Proteomics and peptidomics: moving toward precision medicine in urological malignancies

Comprehensive workflow encompassing discovery, verification, and quantification of indicator peptide in snail mucin using LC-quadrupole Orbitrap high-resolution tandem mass spectrometry

Peptidomics analysis was conducted using high-resolution tandem mass spectrometry (MS2) to determine the peptide profile of snail-derived mucin extract (SM). The study was also aimed to identify an indicator peptide and validate a quantification method for this peptide. The peptide profiling and identification were conducted using discovery-based peptidomics analysis employing data-dependent acquisition, whereas the selected peptides were verified and quantified using parallel reaction monitoring acquisition. Among the 16 identified peptides, the selected octapeptide (TEAPLNPK) was quantified via precursor ion ionization (m/z 435.2400), followed by quantification of the corresponding quantifier ion fragment (m/z 639.3824) using MS2. The quantification method was optimized and validated in terms of specificity, linearity, accuracy, precision, and limit of detection/quantification. The validated method accurately quantified the TEAPLNPK content in the SM as 7.5 ± 0.2 μg/g. Our study not only identifies an indicator peptide from SM but also introduces a novel validation method, involving precursor ion ionization and quantification of specific fragments. Our findings may serve as a comprehensive workflow for the monitoring, selection, and quantification of indicator peptides from diverse food resources.

Untargeted Analysis of Short-Chain Peptides in Urine Samples Short Peptides Analysis

Short-chain peptides have attracted increasing attention in different research fields, including biomarker discovery, but also a well-known analytical challenge in complex matrices due to their low abundance compared to other molecules, which can cause extensive ion suppression during mass spectrometric acquisition. Moreover, there is a lack of analytical workflows for their comprehensive characterization since ordinary peptidomics strategies cannot identify them. In this context, an enrichment strategy was introduced and developed to isolate and clean up short-chain peptides by graphitized carbon black solid phase extraction. For better coverage of peptide polarity, urine samples were analyzed by ultrahigh performance liquid chromatography by reversed-phase and hydrophilic interaction liquid chromatography. High-resolution mass spectrometry allowed the detection of the eluting peptides by data-dependent mode using a suspect screening strategy with an inclusion list; peptides were identified by a semiautomated workflow implemented on Compound Discoverer. The complementarity of the orthogonal separation strategy was confirmed by peptide identification, resulting in 101 peptides identified from the RP runs, and 111 peptides from the HILIC runs, with 60 common identifications.

Peptidomics Strategies to Evaluate Cancer Diagnosis, Prognosis, and Treatment

Peptides have potential bioactive functions, and the peptidomics landscape has been broadly investigated for various diseases, including cancer. In this chapter, we reviewed the past four years of literature available and selected 16 peer-reviewed publications exploring peptidomics in diagnosis, prognosis, and treatment in cancer research. We highlighted their main aims, mass spectrometry-based peptidomics, multi-omics, data-driven and in silico strategies, functional assays, and clinical applications. Moreover, we underscored several levels of difficulties in translating the peptidomics findings to clinical practice, aiming to learn with the accumulated knowledge and guide upcoming studies. Finally, this review reinforces the peptidomics robustness in discovering potential candidates for monitoring the several stages of cancer disease and therapeutic treatment, leveraging the management of cancer patients in the future.

Deciphering Urogenital Cancers through Proteomic Biomarkers: A Systematic Review and Meta-Analysis

Urogenital cancers, which include prostate, bladder, and kidney malignancies, exert a substantial impact on global cancer-related morbidity and mortality. Proteomic biomarkers, emerging as valuable tools, aim to enhance early detection, prognostic accuracy, and the development of personalized therapeutic strategies. This study undertook a comprehensive systematic review and meta-analysis of the existing literature investigating the role and potential of proteomic biomarkers in plasma, tissue, and urine samples in urogenital cancers. Our extensive search across several databases identified 1879 differentially expressed proteins from 37 studies, signifying their potential as unique biomarkers for these cancers. A meta-analysis of the significantly differentially expressed proteins was executed, accentuating the findings through visually intuitive volcano plots. A functional enrichment analysis unveiled their significant involvement in diverse biological processes, including signal transduction, immune response, cell communication, and cell growth. A pathway analysis highlighted the participation of key pathways such as the nectin adhesion pathway, TRAIL signaling pathway, and integrin signaling pathways. These findings not only pave the way for future investigations into early detection and targeted therapeutic approaches but also underscore the fundamental role of proteomics in advancing our understanding of the molecular mechanisms underpinning urogenital cancer pathogenesis. Ultimately, these findings hold remarkable potential to significantly enhance patient care and improve clinical outcomes.

Neurobiochemical, Peptidomic, and Bioinformatic Approaches to Characterize Tauopathy Peptidome Biomarker Candidates in Experimental Mouse Model of Traumatic Brain Injury

Traumatic brain injury (TBI) is a multidimensional damage, and currently, no FDA-approved medicine is available. Multiple pathways in the cell are triggered through a head injury (e.g., calpain and caspase activation), which truncate tau and generate variable fragment sizes (MW 400-45,000 K). In this study, we used an open-head TBI mouse model generated by controlled cortical impact (CCI) and collected ipsilateral (IC) and contralateral (CC) mice htau brain cortices at one (D1) three (D3), and seven (D7) days post-injury. We implemented immunological (antibody-based detection) and peptidomic approaches (nano-reversed-phase liquid chromatography/tandem mass spectrometry) to investigate proteolytic tau peptidome (low molecular weight (LMW) < 10 K)) and pathological phosphorylation sites (high-molecular-weight (HMW); > 10 K) derived from CCI-TBI animal models. Our immunoblotting analysis verified tau hyperphosphorylation, HMW, and HMW breakdown products (HMW-BDP) formation of tau (e.g., pSer₂₀₂, pThr₁₈₁, pThr₂₃₁, pSer₃₉₆, and pSer₄₀₄), following CCI-TBI. Peptidomic data revealed unique sequences of injury-dependent proteolytic peptides generated from human tau protein. Among the N-terminal tau peptides, EIPEGTTAEEAGIGDTPSLEDEAAGHVTQA (a.a. 96-125) and AQPHTEIPEGTTAEEAGIGDTPSLEDEAAGHVTQARM (a.a. 91-127). Examples of tau C-terminal peptides identified include NVSSTGSIDMVDSPQLATLADEVSASLAKQGL (a.a. 410-441) and QLATLADEVSASLAKQGL (a.a. 424-441). Our peptidomic bioinformatic tools showed the association of proteases, such as CAPN1, CAPN2, and CTSL; CASP1, MMP7, and MMP9; and ELANE, GZMA, and MEP1A, in CCI-TBI tau peptidome. In clinical trials for novel TBI treatments, it might be useful to monitor a subset of tau peptidome as targets for biomarker utility and use them for a "theranostic" approach.

Circulating Peptidome Is Strongly Altered in COVID-19 Patients

Whilst the impact of coronavirus disease 2019 (COVID-19) on the host proteome, metabolome, and lipidome has been largely investigated in different bio-fluids, to date, the circulating peptidome remains unexplored. Thus, the present study aimed to apply an untargeted peptidomic approach to provide insight into alterations of circulating peptides in the development and severity of SARS-CoV-2 infection. The circulating peptidome from COVID-19 severe and mildly symptomatic patients and negative controls was characterized using LC-MS/MS analysis for identification and quantification purposes. Database search and statistical analysis allowed a complete characterization of the plasma peptidome and the detection of the most significant modulated peptides that were impacted by the infection. Our results highlighted not only that peptide abundance inversely correlates with disease severity, but also the involvement of biomolecules belonging to inflammatory, immune-response, and coagulation proteins/processes. Moreover, our data suggested a possible involvement of changes in protein degradation patterns. In the present research, for the first time, the untargeted peptidomic approach enabled the identification of circulating peptides potentially playing a crucial role in the progression of COVID-19.

Cell-Free DNA, MicroRNAs, Proteins, and Peptides as Liquid Biopsy Biomarkers in Prostate Cancer and Bladder Cancer

Liquid biopsy, as a novel noninvasive tool for biomarker discovery, has gained a lot of attention and represents a significant innovation in precision medicine. Due to its minimally invasive nature, liquid biopsy has fewer complications and can be scheduled more frequently to provide individualized snapshots of the disease at successive time points. This is particularly valuable in providing simultaneous measurements of tumor burden during treatment and early detection of tumor recurrence or drug resistance. Blood-based liquid biopsy is an attractive, minimally invasive alternative, which has shown promise in diagnosis, risk stratification, disease monitoring, and more. Urine has gained popularity due to its less invasive sampling, the ability to easily repeat samples, and the ability to track tumor evolution in real time, making it a powerful tool for diagnosis and treatment monitoring, especially in urologic cancers. In this review, we provide a detailed discussion on the potential clinical applications of prostate cancer (PCa) and bladder cancer (BCa), with cell-free DNA (cfDNA), microRNAs (miRNAs), proteins, and peptides as liquid biopsy biomarkers.

Potential Urine Proteomic Biomarkers for Focal Segmental Glomerulosclerosis and Minimal Change Disease

Primary focal segmental glomerulosclerosis (FSGS), along with minimal change disease (MCD), are diseases with primary podocyte damage that are clinically manifested by the nephrotic syndrome. The pathogenesis of these podocytopathies is still unknown, and therefore, the search for biomarkers of these diseases is ongoing. Our aim was to determine of the proteomic profile of urine from patients with FSGS and MCD. Patients with a confirmed diagnosis of FSGS (n = 30) and MCD (n = 9) were recruited for the study. For a comprehensive assessment of the severity of FSGS a special index was introduced, which was calculated as follows: the first score was assigned depending on the level of eGFR, the second score—depending on the proteinuria level, the third score—resistance to steroid therapy. Patients with the sum of these scores of less than 3 were included in group 1, with 3 or more—in group 2. The urinary proteome was analyzed using liquid chromatography/mass spectrometry. The proteome profiles of patients with severe progressive FSGS from group 2, mild FSGS from group 1 and MCD were compared. Results of the label free analysis were validated using targeted LC-MS based on multiple reaction monitoring (MRM) with stable isotope labelled peptide standards (SIS) available for 47 of the 76 proteins identified as differentiating between at least one pair of groups. Quantitative MRM SIS validation measurements for these 47 proteins revealed 22 proteins with significant differences between at least one of the two group pairs and 14 proteins were validated for both comparisons. In addition, all of the 22 proteins validated by MRM SIS analysis showed the same direction of change as at the discovery stage with label-free LC-MS analysis, i.e., up or down regulation in MCD and FSGS1 against FSGS2. Patients from the FSGS group 2 showed a significantly different profile from both FSGS group 1 and MCD. Among the 47 significantly differentiating proteins, the most significant were apolipoprotein A-IV, hemopexin, vitronectin, gelsolin, components of the complement system (C4b, factors B and I), retinol- and vitamin D-binding proteins. Patients with mild form of FSGS and MCD showed lower levels of Cystatin C, gelsolin and complement factor I.

Multi-omics strategies for personalized and predictive medicine: past, current, and future translational opportunities

Precision medicine is driven by the paradigm shift of empowering clinicians to predict the most appropriate course of action for patients with complex diseases and improve routine medical and public health practice. It promotes integrating collective and individualized clinical data with patient specific multi-omics data to develop therapeutic strategies, and knowledgebase for predictive and personalized medicine in diverse populations. This study is based on the hypothesis that understanding patient's metabolomics and genetic make-up in conjunction with clinical data will significantly lead to determining predisposition, diagnostic, prognostic and predictive biomarkers and optimal paths providing personalized care for diverse and targeted chronic, acute, and infectious diseases. This study briefs emerging significant, and recently reported multi-omics and translational approaches aimed to facilitate implementation of precision medicine. Furthermore, it discusses current grand challenges, and the future need of Findable, Accessible, Intelligent, and Reproducible (FAIR) approach to accelerate diagnostic and preventive care delivery strategies beyond traditional symptom-driven, disease-causal medical practice.

OMICS in Chronic Kidney Disease: Focus on Prognosis and Prediction

Chronic kidney disease (CKD) patients are characterized by a high residual risk for cardiovascular (CV) events and CKD progression. This has prompted the implementation of new prognostic and predictive biomarkers with the aim of mitigating this risk. The ‘omics’ techniques, namely genomics, proteomics, metabolomics, and transcriptomics, are excellent candidates to provide a better understanding of pathophysiologic mechanisms of disease in CKD, to improve risk stratification of patients with respect to future cardiovascular events, and to identify CKD patients who are likely to respond to a treatment. Following such a strategy, a reliable risk of future events for a particular patient may be calculated and consequently the patient would also benefit from the best available treatment based on their risk profile. Moreover, a further step forward can be represented by the aggregation of multiple omics information by combining different techniques and/or different biological samples. This has already been shown to yield additional information by revealing with more accuracy the exact individual pathway of disease.

Effects of Psychological Intervention on Perioperative Quality of Life and Serum PSA and FPSA Levels of Patients with Prostate Cancer Treated with Integrated Traditional Chinese and Western Medicine

Objective

To observe the effects of psychological intervention on the perioperative quality of life and serum prostate-specific antigen (PSA) and free PSA (FPSA) levels in patients with prostate cancer treated with integrated traditional Chinese and Western medicine.

Method

A total of 208 prostate cancer patients were selected and randomly divided into a study group with 104 cases and a control group with 104 cases. The control group received a plan of basic nursing combined with integrated traditional Chinese and Western medicine, and the study group received psychological intervention on the basis of the control group. Negative emotion, pain degree, quality of life, maximum urine flow rate, residual urine volume, International Prostate Symptom Score (IPSS), and incidence of adverse reactions were compared between the two groups before and after the treatment. The levels of PSA and FPSA and the long-term efficacy of the two groups of patients before and after treatment were compared.

Results

After nursing, Hamilton Anxiety Scale (HAMA) score, Hamilton Depression Scale (HAMD) score, pain degree, maximum urine flow rate, residual urine volume, IPSS score, emotional function, social function, role function, and physical function scores of patients in two groups were decreased, and the decrease was more significant in the study group. After treatment, serum PSA and FPSA levels in the study group were obviously lower than those in the control group. The two-year cumulative survival rate of the study group was higher than that of the control group. There was no significant difference in the cognitive function score and incidence of adverse reactions between the two groups.

Conclusion

Psychological intervention combined with traditional Chinese and Western medicine in the treatment of prostate cancer can effectively improve the patient's psychological state, reduce the degree of pain in patients, improve the therapeutic effect and the quality of life of patients, and significantly reduce serum PSA and FPSA levels, which could lead to a prolonged life.

Urinary Protein and Peptide Markers in Chronic Kidney Disease

Chronic kidney disease (CKD) is a non-specific type of kidney disease that causes a gradual decline in kidney function (from months to years). CKD is a significant risk factor for death, cardiovascular disease, and end-stage renal disease. CKDs of different origins may have the same clinical and laboratory manifestations but different progression rates, which requires early diagnosis to determine. This review focuses on protein/peptide biomarkers of the leading causes of CKD: diabetic nephropathy, IgA nephropathy, lupus nephritis, focal segmental glomerulosclerosis, and membranous nephropathy. Mass spectrometry (MS) approaches provided the most information about urinary peptide and protein contents in different nephropathies. New analytical approaches allow urinary proteomic-peptide profiles to be used as early non-invasive diagnostic tools for specific morphological forms of kidney disease and may become a safe alternative to renal biopsy. MS studies of the key pathogenetic mechanisms of renal disease progression may also contribute to developing new approaches for targeted therapy.

Peptidomics and Capillary Electrophoresis

Peptides play a crucial role in many vitally important functions of living organisms. The goal of peptidomics is the identification of the "peptidome," the whole peptide content of a cell, organ, tissue, body fluid, or organism. In peptidomic or proteomic studies, capillary electrophoresis (CE) is an alternative technique for liquid chromatography. It is a highly efficient and fast separation method requiring extremely low amounts of sample. In peptidomic approaches, CE is commonly combined with mass spectrometric (MS) detection. Most often, CE is coupled with electrospray ionization MS and less frequently with matrix-assisted laser desorption/ionization MS. CE-MS has been employed in numerous studies dealing with determination of peptide biomarkers in different body fluids for various diseases, or in food peptidomic research for the analysis and identification of peptides with special biological activities. In addition to the above topics, sample preparation techniques commonly applied in peptidomics before CE separation and possibilities for peptide identification and quantification by CE-MS or CE-MS/MS methods are discussed in this chapter.

Proteomic Landscape of Prostate Cancer: The View Provided by Quantitative Proteomics, Integrative Analyses, and Protein Interactomes

Prostate cancer is the second most frequent cancer of men worldwide. While the genetic landscapes and heterogeneity of prostate cancer are relatively well-known already, methodological developments now allow for studying basic and dynamic proteomes on a large scale and in a quantitative fashion. This aids in revealing the functional output of cancer genomes. It has become evident that not all aberrations at the genetic and transcriptional level are translated to the proteome. In addition, the proteomic level contains heterogeneity, which increases as the cancer progresses from primary prostate cancer (PCa) to metastatic and castration-resistant prostate cancer (CRPC). While multiple aspects of prostate adenocarcinoma proteomes have been studied, less is known about proteomes of neuroendocrine prostate cancer (NEPC). In this review, we summarize recent developments in prostate cancer proteomics, concentrating on the proteomic landscapes of clinical prostate cancer, cell line and mouse model proteomes interrogating prostate cancer-relevant signaling and alterations, and key prostate cancer regulator interactomes, such as those of the androgen receptor (AR). Compared to genomic and transcriptomic analyses, the view provided by proteomics brings forward changes in prostate cancer metabolism, post-transcriptional RNA regulation, and post-translational protein regulatory pathways, requiring the full attention of studies in the future.

Addressing the Diagnostic Miscommunication in Pathology

OBJECTIVES

The pathology report serves as a crucial communication tool among a number of stakeholders. It can sometimes be challenging to understand. A communication barrier exists among pathologists, other clinicians, and patients when interpreting the pathology report, leaving both clinicians and patients less empowered when making treatment decisions. Miscommunication can lead to delays in treatment or other costly medical interventions.

METHODS

In this review, we highlight miscommunication in pathology reporting and provide potential solutions to improve communication.

RESULTS

Up to one-third of clinicians do not always understand pathology reports. Several causes of report misinterpretation include the use of pathology-specific jargon, different versions of staging or grading systems, and expressions indicative of uncertainty in the pathologist's report. Active communication has proven to be crucial between the clinician and the pathologist to clarify different aspects of the pathology report. Direct communication between pathologists and patients is evolving, with promising success in proof-of-principle studies. Special attention needs to be paid to avoiding inaccuracy while trying to simplify the pathology report.

CONCLUSIONS

There is a need for active and adequate communication among pathologists, other clinicians, and patients. Clarity and consistency in reporting, quantifying the level of confidence in diagnosis, and avoiding misnomers are key steps toward improving communications.

Mining the Biomarker Potential of the Urine Peptidome: From Amino Acids Properties to Proteases

Native biofluid peptides offer important information about diseases, holding promise as biomarkers. Particularly, the non-invasive nature of urine sampling, and its high peptide concentration, make urine peptidomics a useful strategy to study the pathogenesis of renal conditions. Moreover, the high number of detectable peptides as well as their specificity set the ground for the expansion of urine peptidomics to the identification of surrogate biomarkers for extra-renal diseases. Peptidomics further allows the prediction of proteases (degradomics), frequently dysregulated in disease, providing a complimentary source of information on disease pathogenesis and biomarkers. Then, what does urine peptidomics tell us so far? In this paper, we appraise the value of urine peptidomics in biomarker research through a comprehensive analysis of all datasets available to date. We have mined > 50 papers, addressing > 30 different conditions, comprising > 4700 unique peptides. Bioinformatic tools were used to reanalyze peptide profiles aiming at identifying disease fingerprints, to uncover hidden disease-specific peptides physicochemical properties and to predict the most active proteases associated with their generation. The molecular patterns found in this study may be further validated in the future as disease biomarker not only for kidney diseases but also for extra-renal conditions, as a step forward towards the implementation of a paradigm of predictive, preventive and personalized (3P) medicine.

Bioinformatic Analysis of the Wound Peptidome Reveals Potential Biomarkers and Antimicrobial Peptides

Wound infection is a common and serious medical condition with an unmet need for improved diagnostic tools. A peptidomic approach, aided by mass spectrometry and bioinformatics, could provide novel means of identifying new peptide biomarkers for wound healing and infection assessment. Wound fluid is suitable for peptidomic analysis since it is both intimately tied to the wound environment and is readily available. In this study we investigate the peptidomes of wound fluids derived from surgical drainages following mastectomy and from wound dressings following facial skin grafting. By applying sorting algorithms and open source third party software to peptidomic label free tandem mass spectrometry data we provide an unbiased general methodology for analyzing and differentiating between peptidomes. We show that the wound fluid peptidomes of patients are highly individualized. However, differences emerge when grouping the patients depending on wound type. Furthermore, the abundance of peptides originating from documented antimicrobial regions of hemoglobin in infected wounds may contribute to an antimicrobial wound environment, as determined by in silico analysis. We validate our findings by compiling literature on peptide biomarkers and peptides of physiological significance and cross checking the results against our dataset, demonstrating that well-documented peptides of immunological significance are abundant in infected wounds, and originate from certain distinct regions in proteins such as hemoglobin and fibrinogen. Ultimately, we have demonstrated the power using sorting algorithms and open source software to help yield insights and visualize peptidomic data.

Peptides in Plasma, Urine, and Dialysate: Toward Unravelling Renal Peptide Handling

PURPOSE

The peptidomes of spent hemodialysate, urine, and plasma are investigated, to shed light on peptide handling in the kidney.

EXPERIMENTAL DESIGN

Fifteen plasma, 15 urine, and 13 spent hemodialysate samples are collected from age- and sex-matched subjects with chronic kidney disease. Peptide identification and quantification are performed with capillary electrophoresis-coupled mass spectrometry.

RESULTS

A total of 6278 urinary peptides, 1743 plasma peptides, and 1727 peptides from spent hemodialysate are detected. Of these, sequences can be assigned to 1580, 419, and 352 peptides, respectively. A strong correlation in peptide abundance between urine and spent hemodialysate (p = 3 × 10^-21 , Rho = 0.52), a moderately strong correlation between spent hemodialysate and plasma (p = 4.5 × 10^-5 , Rho = 0.30), and no significant correlation between urine and plasma (p = 0.11, Rho = 0.094) are found. Collagen and fibrinogen alpha peptides are highly abundant in all three body fluids. In spent hemodialysate, thymosin ß4 is one of the most abundant peptides, which is shown to be negatively associated with the estimated glomerular filtration rate (Rho = -0.39, p-value = 3.9 × 10^-81 ).

CONCLUSION AND CLINICAL RELEVANCE

The correlation of peptide abundance in these three body fluids is lower than expected, supporting the hypothesis that tubular reabsorption has a major impact on urinary peptide content. Further investigation of thymosin ß4 in hemodialysis is thus warranted.

Knowledge Translation in Oncology: The Bumpy Ride From Bench to Bedside

OBJECTIVES

Knowledge translation (KT) is the dynamic process of mobilizing best-practice evidence to guide health care decisions.

METHODS

Using a PubMed search, challenges were identified and milestones defined.

RESULTS

Substantial challenges exist in integrating discoveries into patient care, including technical limitations related to genomic testing like turnaround time, standardization, reproducibility, and results interpretation. Other challenges include lack of proper training in genetic counseling for health care providers, clarity of scientific evidence, and ethical, legal and social considerations. In addition, most health care systems lack accessibility to genetic testing services. Moving forward, KT should be addressed at three main frontiers. The first is patients centered for proper understanding and decision making; the second is directed toward health care professionals, including clinical decision support and clarity of roles; and the third addresses resources of health care systems.

CONCLUSIONS

Implementing KT requires developing strategies to enhance awareness and promote behavioral changes congruent with research evidence, designing a systematic approach by health care providers and stakeholders to achieve patient-centered care.

Urinary Peptidomic Biomarkers in Kidney Diseases

In order to effectively develop personalized medicine for kidney diseases we urgently need to develop highly accurate biomarkers for use in the clinic, since current biomarkers of kidney damage (changes in serum creatinine and/or urine albumin excretion) apply to a later stage of disease, lack accuracy, and are not connected with molecular pathophysiology. Analysis of urine peptide content (urinary peptidomics) has emerged as one of the most attractive areas in disease biomarker discovery. Urinary peptidome analysis allows the detection of short and long-term physiological or pathological changes occurring within the kidney. Urinary peptidomics has been applied extensively for several years now in renal patients, and may greatly improve kidney disease management by supporting earlier and more accurate detection, prognostic assessment, and prediction of response to treatment. It also promises better understanding of kidney disease pathophysiology, and has been proposed as a “liquid biopsy” to discriminate various types of renal disorders. Furthermore, proteins being the major drug targets, peptidome analysis may allow one to evaluate the effects of therapies at the protein signaling pathway level. We here review the most recent findings on urinary peptidomics in the setting of the most common kidney diseases.

A clean-up strategy for identification of circulating endogenous short peptides in human plasma by zwitterionic hydrophilic liquid chromatography and untargeted peptidomics identification

Short peptides, namely di- tri- and tetra peptides, have been proven to play an important diagnostic role in several diseases. Therefore, the development of an analytical approach for their detection and identification is nowadays an important research goal. This paper describes an analytical procedure able to overcome the issues of short peptide isolation, clean-up and identification in plasma samples. Four different protocols were compared and tested to maximize both recovery and total number of identifications of short circulating plasma endogenous peptides. The purified peptides, coming from the four different tested protocols, were separated by zwitterionic hydrophilic liquid chromatography coupled to high-resolution mass spectrometry with the purpose of accomplishing an untargeted investigation based on suspect screening for short peptides in plasma. In particular, the use of Phree™ Phospholipid removal cartridge in combination with a purification step by solid phase extraction on a graphitized carbon black sorbent allowed the identification of the largest number of amino acid sequences (91 short peptides). The clean-up procedure allowed to tackle the issue of the low abundance of such peptides and their suppression during mass-spectrometric analysis. The results indicated that sample preparation is therefore fundamental for short peptide analysis in plasma samples.

Searching for prognostic biomarkers for small renal masses in the urinary proteome

Renal cell carcinoma (RCC) is frequently diagnosed incidentally as an early-stage small renal mass (SRM; pT1a, ≤4 cm). Overtreatment of patients with benign or clinically indolent SRMs is increasingly common and has resulted in a recent shift in treatment recommendations. There are currently no available biomarkers that can accurately predict clinical behavior. Therefore, we set out to identify early biomarkers of RCC progression. We employed a quantitative label-free liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) proteomics approach and targeted parallel-reaction monitoring to identify and validate early, noninvasive urinary biomarkers for RCC-SRMs. In total, we evaluated 115 urine samples, including 33 renal oncocytoma (≤4 cm) cases, 30 progressive and 26 nonprogressive clear cell RCC (ccRCC)-SRM cases, in addition to 26 healthy controls. We identified six proteins, which displayed significantly elevated expression in clear cell RCC-SRMs (ccRCC-SRMs) relative to healthy controls. Proteins C12ORF49 and EHD4 showed significantly elevated expression in ccRCC-SRMs compared to renal oncocytoma (≤4 cm). Additionally, proteins EPS8L2, CHMP2A, PDCD6IP, CNDP2 and CEACAM1 displayed significantly elevated expression in progressive relative to nonprogressive ccRCC-SRMs. A two-protein signature (EPS8L2 and CCT6A) showed significant discriminatory ability (areas under the curve: 0.81, 95% CI: 0.70-0.93) in distinguishing progressive from nonprogressive ccRCC-SRMs. Patients (Stage I-IV) with EPS8L2 and CCT6A mRNA alterations showed significantly shorter overall survival (p = 1.407 × 10^-6 ) compared to patients with no alterations. Our in-depth proteomic analysis identified novel biomarkers for early-stage RCC-SRMs. Pretreatment characterization of urinary proteins may provide insight into early RCC progression and could potentially help assign patients to appropriate management strategies.

The Prospect and Challenges to the Flow of Liquid Biopsy in Africa

Liquid biopsy technologies have the potential to transform cancer patient management as it offers non-invasive diagnosis and real-time monitoring of disease progression and treatment responses. The use of liquid biopsy for non-invasive cancer diagnosis can have pivotal importance for the African continent where access to medical infrastructures is limited, as it eliminates the need for surgical biopsies. To apply liquid biopsy technologies in the African setting, the influence of environmental and population genetic factors must be known. In this review, we discuss the use of circulating tumor cells, cell-free nucleic acids, extracellular vesicles, protein, and other biomolecules in liquid biopsy technology for cancer management with special focus on African studies. We discussed the prospect, barriers, and other aspects that pose challenges to the use of liquid biopsy in the African continent.

MoS2 Field-Effect Transistor-Amyloid-β1-42 Hybrid Device for Signal Amplified Detection of MMP-9

The detection of circulating protein (CP) is very important for the diagnosis and therapeutics of cancer. Conventional techniques based on a specific antibody-antigen interaction are still lacking because of a shortage of cost effectiveness, complicated sandwich structure and tagging process, and inconsistent detection of CP due to the inherent instability of antibodies. Herein, we demonstrate a hybrid device consisting of two-dimensional (2D) nanoscale molybdenum disulfide (MoS₂) field-effect transistor (FET) with an amyloid-β_1-42 (Aβ_1-42) functionalized surface, which amplifies electric signals of the FET in order to detect matrix metalloproteinase-9 (MMP-9), which is a certain type of CP that degrades Aβ_1-42. With the hybrid device, we detected the concentrations of MMP-9 in the range from 1 pM to 10 nM. Moreover, using tapping-mode atomic force microscopy and Kelvin probe force microscopy, we verified that the signal amplification corresponding to the MMP-9 concentrations was caused by the reduced length and the decreased surface potential of degraded Aβ_1-42 due to MMP-9. The hybrid device studied in this paper can be very useful for monitoring MMP-9 activity, as well as serving as a sensing platform for the electrical signal amplification of 2D MoS₂ FET-biosensors.

Digging Deep into Peptidomics Applied to Body Fluids

Peptidomics techniques have allowed the identification of thousands of peptides that are derived from proteins in body fluids, despite the considerable challenges behind sample handling, MS-based identification, data analysis, and integration with bioinformatics tools. Body fluids' naturally occurring peptides are known to perform a variety of local and systemic functions; however, its knowledge is limited. Even so, the biological meaning that can be retrieved from peptidomics applied to the identification of disease markers and to the development of therapies using peptides has driven the progresses made in this field. In this review, a comparative analysis of body fluids' peptidome data retrieved from databases and from scientific papers is performed to identify the biological processes modulated by naturally occurring peptides. This integrative analysis highlights several interesting facts, such as the small overlap between blood-derived serum and plasma, which illustrates the impact of sample handling on these fluids peptidome. Urine is the body fluid with more naturally occurring peptides identified so far, most of which are derived from collagens. In saliva, the majority of peptides are originated from extracellular matrix proteins. Cerebrospinal fluid presents a high number of peptides derived from distinct proteins, mostly involved in the regulation of nervous system homeostasis. The lowest number of endogenous peptides was found in tears, most of which present antimicrobial activity. Collectively, data analysis highlights a peptidome signature for each body fluid, which comprehension will certainly help to improve disease management.

[Preliminary Study on the Biological Markers for I-IIb Stage Non-small Cell Lung Cancer Based on a Serum-peptidomics]

背景与目的

非小细胞肺癌是发病率最高的一种肺恶性肿瘤，早发现、早诊断、早治疗是其治疗原则，因其发病隐匿，缺乏早期筛查的特异性标志物，多数患者就诊时已处于晚期阶段，导致其5年生存率较低，预后不理想。探索一种敏感的生物学标志物，是目前肺癌诊疗的重点。本文基于差异多肽组学技术，旨在筛选Ⅰ期-Ⅱb期非小细胞肺癌的血清生物标志物。

方法

利用纳升超高效液相色谱联合四级杆轨道阱质谱技术，比较和筛选正常健康人群、肺部良性病变患者、非小细胞肺癌患者血清中的差异多肽片段，通过对目标肽段的鉴别和定量分析，探寻非小细胞肺癌早期诊断的肿瘤生物标志物。

结果

通过与人类蛋白质组数据库进行比对，共鉴定到545个多肽，分别来自118个蛋白。将各组样本中肽段的谱图数进行比较，共筛选出201个差异多肽，经定量分析发现峰面积的变异系数（coefficient of variation, CV）≤30%的目标肽段共计7个，其中在各组间呈趋势变化的有2条肽段，分别是肺癌组中表达下调的肽段QGAKIPKPEASFSPR和肺癌组中表达上调的肽段CDDYRLC，它们分别来自于中间α球蛋白抑制因子H4蛋白（ITIH4）和基质γ-羧基谷氨酸蛋白（MGP）。

结论

将血清多肽组学用于筛选肿瘤标志物的研究，可为非小细胞肺癌的早期诊断提供新线索，其中ITIH4与MGP蛋白水解的特异性肽段是潜在早期非小细胞肺癌患者的血清标志物。

Artificial intelligence for aging and longevity research: Recent advances and perspectives

The applications of modern artificial intelligence (AI) algorithms within the field of aging research offer tremendous opportunities. Aging is an almost universal unifying feature possessed by all living organisms, tissues, and cells. Modern deep learning techniques used to develop age predictors offer new possibilities for formerly incompatible dynamic and static data types. AI biomarkers of aging enable a holistic view of biological processes and allow for novel methods for building causal models-extracting the most important features and identifying biological targets and mechanisms. Recent developments in generative adversarial networks (GANs) and reinforcement learning (RL) permit the generation of diverse synthetic molecular and patient data, identification of novel biological targets, and generation of novel molecular compounds with desired properties and geroprotectors. These novel techniques can be combined into a unified, seamless end-to-end biomarker development, target identification, drug discovery and real world evidence pipeline that may help accelerate and improve pharmaceutical research and development practices. Modern AI is therefore expected to contribute to the credibility and prominence of longevity biotechnology in the healthcare and pharmaceutical industry, and to the convergence of countless areas of research.

Peptidome profiling dataset of ovarian cancer and non-cancer proximal fluids: Ascites and blood sera

Despite a large number of proteomic studies of biological fluids from ovarian cancer patients, there is a lack of sensitive screening methods in clinical practice (Kim et al., 2016) (DOI:https://doi.org/10.1111/cas.12987[1]). Low molecular weight endogenous peptides more easily diffuse across endothelial barriers than proteins and can be more relevant biomarker candidates (Meo et al., 2016) (DOI:https://doi.org/10.18632/oncotarget.8931[2], (Bery et al., 2014) DOI:https://doi.org/10.1186/1559-0275-11-13[3], (Huang et al., 2018) DOI:https://doi.org/10.1097/IGC.0000000000001166[4]). Detailed peptidomic analysis of 26 ovarian cancer and 15 non-cancer samples of biological fluids (ascites and sera) were performed using TripleTOF 5600+ mass-spectrometer. Prior to LC-MS/MS analysis, peptides were extracted from biological fluids using anion exchange sorbent with subsequent peptide desorption from the surface of highly abundant proteins. In total, we identified 4874 peptides; 3123 peptides were specific for the ovarian cancer samples. The mass-spectrometry peptidomics data presented in this data article have been deposited to the ProteomeXchange Consortium (Deutsch et al., 2017) (DOI:https://doi.org/10.1093/nar/gkw936[5]) via the PRIDE partner repository with the dataset identifier PXD009382 and https://doi.org/10.6019/PXD009382, http://www.ebi.ac.uk/pride/archive/projects/PXD009382.

The poly-omics of ageing through individual-based metabolic modelling

BACKGROUND

Ageing can be classified in two different ways, chronological ageing and biological ageing. While chronological age is a measure of the time that has passed since birth, biological (also known as transcriptomic) ageing is defined by how time and the environment affect an individual in comparison to other individuals of the same chronological age. Recent research studies have shown that transcriptomic age is associated with certain genes, and that each of those genes has an effect size. Using these effect sizes we can calculate the transcriptomic age of an individual from their age-associated gene expression levels. The limitation of this approach is that it does not consider how these changes in gene expression affect the metabolism of individuals and hence their observable cellular phenotype.

RESULTS

We propose a method based on poly-omic constraint-based models and machine learning in order to further the understanding of transcriptomic ageing. We use normalised CD4 T-cell gene expression data from peripheral blood mononuclear cells in 499 healthy individuals to create individual metabolic models. These models are then combined with a transcriptomic age predictor and chronological age to provide new insights into the differences between transcriptomic and chronological ageing. As a result, we propose a novel metabolic age predictor.

CONCLUSIONS

We show that our poly-omic predictors provide a more detailed analysis of transcriptomic ageing compared to gene-based approaches, and represent a basis for furthering our knowledge of the ageing mechanisms in human cells.

Microextraction combined with microderivatization for drug monitoring and protein modification analysis from limited blood volume using mass spectrometry

In the clinic, ethosuximide is commonly used to treat generalized absence seizures but has recently been repurposed for other diseases. Because of adverse effects and drug interactions, high-throughput therapeutic drug monitoring of ethosuximide is necessary. Microextraction is a simple, effective, rapid, and low consumption of organic solvents method for sample preparation. In this study, microderivatization-increased detection (MDID)-combined microextraction was used to detect ethosuximide by mass spectrometry. Ethosuximide is a difficult to retain and ionize compound in the C18 nano-flow column and ionization interface, respectively. Hence, we developed a fast method for detecting ethosuximide in human plasma by using the MDID strategy (within 2 min). Chemical microderivatization parameters were studied and optimized to increase the sensitivity of ethosuximide detection at trace levels. The linear range for the analysis of ethosuximide in 10 μL plasma was 5-500 μg/mL with a coefficient of determination (r²) ≥ 0.995. The precision and accuracy of intraday and interday analyses of ethosuximide were below 13.0%. Furthermore, modifications of major proteins in plasma and blood cells, induced by ethosuximide, were identified. The proposed method effectively utilizes microliter samples to detect drug plasma concentrations under suitable microextraction procedures toward the eco-friendly goal of low consumption of organic solvents. Graphical abstract ᅟ.

Urinary Markers in Bladder Cancer: An Update

Bladder cancer (BC) is ones of the most common cancer worldwide. It is classified in muscle invasive (MIBC) and muscle non-invasive (NMIBC) BC. NMIBCs frequently recur and progress to MIBCs with a reduced survival rate and frequent distant metastasis. BC detection require unpleasant and expensive cystoscopy and biopsy, which are often accompanied by several adverse effects. Thus, there is an urgent need to develop novel diagnostic methods for initial detection and surveillance in both MIBCs and NMIBCs. Multiple urine-based tests approved by FDA for BC detection and surveillance are commercially available. However, at present, sensitivity, specificity and diagnostic accuracy of these urine-based assays are still suboptimal and, in the attend to improve them, novel molecular markers as well as multiple-assays must to be translated in clinic. Now there are growing evidence toward the use of minimally invasive “liquid biopsy” to identify biomarkers in urologic malignancy. DNA- and RNA-based markers in body fluids such as blood and urine are promising potential markers in diagnostic, prognostic, predictive and monitoring urological malignancies. Thus, circulating cell-free DNA, DNA methylation and mutations, circulating tumor cells, miRNA, IncRNA and mRNAs, cell-free proteins and peptides, and exosomes have been assessed in urine specimens. However, proteomic and genomic data must to be validated in well-designed multicenter clinical studies, before to be employed in clinic oncology.

Urinary kallikrein 10 predicts the incurability of gastric cancer

The current imaging modalities are not sufficient to identify inoperable tumor factors, including distant metastasis and local invasion. Hence, we conducted this study using urine samples to discover non-invasive biomarkers for the incurability of gastric cancer (GC). Urine samples from 111 GC patients were analyzed in this study. The GC cohort was categorized and analyzed according to disease stage and operability. In the discovery phase, protease protein array analysis identified 3 potential candidate proteins that were elevated in the urine of advanced GC patients compared to early GC patients. Among them, urinary kallikrein 10 (KLK10) was positively associated with tumor stage progression. Moreover, the urinary level of KLK10 (uKLK10) was significantly elevated in the urine of patients with inoperable GC compared to operable GC patients (median, 118 vs. 229; P=0.014). The combination of uKLK10, tumor location and tumor size distinguished operability of GC with an area under the curve of 0.859, 82.4% sensitivity and 86.2% specificity. Disease-free survival (DFS) was significantly shorter in GC patients with high uKLK10 compared to those with low uKLK10 (hazard ratio: 3.30 [95% confidence interval, 1.58-6.90] P<0.001). Immunohistochemical analyses also demonstrated a positive correlation between tumor stage and KLK10 expression in GC tissues (r=0.426, P<0.001). In addition, GC patients with high expression of pathological KLK10 (pKLK10) showed a significantly shorter DFS compared to those with low pKLK10 (hazard ratio: 3.79 [95% confidence interval, 1.27-11.24] P=0.010). uKLK10 is a promising non-invasive biomarker for the inoperability and incurability of GC.

The New Age of -omics in Urothelial Cancer - Re-wording Its Diagnosis and Treatment

Unmet needs in urothelial cancer management represent an important challenge in our effort to improve long-term overall and disease-free survival rates with no significant compromise in quality of life. Radical cystectomy with pelvic lymph node dissection is the standard for the management of muscle-invasive, non-metastatic cancers. In spite of a 90% local disease control, up to 50% of patients ultimately die of distant metastasis. Bladder preservation using chemo-radiation is an acceptable alternative, but optimal patient selection remains elusive. Recent research is focused on the employment of tailored-made strategies in urothelial cancer exploiting the potential of theranostics in patient selection for specific therapies. Herein, we review the current knowledge on molecular theranostics in urothelial cancer and we suggest that this is the time to move toward imaging theranostics, if tailored-made disease management and patient stratification is envisaged.

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Urothelial cancer management represents an important challenge.

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Optimum patient stratification and tailored-made theranostics remain elusive.

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Imaging theranostics is envisaged as a cancer roadmap.

Unmasking molecular profiles of bladder cancer

Precision medicine is designed to tailor treatments for individual patients by factoring in each person's specific biology and mechanism of disease. This paradigm shifted from a "one size fits all" approach to "personalized and precision care" requires multiple layers of molecular profiling of biomarkers for accurate diagnosis and prediction of treatment responses. Intensive studies are also being performed to understand the complex and dynamic molecular profiles of bladder cancer. These efforts involve looking bladder cancer mechanism at the multiple levels of the genome, epigenome, transcriptome, proteome, lipidome, metabolome etc. The aim of this short review is to outline the current technologies being used to investigate molecular profiles and discuss biomarker candidates that have been investigated as possible diagnostic and prognostic indicators of bladder cancer.

Converging blockchain and next-generation artificial intelligence technologies to decentralize and accelerate biomedical research and healthcare

The increased availability of data and recent advancements in artificial intelligence present the unprecedented opportunities in healthcare and major challenges for the patients, developers, providers and regulators. The novel deep learning and transfer learning techniques are turning any data about the person into medical data transforming simple facial pictures and videos into powerful sources of data for predictive analytics. Presently, the patients do not have control over the access privileges to their medical records and remain unaware of the true value of the data they have. In this paper, we provide an overview of the next-generation artificial intelligence and blockchain technologies and present innovative solutions that may be used to accelerate the biomedical research and enable patients with new tools to control and profit from their personal data as well with the incentives to undergo constant health monitoring. We introduce new concepts to appraise and evaluate personal records, including the combination-, time- and relationship-value of the data. We also present a roadmap for a blockchain-enabled decentralized personal health data ecosystem to enable novel approaches for drug discovery, biomarker development, and preventative healthcare. A secure and transparent distributed personal data marketplace utilizing blockchain and deep learning technologies may be able to resolve the challenges faced by the regulators and return the control over personal data including medical records back to the individuals.

Quantitative Mass Spectrometry-Based Proteomic Profiling for Precision Medicine in Prostate Cancer

Prostate cancer (PCa) is one of the most frequently diagnosed cancer among men in the western societies. Many PCa patients bear tumors that will not threat their lives if left untreated or if treatment is delayed. Our inability for early identification of these patients has resulted in massive overtreatment. Therefore, there is a great need of finding biomarkers for patient stratification according to prognostic risk; as well as there is a need for novel targets that can allow the development of effective treatments for patients that progress to castration-resistant PCa. Most biomarkers in cancer are proteins, including the widely-used prostate-specific antigen (PSA). Recent developments in mass spectrometry allow the identification and quantification of thousands of proteins and posttranslational modifications from small amounts of biological material, including formalin-fixed paraffin-embedded tissues, and biological fluids. Novel diagnostic and prognostic biomarkers have been identified in tissue, blood, urine, and seminal plasma of PCa patients, and new insights in the ethology and progression of this disease have been achieved using this technology. In this review, we summarize these findings and discuss the potential of this technology to pave the way toward the clinical implementation of precision medicine in PCa.

[Analysis of contribution of protein phosphorylation in the development of the diseases]

In recent decades, studies in the molecular origins of socially significant diseases have made a big step forward with the development and using of high-performance methods in genomics and proteomics. Numerous studies in the framework of the global program "Human Proteome" were aimed at the identification of all possible proteins in various cell cultures and tissues, including cancer. One of the objectives was to identify biomarkers - proteins with high specificity to certain pathologies. However, in many cases, it is shown that the development of the disease is not associated with the appearance of new proteins, but depends on the level of gene expression or forming of proteoforms - splice variants, single amino acid substitutions (SAP variants), and post-translational modifications (PTM) of proteins. PTM may play a key role in the development of pathology because they activate a variety of regulatory or structural proteins in the majority of cell physiological processes. Phosphorylation is among the most significant of these protein modifications.This review will describe methods for analysis of protein phosphorylation used in the studies of such diseases as cancer and neurodegenerative diseases, as well as examples of cases when the modified proteins are involved directly to their development, and screening such significant PTM is used for the diagnosis and choice of treatment.

Clinical multi-omics strategies for the effective cancer management

Cancer is a global health issue as a multi-factorial complex disease, and early detection and novel therapeutic strategies are required for more effective cancer management. With the development of systemic analytical -omics strategies, the therapeutic approach and study of the molecular mechanisms of carcinogenesis and cancer progression have moved from hypothesis-driven targeted investigations to data-driven untargeted investigations focusing on the integrated diagnosis, treatment, and prevention of cancer in individual patients. Predictive, preventive, and personalized medicine (PPPM) is a promising new approach to reduce the burden of cancer and facilitate more accurate prognosis, diagnosis, as well as effective treatment. Here we review the fundamentals of, and new developments in, -omics technologies, together with the key role of a variety of practical -omics strategies in PPPM for cancer treatment and diagnosis.

BIOLOGICAL SIGNIFICANCE

In this review, a comprehensive and critical overview of the systematic strategy for predictive, preventive, and personalized medicine (PPPM) for cancer disease was described in a view of cancer prognostic prediction, diagnostics, and prevention as well as cancer therapy and drug responses. We have discussed multi-dimensional data obtained from various resources and integration of multisciplinary -omics strategies with computational method which could contribute the more effective PPPM for cancer. This review has provided the novel insights of the current applications of each and combined -omics technologies, which showed their powerful potential for the establishment of PPPM for cancer.

CancerPDF: A repository of cancer-associated peptidome found in human biofluids

CancerPDF (Cancer Peptidome Database of bioFluids) is a comprehensive database of endogenous peptides detected in the human biofluids. The peptidome patterns reflect the synthesis, processing and degradation of proteins in the tissue environment and therefore can act as a gold mine to probe the peptide-based cancer biomarkers. Although an extensive data on cancer peptidome has been generated in the recent years, lack of a comprehensive resource restrains the facility to query the growing community knowledge. We have developed the cancer peptidome resource named CancerPDF, to collect and compile all the endogenous peptides isolated from human biofluids in various cancer profiling studies. CancerPDF has 14,367 entries with 9,692 unique peptide sequences corresponding to 2,230 unique precursor proteins from 56 high-throughput studies for ~27 cancer conditions. We have provided an interactive interface to query the endogenous peptides along with the primary information such as m/z, precursor protein, the type of cancer and its regulation status in cancer. To add-on, many web-based tools have been incorporated, which comprise of search, browse and similarity identification modules. We consider that the CancerPDF will be an invaluable resource to unwind the potential of peptidome-based cancer biomarkers. The CancerPDF is available at the web address http://crdd.osdd.net/raghava/cancerpdf/.

Liquid biopsy: a step forward towards precision medicine in urologic malignancies

There is a growing trend towards exploring the use of a minimally invasive "liquid biopsy" to identify biomarkers in a number of cancers, including urologic malignancies. Multiple aspects can be assessed in circulating cell-free DNA, including cell-free DNA levels, integrity, methylation and mutations. Other prospective liquid biopsy markers include circulating tumor cells, circulating RNAs (miRNA, lncRNAs and mRNAs), cell-free proteins, peptides and exosomes have also emerged as non-invasive cancer biomarkers. These circulating molecules can be detected in various biological fluids, including blood, urine, saliva and seminal plasma. Liquid biopsies hold great promise for personalized medicine due to their ability to provide multiple non-invasive global snapshots of the primary and metastatic tumors. Molecular profiling of circulating molecules has been a stepping-stone to the successful introduction of several non-invasive multi-marker tests into the clinic. In this review, we provide an overview of the current state of cell-free DNA-based kidney, prostate and bladder cancer biomarker research and discuss the potential utility other circulating molecules. We will also discuss the challenges and limitations facing non-invasive cancer biomarker discovery and the benefits of this growing area of translational research.

Prostate cancer proteomics: Current trends and future perspectives for biomarker discovery

The clinical and fundamental research in prostate cancer - the most common urological cancer in men - is currently entering the proteomic and genomic era. The focus has switched from one single marker (PSA) to panels of biomarkers (including proteins involved in ribosomal function and heat shock proteins). Novel genetic markers (such as Transmembrane protease serine 2 (TMPRSS2)-ERG fusion gene mRNA) or prostate cancer gene 3 (PCA3) had already entered the clinical practice, raising the question whether subsequent protein changes impact the evolution of the disease and the response to treatment. Proteomic technologies such as MALDI-MS, SELDI-MS, i-TRAQ allow a qualitative/quantitative analysis of the proteome variations, in both serum and tumor tissue. A new trend in prostate cancer research is proteomic analysis of prostasomes (prostate-specific exosomes), for the discovery of new biomarkers. This paper provides an update of novel clinical tests used in research and clinical diagnostic, as well as of potential tissue or fluid biomarkers provided by extensive proteomic research data.

Characterization of the Low-Molecular-Weight Human Plasma Peptidome

The human plasma proteome represents an important secreted sub-proteome. Proteomic analysis of blood plasma with mass spectrometry is a challenging task. The high complexity and wide dynamic range of proteins as well as the presence of several proteins at very high concentrations complicate the profiling of the human plasma proteome. The peptidome (or low-molecular-weight fraction, LMF) of the human plasma proteome is an invaluable source of biological information, especially in the context of identifying plasma-based markers of disease. Peptides are generated by active synthesis and proteolytic processing, often yielding proteolytic fragments that mediate a variety of physiological and pathological functions. As such, degradomic studies, investigating cleavage products via peptidomics and top-down proteomics in particular, have warranted significant research interest. However, due to their molecular weight, abundance, and solubility, issues with identifying specific cleavage sites and coverage of peptide fragments remain challenging. Peptidomics is currently focused toward comprehensively studying peptides cleaved from precursor proteins by endogenous proteases. This protocol outlines a standardized rapid and reproducible procedure for peptidomic profiling of human plasma using centrifugal ultrafiltration and mass spectrometry. Ultrafiltration is a convective process that uses anisotropic semipermeable membranes to separate macromolecular species on the basis of size. We have optimized centrifugal ultrafiltration (cellulose triacetate membrane) for plasma fractionation with respect to buffer and solvent composition, centrifugal force, duration, and temperature to facilitate recovery >95% and enrichment of the human plasma peptidome. This method serves as a comprehensive and facile process to enrich and identify a key, underrepresented sub-proteome of human blood plasma.

Personalized Proteomics: The Future of Precision Medicine

Medical diagnostics and treatment has advanced from a one size fits all science to treatment of the patient as a unique individual. Currently, this is limited solely to genetic analysis. However, epigenetic, transcriptional, proteomic, posttranslational modifications, metabolic, and environmental factors influence a patient’s response to disease and treatment. As more analytical and diagnostic techniques are incorporated into medical practice, the personalized medicine initiative transitions to precision medicine giving a holistic view of the patient’s condition. The high accuracy and sensitivity of mass spectrometric analysis of proteomes is well suited for the incorporation of proteomics into precision medicine. This review begins with an overview of the advance to precision medicine and the current state of the art in technology and instrumentation for mass spectrometry analysis. Thereafter, it focuses on the benefits and potential uses for personalized proteomic analysis in the diagnostic and treatment of individual patients. In conclusion, it calls for a synthesis between basic science and clinical researchers with practicing clinicians to design proteomic studies to generate meaningful and applicable translational medicine. As clinical proteomics is just beginning to come out of its infancy, this overview is provided for the new initiate.

Precision or Personalized Medicine for Cancer Chemotherapy: Is there a Role for Herbal Medicine

Although over 100 chemotherapeutic agents are currently available for the treatment of cancer patients, the overall long term clinical benefit is disappointing due to the lack of effectiveness or severe side effects from these agents. In order to improve the therapeutic outcome, a new approach called precision medicine or personalized medicine has been proposed and initiated by the U.S. National Institutes of Health. However, the limited availability of effective medications and the high cost are still the major barriers for many cancer patients. Thus alternative approaches such as herbal medicines could be a feasible and less costly option. Unfortunately, scientific evidence for the efficacy of a majority of herbal medicines is still lacking and their development to meet FDA approval or other regulatory agencies is a big challenge. However, herbal medicines may be able to play an important role in precision medicine or personalized medicine. This review will focus on the existing and future technologies that could speed the development of herbal products for treatment of resistant cancer in individual patients. Specifically, it will concentrate on reviewing the phenotypic (activity based) rather than genotypic (mechanism based) approach to develop herbal medicine useful for personalized cancer chemotherapy.