Mass spectrometry based proteomics and metabolomics in personalized oncology

Journal Pre-proof Mass spectrometry based proteomics and metabolomics in personalized oncology

Tomasz Kowalczyk, Michal Ciborowski, Joanna Kisluk, Adam Kretowski, Coral Barbas PII:

S0925-4439(20)30029-6

DOI:

https://doi.org/10.1016/j.bbadis.2020.165690

Reference:

BBADIS 165690

To appear in:

BBA - Molecular Basis of Disease

Received date:

3 September 2019

Revised date:

18 December 2019

Accepted date:

15 January 2020

Please cite this article as: T. Kowalczyk, M. Ciborowski, J. Kisluk, et al., Mass spectrometry based proteomics and metabolomics in personalized oncology, BBA Molecular Basis of Disease(2020), https://doi.org/10.1016/j.bbadis.2020.165690

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© 2020 Published by Elsevier.

Journal Pre-proof Mass spectrometry based proteomics and metabolomics in personalized oncology

Tomasz Kowalczyk1, Michal Ciborowski1, Joanna Kisluk2, Adam Kretowski1,3, Coral Barbas4

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Metabolomics Laboratory, Clinical Research Centre, Medical University of Bialystok,

Bialystok, Poland. Department of Clinical Molecular Biology, Medical University of Bialystok, Bialystok,

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Poland.

Department of Endocrinology, Diabetology and Internal Medicine, Medical University of

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Bialystok, Bialystok, Poland.

Centre for Metabolomics and Bioanalysis (CEMBIO), Facultad de Farmacia, Universidad

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CEU San Pablo, Madrid, Spain

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spectrometry

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Keywords: personalized medicine, oncology, biomarkers, proteomics, metabolomics, mass

To whom correspondence should be addressed: Coral Barbas, Pharmacy Faculty, Campus

Monteprincipe, San Pablo-CEU University, 28668 Boadilla del Monte, Madrid, Spain, e-mail: [email protected]

Journal Pre-proof Abbreviations list: AUC - area under the curve CE - capillary electrophoresis CI - confidence interval CID - collision-induced dissociation DDA - data-dependent acquisition

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DDI - data-independent acquisition

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GC - gas chromatography

IHC - immunohistochemistry

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iodoTMT - iodoacetyl tandem mass tags

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HR - hazard ratio

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iTRAQ - isobaric tags for relativeand absolute quantitation

LC - liquid chromatography

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iTRAQH - isobaric tags for relativeand absolute quantitation hydrazide

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LSI – lipidomics standards initiative

MRM - multiple monitoring reactions MS - mass spectrometry

NMR -nuclear magnetic resonance PRM -parallel reaction monitoring QQQ - triple quadruple

ROC - receiver operating characteristics SRM - selected reaction monitoring SWATH - sequential acquisition of all the theoretical ionic spectra TMT - tandem mass tags

Journal Pre-proof Abstract

Precision medicine (PM) means the customization of healthcare with decisions and practices adjusted to the individual patient. It includes personalized diagnostics, patients‟ subclassification, individual treatment selection and the monitoring of its effectiveness. Currently, in oncology, PM is based on the molecular and cellular features of a tumor, its microenvironment and the patient‟s genetics and lifestyle. Surprisingly, the available targeted therapies were found effective only in a subset of patients. An in-depth understanding of

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tumour biology is crucial to improve their effectiveness and develop new therapeutic targets. Completion of genetic information with proteomics and metabolomics can give broader

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knowledge about tumor biology which consequently provides novel biomarkers and indicates

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new therapeutic targets. Recently, metabolomics and proteomics have extensively been applied in the field of oncology. In the context of PM, human studies, with the use of mass

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spectrometry (MS) which allows the detection of thousands of molecules in a large number of samples, are the most valuable. Such studies, focused on cancer biomarkers discovery or

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patients‟ stratification, are presented in this review. Moreover, the technical aspects of MS-

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based clinical proteomics and metabolomics are described.

Journal Pre-proof 1. Introduction

Precision medicine means the customization of healthcare, with decisions and practices being tailored to the individual patient by the use of genetic or other information. In practice, not to single patients but groups of individuals are classified into subpopulations that are uniquely or disproportionately susceptible to a particular disease or more prone to a specific treatment. Therefore, personalized medicine means not only personalized treatment but also personalized diagnostics, both for earlier detection of disease and for the selection of

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appropriate treatment and monitoring its effectiveness. It also aims to sub-classify patients into specific disease phenotypes [1, 2]. While the concept of personalized medicine is not

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new, e.g. transfusion patients have been matched with donors according to blood type for

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more than a century, it has gained momentum in the last two decades [3, 4]. Advances in genetics and other omics platforms together with the growing availability of health data,

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present an opportunity to make precise personalized patient care a clinical reality. „Omics‟ platforms (genomics, transcriptomics, proteomics, and metabolomics) together with advanced

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bioinformatic tools may allow for more sophisticated patients‟ characterization and the creation of pipelines and scenarios required for personalized medicine[4]. The first step

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toward personalized medicine was the completion of the Human Genome Project in 2003. This allowed the discovery of single nucleotide polymorphisms (SNPs) which make each

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individual unique., SNPs characterization in different pathologies and the evaluation of SNPsrelated treatment success have made it possible to associate different molecular signatures with the diagnosis, prognosis and therapy given to particular patients [5]. Currently, in oncology, precision medicine is based on the molecular and cellular features of a tumor, as well as those of its microenvironment and additional traits of the individual, such as genetics and lifestyle[6]. Defining genetic alterations allows granting an individual, patient-tailored, therapy in accordance with the genomic as well as the epigenomic characteristics of the tumor [7].Several discoveries have been made in that area in the last decades. The identification of breast cancer type 1 (BRCA1) and type 2 (BRCA2) germline mutations allowed the selection of individuals at risk of breast and ovarian cancer giving them the possibility to prevent cancer development by undergoing prophylactic surgery. The Ewings sarcoma-friend leukemia integration 1 transcription factor (EWS-FLI1) fusion gene was found as an indicator of an Ewing sarcoma. Alterations, such as tyrosine kinase-type cell surface receptor (HER2) amplification in breast cancer, K-ras (KRAS) and proto-oncogene B-

Journal Pre-proof raf (BRAF) mutations in colorectal cancer or breakpoint cluster region-Abelson murine leukemia viral oncogene homolog 1 (BCR-Abl) fusion in chronic myelogenous leukemia, are currently routinely examined [7, 8]. Lynch et al. [9] and Paez et al. [10] reported the presence of activating mutations in the tyrosine kinase domain of the epidermal growth factor (EGFR) gene in patients who had a dramatic response to the EGFR tyrosine kinase inhibitor (TKI) [11]. Since then, personalized medicine for lung cancer has become a reality. Currently, personalized medicine used to treat lung cancer uses identified genetic mutations that are characteristic of tumor growth and survival. Mutations of K-RAS, EGFR, BRAF and phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIC3CA) genes are

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most commonly described and used in the targeted treatment of lung cancer. Moreover, anaplastic lymphoma kinase (ALK) gene rearrangement and (mesenchymal-epithelial

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transition factor) MET gene amplification are also often used in personalized lung cancer

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medicine [12]. Isocitrate dehydrogenase 1 (IDH1) and 2 (IDH2) gene‟s mutations are widely reported in gliomas; tumor suppressor 53 (TP53) and ATRX chromatin remodeler (ATRX)

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gene‟s mutations are a driving force in diffuse and anaplastic astrocytomas; while 1p/19q codeletion and telomerase reverse transcriptase (TERT) promoter mutation are characteristic in

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oligodendrogliomas [7]. The discovery of genetic abnormalities and understanding of tumor immunology allowed the development of specific therapies with a high precision of molecular

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targeting. Oncoproteins or oncogenes, which are mainly involved in various signaling pathways and are essential to tumor development and survival, are the primary targets of these

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novel drugs. Specific monoclonal antibodies (trastuzumab or pertuzumab), small molecule tyrosine kinase inhibitors (lapatinib, afatinib or neratinib) and antibody-drug conjugates (Trastuzumab–emtansine) are available targeted treatment options for HER2 positive breast cancer. Among other molecular targets EGFR for non-small cell lung cancer, fms related tyrosine kinase 3-internal tandem duplication (FLT3-ITD) for acute myeloid leukemia, vascular endothelial growth factor (VEGF) and (mammalian target of rapamycin kinase) mTOR renal cell carcinoma as well as vascular endothelial growth factor receptor (VEGFR) for hepatocellular carcinoma, can be mentioned [13]. Although targeted therapy provides new treatment opportunities, even in individuals with similar clinical cancer phenotypes, it has been found effective only in a specific subset of patients [13, 14]. Further research has shown that differential drug response is often a result of dissimilarity in genetic alterations which may contribute to cancer progression by allowing growth and spread of the malignancy or contribute to drug effectiveness if there are mutations

Journal Pre-proof in genes involved in drug metabolism[14]. Moreover, mutations targeted by the drugs currently available are often present only in part of the patients with a particular cancer type. In the case of breast cancer patients HER2 gene amplification and HER2 protein overexpression accounts for about 25% of all cases, activating EGFR mutations occur in only 1–3% of patients with NSCLC, while about 20–30% of patients with acute myeloid leukemia harbor an internal tandem duplication mutation of the FMS-like tyrosine kinase receptor (FLT3-ITD mutation) [13]. Therefore, an in-depth understanding of the biology of the tumor, including molecular changes and altered signalling pathways, is crucial for the identification of patients who are likely to benefit from targeted treatments. It may also contribute to the

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development of new therapeutic targets for non-responders to the currently available therapies or patients not bearing the mutations covered by currently available drugs [14]. Consequently,

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to make the next step forward on the way to precision medicine, additional research, going

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beyond genetics studies, is necessary. Completion of genetic information with proteomics and metabolomics information can give a broader knowledge about tumor development and

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progression. The abovementioned “omics” approaches can be applied to search for new biomarkers for early detection or subtyping, to indicate new therapeutic targets and to predict

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and evaluate the effectiveness of treatment. In the last decade several valuable studies showing the application of metabolomics or proteomics in the field of oncology have been

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published [15, 16]. In the context of personalized medicine human studies are the most valuable; especially these in which mass spectrometry was used to detect metabolites or

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proteins, as this technology allows for the detection of thousands of proteins or metabolites in a large number of samples [17, 18]. Extensive analysis of the proteome and metabolome enables the in-depth study of tumor biology but also poses many challenges for scientists (Figure 1.). Consequently, in this review technical aspects of MS-based clinical proteomics and metabolomics will be described. Moreover, recent human studies in which these MSbased “omics” approaches were used for cancer biomarkers discovery or cancer patients‟ stratification based on proteins or metabolites level will be presented.

2. Methods

In order to collect scientific articles describing the application of proteomics or metabolomics to seek cancer biomarkers, the phrases “cancer biomarkers proteomics” and

Journal Pre-proof “cancer biomarkers metabolomics” were searched in PubMed(the last search was performed on July 18th, 2019) returning 6643 and 1607 records, respectively. Due to the large number of records, especially in the case of proteomics, only papers published in the last 4 years were selected (since 1st of January 2016), which resulted in 1867 and 911 records for proteomics and metabolomics, respectively. Each retrieved record was inspected and only biomarkersoriented, large-scale human original studies in which proteomics or metabolomics was performed using an MS-based approach were selected and are presented in this review.

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3. LC-MS-based clinical proteomics

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Proteomics studies aim not only to identify and quantify (or semi-quantify) proteins in a biological sample, but also, very frequently, protein post-translation modifications (PTMs)

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can be evaluated, which gives information on epigenetic inheritance and relaxation or chromatin compression and recruitment for example in histone research [19]. There are

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different strategies for proteome study. The top-down approach is applied for a one-time

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analysis of the whole protein. The advantage of this method is a good identification of proteins, including unique proteoforms and PTMs. On the other hand, this strategy can suffer from a dynamic range challenge where the same highly abundant ions are repeatedly

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fragmented [20]. The bottom-up strategy is based on the proteolytic digestion of pre-purified proteins and their subsequent analysis with a mass spectrometer. This method allows the

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identification of a large number of proteins based on peptide sequences. However, certain limitations exist, e.g.: loss of PTMs information, extended run times on multi-dimensional LC and loss of low abundant peptides masked by high abundant protein information [21].The third strategy is the shotgun proteomics approach, presented in 1998 by Yates III [22], which is a modified bottom-up approach. The main modification is related to sample preparation. Protein samples are digested into peptides by trypsin or other proteases and the amino acid sequence of each peptide is determined by tandem mass spectrometry. Based on the list of peptides identified by the mass spectrometer the protein composition of the sample is deduced [23]. So far, this last approach has been extensively applied in clinical research, especially in oncology. Several studies using this strategy to analyze clinical samples have already been performed and reviewed [24, 25]. In clinical proteomics research, it is very important to obtain quantitative information about the proteins‟ level. Such a type of data is very important especially in the studies

Journal Pre-proof focused on searching for new biomarkers or monitoring the effectiveness of treatment. Several quantitative proteomic methods, such as metabolic labelling, chemical labelling, or label-free protocols can be used to quantify protein(s) or peptide(s). Quantification of proteins in clinical samples such as tissue or biological fluids is largely carried out using chemical labelling techniques. Isobaric tags, for example tandem mass tags (TMT), isobaric tags for relative and absolute quantitation (iTRAQ), iodoacetyl tandem mass tags (iodoTMT), or isobaric tags for relative and absolute quantitation hydrazide (iTRAQH), label both N-terminus and the lysine side chain of a peptide in the digested mixture. Labelling tags do not affect peptides chromatographic separation but can be observed after MS/MS analysis. Upon fragmentation,

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the mass of the reporter ion is separated from the peptide. As tags with different masses are available, samples with different tags can be pooled and analysed as one sample. The ratio of

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the intensity of each reporter ion is used to obtain quantitative information on the labelled

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peptides between samples [26]. Labelling methods significantly save the working time of the device and allows the comparison of a large number of biological samples, which is very

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important in the biomarkers-oriented studies. However, the use of TMT or iTRAQis not a fully optimal method for detecting differences in protein profiles in individual tumors, as

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multiplexing methods also label low signal peptides at background levels [27]. The other method for quantification of proteins is label-free (LF) quantification. In this method, no

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chemicals are used for labelling, but quantitative information is obtained based on the measurement of chromatographic peak‟s area and integration with MS analysis. This

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approach provides quantitative protein data with high proteome coverage, giving a greater amount of protein identification than chemical labels [27, 28] (Figure 2.). However, it is not recommended for analysis of large sets of samples, as with time chromatography becomes unreproducible and MS loses its sensitivity due to ion source contamination. Therefore, this approach is often used in clinical trials with small sample sets [29]. A similar method to LF, which doesn‟t use chemical labels, is the sequential acquisition of all the theoretical ionic spectra (SWATH). SWATH-MS is based on the cyclic acquisition of precursor ions with solid isolation windows that cover the entire m/z range. All ionized precursor peptides in the sample are fragmented and their fragmentation spectra are collected allowing the retrospective analysis of peptides of interest using spectral libraries. SWATH-MS combines the advantages of high repeatability and sensitivity of targeted methods, such as selected monitoring reactions (SRM) or multiple monitoring reactions (MRM) with the increased proteome depth typically observed in the case of data-dependent MS analysis [30]. SWATH-MS is versatile and is used

Journal Pre-proof in a variety of applications, including quantitative protein determination in personalized oncology [31]. Another approach to the studies of proteins, which can be useful in personalized oncology, is targeted proteomics [32]. It is used for the quantitative determination of particular proteins and can be useful for the validation of potential biomarkers. Targeted proteomics is based on data-independent acquisition (DIA) strategy. DIA works as a previously defined MS/MS fragmentation, and data collection provides a more sensitive and accurate determination of the amount of protein compared to the data-dependent acquisition (DDA) (Figure 3.). Targeted DIA is also used to quantify small molecules, peptides, and PTMs. In targeted proteomics,

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two types of targeted DIA analyses are used: selected reaction monitoring and parallel

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reaction monitoring (PRM). The second requires the use of a high-resolution accurate MS analyser [33]. In the SRM analysis, a single fragment ion is monitored, while in PRM the

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instrument records all fragments of peptides from an analytical sample with a high mass

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resolution [34] (Figure 4).

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4. MS-based clinical metabolomics

Similarly to proteomics, metabolomics research aims to identify and quantify (or semiquantify) small molecule metabolites present in the studied sample [35]. Two analytical

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techniques dominate metabolomics research: stand-alone nuclear magnetic resonance (NMR) or mass spectrometry combined with different separation methods, i.e. liquid chromatography

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(LC), gas chromatography (GC) or capillary electrophoresis (CE). This combination of analytical platforms enables detection, characterization and quantification of low-molecularweight metabolites from different classes. However, as a stand-alone technique, each of them has limited capabilities for the detection of metabolites. NMR can uniquely identify and quantify a wide range of organic compounds but is limited to metabolite concentrations in the micromolar range or above. Amino acids, vitamins, thiols, carbohydrates, peptides, nucleotides and nucleosides have been measured using NMR. The LC-MS method is better suited for the analysis of labile and non-volatile nonpolar (reversed-phase chromatography) and polar (normal phase chromatography) compounds in their native forms. GC-MS is a good choice for separation and quantification of the volatile metabolites. It has been used to analyze several classes of compounds including organic acids, most amino acids, sugars, sugar alcohols, aromatic amines and fatty acids. CE-MS is a perfect tool for the study of polar and ionic metabolites, including inorganic ions, organic acids, amino acids, vitamins, thiols, carbohydrates, peptides nucleotides and nucleosides [36]. However, considering the use of a

Journal Pre-proof single analytical technique for metabolomics study, LC-MS provides the highest metabolome coverage[35]. Consequently, despite of the undoubted contribution of NMR spectroscopy to study the multicomponent metabolic composition of biofluids, cells, and tissues [37], MS, especially LC-MS, has a higher sensitivity and is able to rapidly resolve and identify individual metabolites in complex mixtures, making it an ideal tool for high throughput precision metabolomics of clinical samples [38, 39]. Moreover, an LC-MS system is able to detect thousands of features within a single run and when used in a targeted manner can be successfully used in large-scale clinical studies [38, 40]. A basic characteristic of different MS-based analytical approaches used in metabolomics is presented in Table 1. As lipids

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comprise about one-third of all metabolites and using LC-MS even thousands of individual lipids can be measured, a separate area of metabolomics, called lipidomics, evolved. LIPID

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MAPS is a web-based source of information about lipids. It was established in 2003 and is

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constantly developing. Currently it is not only a lipid database but also a lipid biochemistry encyclopedia which additionally contains tools for lipid‟s drawing and bioinformatics

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analysis. In 2018 Lipidomics Standards Initiative (LSI) was established which aims to develop guidelines for the entire lipidomics workflow, i.e. samples collection, as well as lipid

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extraction, MS analysis, identification and quantification [41]. Changes in the metabolism of lipids are related to cancer development. These molecules are important building blocks of

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cancer cells and a source of energy [42]. Lipidomics studies substantially increase our knowledge about the role of these molecules in cancer, recently the role of lipids in colorectal

reviewed.

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cancer [43], acute myeloid leukaemia [44] and hepatocellular carcinoma [45] have been

Independent of the applied technique, metabolites can be measured using different approaches, i.e. metabolic fingerprinting, metabolic profiling or a target study of the metabolite. Metabolic fingerprinting aims to detect and semi-quantify metabolites present in a biological sample [46]. The number of metabolites that are measured is limited to those that were extracted using the chosen methodology and are detectable by selected analytical technique. This type of analysis is also called untargeted metabolomics, as it is not focused on particular metabolite(s), therefore it allows the discovery of novel metabolic pathways disturbed by the disease or affected by the treatment without a prior hypothesis. Considering clinical applications, this approach has potential in biomarker-oriented discovery and interventional studies aiming to evaluate the effectiveness of the treatment [16]. However, untargeted studies have their limitations. In the case of MS-based studies, only a limited

Journal Pre-proof number of samples can be analysed. The reason for that is the continuous contamination of the ion source during the sequence of analyses and subsequent decrease of sensitivity caused by a complex composition of samples. Another limitation is the lack of quantitative information about measured metabolites. In this type of study, usually the relative difference of metabolite‟s levels between studied groups is reported together with its statistical significance. Considering clinical applications aiming to use the data about metabolites‟ level for diagnostic purposes, this information should be quantitative, presented as a concentration of metabolite, not signal recorded by the instrument or value related to the control group [16]. In the context of personalized medicine, due to abovementioned limitations, untargeted methods are

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currently used as discovery tools. It means, that obtained results should be considered preliminary and require validation on a bigger cohort by using targeted metabolomics

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methods, such as metabolic profiling or target analysis of metabolite(s), in order to prove their

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clinical value. Targeted metabolomics enable the quantitative measurement of small molecules from all classes to which chemical standards are available. The additional value of

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a targeted approach is a sample treatment protocol, which can be adjusted to a particular class of metabolites which are going to be measured. In this way more selective analytical protocols

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can be used, allowing for more effective extraction and elimination of other molecules that can interfere in the ion source, which can significantly improve the detection limit. Targeted

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metabolomics is mainly performed using LC-MS equipment with triple quadruple (QQQ) detector and MRM mode. In this mode, a metabolite ion is resolved and isolated in the first

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quadrupole (Q1). The second quadrupole (Q2) works as a collision cell where the ion undergoes a collision-induced dissociation (CID) and is fragmented. Obtained fragment ions are accelerated into the third quadrupole, which also acts as a mass filter, seeking for a previously defined m/z of a fragment ion, which is then introduced to the detector (Figure 4). The combination of retention time and fragmentation pattern are usually specific for one metabolite and the intensity can be compared to the intensity of a standard of this metabolite or standard of other metabolites from the same class (having similar structure and properties). The highest precision of the concentration measurement can be achieved by spiking the sample with isotopically labelled metabolites‟ standards, which also allows controlling drift in instrument performance [47]. Recently, the SWATH technology; used in targeted proteomics with MRM analysis has also been applied to analyze metabolites. Using the SWATH to MRM approach, a high-coverage targeted metabolomics method with 1,303 metabolites in one injection was developed to profile colorectal cancer (CRC) tissues and adjacent normal tissues

Journal Pre-proof surgically excised from CRC patients. The proposed SWATH to MRM approach demonstrated high reproducibility, sensitivity, and dynamic range [48].

5. From clinical studies to personalized medicine

Before introducing to clinical practice, potential biomarkers require proper validation which should be performed in a large group of patients ideally with different ethnicity and

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geographical distribution. Depending on the type of study (e.g. discovery or validation), the number of patients should be inversely proportional to the number of molecules measured.

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The applied methodology should be quantitative, in order to introduce adequate cut-off values, and optimized for the measurement of selected molecules. Evaluation of the clinical

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utility of quantified molecules as biomarkers can be performed by different statistical tests

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(Table 2). Among them, the most popular is calculation of the area under the curve (AUC) for the receiver operating characteristic curve (ROC). Generally, AUC values below 0.7 are

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considered very poor, between 0.80-0.95 good and very good while above 0.95 are seen as excellent in terms of diagnostic accuracy. Biological material selected for this purpose should

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be easy to collect, ideally blood (serum or plasma) or urine, but cancer tissue can also be used to search for potential biomarkers. The use of cellular organelles such as mitochondria or

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ribosomes in omics research is also observed. Studies on extracellular vesicles (EV) may also provide interesting and important results. EVs are lipid bilayer-delimited particles that are released by the cells in response to cell activation, exposure to complement proteins, injury or cellular stress. In addition, EVs may be isolated from different body fluids. and are involved in a wide range of processes that underlie cancer progression, e.g. angiogenesis, lymphogenesis, cell migration, cell proliferation or metastasis [49].The huge research potential that ensues from the biological functions performed by EV is currently very often used to search for new biomarkers, especially in precision oncological medicine [50, 51]. As proteomics and metabolomics are relatively young disciplines, considering research seeking for oncological biomarkers, there are many more small discovery studies [52], than those including discovery and validation phases [53]. However, there are also discovery studies performed in a large group of patients [54], as well as studies aiming to validate biomarkers proposed by other researchers [55]. In this chapter, recent (from the last 4 years) studies focused on potential protein and metabolite oncological biomarkers will be presented. We

Journal Pre-proof have focused on human studies performed on large sets (at least one hundred) of patients, in order to introduce to the readers the studies which bring closer MS-based proteomics or metabolomics to be used as tools allowing for precision oncological medicine in clinical practice.

5.1. MS-based proteomics

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In total 32 articles remained after manual inspection of research papers focused on the application of proteomics to search for oncological biomarkers. Only the most recent (2017-

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2019) studies, grouped by the location of the tumor, are described below; while all of them are

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summarized in Table 3.

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Urinary system

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Bladder cancer (BC) is the most common malignancy of the urinary tract, characterized by a high rate of relapse within five years after surgical resection. Duriez et al.

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[56] used the selected reaction monitoring (SRM) method to analyze urine samples and evaluate the diagnostic utility of potential biomarkers of BC described in the literature. Out of

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the 108 potential biomarkers, only 6 proteins were found significantly different between urine samples of controls and BC patients. The study confirmed the strong relationship of such proteins as thrombospondin-1 (TSP1), uromodulin (UROM), phospholipid transfer protein (PLTP) and keratin - type I cytoskeletal 19 protein (K1C19) with disease status and risk factor. Ten proteins, especially aminopeptidase-N (AMPN), alpha-N-acetylglucosaminidase (ANAG) and tumor necrosis factor (TNFA) show the strongest association with the prevalent BC prognostic risk group. Of all the proven biomarkers lysosome-associated membrane glycoprotein 1 (LAMP1) has demonstrated the highest potential to be used as a diagnostic marker of relapse in patients with an earlier history of BC. Urine is the first choice of samples to look for urinary tract cancer biomarkers. However, a tumor tissue sample is a valuable source of the molecular composition of the cancerous tissue, providing information about the molecules, which later can be found in biofluids (urine or blood). Zheng et al. [57] used LC-MS-based proteomics with isobaric labeling (iTRAQ) to analyze 18 pairs of tumor and adjacent control tissue samples obtained

Journal Pre-proof from patients with renal cell carcinoma (RCC). Out of 2985 quantified proteins, 38 were found up-regulated and 174 down-regulated in different stages of renal carcinoma. Among others, the diagnostic utility of phosphoinositide-dependent protein kinase 1 (PDZK1) protein has been validated using different approaches. PDZK1 mRNA was evaluated in a cohort of 532 RCC and 72 normal tissue samples indicating poor prognosis of RCC in patients with low PDZK1 mRNA levels. Validation of PDZK1 protein was also performed in an independent group of patients (n=202) using immunohistochemistry and western blotting. As obtained by the ROC analysis (AUC=0.877), the sensitivity and specificity of the level of PDZK1 protein in the classification of RCC patients with a good and poor prognosis for overall survival were

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96.6% and 71.4%, respectively.

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Progesterone receptor membrane component 1 (PGRMC1) is a frequently observed oncological biomarker [58]. Its utility as a biomarker of RCC has also been evaluated. Zhang

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et al. [59] performed proteomics on RCC SILAC cell line and observed up-regulation of this protein in renal cell carcinoma cells. Moreover, analysis of tissue biopsies obtained from 135

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renal cancer patients showed an almost 64% higher level of PGRMC1 in cancer in

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comparison to non-cancerous tissue. The high level of PGRMC1 was also associated with the grade of malignancy. Moreover, testing of serum samples obtained from RCC patients and

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Digestive system

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controls indicated an almost 2-fold increase of PGRMC1 level in cancer patients.

Any cancer affecting the digestive system belongs to a group of gastrointestinal (GI) cancers, which are responsible for more cancer deaths than cancers in any other system in the body. Among different types of GI cancers stomach, biliary system, liver, pancreas, small intestine, large intestine or colon cancer can be mentioned [60]. Arbelaiz et al. [51] studied serum samples obtained from patients with primary sclerosing cholangitis (PSC), intrahepatic cholangiocarcinoma (iCCA), hepatocellular carcinoma (HCC) and controls. The extracellular vesicles (EV) isolated from the serum samples were analysed using transmission electron microscopy, nanoparticle tracking analysis and immunoblotting. In addition, proteome analyzes using LC-MS were also performed. This enabled the identification of 95 proteins differentially expressed between CCA and controls, 161 between PSC and controls, 50 between CCA and PSC, as well as 98 between HCC and controls. Of all identified proteins aminopeptidase N (AMPN), pantetheinase (VNN1) and

Journal Pre-proof polymeric immunoglobulin receptor (PIGR) showed the best diagnostic capability to distinguish between CCA and controls with AUC 0.878, 0.876 and 0.844 for each protein, respectively. However, for the comparison of PSC patients with controls AMPN, ficolin-1 (FCN1) and neprilysin (NEP) presented the best diagnostic value with AUC 0.789, 0.771 and 0.761, respectively. When comparing two patients‟ groups (PSC and HCC), fibrinogen gamma chain (FIBG), alpha-1-acid glycoprotein 1 (A1AG1) and S100A8 (S10A8) were identified. The diagnostic value of these proteins was evaluated as AUC=0.819. For the comparison of the HCC and control group the two proteins with the highest diagnostic potential were proposed: galectin-3-binding protein (LG3BP) with AUC=0.904 and polymeric

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immune receptor (PIGR) with AUC=0.837. In addition, the level of VNN1, C-reactive protein (CRP), FIBG, immunoglobulin heavy constant alpha 1 (IGHA1), A1AG1 and gamma-

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glutamyltransferase 1 (GGT1) proteins were found increased in serum EV of CCA patients

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compared to all PSC, HCC or healthy individuals. Only the level of LG3BP was found increased in HCC as compared to all CCA, PSC and healthy controls.

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HCC has also been studied by Yu et al. [61] who used a targeted proteomic approach to evaluate the effectiveness of treatment in patients with this type of cancer. The exploratory

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and validation stages were performed on serum samples (n=180) originating before and 6 months after transarterial chemoembolization (TACE). MRM analyses identified leucine-rich

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alpha-2-glycoprotein (LRG1), serum amyloid P component (APCS), butyrylcholinesterase (BCHE), complement component C7 (C7), and ficolin-3 (FCN3) potential biomarkers with

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the ability to predict complete response after TACE (AUC=0.881 in the training set and 0.813 in the validation set).

The same analytical method was used by Kim et al. [62] to check the effectiveness of the treatment of patients with hepatocellular carcinoma with sorafenib. Based on the studies of serum samples a panel of three proteins comprising CD5 antigen-like (CD5L), immunoglobulin J (IGJ), galectin-3-binding protein (LGALS3BP) were proposed as potential biomarkers for treatment effectiveness. This panel was proposed as a significant predictor (AUC>0.95) of the efficacy of HCC treatment with sorafenib. In addition, the low level of these proteins was found to be a predictor of the overall survival time [hazard ratio (HR), 2,728; 95% confidence interval (CI), 1.312-5.672; p=0.007] and an independent predictive factor of rapid progression (HR, 2.631, 95% CI, 1.448-4.780, p=0.002). A combined proteomic, phosphoproteomic and gene expression analyses of blood as well as tumor and control tissue samples was performed by Vasaikar et al. to study colon cancer

(CRC).

Phosphoproteomics

data

associated

retinoblastoma

(Rb)

protein

Journal Pre-proof phosphorylation with increased proliferation and decreased apoptosis in colon cancer, suggesting that targeting Rb phosphorylation may be a new therapeutic option for CRC [63]. Another worth mentioning study on CRC was performed by Mori et al. [64] who used LC-MS-based tissue proteomics to find predictors of distant metastasis of colon cancer. Fiftyfive proteins associated with the metastasis of colon cancer to the lymph nodes have been identified. Among them, the expression of ezrine was significantly higher at both protein and mRNA levels in cancer tissue of CRC patients. High expression of this protein was strongly associated with metastasis and poor prognosis (OR 2.3088, 95% CI 1.1513-4.7120, P = 0.0183).

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Pancreatic cancer (PC) is characterized by a very poor prognosis. About 7% of people will survive five years, except patients diagnosed at an early stage of the disease, for whom

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the five-year survival rate rises to about 25% [65]. Sogawa et al. [66] analysed the serum

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samples of patients with pancreatic ductal carcinoma (PDAC) before and after surgery using LC-MS proteomics supported with TMT labeling. Out of the 20 proteins identified as

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potential biomarkers, the diagnostic potential of two C4b-binding protein α-chain (C4BPA) and polymeric immunoglobulin receptor (PIGR) were confirmed by validation. The levels of

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C4BPA and PIGR were statistically increased in preoperative patients versus postoperative patients. The level of C4BPA was higher in patients with PDAC in comparison to controls,

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patients with inflammation, pancreatitis or other cancers, including cholangiocarcinomas. The diagnostic utility was evaluated by receiver operating characteristic curve (ROC) analysis

(AUC=0.912).

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giving AUC=0.860, which was even better in the case of the early stage of PDAC

Pancreatic cancer was also studied by Park et al. [54] who used both proteomics and transcriptomics data to detect the potential biomarkers in the serum of PC patients (n=638). Using shotgun proteomics four proteins apolipoprotein A-IV (APOA4), apolipoprotein CIII (APOC3), insulin-like growth factor-binding protein 2 (IGFBP2) and tissue inhibitor of metalloproteinase 1 (TIMP1) discriminating PC patients from controls were identified. In addition, all results were validated using SIM-MRM technique. The combination of two potential biomarkers (APOA4 and TIMP1) with a common tumor biomarker (CA19-9) demonstrated better performance (AUC>0.934) for distinguishing early PC from pancreatitis than any other panel of biomarkers.

Respiratory system

Journal Pre-proof Among malignancies of the respiratory system, the most prevalent and lethal is lung cancer, which at the same time is the most common cancer in the world and the most common cause of death from cancer [67]. Lopez-Sanchez et al. [68] used proteomics to analyze exhaled breath condensate (EBC) of patients with lung cancer in order to identify potential biomarkers. EBC samples obtained from cancer patients contained more dermcidin and less hornerinin compared to EBC samples obtained from the control group. Moreover, several cytokeratins (KRT6A, KRT6B, and KRT6C) were significantly elevated in patients with lung cancer showing proper diagnostic capabilities, as evaluated by ROC curve analysis (AUC=0.82). In addition, the

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level of cytokeratins positively correlated with the size of the tumor.

The concentration of cytokeratins in pleural effusions of lung cancer patients and

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controls was also studied by Perzanowska et al. [69] using targeted (MRM-based) LC-MS

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method. A significant difference in the abundance of cytokeratins: CK-7, -8, -17, -18, -19 and CK-19 FGPGVAFR peptide was observed between cancer and control groups. By combining

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five the most important markers using a logistic regression model, discriminating power was obtained at the AUC of 0.912 (95% CI: 0.828-0.996). Moreover, the proposed panel of

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biomarkers was also able to discriminate between small cell lung cancer as well as the two

CI: 0.753-0.990).

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Reproductive system

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NSCLC subtypes (adenocarcinoma and squamous cell carcinoma) with AUC of 0.872 (95%

Cancers of the reproductive system can affect men and women and occur in the reproductive organs. In women, breast and cervical cancers are the most common [70] while in men, prostate and testicular cancers [71]. Barnabas et al. [72] applied proteomics to samples of utero-tubal lavage in order to search for potential biomarkers allowing the early diagnosis of ovarian cancer (OC). Nine differentiating proteins, among which six were higher and three lower in the OC patients in comparison to controls, were found. Five of these proteins: S100A2, S100A14, serpin B5 (SERPINB5), involucrin (IVL) and calcium-activated chloride channel regulator 4 (CLCA4) were also statistically significant between the control and patient samples in the discovery cohort demonstrating 83% of sensitivity, 100% of specificity and AUC of 0.99. Endometrium carcinoma (EC) was studied by Martinez-Garcia et al. [73] using LCPRM method. Uterine aspirates samples were analysed and 28 proteins significantly discriminating EC patients from the control group were identified. The level of two proteins,

Journal Pre-proof metalloproteinase 9 (MMP9) and pyruvate kinase (KPYM), exhibited 94% sensitivity and 87% specificity for detecting EC cases (AUC = 0.960). Moreover, the panel of nine proteins was significantly increased in endometrioid EC as compared to serous EC. The combination of three of these proteinsβ-Catenin (CTNB1), exportin-2(XPO2), and macrophage-capping protein (CAPG) achieved 95% sensitivity and 96% specificity for the discrimination of these subtypes. Qing et al. [74] identified potential biomarkers of cervical cancer (CC) and papillomavirus infection, which is one of the causes of CC. The primary study was performed on 76 tissue samples and 67 proteins were identified as differentially expressed in the 16-

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positive human papillomavirus of squamous cell carcinoma compared to the normal cervix. Transcriptomic assays enabled the detection of upregulation of acid ceramidase (ASAH1),

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poly(rC)-binding protein 2 (PCBP2), probable ATP-dependent RNA helicase DDX5 (DDX5),

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DNA replication licensing factor MCM5 (MCM5), transgelin-2 (TAGLN2), heterogeneous nuclear ribonucleoprotein A1 (hnRNPA1), alpha-enolase (ENO1), thymidine phosphorylase

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(TYPH), cytochrome c1 (CYC) and DNA replication licensing factor MCM4 (MCM4) in squamous cell carcinoma compared to the normal cervix. A validation study confirmed the

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overexpression of PCBP2, hnRNPA1, ASAH1 and DDX5 proteins in squamous cell carcinoma and intraepithelial cervical II-III neoplasia compared to normal controls. The

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results suggest that the overexpression of ASAH1, PCBP2, DDX5 and hnRNPA1 and possibly MCM4, MCM5, CYC, ENO1 and TYPH is increased during cervical carcinogenesis

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associated with human papillomavirus infection. Evaluation of cytochrome P450 reductase (CYPOR) protein expression as a prognostic biomarker for triple-negative breast cancer (TNBC) was performed by Pedersen et al. [75]. In a mass spectrometry-based global proteomic study of 44 FFPE primary TNBC tumors and 10 corresponding metastases, it was found that CYPOR expression correlates with patient outcome. This observation was further confirmed using immunohistochemistry (IHC) and publicly available gene expression data. A multivariate Cox regression analysis identified CYPOR as an independent prognostic factor for shorter recurrence free survival of TNBC patients (p=0.032, HR = 2.19, 95% CI 1.07–4.47). Zeng et al. [69] conducted a study aiming to detect potential biomarkers of breast cancer metastasis. By the use of iTRAQ-based quantitative tissue proteomics, the authors have shown that a reduced level of nucleobindin-2 (NUCB2) protein was observed in metastatic lymph nodes. The overall survival time of patients with positive expression of NUCB2 protein was shorter than those with negative NUCB2 expression (p=0.004).

Journal Pre-proof Moreover, statistical models have indicated that NUCB2 is a risk factor in breast cancer patients and can be used as a potential biomarker for breast cancer metastasis and a prognostic predictor. Among cancers of the male reproductive system, prostate cancer (PC) was studied by Sequeiros et al. [50] who analysed urinary extracellular vesicles (uEV), obtained from prostate cancer patients and a control group, by use of targeted LC-MS proteomics with a selected reaction monitoring approach. The patients were divided into low-grade PC and highgrade PC. Based on the literature search, 64 proteins (described as candidate biomarkers) were selected for validation. Eleven of them showed significantly higher and three significantly

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lower levels in uEV of PC patients in comparison to the control group. The best classification model between PC and controls was obtained for a combination of two proteins protein-

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glutamine gamma-glutamyltransferase 4 (TGM4) and adseverin (ADSV) with AUC=0.65.

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Whilst a panel of five proteins phospholipid phosphatase (PPAP), phosphoserine aminotransferase (PSA), CD63 antigen (CD63), N-sulphoglucosaminesulphohydrolase

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(SPHM), glycerol kinase(GLPK5) was found as the best classifier of low-grade and highgrade PC (AUC=0.7). In addition, the validation of obtained results was also performed using

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IHC. The study confirmed the strong link between the urinary EVc proteome and the changes in PC tissues. A high level of ADSV protein, which is critical for cell proliferation, has been

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patients with poor prognosis.

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observed in PC tissue. Moreover, a low level of TGM4 (AUC = 0.803) was detected in

Head and neck region

There is a group of tumors called “head and neck cancers” which are mostly of the squamous cell carcinoma type and are commonly located in the oral cavity, oropharynx, nasopharynx, hypopharynx and larynx. The prevalence of this type of cancer is more common in less developed countries where the occurrence rate is about 35-45% [76, 77]. There are also other types of cancer located in the head and neck region, e.g. brain tumors, sarcoma, esophageal, eye or thyroid cancer, but their diagnosis and treatment are very different. Chen et al. [78] studied oral cancer (OC) in the Taiwan population. Integrated omics data obtained for cancer tissue showed a predominant mutation signature associated with cytidine deaminase proteins family (APOBEC), which correlated with the upregulation of APOBEC3A expression in the APOBEC3 gene cluster at 22q13. APOBEC3A protein was found more expressed in tumors carrying APOBEC3B deletion. The validation study showed

Journal Pre-proof that a high level of APOBEC3A expression is associated with better overall survival, especially among patients carrying APOBEC3B-deletion alleles. Oral squamous cell carcinoma (OSCC) was studied by Carnielli et al. [79] who analysed neoplastic islands and stroma from the invasive tumor front and inner tumor to identify differentially expressed proteins. Such proteins as cystatin-B (CSTB), leukotriene A4 hydrolase (LTA4H), protein NDRG1 (NDRG1), phosphoglycerate kinase 1 (PGK1) from the neoplastic island and collagen alpha-1(VI) chain (COL6A1), integrin alpha-V (ITGAV), myoglobin (MB) from the tumor-stromal were taken for further validation using IHC and SRM-MS analyzes. The IHC test indicated the low expression of cystatin B in tumor islands

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from the invasive tumor front as an independent marker of local recurrence. Targeted proteomics of saliva samples showed low expression of LTA4H, PGK1, NDRG1, COL6A1,

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and ITGAV which was associated with lymph node metastasis and advanced clinical staging

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for patients with OSCC.

Lin et al. [80] conducted serum proteomics in order to study nasopharyngeal

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carcinoma (NPC). The highest diagnostic utility was observed for peroxiredoxin-2 (PRDX2) (AUC=0.614) and PRDX3 (AUC=0.6) proteins, which were found elevated in the patients as

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compared to the controls. In addition, the results were combined and correlated with viral capsid antibodies(VCA-IgA), which allowed for the creation of the panel of biomarkers able

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to diagnose early NPC with 50% sensitivity and 95% specificity (AUC=0.754).

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5.2. MS-based metabolomics

In total 79 articles remained after manual inspection of research papers focused on the application of metabolomics to search for oncological biomarkers. Due to the large number of articles and considering the fact that validated biomarkers-oriented metabolomics studies (including cancer biomarkers) have been reviewed recently[52], only the most recent studies (2018-2019), in which potential biomarkers have been validated in an additional cohort of patients are commented on below. All articles are summarized in Table 4, except for the publications described in the review[52]. Urinary system Liu et al. [81] used LC-MS-based metabolomics in urine to identify biomarkers that enable early diagnosis of bladder cancer. A discovery phase enabled the detection of a panel

Journal Pre-proof of biomarkers (trans-2-dodecenoylcarnitine, serine-valine, feruloyl-2-hydroxyputrescine, and 3-hydroxynonanoyl-carnitine) characteristic for BC. The obtained panel showed a high ability to discriminate BC and controls with AUC=0.956, as evaluated by ROC analysis. Moreover, the panel of 3 metabolites (indolylacryloylglycine, N2-galacturonyl-L-lysine, and aspartyl glutamate) permitted differentiating between early and advanced stages of BC (AUC=0.937). Many neoplastic changes can occur in the genitourinary system, especially in women. The most frequently occurring cancer is cervical cancer (CC). In the study carried out by Khan et al. [82] a panel of potential biomarkers enabling detection of CC and a distinction between cancer and cervical intraepithelial neoplasia (CIN) were identified. Based on

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untargeted and targeted LC-MS-based metabolomics studies seven metabolites (AMP,

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aspartate, glutamate, hypoxanthine, lactate, proline, and piroglutamate) discriminating CIN and cervical cancer from the control group (AUC>0.8) have been identified. In addition,

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elevated levels of these metabolites along with positive HPV status correlated with the risk of

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tumor progression. Digestive system

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The main goal of the research conducted by Corona et al. [83] was to use metabolic profiling of serum to find additional biomarkers that could be integrated with currently used

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protein biomarkers (pepsinogens (PGs) and gastrin 17) to improve the diagnosis of gastric cancer and the selection of first-degree relatives at higher risk of GC development. In total,

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188 serum metabolites (amino acids, biogenic amines, acylcarnitines, phosphatidylcholines, sphingomyelins, and hexoses) were profiled by tandem mass spectrometry using the Biocrates AbsoluteIDQ p180 kit. The study cohort included GC patients and their healthy first-degree relatives as a control group. Out of 40 metabolites selected as potential GC biomarkers based on the results from the training set, 9 were further confirmed in the validation set. In comparison to the control group, GC patients had lower levels of selected hydroxylated sphingomyelins (SM) and phosphatidylcholines as well as higher levels of selected acylcarnitines. The panel of markers composed of metabolites and PGs had better diagnostic utility than metabolites alone, AUC increased from 0.811 to 0.9. The predictive risk algorithm composed of the acylcarnitine C16, SM(OH)22:1 and PG-II serum levels has the potential to indicate first-degree relatives at higher risk of GC development. Another cancer of the digestive system, hepatocellular carcinoma, also lacks the effective diagnostic method for early detection. Therefore, to search for novel HCC biomarkers, Luo et al. [84] applied untargeted serum metabolomics, which was followed by a

Journal Pre-proof targeted approach, in order to validate the potential biomarkers. Candidate biomarkers were selected by untargeted metabolomics performed on the samples from the discovery set, and the reliability of 8 of them was confirmed by the same metabolomics approach which was applied to the samples from the test set. By using binary logistic regression analysis, the ideal biomarker panel consisting of phenylalanyl-tryptophan (Phe-Trp) and glycocholate (GCA) was selected. Finally, the serum concentrations of Phe-Trp and GCA were determined in the samples from the validation set by the isotope-labeled quantification method. This novel biomarker panel was found better for HCC diagnosis than a-fetoprotein (AFP) test. AUC were 0.930, 0.892, and 0.807 for the metabolites panel vs 0.657, 0.725, and 0.650 for AFP in the

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discovery set, test set, and the validation set, respectively. Furthermore, the new biomarker panel was significantly more effective than AFP in identifying patients at the early stage of

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HCC.

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Respiratory system

As previously mentioned, lung cancer, and in particular non-small cell lung cancer

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(NSCLC), has the highest mortality in the world [67]. Therefore, prognosis and early

metabolomics

followed

by

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diagnosis of lung cancer are of great importance. Chen et al. [85] performed untargeted serum metabolic

profiling

of

serum

monounsaturated

and

na

polyunsaturated phosphatidylcholines in patients with early NSCLC stage and healthy controls. A significant increase in the level of PCs (15:0/18:1, 18:0/16:0 and 18:0/20:1) in patients‟ serum was observed. On the other hand, the level of 12 other PCs was significantly

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reduced in patients. Although dysregulation of PCs metabolism is observed at an early stage of NSCLC development, due to their broad role in metabolism and regulation in health and disease [86], their diagnostic specificity as NSCLC biomarkers is questionable.

6. Summary and future perspectives

In the field of oncology, several biomarkers-oriented studies using proteomics and metabolomics approaches have been published in the last years. Although several of these biomarkers have been validated in a large cohort of patients their clinical utility is still unsatisfactory. In the case of proteins, the problem lies in the lack of specificity of the proposed biomarkers to a particular type of cancer. Protein biomarkers, like carcinoembryonic antigen(CEA) or other cancer antigens (CA), discovered for a particular type of cancer are

Journal Pre-proof soon reported as altered in the other types of cancer [87]. Small molecule potential biomarkers still need such validation in many cases. However, just based on the available literature, phospholipids can be considered as general biomarkers of cancer, not specific to any type [88]. One of the ways to improve the specificity is to move from a single to multiplex biomarkers, a so-called signature, which can additionally provide significantly increased diagnostic accuracy [89]. Such signatures should include information from each level of systems biology from genomics through transcriptomics and proteomics to metabolomics. The support of genomics with other omics data may be crucial to moving personalized medicine to the next level. Single or panel biomarkers still require multi-center validation, on

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the international scale. To perform such large-scale studies, collaboration initiatives with involvement of disease-oriented professional societies, governments and academia are needed.

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Therefore, in our opinion, considering protein and metabolite potential cancer biomarkers it is

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time to move from a discovery phase to large-scale, multi-country validation. The most

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promising biomarkers (also genetic) should be selected for this purpose.

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Acknowledgment

MC, JK and AK, acknowledge funding from the Polish National Research Centre

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(2014/13/B/NZ5/01256).TK, MC, JK and AK acknowledge funding from the National Centre for Research and Development in the framework of Programme “Prevention practices and of

civilization

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treatment

diseases”

–

STRATEGMED

(contract

no.

STRATEGMED2/266484/2/NCBR/2015). TK, MC, and AK acknowledge funding from the leading National Research Centre in Poland (61/KNOW-16 and 66/KNOW/16).CB acknowledges to Ministerio de Ciencia, Innovación y Universidades of Spain (Grant CTQ2014-55279-R). We would also like to thank Anna Czajkowska for creating diagrams presenting the proteomics and metabolomics methods in personalized medicine as well as Dan Cherry for his careful English editing and proofreading.

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Figure 1. Challenges of MS-based clinical proteomics and metabolomics research.

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Panel A presents the challenges of MS-based clinical proteomics. Proteins can be modified by protein-protein interactions, which are crucial for many important cellular processes including signal transduction or molecular transport. In addition, post-translational modifications can significantly affect protein functions in the cell, especially in oncological diseases. The second challenge is to use an appropriate analytical method to search for novel biomarkers or to validate them. Panel B presents the challenges of MS-based clinical metabolomics. In the “omics” cascade metabolome is the most susceptible to the modifications related to our lifestyle (diet, physical activity or drugs or supplements). Moreover, metabolites have diverse physicochemical properties what makes a measurement of the whole metabolome challenging. There is no single analytical tool that can measure a complete metabolome. One of the biggest bottle-necks of clinical metabolomics is metabolites identification. In comparison to proteins, metabolites fragmentation pattern is relatively unpredictable. Furthermore, the chemical

Journal Pre-proof standards, which are crucial to confirm identification and to quantify metabolites are often not

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Figure 2. Quantitative methods used to analyse proteins in clinical samples.

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Figure 3. Two different MS strategies used for protein analysis. Panel A presents a data-independent acquisition method of ions analysis. In the DIA method, all ions in the different selected mass-charge (m/z) range windows are fragmented.

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The analysis are repeated as the MS performed the full m/z range. Panel B presents a data-dependent acquisition method of ions analysis. In the DDA method, MS collects all signals that are higher than noise in the full mass spectrum. Then the most intense ones are selected for fragmentation, creating tandem mass spectra (MS / MS), which are compared to those in the database.

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Figure 4. Targeted MS methods used in proteomics and metabolomics research for

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Table 1. A basic characteristics of different MS-based analytical approaches used in metabolomics research.

Analytical method Advantages LC-MS High sensitivity and resolution, simple sample treatment, analysis of metabolites from different classes GC-MS Good resolution and selectivity, high separation efficiency, reproducible retention times, simplified identification with public and commercial databases CE-MS High resolution and separation efficiency, very simple sample treatment, low sample volume required

l a

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Disadvantages Often difficult identification, ion suppression, sensitive to matrix effect Derivatization of metabolites is needed, only for thermally stable, volatile or transferrable to volatile metabolites Only for charged molecules, low migration time reproducibility, in-source fragmentation, sensitive to the presence of salts in the sample

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Table 2. The main statistical methods which can be used to evaluate the efficacy and validity of the biomarkers.

Name Area under the curve for the receiver operating characteristic curve Hazard ratios Diagnostic odds ratios

Abbreviation AUC

HR DOR

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Journal Pre-proof Table 3. LC-MS-based proteomics studies oriented on oncological biomarkers discovery performed in large sample cohorts. Type of cancer

Type of sample

Number of patients

Main proteins

Validation

Type of markers

Ref.

Bladder cancer

urine

n=121

Panel of proteins

yes

Diagnostic

[56]

Renal carcinoma

FF tissue and

PDZ domain containing 1

yes

Prognostic and prediction of survival time

[57]

FFPE tissue

Discovery set n=18, Validation set 1 n=604, set 2 n=15, set 3 n=90

Renal carcinoma

tissue and serum

n=270 tissue, n=30 serum

Progesterone receptor membrane component 1 (PGRMC1)

yes

Diagnostic and treatment efficacy

[59]

Renal carcinoma

FF tissue, FFPE tissue

Discovery set n=18, Validation set 1 n=604, set 2 n=15, set 3 n=90

Serpin peptidase inhibitor clade H member 1 (SERPINH1)

yes

Prognostic

[90]

Cholangiocarcinoma

serum, mice serum, cell culture

n=134

Alpha-2-macroglobulin, galectin-3binding protein, fibronectin, complement C3, clusterin, alphaenolase, annexin A1

yes

Diagnostic and classification

[51]

Colon cancer

tissue and blood

n=110

Panels of proteins

yes

Treatment efficacy

[63]

Colon cancer

FF tissue and FFPE tissue

Discovery set n=20, Validation set 1 n=195, set 2 n=170

yes

Prediction of metastasis

[64]

ezrin

serum

Discovery set n=40, Training set n=152,

yes

Early diagnosis

[91]

Pancreatic cancer

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Vitamin K-dependent protein Z (PROZ), tumor necrosis factor receptor superfamily

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member 6b (TNFRSF6B)

Pancreatic cancer, stomach, colorectal, liver, biliary tract

serum

Discovery set n=3, Validation set 1 n=14, set 2 n=112, set 3 n=146

C4b-binding protein a-chain (C4BPA), polymeric immunoglobulin receptor (PIGR)

Pancreatic cancer

FF tissue and

Discovery set n=10, Targeted set n=8, Validation set n=143 Discovery set n=182, Validation set n=456

FFPE tissue Pancreatic cancer

serum

l a

Discovery set n=362, early stage set n=103, all stage set n=429

yes

Diagnostic

[66]

Brain acid soluble protein 1 (BASP1)

yes

Prognostic

[92]

apolipoprotein A-IV, apolipoprotein CIII, insulin-like growth factor binding protein 2, tissue inhibitor of metalloproteinase 1

yes

Early diagnosis

[54]

insulin-like growth factor-binding protein (IGFBP2 and IGFBP3)

yes

Early diagnosis

[93]

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Pancreatic cancer, Esophagus cancer, Gastric cancer, Cholangiocarcinoma, Hepatocellular carcinoma, colon cancer, duodenal carcinoma

plasma

Pancreatic cancer

cyst fluid

n=110

tripeptidyl peptidase 1

yes

Early diagnosis and treatment response

[94]

Hepatocellular Carcinoma

serum

Discovery set n=100, Validation set n=80

leucine-rich alpha-2-glycoprotein (LRG1), serum amyloid P-component (APCS), cholinesterase (BCHE),

yes

Diagnostic and treatment efficacy

[61]

n r u

o J

Journal Pre-proof complement component C7 (C7), and ficolin-3 (FCN3) Hepatocellular Carcinoma

serum

Training set n=65, Validation set n=50

CD5 antigen-like (CD5L), immunoglobulin J (IGJ), and galectin-3-binding protein (LGALS3BP)

Lung cancer

serum

n=120

paraoxonase/arylesterase 1 (PON1)

Lung cancer

breath condensate

n=192

Dermcidin, hornerin, cytokeratins,

Lung cancer

serum

Discovery set n=40, Validation set n=512

l a

yes

Treatment response

[62]

yes

Early diagnosis

[95]

no

Diagnostic

[68]

yes

Diagnostic and histological defining

[96]

cytokeratins

yes

Treatment efficacy

[69]

f o

ro

p e

r P

alpha-enolase (ENO1)

Lung cancer

pleural effusion

n=118

Pleural mesothelioma

FFPE tissue, plasma, cell culture

Cohort 1 Discovery set n=75, Cohort 2 Validation set 1 n=97, set 2 n=74

secreted protein acidic and rich in cysteine (SPARC)

yes

Prognostic

[97]

Ovarian cancer

Flashed uterine sample

Discovery set n=24, Validation set n=152

Serpin B5 SERPINB5, protein S100A2, S100A14, Calciumactivated chloride channel regulator 4 (CLCA4), myosin-11 (MYH11), Involucrin (IVL), CD109 antigen, Nicotinamide Nmethyltransferase (NNMT), Ectonucleotide pyrophosphatase/phosphodie

yes

Early diagnosis

[72]

rn

J

u o

Journal Pre-proof sterase family member 3 (ENPP3) Breast cancer

FF tissue and FFPE tissue

Discovery set n=23, Validation set n=106

Breast cancer

FF tissue and FFPE tissue

n=192

Breast cancer

FFPE tissue

Discovery set n=54, Validation set n=113

Cytochrome P450 reductase (CYPOR)

Endometrial cancer

uterine aspirate

n=116

Endometrial Cancer

serum

Prostate cancer

urine and FFPE tissue

Cervical carcinoma

FF tissue and

Nucleobindin-2 (NUCB2)

yes

Metastasis

[98]

yes

Prognostic and prediction of metastasis

[99]

yes

[75]

e

Prognostic and prediction of survival time

Metalloproteinase-9, Pyruvate kinase (KPYM), b-Catenin (CTNB1), exportin-2 (XPO2), Macrophage-capping protein (CAPG)

yes

Diagnostic and [100] classification

CA125 and CA 15-3

no

Early diagnosis

[101]

Discovery set n=107, Validation set n= 234

Transglutaminase-4 Adseverin (ADSV), Nsulphoglucosaminesulphohydrolase (SPHM), Putative glycerol kinase 5 (GLPK5)

yes

Diagnostic and prognostic

[50]

n=76, n=116

Acid ceramidase (ASAH1), Poly(rC)-binding protein 2

yes

Diagnostic and

[74]

f o

protein S100-A8

rn

n=224

u o

J

l a

r P

o r p

Journal Pre-proof FFPE tissue

Oral cancer

(PCBP2), Heterogeneous nuclear ribonucleoprotein A1 (hnRNPA1), Minichromosome maintenance complex component 4 and 5 (MCM4,MCM5), Cytochrome c (CYC), Alpha-enolase (ENO1), Thymidine phosphorylase (TYPH)

salvia

f o

n=119

Complement factor H (CFAH), Afamin (AFAM), Gelisolin (GELS), serum amyloid Pcomponent (SAMP), Vitamin Dbinding protein(VTDB)

Oral cancer

tissue

Cohort 1 n=50, cohort 2 n=188

Oral cancer

FFPE tissue and salvia

Discovery set n=20, Validation set n=241

Nasopharyngeal carcinoma

cell culture and serum

l a

o J

n r u

Discovery set n=14, Validation set n=229

o r p

e

r P

prognostic

yes

Diagnostic and [102] prognostic

Apolipoprotein B mRNA Editing enzyme, Catalytic polypeptide-associated signature 3A (APOBEC3A)

yes

Prognostic

[78]

Cystatin B (CSTB), protein NDRG1, Phosphoglycerate kinase 1 (PGK1), Integrin alpha-V (ITGAV)

yes

Prognosis of lymph metastasis

[79]

Peroxiredoxin 2 and 3 (PRDX2, PRDX3)

yes

Diagnostic

[80]

FFPE - Formalin-fixed, Paraffin-embedded tissue; FF – fresh frozen tissue;

Table 4. MS-based metabolomics studies oriented on oncological biomarkers discovery performed in in large sample cohorts.

Journal Pre-proof Type of cancer Colon cancer

Type of sample

Number of patients

Validation

Type of marker

plasma

n=252

no

Prognostic and diagnostic

[103]

f o

no

Prognostic

[104]

no

Diagnostic

[105]

Lysophosphatidylcholine, phosphatidylcholine, taurine, hypoxanthine, valine, leucine, bilirubin, 1-methylnicotinamide

no

Diagnostic

[106]

n=166

Acylcarnitines, amino acids

no

Diagnostic

[107]

n=114

very-long-chain dicarboxylic acid

no

Diagnostic

[108]

Main metabolites Anserine, Trimethylamine Noxide, L-Targinine, gammaGlutamylgammaaminobutyraldehyde, Indoxyl sulfate, Pyridoxal 5′phosphate

ro

Colorectal cancer

serum

n=132

Ceramide, fatty acid, ULCFA

Colorectal cancer

feces

n=129

glycerolipids, glycerophospholipids, sterol lipids, sphingolipids

Colorectal cancer

plasma

l a

n=621

n r u

o J

p e

r P

Colorectal cancer

blood

Colorectal cancer

plasma

Colorectal cancer

serum

Discovery set n=90, Validation set n=150

xanthine, hypoxanthine and Dmannose

yes

Colorectal cancer

urinary

n=163

Isoleucine, leucine, valine, (2Z)-3-methylglutaconic acid, 2-ethylhydracrylic acid, 2-

no

Ref.

Early diagnosis [109]

Prognostic

[110]

Journal Pre-proof methyl-3-hydroxybutyrate Colorectal cancer

plasma

n=572

Palmitoleic acid, 2-ketoglutaric acid, Fumaric acid, ornithine,

yes

Early diagnosis [111]

Colorectal cancer

plasma

n=250

Picolinic acid,

no

Diagnostic and prognostic

[112]

yes

Diagnostic

[113]

Selenocystine, Phosphatidylcholines

o r p

f o

Colorectal cancer

serum

Training set n=378, validation set n=165

Serine, cystine, Octadecenoic acid, lacyid acid, butanoic acid, citric acid, glicerol,

Rectal cancer

serum

n=105

4-Imidazoleacetic acid, dValerolactam, NMethylethanolamine phosphate, Oleanolic Acid Acetate,

no

Prognostic

[114]

Gastric cancer

plasma

n=166

glutamine, ornithine, histidine, argininę, tryptophan

no

Diagnostic

[115]

Gastric cancer

serum

Training set n=86, Validation set n=39

Acylcarnitines, sphingomyelins, phosphatidylcholines

yes

Prognostic

[83]

Hepatocellular carcinoma

serum

n=209

linoleic acid, arachidonic acid, tyrosine, lysophosphatidylcholine,, oleamide, 5-hydroxyhexanoic acid, androsterone sulfate

no

Diagnostic

[116]

Hepatocellular carcinoma

serum

n=147

glutamic acid, tyrosine, Lysophosphatidylcholine,

no

Prognostic

[117]

n r u

l a

o J

r P

e

Journal Pre-proof phosphatidylcholine, sphingomyelins, Hepatocellular carcinoma

plasma

n=128

Valine, serine, glicyne, linoleic acid, glutamic acid,

yes

Hepatocellular carcinoma

serum

n=139

malate, citrate, succinate, lysine, carnitine, proline, ornithine, serine, phenylalanine, tyrosine, arachidonic acid arabinose, galactose, uric acid

yes

Hepatocellular carcinoma

plasma

n=364

Cholangiocarcino ma

urine

n=211

Pancreatitis cancer

serum

n=320

Pancreatitis cancer

serum

Liver cancer

Bladder cancer

e

l a

r P

Diagnostic

[119]

Prognostic

[120]

f o

o r p

Leucine, lysine, glutamine, phenylalanine, tyrosine, glutamate, kynurenine

Early diagnosis [118]

no

acylcarnitine, bile acid, purine

no

Lysophosphatidylcholine, phosphatidylcholine

no

Prognostic

[122]

n=157

Galactose, succinate, phenylalanine, glutamate, mannose, arabitol

yes

Diagnostic

[123]

serum

n=463

Tyrosine, glycochenodeoxycholic acid, glycocholic acid

no

Prognostic and diagnostic

[124]

tissue

n=126

phosphatidylserine, phosphatidylethanolamines, phosphocholines,

no

Prognostic

[125]

n r u

o J

Early diagnosis [121]

Journal Pre-proof diacylglycerols Bladder cancer

urine

n=284

3-hydroxy-cis5-tetradecenoylcarnitine, 6ketoestriol, beta-cortolone, tetrahydrocorticosterone, heptylmalonic acid,

Bladder cancer

urine

n=262

indolylacryloylglycine, N2galacturonyl-L-lysine, aspartylglutamate, trans-2dodecenoylcarnitine,serinylvaline, feruloyl-2hydroxyputrescine, 3hydroxynonanoyl carnitine

no

f o

Early diagnosis [126]

yes

Diagnostic

[81]

5-hydroxyvaleric acid, cholesterol, 3-phosphoglyceric acid, glycolic acid

yes

Diagnostic

[127]

l a

o r p

e

r P

Bladder cancer

urine

Discovery set n=85, Validation set n=96

Bladder cancer

urine

n=152

imidazoleacetic acid,

no

Diagnostic

[128]

n=126

Acylcarnitine, phosphatidylcholine, prolinę, tyrosine

yes

Diagnostic and prognostic

[129]

Estradiol, 2-amino-Imidazole-5carboxylic acid, 2,6-di-t-butyl4-hydroxymethylene,2,3,5,6detetrahydrocyclohexanone tryptophan, kynurenine, anthranilate

no

Diagnostic

[130]

no

Diagnostic

[131]

Endometrial cancer

plasma

n r u

o J

Prostate cancer

urine

Training set n=108, Testing set n=75

Prostate cancer

serum

n=146

Journal Pre-proof Prostate cancer

plasma

n=1077

acylcarnitines, amino acids, biogenic amines, glycerophospholipids, hexose, sphingolipids

no

Diagnostic

[132]

Prostate cancer

serum

n=760

Pyroglutamine, gammaglutamylphenylalanine, phenylpyruvate, Nacetylcitrulline, stearoylcarnitine

no

Prognostic

[133]

yes

Prognostic

[134]

o r p

f o

Prostate cancer

plasma

Discovery set n=96, Validation set n=63

Ceramide, sphingomyelin, phosphatidylcholine

Prostate cancer

blood

n=337

N-acetyl-3-methylhistidine, 3methylhistidine, 2'-deoxyuridine, oleoyl-linoleoylglycerophosphoinositol (GPI), palmitoyl-linoleoyl-GPI, cholate, inositol 1-phosphate

no

Diagnostic and prognostic

[135]

n=104

lysine, arginin, histidine, 7methylguanosine and 7methylguanine

yes

Diagnostic

[136]

n=106

14-Methylhexadecanoic acid, Pantothenic acid, Heptadecanoic acid, uracil, Fructose-6-phosphate

no

Diagnostic and classification

[137]

Discovery set n=25, validation set n=67

sphingosine-1-phosphate

yes

Diagnostic

[138]

l a

Prostate cancer

urine

Prostate cancer

tissue

Prostate cancer

tissue

n r u

o J

r P

e

Journal Pre-proof Breast cancer

plasma

n=201

Palmitic acid, 2Hydroxybenzoic acid, 4Pyridoxic acid, 5Aminolevulinic acid,

yes

Diagnostic

[139]

Breast cancer

salvia

n=166

Spermine

yes

Diagnostic

[140]

Breast cancer

plasma

n=111

Caproic acid , Taurine, Stearamide, Linoleic Acid

no

f o

Diagnostic

[141]

yes

Diagnostic and prognostic

[142]

Leucine, γ-linolenic acid, Polyunsaturated fatty aci, Lysophosphatidylcholine, palmitic acid, retinoic acid

no

Diagnostic

[143]

Breast cancer

serum

n=116

Breast cancer

serum

n=172

e

taurine, glutamic acid, ethylmalonic acid

l a

n r u

o J

o r p

r P

Breast cancer

plasma

Discovery set n=180, Validation set n=159

Acylcarnitines, threonine, tryptophan, ornithine, sphingomyelins,

yes

Early diagnosis [144]

Breast cancer

serum and plasma

Training set n=167, Test sets n=242

Taurine, hypotaurine, Pyruvate, amino acids: succinate, choline, serine, glycine, alaninę, aspartate,

yes

Diagnostic

[145]

Breast cancer

plasma

n=175

carnitine, lysophosphatidylcholine,

no

Diagnostic and classification

[146]

Journal Pre-proof proline, alanine, lysophosphatidylcholine, glycochenodeoxycholic acid, valine, 2-octenedioic acid Breast, prostate, and colorectal cancer

plasma

Breast, n=362 Prostate, n=310 Colorectal, n=163

Lysophosphatidylcholine, phosphatidylcholine

Cervical cancer

plasma

n=217

aspartate, glutamate, hypoxanthine, lactate, proline, pyroglutamate

Cervical cancer

blood

n=285

Risk factors

[147]

yes

Diagnostic

[82]

bilirubin, Lysophosphatidylcholine, noleoyl threonine, 12hydroxydodecanoic acid, tetracosahexaenoic acid

yes

Diagnostic

[148]

Glycerophospholipids,

yes

Prognostic

[149]

n r u

l a

f o

o r p

e

r P

no

Ovarian cancer

serum, tissue, ascites and blood

Discovery set n=130, Validation set n=165

Ovarian cancer

plasma

n=298

Small peptides (SPs)

yes

Diagnostic

[150]

Ovarian cancer

plasma

n=120

Kynurenine, Acetylcarnitine, phosphatidylcholine, lysophosphatidylethanolamine

no

Prognostic

[151]

Ovarian cancer

serum and tissue

Serum n=258 (158 OC patients), Tissue n=124 (112 patients matched

hydroxybutyric acid

yes

Diagnostic and prognostic

[152]

o J

Journal Pre-proof with serum) Glioblastoma

plasma

n=159

Arginine, methionine, kynurenate

yes

Prognostic

[153]

Glioblastoma

serum

n=220

α-tocopherol, γ-tocopherol

no

Prognostic

[154]

Glioma

serum

n=128

2-Oxoarginine, cysteine, alphaketoglutarate, chenodeoxycholate, argininate

no

[155]

Leukemia

serum

n=186

Stearic acid, oleic acid

f o

Diagnostic

no

Prognostic

[156]

Oral cancer

salvia

n=194

Glycine, proline

yes

Diagnostic

[157]

Serum, plasma and tissue

n=334

Lysophosphatidylcholine, phosphatidylcholine, tryptophan, L-proline, glutamic acid, L-alanine

no

Diagnostic

[158]

Lung cancer

serum

n=136

Cysteine, serine, 1monooleoylglycerol

no

Diagnostic

[159]

Lung cancer

urine

n=529

creatine riboside, Nacetylneuraminic acid, cortisol sulfate

no

Lung cancer

plasma

n=211

cortisol,cortisone, 4methoxyphenylacetic acid

no

Diagnostic

[161]

Lung cancer

serum

Discovery set n=180, Validation set n=60

saturated and monounsaturated phosphatidylcholines

yes

Diagnostic

[85]

Thyroid cancer

n r u

o J

-p

re

P l a

ro

Early diagnosis [160]

Journal Pre-proof Lung cancer

serum

Training set n=10, validation set n=124

Cysteine, Glyoxylic acid, triglyceride, diglyceride, Phosphatidylinositol, Inosinic acid, Docosapentaenoic acid, Arachidonic acid, Phosphatidylcholine, Myristoleic acid

Lung cancer

serum

n=153

phenylalanine , citrulline, aspartic acid, β-alanine

Lung cancer

serum

n=284

Panel of metabolites

l a

o J

n r u

e

r P

Prediction of treatment efficacy

[162]

no

f o

Diagnostic

[163]

no

Diagnostic

[164]

o r p

yes

Journal Pre-proof Highlights 1. Personalized oncology relies on a tumor‟s genetic, molecular, cellular features. 2. The support of genomics with other omics data may improve personalized medicine.

f o

3. Proteomics and metabolomics added to genetics improve the knowledge about cancer. 4. Recently, several proteins and metabolites were proposed as oncological biomarkers.

o r p

5. Novel omics-based biomarkers require multi-center, international scale validation.

l a

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n r u

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e

Figure 1

Figure 2

Figure 3

Figure 4