This theory of information transfer remains salient to genomic medicine and genome-based drug therapy where, in its most simplistic interpretation, DNA is transcribed into RNA and then translated into protein.
While the critical concepts of information transfer and the emergence of new properties across biological scales are fundamental to this theory, they are often lost.
We present a case study of a prospective–retrospective approach for a continuous biomarker identified after patient enrollment but defined prospectively before the unblinding of data.
An analysis of the strengths and weaknesses of this approach and the challenges encountered in its practical application are also provided.
Complex signatures, describing multiple independent biological components, are also easily identified.
The use of gene signatures and Principal Component Analysis  (PCA) is a popular combination, but a recent publication has clearly shown drawbacks with this combination .
During early clinical development, prospective identification of a predictive biomarker and validation of an assay method may not always be feasible.
Dichotomizing a continuous biomarker measure to classify responders also leads to challenges.
It is even more remarkable that this straightforward model seamlessly incorporates genome-wide complexity and thus fundamentally differs from previous multiscale combinatorial approaches that reduce drug repositioning to prioritized molecular mechanisms.Gene signatures are used to represent a biological event and have the potential to describe complex biology better and more robustly than a single gene.There exists a large amount of literature on how to properly analyze microarray data and derive signatures [3–7], validate biomarkers [3, 8, 9], and, in particular, validate prognostic models [10–13], all in response to the poor reproducibility rate in publications [14–18]. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Many gene-expression signatures exist for describing the biological state of profiled tumors.Our hypothesis is that gene signatures can be validated when applied to new datasets, using inherent properties of PCA. Uniqueness: the general direction of the data being examined can drive most of the observed signal.Other biomarkers, such as ) mutation status, were also evaluated in an exploratory fashion. low HRG m RNA levels was set at the median delta threshold cycle.