Restructuring proteomics through verification

Mass spectrometry-assisted gel-based proteomics in cancer biomarker discovery: approaches and application

There is a critical need for the discovery of novel biomarkers for early detection and targeted therapy of cancer, a major cause of deaths worldwide. In this respect, proteomic technologies, such as mass spectrometry (MS), enable the identification of pathologically significant proteins in various types of samples. MS is capable of high-throughput profiling of complex biological samples including blood, tissues, urine, milk, and cells. MS-assisted proteomics has contributed to the development of cancer biomarkers that may form the foundation for new clinical tests. It can also aid in elucidating the molecular mechanisms underlying cancer. In this review, we discuss MS principles and instrumentation as well as approaches in MS-based proteomics, which have been employed in the development of potential biomarkers. Furthermore, the challenges in validation of MS biomarkers for their use in clinical practice are also reviewed.

Clinical proteomics in cancer: Where we are

Proteomics has emerged as a promising field in the post-genomic era. Notwithstanding the great advances provided by gene expression analysis in cancer, the lack of a correlation between gene expression and protein levels has highlighted the need for a proteomic focus on cancer. Although the increasing knowledge regarding cancer biology, a reliable marker to improve diagnosis, prognosis and treatment for cancer patients is not a reality at present. In this review, we address the main considerations regarding proteomics-based studies and their clinical applications on cancer research, highlighting some considerations related to strengths and limitations of proteomics-based studies and its application to clinical practice.

The Role of Proteomics in Biomarker Development for Improved Patient Diagnosis and Clinical Decision Making in Prostate Cancer

Prostate Cancer (PCa) is the second most commonly diagnosed cancer in men worldwide. Although increased expression of prostate-specific antigen (PSA) is an effective indicator for the recurrence of PCa, its intended use as a screening marker for PCa is of considerable controversy. Recent research efforts in the field of PCa biomarkers have focused on the identification of tissue and fluid-based biomarkers that would be better able to stratify those individuals diagnosed with PCa who (i) might best receive no treatment (active surveillance of the disease); (ii) would benefit from existing treatments; or (iii) those who are likely to succumb to disease recurrence and/or have aggressive disease. The growing demand for better prostate cancer biomarkers has coincided with the development of improved discovery and evaluation technologies for multiplexed measurement of proteins in bio-fluids and tissues. This review aims to (i) provide an overview of these technologies as well as describe some of the candidate PCa protein biomarkers that have been discovered using them; (ii) address some of the general limitations in the clinical evaluation and validation of protein biomarkers; and (iii) make recommendations for strategies that could be adopted to improve the successful development of protein biomarkers to deliver improvements in personalized PCa patient decision making.

Targeted proteomics of solid cancers: from quantification of known biomarkers towards reading the digital proteome maps

The concept of personalized medicine includes novel protein biomarkers that are expected to improve the early detection, diagnosis and therapy monitoring of malignant diseases. Tissues, biofluids, cell lines and xenograft models are the common sources of biomarker candidates that require verification of clinical value in independent patient cohorts. Targeted proteomics - based on selected reaction monitoring, or data extraction from data-independent acquisition based digital maps - now represents a promising mass spectrometry alternative to immunochemical methods. To date, it has been successfully used in a high number of studies answering clinical questions on solid malignancies: breast, colorectal, prostate, ovarian, endometrial, pancreatic, hepatocellular, lung, bladder and others. It plays an important role in functional proteomic experiments that include studying the role of post-translational modifications in cancer progression. This review summarizes verified biomarker candidates successfully quantified by targeted proteomics in this field and directs the readers who plan to design their own hypothesis-driven experiments to appropriate sources of methods and knowledge.

Measurement of glycosylated alpha-fetoprotein improves diagnostic power over the native form in hepatocellular carcinoma

Serum alpha-fetoprotein (AFP) has long been used as a diagnostic marker for hepatocellular carcinoma (HCC), albeit controversially. Although it remains widely used in clinics, the value of AFP in HCC diagnosis has recently been challenged due to its significant rates of false positive and false negative findings. To improve the efficacy of AFP as HCC diagnostic marker, we developed a method of measuring total and glycosylated AFP by multiple reaction monitoring (MRM)-MS. In this study, we verified the total amount of AFP (nonglycopeptide levels) and the degree of glycosylated AFP (deglycopeptide levels) in 60 normal (41 men and 19 women; mean age 53 years; range 32–74 years), 35 LC (23 men and 12 women; mean age 56 years; range 43–78 years; HBV-related), and 60 HCC subjects (42 men and 18 women; mean age 58 years; range 38–76 years; HBV-related; 30 stage I, 15 stage II, and 10 stage III). By MRM-MS analysis, the nonglycopeptide had 56.7% sensitivity, 68.3% specificity, and an AUC of 0.687 [cutoff value: ≥0.02 (light/heavy ratio)], comparing the normal and HCC group, whereas the deglycopeptide had 93.3% sensitivity, 68.3% specificity, and an AUC of 0.859 [cutoff value: ≥0.02 (light/heavy ratio)]. In comparing the stage I HCC subgroup with the LC group, the nonglycopeptide had a sensitivity of 66.7%, specificity of 80.0%, and an AUC of 0.712 [cutoff value: ≥0.02 (light/heavy ratio)], whereas the deglycopeptide had a sensitivity of 96.7%, specificity of 80.0%, and an AUC of 0.918 [cutoff value: ≥0.02 (light/heavy ratio)]. These data demonstrate that the discriminatory power of the deglycopeptide is greater than that of the nonglycopeptide. We conclude that deglycopeptide can distinguish cancer status between normal subjects and HCC patients better than nonglycopeptide.

Quantitative analysis of aberrant protein glycosylation in liver cancer plasma by AAL-enrichment and MRM mass spectrometry

A lectin-coupled mass spectrometry (MS) approach was employed to quantitatively monitor aberrant protein glycosylation in liver cancer plasma. To do this, we compared the difference in the total protein abundance of a target glycoprotein between hepatocellular carcinoma (HCC) plasmas and hepatitis B virus (HBV) plasmas, as well as the difference in lectin-specific protein glycoform abundance of the target glycoprotein. Capturing the lectin-specific protein glycoforms from a plasma sample was accomplished by using a fucose-specific aleuria aurantia lectin (AAL) immobilized onto magnetic beads via a biotin-streptavidin conjugate. Following tryptic digestion of both the total plasma and its AAL-captured fraction of each HCC and HBV sample, targeted proteomic mass spectrometry was conducted quantitatively by a multiple reaction monitoring (MRM) technique. From the MRM-based analysis of the total plasmas and AAL-captured fractions, differences between HCC and HBV plasma groups in fucosylated glycoform levels of target glycoproteins were confirmed to arise from both the change in the total protein abundance of the target proteins and the change incurred by aberrant fucosylation on target glycoproteins in HCC plasma, even when no significant change occurs in the total protein abundance level. Combining the MRM-based analysis method with the lectin-capturing technique proved to be a successful means of quantitatively investigating aberrant protein glycosylation in cancer plasma samples. Additionally, it was elucidated that the differences between HCC and control groups in fucosylated biomarker candidates A1AT and FETUA mainly originated from an increase in fucosylation levels on these target glycoproteins, rather than an increase in the total protein abundance of the target glycoproteins.

Proteogenomic convergence for understanding cancer pathways and networks

During the past several decades, the understanding of cancer at the molecular level has been primarily focused on mechanisms on how signaling molecules transform homeostatically balanced cells into malignant ones within an individual pathway. However, it is becoming more apparent that pathways are dynamic and crosstalk at different control points of the signaling cascades, making the traditional linear signaling models inadequate to interpret complex biological systems. Recent technological advances in high throughput, deep sequencing for the human genomes and proteomic technologies to comprehensively characterize the human proteomes in conjunction with multiplexed targeted proteomic assays to measure panels of proteins involved in biologically relevant pathways have made significant progress in understanding cancer at the molecular level. It is undeniable that proteomic profiling of differentially expressed proteins under many perturbation conditions, or between normal and "diseased" states is important to capture a first glance at the overall proteomic landscape, which has been a main focus of proteomics research during the past 15-20 years. However, the research community is gradually shifting its heavy focus from that initial discovery step to protein target verification using multiplexed quantitative proteomic assays, capable of measuring changes in proteins and their interacting partners, isoforms, and post-translational modifications (PTMs) in response to stimuli in the context of signaling pathways and protein networks. With a critical link to genotypes (i.e., high throughput genomics and transcriptomics data), new and complementary information can be gleaned from multi-dimensional omics data to (1) assess the effect of genomic and transcriptomic aberrations on such complex molecular machinery in the context of cell signaling architectures associated with pathological diseases such as cancer (i.e., from genotype to proteotype to phenotype); and (2) target pathway- and network-driven changes and map the fluctuations of these functional units (proteins) responsible for cellular activities in response to perturbation in a spatiotemporal fashion to better understand cancer biology as a whole system.

Differential proteomic approach for identification and verification of aberrantly glycosylated proteins in adenocarcinoma lung cancer (ADLC) plasmas by lectin-capturing and targeted mass spectrometry

To investigate quantitative differences in aberrant glycosylation of target glycoproteins between noncancerous group and patient group with adenocarcinoma lung cancer (ADLC), differential proteomic approach was developed by cooperatively using comparative lectin-capturing, targeted mass spectrometry (MRM MS), and antibody/lectin sandwich ELISA. Plasma samples comparatively prepared from 3 ADLC patients and 3 controls, with and without lectin-fractionation using fucose-specific Aleuria aurantia lectin (AAL), were trypsin-digested and analyzed for target glycoproteins, alpha-1-acid glycoprotein (AGP) and ceruloplasmin (CP), by MRM MS. From the MRM MS data the abundance levels of AAL-captured glycoforms of both targets were significantly higher in ADLC cases compared to controls, although the levels in total protein abundance were comparable between ADLC and control groups. This difference between ADLC and control groups in the fucosylated glycoform levels was originated mainly from aberrant fucosylation on the targets in ADLC plasmas rather than change in total protein abundance of the targets, and also confirmed by sandwich ELISA. AGP and CP were further verified to be biomarker candidates by MRM-based analysis of AAL-captured plasmas (30 ADLC cases, 30 controls), with AUROC 0.758 and 0.847 respectively. This differential proteomic approach can be useful for identifying and verifying biomarker candidate involved in aberrant protein glycosylation.

BIOLOGICAL SIGNIFICANCE

The present paper introduces an efficient differential proteomic method to investigate quantitative differences in aberrant protein glycosylation of serological glycoproteins between noncancerous group and lung cancer patient group. This differential proteomic approach consisting of the targeted MRM MS of comparatively lectin-captured plasma fractions and the antibody/lectin sandwich ELISA-based assay was evaluated to be useful for identification of aberrantly fucosylated glycoproteins AGP and CP in lung cancer plasmas. In addition, we have demonstrated that the MRM MS-based differential proteomic approach is also useful for high-throughput verification of the aberrantly fucosylated glycoproteins AGP and CP using the large number of individual plasmas. Therefore, the present MRM MS-based differential proteomic strategy with lectin-capturing can be a powerful tool for high-throughput verification of aberrantly glycosylated biomarker candidates, identified preliminary by mass profiling experiments in proteomic fields but requiring further validation using a large number of cohorts.

Combining bioinformatics and MS-based proteomics: clinical implications

Clinical proteomics research aims at i) discovery of protein biomarkers for screening, diagnosis and prognosis of disease, ii) discovery of protein therapeutic targets for improvement of disease prevention, treatment and follow-up, and iii) development of mass spectrometry (MS)-based assays that could be implemented in clinical chemistry, microbiology or hematology laboratories. MS has been increasingly applied in clinical proteomics studies for the identification and quantification of proteins. Bioinformatics plays a key role in the exploitation of MS data in several aspects such as the generation and curation of protein sequence databases, the development of appropriate software for MS data treatment and integration with other omics data and the establishment of adequate standard files for data sharing. In this article, we discuss the main MS approaches and bioinformatics solutions that are currently applied to accomplish the objectives of clinical proteomic research.

Mass spectrometry in cancer biomarker research: a case for immunodepletion of abundant blood-derived proteins from clinical tissue specimens

The discovery of clinically relevant cancer biomarkers using mass spectrometry (MS)-based proteomics has proven difficult, primarily because of the enormous dynamic range of blood-derived protein concentrations and the fact that the 22 most abundant blood-derived proteins constitute approximately 99% of the total plasma protein mass. Immunodepletion of clinical body fluid specimens (e.g., serum/plasma) for the removal of highly abundant proteins is a reasonable and reproducible solution. Often overlooked, clinical tissue specimens also contain a formidable amount of highly abundant blood-derived proteins present in tissue-embedded networks of blood/lymph capillaries and interstitial fluid. Hence, the dynamic range impediment to biomarker discovery remains a formidable obstacle, regardless of clinical sample type (solid tissue and/or body fluid). Thus, we optimized and applied simultaneous immunodepletion of blood-derived proteins from solid tissue and peripheral blood, using clear cell renal cell carcinoma as a model disease. Integrative analysis of data from this approach and genomic data obtained from the same type of tumor revealed concordant key pathways and protein targets germane to clear cell renal cell carcinoma. This includes the activation of the lipogenic pathway characterized by increased expression of adipophilin (PLIN2) along with 'cadherin switching', a phenomenon indicative of transcriptional reprogramming linked to renal epithelial dedifferentiation. We also applied immunodepletion of abundant blood-derived proteins to various tissue types (e.g., adipose tissue and breast tissue) showing unambiguously that the removal of abundant blood-derived proteins represents a powerful tool for the reproducible profiling of tissue proteomes. Herein, we show that the removal of abundant blood-derived proteins from solid tissue specimens is of equal importance to depletion of body fluids and recommend its routine use in the context of biological discovery and/or cancer biomarker research. Finally, this perspective presents the background, rationale and strategy for using tissue-directed high-resolution/accuracy MS-based shotgun proteomics to detect genuine tumor proteins in the peripheral blood of a patient diagnosed with nonmetastatic cancer, employing concurrent liquid chromatography-MS analysis of immunodepleted clinical tissue and blood specimens.

Statistical design for biospecimen cohort size in proteomics-based biomarker discovery and verification studies

Protein biomarkers are needed to deepen our understanding of cancer biology and to improve our ability to diagnose, monitor, and treat cancers. Important analytical and clinical hurdles must be overcome to allow the most promising protein biomarker candidates to advance into clinical validation studies. Although contemporary proteomics technologies support the measurement of large numbers of proteins in individual clinical specimens, sample throughput remains comparatively low. This problem is amplified in typical clinical proteomics research studies, which routinely suffer from a lack of proper experimental design, resulting in analysis of too few biospecimens to achieve adequate statistical power at each stage of a biomarker pipeline. To address this critical shortcoming, a joint workshop was held by the National Cancer Institute (NCI), National Heart, Lung, and Blood Institute (NHLBI), and American Association for Clinical Chemistry (AACC) with participation from the U.S. Food and Drug Administration (FDA). An important output from the workshop was a statistical framework for the design of biomarker discovery and verification studies. Herein, we describe the use of quantitative clinical judgments to set statistical criteria for clinical relevance and the development of an approach to calculate biospecimen sample size for proteomic studies in discovery and verification stages prior to clinical validation stage. This represents a first step toward building a consensus on quantitative criteria for statistical design of proteomics biomarker discovery and verification research.

The current status of clinical proteomics and the use of MRM and MRM(3) for biomarker validation

The transfer of biomarkers from the discovery field to clinical use is still, despite progress, on a road filled with pitfalls. Since the emergence of proteomics, thousands of putative biomarkers have been published, often with overlapping diagnostic capacities. The strengthening of the robustness of discovery technologies, particularly in mass spectrometry, has been followed by intense discussions on establishing well-defined evaluation procedures for the identified targets to ultimately allow the clinical validation and then the clinical use of some of these biomarkers. Some of the obstacles to the evaluation process have been the lack of the availability of quick and easy-to-develop, easy-to-use, robust, specific and sensitive alternative quantitative methods when immunoaffinity-based tests are unavailable. Multiple reaction monitoring (MRM; also called selected reaction monitoring) is currently proving its capabilities as a complementary or alternative technique to ELISA for large biomarker panel evaluation. Here, we present how MRM(3) can overcome the lack of specificity and sensitivity often encountered by MRM when tracking minor proteins diluted by complex biological matrices.

A lectin-coupled, targeted proteomic mass spectrometry (MRM MS) platform for identification of multiple liver cancer biomarkers in human plasma

Aberrantly glycosylated proteins related to liver cancer progression were captured with specific lectin and identified from human plasma by multiple reaction monitoring (MRM) mass spectrometry as multiple biomarkers for hepatocellular carcinoma (HCC). The lectin fractionation for fucosylated protein glycoforms in human plasma was conducted with a fucose-specific aleuria aurantia lectin (AAL). Following tryptic digestion of the lectin-captured fraction, plasma samples from 30 control cases (including 10 healthy, 10 hepatitis B virus [HBV], and 10 cirrhosis cases) and 10 HCC cases were quantitatively analyzed by MRM to identify which glycoproteins are viable HCC biomarkers. A1AG1, AACT, A1AT, and CERU were found to be potent biomarkers to differentiate HCC plasma from control plasmas. The AUROC generated independently from these four biomarker candidates ranged from 0.73 to 0.92. However, the lectin-coupled MRM assay with multiple combinations of biomarker candidates is superior statistically to those generated from the individual candidates with AUROC more than 0.95, which can be an alternative to the immunoassay inevitably requiring tedious development of multiple antibodies against biomarker candidates to be verified. Eventually the lectin-coupled, targeted proteomic mass spectrometry (MRM MS) platform was found to be efficient to identify multiple biomarkers from human plasma according to cancer progression.

A targeted proteomics-based pipeline for verification of biomarkers in plasma

High-throughput technologies can now identify hundreds of candidate protein biomarkers for any disease with relative ease. However, because there are no assays for the majority of proteins and de novo immunoassay development is prohibitively expensive, few candidate biomarkers are tested in clinical studies. We tested whether the analytical performance of a biomarker identification pipeline based on targeted mass spectrometry would be sufficient for data-dependent prioritization of candidate biomarkers, de novo development of assays and multiplexed biomarker verification. We used a data-dependent triage process to prioritize a subset of putative plasma biomarkers from >1,000 candidates previously identified using a mouse model of breast cancer. Eighty-eight novel quantitative assays based on selected reaction monitoring mass spectrometry were developed, multiplexed and evaluated in 80 plasma samples. Thirty-six proteins were verified as being elevated in the plasma of tumor-bearing animals. The analytical performance of this pipeline suggests that it should support the use of an analogous approach with human samples.