Shotgun-proteomics-based clinical testing for diagnosis and classification of amyloidosis.

Special feature: perspective Received: 9 May 2013

Revised: 1 August 2013

Accepted: 16 August 2013

Published online in Wiley Online Library

(wileyonlinelibrary.com) DOI 10.1002/jms.3264

Shotgun-proteomics-based clinical testing for diagnosis and classification of amyloidosis Jason D. Theis,a* Surendra Dasari,b Julie A. Vrana,a Paul J. Kurtina and Ahmet Dogana Shotgun proteomics technology has matured in the research laboratories and is poised to enter clinical laboratories. However, the road to this transition is sprinkled with major technical unknowns such as long-term stability of the platform, reproducibility of the technology and clinical utility over traditional antibody-based platforms. Further, regulatory bodies that oversee the clinical laboratory operations are unfamiliar with this new technology. As a result, diagnostic laboratories have avoided using shotgun proteomics for routine diagnostics. In this perspectives article, we describe the clinical implementation of a shotgun proteomics assay for amyloid subtyping, with a special emphasis on standardizing the platform for better quality control and earning clinical acceptance. This assay is the first shotgun proteomics assay to receive regulatory approval for patient diagnosis. The blueprint of this assay can be utilized to develop novel proteomics assays for detecting numerous other disease pathologies. Copyright © 2013 John Wiley & Sons, Ltd. Keywords: amyloidosis; diagnostic test; clinical; mutant protein; tandem mass; spectrometry; proteomics

Introduction

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Amyloidosis Amyloidosis refers to a complex of diseases that are characterized by an abnormal extracellular deposition of misfolded proteins into various organs. Proteins in the deposits polymerize into insoluble fibrils in β-pleated sheet format. Over time, the accumulating amyloid damages the tissue microenvironment and causes organ failure. In the tissues, amyloid deposits are identified by their distinct optical and electromagnetic characteristics. However, the protein composition of the amyloid fibrils is the determining factor of the disease phenotype. For instance, the accumulation of amyloid beta (Aβ) protein in the brain causes Alzheimer’s disease. To date, over 26 different types of localized and systemic amyloidosis syndromes have been identified, and a single organ is susceptible to multiple types of amyloids. The diagnosis of amyloidosis presents a unique challenge for medical practitioners because the presentation is often subtle and may be accompanied with a plethora of clinical signs and

* Correspondence to: Jason D. Theis, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA. E-mail: [email protected] a Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA b Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA

Copyright © 2013 John Wiley & Sons, Ltd.

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Shotgun proteomics is a powerful analytical method for characterizing the complex proteomes of various types of biological specimens, including body fluids, tissues and cells. Since its inception in the early 1990s, this technology has improved exponentially in sensitivity, which is underscored by several recent studies that have detected up to 10 000 proteins from both cell lysates as well as formalin-fixed paraffin-embedded (FFPE) tissues.[1] The depth of information provided by the contemporary technologies has ushered a new era for clinical proteomics. A growing number of researchers are using proteomics for discovering clinically actionable biomarkers. An ambitious partnership between the Clinical Proteomic Tumor Analysis Consortium and The Cancer Genome Atlas aims to fully characterize the proteomic changes underlying the development and progression of 20 different cancers. Despite the success of shotgun proteomics in research laboratories, clinical laboratories have hesitated at adapting this technology for routine diagnostics. Several technical and procedural hurdles stand in the way of implementing a shotgun proteomics diagnostic test. A major technical hurdle is achieving consistent long-term performance demanded by the clinical testing environment. Unfortunately, shotgun proteomics methods have gathered infamy for being unstable and producing inconsistent results. The complexity of the proteomics method needs to be controlled in order to generate highly repeatable (precision) results. According to Tabb,[2] repeatability refers to the similarity between the results obtained from analyzing a reference sample over long periods using identical conditions (instrument, protocols, etc.). A major procedural hurdle is the training of physician/clinical laboratory workforce to accept the new untargeted testing modality and gaining clinical acceptance for the assays. This is particularly challenging because shotgun proteomics is a semiquantitative assay that physicians are not trained to interpret in a clinical context. Finally, winning

regulatory compliance for shotgun proteomics assays is also challenging because the agencies that oversee the practices of clinical laboratories are not familiar with the platform. In this perspectives article, we will discuss solutions to these hurdles through a case study of a shotgun-proteomics-based diagnostic assay for amyloid subtyping. We will also highlight other potential translational applications for this technology.

J. D. Theis et al. symptoms mimicking a number of different conditions. The clinical management options for amyloidosis are highly varied (such as high-dose chemotherapy, stem cell transplantation or liver transplantation) and intricately tied to the subtype of the deposit. For instance, hereditary transthyretin amyloidosis, which often leads to cardiac dysfunction, is treated with a liver transplantation whereas immunoglobulin-associated amyloidosis, presenting with the same clinical syndrome, is treated by stem cell transplantation. Hence, a clinician must know the protein composition of the amyloid deposits before devising an appropriate therapy. In routine clinical practice, diagnosis and classification of amyloidosis is made in two steps. First, the presence of amyloid deposits in tissue biopsy has to be established as the cause of organ damage. Although numerous techniques can be utilized to achieve this, Congo red (CR) staining remains the gold standard method. CR specifically binds to the β -pleated sheet physical structure and appears reddish brown and produces apple-green birefringence under polarized light. Next, the protein composition (subtype) of the amyloid deposits is established by using either clinical, genetic, serological surrogates or by directly phenotyping the tissue with immunohistochemistry (IHC). This process resembles a guided search wherein the clinical presentation is used to predict the potential subtypes, and the clinical surrogates and IHC are used to deduce the amyloid type. However, IHC often produces ambivalent results because of the high background staining from serum contamination, epitope loss from the formalin-fixation-induced protein cross-linking and lack of specific antibodies for all different amyloid subtypes. These inadequacies of an IHC-based test create an opportunity for a shotgun-proteomics-based diagnostic application to accurately subtype amyloids and improve patient care. Further, a single proteomics assay contains all the information necessary to subtype an amyloid whereas an IHC assay requires the use of multiple antibodies to hone in on the subtype, which drives up the cost of an IHC-based diagnostic panel.

Overview of proteomics research in amyloidosis subtyping

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Mass-spectrometry-based amyloid proteomics is a relatively mature field of study. Over the past decade, researchers have developed numerous proteomics techniques for detecting the amyloidogenic proteins from various types of samples. All of these methods delineate into three distinct categories: (a) immunoaffinity-aided top-down proteomics of serum,[3–8] (b) shotgun proteomics of fresh-frozen tissues,[9] FFPE tissues[10,11] and abdominal subcutaneous fat aspirates[11–13] and (c) histologydriven mass spectrometry imaging (MSI) of FFPE tissues.[14] Kishikawa et al.[3] pioneered the approach of immunoprecipitating the transthyretin (ATTR) protein from the patient sera and subjecting the precipitates to protein mass fingerprinting via liquid chromatography mass spectrometry (LC-MS). Mass signatures are analyzed to detect transthyretin mutations. This approach has the same limitations of an IHC-based test wherein a multitude of special antibodies are required to fully subtype a patient. In addition, a serum-based amyloidosis test is far less sensitive and specific compared to a direct amyloid-tissue-based assay. In 2004, Liao et al.[9] solved these problems by coupling the laser capture microdissection technique with the shotgun proteomics method to analyze amyloid deposits from human

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brain tissue. Proteins in the captures were separated with 1-D gel electrophoresis and digested using trypsin. Resulting peptides were analyzed with liquid chromatography tandem mass spectrometry (LC-MS/MS). MS/MS spectra were processed with protein identification software to identify the amyloidogenic proteins present in the captures. In this study, amyloid plaques showed an 80-fold enrichment of Aβ compared to the surrounding normal tissue. Brambilla et al.[12] adapted the multidimensional protein identification technology[15] to analyze the proteins extracted from the fat tissue aspirates taken from patients with systemic amyloidosis. Both of these methods use unnecessary prefractionation steps that are not viable in a clinical laboratory that processes thousands of specimens per year. Murphy et al.[11] extended the shotgun proteomics method to the FFPE tissues. In this method, amyloid deposits were scraped from the FFPE tissues. Proteins were extracted from the scraped tissue fragments using a multiday (8–10 days) incubation technique and subjected to LC-MS/MS. This method not only is time consuming but also suffers from high sample-to-sample variability because the imprecise scrape off technique collects signal from the surrounding normal tissue areas. Recently, Seeley et al.[14] utilized the high-throughput MSI technology to detect serum amyloid A protein from an FFPE spleen with possible amyloidosis. However, this technology has not been clinically implemented and validated. Further, MSI is also limited to tissue-based diagnostics, thereby ignoring the whole spectrum of systemic amyloidosis cases that can be diagnosed from fine needle fat aspirates. We addressed these problems by implementing and validating a novel clinical testing proteomics platform for diagnosis and classification of amyloidosis.

Development of a clinical shotgun proteomics assay for amyloid subtyping Figure 1 illustrates the high-level workflow of the shotgunproteomics-based amyloidosis test.[10] This process starts by staining an FFPE tissue section with CR to highlight the amyloid deposits. Stained sections are mounted on special Director slides (OncoPlexDx, Rockville, MD). Positively stained areas are subjected to laser microdissection (LMD) wherein the amyloid deposits are visualized under a microscope and resected with a low-power laser. Four microdissections are performed for each case, and each microdissection collects material from an area of 60 000 μm2 across a 10-μm-thick tissue section. Material from each microdissection is captured into a separate tube containing 35 μl of 10 mM Tris/1 mM EDTA/0.002% Zwittergent 3-16 (Calbiochem, San Diego, CA) buffer via gravity. In the context of direct tissue analysis, LMD offers to selectively enrich certain tissue areas of interest (such as adenoma and amyloid deposits) while minimizing the contribution from the background tissue for downstream proteomics analysis. Proteins in the microdissected FFPE fragments are chemically cross-linked, and hence, they are not directly amenable for massspectrometer-based proteomic analysis. Proteomics researchers have developed special methods for solubilizing the FPPE proteins.[1,10,16–19] We utilize a simple heat-mediated antigen retrieval method (98 °C for 90 min) for extracting proteins from the FFPE matrix.[10,16] Several studies have shown that protein yields obtained from the FFPE tissues using this method are comparable to that of yields obtained from fresh-frozen tissues.[16,20] Extracted proteins are denatured via sonication in a


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Shotgun-proteomics-based clinical testing

Congo Red Staining

Laser Microdissection

Protein Identification Report

Heat-mediated Antigen Retrieval

Bioinformatics

Trypsin Digestion

Tandem Mass Spectrometry

Figure 1. Shotgun proteomics workflow for characterizing the protein content of amyloid deposits.

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least four MS/MS spectral matches are considered for clinical interpretation. For every case, we create a personalized clinical proteomic profile that lists all the confident protein identifications in each of the microdissection along with their respective MS/MS spectral counts. The number of MS/MS spectra matching to a protein is considered as a semiquantitative measure of its abundance.[25] A pathologist calls the amyloidosis subtype by considering the most abundant amyloidogenic protein detected across all microdissections as well as other clinical factors. We also extended the FFPE-tissue-based proteomics method to analyze amyloid deposits in abdominal fat aspirates. However, discussion of this modified method is out of the scope of this perspectives article.

Regulatory factors in the implementation of a clinical proteomics assay Development of a shotgun proteomics assay is trivial compared to the process of implementing it in a clinical testing environment. Regulations from a wide variety of federal agencies and laws must be considered before implementing such assays in a clinical laboratory. The main federal regulatory framework is set by the 1988 Clinical Laboratory Improvement Amendments (CLIA), which requires regular inspections of the laboratories to ensure compliance with the quality standards set by the law. This enforcement is typically performed by the College of American Pathologists (CAP) whose Laboratory Accreditation Program establishes regular, unannounced inspections of the clinical laboratories, and the laboratories that are determined to be compliant with the CAP accreditation standards are deemed to be ‘CLIA approved’. In addition to CAP/CLIA, the Health Insurance Portability and Accountability Act of 1996 addresses the patient privacy and security aspects by regulating all the health information transactions that contain identifiable data. The Centers for Disease Control and the Occupational Safety and Health Administration regulate the public health and the employee safety aspects of a clinical laboratory. Recently, the United States Food and Drug Administration has also expressed interest in extending



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water bath for 60 min. Proteins are digested into peptides using 0.5 μg of trypsin (Promega, Madison, WI) and an overnight incubation at 37 °C. Tryptic digests are reduced with 3 μl of 0.1 M dithiothreitol and analyzed with nanoflow LC-MS/MS. Five microliters of the peptide mixtures from each dissection are diluted with 20 μl of 0.5% trifluroacetic acid and 0.15% formic acid buffer. Twenty microliters of the sample is loaded onto a 0.25 μl Optipak trap (New Objective, Woburn, MA) packed with Magic C8 beads (Michrom Bioresources, Auburn, CA). Peptides are separated on a 75 μm × 15 cm Magic C18 column (Precision Capillary Columns, San Clemente, CA) using a 60-min gradient of 5%–55% ACN in 0.1% formic acid. Eluting peptides are analyzed on an LTQ-Orbitrap (Thermo-Fisher, Waltham, MA) mass spectrometer operated in data-dependent mode. Full MS scans are collected in the Orbitrap at a resolution of 60 000. The accuracy of the precursor masses is improved via real-time lock mass calibration. The top five most intense precursor ions are sequentially isolated for collision-induced dissociation, and the resulting tandem mass spectra (MS/MS) are collected in the linear ion trap portion of the instrument. Raw data are processed by bioinformatics software to characterize the proteomic composition of an amyloid deposit. The tandem mass spectra of each case are matched against a composite protein sequence database using three different search engines (Sequest,[21] X!Tandem[22] and Mascot[23]). The composite database contains protein sequences obtained from the SwissProt database selected for human subspecies, known human immunoglobulin variant domains and known amyloidogenic mutations collected from literature and common contaminants. Reversed protein sequences are appended to the database for estimating the false discovery rates of the identifications.[24] The search engines are configured to detect semitryptic peptides from the composite database while looking for the following variable modifications: oxidation of methionine (+15.996 Da) and n-terminal pyroglutamic acid ( 17.023 Da). Peptide identification results are filtered using Scaffold software (Proteome Software, Portland, OR), and filtered peptides are assembled into protein identifications. Candidate proteins with at least one high-confidence (probability of identification >90%) unique peptide identification and at

J. D. Theis et al. its oversight over laboratory developed tests (LDTs) such as the amyloid shotgun proteomics assay, potentially creating additional complexities in the regulatory landscape for the clinical laboratories. It is not an easy task to implement a shotgun proteomics assay that complies with the complex, often overlapping and sometimes conflicting weave of regulation. This becomes even more daunting for large reference pathology laboratories serving at the national level, where the compliance with not only the federal regulations but also individual state regulations may be necessary. Additionally, shotgun proteomics is such a novel technique for clinical laboratories that none of the regulatory agencies has issued specific guidelines for implementing, validating and maintaining the compliance of this testing modality. We addressed this guideline gap by synthesizing the regulatory requirements into a Quality Management System (QMS). This system is composed of 12 Quality System Essentials (QSEs) that describe how to build regulatory-level quality into the structure, processes and outcomes of a clinical laboratory as well as its products and services (Fig. 2). These QSEs are derived by combining the clinical laboratory standards proposed by the Clinical and Laboratory Standards Institute, the American Association of Blood Banks, the International Organization for Standardization and the Good Clinical Laboratory Practices framework.[26] Implementing a diagnostic test using the QMS automatically builds quality into the laboratory processes, with federal and state regulatory compliance as a natural outcome. We conceptually deconstructed the amyloid shotgun proteomics research method and reengineered it as a diagnostic test using the QMS structure.

Implementation in a clinical diagnostic laboratory Here, we discuss the overall implementation of an amyloid shotgun proteomics assay in a clinical environment, which leveraged different QSEs to standardize the following components

of the diagnostic test: preanalytical, analytical, bioinformatics, personnel, supplies/reagents and instrumentation. This QMS-driven model of implementation allowed us to minimize the intra-assay variability and improve assay reproducibility, thereby achieving consistent high-quality performance over long periods. It is commonly understood that the preanalytical phase hosts the most prominent sources of error that can echo through the entire pipeline (Fig. 3). For instance, collecting inadequate amounts of tissues for proteomic analysis leads to lower protein identification rates. Tissues contaminated with high levels of serum tend to have a lower coverage of the tissue proteome because of the increased competition from the serum proteome for the instrument time. Uncontrolled in vitro proteolysis of the collected proteins introduces random fluctuations in the number of peptides and spectra detected per protein. These preanalytical errors often lead to an inconclusive diagnostic result and waste precious clinical specimens. A majority of such catastrophic failures can be averted by standardizing the procedures for tissue microtomy, tissue quality control (QC) and sample storage conditions. The analytical space of the MS-based proteomics method is vast and includes many critical steps: protein extraction from the sample matrix, protein denaturation, trypsin digestion, sample loading, liquid chromatography, peptide ionization, MS/MS data acquisition and bioinformatics. The parameters controlling each of these steps are also numerous. This introduces a high degree of variability in the proteomics method, which is highlighted by a multilaboratory study conducted by the Human Proteome Organization to characterize the proteome content of a defined protein mixture that resulted in groups of laboratories detecting different sets of proteins from the same reference sample.[27] Several independent researchers also noted the poor reproducibility of the proteomics method across different laboratories and sometimes within a laboratory.[28,29] Clinical laboratories must reduce this variable performance of the shotgun proteomics method before employing it as a diagnostic platform.

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Figure 2. The Quality Management System (QMS) is a group of interrelated and integrated processes that ensure consistent and predictable results from a diagnostic test, with regulatory compliance as the natural outcome. The QMS includes a total of eight Quality System Essentials (QSEs) that imbibe quality throughout the structural and process elements of a diagnostic assay. The four outcome QSEs ensure continual maintenance of the quality over long periods.



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Laser Microdissection

Specimen Processing Fixation Quality Sample Quantity

Sample Preparation

Staining Quality

Antigen Retrieval

Micrometer Calibration

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Protein Denaturation

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Variability Gradient Stability

Data Acquisition Settings

Column Condition

Calibration and Tuning

Digest Loading

Ionization Efficiency

Liquid Chromatography

Software Versions Search Engine Settings Protein Sequence DB

Tandem Mass Spectrometry

Bioinformatics

Figure 3. Configuration and performance of many critical subtasks can alter the final protein identification report generated for clinical interpretation. This Ishikawa diagram outlines few potential sources of the variability in the amyloid shotgun proteomics assay. For instance, incomplete trypsin digestion reduces the number of peptides and proteins detected in a sample. We utilize the QSEs to minimize the variability of the assay.

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for identification. The final protein identification report is automatically generated according to the clinical specifications. Laboratory personnel are central to achieving the highest quality standards set for any clinical assay. Research laboratories typically tend to have one to two personnel performing the entire proteomics experiment. Clinical laboratories, however, process hundreds of samples on a continual basis using tens of personnel. Standardizing the roles of each technician, defining quality measures for each task and establishing communication channels are very important in order to achieve smooth operations and produce high-quality data. For instance, technicians performing the microtomy, CR staining and LMD must ensure the integrity of the clinical specimen and the quality of the sampled proteome. The technician operating the mass spectrometer should assess whether the instrument is performing within the standard operating limits before submitting a new batch of samples, troubleshoot the drift in performance (if any), assess the quality of the acquired data and submit the data files for bioinformatics analysis. Technicians must document and report any deviations from the SOPs as well as quality issues. We also created training modules, proficiency tests and competency tests for all the technicians involved in the amyloid shotgun proteomics testing. This high-training and high-complexity testing environment equalizes the performance levels of all the laboratory personnel, thereby ensuring the overall quality and consistency of the diagnostic test. Finally, clinical laboratories must ensure that the quality of all the supplies/reagents obtained from the outside vendors meets the criteria set by the laboratory. This is accomplished by tracking each lot of consumables and testing them via random sampling. For instance, the enzyme activity of a trypsin lot must be checked and documented before using it for the clinical test. Otherwise, interlot quality differences can introduce digestion variability in the final diagnostic test. Besides consumables, all the equipment must be periodically validated and their performance documented before analyzing clinical specimens.



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This issue was partly addressed when the National Cancer Institute (NCI) created the Clinical Proteomics Technologies for Cancer (CPTC) initiative to identify the underpinnings of high variability in proteomics experiments. CPTC reported that both interlaboratory and intralaboratory variation decreased when all parties were following a predefined standard operating procedure (SOP) for analyzing the samples.[30] Following the recommendations of the QMS and CPTC, we standardized all the analytical protocols of our amyloid proteomics test and created step-by-step SOPs. The overall number of tasks required to prepare the tissue for MS/MS was also minimized in order to reduce the number of sources for variability. These two measures when combined ensured consistent sample preparation and repeatable identification of both amyloidogenic proteins as well as mutant peptides. Bioinformatics introduces an invisible source of variability in the shotgun proteomics method. This is highlighted by an Association for Biomolecular Resources study to analyze a single shotgun proteomics data set using various protein identification pipelines, which resulted in a wide range of protein identification sets being reported despite a common starting point.[31] Interestingly, laboratories produced different sets of identifications even when they were using the same data set, protein identification pipeline and protein sequence database. This is not surprising because a protein identification pipeline has many configuration choices affecting the final result: creation of peak lists, choosing a protein sequence database, configuring the database search engines, selecting methods for assessing the confidence of peptide identifications and assembling the confident peptides into protein identifications. For example, choosing a suboptimal precursor mass tolerance for peptide-spectrum matching would result in lower peptide and protein identification rates. To remedy this, we optimized the configuration of our entire protein identification pipeline and preprogrammed it into the SWiFT environment.[32] The instrument operator simply chooses an instrument-specific configuration and submits the raw data files

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Quality monitoring and assessment of the amyloid shotgun proteomics assay Quality control of a clinical shotgun proteomics assay is an active endeavor. Consistent specimen handling, sample preparation, data acquisition and data analysis are necessary to achieve the quality standards established by the regulatory agencies. However, shotgun proteomic assays are very complex, encompassing numerous sample processing steps. Subpar performance of a single processing step can cast doubt on the final results of these assays. Hence, it is crucial to calibrate, establish, and track the quality parameters of a shotgun proteomics assay when analyzing clinical samples. Typically, proteomics researchers use known QC standards to measure the variability of the method and perform QC. The popular QC standards include trypsin digests of bovine serum albumin (low complexity), Sigma’s 48-protein Universal Proteomics Standard (medium complexity) or yeast whole cell lysates (high complexity). These standards are very effective in tuning and calibrating the shotgun proteomics method, but they fail to cover the other critical aspects of the amyloid proteomics test, including slide preparation, CR staining, LMD, FFPE antigen extraction and clinical interpretation of the protein identification report. We developed a process-level QC method for the amyloid shotgun proteomics assay. Reference FFPE blocks were created for each of the four major subtypes (AL-K, AL-L, AA and ATTR) using amyloid tissues collected from gold standard cases with confirmed diagnosis. A single reference block is selected and inserted into each batch of patient samples. The entire batch, including the QC sample, is processed through the diagnostic testing pipeline. Protein identification results from the batch control are compared against the historical records to assess whether the data quality from the batch is sufficient for making clinical decisions. Independently, all four reference blocks are repeatedly analyzed once every week. LC-MS/MS performance metrics are computed from each of the weekly controls and compared against the respective distributions of historical values

using MassQC (Proteome Software, Portland, OR). MassQC accepts a QC raw data file, extracts a total of 28 performance metrics described by Rudnick et al.,[33] adds them to a metrics database, independently aggregates each metric across time and displays the historical trend observed for each metric. Standard operating limits (SOLs) were established for each metric in order to detect the performance drifts that might impact the diagnostic test, such as broadening of chromatographic peaks, increase in the mass measurement error and decrease in the number of identified peptides. Deviations from the SOLs are documented. Potential sources of variability are identified and corrected using normal FFPE tissues and medium-complexity QC protein mixtures. These weekly QCs when coupled with preventive maintenance lead to a highly stable system that can generate reproducible results (Fig. 4). The entire pipeline must pass the daily batch QC as well as the weekly QC for it to process clinical samples. Quality audits are integral for meeting the regulatory compliance. These audits are designed to shift the focus away from the purely procedural QC toward measuring the overall effectiveness of the QC system. We constructed an internal audit system wherein an independent QC expert periodically measures the overall performance of the amyloid shotgun proteomics assay using blinded clinical samples with a pre-established diagnosis. We also utilize the metrics from these audits to continually improve the operational processes underlying the assay.

Clinical validation of the amyloidosis proteomics assay Performance characteristics of new assays implemented in a clinical laboratory must be validated according to the regulatory criteria before employing them for patient care. Our amyloid shotgun proteomics assay was developed inside a clinical laboratory, and it utilizes only well-documented protein biomarkers for typing the amyloid deposits.[34] This type of assay is considered

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Figure 4. QA/QC of the shotgun proteomics platform is crucial for producing highly reproducible results. This figure illustrates the average of the chromatogram peak widths and the variability in the chromatogram peak widths measured over a period of fourteen months using QC samples.



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Shotgun-proteomics-based clinical testing as an LDT by the regulatory agencies, and it must be validated according to the CLIA guidelines. These specifications broadly describe how to perform the validation studies, but specific details of the approaches used for shotgun proteomics assays are not published. Guidance in the form of specific examples is also not available because of the novelty of shotgun proteomics in a clinical laboratory. We developed a rigorous validation procedure for the amyloid proteomics test following the CLIA guidelines. CLIA requires that the laboratories must establish the following six criteria for all the LDTs: accuracy, analytical precision, analytical sensitivity, analytical specificity, reference range and reportable range. Accuracy refers to the proportion of true results (both true positives and true negatives) produced by a diagnostic test when analyzing a large cohort of samples. We computed the accuracy of the amyloid shotgun proteomics test using a validation cohort of 50 amyloidosis cases of different subtypes (positives) and 12 controls (negatives). FFPE blocks from the validation cohort were analyzed following the SOPs of the assay. Clinical diagnoses were made for all cases and controls and compared against the gold standard (IHC). Analytical precision refers to the ability of the clinical test to recapitulate the same result from repeated analysis of the same clinical samples. This was computed by analyzing duplicate injections of confirmed ATTR, AL-K, AL-L and AA cases as well as controls performed every day over a 20-day period. Analytical specificity refers to the ability of the diagnostic test to detect the amyloidogenic protein that is specific to a given subtype while remaining negative for the other subtyping proteins. This was computed by analyzing 20 samples of different subtypes in an interleaved fashion. Protein identification reports from each sample were scrutinized for making subtyping calls. Analytical sensitivity of the diagnostic test was computed by analyzing serial dilutions of the amyloid lysates captured from 20 subjects with the four most common subtypes. Protein identification reports from all the dilution experiments were analyzed, subtyping calls were made and the limit of detection threshold beyond which the MS assay produces inconclusive clinical results were established. The reference range

and reportable range parameters are not applicable to the amyloid shotgun proteomics assay because it is semiquantitative, and the results of the test must be interpreted by a licensed pathologist in a clinical context.

Training clinicians to interpret protein identification reports in a clinical context Shotgun proteomics produces identification reports containing multiple proteins and their respective MS/MS spectral counts. Figure 5 shows a typical protein identification report produced by the amyloidosis test. Finding clinically actionable patterns in these complex identification reports is challenging for both clinicians and researchers. We created simple heuristics to derive potential subtypes from the report. This process highlights all known amyloidogenic proteins and mutations in the report that have at least four MS/MS spectral matches (Fig. 5). Highlighted proteins are ranked by the decreasing order of their spectral counts. Subtypes are identified based on the most abundant amyloidogenic proteins and mutations detected from the deposit. A pathologist correlates the potential subtypes with other clinical findings to arrive at a final diagnosis. Training data sets, tutorials and support networks were created to accommodate pathologists that are new to the shotgun-proteomics-based amyloid testing. One might question the wisdom behind choosing an untargeted proteomics method over targeted protein quantification methods such as multiple reaction monitoring (MRM) for a clinical test. MRM assays utilize the intensity of highly repeatable peptides to measure the abundances of proteins. These assays are easier to interpret in a clinical context because they provide absolute abundances of few preselected proteins. However, proteins in amyloid deposits are not amenable for targeted quantification because they are in a truncated form, and they often fail to yield proteotypic peptides that can be repeatedly seen across all patient samples.

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Figure 5. Protein identification report derived from seven patients each with different amyloid subtype. Numerical values indicate the number of spectra assigned to each protein. AApoA1 stands for Apolipoprotein A1 amyloidosis, AL-Kappa stands for kappa light chain amyloidosis, ATTR stands for transthyretin amyloidosis, AL-Lambda stands for lambda light-chain amyloidosis, AANF stands for atrial natriuretic factor amyloidosis, AA stands for secondary amyloidosis and ALys stands for lysozyme amyloidosis.

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A shotgun-proteomics-based diagnostic assay is more complex and potentially more expensive to develop and implement when compared to its IHC-based counterpart. Hence, an honest cost– benefit analysis must be performed before converting an existing clinical test from IHC format to MS-based format. One reason for such conversion is that an MS-based assay can scan for multiple targets in one tissue section (multiplexing) whereas the counter IHC test has to utilize a panel of antibodies spread over multiple tissue sections. This multiplexing capability reduces the overall cost of a diagnostic test while minimizing the amount of precious biopsy tissue used for testing. These benefits could be maximized by converting an IHC test with high failure rate. For example, an IHC-based amyloid subtyping test has a failure rate of 58% as a result of unreliable antibody–antigen chemistry.[10] In contrast, our shotgun-proteomics-based amyloid classification assay has a failure rate of 2%.[10] Such a magnitude of reduction in the failure rate of an assay is highly desirable because it vastly improves patient outcomes while reducing the net health care expenditure. Finally, a shotgun proteomics clinical assay would generate massive amounts of protein identification data that can be mined for potential new diagnostic, therapeutic and prognostic biomarkers. For instance, we collected approximately 6000 clinical amyloid proteome profiles since the assay was launched in 2008. Efforts are under way to mine this data set for new biomarkers.

Perspectives Diagnostic laboratories can host optimal biomarker discovery pipelines

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A typical protein biomarker discovery pipeline has three phases: discovery, verification and clinical validation. Often, academic laboratories carry out the discovery work using research-grade samples from underpowered cohorts (n = 20–30). A brief review of the literature for protein biomarkers identifies hundreds of putative markers for each disease, and a majority of those biomarkers are idiosyncratic to the research study. Promising candidates from the discovery phase are verified by analyzing their signatures in medium-sized clinical cohorts (n > 100) using standardized analytical platforms. Rapid assays are developed to validate the top performing biomarkers in the context of large clinical cohorts (n > 200) with diversified patient populations. Ultimately, the viability of the validated biomarkers for routine clinical use is demonstrated in a clinical setting (improvement over gold standard diagnosis, cost, clinical outcomes, etc.). This type of dispersed biomarker development involves multiple parties, uses different cohorts at each phase, uses nonstandardized analytical platforms and lacks end-to-end clinical testing oversight. Hence, it is unsurprising that a majority of biomarkers found in the discovery phase never reach the clinic. In remedy, we created an integrated biomarker development pipeline by duplicating the CAP/CLIA-certified amyloid shotgun proteomic testing platform. Operating under a direct clinical oversight, this pipeline seeks protein biomarkers for subtyping novel amyloid deposits. We utilized this pipeline to analyze the proteomic content of amyloid deposits from a cohort of 147


patients with amyloidosis symptoms but no clinical diagnosis. The MS/MS data failed to detect any of the previously known amyloidogenic proteins across all patient samples. However, the expression of leukocyte cell-derived chemotaxin-2 (Lect2) protein was consistently present across all amyloid deposits but absent from matched control samples (Fig. 6(A)). This was further attested by a case report of a patient presented with nephrotic syndrome whose amyloid fibrils were enriched with Lect2.[35] To date, our study represents the largest cohort of Lect2 amyloids detected from a variety of organs (Fig. 6(B)). The clinical acceptance of Lect2 amyloidosis as a latest systemic type of amyloidosis is also slowly gaining traction,[36,37] which is important because Lect2 amyloidosis patients are often misdiagnosed and may be subjected to nonbeneficial treatments. We strongly believe that the discovery and clinical acceptance of Lect2 as a novel amyloidogenic protein was greatly accelerated by the biomarker developmental work of the clinical laboratories.

Biomarker development for renal disease applications Determining the pathology of kidney diseases is a laborious task, which involves examining the biopsied material with different testing modalities: light microscopy, immunofluorescence

(A)

Amyloid Subtypes

Cost–benefit analysis of shotgun-proteomics-based versus IHC-based amyloid subtyping

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Kidney, 92

Figure 6. (A) Amyloid deposits from 147 patients and 70 controls were subjected to shotgun protein identification. The distribution of the numbers of MS2 spectra that matched to Lect2 protein between the two cohorts is shown. (B) This figure illustrates the number of Lect2 amyloid cases per tissue organ of origin. Misc stands for one case for each of the following organs: small bowel, parathyroid, gall bladder and pancreas. Unk stands for tissue of unknown origin. A majority of the Lect2 amyloids in our cohort originated from kidney and liver.


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Shotgun-proteomics-based clinical testing microscopy, electron microscopy and multiple IHC stains. These techniques have identified the pathogenesis of various kidney diseases. However, the targeted nature of these tests often fails to uncover the molecular etiology of the disease. Further, intertechnique disagreements often create gray zones that hamper accurate diagnosis. LMD-coupled shotgun proteomics has been quietly revolutionizing the field of renal disease pathology by enabling the diagnosis and accurate subtyping with one assay. In previous studies, LMD-LC-MS/MS has been utilized to examine the renal tissue protein expression in various glomerular diseases associated with organized deposits including amyloidosis, fibrillary glomerulonephritis (FG), membranoproliferative glomerulonephritis (MPGN) and immunotactoid glomerulopathy.[38–41] Shotgun proteomics was able to identify distinct proteomic signatures in the renal fibrils formed because of either amyloidosis or FG, both of which are indistinguishable under a microscope. Sethi et al.[40] distinguished between immune complex mediated MPGN (enrichment of immunoglobulins and complement factor C3) and complement mediated MPGN (enrichment complement factors C3 and C9 with little or no immunoglobulins) utilizing shotgun proteomics. These findings were translated into an ancillary test for diagnosing and subtyping glomerulonephritis.

Clinical translation of oncology markers

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Proteomics technology in the clinical laboratories The ability of the MS-based proteomics to simultaneously scan for multiple disease markers (multiplexing) from a single sample of tissue or biofluid is very appealing for a clinical laboratory. To this end, proteomics has two distinct technologies to offer: shotgun proteomics and targeted proteomics. Shotgun proteomics is an untargeted method for identifying the proteins present in a sample and was discussed extensively as a part of this perspectives article. This semiquantitative assay could be utilized to recognize the distribution of certain protein biomarkers in the tissues. This method is implemented once in a laboratory, and then the optimized method can be repeatedly utilized to translate multiplex immunoassay panels into new clinical tests. In contrast, the targeted proteomics assays are quantitative, and they are typically implemented by utilizing either a multiple reaction monitoring (MRM) or single reaction monitoring (SRM) of peptides from proteins present in a biomarker panel. The sensitivity and specificity of these assays are comparable to that of the enzyme-linked immunosorbent assay. The targeted proteomics assays have a broad applicability in the areas of tissue-based and biofluid-based quantitative diagnostics. However, development, validation and implementation of a new MRM/SRM assay are very time consuming for a clinical laboratory. Targeted assays can also be implemented in a much simpler pseudoselected reaction monitoring (pSRM) framework,[62] which draws up on the simplicity of the shotgun proteomics and sensitivity of the targeted assays such as MRM/SRM. This method performs targeted MS/MS analyses of every precursor m/z in a list, transitions are extracted from the MS/MS and peak areas are summed and normalized to a spiked-in internal standard. The pSRM method has the potential to become a rapid test development platform for quantitative proteomic assays. Clinical laboratories implementing proteomics assays must ensure that the technology measures up to the real-world performance characteristics that are expected from a clinical testing platform. The issues of preanalytical standardization, analytical standardization, QC, diagnostic accuracy, regulatory validation and clinical utility must be thoroughly addressed. This insurmountable task can be accomplished if clinical chemists engage with the broader proteomics research community. For example, the NCI’s CPTC initiative has already characterized the methods necessary for achieving a high degree of reproducibility when using both untargeted and targeted proteomics technologies. These findings are readily applicable for any new proteomics assay development. Further, diagnostic laboratories must also educate the proteomics research community about the unique needs of a clinical testing environment, including finding areas with unmet clinical needs, requiring preliminary studies with sufficient evidence to justify new assay development, proving clinical utility and gaining clinical acceptance through early champions and clinical trials. This type of symbiotic relationship will rapidly accelerate the development and deployment of the next-generation proteomics-based diagnostics.



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Researchers have utilized shotgun proteomics to study different types of tumors including oral,[42–44] breast,[45–47] lung,[48,49] pancreatic,[50–52] prostate,[53,54] ovarian[55,56] and colon.[57,58] This lead to the discovery of several protein biomarkers that fall into three application areas: diagnostic markers for differentiating between tumor versus normal tissue, tumor subtyping and tumor grading; prognostic markers for risk stratification and therapy personalization; and therapeutics markers for detecting dose response and chemoresistance. For example, Wisniewski et al.[59] utilized LMD-LC-MS/MS to analyze FFPE tissues of normal colon mucosa and adenocarcinoma. A total of 1808 protein biomarkers were detected, including 34 cell-surface proteins that were significantly upregulated in cancer tissues. These novel cellsurface markers can be translated into novel shotgun proteomics targets that can be employed for scanning large numbers of samples. Breast cancer offers the most clear-cut example of the power of molecular prognostic markers. It is well understood that morphologically similar breast tumors can be subtyped into four distinct clinical phenotypes using the expression status of the estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2). Each subtype has a distinct clinical outcome. Tumors with an elevated HER2 expression develop resistance to the trastuzumab (Herceptin) treatment. Patients whose tumors do not express ER, PR and HER2 (triple negative tumors) have poor prognosis because they do not respond well to most of the available treatments. Commercial entities such as OncoPlexDX have started translating this knowledge into LMD-assisted proteomics assays that can accurately subtype breast tumors and facilitate guided therapy.[60] Azad et al.[61] went one step ahead with the development of protein biomarkers for which changes during the early stages of sorafenib and bevacizumab therapy can predict the future clinical benefit received by the patients. These types of therapeutic markers are indispensable in stopping

ineffective treatments and preventing unnecessary harm to the patient. The demand for proteomics assays will only increase with time as more diagnostic, prognostic and therapeutic markers are uncovered for various cancers.

J. D. Theis et al. Acknowledgements J.D. Theis, J.A. Vrana, P.J. Kurtin and A. Dogan were supported by the Department of Laboratory Medicine and Pathology, Mayo Clinic. S. Dasari was supported by the Center for Individualized Medicine, Mayo Clinic. Bioinformatics support was provided by Roman Zenka through the Proteomics Core, Mayo Clinic. Quality systems support was provided by Melanie Hintz through the Department of Laboratory Medicine and Pathology, Mayo Clinic. A part of the FFPE extraction methodology was licensed from OncoPlexDx (Rockville, MD).

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Psychological Classification Versus Clinical Diagnosis.

SCFE: clinical aspects, diagnosis, and classification.

Combined clinical and genetic testing algorithm for cervical cancer diagnosis.

Diagnosis of amyloidosis.

The clinical features, diagnosis and classification of dermatomyositis.

Nomenclature 2014: Amyloid fibril proteins and clinical classification of the amyloidosis.

Incidence, diagnosis and prognosis of cardiac amyloidosis.

Cardiac amyloidosis: a challenging diagnosis.

Improving strategies for the diagnosis of cardiac amyloidosis.

Systemic AA amyloidosis: epidemiology, diagnosis, and management.

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Familial renal amyloidosis. Case reports, literature review and classification.

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Recommendations for presymptomatic genetic testing and management of individuals at risk for hereditary transthyretin amyloidosis.

Diagnosis and classification of psoriasis.

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Clinical malaria diagnosis: rule-based classification statistical prototype.

DNA diagnosis for hereditary cerebral hemorrhage with amyloidosis (Dutch type)

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