**1. Introduction**

270 Biomarker

Wingernfeld, K.; Schulz, M.; Damkroeger, A.; Philippsen, C.; Rose, M. & Driessen, M. (2010).

Winkler, H. & Fischer-Colbrie, R. (1992). The chromogranins A and B: the first 25 years and

Wolf, O.T. & Kirschbaum, C. (1999). Actions of Dehydroepiandrosterone and its sulfate in

Yamaguchi, M., Kanemori, T., Knemaru, M., Takai, N., Yasufumi, M. & Yoshida, H. (2004).

Yamakoshi, T.; Park, S.B.; Jang, W.C.; Kim, K.; Yamakoshi, Y. & Hirose, H. (2009).

Yokoyama, K. & Araki, S. (1993). *Nihongo ban POMS tebiki* (the guide of profile of mood

monotonous driving. *Med Biol Eng Comput*., Vol.47, pp.449-456.

states Japanese version, 5th ed.), Kaneko Shobo, Tokyo, Japan

future perspectives. *Neuroscience*, Vol.49, No.3, pp.497-528

humans. *Brain Res Rev.*, Vol.30, No.3, pp.264-288

No.1, pp.179-181

Vol.20, pp.491-497

The Dirunal Course of Salivary Alpha-amylase in Nurses: An Investigation of Potential Confounders and Associations with Stress. *Biological Psychology*, Vol.85,

the central nervous system: Effects on cognition and emotion in animals and

Performance evaluation of salivary amylase activity monitor. *Biosens.Bioelectron.*,

Relationship between salivary chromogranin-A and stress induced by simulated

Imagine a simple clinical test that can not only diagnose a disease, but that can also identify the exact, personal therapeutic regime to cure it. Not only that, imagine tests that can accurately predict the potential of developing a disease and provide an individualized roadmap on how it will progress. Now imagine that all you had to do was to spit in a vial, or have a few hairs plucked for the analysis. While the promise of "personalized medicine" is technologically a reality, it relies on the development of disease and progression biomarkers.

The ideal biomarker should have a number of characteristics, including: having an analyte that is accessible using noninvasive protocols, inexpensive to quantify, specific to the disease of interest, translatable from model systems to humans, and the ability to provide a reliable early indication of disease before clinical symptoms appear. Biomarkers that can be used to stratify disease and assess response to therapeutics are also medically valuable.

Although most current biomarkers utilize protein or metabolic analytes, it can be difficult to develop new protein-based biomarkers. This is due to the inherent complexity of the protein composition of biological samples, the assorted posttranslational modifications of proteins, and the low abundance of many proteins of interest in most biological samples (especially blood). Similarly, the detection of metabolic analytes is difficult due to the complex biological matrix from which they are measured.

Detecting specific nucleic acids, while not trivial, is generally much easier. Synthetic complimentary oligonucleotides can deliver sufficient detection specificity in most cases, and PCR or other DNA amplification methods can be used to improve the detection limit. There are numerous examples of genomic biomarkers that have become powerful tools for molecular diagnostics and outcome prediction (Cronin et al., 2007; Guttmacher & Collins, 2002; Hamburg & Collins, 2010; Klein et al., 2009; Tainsky, 2009; L. J. van 't Veer et al., 2002). RNA and DNA biomarkers are used routinely for screening patients to diagnose and subtype disease, as well as to monitor therapy and predict progression. Discovery of microRNAs, and lately lncRNAs (long non–coding RNAs), further increased their importance and broadened their clinical application (Gibb, Brown, & Lam, 2011; Laterza et

Novel Tissue Types for the Development of Genomic Biomarkers 273

Peripheral blood remains the most commonly studied tissue due to the minimally invasive nature of sample collection and the vascularization of most tissues. Peripheral whole blood is a rich source of validated and potential biomarkers, whether they are protein, genomic, or metabolic in nature. While the methods for extraction and profiling of blood DNA are well established, the isolation of RNA and microRNA from whole blood, and studies on their transcript abundance (commonly called gene expression studies), still pose many technical challenges. These include transcriptomic changes induced by *ex vivo* handling and the

Pre-analytical variables such as the degradation of RNA by endogenous RNases and unintentional expression of individual genes after drawing blood could lead to false assessment of potential markers. The introduction of blood collection systems containing stabilizing additives has significantly improved the RNA quantity and quality of blood samples (Rainen et al., 2002; Thach, 2003). RNA stabilization systems have the advantage of storing the collected samples at more accessible temperatures before shipment to the laboratory for analysis, resulting in reduced pre-analytical variability. A well-described method for RNA stabilization in human blood is the PAXgeneTM system (Chai et al., 2005; Rainen et al., 2002). The Tempus™ Whole Blood RNA isolation system offers an alternative approach to peripheral blood RNA isolation suitable for gene expression profiling as well (Asare et al., 2008). Recently RNAlaterTM, a common stabilization reagent for RNA in cells and tissues, has been successfully used for RNA stabilization in human peripheral blood (Weber et al., 2010). The downside of the latter method is that pre-filled RNAlaterTM blood

All the described methods are able to stabilize transcription and isolate total RNA with good quality and in appropriate quantities. However, RNA stabilization/isolation methods can critically impact differential expression results. For example, the failure of PAXgeneTM to stabilize specific transcripts was reported in several studies (Asare et al., 2008; Kågedal et al., 2005). Until more broad studies are done, it is recommended that a researcher should prevalidate the whole blood stabilization/isolation conditions with the transcripts of interest. We find that strict adherence to the manufacturer's protocol for collection and storage, including how the reagent is mixed with the blood at the time of collection, is critical to

The discovery of microRNAs has opened new opportunities for markers in the diagnosis of cancer (Wang et al., 2009). MicroRNAs are small (typically ~22 nt in size) regulatory RNA molecules that function to modulate the activity of specific mRNA targets and play important roles in a wide range of physiologic and pathologic processes (Mattick & Makunin, 2005). MicroRNAs are an ideal class of blood-based biomarkers for disease detection because: (i) miRNA expression is frequently dysregulated in disease, (ii) expression patterns of miRNAs are tissue-specific, and (iii) miRNAs have unusually high stability in most tissues and can be recovered from formalin-fixed, paraffin embedded

**2. Clinically important tissues** 

interference of highly abundant globin mRNA.

collection tubes are not currently available commercially.

successful expression profiling.

samples.

**2.1 Blood** 

**2.1.1 Whole blood** 

al., 2009). Low complexity, no known post-processing modifications, simple detection and amplification methods, tissue-specific expression profiles, and sequence conservation between humans and model organisms make extracellular miRNAs ideal candidates for genomic biomarkers to reflect and study various physiopathological conditions of the body.

Ideally, the most clinically powerful information would come directly from the tissue of interest. To understand cancer, one must look at malignant cells, much as one must analyze brain tissue to understand the complexities of neuroscience. However, many of these tissues are difficult to access or impossible to reach without potential injury to the patient. Alternative, or "surrogate", tissues can provide a means of assessing the genomic changes in the tissue of interest, without fear of harming the donor. For example, surrogate tissues may contact the tissue of interest and retain sloughed cells, secreted molecules or the contents of dying cells. While these molecular signals may not exactly mirror the tissue of origin, in many cases they are reproducible and can clearly point to underlying biology. Clinical material suitable for biomarker testing can be divided into 2 different types. The first are those that require minimally invasive procedures to obtain. This type includes blood, cerebrospinal fluid, tissue biopsies and so on. Type 2 tissues are those that can be obtained without any invasive means: hair, saliva, tears, epidermal cells, urine, etc. In some cases, acquisition of the material may not be passive. Examples of Type 1 and Type 2 samples are listed in Table 1.


Table 1. Example sample types for the development of genomic biomarkers.

The easier it is to provide a sample for biomarker testing, the greater will be the utilization and utility. There is emerging data that many tissues and fluids that have been largely ignored, hold numerous important analytes that can be exploited for biomarker development. Relative ease of acquisition and rich genomic information, make these surrogate tissues ideally suited for the development of new biomarkers. By casting a wider net over the potential sources of biomarkers, we can increase the odds of finding clinically important ones that will make predictive, personalized healthcare a reality (Hood & Friend, 2011). In this review we will provide examples of various surrogate tissues that are being utilized for the development of genomic biomarkers, and highlight important concepts for successful collection and handling of them.
