Gordon Whiteley

Targeted Peptide Measurements in Biology and Medicine: Best Practices for Mass Spectrometry-based Assay Development Using a Fit-for-Purpose Approach

Adoption of targeted mass spectrometry (MS) approaches such as multiple reaction monitoring (MRM) to study biological and biomedical questions is well underway in the proteomics community. Successful application depends on the ability to generate reliable assays that uniquely and confidently identify target peptides in a sample. Unfortunately, there is a wide range of criteria being applied to say that an assay has been successfully developed. There is no consensus on what criteria are acceptable and little understanding of the impact of variable criteria on the quality of the results generated. Publications describing targeted MS assays for peptides frequently do not contain sufficient information for readers to establish confidence that the tests work as intended or to be able to apply the tests described in their own labs. Guidance must be developed so that targeted MS assays with established performance can be made widely distributed and applied by many labs worldwide. To begin to address the problems and their solutions, a workshop was held at the National Institutes of Health with representatives from the multiple communities developing and employing targeted MS assays. Participants discussed the analytical goals of their experiments and the experimental evidence needed to establish that the assays they develop work as intended and are achieving the required levels of performance. Using this “fit-for-purpose” approach, the group defined three tiers of assays distinguished by their performance and extent of analytical characterization. Computational and statistical tools useful for the analysis of targeted MS results were described. Participants also detailed the information that authors need to provide in their manuscripts to enable reviewers and readers to clearly understand what procedures were performed and to evaluate the reliability of the peptide or protein quantification measurements reported. This paper presents a summary of the meeting and recommendations.

Discovering Clinical Biomarkers of Ionizing Radiation Exposure with Serum Proteomic Analysis

In this study, we sought to explore the merit of proteomic profiling strategies in patients with cancer before and during radiotherapy in an effort to discover clinical biomarkers of radiation exposure. Patients with a diagnosis of cancer provided informed consent for enrollment on a study permitting the collection of serum immediately before and during a course of radiation therapy. High-resolution surface-enhanced laser desorption and ionization-time of flight (SELDI-TOF) mass spectrometry (MS) was used to generate high-throughput proteomic profiles of unfractionated serum samples using an immobilized metal ion-affinity chromatography nickel-affinity chip surface. Resultant proteomic profiles were analyzed for unique biomarker signatures using supervised classification techniques. MS-based protein identification was then done on pooled sera in an effort to begin to identify specific protein fragments that are altered with radiation exposure. Sixty-eight patients with a wide range of diagnoses and radiation treatment plans provided serum samples both before and during ionizing radiation exposure. Computer-based analyses of the SELDI protein spectra could distinguish unexposed from radiation-exposed patient samples with 91% to 100% sensitivity and 97% to 100% specificity using various classifier models. The method also showed an ability to distinguish high from low dose-volume levels of exposure with a sensitivity of 83% to 100% and specificity of 91% to 100%. Using direct identity techniques of albumin-bound peptides, known to underpin the SELDI-TOF fingerprints, 23 protein fragments/peptides were uniquely detected in the radiation exposure group, including an interleukin-6 precursor protein. The composition of proteins in serum seems to change with ionizing radiation exposure. Proteomic analysis for the discovery of clinical biomarkers of radiation exposure warrants further study.

Clinical proteomics and biomarker discovery.

Abstract: 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.

Serum proteomic analysis identifies a highly sensitive and specific discriminatory pattern in stage 1 breast cancer

BackgroundMass spectrometry (MS)-based profiling was used to determine whether ion fingerprints could distinguish women with stage 1 breast cancer from women without breast cancer.MethodsThe initial study population consisted of 310 subjects: 155 women with yearly negative breast examination and negative mammography findings for at least 4 years, and 155 women undergoing surgery for pathology-proven stage 1 invasive ductal carcinoma. High-resolution SELDI-TOF (surface-enhanced laser desorption ionization–time of flight) analysis was performed on serum obtained from blood samples collected before mammography in controls, and before surgery in patients with breast cancer. Samples were divided into a training (109 controls and 109 cancers) and blinded (46 controls and 46 cancers) testing set; each group had similar age demographics. In addition, an independent study set of 46 serum samples was analyzed 14 months after the initial study to validate the robustness of the classifier.ResultsA discriminatory profile consisting of seven ion peaks found in the training set, when applied to the blinded test set, achieved a sensitivity and specificity of 95.6% and 86.5%, respectively. This same seven-peak profile achieved a 96.5% sensitivity and 85.7% specificity, with correct identification of all of 17 T1a tumors when applied to the validation study set.ConclusionsMass spectrometry profiling of human serum generated a robust classifier composed of seven low-molecular-weight ions that yielded a highly sensitive and specific diagnostic procedure for the discrimination of women with stage 1 breast cancer compared with women without breast cancer in this research study set.

Proteomics in Clinical Trials and Practice Present Uses and Future Promise

The study of clinical proteomics is a promising new field that has the potential to have many applications, including the identification of biomarkers and monitoring of disease, especially in the field of oncology. Expression proteomics evaluates the cellular production of proteins encoded by a particular gene and exploits the differential expression and post-translational modifications of proteins between healthy and diseased states. These biomarkers may be applied towards early diagnosis, prognosis, and prediction of response to therapy. Functional proteomics seeks to decipher protein-protein interactions and biochemical pathways involved in disease biology and targeted by newer molecular therapeutics. Advanced spectrometry technologies and new protein array formats have improved these analyses and are now being applied prospectively in clinical trials. Further advancement of proteomics technology could usher in an era of personalized molecular medicine, where diseases are diagnosed at earlier stages and where therapies are more effective because they are tailored to the protein expression of a patient’s malignancy.

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.

Proteomics as a Tool for Biomarker Discovery

Novel technologies are now being advanced for the purpose of identification and validation of new disease biomarkers. A reliable and useful clinical biomarker must a) come from a readily attainable source, such as blood or urine, b) have sufficient sensitivity to correctly identify affected individuals, c) have sufficient specificity to avoid incorrect labeling of unaffected persons, and d) result in a notable benefit for the patient through intervention, such as survival or life quality improvement. Despite these critical descriptors, the few available FDA-approved biomarkers for cancer do not completely fit this definition and their benefits are limited to a small number of cancers. Ovarian cancer exemplifies the need for a diagnostic biomarker of early stage disease. Symptoms are present but not specific to the disease, delaying diagnosis until an advanced and generally incurable stage in over 70% of affected women. As such, diagnostic intervention in the form of oopherectomy can be performed in the appropriate at-risk population if identified such as with a new accurate, sensitive, and specific biomarker. If early stage disease is identified, the requirement for survival and life quality improvement will be met. One of the new technologies applied to biomarker discovery is tour-de-force analysis of serum peptides and proteins. Optimization of mass spectrometry techniques coupled with advanced bioinformatics approaches has yielded informative biomarker signatures discriminating presence of cancer from unaffected in multiple studies from different groups. Validation and randomized outcome studies are needed to determine the true value of these new biomarkers in early diagnosis, and improved survival and quality of life.

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.

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 Consortiums (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.