95Bioinformatics to Sort Biomarker Data
for Personalized Medicine
Bioinformatics methods are being applied for the development and validation of
new genomic biomarkers that are useful for selecting the right treatments for the
right patients. The established heterogeneity of disease based on genomic bio-
markers requires development of new paradigms of design and analysis of clinical
trials for assessing the validity and clinical utility of new treatments and the com-
panion biomarkers in personalized medicine. Stratifi cation prior to clinical trial
would involve measurement of a relevant biomarkers and separation of the study
population into biomarkers positive and biomarker negative groups; each group is
randomized into those to be treated with a new drug vs control drug or placebo
(Matsui 2013 ).
In 2012, the Center of Excellence for the Prevention of Organ Failure (PROOF)
and IO Informatics started collaboration to develop a web-based software applica-
tion addressing chronic heart, lung, and kidney diseases. The application will be
developed so that clinicians can use it on handheld device and other technology, and
it will be used with blood tests developed by the PROOF Center that target chronic
disease and transplantation. The application will give an overall score indicating
patient risk level and associated clinical recommendations to help guide decision
making. The scores and recommendations will be based on gene expression data,
protein expression data, and longitudinal clinical observations. Future applications
of the technology will enable automated, pre-symptomatic screening for biomarker-
based risk events, disease severity characterization, and treatments that are suitable
for individual patients.
Use of Pharmacogenetics in Clinical Pharmacology xvi
Innovative clinical trial designs are needed to address the diffi culties and issues in
the development and validation of biomarker-based personalized therapies. A new
clinical trial design that captures the strengths of the frequentist and Bayesian
approaches has been proposed to address some of these issues (Lai et al. 2012 ).
There are advantages of using likelihood inference and interim analysis to meet the
challenges in the sample size needed and in the constantly evolving biomarker
landscape and genomic and proteomic technologies.
The statistical method used nearly exclusively to design and monitor clinical tri-
als today, a method called frequentist or Neyman-Pearson (for the statisticians who
advocated its use), is so narrowly focused and rigorous in its requirements that it
limits innovation and learning. A solution is to adopt a system called the Bayesian
method, a statistical approach more in line with how science works (Berry 2006 ).
The main difference between the Bayesian approach and the frequentist approach to
clinical trials has to do with how each method deals with uncertainty, an inescapable
Bioinformatics to Sort Biomarker Data for Personalized Medicine