognostic biomarkers for a clinical outcome of weak genomic signals, illustrated the benefits of careful study design and data normalization, and provided a practical approach to identify handling effects and select a beneficial normalization method. Our work calls for careful study design and data analysis for the discovery of robust and translatable molecular biomarkers. More... »

PAGES

27

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s12920-016-0187-4

DOI

http://dx.doi.org/10.1186/s12920-016-0187-4

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1039135797

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/27282150


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