From Roche Diagnostics GmbH, Centralized Diagnostics, Nonnenwald 2, D-82377 Penzberg, Germany; and ¶ Hoffmann-La Roche Ltd., Roche Center for Medical Genomics, CH-4070 Basel, Switzerland
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ABSTRACT |
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A biomarker is a molecule that indicates an alteration of the physiological state of an individual in relation to health or disease state, drug treatment, toxins, and other challenges of the environment. By this definition, a biomarker is not static, it is changing over time. Therefore, a genetic predisposition, like a single nucleotide polymorphism, is not considered a biomarker.
Biomarkers play an important role in the pharmaceutical industry and are assuming an ever greater role in drug discovery and development (1). The potential benefit of biomarkers is to allow earlier, more robust drug safety and efficacy measurements. Generally, a better understanding of the mechanism of disease progression and therapeutic intervention is needed. The major challenge is the selection and validation of biomarkers including clinical endpoint validation. This requires extended clinical studies to compare the new biomarkers with clinical endpoints determined by other means. Only when this correlation is established will biomarkers identified by proteomics be accepted as surrogate markers.
Many diagnostic markers are also biomarkers. But not all biomarkers meet the criteria set for diagnostic markers in respect to specificity and/or sensitivity. They can still be used for e.g. monitoring inflammation during drug treatment. Inflammation markers generally lack the specificity to identify a given disease, but their reduction during drug treatment can be taken as an early indication of drug efficacy.
Before focusing in the context of "biomarkers" and "diagnostic markers" exclusively on proteomics activities and consequently protein-based markers, one should have in mind that a whole category of markers is actually based on non-protein targets. Although not the subject of this review, diagnostic approaches targeting DNA and specific gene alterations in the DNA (single nucleotide polymorphisms) based on amplification technologies such as PCR are getting more and more attention in the context of predisposition testing, e.g. correlating single nucleotide polymorphisms with the probability of developing a certain disease.
On the mRNA level, expression profiles of given biological material are monitored based on hybridization events using chip-based test formats (see "Genchips"). One of the many applications of such mRNA expression chips is the stratification of patients, e.g. to classify individuals for their ability to metabolize certain drugs.
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POSITIONING OF DIAGNOSTIC MARKERS |
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DECISION MAKING IN THE INDUSTRY |
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These fundamental questions emphasize the different decision trees for the selection of targets between the academic world and the industry. Colorectal cancer (CRC) can serve as an example. CRC is the third most common cancer worldwide, with an incidence of >400,000 new cases a year and a prevalence of >1.6 million in seven major markets (2). The lifetime risk of developing CRC is about 6%, with a sharp increase over the age of 50, making age the most important risk factor for the disease, ahead of genetic and/or familial traits. Of diagnostic relevance is the fact that CRC can be effectively cured through early detection and intervention. Some 37% of all CRC cases are detected in their early localized stages or stages preceding the cancer event (Dukes stage A) or in stages where no invasion of the lymph nodes has yet occurred (Dukes stage B), conditions with a 5-year survival rate of 93% after curative surgery. After infiltration of the lymph nodes (regional stages, Dukes stage C, another 37% of the cases), the survival rate drops to 63%, to fall to only 9% in the distant stages correlated with metastasis (Dukes stage D). This segmentation already underlines the need for early diagnosis in a market segment of important size. The problem of early diagnosis for CRC is compounded by the fact that the available screening methods do have considerable limitations. Still widely used is the test for occult blood in stool. Some nonmalignant conditions (hemorrhoids and ulcers) can lead to false-positive results, as can certain diets and medications (3). Despite the fact that fecal occult blood testing does not reach a satisfactory level of sensitivity and specificity, the test reduces CRC mortality by detecting "true positives" and thus is recommended by many national cancer agencies simply because of the absence of a better test. Colonoscopy is seen as a "gold standard" in CRC testing, but the testing procedure is, by its very nature, highly invasive and thus does not have the wide-spread compliance of the target population (people >55 years of age).
In summary, in CRC screening there is a large diagnostic gap between the established test regimen of stool testing (with all its limitations) on one side and a reliable early detection procedure (which lacks the much needed acceptance) on the other side. Any new screening marker for the early detection of CRC would be a definite improvement likely be accepted by the patients and the medical community. The example of CRC can therefore be viewed as a model target area of high interest for the diagnostic industry fulfilling most (if not all) industry conditions referred to in the questions above ("Decision Making in the Industry"). That this overlap between key industry considerations and CRC is a close to perfect match is evidenced by the many parallel R&D activities in CRC across the industry to identify suitable diagnostic markers.
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SEARCH FOR NEW MARKERS: GENERAL WORKFLOW |
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PHASE I: DISCOVERY PHASE |
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Proteomics Methods for the Identification of Diagnostic Markers
Proteomics research combines high-resolution separation techniques applied to complex protein mixtures with state-of-the-art identification methods such as mass spectrometry (MS). It is generally agreed that none of the existing separation and identification methodologies on its own can give a full account of the protein composition or the protein expression in complex mixtures, e.g. biological fluids such as serum, synovial fluid, cerebrospinal fluid, urine, whole cells, cell fractions, or tissue extracts. This limitation, however, has not prevented the use of existing methods (or the combination of several existing technologies) to provide valuable information on a wide range of proteins, especially when either their absence or presence or their level of expression can be correlated to a disease state. There are two strategies for the search of protein biomarkers in body compartments. One is the direct search in the peripheral fluids where the marker concentration is expected to be relatively low and todays detection methods will not allow their identification. The other approach is the search for new markers in diseased tissue where the marker protein is presumably present at a much higher concentration, facilitating the protein identification by MS as discussed below. An inherent risk in the tissue approach is the fact that the candidate marker identified in e.g. tissue cannot later be detected in peripheral fluid such of serum.
Removal of Abundant Proteins from Serum
The biggest hurdle to overcome in the discovery phase is the fact that the analytical tools used at the end of the process chain such as MS have a definite detection limit for finite amounts of proteins (or peptides derived thereof). To fully exploit the sensitivity limits for peptide identification by MS, it is necessary to enrich the remaining protein mixture for the potential marker candidate. To this end, the concentration of abundant proteins present in complex peripheral fluids such as serum has to be reduced as much as possible. The seven most abundant proteins in serum (albumin, immunoglobulins both large and small chains, transferrin, -macroglobulin, antitrypsin, and haptoglobulin) already amount to some 97% of the total serum proteins (4). If one adds up the 30 most abundant proteins present in a concentration of >100 µg/ml serum and assuming an average total protein content of some 80 mg/ml serum, then these "nontarget" proteins amount to about 99% of the total proteins, leaving just 1% of all proteins present as prime targets for the identification of novel markers. Assuming that the markers of interest are not bound or complexed to one of those major proteins, they in fact have to be removed early in the process in order to reduce the complexity. Size-exclusion chromatography under both denaturating and nondenaturating conditions is one of the options at hand to separate abundant proteins of a larger size from those in the 20- to 50-kDa range. For albumin removal, common affinity adsorbers based on dyes such as Sepharose Blue® and other modifications are used (5). Immunoglobulins are commonly removed using Protein G affinity chromatography (6). In our proteomics depletion strategy, we are using a series of immunoadsobers with specific monoclonal antibodies immobilized to magnetic beads on a preparative scale. On an analytical scale, similar immunoadsorbers (Multiple Affinity Removal System) are commercially available, removing six abundant proteins such as albumin, IgG, IgA, transferrin, antitrypsin, and haptoglobulin (Agilent Technologies, Waldbronn, Germany). This attempted reduction of complexity becomes even more challenging if one looks at the dynamic range of the protein distribution (Fig. 2). Taking albumin with about 50 mg/ml as an example, if any purification scheme were able to remove 99.9% of albumin from the serum, the remaining (contaminating) concentration of albumin would still be 50 µg/ml or a factor 50,000 higher than well-known tumor markers such as the prostate-specific antigen present in about 1 ng/ml.
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Tissue Approach
In the tissue-based discovery of protein markers, the large difference in protein expression has to be overcome to a certain extent. The following methods can be used for fractionation:
Protein Identification Methods
Mass spectroscopy is the method of choice for the analysis of proteins and peptides. One modification of MS, the matrix-assisted laser desorption/ionization technology (MALDI) (18), is ideally suited for analyzing the low-complexity mixtures of proteins derived from protein spots after separation by 2D electrophoresis (see "2D Gel Electrophoresis").
Another modification of MS is electrospray ionization (ESI), which is a solution-based analytical technology (19) optimized for coupling liquid chromatography (LC) technology directly to the MS detection units. We are using the ESI-MS modification preferentially for proteins and peptides of low molecular mass that cannot be resolved by 2D gels. Another class of proteins that is not amenable for the separation by 2D gels and lends itself to the analysis by ESI are the hydrophobic membrane proteins. These proteins are analyzed by preparing the corresponding subcellular fractions (see "Tissue Approach") followed by one-dimensional SDS electrophoresis. The bands are cut out, the proteins in the gel pieces are digested by trypsin, and the resulting peptides are injected onto a capillary reversed-phase column coupled to MS (LC-MS). The separated peptides are directly sprayed into the mass spectrometer, and the peptide masses are recorded (20). In MS modifications such as ion trap instruments (21) or quadrupole instruments (22), the peptides can be further fragmented and the fragment information can be used for further database searches.
One of the most important recent developments in MS is the increased quality of the mass spectral results. This is made possible by the introduction of a new generation of mass spectrometers, the "time-of-flight/time-of-flight" (TOF/TOF) instruments (23). With MALDI-TOF/TOF instruments, MS fragmentation spectra can be obtained within a few seconds.
To optimally use of the analytical capability of these instruments, prefractionation schemes have to be developed to overcome the differences in expression rates of proteins (see "Removal of Abundant Proteins from Serum"). Those fractionation methods need to be applied before the proteins are separated by 2D gels or, in the case of shotgun approaches, before the proteins are digested with trypsin and separated by high-performance LC.
2D Gel Electrophoresis
After nearly 30 years (24), 2D gel electrophoresis is still the method of choice for high-resolution protein separation (25). On a 2D gel, up to 1,000 protein species can be separated followed by the identification with MS. Why is there still a resistance to the widespread use of 2D gels? One reason is the substantial workload involved in performing this separation technology. The hands-on time has somewhat been reduced by the availability of prefabricated strips for the first dimension (isoelectric focusing) and with pre-cast gels used in the second dimension. Another hurdle in the acceptance of 2D gels are the in-built limitations of the technology. Several classes of proteins are difficult to resolve, among them the very basic proteins, small proteins, and the hydrophobic proteins like G-coupled protein receptors with several transmembrane regions. Despite all these limitations, 2D gels were successfully used for differential protein display many years even before the term "proteomics" was coined.
In most of the proteomics-based work, a 2D gel-matching procedure (gel imaging) is used to compare two sets of protein mixes run under highly standardized conditions. A minimum of two parallel gels (preferentially three gels) have to be run to obtain a sound basis for applying the image technology and to directly compare preparations of protein mixtures from "disease" and "healthy" states. The imaging software recognizes the spot differences, and only those different spots are further analyzed. But even the most advanced imaging software packages still need a manual intervention to assist in the correct matching. Even under the most standardized conditions, not all protein spots on two given gels can be correctly matched, and thus ambiguities will arise. In addition, with the imaging approach misleading protein identifications can occur because proteins are identified irrespective of their origin including protein isoforms. Assuming that a given protein is present in three isoforms in the "disease" state and only in two isoforms in the "healthy" state, the expression of an additional protein in the "disease" state would be scored by the imaging technology as a "new protein," whereas in fact the same protein (in an additional isoform) has been expressed.
To avoid some of these problems related to the image analysis of 2D gels, we have implemented in our proteomics strategy the picking of all spots on the gels and determine the differences in corresponding gel sets after the peptide analysis by MS. The resulting protein list is more extensive and complete than the lists obtained by the imaging procedure. Moreover, we have observed in many occasions that two different proteins can be hidden in one single protein spot, a fact only revealed after comparing the peptide files. These "hidden" proteins would have been missed by applying the "imaging technology." We have automated our approach to analyze each and every spot after 2D gel electrophoresis to a very high degree. We use appropriate software and "spot-picking robots" that transfer the gel pieces to microtiter plates, which in turn are processed in automated washing stations followed by automated trypsin digestion and subsequent spotting on MS target plates. With this automated workflow, we are able to process as many as 15,000 spots per day.
Multidimensional Chromatography
While the use of one-dimensional and 2D gels is considered essential to many proteomics approaches, the emergence of shotgun sequencing based on high-performance LC and tandem MS (MS/MS) is a powerful alternative (26). Both single-dimensional high-pressure LC and multidimensional LC (LC/LC) can be directly interfaced on-line with MS to allow for automated collection of large datasets (20). This approach is known as MudPIT (27). In the shotgun proteomics approach, one does not analyze the intact proteins but peptides generated by specific enzymes such as trypsin. This fragmentation can offer definite advantages because even very large proteins, very hydrophobic, or very basic proteins (protein classes that are difficult to handle) will give rise to peptides of sufficient size. However, because several peptides are generated from each protein, the complexity of the mixture to be analyzed is increased. Consequently, more instrument time and computing power are needed for the shotgun approach as compared with the 2D gel approach. In addition, in the shotgun approach, the information about the integrity of the original protein or its potential posttranslational modifications are lost to a large extent. The shotgun approach is therefore better suited for less complex mixtures. With the increased number of different peptides, the matching of the mass spectral fingerprints with the protein databases becomes more demanding and time consuming and leads, without careful manual inspection of the spectra, easily to false-positive identification of proteins, e.g. the tentative identification based on one peptide only ("one-hit wonders"). We have extensively studied the information gain achieved with the MudPIT approach in comparison to the 2D gel approach. We consistently find that both technologies are highly complementary to each other, e.g. proteins identified by the 2D gel/MALDI approach were not found with MudPIT. On the other hand, additional proteins were in fact found with MudPIT that were never seen with the 2D gel/MS method. In consequence, we routinely analyze all protein mixtures by both strategies in parallel.
Peptidomics
Peptides play a central role in many physiological processes. In order to analyze comprehensively all "natural" peptides and small proteins of a whole organism (peptidome), an approach described as peptidomics is used (28). Neither the 2D gel approach (which is limited to proteins >10 kDa (29)) nor the shotgun approach can therefore be used to analyze the naturally occurring peptides. The workflow of peptidomics usually starts with the removal of the proteins over 20 kDa by size-exclusion filtration or size fractionation with membranes of suitable pore size. Alternatively, precipitation techniques using trichloroacetic acid can be employed. Next, the peptides can be identified by fragmentation in modern MS/MS instruments like the TOF/TOF or quadrupole/TOF instruments.
SELDI
The surface-enhanced laser desorption/ionization (SELDI)-TOF-MS technology (30) uses chromatographic surfaces coupled to the target plate. The plates are then extensively washed and the bound material is directly analyzed by MALDI-MS. SELDI covers peptides and proteins predominantly in the low molecular mass range. This technology is limited to the major abundant peptides and proteins as long as a suitable upfront purification scheme is not integrated (31). The SELDI technology leads only to a pattern and not to the identification of peptides as is the case in the peptidomics approach (28). In the published examples using SELDI, only about 2050 well-resolved peaks are detected (32), compared with several hundred peptides species detected in the peptidomics strategy. For the best possible reproducibility and mass accuracy using SELDI, high-resolution MS is mandatory.
MALDI Imaging
MALDI-MS has been used to generate protein and peptide mass lists of tissues directly mounted to the target plate of the mass spectrometer. The plate is scanned in the corresponding xy coordinates to give a list of masses that correspond to the molecular components present (33). The high sensitivity of the technique (low femtomole to attomole levels for proteins and peptides) allows the study of biomarkers for example in tissue sections from cancer patients to generate a molecular fingerprint of each tumor type. Using this technology, Yanagisawa et al. were able to classify several subtypes of lung cancer tumors (34).
Genchips
In several recent studies, cDNA microarray analysis has revealed significantly elevated expression of the prostasin and osteopontin genes in ovarian cancers, and correspondingly high levels of these proteins have subsequently been detected in the serum of ovarian cancer patients (35). This proves that transcript profiling to identify genes encoding secreted proteins in human carcinomas can be achieved using commercially available microarrays. Genchips led to the identification of 2,300 genes and classified them as being extracellular. Seventy-four of these genes were overexpressed in one or more carcinoma types relative to healthy control tissue. The overexpression of several of these genes was confirmed at the protein level in the serum of cancer patients.
Selective Ion Monitoring
Before starting the development of immunological tools for a given marker candidate (phase II in Fig. 1), especially if the discovery phase was conducted using tissue as sample material, we verify the presence of the marker candidate in question in serum by applying the multiple reaction monitoring technology (MRM) (36, 37). MRM combines the quantitative bioanalytical LC-MS/MS with stable isotope labeling of peptides. MRM has been shown to be a powerful technology for the quantitative analysis of peptides in complex mixtures like plasma and is well established in pharmacokinetic studies of small molecules but has not yet found a widespread use in proteomics studies. Recent publications describe the use of 13C-labeled peptides as internal standards for an accurate quantification of proteins in plasma (35, 38). Two or three tryptic peptides of the protein to be analyzed are selected for peptide synthesis based on their ionization properties. During synthesis, 13C is incorporated into the leucine moieties of the peptides. The plasma proteins to be analyzed are digested with trypsin and the labeled peptides are spiked in. The ionization properties for the labeled synthetic peptides are the same as for the naturally occurring nonlabeled peptides, the only difference being the mass difference due to the number of 13C atoms incorporated in the amino acid leucine of the synthetic peptides. Knowing the concentration of the labeled peptide, a direct quantification between the synthetic and the corresponding "natural" peptides can be made. In a recent study, candidate markers for RA identified in synovial fluid could be quantified in directly in serum.2
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PHASE II: PROTOTYPE DEVELOPMENT |
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Immunological Tools
Many of the strategies to set up immunological prototype assays are explained in the respective tutorials and are not the subject of this review (39). For reasons of throughput, we are working in the prototype developmental phase exclusively with polyclonal antibodies raised in rabbits using the 105-day bleeds throughout. For immunization, we follow a dual strategy. Several peptides are selected per protein using appropriate software such as compiled on the Expert Protein Analysis System (ExPASy; at us.expasy.org) to identify nonhomologues regions of high immunogenicity. In parallel, we express the full-length protein by means of recombinant expression systems starting with but not restricted to Escherichia coli. Although each particular assay requires a tailored strategy and many options have to be considered, one of the many modifications we use is as follows. Anti-rabbit antibodies are immobilized on the walls of microtiter plates (MTP), and the raw serum (containing the antibodies in question) is added followed by the biotinylated peptide used to immunize. The antibody-captured biotinylated peptide is than detected by streptavidin coupled to horseradish peroxidase. This robust entry-screening system allows a first estimation of the combined antibody concentration and antibody affinity and thus a selection of the serum charge to be followed-up. Next, the IgG fraction of the selected polyclonal antibody pool is purified using conventional purification schemes. The recombinant full-length protein is used to establish a standard curve to obtain more detailed information as to binding characteristics of the antibodies intended for the set-up of prototype assays.
In certain applications such as cancer, it is helpful to test the specificity of the antibodies before setting up the prototype assay format. To this effect, we use e.g. lysates of different cancer tissues and probe them in the Western blot format with the antibodies in question, often enriching the antigens by immunoprecipitating the lysates beforehand. Alternatively, immunohistological screens with immobilized cells obtained by LCM from various tumors (see "Tissue Approach") are used to get a first impression about the specificity of the antibodies.
ROC Curves
After deciding on a suitable assay format and optimizing the prototype assay for minimal interference (eliminating matrix effects), a receiver-operator characteristics (ROC) plot is established (40) using a panel of a minimum of 250 control samples and 250 highly characterized samples of diseased individuals. The clinical performance of a laboratory test depends on its diagnostic accuracy or the ability to correctly classify subjects into clinically relevant subgroups (e.g. "healthy" versus "diseased"). The ROC plot depicts the overlap between the two populations by plotting the sensitivity versus (1 - specificity) (Fig. 3). The true-positive fraction (sensitivity) is plotted on the y axis and is defined as the (number of true-positive test results)/[(number of true-positive results) + (number of false-negative test results)]. This is referred to as "positivity" in the presence of a disease and is calculated solely from the affected subgroup. On the x axis, the false-positive fraction is plotted or (1 - specificity) defined as the (number of false-positive results)/[(number of true-negative results) + (number of false-positive results)]. It is an index of specificity and is calculated entirely from the unaffected subgroup. Because the true- and false-positive fractions are calculated from two different subgroups, the ROC plot is independent of the prevalence of disease in the sample cohort.
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Fig. 3 shows an example of a ROC plot measuring the C-reactive protein (CRP) in a cohort of "healthy controls" versus patients with confirmed RA. The ROC area is 87%, indicating a rather high discrimination power of CRP in the two reference populations and confirming the validity of CRP measurements in RA. Depending on the diagnostic question at hand, one can set either the specificity or the sensitivity of the assay under investigation and can directly read off the corresponding sensitivity and specificity. In the example shown in Fig. 3, the specificity was set arbitrarily at 80%, resulting in a corresponding sensitivity of 76% (Fig. 3, red line). If one wishes to adjust the assay to a higher specificity, e.g. 90%, one has to take a reduced sensitivity of 64% into account (Fig. 3, blue line).
This balancing of specificity versus sensitivity is of great importance in diagnostics if one considers the different requirements for different diagnostic applications (see "Positioning of Diagnostic Markers"). Taking a potential screening marker in oncology as an example, in this particular application one does not want to miss any asymptomatic individuals that are about to develop cancer. Consequently, the sensitivity of an ideal screening marker has to be in the >9599% range. Inevitably, based on the corresponding lower specificity of the marker (see ROC plot), few individuals will be included that in fact do not develop the disease ("false positives"). Apart from the emotional stress caused by such a misclassification, undue therapeutic intervention has to be avoided by the immediate confirmation of an initial positive test result by other diagnostic measures. The opposite requirements exist for e.g. differentiation markers used after a condition is diagnosed with some degree of certainty. In such a diagnostic situation, related disease complexes (requiring different treatment) have to be ruled out and consequently the specificity of the assay has to be very high in favor of a lower sensitivity. Such a scenario is given if one tries e.g. to discriminate RA from rheumatologic conditions "other than RA" showing in fact similar or overlapping symptoms.
After the completion of performance characteristics with samples out of the highly defined collectives, the prototype assays are then evaluated for the first time in a clinical setting (-site testing, Fig. 1). This is to confirm the intended use of the assay (verification of specificity) and to conduct a technical feasibility for robustness under "non-R&D" conditions with samples as they will be available in the commercial setting once the developmental phase is concluded.
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PHASE III: PRODUCT DEVELOPMENT |
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For reasons of product safety, reproducibility, and constant supply of the raw materials, one of the first steps in product development will usually be the replacement of the polyclonal antibodies in the prototype assay configuration by monoclonal antibodies for the envisioned commercial product (Fig. 1). This replacement will have to be verified by extensive test series to show the equivalency of the two antibody classes. Next, the manual prototype assays in the MTP format used in phase II will have to be converted into an automated assay format such as Elecsys® format, the automated immunochemistry analyzer in place in our affiliation. This complex undertaking requires the (re)design of all assay reagents to make them compatible with the magnetic beads replacing the MTP walls as the solid phase and electrochemoluminescence used as the detection system in the Elecsys® test format. The automated assay system differs from the MTP formats by a much faster time-to-result (18 min versus hours for MTP assays) and covers a much larger dynamic range (five orders of magnitude versus two orders of magnitude in the corresponding MTP format). Again, test series with selected samples are used to establish the technical performance of the assay. This includes, among other parameters, precision data, recovery rates of the analytes, reagent stability, and lot-to-lot variation. Upon confirmation of all those performance data, the standard-operating procedures are finalized and the assay is transferred to production. Different production lots are again assayed for their performance and reproducibility after different times of storage under different conditions. Upon meeting those specifications, the production lots are released for ß-site testing planned according to rigid criteria by the Clinical Trials Department (Fig. 1). In these test series with the final product (no alterations of the design or of the entire assay handling are allowed anymore), large patient collectives are tested and datasets collected that are used to formulate the final specification claims as will be included in the package inserts. Furthermore, these data are critical to file for approval both with the national and international registration agencies. In parallel to these product developmental activities, the prelaunch activities have to be set in motion to inform the medical community and the world-wide net of affiliates that a new marker is about to be launched and commercially available.
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ACKNOWLEDGMENTS |
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FOOTNOTES |
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Published, MCP Papers in Press, January 28, 2004, DOI 10.1074/mcp.M400007-MCP200
1 The abbreviations used are: RA, rheumatoid arthritis; CRC, colorectal cancer; MS, mass spectrometry; 2D, two dimensional; MudPIT, multidimensional protein identification technology; MALDI, matrix-assisted laser desorption/ionization; ESI, electrospray ionization; LC, liquid chromatography; TOF, time-of-flight; MS/MS, tandem MS; SELDI, surface-enhanced laser desorption/ionization; MRM, multiple reaction monitoring; MTP, microtiter plate; ROC, receiver-operator characteristic; CRP, C-reactive protein.
2 B. Guild, personal communication.
* The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
To whom correspondence should be addressed: Roche Diagnostics GmbH, Nonnenwald 2, D-82377 Penzberg, Germany. Tel.: 0049- 8856-60-4145; E-mail: werner.zolg{at}roche.com
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REFERENCES |
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