Comparison of Eight Technologies to Determine Genotype at the UGT1A1 (TA)n Repeat Polymorphism: Potential Clinical Consequences of Genotyping Errors?

To ensure accuracy of UGT1A1 (TA)n (rs3064744) genotyping for use in pharmacogenomics-based irinotecan dosing, we tested the concordance of several commonly used genotyping technologies. Heuristic genotype groupings and principal component analysis demonstrated concordance for Illumina sequencing, fragment analysis, and fluorescent PCR. However, Illumina sequencing and fragment analysis returned a range of fragment sizes, likely arising due to PCR "slippage". Direct sequencing was accurate, but this method led to ambiguous electrophoregrams, hampering interpretation of heterozygotes. Gel sizing, pyrosequencing, and array-based technologies were less concordant. Pharmacoscan genotyping was concordant, but it does not ascertain (TA)8 genotypes that are common in African populations. Method-based genotyping differences were also observed in the publication record (p < 0.0046), although fragment analysis and direct sequencing were concordant (p = 0.11). Genotyping errors can have significant consequences in a clinical setting. At the present time, we recommend that all genotyping for this allele be conducted with fluorescent PCR (fPCR).

Anti-peptide monoclonal antibodies generated for immuno-multiple reaction monitoring-mass spectrometry assays have a high probability of supporting Western blot and ELISA.

Immunoaffinity enrichment of peptides coupled to targeted, multiple reaction monitoring-mass spectrometry (immuno-MRM) has recently been developed for quantitative analysis of peptide and protein expression. As part of this technology, antibodies are generated to short, linear, tryptic peptides that are well-suited for detection by mass spectrometry. Despite its favorable analytical performance, a major obstacle to widespread adoption of immuno-MRM is a lack of validated affinity reagents because commercial antibody suppliers are reluctant to commit resources to producing anti-peptide antibodies for immuno-MRM while the market is much larger for conventional technologies, especially Western blotting and ELISA. Part of this reluctance has been the concern that affinity reagents generated to short, linear, tryptic peptide sequences may not perform well in traditional assays that detect full-length proteins. In this study, we test the feasibility and success rates of generating immuno-MRM monoclonal antibodies (mAbs) (targeting tryptic peptide antigens) that are also compatible with conventional, protein-based immuno-affinity technologies. We generated 40 novel, peptide immuno-MRM assays and determined that the cross-over success rates for using immuno-MRM monoclonals for Western blotting is 58% and for ELISA is 43%, which compare favorably to cross-over success rates amongst conventional immunoassay technologies. These success rates could most likely be increased if conventional and immuno-MRM antigen design strategies were combined, and we suggest a workflow for such a comprehensive approach. Additionally, the 40 novel immuno-MRM assays underwent fit-for-purpose analytical validation, and all mAbs and assays have been made available as a resource to the community via the Clinical Proteomic Tumor Analysis Consortium's (CPTAC) Antibody (http://antibodies.cancer.gov) and Assay Portals (http://assays.cancer.gov), respectively. This study also represents the first determination of the success rate (92%) for generating mAbs for immuno-MRM using a recombinant B cell cloning approach, which is considerably faster than the traditional hybridoma approach.

Recommendations for the Generation, Quantification, Storage, and Handling of Peptides Used for Mass Spectrometry-Based Assays.

BACKGROUND: For many years, basic and clinical researchers have taken advantage of the analytical sensitivity and specificity afforded by mass spectrometry in the measurement of proteins. Clinical laboratories are now beginning to deploy these work flows as well. For assays that use proteolysis to generate peptides for protein quantification and characterization, synthetic stable isotope-labeled internal standard peptides are of central importance. No general recommendations are currently available surrounding the use of peptides in protein mass spectrometric assays. CONTENT: The Clinical Proteomic Tumor Analysis Consortium of the National Cancer Institute has collaborated with clinical laboratorians, peptide manufacturers, metrologists, representatives of the pharmaceutical industry, and other professionals to develop a consensus set of recommendations for peptide procurement, characterization, storage, and handling, as well as approaches to the interpretation of the data generated by mass spectrometric protein assays. Additionally, the importance of carefully characterized reference materials-in particular, peptide standards for the improved concordance of amino acid analysis methods across the industry-is highlighted. The alignment of practices around the use of peptides and the transparency of sample preparation protocols should allow for the harmonization of peptide and protein quantification in research and clinical care.

A dataset describing a suite of novel antibody reagents for the RAS signaling network.

RAS genes are frequently mutated in cancer and have for decades eluded effective therapeutic attack. The National Cancer Institute's RAS Initiative has a focus on understanding pathways and discovering therapies for RAS-driven cancers. Part of these efforts is the generation of novel reagents to enable the quantification of RAS network proteins. Here we present a dataset describing the development, validation (following consensus principles developed by the broader research community), and distribution of 104 monoclonal antibodies (mAbs) enabling detection of 27 phosphopeptides and 69 unmodified peptides from 20 proteins in the RAS network. The dataset characterizes the utility of the antibodies in a variety of applications, including Western blotting, immunoprecipitation, protein array, immunohistochemistry, and targeted mass spectrometry. All antibodies and characterization data are publicly available through the CPTAC Antibody Portal, Panorama Public Repository, and/or PRIDE databases. These reagents will aid researchers in discerning pathways and measuring expression changes in the RAS signaling network.

Direct molecular dissection of tumor parenchyma from tumor stroma in tumor xenograft using mass spectrometry-based glycoproteomics.

The most widely used cancer animal model is the human-murine tumor xenograft. Unbiased molecular dissection of tumor parenchyma versus stroma in human-murine xenografts is critical for elucidating dysregulated protein networks/pathways and developing therapeutics that may target these two functionally codependent compartments. Although antibody-reliant technologies (e.g., immunohistochemistry, imaging mass cytometry) are capable of distinguishing tumor-proper versus stromal proteins, the breadth or extent of targets is limited. Here, we report an antibody-free targeted cross-species glycoproteomic (TCSG) approach that enables direct dissection of human tumor parenchyma from murine tumor stroma at the molecular/protein level in tumor xenografts at a selectivity rate presently unattainable by other means. This approach was used to segment/dissect and obtain the protein complement phenotype of the tumor stroma and parenchyma of the metastatic human lung adenocarcinoma A549 xenograft, with no need for tissue microdissection prior to mass-spectrometry analysis. An extensive molecular map of the tumor proper and the associated microenvironment was generated along with the top functional N-glycosylated protein networks enriched in each compartment. Importantly, immunohistochemistry-based cross-validation of selected parenchymal and stromal targets applied on human tissue samples of lung adenocarcinoma and normal adjacent tissue is indicative of a noteworthy translational capacity for this unique approach that may facilitate identifications of novel targets for next generation antibody therapies and development of real time preclinical tumor models.

A robust biomarker discovery pipeline for high-performance mass spectrometry data.

A high-throughput software pipeline for analyzing high-performance mass spectral data sets has been developed to facilitate rapid and accurate biomarker determination. The software exploits the mass precision and resolution of high-performance instrumentation, bypasses peak-finding steps, and instead uses discrete m/z data points to identify putative biomarkers. The technique is insensitive to peak shape, and works on overlapping and non-Gaussian peaks which can confound peak-finding algorithms. Methods are presented to assess data set quality and the suitability of groups of m/z values that map to peaks as potential biomarkers. The algorithm is demonstrated with serum mass spectra from patients with and without ovarian cancer. Biomarker candidates are identified and ranked by their ability to discriminate between cancer and noncancer conditions. Their discriminating power is tested by classifying unknowns using a simple distance calculation, and a sensitivity of 95.6% and a specificity of 97.1% are obtained. In contrast, the sensitivity of the ovarian cancer blood marker CA125 is approximately 50% for stage I/II and approximately 80% for stage III/IV cancers. While the generalizability of these markers is currently unknown, we have demonstrated the ability of our analytical package to extract biomarker candidates from high-performance mass spectral data.

Evaluation of the selectivity and sensitivity of isoform- and mutation-specific RAS antibodies.

There is intense interest in developing therapeutic strategies for RAS proteins, the most frequently mutated oncoprotein family in cancer. Development of effective anti-RAS therapies will be aided by the greater appreciation of RAS isoform-specific differences in signaling events that support neoplastic cell growth. However, critical issues that require resolution to facilitate the success of these efforts remain. In particular, the use of well-validated anti-RAS antibodies is essential for accurate interpretation of experimental data. We evaluated 22 commercially available anti-RAS antibodies with a set of distinct reagents and cell lines for their specificity and selectivity in recognizing the intended RAS isoforms and mutants. Reliability varied substantially. For example, we found that some pan- or isoform-selective anti-RAS antibodies did not adequately recognize their intended target or showed greater selectivity for another; some were valid for detecting G12D and G12V mutant RAS proteins in Western blotting, but none were valid for immunofluorescence or immunohistochemical analyses; and some antibodies recognized nonspecific bands in lysates from "Rasless" cells expressing the oncoprotein BRAFV600E Using our validated antibodies, we identified RAS isoform-specific siRNAs and shRNAs. Our results may help to ensure the accurate interpretation of future RAS studies.

SELDI-TOF plasma profiles distinguish individuals in a protein C-deficient family with thrombotic episodes occurring before age 40.

We tested the hypothesis that differences in the low-molecular-weight (500-20,000 Da) proteomic profile of plasma may be detectable between members of a protein C-deficient family who have suffered thrombotic events before age 40 compared to family members without a history of venous thrombosis. Unfractionated plasma samples from members of a previously described large thrombophilic kindred with type I protein C deficiency were applied to ProteinChip weak cation exchange interaction arrays (WCX2; Ciphergen Biosystems, Fremont, CA, USA) and subjected to SELDI-TOF (surface-enhanced laser desorption/ionization time-of-flight) mass spectrometry using the Ciphergen PBSII ProteinChip System (Ciphergen Biosystems). Profiles were analyzed by a boosted decision-tree algorithm. When individuals who had presented with deep venous thrombosis (DVT) before the age of 40 (n = 21) were compared to age-matched, healthy family members (n = 50), the proteomic patterns defined by the decision-tree analysis could classify the entity of DVT before age 40 with 67% sensitivity, at a specificity of 86%. When a small group of cases with history of superficial venous thrombosis (n = 6) was added to the case group, the sensitivity was 87.5% at a specificity of 80%. These data support the hypothesis that members of the protein C deficient Vermont kindred II who suffer a thrombotic event before age 40 display significant differences in low-molecular-weight proteomics profile compared to those who remain disease-free. This is the first study to apply SELDI-TOF technology in conjunction with a bioinformatics tool to analyze low-molecular-weight proteomic patterns in patients with venous thrombosis.

Proteomic patterns for cancer diagnosis--promise and challenges.

Proteomic patterns have been discovered for a variety of cancers and cancer related diseases. The platforms used have been both mass spectrometry and microarrays and the incorporation of computer informatics has resulted in innovative possibilities for novel diagnostics.

Novel approaches to visualization and data mining reveals diagnostic information in the low amplitude region of serum mass spectra from ovarian cancer patients.

The ability to identify patterns of diagnostic signatures in proteomic data generated by high throughput mass spectrometry (MS) based serum analysis has recently generated much excitement and interest from the scientific community. These data sets can be very large, with high-resolution MS instrumentation producing 1-2 million data points per sample. Approaches to analyze mass spectral data using unsupervised and supervised data mining operations would greatly benefit from tools that effectively allow for data reduction without losing important diagnostic information. In the past, investigators have proposed approaches where data reduction is performed by a priori "peak picking" and alignment/warping/smoothing components using rule-based signal-to-noise measurements. Unfortunately, while this type of system has been employed for gene microarray analysis, it is unclear whether it will be effective in the analysis of mass spectral data, which unlike microarray data, is comprised of continuous measurement operations. Moreover, it is unclear where true signal begins and noise ends. Therefore, we have developed an approach to MS data analysis using new types of data visualization and mining operations in which data reduction is accomplished by culling via the intensity of the peaks themselves instead of by location. Applying this new analysis method on a large study set of high resolution mass spectra from healthy and ovarian cancer patients, shows that all of the diagnostic information is contained within the very lowest amplitude regions of the mass spectra. This region can then be selected and studied to identify the exact location and amplitude of the diagnostic biomarkers.

Clinical proteomics and biomarker discovery.

Early detection of disease generally provides much-improved outcomes by a definitive medical procedure or through lifestyle modification along with specific medical management strategies. For serum biomarkers, which are central to the diagnosis of many diseases, to become truly useful sentinels of pathogenesis, their sensitivity and specificity in both early detection and recurrence monitoring must be improved. Currently, the detection and monitoring of disease markers is based on solitary proteins, and this approach is not always reliable. New classes of biomarkers derived from mass spectroscopy analysis of the low molecular weight proteome have shown improved abilities in the early detection of disease and hence in patient risk stratification and outcome. The development of a modular platform technology with sufficient flexibility and design abstractions allowing for concurrent experimentation, test, and refinement will help speed the progress of mass spectroscopy-derived proteomic pattern-based diagnostics from the scientific laboratory to the medical clinic. For acceptance by scientists, physicians, and regulatory personnel, new bioinformatic tools are essential system components for data management, analysis, and intuitive display of these new and complex data. Clinically engineered mass spectroscopy systems are essential for the further development and validation of multiplexed biomarkers that have shown tremendous promise for the early detection of disease.

Analytical considerations for mass spectrometry profiling in serum biomarker discovery.

The potential of using mass spectrometry profiling as a diagnostic tool has been demonstrated for a wide variety of diseases. Various cancers and cancer-related diseases have been the focus of much of this work because of both the paucity of good diagnostic markers and the knowledge that early diagnosis is the most powerful weapon in treating cancer. The implementation of mass spectrometry as a routine diagnostic tool has proved to be difficult, however, primarily because of the stringent controls that are required for the method to be reproducible. The method is evolving as a powerful guide to the discovery of biomarkers that could, in turn, be used either individually or in an array or panel of tests for early disease detection. Using proteomic patterns to guide biomarker discovery and the possibility of deployment in the clinical laboratory environment on current instrumentation or in a hybrid technology has the possibility of being the early diagnosis tool that is needed.

Proteomic patterns for classification of ovarian cancer and CTCL serum samples utilizing peak pairs indicative of post-translational modifications.

Proteomic patterns as a potential diagnostic technology has been well established for several cancer conditions and other diseases. The use of machine learning techniques such as decision trees, neural networks, genetic algorithms, and other methods has been the basis for pattern determination. Cancer is known to involve signaling pathways that are regulated through PTM of proteins. These modifications are also detectable with high confidence using high-resolution MS. We generated data using a prOTOF mass spectrometer on two sets of patient samples: ovarian cancer and cutaneous t-cell lymphoma (CTCL) with matched normal samples for each disease. Using the knowledge of mass shifts caused by common modifications, we built models using peak pairs and compared this to a conventional technique using individual peaks. The results for each disease showed that a small number of peak pairs gave classification equal to or better than the conventional technique that used multiple individual peaks. This simple peak picking technique could be used to guide identification of important peak pairs involved in the disease process.

A novel, high-throughput workflow for discovery and identification of serum carrier protein-bound peptide biomarker candidates in ovarian cancer samples.

BACKGROUND: Most cases of ovarian cancer are detected at later stages when the 5-year survival is approximately 15%, but 5-year survival approaches 90% when the cancer is detected early (stage I). To use mass spectrometry (MS) of serum proteins for early detection, a seamless workflow is needed that provides an opportunity for rapid profiling along with direct identification of the underpinning ions. METHODS: We used carrier protein-bound affinity enrichment of serum samples directly coupled with MALDI orthagonal TOF MS profiling to rapidly search for potential ion signatures that contained discriminatory power. These ions were subsequently directly subjected to tandem MS for sequence identification. RESULTS: We discovered several biomarker panels that enabled differentiation of stage I ovarian cancer from unaffected (age-matched) patients with no evidence of ovarian cancer, with positive results in >93% of samples from patients with disease-negative results and in 97% of disease-free controls. The carrier protein-based approach identified additional protein fragments, many from low-abundance proteins or proteins not previously seen in serum. CONCLUSIONS: This workflow system using a highly reproducible, high-resolution MALDI-TOF platform enables rapid enrichment and profiling of large numbers of clinical samples for discovery of ion signatures and integration of direct sequencing and identification of the ions without need for additional offline, time-consuming purification strategies.

High-resolution serum proteomic profiling of Alzheimer disease samples reveals disease-specific, carrier-protein-bound mass signatures.

BACKGROUND: Researchers typically search for disease markers using a "targeted" approach in which a hypothesis about the disease mechanism is tested and experimental results either confirm or disprove the involvement of a particular gene or protein in the disease. Recently, there has been interest in developing disease diagnostics based on unbiased quantification of differences in global patterns of protein and peptide masses, typically in blood from individuals with and without disease. We combined a suite of methods and technologies, including novel sample preparation based on carrier-protein capture and biomarker enrichment, high-resolution mass spectrometry, a unique cohort of well-characterized persons with and without Alzheimer disease (AD), and powerful bioinformatic analysis, that add statistical and procedural robustness to biomarker discovery from blood. METHODS: Carrier-protein-bound peptides were isolated from serum samples by affinity chromatography, and peptide mass spectra were acquired by a matrix-assisted laser desorption/ionization (MALDI) orthogonal time-of-flight (O-TOF) mass spectrometer capable of collecting data over a broad mass range (100 to >300,000 Da) in a single acquisition. Discriminatory analysis of mass spectra was used to process and analyze the raw mass spectral data. RESULTS: Coupled with the biomarker enrichment protocol, the high-resolution MALDI O-TOF mass spectra provided informative, reproducible peptide signatures. The raw mass spectra were analyzed and used to build discriminant disease models that were challenged with blinded samples for classification. CONCLUSIONS: Carrier-protein enrichment of disease biomarkers coupled with high-resolution mass spectrometry and discriminant pattern analysis is a powerful technology for diagnostics and population screening. The mass fingerprint model successfully classified blinded AD patient and control samples with high sensitivity and specificity.