Diagnostic Signatures

For diagnostic signatures to be relevant in a clinical setting, a number of factors need to be considered. Ideally, clinically useful diagnostic signatures should be measurable in a readily accessible body fluid such as serum, urine, or saliva, making diagnosis noninvasive. A recognizable signature should be evident prior to the onset of clinical symptoms. Early diagnostic signatures would be valuable for monitoring patients for postoperative infection and for population-screening, as prostate-specific antigen is used to screen for prostate cancer. Furthermore, diagnostically useful signatures should be specific for a given disease. Certain signatures may be common to diseases such as cancers or infections, but useful signatures should distinguish between tumor classes and pathogen types. Lastly, signatures are present in a dynamic biological system and normal variation among healthy individuals must be taken into account. Genetic factors, age, gender, time of day, or environmental conditions such as diet and stress all contribute to variation. This issue presents a serious challenge in recognizing diagnostically useful signatures.

Researchers must first discover diagnostic signatures which meet these criteria. However, identifying diagnostic signatures will only be useful to the clinician if they can be detected routinely and efficiently. Therefore, an ideal diagnostic signature-detecting system for the clinician's office should allow for on-site analysis for immediate detection or confirmation of a specific disease state, thereby allowing for the necessary targeted treatment. The assay should be sensitive, accurate, and reproducible to prevent false negatives and positives. The system should be inexpensive and easy to use, requiring minimal technical expertise and sample processing to prevent any additional variance.

We will focus on progress in detecting clinically useful diagnostic signatures and the development of protein-detecting microarrays to measure these signatures in the clinician's office.

2.1. Signature Discovery Tools

The availability of whole genome sequences of organisms starting with a virus (S1), the first bacterium (T4), and the much heralded human genome (L3, V1) has contributed significantly to the understanding of human disease. Analysis of the genome has led to the identification of genetically based diseases and gene variants or polymorphisms that render individuals more susceptible to certain diseases. Depending on developmental stage, age, organ, and environmental factors, a subset of genes is transcribed into messenger RNA (mRNA) that could then be translated into proteins, which are critical for the functional state of a cell. Functional genomics is the study of the transcriptome, i.e., all the genes transcribed into mRNA, while proteomics is the analysis of the proteins expressed under a specific condition, such as disease. Genetic, functional genomic, and proteomic analysis have all contributed to determination of molecular changes related to disease in order to elucidate the cause and develop targeted therapy. In addition, these studies have identified biomarkers/diagnostic signatures that will improve diagnostic accuracy.

Diagnostic signatures can be obtained by measuring either mRNA or protein levels in a given sample. It is preferable to measure protein levels, which more accurately describe the conditions in a biological system. Furthermore, mRNA levels are not necessarily correlative of protein levels and activity. In a study comparing mRNA and protein expression in lung carcinomas, only a subset of the proteins studied (17%) exhibited a significant correlation with mRNA levels (C4). Another subset of proteins had a negative correlation, and various protein isoforms had diVerent protein/ mRNA correlations. In addition to measuring protein expression levels, it would be useful to analyze proteins that have undergone post-translational modifications, which are crucial for protein activity and function. Clearly, protein diagnostic signatures are significantly different from DNA signatures and would be more specific if certain post-translationally modified and protein isoforms are important disease indicators. Investigations on RNA-based diagnostic signatures are currently more prevalent due to the availability of DNA microarrays. Protein signatures require specialized techniques for the separation and detection of proteins and the technology is less developed for large-scale high-throughput analysis.

2.1.1. DNA Diagnostic Signatures

Microarrays represent a new technology that has been used extensively since the first DNA microarray study on differential expression of 45 Arabi-dopsis genes published in 1995 (S2). Many reports have demonstrated the use of DNA microarrays for the investigation of differential gene expression in diseased versus healthy tissue. These studies support the idea that obtaining diagnostic signatures for specific disease states is possible. However, very few studies fulfill the criteria outlined for clinically useful diagnostic signatures. Typically, mRNA is isolated from tissue samples rather than readily accessible body fluids, such as urine, plasma, cerebrospinal fluid, and saliva. Most studies compare healthy and diseased tissues rather than comparing diseases to determine whether signatures are specific. Normal variation is rarely reported and most study groups are too small to take the variation of the normal population into account.

One study illustrated that blood genomic signatures could be used to distinguish among experimentally induced disease states in rats. The gene expression patterns for 8740 genes in leukocytes was determined on an Affymetrix GeneChip® 24 hours after adult rats were subjected to ischemic strokes, hemorrhagic strokes, sham surgeries, kainite-induced seizures, hyp-oxia, or insulin-induced hypoglycemia (T3). There were overall similarities in the response patterns in the six experimental conditions compared to the controls, but each disease condition could be identified by unique gene expression patterns. Animal studies are not directly comparable to humans as there are multiple environmental factors and genetic diversity of the human population to take into account. However, this is a significant study as a proof of principle that disease states can be detected and differentiated in readily accessible body fluids.

Other DNA microarray studies indicate that presymptomatic diagnostic signatures are obtainable. DNA microarray analysis of mRNA samples from a chimpanzee's liver during acute resolving Hepatitis C virus infection was performed. The study provided insight into the liver response to viral infection. Although the study was not developed to determine a diagnostic signature, changes in gene expression could be noticed as early as day 2 post-infection (B4). Early changes in gene expression in a murine model for allogenic bone marrow transplant indicated that acute graft-versus-host disease could be detected before the development of histological changes in the liver (I1). These animal studies in controlled settings indicate that early diagnosis is possible.

2.1.2. Protein Diagnostic Signatures

Various methodologies are being developed to detect and quantify the specific combinations of proteins associated with a particular disease. There are significant issues that must be taken into consideration to achieve this goal. The methodology should detect proteins from complex biological samples and should be sensitive to detect low-abundant proteins, which are potentially important diagnostic markers. In addition, other challenges include the solubility of the protein (i.e., membrane proteins have low solubility in aqueous media) and different isoforms and post-translationally modified proteins must be identified. Numerous technologies have been developed to undertake this daunting task, but we focus on reports that are clinically relevant.

Mass spectrometry (MS) methodologies have most commonly been used to detect proteins (biomarkers) associated with a particular disease (P2, P5, P7, W5). The most well-established technique for determination of protein biomarkers is two-dimensional polyacrylamide gel electrophoresis coupled with mass spectrometry (2D/MS) (G3, H1). Several thousand proteins can be separated simultaneously according to their charge and molecular mass by 2D electrophoresis and visualized by silver staining. Subsequently, the protein spots of interest are excised from the gel, trypsinized, and analyzed by either matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) MS or electrospray ionization (ESI) MS. Overall, 2D/MS has proven to be a reliable tool to differentiate between proteins expressed under different cellular conditions and to detect proteins with various isoforms or post-translational modifications. Although low abundance proteins, proteins of very low or high molecular weight, and less soluble proteins are not easily detected; thus, information is lost for potentially important diagnostic markers.

This methodology has been employed for the identification of potential protein diagnostic signatures. By comparing the protein expression in lung adenocarcinoma tissue samples and uninvolved lung samples by 2D/MS, nine proteins were identified to have increased expression levels (1.4- to 10.6-fold) in lung adenocarcinoma tissue samples (C3). Furthermore, multiple protein isoforms were upregulated for a number of these proteins, but one protein isoform for cytosolic inorganic pyrophosphate (P4HB) was significantly overexpressed while another isoform was unchanged relative to normal lung tissue. Thus, this study suggests that 2D/MS is a powerful tool to identify potential biomarkers, including specific isoforms with diagnostic potential.

The cited study was done using tissue samples, but potential biomarkers for hepatitis B virus (HBV) infection were determined using serum samples (H2). Although there are a number of biomarkers available for HBV infection, no single serological test can unequivocally diagnose the infection. Therefore, an assay able to detect multiple biomarkers should be more accurate to diagnose HBV. The expression levels of seven proteins were significantly changed in HBV-infected sera as determined by 2D/MS. This protein profile suggests that these serum proteins may be useful in diagnosis, but additional investigations are needed to determine specificity of the pattern with regard to other types of infection and liver inflammation.

Inflammatory response markers have been detected with 2D/MS in the urine of stroke-prone rats at least 4 weeks before a stroke occurred and before the appearance of anomalous features could be detected in the brain by MRI (S4). The specificity of inflammatory response markers still needs to be determined; however, this study suggests that proteins in a readily obtainable body fluid can be used as early diagnostic markers.

An alternative mass spectroscopy technique that is rapidly gaining recognition for its potential in clinical proteomics is surface-enhanced laser desorption ionization time-of-flight (SELDI-TOF) (I3, I4, W4). Using the SELDI-TOF technology, only a small amount of serum sample (one microliter) is required to provide a diagnostic signature for a particular disease in a relatively short time and therefore a potentially high-throughput clinical proteomic tool.

A critical aspect to this technique is that proteins from a serum sample are bound to a ProteinChip® (Ciphergen Biosystems Inc., Fremont, CA, USA) based upon common physicochemical properties such as charge and hydro-phobicity or, more specifically, adhered to the surface via a specific antibody or ligand, while the rest of the sample is washed away. Next, the adhered proteins are ionized and analyzed similar to MALDI-TOF. Since a low-end mass spectrometer lacking MS/MS capabilities is used in SELDI-TOF, the proteins or peptides in the sample are not individually identified; instead, profiles specific to serum samples are compiled using highly sophisticated bioinformatics.

Diseased and healthy tissues have been differentiated by MALDI-TOF, as illustrated in the MS protein profile for tumor and normal lung tissue samples with the discriminatory peaks in the spectrum marked by an asterisk

(Fig. 1) (Y1). SELDI-TOF has also been used to distinguish protein profiles. The first reports of using SELDI-MS to reveal diagnostic signatures in readily accessible body fluids were in nipple aspirate fluid to detect breast cancer (P1) and in serum to detect ovarian cancer (P3). To detect ovarian cancer-specific protein profiles, serum proteins were bound to a C16 hydrophobic interaction ProteinChip® (P3). The mass spectra patterns from 50 ovarian cancer patients were compared to those from 50 unaffected controls. An iterative searching algorithm process identified a small subset of key values that segregated the cancer patients from the unaffected population. The cluster patterns were distinguishable for 50 ovarian cancer cases, including 18 stage I cases and nearly all control samples. This promising study suggests that a readily attainable serum sample could be used for initial screening of patients for ovarian cancer. The identities of the discriminatory peptides in the ovarian cancer sample were not deduced, which illustrates the limitation of the commercial Ciphergen system. The specificity of the protein profile for stage I ovarian cancer still needs to be determined, since the diagnostic signatures could be attributed to general metabolic changes caused by tumors. Combining profiles from additional subset-specific protein chips increased specificity (P4).

The Ciphergen system has also been used to determine serum proteomic patterns in other cancers such as prostate cancer (A1, B2), hepatocellular carcinoma (P6), and non-small cell lung cancer (X1). The protein patterns were determined from a combination of data from more than one capture array, thereby increasing the specificity of the diagnostic signature.

Another MS-based technique has been used in an attempt to determine the normal peptides present in bodily fluids. Peptides present in a normal urine sample were separated by high-resolution capillary electrophoresis (CE). A peptide pattern was established by analyzing the mass spectra from 18 samples (W3). The patterns contained ion peaks from 247 peptides (out of more than 1000 detected) that were present in more than 50% of the samples. The data was compared to five samples from patients with renal disease and impaired renal function. An alternative pattern was evident for samples from diseased individuals with additional ion peaks in the spectra and the absence of previously observed peaks. Even though a small number of samples were analyzed, valuable information about biological samples was obtained rapidly, illustrating the significance of MS.

2.2. Signature-Detecting Platforms in the Clinic

DNA microarrays have been a useful research tool. However, there are only a few examples of DNA microarrays used to identify clinically relevant diagnostic signatures, as outlined earlier. The complexity of the technique hinders

  1. 1. Representative example of potential protein diagnostic profiles obtained by MALDI-TOF Mass Spectroscopy (MS) from tumor and normal lung tissue samples shown with the molecular weight calculation (m/z values). Asterisks indicate examples of the MS peaks identified by statistical analyses as optimum discriminatory patterns between normal and tumor. Below: hierarchical cluster analysis of 42 lung tumors and eight normal lung tissues in the training cohort according to the protein expression patterns of 82 MS signals. Each row represents an individual proteomic signal and each column represents an individual sample. The dendrogram at the top shows the similarity in protein expression profiles of the samples. Substantially raised (red) expression of the proteins is noted in individual tumor and normal lung tissue
  2. 1. Representative example of potential protein diagnostic profiles obtained by MALDI-TOF Mass Spectroscopy (MS) from tumor and normal lung tissue samples shown with the molecular weight calculation (m/z values). Asterisks indicate examples of the MS peaks identified by statistical analyses as optimum discriminatory patterns between normal and tumor. Below: hierarchical cluster analysis of 42 lung tumors and eight normal lung tissues in the training cohort according to the protein expression patterns of 82 MS signals. Each row represents an individual proteomic signal and each column represents an individual sample. The dendrogram at the top shows the similarity in protein expression profiles of the samples. Substantially raised (red) expression of the proteins is noted in individual tumor and normal lung tissue its use by nonexperts. Detecting differential gene expression requires a number of sample preparation steps. Furthermore, significant differences in measurable gene expression can be introduced by small variations in sample collection and preservation, RNA quality, cDNA amplification methods, probe labeling, hybridization, and washing conditions. DNA microarrays will be useful to identify individuals susceptible to genetic-based diseases. However, clinical use of DNA microarrays will most likely be done in specialized centers due to the cost and technical expertise required for reproducible results.

Mass spectrometry techniques are used to determine diVerential patterns of protein expression but cannot be employed for rapid detection and quantification of specific proteins. Although 2D/MS has been proven to be a powerful tool to analyze protein mixtures, there are limitations that prevent it from use in the clinic. Separation of proteins by 2D is tedious, labor intensive, and not amenable to high-throughput strategies. Mass spectrome-try techniques used to determine protein profiles (i.e., SELDI-TOF) have the potential for high-throughput strategies and automation. These methodologies do not require the extensive sample processing required by 2D/MS. However, additional SELDI-TOF studies must be carried out to ascertain its accuracy in detecting positives while reducing the incidence of false-positives, prior to its use as a clinical diagnostic tool. However, the expense of mass spectrometers may limit their use to specialized centers.

Currently, most clinical assays detect protein biomarkers by ELISA. Though ELISA is a well-established technique for diagnostic assays, it is limited to detection of single biomarkers. In the future, we envisage that multiplex ELISAs in the form of protein-detecting microarray-based assays will be used in the clinician's office, or even in the home, for rapid detection of multiple proteins in biological samples. unlike DNA microarrays, protein-detecting arrays would require minimal sample processing, thus reducing the variability and need for experienced individuals to obtain reproducible and accurate results.

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