An improved quantitative mass spectrometry analysis of tumor specific mutant proteins at high sensitivity

Mass spectrometry-based targeted proteomics for analysis of protein mutations

Cancers are caused by accumulated DNA mutations. This recognition of the central role of mutations in cancer and recent advances in next-generation sequencing, has initiated the massive screening of clinical samples and the identification of 1000s of cancer-associated gene mutations. However, proteomic analysis of the expressed mutation products lags far behind genomic (transcriptomic) analysis. With comprehensive global proteomics analysis, only a small percentage of single nucleotide variants detected by DNA and RNA sequencing have been observed as single amino acid variants due to current technical limitations. Proteomic analysis of mutations is important with the potential to advance cancer biomarker development and the discovery of new therapeutic targets for more effective disease treatment. Targeted proteomics using selected reaction monitoring (also known as multiple reaction monitoring) and parallel reaction monitoring, has emerged as a powerful tool with significant advantages over global proteomics for analysis of protein mutations in terms of detection sensitivity, quantitation accuracy and overall practicality (e.g., reliable identification and the scale of quantification). Herein we review recent advances in the targeted proteomics technology for enhancing detection sensitivity and multiplexing capability and highlight its broad biomedical applications for analysis of protein mutations in human bodily fluids, tissues, and cell lines. Furthermore, we review recent applications of top-down proteomics for analysis of protein mutations. Unlike the commonly used bottom-up proteomics which requires digestion of proteins into peptides, top-down proteomics directly analyzes intact proteins for more precise characterization of mutation isoforms. Finally, general perspectives on the potential of achieving both high sensitivity and high sample throughput for large-scale targeted detection and quantification of important protein mutations are discussed.

Mass spectrometric detection of KRAS protein mutations using molecular imprinting

Cancer is a disease of cellular evolution where single base changes in the genetic code can have significant impact on the translation of proteins and their activity. Thus, in cancer research there is significant interest in methods that can determine mutations and identify the significant binding sites (epitopes) of antibodies to proteins in order to develop novel therapies. Nano molecularly imprinted polymers (nanoMIPs) provide an alternative to antibodies as reagents capable of specifically capturing target molecules depending on their structure. In this study, we used nanoMIPs to capture KRAS, a critical oncogene, to identify mutations which when present are indicative of oncological progress. Herein, coupling nanoMIPs (capture) and liquid chromatography-mass spectrometry (detection), LC-MS has allowed us to investigate mutational assignment and epitope discovery. Specifically, we have shown epitope discovery by generating nanoMIPs to a recombinant KRAS protein and identifying three regions of the protein which have been previously assigned as epitopes using much more time-consuming protocols. The mutation status of the released tryptic peptide was identified by LC-MS following capture of the conserved region of KRAS using nanoMIPS, which were tryptically digested, thus releasing the sequence of a non-conserved (mutated) region. This approach was tested in cell lines where we showed the effective genotyping of a KRAS cell line and in the plasma of cancer patients, thus demonstrating its ability to diagnose precisely the mutational status of a patient. This work provides a clear line-of-sight for the use of nanoMIPs to its translation from research into diagnostic and clinical utility.

Personalized Proteome: Comparing Proteogenomics and Open Variant Search Approaches for Single Amino Acid Variant Detection

Discovery of variant peptides such as a single amino acid variant (SAAV) in shotgun proteomics data is essential for personalized proteomics. Both the resolution of shotgun proteomics methods and the search engines have improved dramatically, allowing for confident identification of SAAV peptides. However, it is not yet known if these methods are truly successful in accurately identifying SAAV peptides without prior genomic information in the search database. We studied this in unprecedented detail by exploiting publicly available long-read RNA sequences and shotgun proteomics data from the gold standard reference cell line NA12878. Searching spectra from this cell line with the state-of-the-art open modification search engine ionbot against carefully curated search databases resulted in 96.7% false-positive SAAVs and an 85% lower true positive rate than searching with peptide search databases that incorporate prior genetic information. While adding genetic variants to the search database remains indispensable for correct peptide identification, inclusion of long-read RNA sequences in the search database contributes only 0.3% new peptide identifications. These findings reveal the differences in SAAV detection that result from various approaches, providing guidance to researchers studying SAAV peptides and developers of peptide spectrum identification tools.

Biological Applications for LC-MS-Based Proteomics

Since its inception, liquid chromatography-mass spectrometry (LC-MS) has been continuously improved upon in many aspects, including instrument capabilities, sensitivity, and resolution. Moreover, the costs to purchase and operate mass spectrometers and liquid chromatography systems have decreased, thus increasing affordability and availability in sectors outside of academic and industrial research. Processing power has also grown immensely, cutting the time required to analyze samples, allowing more data to be feasibly processed, and allowing for standardized processing pipelines. As a result, proteomics via LC-MS has become popular in many areas of biological sciences, forging an important seat for itself in targeted and untargeted assays, pure and applied science, the laboratory, and the clinic. In this chapter, many of these applications of LC-MS-based proteomics and an outline of how they can be executed will be covered. Since the field of personalized medicine has matured alongside proteomics, it has also come to rely on various mass spectrometry methods and will be elaborated upon as well. As time goes on and mass spectrometry evolves, there is no doubt that its presence in these areas, and others, will only continue to grow.

Multiplex assay for multiomics advances in personalized-precision medicine

Building the future of precision medicine is the main focus in cancer domain. Clinical trials are moving toward an array of studies that are more adapted to precision medicine. In this domain, there is an enhanced need for biomarkers, monitoring devices, and data-analysis methods. Omics profiling using whole genome, epigenome, transcriptome, proteome, and metabolome can offer detailed information of the human body in an integrative manner. Omes profiles reflect more accurately real-time physiological status. Personalized omics analyses both disease as a whole and the main disease processes, for a better understanding of the individualized health. Through this, multi-omic approaches for health monitoring, preventative medicine, and personalized treatment can be targeted simultaneously and can lead clinicians to have a comprehensive view on the diseasome.

Natural organic matter contained in clay rock pore water: Direct quantification at the molecular level using electrospray ionization mass spectrometry

RATIONALE

Natural organic matter (NOM) is present in the environment and could influence the migration of heavy metals/radionuclides. The dissolved fraction of NOM (DOM) is usually quantified using total organic carbon analysis or UV-visible spectrometry. Nonetheless, analysis using pattern recognition cannot provide the full spectrum of organic molecules contained in waters, especially low-molecular-weight compounds. In the context of nuclear performance assessment studies, ground waters may contain DOM and a key aspect is to quantify different categories of NOM types in order to further evaluate the transport and fate of radionuclides in the environment.

METHODS

Thus, a method for the quantification of DOM at the molecular level was developed, based on electrospray ionization mass spectrometry (ESI-MS). This method simultaneously gives structural information on DOM and the individual concentrations of these low-molecular-weight compounds without pretreatment and/or preconcentration of the samples.

RESULTS

Several methods of quantification (internal calibration, calibrated addition of external standard, sequential tandem mass spectrometry) have been optimized and successfully applied to real natural samples. They are discussed in this paper with a focus on acidic compounds, which are the compounds that most probably could influence the migration of heavy metals and radionuclides in the clay rock pore water from the French Callovo-Oxfordian (COx) nuclear repository site.

CONCLUSIONS

Quantification of in situ dissolved NOM from the COx has been performed using ESI-MS. For the first time to our knowledge, it was possible to give a quite exhaustive and quantitative inventory of the small organic compounds present without proceeding to any chemical treatment or sample crushing and for naturally occurring concentrations.

Personalized medicine-a modern approach for the diagnosis and management of hypertension

The main goal of treating hypertension is to reduce blood pressure to physiological levels and thereby prevent risk of cardiovascular disease and hypertension-associated target organ damage. Despite reductions in major risk factors and the availability of a plethora of effective antihypertensive drugs, the control of blood pressure to target values is still poor due to multiple factors including apparent drug resistance and lack of adherence. An explanation for this problem is related to the current reductionist and ‘trial-and-error’ approach in the management of hypertension, as we may oversimplify the complex nature of the disease and not pay enough attention to the heterogeneity of the pathophysiology and clinical presentation of the disorder. Taking into account specific risk factors, genetic phenotype, pharmacokinetic characteristics, and other particular features unique to each patient, would allow a personalized approach to managing the disease. Personalized medicine therefore represents the tailoring of medical approach and treatment to the individual characteristics of each patient and is expected to become the paradigm of future healthcare. The advancement of systems biology research and the rapid development of high-throughput technologies, as well as the characterization of different –omics, have contributed to a shift in modern biological and medical research from traditional hypothesis-driven designs toward data-driven studies and have facilitated the evolution of personalized or precision medicine for chronic diseases such as hypertension.

Variant peptide detection utilizing mass spectrometry: laying the foundations for proteogenomic identification and validation

BACKGROUND

Proteogenomics is an emerging field at the intersection of genomics and proteomics. Many variant peptides corresponding to single nucleotide variations (SNVs) are associated with specific diseases. The aim of this study was to demonstrate the feasibility of proteogenomic-based variant peptide detection in disease models and clinical specimens.

METHODS

We sought to detect p53 single amino acid variant (SAAV) peptides in breast cancer tumor samples that have been previously subjected to sequencing analysis. Initially, two cancer cell lines having a cellular tumor antigen p53 (TP53) mutation and one wild type for TP53 were analyzed by selected reaction monitoring (SRM) assays as controls. One pool of wild type and one pool of mutated for TP53 cytosolic extracts were assayed with a shotgun proteogenomic workflow. Furthermore, 18 individual samples having a mutation in TP53 were assayed by SRM.

RESULTS

Two mutant p53 peptides were successfully detected in two cancer cell lines as expected from their DNA sequence. Wild type p53 peptides were detected in both cytosolic pools, however, none of the mutant p53 peptides were identified. Mutations at the protein level were detected in two cytosolic extracts and whole tumor lysates from the same patients by SRM analysis. Six thousand and six hundred and twenty eight non-redundant proteins were identified in the two cytosolic pools, thus greatly improving a previously reported cytosolic proteome.

CONCLUSIONS

In the current study we show the great potential of using proteogenomics for the direct identification of cancer-associated mutations in clinical samples and we discuss current limitations and future perspectives.

Quantification of mutant SPOP proteins in prostate cancer using mass spectrometry-based targeted proteomics

BACKGROUND

Speckle-type POZ protein (SPOP) is an E3 ubiquitin ligase adaptor protein that functions as a potential tumor suppressor, and SPOP mutations have been identified in ~10% of human prostate cancers. However, it remains unclear if mutant SPOP proteins can be utilized as biomarkers for early detection, diagnosis, prognosis or targeted therapy of prostate cancer. Moreover, the SPOP mutation sites are distributed in a relatively short region with multiple lysine residues, posing significant challenges for bottom-up proteomics analysis of the SPOP mutations.

METHODS

To address this issue, PRISM (high-pressure, high-resolution separations coupled with intelligent selection and multiplexing)-SRM (selected reaction monitoring) mass spectrometry assays have been developed for quantifying wild-type SPOP protein and 11 prostate cancer-derived SPOP mutations.

RESULTS

Despite inherent limitations due to amino acid sequence constraints, all the PRISM-SRM assays developed using Arg-C digestion showed a linear dynamic range of at least two orders of magnitude, with limits of quantification ranged from 0.1 to 1 fmol/μg of total protein in the cell lysate. Applying these SRM assays to analyze HEK293T cells with and without expression of the three most frequent SPOP mutations in prostate cancer (Y87N, F102C or F133V) led to confident detection of all three SPOP mutations in corresponding positive cell lines but not in the negative cell lines. Expression of the F133V mutation and wild-type SPOP was at much lower levels compared to that of F102C and Y87N mutations; however, at present, it is unknown if this also affects the biological activity of the SPOP protein.

CONCLUSIONS

In summary, PRISM-SRM enables multiplexed, isoform-specific detection of mutant SPOP proteins in cell lysates, providing significant potential in biomarker development for prostate cancer.

Advances in targeted proteomics and applications to biomedical research

Targeted proteomics technique has emerged as a powerful protein quantification tool in systems biology, biomedical research, and increasing for clinical applications. The most widely used targeted proteomics approach, selected reaction monitoring (SRM), also known as multiple reaction monitoring (MRM), can be used for quantification of cellular signaling networks and preclinical verification of candidate protein biomarkers. As an extension to our previous review on advances in SRM sensitivity (Shi et al., Proteomics, 12, 1074-1092, 2012) herein we review recent advances in the method and technology for further enhancing SRM sensitivity (from 2012 to present), and highlighting its broad biomedical applications in human bodily fluids, tissue and cell lines. Furthermore, we also review two recently introduced targeted proteomics approaches, parallel reaction monitoring (PRM) and data-independent acquisition (DIA) with targeted data extraction on fast scanning high-resolution accurate-mass (HR/AM) instruments. Such HR/AM targeted quantification with monitoring all target product ions addresses SRM limitations effectively in specificity and multiplexing; whereas when compared to SRM, PRM and DIA are still in the infancy with a limited number of applications. Thus, for HR/AM targeted quantification we focus our discussion on method development, data processing and analysis, and its advantages and limitations in targeted proteomics. Finally, general perspectives on the potential of achieving both high sensitivity and high sample throughput for large-scale quantification of hundreds of target proteins are discussed.

Proteogenomics for understanding oncology: recent advances and future prospects

The concept of proteogenomics has emerged rapidly as a valuable approach to integrate mass spectrometry-derived proteomic data with genomic and transcriptomic data. It is used to harness the full potential of the former dataset in the discovery of potential biomarkers, therapeutic targets and novel proteins associated with various biological processes including diseases. Proteogenomic strategies have been successfully utilized to identify novel genes and redefine annotation of existing gene models in various genomes. In recent years, this approach has been extended to the field of cancer biology to unravel complexities in the tumor genomes and proteomes. Standard proteomics workflows employing translated cancer genomes and transcriptomes can potentially identify peptides from mutant proteins, splice variants and fusion proteins in the tumor proteome, which in addition to the currently available biomarker panels can serve as potential diagnostic and prognostic biomarkers, besides having therapeutic utility. This review focuses on the role of proteogenomics to understand cancer biology.

The clinical impact of recent advances in LC-MS for cancer biomarker discovery and verification

Mass spectrometry (MS) -based proteomics has become an indispensable tool with broad applications in systems biology and biomedical research. With recent advances in liquid chromatography (LC) and MS instrumentation, LC-MS is making increasingly significant contributions to clinical applications, especially in the area of cancer biomarker discovery and verification. To overcome challenges associated with analyses of clinical samples (for example, a wide dynamic range of protein concentrations in bodily fluids and the need to perform high throughput and accurate quantification of candidate biomarker proteins), significant efforts have been devoted to improve the overall performance of LC-MS-based clinical proteomics platforms. Reviewed here are the recent advances in LC-MS and its applications in cancer biomarker discovery and quantification, along with the potentials, limitations and future perspectives.

Absolute Quantification of Endogenous Ras Isoform Abundance

Ras proteins are important signalling hubs situated near the top of networks controlling cell proliferation, differentiation and survival. Three almost identical isoforms, HRAS, KRAS and NRAS, are ubiquitously expressed yet have differing biological and oncogenic properties. In order to help understand the relative biological contributions of each isoform we have optimised a quantitative proteomics method for accurately measuring Ras isoform protein copy number per cell. The use of isotopic protein standards together with selected reaction monitoring for diagnostic peptides is sensitive, robust and suitable for application to sub-milligram quantities of lysates. We find that in a panel of isogenic SW48 colorectal cancer cells, endogenous Ras proteins are highly abundant with ≥260,000 total Ras protein copies per cell and the rank order of isoform abundance is KRAS>NRAS≥HRAS. A subset of oncogenic KRAS mutants exhibit increased total cellular Ras abundance and altered the ratio of mutant versus wild type KRAS protein. These data and methodology are significant because Ras protein copy number is required to parameterise models of signalling networks and informs interpretation of isoform-specific Ras functional data.

Screening protein isoforms predictive for cancer using immunoaffinity capture and fast LC-MS in PRM mode

PURPOSE

We report an immunocapture strategy to extract proteins known to harbor driver mutations for a defined cancer type before the simultaneous assessment of their mutational status by MS. Such a method bypasses the sensitivity and selectivity issues encountered during the analysis of unfractionated complex biological samples.

EXPERIMENTAL DESIGN

Fast LC separations using short nanobore columns hyphenated with a high-resolution quadrupole-orbitrap mass spectrometer have been devised to take advantage of fast MS cycle times in conjunction with sharp chromatographic peak widths to accelerate the sample analysis throughput. Such an analytical platform is well suited to analyze simple protein mixtures obtained after immunoaffinity enrichment.

RESULTS

After establishing the technical performance of the platform, the method was applied to the quantitative profiling of cellular Ras and EGFR protein isoforms, as well as serum amyloid A isoforms in plasma.

CONCLUSIONS AND CLINICAL RELEVANCE

Immunoaffinity purification combined with fast LC-MS detection for the detection of driver mutations in tissue and tumor biomarkers in plasma samples can assist clinicians to select an optimal therapeutic intervention for patients.

Quantitative mass spectrometry for colorectal cancer proteomics

This review documents the uses of quantitative MS applied to colorectal cancer (CRC) proteomics for biomarker discovery and molecular pathway profiling. Investigators are adopting various labeling and label-free MS approaches to quantitate differential protein levels in cells, tumors, and plasma/serum. We comprehensively review recent uses of this technology to examine mouse models of CRC, CRC cell lines, their secretomes and subcellular fractions, CRC tumors, CRC patient plasma/serum, and stool samples. For biomarker discovery these approaches are uncovering proteins with potential diagnostic and prognostic utility, while in vitro cell culture experiments are characterizing proteomic and phosphoproteomic responses to disrupted signaling pathways due to mutations or to inhibition of drugable enzymes.

Proteomic approaches in biomarker discovery: new perspectives in cancer diagnostics

Despite remarkable progress in proteomic methods, including improved detection limits and sensitivity, these methods have not yet been established in routine clinical practice. The main limitations, which prevent their integration into clinics, are high cost of equipment, the need for highly trained personnel, and last, but not least, the establishment of reliable and accurate protein biomarkers or panels of protein biomarkers for detection of neoplasms. Furthermore, the complexity and heterogeneity of most solid tumours present obstacles in the discovery of specific protein signatures, which could be used for early detection of cancers, for prediction of disease outcome, and for determining the response to specific therapies. However, cancer proteome, as the end-point of pathological processes that underlie cancer development and progression, could represent an important source for the discovery of new biomarkers and molecular targets for tailored therapies.

Large-scale mass spectrometric detection of variant peptides resulting from nonsynonymous nucleotide differences

Each individual carries thousands of nonsynonymous single nucleotide variants (nsSNVs) in their genome, each corresponding to a single amino acid polymorphism (SAP) in the encoded proteins. It is important to be able to directly detect and quantify these variations at the protein level to study post-transcriptional regulation, differential allelic expression, and other important biological processes. However, such variant peptides are not generally detected in standard proteomic analyses due to their absence from the generic databases that are employed for mass spectrometry searching. Here we extend previous work that demonstrated the use of customized SAP databases constructed from sample-matched RNA-Seq data. We collected deep-coverage RNA-Seq data from the Jurkat cell line, compiled the set of nsSNVs that are expressed, used this information to construct a customized SAP database, and searched it against deep-coverage shotgun MS data obtained from the same sample. This approach enabled the detection of 421 SAP peptides mapping to 395 nsSNVs. We compared these peptides to peptides identified from a large generic search database containing all known nsSNVs (dbSNP) and found that more than 70% of the SAP peptides from this dbSNP-derived search were not supported by the RNA-Seq data and thus are likely false positives. Next, we increased the SAP coverage from the RNA-Seq derived database by utilizing multiple protease digestions, thereby increasing variant detection to 695 SAP peptides mapping to 504 nsSNV sites. These detected SAP peptides corresponded to moderate to high abundance transcripts (30+ transcripts per million, TPM). The SAP peptides included 192 allelic pairs; the relative expression levels of the two alleles were evaluated for 51 of those pairs and were found to be comparable in all cases.

Promise of personalized omics to precision medicine

The rapid development of high-throughput technologies and computational frameworks enables the examination of biological systems in unprecedented detail. The ability to study biological phenomena at omics levels in turn is expected to lead to significant advances in personalized and precision medicine. Patients can be treated according to their own molecular characteristics. Individual omes as well as the integrated profiles of multiple omes, such as the genome, the epigenome, the transcriptome, the proteome, the metabolome, the antibodyome, and other omics information are expected to be valuable for health monitoring, preventative measures, and precision medicine. Moreover, omics technologies have the potential to transform medicine from traditional symptom-oriented diagnosis and treatment of diseases toward disease prevention and early diagnostics. We discuss here the advances and challenges in systems biology-powered personalized medicine at its current stage, as well as a prospective view of future personalized health care at the end of this review.