Mutant proteins as cancer-specific biomarkers

Mass Spectrometry-Based Proteomics for Next-Generation Precision Oncology

Cancer is the leading cause of death worldwide characterized by patient heterogeneity and complex tumor microenvironment. While the genomics-based testing has transformed modern medicine, the challenge of diverse clinical outcomes highlights unmet needs for precision oncology. As functional molecules regulating cellular processes, proteins hold great promise as biomarkers and drug targets. Mass spectrometry (MS)-based clinical proteomics has illuminated the molecular features of cancers and facilitated discovery of biomarkers or therapeutic targets, paving the way for innovative strategies that enhance the precision of personalized treatment. In this article, we introduced the tools and current achievements of MS-based proteomics, choice of discovery and targeted MS from discovery to validation phases, profiling sensitivity from bulk samples to single-cell level and tissue to liquid biopsy specimens, current regulatory landscape of MS-based protein laboratory-developed tests (LDTs). The challenges, success and future perspectives in translating research MS assay into clinical applications are also discussed. With well-designed validation studies to demonstrate clinical benefits and meet the regulatory requirements for both analytical and clinical performance, the future of MS-based assays is promising with numerous opportunities to improve cancer diagnosis, treatment, and monitoring.

MMRT: MultiMut Recursive Tree for predicting functional effects of high-order protein variants from low-order variants

Protein sequences primarily determine their stability and functions. Mutations may occur at one, two, or three positions at the same time (low-order variants) or at multiple positions simultaneously (high-order variants), which affect protein functions. So far, low-order variants, such as single variants, double variants, and triple variants, have been well-studied through high-throughput experimental scanning techniques and computational prediction methods. However, research on high-order variants remains limited because of the difficulty of scanning an exponentially large number of potential variant combinations. Nonetheless, studying higher-order variants is crucial for understanding the pathogenesis of complex diseases, advancing protein engineering, and driving precision medicine. In this work, we introduce a novel deep learning model, namely MultiMut Recursive Tree (MMRT), to address this challenge of predicting the functional effects of high-order variants. MMRT integrates deep learning with a recursive tree framework to leverage the information from low-order variants to predict functional effects of high-order variants. We evaluated MMRT on datasets comprising 685,593 high-order variants. Our results (mean Spearman's correlation coefficient 0.55) demonstrated that MMRT outperformed three existing state-of-the-art methods: ESM (evolutionary scale modeling), DeepSequence, and ECNet (evolutionary context-integrated neural network). MMRT thus provides more accurate prediction of the functional effects of high-order protein variants, offering great potential for aiding the interpretation of variants in human disease studies.

Fast, sensitive detection of protein homologs using deep dense retrieval

The identification of protein homologs in large databases using conventional methods, such as protein sequence comparison, often misses remote homologs. Here, we offer an ultrafast, highly sensitive method, dense homolog retriever (DHR), for detecting homologs on the basis of a protein language model and dense retrieval techniques. Its dual-encoder architecture generates different embeddings for the same protein sequence and easily locates homologs by comparing these representations. Its alignment-free nature improves speed and the protein language model incorporates rich evolutionary and structural information within DHR embeddings. DHR achieves a >10% increase in sensitivity compared to previous methods and a >56% increase in sensitivity at the superfamily level for samples that are challenging to identify using alignment-based approaches. It is up to 22 times faster than traditional methods such as PSI-BLAST and DIAMOND and up to 28,700 times faster than HMMER. The new remote homologs exclusively found by DHR are useful for revealing connections between well-characterized proteins and improving our knowledge of protein evolution, structure and function.

Thermodynamic coupling of the tandem RRM domains of hnRNP A1 underlie its pleiotropic RNA binding functions

The functional properties of RNA binding proteins (RBPs) require allosteric regulation through interdomain communication. Despite the importance of allostery to biological regulation, only a few studies have been conducted to describe the biophysical nature by which interdomain communication manifests in RBPs. Here, we show for hnRNP A1 that interdomain communication is vital for the unique stability of its amino-terminal domain, which consists of two RNA recognition motifs (RRMs). These RRMs exhibit drastically different stability under pressure. RRM2 unfolds as an individual domain but remains stable when appended to RRM1. Variants that disrupt interdomain communication between the tandem RRMs show a significant decrease in stability. Carrying these mutations over to the full-length protein for in vivo experiments revealed that the mutations affected the ability of the disordered carboxyl-terminal domain to engage in protein-protein interactions and influenced the protein's RNA binding capacity. Collectively, this work reveals that thermodynamic coupling between the tandem RRMs of hnRNP A1 accounts for its allosteric regulatory functions.

Applicability of selected reaction monitoring for precise screening tests

INTRODUCTION

The proactive identification of diseases through screening tests has long been endorsed as a means to preempt symptomatic onset. However, such screening endeavors are fraught with complications, such as diagnostic inaccuracies, procedural risks, and patient unease during examinations. These challenges are amplified when screenings for multiple diseases are administered concurrently. Selected Reaction Monitoring (SRM) offers a unique advantage, allowing for the high-throughput quantification of hundreds of analytes with minimal interferences.

AREAS COVERED

Our research posits that SRM-based assays, traditionally tailored for single-disease biomarker profiling, can be repurposed for multi-disease screening. This innovative approach has the potential to substantially alleviate time, labor, and cost demands on healthcare systems and patients alike. Nonetheless, there are formidable methodological hurdles to overcome. These include difficulties in detecting low-abundance proteins and the risk of model overfitting due to the multiple functionalities of single proteins across different disease spectrums - issues especially pertinent in blood-based assays where detection sensitivity is constrained. As we move forward, technological strides in sample preparation, online extraction, throughput, and automation are expected to ameliorate these limitations.

EXPERT OPINION

The maturation of mass spectrometry's integration into clinical laboratories appears imminent, positioning it as an invaluable asset for delivering highly sensitive, reproducible, and precise diagnostic results.

Detection and Quantitation of Endogenous Membrane-Bound RAS Proteins and KRAS Mutants in Cancer Cell Lines Using 1D-SDS-PAGE LC-MS2

In healthy cells, membrane-anchored wild-type RAS proteins (i.e., HRAS, KRAS4A, KRAS4B, and NRAS) regulate critical cellular processes (e.g., proliferation, differentiation, survival). When mutated, RAS proteins are principal oncogenic drivers in approximately 30% of all human cancers. Among them, KRAS mutants are found in nearly 80% of all patients diagnosed with RAS-driven malignancies and are regarded as high-priority anti-cancer drug targets. Due to the lack of highly qualified/specific RAS isoform and mutant RAS monoclonal antibodies, there is a vital need for an effective antibody-free approach capable of identifying and quantifying membrane-bound RAS proteins in isoform- and mutation-specific manner. Here, we describe the development of a simple antibody-free protocol that relies on ultracentrifugation to isolate the membrane fraction coupled with single-dimensional (1D) sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) to fractionate and enrich membrane-bound endogenous RAS isoforms. Next, bottom-up proteomics that utilizes in-gel digestion followed by reversed-phase high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS2) is used for detection and relative quantitation of all wild-type RAS proteins (i.e., HRAS, KRAS4A, KRAS4B, and NRAS) and corresponding RAS mutants (e.g., G12D, G13D, G12S, G12V). Notably, this simple 1D-SDS-PAGE-HPLC-MS2-based protocol can be automated and widely applied to multiple cancer cell lines to investigate concentration changes in membrane-bound endogenous RAS proteins and corresponding mutants in the context of drug discovery.

Identification and verification of plasma protein biomarkers that accurately identify an ectopic pregnancy

BACKGROUND

Differentiating between a normal intrauterine pregnancy (IUP) and abnormal conditions including early pregnancy loss (EPL) or ectopic pregnancy (EP) is a major clinical challenge in early pregnancy. Currently, serial β-human chorionic gonadotropin (β-hCG) and progesterone are the most commonly used plasma biomarkers for evaluating pregnancy prognosis when ultrasound is inconclusive. However, neither biomarker can predict an EP with sufficient and reproducible accuracy. Hence, identification of new plasma biomarkers that can accurately diagnose EP would have great clinical value.

METHODS

Plasma was collected from a discovery cohort of 48 consenting women having an IUP, EPL, or EP. Samples were analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS) followed by a label-free proteomics analysis to identify significant changes between pregnancy outcomes. A panel of 14 candidate biomarkers were then verified in an independent cohort of 74 women using absolute quantitation by targeted parallel reaction monitoring mass spectrometry (PRM-MS) which provided the capacity to distinguish between closely related protein isoforms. Logistic regression and Lasso feature selection were used to evaluate the performance of individual biomarkers and panels of multiple biomarkers to predict EP.

RESULTS

A total of 1391 proteins were identified in an unbiased plasma proteome discovery. A number of significant changes (FDR ≤ 5%) were identified when comparing EP vs. non-EP (IUP + EPL). Next, 14 candidate biomarkers (ADAM12, CGA, CGB, ISM2, NOTUM, PAEP, PAPPA, PSG1, PSG2, PSG3, PSG9, PSG11, PSG6/9, and PSG8/1) were verified as being significantly different between EP and non-EP in an independent cohort (FDR ≤ 5%). Using logistic regression models, a risk score for EP was calculated for each subject, and four multiple biomarker logistic models were identified that performed similarly and had higher AUCs than models with single predictors.

CONCLUSIONS

Overall, four multivariable logistic models were identified that had significantly better prediction of having EP than those logistic models with single biomarkers. Model 4 (NOTUM, PAEP, PAPPA, ADAM12) had the highest AUC (0.987) and accuracy (96%). However, because the models are statistically similar, all markers in the four models and other highly correlated markers should be considered in further validation studies.

Dietary Oncopharmacognosy as a Crosswalk between Precision Oncology and Precision Nutrition

While diet and nutrition are modifiable risk factors for many chronic and infectious diseases, their role in cancer prevention and control remains under investigation. The lack of clarity of some diet-cancer relationships reflects the ongoing debate about the relative contribution of genetic factors, environmental exposures, and replicative errors in stem cell division as determinate drivers of cancer risk. In addition, dietary guidance has often been based upon research assuming that the effects of diet and nutrition on carcinogenesis would be uniform across populations and for various tumor types arising in a specific organ, i.e., that one size fits all. Herein, we present a paradigm for investigating precision dietary patterns that leverages the approaches that led to successful small-molecule inhibitors in cancer treatment, namely understanding the pharmacokinetics and pharmacodynamics of small molecules for targeting carcinogenic mechanisms. We challenge the scientific community to refine the paradigm presented and to conduct proof-in-concept experiments that integrate existing knowledge (drug development, natural products, and the food metabolome) with developments in artificial intelligence to design and then test dietary patterns predicted to elicit drug-like effects on target tissues for cancer prevention and control. We refer to this precision approach as dietary oncopharmacognosy and envision it as the crosswalk between the currently defined fields of precision oncology and precision nutrition with the goal of reducing cancer deaths.

Variant biomarker discovery using mass spectrometry-based proteogenomics

Genomic diversity plays critical roles in risk of disease pathogenesis and diagnosis. While genomic variants-including single nucleotide variants, frameshift variants, and mis-splicing isoforms-are commonly detected at the DNA or RNA level, their translated variant protein or polypeptide products are ultimately the functional units of the associated disease. These products are often released in biofluids and could be leveraged for clinical diagnosis and patient stratification. Recent emergence of integrated analysis of genomics with mass spectrometry-based proteomics for biomarker discovery, also known as proteogenomics, have significantly advanced the understanding disease risk variants, precise medicine, and biomarker discovery. In this review, we discuss variant proteins in the context of cancers and neurodegenerative diseases, outline current and emerging proteogenomic approaches for biomarker discovery, and provide a comprehensive proteogenomic strategy for detection of putative biomarker candidates in human biospecimens. This strategy can be implemented for proteogenomic studies in any field of enquiry. Our review timely addresses the need of biomarkers for aging related diseases.

Ras protein abundance correlates with Ras isoform mutation patterns in cancer

Activating mutations of Ras genes are often observed in cancer. The protein products of the three Ras genes are almost identical. However, for reasons that remain unclear, KRAS is far more frequently mutated than the other Ras isoforms in cancer and RASopathies. We have quantified HRAS, NRAS, KRAS4A and KRAS4B protein abundance across a large panel of cell lines and healthy tissues. We observe consistent patterns of KRAS > NRAS»HRAS protein expression in cells that correlate with the rank order of Ras mutation frequencies in cancer. Our data provide support for the model of a sweet-spot of Ras dosage mediating isoform-specific contributions to cancer and development. We suggest that in most cases, being the most abundant Ras isoform correlates with occupying the sweet-spot and that HRAS and NRAS expression is usually insufficient to promote oncogenesis when mutated. However, our results challenge the notion that rare codons mechanistically underpin the predominance of KRAS mutant cancers. Finally, direct measurement of mutant versus wildtype KRAS protein abundance revealed a frequent imbalance that may suggest additional non-gene duplication mechanisms for optimizing oncogenic Ras dosage.

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.

CRISPR-Cas system manipulating nanoparticles signal transduction for cancer diagnosis

Early diagnosis of cancer is important to improve the survival rate and relieve patient pain. Sensitive detection of cancer related biomarkers in body fluids is a critical approach for the early diagnosis of cancer. The clustered regularly interspaced short palindromic repeat-associated protein (CRISPR-Cas) system has emerged as a molecular manipulation technology because of its simple detection procedure, high base resolution, and isothermal signal amplification. Recently, various nanomaterials with unique optical and electrical characteristics have been introduced as the novel signal transducers to enhance the detection performance of CRISPR-Cas-based nanosensors. This review summarizes the working mechanisms of the CRISPR-Cas system for biosensing. It also enumerates the strategies of CRISPR-manipulated nanosensors based on various signal models for cancer diagnosis, including colorimetric, fluorescence, electrochemical, electrochemiluminescence, pressure, and other signals. Finally, the prospects and challenges of CRISPR-Cas-based nanosensors for cancer diagnostic are also discussed. This article is categorized under: Diagnostic Tools > Biosensing.

Surgical treatment for pancreatic cystic lesions-implications from the multi-center and prospective German StuDoQ|Pancreas registry

PURPOSE

The detection of pancreatic cystic lesions (PCL) causes uncertainty for physicians and patients, and international guidelines are based on low evidence. The extent and perioperative risk of resections of PCL in Germany needs comparison with these guidelines to highlight controversies and derive recommendations.

METHODS

Clinical data of 1137 patients who underwent surgery for PCL between 2014 and 2019 were retrieved from the German StuDoQ|Pancreas registry. Relevant features for preoperative evaluation and predictive factors for adverse outcomes were statistically identified.

RESULTS

Patients with intraductal papillary mucinous neoplasms (IPMN) represented the largest PCL subgroup (N = 689; 60.6%) while other entities (mucinous cystic neoplasms (MCN), serous cystic neoplasms (SCN), neuroendocrine tumors, pseudocysts) were less frequently resected. Symptoms of pancreatitis were associated with IPMN (OR, 1.8; P = 0.012) and pseudocysts (OR, 4.78; P < 0.001), but likewise lowered the likelihood of MCN (OR, 0.49; P = 0.046) and SCN (OR, 0.15, P = 0.002). A total of 639 (57.2%) patients received endoscopic ultrasound before resection, as recommended by guidelines. Malignancy was histologically confirmed in 137 patients (12.0%), while jaundice (OR, 5.1; P < 0.001) and weight loss (OR, 2.0; P = 0.002) were independent predictors. Most resections were performed by open surgery (N = 847, 74.5%), while distal lesions were in majority treated using minimally invasive approaches (P < 0.001). Severe morbidity was 28.4% (N = 323) and 30d mortality was 2.6% (N = 29). Increased age (P = 0.004), higher BMI (P = 0.002), liver cirrhosis (P < 0.001), and esophageal varices (P = 0.002) were independent risk factors for 30d mortality.

CONCLUSION

With respect to unclear findings frequently present in PCL, diagnostic means recommended in guidelines should always be considered in the preoperative phase. The therapy of PCL should be decided upon in the light of patient-specific factors, and the surgical strategy needs to be adapted accordingly.

Contribution of mechanical homeostasis to epithelial-mesenchymal transition

BACKGROUND

Metastasis refers to the spread of cancer cells from a primary tumor to other parts of the body via the lymphatic system and bloodstream. With tremendous effort over the past decades, remarkable progress has been made in understanding the molecular and cellular basis of metastatic processes. Metastasis occurs through five steps, including infiltration and migration, intravasation, survival, extravasation, and colonization. Various molecular and cellular factors involved in the metastatic process have been identified, such as epigenetic factors of the extracellular matrix (ECM), cell-cell interactions, soluble signaling, adhesion molecules, and mechanical stimuli. However, the underlying cause of cancer metastasis has not been elucidated.

CONCLUSION

In this review, we have focused on changes in the mechanical properties of cancer cells and their surrounding environment to understand the causes of cancer metastasis. Cancer cells have unique mechanical properties that distinguish them from healthy cells. ECM stiffness is involved in cancer cell growth, particularly in promoting the epithelial-mesenchymal transition (EMT). During tumorigenesis, the mechanical properties of cancer cells change in the direction opposite to their environment, resulting in a mechanical stress imbalance between the intracellular and extracellular domains. Disruption of mechanical homeostasis may be one of the causes of EMT that triggers the metastasis of cancer cells.

Radiothermometric Study of the Effect of Amino Acid Mutation on the Characteristics of the Enzymatic System

The radiothermometry (RTM) study of a cytochrome-containing system (CYP102 A1) has been conducted in order to demonstrate the applicability of RTM for monitoring changes in the functional activity of an enzyme in case of its point mutation. The study has been performed with the example of the wild-type cytochrome (WT) and its mutant type A264K. CYP102 A1 is a nanoscale protein-enzymatic system of about 10 nm in size. RTM uses a radio detector and can record the corresponding brightness temperature (T_br) of the nanoscale enzyme solution within the 3.4–4.2 GHz frequency range during enzyme functioning. It was found that the enzymatic reaction during the lauric acid hydroxylation at the wild-type CYP102 A1 (WT) concentration of ~10⁻⁹ M is accompanied by T_br fluctuations of ~0.5–1 °C. At the same time, no T_br fluctuations are observed for the mutated forms of the enzyme CYP102 A1 (A264K), where one amino acid was replaced. We know that the activity of CYP102 A1 (WT) is ~4 orders of magnitude higher than that of CYP102 A1 (A264K). We therefore concluded that the disappearance of the fluctuation of T_br CYP102 A1 (A264K) is associated with a decrease in the activity of the enzyme. This effect can be used to develop new methods for testing the activity of the enzyme that do not require additional labels and expensive equipment, in comparison with calorimetry and spectral methods. The RTM is beginning to find application in the diagnosis of oncological diseases and for the analysis of biochemical processes.

The Chi-Square Test of Distance Correlation

Distance correlation has gained much recent attention in the data science community: the sample statistic is straightforward to compute and asymptotically equals zero if and only if independence, making it an ideal choice to discover any type of dependency structure given sufficient sample size. One major bottleneck is the testing process: because the null distribution of distance correlation depends on the underlying random variables and metric choice, it typically requires a permutation test to estimate the null and compute the p-value, which is very costly for large amount of data. To overcome the difficulty, in this paper we propose a chi-square test for distance correlation. Method-wise, the chi-square test is non-parametric, extremely fast, and applicable to bias-corrected distance correlation using any strong negative type metric or characteristic kernel. The test exhibits a similar testing power as the standard permutation test, and can be utilized for K-sample and partial testing. Theory-wise, we show that the underlying chi-square distribution well approximates and dominates the limiting null distribution in upper tail, prove the chi-square test can be valid and universally consistent for testing independence, and establish a testing power inequality with respect to the permutation test.

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.

Current Approaches in Supersecondary Structures Investigation

Proteins expressed during the cell cycle determine cell function, topology, and responses to environmental influences. The development and improvement of experimental methods in the field of structural biology provide valuable information about the structure and functions of individual proteins. This work is devoted to the study of supersecondary structures of proteins and determination of their structural motifs, description of experimental methods for their detection, databases, and repositories for storage, as well as methods of molecular dynamics research. The interest in the study of supersecondary structures in proteins is due to their autonomous stability outside the protein globule, which makes it possible to study folding processes, conformational changes in protein isoforms, and aberrant proteins with high productivity.

The role of proteomics in the multiplexed analysis of gene alterations in human cancer

INTRODUCTION

Proteomics has played a pivotal role in identifying proteins perturbed in disease conditions when compared with healthy samples. Study of dysregulated proteins aids in identifying diagnostic markers and potential therapeutic targets. Cancer is an outcome of interplay of several such disarrayed proteins and molecular pathways which perturb cellular homeostasis, resulting in transformation. In this review, we discuss various facets of proteomic approaches, including tools and technological advancements, aiding in understanding differentially expressed molecules and signaling mechanisms.

AREAS COVERED

In this review, we have taken the approach of documenting the different methods of proteomic studies, ranging from labeling techniques, data analysis methods, and the nature of molecule detected. We summarize each technique and provide a glimpse of cancer research carried out using them, highlighting the advantages and drawbacks in comparison with others. Literature search using online resources, such as PubMed and Google Scholar were carried out for this approach.

EXPERT OPINION

Technological advancements in proteomics studies have come a long way from the study of two-dimensional mapping of proteins separated on gels in the early 1970s. Higher precision in molecular identification and quantification (high throughput), and greater number of samples analyzed have been the focus of researchers.

Building Personalized Cancer Therapeutics through Multi-Omics Assays and Bacteriophage-Eukaryotic Cell Interactions

Bacteriophage-eukaryotic cell interaction provides the biological foundation of Phage Display technology, which has been widely adopted in studies involving protein-protein and protein-peptide interactions, and it provides a direct link between the proteins and the DNA encoding them. Phage display has also facilitated the development of new therapeutic agents targeting personalized cancer mutations. Proteins encoded by mutant genes in cancers can be processed and presented on the tumor cell surface by human leukocyte antigen (HLA) molecules, and such mutant peptides are called Neoantigens. Neoantigens are naturally existing tumor markers presented on the cell surface. In clinical settings, the T-cell recognition of neoantigens is the foundation of cancer immunotherapeutics. This year, we utilized phage display to successfully develop the 1st antibody-based neoantigen targeting approach for next-generation personalized cancer therapeutics. In this article, we discussed the strategies for identifying neoantigens, followed by using phage display to create personalized cancer therapeutics-a complete pipeline for personalized cancer treatment.

Protein detection in blood with single-molecule imaging

The ability to characterize individual biomarker protein molecules in patient blood samples could enable diagnosis of diseases at an earlier stage, when treatment is typically more effective. Single-molecule imaging offers a promising approach to accomplish this goal. However, thus far, single-molecule imaging methods have not been translated into the clinical setting. The detection limit of these methods has been confined to the picomolar (10^-12 M) range, several orders of magnitude higher than the circulating concentrations of biomarker proteins present in many diseases. Here, we describe single-molecule augmented capture (SMAC), a single-molecule imaging technique to quantify and characterize individual protein molecules of interest down to the subfemtomolar (<10^-15 M) range. We demonstrate SMAC in a variety of applications with human blood samples, including the analysis of disease-associated secreted proteins, membrane proteins, and rare intracellular proteins. SMAC opens the door to the application of single-molecule imaging in noninvasive disease profiling.

Use of the Molecular Dynamics Method to Investigate the Stability of α-α-Corner Structural Motifs in Proteins

This study investigated the stability of structural motifs via molecular dynamics, using α-α-corners as an example. A molecular dynamics experiment was performed on a sample of α-α-corners selected by the authors from the PDB database. For the first time during a molecular dynamics experiment, we investigated the characteristics of structural motifs by describing their geometry, including the interplanar distance, area of polygon of the helices projections intersection, and torsion angles between axes of helices in helical pairs. The torsion angles for the constriction amino acids in the equilibrium portion of the molecular dynamics trajectory were analyzed. Using the molecular dynamics method, α-α-corners were found to be autonomous structures that are stable in aquatic environments.

Establishing a mass spectrometry-based system for rapid detection of SARS-CoV-2 in large clinical sample cohorts

The outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is pressing public health systems around the world, and large population testing is a key step to control this pandemic disease. Here, we develop a high-throughput targeted proteomics assay to detect SARS-CoV-2 nucleoprotein peptides directly from nasopharyngeal and oropharyngeal swabs. A modified magnetic particle-based proteomics approach implemented on a robotic liquid handler enables fully automated preparation of 96 samples within 4 hours. A TFC-MS system allows multiplexed analysis of 4 samples within 10 min, enabling the processing of more than 500 samples per day. We validate this method qualitatively (Tier 3) and quantitatively (Tier 1) using 985 specimens previously analyzed by real-time RT-PCR, and detect up to 84% of the positive cases with up to 97% specificity. The presented strategy has high sample stability and should be considered as an option for SARS-CoV-2 testing in large populations.

RNA oxidation in chromatin modification and DNA-damage response following exposure to formaldehyde

Formaldehyde is an environmental and occupational chemical carcinogen implicated in the damage of proteins and nucleic acids. However, whether formaldehyde provokes modifications of RNAs such as 8-oxo-7,8-dihydroguanine (8-oxoG) and the role that these modifications play on conferring long-term adverse health effects remains unexplored. Here, we profile 8-oxoG modifications using RNA-immunoprecipitation and RNA sequencing (8-oxoG RIP-seq) to identify 343 RNA transcripts heavily enriched in oxidations in human bronchial epithelial BEAS-2B cell cultures exposed to 1 ppm formaldehyde for 2 h. RNA oxidation altered expression of many transcripts involved in chromatin modification and p53-mediated DNA-damage responses, two pathways that play key roles in sustaining genome integrity and typically deregulated in tumorigenesis. Given that these observations were identified in normal cells exhibiting minimal cell stress and death phenotypes (for example, lack of nuclear shrinkage, F-actin alterations or increased LDH activity); we hypothesize that oxidative modification of specific RNA transcripts following formaldehyde exposure denotes an early process occurring in carcinogenesis analogous to the oxidative events surfacing at early stages of neurodegenerative diseases. As such, we provide initial investigations of RNA oxidation as a potentially novel mechanism underlying formaldehyde-induced tumorigenesis.

Kiss1R Identification and Biodistribution Analysis Employing a Western Ligand Blot and Ligand-Derivative Stain with a FITC-Kisspeptin Derivative

It is not always easy to establish specific antibodies against receptors. Most receptors are hydrophobic and have complicated three-dimensional structures, making them difficult to use as immunogens. Thus, we developed receptor detection methods with a fluorescein-labeled ligand as an antibody alternative, which we referred to as a western ligand blot (WLB) and ligand derivative stain (LDS). Kisspeptin receptor (Kiss1R) was detected by its ligand. Kiss1R expression was confirmed in eight human cell lines by the WLB and in four pathological tissues by the LDS. Next, Kiss1R was stained by LDS in organs, revealing Kiss1R expression by [67 Ga]Ga-DOTA-kisspeptin 10 accumulation. As a result, Kiss1R-expressing cells in each organ could be stained with fluorescein-labeled kisspeptin 14 instead of an antibody and observed by light microscopy. The combination of the WLB and LDS allows identification of receptors in tissues, which can be readily applied to target receptor detection by a synthetic ligand derivative.

Mutant Proteomics of Lung Adenocarcinomas Harboring Different EGFR Mutations

Epidermal growth factor receptor EGFR major driver mutations may affect downstream molecular networks and pathways, which would influence treatment outcomes of non-small cell lung cancer (NSCLC). This study aimed to unveil profiles of mutant proteins expressed in lung adenocarcinomas of 36 patients harboring representative driver EGFR mutations (Ex19del, nine; L858R, nine; no Ex19del/L858R, 18). Surprisingly, the orthogonal partial least squares discriminant analysis performed for identified mutant proteins demonstrated the profound differences in distance among the different EGFR mutation groups, suggesting that cancer cells harboring L858R or Ex19del emerge from cellular origins different from L858R/Ex19del-negative cells. Weighted gene coexpression network analysis, together with over-representative analysis, identified 18 coexpressed modules and their eigen proteins. Pathways enriched differentially for both the L858R and Ex19del mutations included carboxylic acid metabolic process, cell cycle, developmental biology, cellular responses to stress, mitotic prophase, cell proliferation, growth, epithelial to mesenchymal transition (EMT), and immune system. The IPA causal network analysis identified the highly activated networks of PARPBP, HOXA1, and APH1 under the L858R mutation, whereas those of ASGR1, APEX1, BUB1, and MAPK10 were highly activated under the Ex19del mutation. Interestingly, the downregulated causal network of osimertinib intervention showed the highest significance in overlap p-value among most causal networks predicted under the L858R mutation. We also identified the causal network of MAPK interacting serine/threonine kinase 1/2 (MNK1/2) highly activated differentially under the L858R mutation. Tumor-suppressor AMOT, a component of the Hippo pathways, was highly inhibited commonly under both L858R and Ex19del mutations. Our results could identify disease-related protein molecular networks from the landscape of single amino acid variants. Our findings may help identify potential therapeutic targets and develop therapeutic strategies to improve patient outcomes.

Comparative Proteome Profiling and Mutant Protein Identification in Metastatic Prostate Cancer Cells by Quantitative Mass Spectrometry-based Proteogenomics

BACKGROUND/AIM

Prostate cancer (PCa) is the most frequent cancer found in males worldwide. The aim of this study was to identify new biomarkers using mutated peptides for the prognosis and prediction of advanced PCa, based on proteogenomics.

MATERIALS AND METHODS

The tryptic peptides were analyzed by tandem mass tag-based quantitative proteomics. Proteogenomics were used to identify mutant peptides as novel biomarkers in advanced PCa.

RESULTS

Using a human database, increased levels of INTS7 and decreased levels of SH3BGRL were found to be associated with the aggressiveness of PCa. Using proteogenomics and a cancer mutation database, 70 mutant peptides were identified in PCa cell lines. Using parallel reaction monitoring, the expression of seven mutant peptides was found to be altered in tumors, amongst which CAPN2 D22E was the most significantly up-regulated mutant peptide in PCa tissues.

CONCLUSION

Altered mutant peptides present in PCa tissue could be used as new biomarkers in advanced PCa.

Separation and characterization of KRas proteins and tryptic peptides using enhanced-fluidity liquid chromatography tandem mass spectrometry

Enhanced-fluidity, reversed-phase liquid chromatography was developed using custom instrumentation for separation and characterization of intact KRas proteins and tryptic peptides. The KRas, HRas and NRas function as GDP-GTP regulated binary switches in many signalling pathways, and mutations in Ras proteins are frequently found in human cancers and represent poor prognosis markers for patients. Mutations of the KRas isoform constitute some of the most common aberrations among all human cancers and intensive drug discovery efforts have been directed toward targeting the KRas protein. Separation and characterization of the KRas protein and tryptic peptides are helpful for exploring targeting, which has not been fully investigated using liquid chromatography-tandem mass spectrometry. EFLC-MS provided improved chromatographic performance compared to traditional HPLC-MS in terms of shorter analysis time, increased ion intensity and a shift to higher charge states for intact KRas proteins.

Direct Detection and Quantification of Neoantigens

Many immunotherapeutic approaches under development rely on T-cell recognition of cancer-derived peptides bound to human leukocyte antigen molecules on the cell surface. Direct experimental demonstration that such peptides are processed and bound is currently challenging. Here, we describe a method that meets this challenge. The method entailed an optimized immunoprecipitation protocol coupled with two-dimensional chromatography and mass spectrometry. The ability to detect and quantify minute amounts of predefined antigens should be useful both for basic research in tumor immunology and for the development of rationally designed cancer vaccines.

Relative stabilities of wild-type and mutant glial fibrillary acidic protein in patients with Alexander disease

Alexander disease (AxD) is an often fatal astrogliopathy caused by dominant gain-of-function missense mutations in the glial fibrillary acidic protein (GFAP) gene. The mechanism by which the mutations produce the AxD phenotype is not known. However, the observation that features of AxD are displayed by mice that express elevated levels of GFAP from a human WT GFAP transgene has contributed to the notion that the mutations produce AxD by increasing accumulation of total GFAP above some toxic threshold rather than the mutant GFAP being inherently toxic. A possible mechanism for accumulation of GFAP in AxD patients is that the mutated GFAP variants are more stable than the WT, an attribution abetted by observations that GFAP complexes containing GFAP variants are more resistant to solvent extraction. Here we tested this hypothesis by determining the relative levels of WT and mutant GFAP in three individuals with AxD, each of whom carried a common but different GFAP mutation (R79C, R239H, or R416W). Mass spectrometry analysis identified a peptide specific to the mutant or WT GFAP in each patient, and we quantified this peptide by comparing its signal to that of an added [¹⁵N]GFAP standard. In all three individuals, the level of mutant GFAP was less than that of the WT. This finding suggests that AxD onset is due to an intrinsic toxicity of the mutant GFAP instead of it acting indirectly by being more stable than WT GFAP and thereby increasing the total GFAP level.

Current status of clinical proteogenomics in lung cancer

Introduction: Lung cancer is the leading cause of cancer death worldwide. Proteogenomics, a way to integrate genomics, transcriptomics, and proteomics, have emerged as a way to understand molecular causes in cancer tumorigenesis. This understanding will help identify therapeutic targets that are urgently needed to improve individual patient outcomes. Areas covered: To explore underlying molecular mechanisms of lung cancer subtypes, several efforts have used proteogenomic approaches that integrate next generation sequencing (NGS) and mass spectrometry (MS)-based technologies. Expert opinion: A large-scale, MS-based, proteomic analysis, together with both NGS-based genomic data and clinicopathological information, will facilitate establishing extensive databases for lung cancer subtypes that can be used for further proteogenomic analyzes. Proteogenomic strategies will further be understanding of how major driver mutations affect downstream molecular networks, resulting in lung cancer progression and malignancy, and how therapy-resistant cancers resistant are molecularly structured. These strategies require advanced bioinformatics based on a dynamic theory of network systems, rather than statistics, to accurately identify mutant proteins and their affected key networks.

Proteogenomic Analysis of Protein Sequence Alterations in Breast Cancer Cells

Cancer evolves as a result of an accumulation of mutations and chromosomal aberrations. Developments in sequencing technologies have enabled the discovery and cataloguing of millions of such mutations. The identification of protein-level alterations, typically by using reversed-phase protein arrays or mass spectrometry, has lagged, however, behind gene and transcript-level observations. In this study, we report the use of mass spectrometry for detecting the presence of mutations-missense, indels and frame shifts-in MCF7 and SKBR3 breast cancer, and non-tumorigenic MCF10A cells. The mutations were identified by expanding the database search process of raw mass spectrometry files by including an in-house built database of mutated peptides (XMAn-v1) that complemented a minimally redundant, canonical database of Homo sapiens proteins. The work resulted in the identification of nearly 300 mutated peptide sequences, of which ~50 were characterized by quality tandem mass spectra. We describe the criteria that were used to select the mutated peptide sequences, evaluate the parameters that characterized these peptides, and assess the artifacts that could have led to false peptide identifications. Further, we discuss the functional domains and biological processes that may be impacted by the observed peptide alterations, and how protein-level detection can support the efforts of identifying cancer driving mutations and genes. Mass spectrometry data are available via ProteomeXchange with identifier PXD014458.

A dual-functional molecular strategy for in situ suppressing and visualizing of neuraminidase in aqueous solution using iridium(iii) complexes

We have designed for the first time a dual-functional luminescent probe and inhibitor of neuraminidase (NA), a key influenza target. The lead iridium(iii) complex exhibited enhanced inhibition against NA compared to the FDA-approved antiviral drug, oseltamivir, and could detect NA even in the presence of an autofluorescent background.

Targeted Proteomics Comes to the Benchside and the Bedside: Is it Ready for Us?

While mass spectrometry (MS)-based quantification of small molecules has been successfully used for decades, targeted MS has only recently been used by the proteomics community to investigate clinical questions such as biomarker verification and validation. Targeted MS holds the promise of a paradigm shift in the quantitative determination of proteins. Nevertheless, targeted quantitative proteomics requires improvisation in making sample processing, instruments, and data analysis more accessible. In the backdrop of the genomic era reaching its zenith, certain questions arise: is the proteomic era about to come? If we are at the beginning of a new future for protein quantification, are we prepared to incorporate targeted proteomics at the benchside for basic research and at the bedside for the good of patients? Here, an overview of the knowledge required to perform targeted proteomics as well as its applications is provided. A special emphasis is placed on upcoming areas such as peptidomics, proteoform research, and mass spectrometry imaging, where the utilization of targeted proteomics is expected to bring forth new avenues. The limitations associated with the acceptance of this technique for mainstream usage are also highlighted. Also see the video abstract here https://youtu.be/mieB47B8gZw.

Discovering and deciphering relationships across disparate data modalities

Understanding the relationships between different properties of data, such as whether a genome or connectome has information about disease status, is increasingly important. While existing approaches can test whether two properties are related, they may require unfeasibly large sample sizes and often are not interpretable. Our approach, 'Multiscale Graph Correlation' (MGC), is a dependence test that juxtaposes disparate data science techniques, including k-nearest neighbors, kernel methods, and multiscale analysis. Other methods may require double or triple the number of samples to achieve the same statistical power as MGC in a benchmark suite including high-dimensional and nonlinear relationships, with dimensionality ranging from 1 to 1000. Moreover, MGC uniquely characterizes the latent geometry underlying the relationship, while maintaining computational efficiency. In real data, including brain imaging and cancer genetics, MGC detects the presence of a dependency and provides guidance for the next experiments to conduct.

BRAF protein immunoprecipitation, elution, and digestion from cell extract using a microfluidic mixer for mutant BRAF protein quantification by mass spectrometry

This study utilized a microfluidic mixer for the sample pretreatment of cell extracts for target protein quantification by mass spectrometers, including protein immunoprecipitation and protein enzymatic digestion. The time of sample pretreatment was reduced and thus the throughput of quantitative mutant proteins was increased by using the proposed method. Whole cell lysates of the cancer cell line HT-29 with gene mutations were used as the sample. The target protein BRAF was immunoprecipitated using magnetic beads in a pneumatic micromixer. Purified protein was then eluted and digested by trypsin in another two micromixers to yield peptide fragments in the solution. Using stable isotope-labeled standard as the internal control, wild-type and mutant BRAF proteins were quantified using mass spectrometry, which could be used for cancer screening. Compared with conventional methods in which protein immunoprecipitation lasts overnight, the micromixer procedure takes only 1 h, likely improving the throughput of mutant BRAF protein quantification by mass spectrometry. Graphical abstract Three micromixers were used to reduce the sample pretreatment time of cell extracts for target protein quantification by mass spectrometers, including protein immunoprecipitation, protein elution, and protein enzymatic digestion.

Comparative Secretome Profiling and Mutant Protein Identification in Metastatic Prostate Cancer Cells by Quantitative Mass Spectrometry-based Proteomics

BACKGROUND

Secreted proteins play an important role in promoting cancer (PCa) cell migration and invasion. Proteogenomics helps elucidate the mechanism of diseases, discover therapeutic targets, and generate biomarkers for diagnosis through protein variations.

MATERIALS AND METHODS

We carried out mass a spectrometry-based proteomic analysis of the conditioned media (CM) from two human prostate cancer cell lines, belonging to different metastatic sites, to identify potential metastatic and/or aggressive factors.

RESULTS

We identified a total of 598 proteins, among which 561 were quantified based on proteomic analysis. Among the quantified proteins, 128 were up-regulated and 83 were down-regulated in DU145/PC3 cells. Six mutant peptides were identified in the CM of prostate cancer cell lines using proteogenomics approach.

CONCLUSION

This is the first proteogenomics study in PCa aiming at exploring a new type of metastatic factor, which are mutant peptides, predicting a novel biomarker of metastatic PCa for diagnosis, prognosis and drug targeting.

Precise characterization of KRAS4b proteoforms in human colorectal cells and tumors reveals mutation/modification cross-talk

Mutations of the KRAS gene are found in human cancers with high frequency and result in the constitutive activation of its protein products. This leads to aberrant regulation of downstream pathways, promoting cell survival, proliferation, and tumorigenesis that drive cancer progression and negatively affect treatment outcomes. Here, we describe a workflow that can detect and quantify mutation-specific consequences of KRAS biochemistry, namely linked changes in posttranslational modifications (PTMs). We combined immunoaffinity enrichment with detection by top-down mass spectrometry to discover and quantify proteoforms with or without the Gly13Asp mutation (G13D) specifically in the KRAS4b isoform. The workflow was applied first to isogenic KRAS colorectal cancer (CRC) cell lines and then to patient CRC tumors with matching KRAS genotypes. In two cellular models, a direct link between the knockout of the mutant G13D allele and the complete nitrosylation of cysteine 118 of the remaining WT KRAS4b was observed. Analysis of tumor samples quantified the percentage of mutant KRAS4b actually present in cancer tissue and identified major differences in the levels of C-terminal carboxymethylation, a modification critical for membrane association. These data from CRC cells and human tumors suggest mechanisms of posttranslational regulation that are highly context-dependent and which lead to preferential production of specific KRAS4b proteoforms.

Nanoplasmonic sensors for detecting circulating cancer biomarkers

The detection of cancer biomarkers represents an important aspect of cancer diagnosis and prognosis. Recently, the concept of liquid biopsy has been introduced whereby diagnosis and prognosis are performed by means of analyzing biological fluids obtained from patients to detect and quantify circulating cancer biomarkers. Unlike conventional biopsy whereby primary tumor cells are analyzed, liquid biopsy enables the detection of a wide variety of circulating cancer biomarkers, including microRNA (miRNA), circulating tumor DNA (ctDNA), proteins, exosomes and circulating tumor cells (CTCs). Among the various techniques that have been developed to detect circulating cancer biomarkers, nanoplasmonic sensors represent a promising measurement approach due to high sensitivity and specificity as well as ease of instrumentation and operation. In this review, we discuss the relevance and applicability of three different categories of nanoplasmonic sensing techniques, namely surface plasmon resonance (SPR), localized surface plasmon resonance (LSPR) and surface-enhanced Raman scattering (SERS), for the detection of different classes of circulating cancer biomarkers.

An immunocapture-LC-MS-based assay for serum SPINK1 allows simultaneous quantification and detection of SPINK1 variants

Pancreatic secretory trypsin inhibitor Kazal type 1 (SPINK1) is a 6420 Da peptide produced by the pancreas, but also by several other tissues and many tumors. Some mutations of the SPINK1 gene, like the one causing amino acid change N34S, have been shown to confer susceptibility to recurrent or chronic pancreatitis. Detection of such variants are therefore of clinical utility. So far SPINK1 variants have been determined by DNA techniques. We have developed and validated an immunocapture-liquid chromatography-mass spectrometric (IC-LC-MS) assay for the detection and quantification of serum SPINK1, N34S-SPINK1, and P55S-SPINK1. We compared this method with a time-resolved immunofluorometric assay (TR-IFMA) for serum samples and primer extension analysis of DNA samples. We used serum and DNA samples from patients with acute pancreatitis, renal cell carcinoma, or benign urological conditions. With the help of a zygosity score calculated from the respective peak areas using the formula wild-type (wt) SPINK1/(variant SPINK1 + wt SPINK1), we were able to correctly characterize the heterozygotes and homozygotes from the samples with DNA information. The score was then used to characterize the apparent zygosity of the samples with no DNA characterization. The IC-LC-MS method for SPINK1 was linear over the concentration range 0.5-1000 μg/L. The limit of quantitation (LOQ) was 0.5 μg/L. The IC-LC-MS and the TR-IFMA assays showed good correlation. The median zygosity score was 1.00 (95% CI 0.98-1.01, n = 11), 0.55 (95% CI 0.43-0.61, n = 14), and 0.05 (range 0.04-0.07, n = 3) for individuals found to be wt, heterozygous, and homozygous, respectively, for the N34S-SPINK1 variant by DNA analysis. When DNA samples are not available, this assay facilitates identification of the N34S- and P55S-SPINK1 variants also in archival serum samples.

15N isotopic labelling for in-cell protein studies by NMR spectroscopy and single-cell IR synchrotron radiation FTIR microscopy: a correlative study

The effect of ¹⁵N-enrichment on human cells analyzed by correlative in-cell NMR and single-cell SR-FTIR experiments.

Selected reaction monitoring approach for validating peptide biomarkers

We here describe a selected reaction monitoring (SRM)-based approach for the discovery and validation of peptide biomarkers for cancer. The first stage of this approach is the direct identification of candidate peptides through comparison of proteolytic peptides derived from the plasma of cancer patients or healthy individuals. Several hundred candidate peptides were identified through this method, providing challenges for choosing and validating the small number of peptides that might prove diagnostically useful. To accomplish this validation, we used 2D chromatography coupled with SRM of candidate peptides. We applied this approach, called sequential analysis of fractionated eluates by SRM (SAFE-SRM), to plasma from cancer patients and discovered two peptides encoded by the peptidyl-prolyl cis-trans isomerase A (PPIA) gene whose abundance was increased in the plasma of ovarian cancer patients. At optimal thresholds, elevated levels of at least one of these two peptides was detected in 43 (68.3%) of 63 women with ovarian cancer but in none of 50 healthy controls. In addition to providing a potential biomarker for ovarian cancer, this approach is generally applicable to the discovery of peptides characteristic of various disease states.

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.

Identification of trunk mutations in gastric carcinoma: a case study

Background

Intratumor heterogeneity (ITH) poses an urgent challenge for cancer precision medicine because it can cause drug resistance against cancer target therapy and immunotherapy. The search for trunk mutations that are present in all cancer cells is therefore critical for each patient.

Case presentation

In this study, we aimed to evaluate the efficiency of multiregional sequencing for the identification of trunk mutations present in all regions of a tumor as a case study. We applied multiregional whole-exome sequencing (WES) to investigate the genetic heterogeneity and homogeneity of a case of gastric carcinoma. Approximately 83% of common missense mutations present in two samples and approximately 89% of common missense mutations present in three samples were trunk mutations. Notably, trunk mutations appeared to have higher variant allele frequencies (VAFs) than non-trunk mutations.

Conclusions

Our results indicate that small-scale multiregional sampling and subsequent screening of low VAF somatic mutations might be a cost-effective strategy for identifying the majority of trunk mutations in gastric carcinoma.

Electronic supplementary material

The online version of this article (doi:10.1186/s12920-017-0285-y) contains supplementary material, which is available to authorized users.