Study design and data analysis considerations for the discovery of prognostic molecular biomarkers: a case study of progression free survival in advanced serous ovarian cancer

open access: (technical)
Study design and data analysis considerations for the discovery of prognostic molecular biomarkers: a case study of progression free survival in advanced serous ovarian cancer
Published: 10 June 2016

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.

Background

Accurate discovery of molecular biomarkers that are prognostic of a clinical outcome is an important yet challenging task [1]. A main reason for the difficulty is the combination of the typically weak signal for a clinical outcome and the frequently strong noise due to microarray handling effects [2]. In particular, array handling effects can increase data variability and often confound with the outcome of interest, which have been reported profoundly in high-throughput genomic studies as a reason for dubious or even erroneous findings [3].....

 A set of 192 untreated primary gynecologic tumor samples (96 endometrioid endometrial tumors and 96 serous ovarian tumors) were collected at Memorial Sloan Kettering Cancer Center during the period of 2000–2012.