**2.7 Urine**

278 Biomarker

Yao et al. identified the overexpression of type I IFN-inducible genes in psoriatic biopsies by comparing biopsies of normal, healthy donor skin and non-lesional skin to psoriatic donor skin (Yao et al., 2008). To better understand the degree of type I IFN-inducible gene overexpression in psoriasis, blood from healthy donors and normal keratinocyctes (EpiDerm, MatTek, Inc.) were stimulated with various members of the type I IFN family. Ex vivo blood and in-vitro keratinocyte data showed overall agreement in up-regulated type I IFN-inducible genes. While only 1% of upregulated probes from the stimulation study were overexpressed in non-lesional compared to normal skin, 11.7% of the upregulated probes were overexpressed in lesional compared to non-lesional skin, suggesting type I IFNs may

Hair follicles are different from skin and blood, in that they are made up of stem cells, which control the growth and cycling of hair. The stem cells are contained within the follicle and are often called the bulge. It is this fact which makes hair follicle gene expression particularly intriguing: "stem cells in the epidermis and hair follicle serve as the ultimate source of cells for both of these tissues, understanding the control of their proliferation and differentiation is key to understanding disorders related to disruption in these processes,"

Advances in hair follicle extraction, isolation, and amplification techniques along with the relative ease of collection of the tissue, and the abundance on most, hair follicle collection is being increasingly examined as a good investigatory and clinical biomarker tissue. To date most research has been in diseases involving skin conditions (Ohyama et al., 2006). However, hair follicles are also being examined for markers in to quantify exposures to pharmaceuticals (Reiter et al., 2008) or toxicology to certain drug targets (Kim et al., 2006). Hair follicles are obtained using tweezers, grasping at the hair as near to the scalp as possible, and quickly yanking upwards. The follicle should be clearly present and immediately preserved in the appropriate preservation solution. For those with longer hair, we have found it helpful to cut the hair close to the follicle, before preservation. Although it is possible to achieve results with a single or a few (3 follicles), it is often better to acquire a larger set (15 follicles), to ensure the needed mass for evaluation will be met. The follicles for the experiment should be taken from a similar location for each extraction, as there might be slight gene expression changes with different hair locations (head, arm, and eyebrow). We recommend behind the ear for collection of the desired hairs for most applications. There are several different preservation solutions such as RNAlater (Ambion) or SD Lysis Buffer (Promega). Following preservation, follow the manufacturer guidelines on storage and

Often overlooked, stool is an important source of potential biomarkers for a number of clinical indications. While the identification of infection and various metabolic imbalances are easily identified, feces can also yield RNA, DNA and miRNA for use in biomarker development. This is largely due to the shedding of epithelial cells in the gastrointestinal track (Osborn & Ahlquist, 2005). With the use of highly sensitive detection techniques, one

be a prospective target for psoriatic treatment.

**2.5 Hair follicles** 

(Cotsarelis, 2006).

extraction/isolation of the RNA.

**2.6 Feces** 

Urine is an ideal source for the identification of new biomarkers as it is easily and noninvasively collected. It has long been a standard fluid for the measurement of metabolites, proteins, and infectious agents. Recent data has demonstrated that not only can these traditional analytes can be identified, but RNA, DNA miRNA can be extracted and profiled. While less stable than the other nucleic acids, mRNA can be detected in urine. Keller and colleagues have demonstrated that this stability is likely due to protection of the mRNA in protein/lipid vesicles called exosomes (Keller et al., 2011; Nilsson et al., 2009). Further, mRNA patterns from urine sediments have been suggested for the development of ovulation and fertility biomarkers (Campbell & Rockett, 2006). miRNAs have also been uniquely identified in urine (Weber et al., 2010), and their stability has also been linked to exosomes (Record et al., 2011; Valadi et al., 2007). Differential detection of miRNAs in urine is showing promise in the non-invasive detection of lupus, nephropathy, renal allograft rejection and urothelial cancer (Lorenzen et al., 2011; Wang et al., 2010; Wang et al., 2011; Yamada et al., 2011).

Urinary DNA is a complex target, with both host and non-host DNA being present and clinically relevant. Patient DNA is readily extracted from urine with methylation patterns that have been shown to have utility in the diagnosis of cancer and kidney injury (Chen et

Novel Tissue Types for the Development of Genomic Biomarkers 281

methodologies for mtDNA sequencing, such as Sanger sequencing or hybridization-based resequencing, are substantially impacted by the presence of normal cells. This background of normal cells attenuates the positive mutational signals, leading to poor discrimination of bases. While Zhu and colleagues did not find this to be true in their study, it is likely that as next generation sequencing methodologies are applied to NAF profiling, we will be able to discriminate and quantify the differences between normal and tumor cells with high

Genomic and mitochondrial DNA statuses are important factors in understanding the genetic context of disease. However, tumorigenesis is a dynamic process that is influenced by heredity and environment. RNA profiling is a way of linking these factors in a measurable way. Due to their low numbers, breast fluid-derived cells are difficult targets for gene expression profiling. With recent advances in mRNA amplification methodologies, there are now tools that allow these studies (Van Gelder et al., 1990). For example, Single-Primer, Isothermal Amplification (SPIA) is one of several techniques that can amplify and label mRNA for microarray or RT-qPCR analysis (Kurn et al., 2005). Various studies have shown the utility of gene expression in identifying gene expression patterns of tumors that subclassify breast cancer and help to predict outcome (Cronin et al., 2007; Ma et al., 2003; van de Vijver et al., 2002). It is conceivable that these same transcript signatures will be

The utility of a given sample to yield a clinically meaningful result is dependent on many factors. These include when and how samples were collected, the preservation method used to stabilize the analytes, shipping and storage effects, and the correct association of patient data with the sample. Variation in any of these areas can have a substantial impact on the

There is conflicting data as far as the effect of time delay between sample collection and the time of extraction of RNA. Some studies report that any delay in getting the sample from the living state to a preserved state (frozen, in formalin (FFPE) or RNAlater) will decrease the quality of the sample (Hong et al., 2010). There are other studies that indicate that there is at least a 16 hour window in which the sample collection and the QC metrics of BioAnalyzer assessment do not show any degradation (Micke et al., 2006). In our experience, we have found that any interruption of sample collection state en route to preservation could lead to degradation of the RNA (unpublished observation). Lisowski and colleagues found that as FFPE sample slices aged, signal intensity by in situ hybridization (ISH) was impacted. If they sliced from the block right before extracting RNA, the signal was clearer and stronger (Lisowski et al., 2001). While some tissues are considered homogenous, studies by Irwin and Dyroff show that there are different physiological responses to different sections of liver in

With the advent of electronic tracking by the shipping industry, as well as a societal expectation of overnight shipments, samples can safely and quickly travel from a clinical

resolution (Zhu et al., 2005).

**3.1 Sample collection** 

usefulness of a sample.

**3.2 Shipping and storage** 

obtained from isolated cells from ductal fluid.

**3. Factors that impact genomic sample quality and utility** 

response to drugs (Dyroff et al., 1986; Irwin et al., 2005).

al., 2011; Kang et al., 2011). Microbial DNA is also extracted in urine. Through the expanding discipline of microbial metagenomics, we now understand that the relative distribution of microbial DNA has important clinical utility (Nelson et al., 2010; Virgin & Todd, 2011). New improvements in next generation sequencing and microarray technology are showing how the interactions between microbial communities and their host are measurable and are correlated with the health of the host. Urine, like feces, has the potential to provide an easily accessed fluid type, whose flora may provide an exquisitely sensitive measure of pathological state. For example, the microbiome of urine can be used to monitor asymptomatic sexually transmitted disease and is highly correlated to data generated from the urethra swabs (Dong et al., 2011; Nelson et al., 2010). As more work is done in this field, it is likely that more examples will be uncovered.

### **2.8 Nipple aspirate fluid**

The breast is a complex organ whose architecture is intertwined with its biology. Even the structure of the nipple is multifaceted and not completely well understood (Love & Barsky, 2004). However, it does provide unique access to fluid that can be leveraged for biomarker development. Nipple aspirate fluid (NAF) and ductal lavage contain cells that have been used for the diagnosis and monitoring of breast cancer (Lang & Kuerer, 2007; Li et al., 2005; Mendrinos et al., 2005; Sauter et al., 1997). NAF is generally obtained either through spontaneous emission or suction, while ductal lavage requires the use of a microcatheter to enter the duct orifice to rinse and collect fluid. Although more invasive, ductal lavage yields more cells (Dooley et al., 2001; Li et al., 2005). These cells originate from the ductal epithelium and by studying them in the NAF, we can glean important information about the active biology within the ducts without the risks associated with biopsy (Dooley et al., 2001; King & Love, 2006; Miller et al., 2006). Much of this work has focused on the early identification of neoplasia using proteomic or cytological analysis of the cells isolated from this fluid (Dooley et al., 2001; Harigopal & Chhieng, 2010; King & Love, 2006; Mendrinos et al., 2005; Wrensch et al., 1992; Wrensch et al., 2001). Recent work has focused on the genomic profiling of NAF cells in order to identify early biomarkers that may predict progression, before morphological changes are evident. For example, the methylation of key tumor suppressor genes can be a highly effective means of predicting tumorgenesis. Preliminary work using NAF samples has demonstrated this as a feasible biomarker of early cancer detection (Krassenstein, 2004). However, measuring the methylation status of key genes in NAF-derived cells is generally not a sensitive enough technique on its own to diagnose disease or predict progression (Euhus et al., 2007; Fackler et al., 2006; Locke et al., 2007).

Mitochondrial sequencing has been shown to be a sensitive way of identifying neoplastic tissues (Czarnecka et al., 2006; Jakupciak et al., 2008; Jakupciak et al., 2008). Mutations in the mitochondrial genome are often found at higher rates than in normal tissues. It is likely that in many cases, these mutations are directly linked to disease pathogenesis, while in others this linkage may only be an effect of other processes. Various groups have applied different techniques to sequence mtDNA from NAF. Zhu and colleagues showed that mutations in mtDNA can be detected non-invasively from NAF using sequencing (Zhu et al., 2005). Jakupciak and colleagues used a mitochondrial resequencing microarray and were able to demonstrate the detection of mutations and a high correlation to traditional sequencing methods (Jakupciak et al., 2008). These methods show great promise for clinical use, although further work is required to validate the approaches. Interestingly, traditional methodologies for mtDNA sequencing, such as Sanger sequencing or hybridization-based resequencing, are substantially impacted by the presence of normal cells. This background of normal cells attenuates the positive mutational signals, leading to poor discrimination of bases. While Zhu and colleagues did not find this to be true in their study, it is likely that as next generation sequencing methodologies are applied to NAF profiling, we will be able to discriminate and quantify the differences between normal and tumor cells with high resolution (Zhu et al., 2005).

Genomic and mitochondrial DNA statuses are important factors in understanding the genetic context of disease. However, tumorigenesis is a dynamic process that is influenced by heredity and environment. RNA profiling is a way of linking these factors in a measurable way. Due to their low numbers, breast fluid-derived cells are difficult targets for gene expression profiling. With recent advances in mRNA amplification methodologies, there are now tools that allow these studies (Van Gelder et al., 1990). For example, Single-Primer, Isothermal Amplification (SPIA) is one of several techniques that can amplify and label mRNA for microarray or RT-qPCR analysis (Kurn et al., 2005). Various studies have shown the utility of gene expression in identifying gene expression patterns of tumors that subclassify breast cancer and help to predict outcome (Cronin et al., 2007; Ma et al., 2003; van de Vijver et al., 2002). It is conceivable that these same transcript signatures will be obtained from isolated cells from ductal fluid.
