The discovery of reliable biomarkers, which are eligible for the prediction of both disease progression and response to treatment, means a great challenge in the management of multiple sclerosis (MS), a devastating disease of the central nervous system. The results of recent proteomic findings from the cerebrospinal fluid of MS patients hold promise of finding ideal biomarkers in the near future.
by Dr J. Füvesi, Dr C. Rajda, Dr D. Zádori, Dr K. Bencsik, Prof. Dr L. Vécsei and Prof. Dr J. Bergquist
Multiple Sclerosis
Multiple sclerosis is a demyelinative disorder of the central nervous system that affects mainly young adults. It has a great impact on quality of life, social and family life, and the careers of the patients.
In the majority of cases the disease starts with a relapsing–remitting (RR) phase. After a variable period of time it turns into a secondary progressive (SP) phase characterized by the gradual accumulation of residual symptoms. In 10–15% of cases a continuous progression is observed from the very beginning, this is the primary progressive (PP) form. In very rare fulminant cases frequent relapses with incomplete remissions cause severe disability or even death in a short duration of time.
The diagnosis of multiple sclerosis is still mainly clinical, supported by MRI and cerebrospinal fluid (CSF) analysis findings. The revised McDonald Criteria [1] allow earlier diagnosis, especially in PP MS. The routine diagnostic CSF analysis in MS includes the detection of oligoclonal bands and quantitative IgG analysis. Isoelectric focusing (IEF) on agarose gels followed by immunoblotting is considered the ‘gold standard’ for detecting the presence of oligoclonal bands [2]. The sensitivity of the method is above 95% and the specificity is more than 86%. An increased IgG index and the presence of oligoclonal bands in the CSF support an MS diagnosis.
Although the diagnosis is quite straightforward in most cases, taking into account clinical findings and paraclinical tests, there are still no specific biomarkers to confirm the diagnosis nor do we have any validated prognostic markers to follow the progression of the disorder.
At the time of diagnosis, major problems include the identification of the different clinical forms of the disease and the identification of patients with a potential rapid progression before disability evolves; the differential diagnosis of clinically isolated syndrome (CIS) with optic neuritis as the presenting symptom, where neuromyelitis optica (NMO) spectrum disorder may be an alternative diagnosis. Markers of disease progression are needed to distinguish CIS patients with a high probability to develop clinically definite MS.
There is also a need for biomarkers of response to treatment and biomarkers for better understanding the underlying pathological processes of the disease. This is especially important with the growing variety of treatment options: now it is possible to change therapy in the case of an inadequate treatment response and to escalate MS treatment to more aggressive alternatives. In the near future individualized treatment choices need better classification of patient characteristics.
In order to discover new biomarkers in MS, one should analyse the whole protein content of body fluids, preferentially CSF. Because of its proximity to the central nervous system (CNS), CSF may reflect changes in the CNS that may help differentiate normal and pathological conditions.
Proteomics
Proteomics is the study of protein expression in an organism. There are excellent reviews on proteomic approaches [3–5], so we will discuss here only certain aspects of these methods relevant to multiple sclerosis biomarker research. Mass-spectrometry (MS in Italic to distinguish from multiple sclerosis in this paper) based protein identification strategies include whole-protein analysis (‘top-down’ proteomics) and analysis of enzymatically produced peptides (‘bottom-up’ proteomics) [4]. The latter is the standard for large-scale or high-throughput analysis of highly complex samples, and digestion with trypsin is the most common method. The separation of peptides and proteins is an important element of both approaches.
Mass spectrometry measures the mass-to-charge ratio (m/z) of ionized molecules, and, as multiple distinct peptides can have very similar or identical molecular masses, it can be difficult to identify the overlapping peptides [3]. The use of separation techniques, therefore, reduces the cases of coincident peptide masses simultaneously introduced into the mass spectrometer. One of the most commonly used separation techniques is high-performance liquid chromatography (HPLC) with a capillary column. Peptides of similar molecular mass but different hydrophobicity elute from the LC column and enter the mass spectrometer at different time points, no longer overlapping in the initial MS analysis. Liquid chromatography coupled to mass spectrometry reduces the complexity of the sample and allows more precise protein identification.
In order to limit the risk of systematic errors and achieve a high sample throughput, labelling by means of isobaric tags for relative and absolute quantification (iTRAQ) may be used [6]. Multiple samples may be processed in parallel with this multiplexed approach. The main advantage is that the samples are analysed under exactly the same conditions. The relative abundance of labelled peptides indicates relative changes in protein expression.
LC-MS experiments generate an enormous amount of data, making data analysis one of the most challenging parts of proteomic analysis. Protein identification and quantification is achieved by database searching. Programs, such as Mascot etc., compare observed spectra to predicted spectra for candidate peptides from a protein database. In a recent study Schutzer et al. established a database of the normal human CSF proteome [7].
Proteomics in multiple sclerosis
In recent years a number of papers appeared describing proteomic analysis of CSF or brain tissue of multiple sclerosis patients [8–12]. The first papers in the field analysed pooled samples from a relatively small group of patients [8, 9]. Hammack et al. [8] reported the analysis of a pooled sample of three relapsing–remitting MS patients and a pooled sample of three patients with non-MS inflammatory CNS disorders using two-dimensional gel electrophoresis (2-DE) and peptide mass fingerprinting. They identified four proteins in the gels containing MS CSF that were not reported previously in normal human CSF: CRTAC-1B (cartilage acidic protein), tetranectin (a plasminogen-binding protein), SPARC-like protein (a calcium binding cell signalling glycoprotein) and autotaxin t (a phosphodiesterase).
In the study of Dumont et al. [9] CSF samples from five MS patients (4 RR, one SP) were analysed by 2-DE followed by liquid chromatography tandem mass spectrometry. With this method 15 proteins have been identified that were not previously observed in non-multiple sclerosis CSF 2-DE gels. These proteins were: psoriasin, calmodulin-related protein NB-1, annexin 1, EWI-2, Niemann–Pick disease type C2 protein (NPC-2), semenogelin 1 (SEM1), semenogelin 2 (SEM2), complement factor H-related protein 1 (FHR-1), procollagen C-proteinase enhancer protein (PCPE), aldolase A, N-acetyllactosaminide β-1,3-N-acetylglucosaminyl-transferase, tetranectin, cystatin A, superoxide dismutase 3 and glutathione peroxidase.
Later, publications started to focus on the differentiation of the clinical forms of the disease. Lehmensiek et al. compared CSF samples from RR MS and clinically isolated syndrome (CIS) patients with controls using two-dimensional difference gel electrophoresis (2-D-DIGE) and matrix-assisted laser desorption/ionization – time of flight (MALDI-TOF) mass spectrometry [10]. In RR MS Ig kappa chain NIG93 protein was increased in concentration, while transferrin isoforms, alpha 1 antitrypsin isoforms, alpha 2-HS glycoprotein, Apo E and transthyretin decreased. In a study of Stoop et al. [11] significant differences were observed comparing the peak lists of spectra from CSF of MS patients and patients with other neurological diseases (OND), and also clinically isolated syndrome (CIS) vs OND. Three differentially expressed proteins were identified in the CSF of MS patients compared to CSF of patients with OND: chromogranin A, clusterin and complement C3.
The same group compared proteome profiles of CSF from RR and PP multiple sclerosis and found that they overlap to a large extent [13]. The main detected difference was that protein jagged-1 was less abundant in PP MS compared to RR MS, whereas vitamin D-binding protein was only detected in the RR MS CSF samples. Ottervald et al. found an increased CSF level of vitamin-D-binding protein in SP MS compared to the control [14]. Recently, impaired vitamin D homeostasis has been linked to multiple sclerosis [15]: high serum levels of 25-hydroxyvitamin D correlated with a reduced risk of MS [16] and vitamin D supplementation was proposed as an add-on therapy [17].
Biomarkers of disease progression are emerging as new targets of proteomics. In our recently published paper we analysed the CSF of a rare fulminant case of MS and compared it with RR MS and control samples [18]. The aim of this study was to identify proteins related to rapid progression. The presented bottom-up strategy, based on isobaric tag labelling in conjunction with enzymatic digestion followed by nanoLC coupled off-line to MALDI TOF/TOF MS resulted in the identification of 78 proteins. Seven proteins were found to be upregulated in both fulminant MS samples but not in the relapsing–remitting case compared to the control. These proteins included Ig kappa and gamma-1 chain C region, complement C4-A, fibrinogen beta chain, serum amyloid A protein, neural cell adhesion molecule 1 and beta-2-glycoprotein 1. These proteins are involved in the immune response, blood coagulation, cell proliferation and cell adhesion.
Disease progression may be examined by analysing CSF samples from CIS patients who remain CIS and CIS patients who convert to clinically definite multiple sclerosis. Comabella et al. [19, 20] analysed pooled CSF samples with
isobaric labelling and mass spectrometry. They found that chitinase 3-like 1, ceruloplasmin and vitamin D-binding protein were upregulated in CSF of patients converted to clinically definite MS. In order to validate their results, the authors determined the levels of these selected proteins by enzyme-linked immunosorbent assay (ELISA) in individual CSF samples. Only chitinase 3-like 1 was validated. In a second validation step CSF chitinase 3-like 1 levels were measured in an independent CIS cohort and its level was again significantly increased in CIS patients who later converted to MS, compared to patients who remained as CIS. High CSF levels of this protein significantly correlated with the number of gadolinium enhancing and T2 lesions on baseline brain MRI scans and disability progression during follow-up.
The search for biomarkers that are able to identify patients at high risk of rapid progression becomes increasingly important with the appearance of more aggressive treatment possibilities. In another ongoing study we currently analyse LC-Fourier transform ion cyclotron resonance (FTICR) MS [20–22] data of a larger set of CSF samples from a variety of clinical forms of MS and matched controls.
Despite the increasing number of studies investigating potential biomarkers of MS disease progression and response to therapy, there is still no protein that is repeatedly identified and validated by different groups. This may be due to the relatively small sample sizes and the heterogeneity of the methods applied. Large scale multi-centre projects using standard methods for collecting, storing and analysing the samples are necessary to validate these preliminary results and integrate candidate biomarkers into the pathomechanism of the disease.
A great step in this direction is the BIOMS project, which aims a standardized sample collection, storage and processing during the preanalytical steps to rule out the differences occurred by sample preparation [23–25] and test the different methods and hypotheses on a great sample number in multiple centres to shed light on the sources of errors using different methods. One of these initiatives was the neurofilament validation study, which is a candidate biomarker in multiple sclerosis [26]. Another validation study tested two different methods of detecting the neutralizing antibodies against interferon-beta therapy, which is a biomarker of therapy in MS [27].
In the future multi-centre studies on standardized samples and methods can bring us closer to solve the questions regarding the pathological processes and the classification of patients to the most appropriate therapy.
Acknowledgement
TÁMOP-4.2.2.A-11/1KONV/-2012-0052 and The Swedish Research Council 621-2011-4423 are gratefully acknowledged for financial support.
References
1. Polman CH, et al. Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Ann Neurol 2011; 69: 292–302.
2. Freedman MS, et al. Recommended standard of cerebrospinal fluid analysis in the diagnosis of multiple sclerosis: a consensus statement. Arch Neurol 2005; 62: 865–870.
3. Karpievitch YV, et al. Liquid Chromatography Mass Spectrometry-Based Proteomics: Biological and Technological Aspects. Ann Appl Stat 2010; 4: 1797–1823.
4. Han X, et al. 3rd Mass spectrometry for proteomics. Curr Opin Chem Biol 2008; 12: 483–490.
5. Becker CH, Bern M. Recent developments in quantitative proteomics. Mutat Res 2011; 722: 171–182.
6. Ross PL, et al. Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents. Mol Cell Proteomics 2004; 3: 1154–1169.
7. Schutzer SE, et al. Establishing the proteome of normal human cerebrospinal fluid. PLoS One 2010; 5: e10980.
8. Hammack BN, et al. Proteomic analysis of multiple sclerosis cerebrospinal fluid. Mult Scler 2004; 10: 245–260.
9. Dumont D, et al. Proteomic analysis of cerebrospinal fluid from multiple sclerosis patients. Proteomics 2004; 4: 2117–2124.
10. Lehmensiek V, et al. Cerebrospinal fluid proteome profile in multiple sclerosis. Mult Scler 2007; 13: 840–849.
11. Stoop MP, et al. Multiple sclerosis-related proteins identified in cerebrospinal fluid by advanced mass spectrometry. Proteomics 2008; 8: 1576–1585.
12. Han MH, et al. Proteomic analysis of active multiple sclerosis lesions reveals therapeutic targets. Nature 2008; 451: 1076–1081.
13. Stoop MP, et al. Proteomics comparison of cerebrospinal fluid of relapsing remitting and primary progressive multiple sclerosis. PLoS One 2010; 5: e12442.
14. Ottervald J, et al. Multiple sclerosis: Identification and clinical evaluation of novel CSF biomarkers. J Proteomics 2010; 73: 1117–1132.
15. Cantorna MT, Mahon BD. Mounting evidence for vitamin D as an environmental factor affecting autoimmune disease prevalence. Exp Biol Med 2004; 229: 1136–1142.
16. Raghuwanshi A, et al. Vitamin D and multiple sclerosis. J Cell Biochem 2008; 105: 338–343.
17. §Myhr KM. Vitamin D treatment in multiple sclerosis. J Neurol Sci 2009; 286: 104–108.
18. Füvesi J, et al. Proteomic analysis of cerebrospinal fluid in a fulminant case of multiple sclerosis. Int J Mol Sci 2012; 13: 7676–7693.
19. Comabella M, et al. Cerebrospinal fluid chitinase 3-like 1 levels are associated with conversion to multiple sclerosis. Brain 2010; 133: 1082–1093.
20. Bergquist J. FTICR mass spectrometry in proteomics. Curr Opin Mol Ther 2003; 5: 310–314.
21. Ramstrom M, et al. Protein identification in cerebrospinal fluid using packed capillary liquid chromatography Fourier transform ion cyclotron resonance mass spectrometry. Proteomics 2003; 3: 184–190.
22. Ramstrom M, et al. Cerebrospinal fluid protein patterns in neurodegenerative disease revealed by liquid chromatography-Fourier transform ion cyclotron resonance mass spectrometry. Proteomics 2004; 4: 4010–4018.
23. Teunissen CE, et al. A consensus protocol for the standardization of cerebrospinal fluid collection and biobanking. Neurology 2009; 73: 1914–1922.
24. Teunissen CE, et al. Short commentary on ‘a consensus protocol for the standardization of cerebrospinal fluid collection and biobanking’. Mult Scler 2010; 16: 129–132.
25. Tumani H, et al. Cerebrospinal fluid biomarkers in multiple sclerosis. Neurobiol Dis 2009; 35: 117–127.
26. Petzold A, et al. Neurofilament ELISA validation. J Immunol Methods 2010; 352: 23–31.
27. Bertolotto A, et al. Development and validation of a real time PCR-based bioassay for quantification of neutralizing antibodies against human interferon-beta. J Immunol Methods 2007; 321: 19–31.
The authors
Judit Füvesi1 PhD, MD; Cecilia Rajda1 PhD, MD; Dénes Zádori1 PhD, MD; Krisztina Bencsik1 PhD, MD; László Vécsei1,2 PhD, MD; and Jonas Bergquist3,4* PhD, MD
1 Department of Neurology, Faculty of Medicine, Albert Szent-Györgyi Clinical Center, University of Szeged, Szeged, Hungary
2 Neuroscience Research Group of Hungarian Academy of Sciences and University of Szeged, Szeged, Hungary
3 Analytical Chemistry, Department of Chemistry-Biomedical Center, Uppsala University, Uppsala, Sweden
4 Science for Life Laboratory (SciLife Lab), Uppsala University, Uppsala, Sweden
*Corresponding author
E-mail: jonas.bergquist@kemi.uu.se
Biomarker panels for the diagnosis of sepsis
, /in Featured Articles /by 3wmediaSepsis is a complex syndrome associated with significant morbidity and mortality. If detected and treated early, septic patients have better prognoses. Unfortunately, identification of sepsis is challenging because its pathophysiology is complex and its clinical signs and symptoms overlap with other inflammatory diseases. This review discusses emerging biomarker panels and their ability to predict sepsis in critically ill patients.
by Dr A. Woodworth and Dr J. Colon-Franco
Sepsis and SIRS: International definitions
Definitions of sepsis and related conditions date back to the 1991 consensus conference held by the American College of Chest Physicians and the Society of Critical Care Medicine [1].
This consensus group introduced the term SIRS to describe the systemic inflammatory response syndrome, a normal response to infection and non-infectious insults like trauma, pancreatitis, and burns [Figure 1]. In SIRS two or more of the following clinical signs manifest: abnormal body temperature (fever or hypothermia), tachypnea, tachycardia and abnormal white blood cell count (leukocytosis or leukopenia).
The consensus group defined sepsis as the presence of SIRS along with a documented infection. Left untreated, septic patients develop severe sepsis, characterized by organ dysfunction, and ultimately septic shock, characterized by organ failure, hypotension and decreased peripheral perfusion. Revision of these definitions in 2001, added ‘suspected infection’ to the classification of sepsis to address numerous clinical cases where microorganisms cannot be confirmed.
For over 20 years, these definitions have provided uniformity in clinical disease recognition and better characterized patient populations for sepsis research. Although recognition of SIRS is relatively straightforward, identification of patients with sepsis among those with SIRS remains challenging. This is due, in part, to overlapping clinical signs and symptoms between SIRS and sepsis as well as inherent difficulty in confirming infectious causes of SIRS.
Sepsis and SIRS: Pathobiology and related syndromes
Sepsis pathobiology is complex and not well characterized [Figure 2] [1]. Historical models describing an overactive proinflammatory response to infection likely oversimplify the process. Sepsis experts now support two distinct pathogenesis models for the progression of sepsis. The sequential response model describes an initial proinflammatory response to a pathogen, SIRS, followed by a compensatory anti-inflammatory response syndrome (CARS). In the second model, known as the mixed antagonist response syndrome, SIRS and CARS occur simultaneously and achieve homeostasis. Severe sepsis and septic shock are associated with an imbalance in the SIRS/CARS equilibrium. Although recent research supports the second model [2], larger studies exploring the underlying pathobiology, including expression of pro- and anti-inflammatory molecules throughout the course of sepsis pathogenesis are needed.
Sepsis diagnosis
Rapid diagnosis and treatment of sepsis reduces mortality. The ‘gold standard’ for sepsis diagnosis is identification of an infectious microorganism in patients with SIRS. Traditionally, pathogens in blood, urine or other body fluids were detected in the laboratory by culturing. Unfortunately, cultures have limited utility because some pathogens are slow growing and contamination is common, leading to a high number of false negative and positive results. Despite their disadvantages, identification of the infecting agent as well as its antibiotic susceptibility and resistance patterns remains crucial to administer or adjust antimicrobial treatment. Direct identification of a pathogen through molecular and proteomic-based approaches may help overcome these disadvantages [3].
Because of its non-specific clinical symptoms and the limited utility of bacterial cultures, researchers have looked to biomarkers to diagnose sepsis. Currently, the diagnostic utility of sepsis biomarkers is limited to confirming, ruling out sepsis or stratifying patients based on disease severity. Lactate, C-reactive protein (CRP) and procalcitonin (PCT) assist in the work-up of patients with suspected sepsis. Lactate, the end product of anaerobic glycolysis, is increased in septic shock and other conditions as a result of excessive energy demand, tissue hypoxia, and/or impaired metabolic pathways.
The Surviving Sepsis Campaign, an international collaboration developed to improve the management, diagnosis, and treatment and reduce mortality rates of sepsis, advocates measuring blood lactate within 6 h of presentation in patients with suspected sepsis [1]. A lactate concentration >4 mmol/L (36 mg/dL) is associated with increased morbidity and mortality and is used to guide sepsis resuscitation protocols. Lactate concentrations increase with severity of sepsis and are most useful for diagnosing septic shock, but lack diagnostic strength to discriminate early sepsis from SIRS.
Expression of proinflammatory molecules is markedly up-regulated in early sepsis. CRP and PCT expression is stimulated by proinflammatory cytokines. CRP is an acute phase reactant that is up-regulated in inflammatory processes, and is not specific for sepsis. PCT, the precursor of calcitonin in thyroidal C-cells, is systemically produced in non-thyroidal tissue in response to inflammation and infection. Compared to CRP, PCT more accurately distinguishes SIRS from sepsis [1, 4]. In critically ill adults, the diagnostic strength of PCT to distinguish sepsis from SIRS is low [1, 4]. This may be due to the fact that like CRP, PCT is overexpressed in non-infectious inflammatory states like surgery or trauma. Unlike CRP, PCT concentrations correlate with sepsis severity. Both CRP and PCT can predict prognosis and response to therapy in septic patients. PCT is also useful in ruling out bacterial infections and is used in algorithms guiding antimicrobial therapy in critically-ill patients [4]. However, because of its questionable diagnostic utility, PCT testing is not universally used in clinical practice.
Thousands of studies have investigated the clinical and diagnostic utility of hundreds of sepsis biomarkers. A recent review of relevant clinical and experimental studies identified 178 proposed sepsis biomarkers [4]. Besides PCT and CRP, 34 others were investigated as diagnostic markers for sepsis. None had sufficient diagnostic strength to differentiate septic patients from those with non-infectious SIRS.
Multiple biomarker panels for sepsis diagnosis
As our understanding of the underlying mechanisms of sepsis evolved, it became evident that a single biomarker could not identify all patients with this heterogeneous syndrome. Instead, a panel of biomarkers, consisting of molecules secreted in the blood throughout the disease process, may better predict sepsis among patients with systemic inflammation [3].
Recent studies [Table 1] explored the utility of novel multimarker panels to predict sepsis. In a prospective cohort study of 151 emergency department (ED) patients with SIRS, both panels of 3 and 6 biomarkers showed superior diagnostic utility for detection of bacterial infection compared to any single biomarker [Table 1; Study #1] [5]. In a separate cohort of 342 ED patients with SIRS, adding 1 to 3 biomarkers and/or clinical parameters did not improve upon PCT alone to predict bacteremia [Table 1; #2] [6]. In the latter study, only patients with documented positive blood cultures were included, excluding possible infection in other sites or fluids and patients with false negative cultures.
In a retrospective pilot study at our institution, 10 inflammatory biomarkers, chosen because of their expression pattern during SIRS and/or CARS, were measured in 63 critically-ill patients with SIRS [Table 1; #3]. Panels of 2 to 6 inflammatory biomarkers measured in multiplex were better able to identify sepsis among patients with SIRS compared to single markers. Because of the small sample size, a 2-marker panel was most predictive of sepsis. PCT and CRP showed limited diagnostic utility alone or in combination with other biomarkers [7]. In a second retrospective cohort study we evaluated the diagnostic utility of 5 inflammatory biomarkers up-regulated in SIRS and/or CARS in 169 ICU patients with SIRS [Table 1; #4]. The 5-biomarker panel outperformed any single biomarker to predict sepsis on the day that patients developed SIRS [8]. Studies are ongoing to validate these findings in a larger population and to compare these results with the diagnostic performance of PCT.
A sepsis risk score, generated from results of multiple biomarkers, may allow easy adoption of these panels into clinical practice [3, 9]. A study evaluating the plasma concentrations of 5 proinflammatory molecules demonstrated that, compared to individual markers, a sepsis score consisting of at least 2 biomarkers elevated above their respective cut-offs better discriminated between SIRS and sepsis in ICU patients [Table 1; #5] [10]. Gibot and colleagues investigated the concentration of three biomarkers in 300 patients consecutively admitted into the ICU [Table 1; #6] [9]. A bioscore of 0, 1, 2 or 3 was assigned based on the number of positive biomarkers (above pre-defined cut-off values). The bioscore surpassed the diagnostic strength of any of the individual biomarker results for the prediction of sepsis. This model was validated in a separate cohort of 228 patients presenting with clinical signs of sepsis. In these studies, the sepsis score strategy was practical with superior diagnostic utility.
Conclusions
Early identification and treatment of septic patients reduces mortality, however, signs and symptoms of early sepsis are similar to non-infectious SIRS. To date, no single biomarker has ample diagnostic strength to identify septic patients among a critically-ill population. A panel of biomarkers may better distinguish patients with sepsis from those with non-infectious SIRS. Most findings are from preliminary studies with small patient cohorts and require additional validation studies. These should be conducted in larger, multicentre populations with distinct validation cohorts. Rapid, automated, multiplexing platforms and/or point-of-care technologies may be necessary to obtain timely results for these multimarker sepsis panels. Combining biomarkers into equations or sepsis-scores that yield an interpretable and meaningful result is paramount for their clinical adoption.
In conclusion, using combinations of biomarkers to predict sepsis is an attractive strategy that may improve real time assessments and reduce morbidity and mortality in septic patients.
References
1. Faix JD. Established and novel biomarkers of sepsis. Biomark Med 2011; 5(2): 117–130.
2. Osuchowski MF, et al. Sepsis chronically in MARS: systemic cytokine responses are always mixed regardless of the outcome, magnitude, or phase of sepsis. J Immunol 2012; 189(9): 4648–56.
3. Casserly B, Read R, Levy MM. Multimarker panels in sepsis. Crit Care Clin 2011; 27(2): 391–405.
4. Pierrakos C, Vincent JL. Sepsis biomarkers: a review. Crit Care 2010; 14(1): R15.
5. Kofoed K, et al. Use of plasma C-reactive protein, procalcitonin, neutrophils, macrophage migration inhibitory factor, soluble urokinase-type plasminogen activator receptor, and soluble triggering receptor expressed on myeloid cells-1 in combination to diagnose infections: a prospective study. Crit Care 2007; 11(2): R38.
6. Tromp M, et al. Serial and panel analyses of biomarkers do not improve the prediction of bacteremia compared to one procalcitonin measurement. J Infect 2012; 65(4): 292–301.
7. Pyle AL, et al. Multiplex cytokine analysis for the differentiation of SIRS and sepsis. Am J Clin Pathol 2010; 134: 509.
8. Pyle AL, et al. A multi-marker approach to differentiate sepsis from SIRS. Am J Clin Pathol 2011; 136: 468–469.
9. Gibot S, et al. Combination biomarkers to diagnose sepsis in the critically ill patient. Am J Respir Crit Care Med 2012; 186(1): 65–71.
10. Selberg O, et al. Discrimination of sepsis and systemic inflammatory response syndrome by determination of circulating plasma concentrations of procalcitonin, protein complement 3a, and interleukin-6. Crit Care Med 2000; 28(8): 2793–2798.
The authors
Alison Woodworth, PhD, DABCC, FACB
Assistant Professor, Pathology, Microbiology and Immunology
Director, Esoteric Chemistry
Vanderbilt University Medical Center
Nashville, TN, USA
Jessica M. Colón-Franco, PhD
Clinical Chemistry Fellow
Department of Pathology, Microbiology and Immunology
Vanderbilt University Medical Center
Nashville, TN, USA
E-mail:
Alison.Woodworth@Vanderbilt.Edu
Proteomics of cerebrospinal fluid for biomarker discovery in multiple sclerosis
, /in Featured Articles /by 3wmediaThe discovery of reliable biomarkers, which are eligible for the prediction of both disease progression and response to treatment, means a great challenge in the management of multiple sclerosis (MS), a devastating disease of the central nervous system. The results of recent proteomic findings from the cerebrospinal fluid of MS patients hold promise of finding ideal biomarkers in the near future.
by Dr J. Füvesi, Dr C. Rajda, Dr D. Zádori, Dr K. Bencsik, Prof. Dr L. Vécsei and Prof. Dr J. Bergquist
Multiple Sclerosis
Multiple sclerosis is a demyelinative disorder of the central nervous system that affects mainly young adults. It has a great impact on quality of life, social and family life, and the careers of the patients.
In the majority of cases the disease starts with a relapsing–remitting (RR) phase. After a variable period of time it turns into a secondary progressive (SP) phase characterized by the gradual accumulation of residual symptoms. In 10–15% of cases a continuous progression is observed from the very beginning, this is the primary progressive (PP) form. In very rare fulminant cases frequent relapses with incomplete remissions cause severe disability or even death in a short duration of time.
The diagnosis of multiple sclerosis is still mainly clinical, supported by MRI and cerebrospinal fluid (CSF) analysis findings. The revised McDonald Criteria [1] allow earlier diagnosis, especially in PP MS. The routine diagnostic CSF analysis in MS includes the detection of oligoclonal bands and quantitative IgG analysis. Isoelectric focusing (IEF) on agarose gels followed by immunoblotting is considered the ‘gold standard’ for detecting the presence of oligoclonal bands [2]. The sensitivity of the method is above 95% and the specificity is more than 86%. An increased IgG index and the presence of oligoclonal bands in the CSF support an MS diagnosis.
Although the diagnosis is quite straightforward in most cases, taking into account clinical findings and paraclinical tests, there are still no specific biomarkers to confirm the diagnosis nor do we have any validated prognostic markers to follow the progression of the disorder.
At the time of diagnosis, major problems include the identification of the different clinical forms of the disease and the identification of patients with a potential rapid progression before disability evolves; the differential diagnosis of clinically isolated syndrome (CIS) with optic neuritis as the presenting symptom, where neuromyelitis optica (NMO) spectrum disorder may be an alternative diagnosis. Markers of disease progression are needed to distinguish CIS patients with a high probability to develop clinically definite MS.
There is also a need for biomarkers of response to treatment and biomarkers for better understanding the underlying pathological processes of the disease. This is especially important with the growing variety of treatment options: now it is possible to change therapy in the case of an inadequate treatment response and to escalate MS treatment to more aggressive alternatives. In the near future individualized treatment choices need better classification of patient characteristics.
In order to discover new biomarkers in MS, one should analyse the whole protein content of body fluids, preferentially CSF. Because of its proximity to the central nervous system (CNS), CSF may reflect changes in the CNS that may help differentiate normal and pathological conditions.
Proteomics
Proteomics is the study of protein expression in an organism. There are excellent reviews on proteomic approaches [3–5], so we will discuss here only certain aspects of these methods relevant to multiple sclerosis biomarker research. Mass-spectrometry (MS in Italic to distinguish from multiple sclerosis in this paper) based protein identification strategies include whole-protein analysis (‘top-down’ proteomics) and analysis of enzymatically produced peptides (‘bottom-up’ proteomics) [4]. The latter is the standard for large-scale or high-throughput analysis of highly complex samples, and digestion with trypsin is the most common method. The separation of peptides and proteins is an important element of both approaches.
Mass spectrometry measures the mass-to-charge ratio (m/z) of ionized molecules, and, as multiple distinct peptides can have very similar or identical molecular masses, it can be difficult to identify the overlapping peptides [3]. The use of separation techniques, therefore, reduces the cases of coincident peptide masses simultaneously introduced into the mass spectrometer. One of the most commonly used separation techniques is high-performance liquid chromatography (HPLC) with a capillary column. Peptides of similar molecular mass but different hydrophobicity elute from the LC column and enter the mass spectrometer at different time points, no longer overlapping in the initial MS analysis. Liquid chromatography coupled to mass spectrometry reduces the complexity of the sample and allows more precise protein identification.
In order to limit the risk of systematic errors and achieve a high sample throughput, labelling by means of isobaric tags for relative and absolute quantification (iTRAQ) may be used [6]. Multiple samples may be processed in parallel with this multiplexed approach. The main advantage is that the samples are analysed under exactly the same conditions. The relative abundance of labelled peptides indicates relative changes in protein expression.
LC-MS experiments generate an enormous amount of data, making data analysis one of the most challenging parts of proteomic analysis. Protein identification and quantification is achieved by database searching. Programs, such as Mascot etc., compare observed spectra to predicted spectra for candidate peptides from a protein database. In a recent study Schutzer et al. established a database of the normal human CSF proteome [7].
Proteomics in multiple sclerosis
In recent years a number of papers appeared describing proteomic analysis of CSF or brain tissue of multiple sclerosis patients [8–12]. The first papers in the field analysed pooled samples from a relatively small group of patients [8, 9]. Hammack et al. [8] reported the analysis of a pooled sample of three relapsing–remitting MS patients and a pooled sample of three patients with non-MS inflammatory CNS disorders using two-dimensional gel electrophoresis (2-DE) and peptide mass fingerprinting. They identified four proteins in the gels containing MS CSF that were not reported previously in normal human CSF: CRTAC-1B (cartilage acidic protein), tetranectin (a plasminogen-binding protein), SPARC-like protein (a calcium binding cell signalling glycoprotein) and autotaxin t (a phosphodiesterase).
In the study of Dumont et al. [9] CSF samples from five MS patients (4 RR, one SP) were analysed by 2-DE followed by liquid chromatography tandem mass spectrometry. With this method 15 proteins have been identified that were not previously observed in non-multiple sclerosis CSF 2-DE gels. These proteins were: psoriasin, calmodulin-related protein NB-1, annexin 1, EWI-2, Niemann–Pick disease type C2 protein (NPC-2), semenogelin 1 (SEM1), semenogelin 2 (SEM2), complement factor H-related protein 1 (FHR-1), procollagen C-proteinase enhancer protein (PCPE), aldolase A, N-acetyllactosaminide β-1,3-N-acetylglucosaminyl-transferase, tetranectin, cystatin A, superoxide dismutase 3 and glutathione peroxidase.
Later, publications started to focus on the differentiation of the clinical forms of the disease. Lehmensiek et al. compared CSF samples from RR MS and clinically isolated syndrome (CIS) patients with controls using two-dimensional difference gel electrophoresis (2-D-DIGE) and matrix-assisted laser desorption/ionization – time of flight (MALDI-TOF) mass spectrometry [10]. In RR MS Ig kappa chain NIG93 protein was increased in concentration, while transferrin isoforms, alpha 1 antitrypsin isoforms, alpha 2-HS glycoprotein, Apo E and transthyretin decreased. In a study of Stoop et al. [11] significant differences were observed comparing the peak lists of spectra from CSF of MS patients and patients with other neurological diseases (OND), and also clinically isolated syndrome (CIS) vs OND. Three differentially expressed proteins were identified in the CSF of MS patients compared to CSF of patients with OND: chromogranin A, clusterin and complement C3.
The same group compared proteome profiles of CSF from RR and PP multiple sclerosis and found that they overlap to a large extent [13]. The main detected difference was that protein jagged-1 was less abundant in PP MS compared to RR MS, whereas vitamin D-binding protein was only detected in the RR MS CSF samples. Ottervald et al. found an increased CSF level of vitamin-D-binding protein in SP MS compared to the control [14]. Recently, impaired vitamin D homeostasis has been linked to multiple sclerosis [15]: high serum levels of 25-hydroxyvitamin D correlated with a reduced risk of MS [16] and vitamin D supplementation was proposed as an add-on therapy [17].
Biomarkers of disease progression are emerging as new targets of proteomics. In our recently published paper we analysed the CSF of a rare fulminant case of MS and compared it with RR MS and control samples [18]. The aim of this study was to identify proteins related to rapid progression. The presented bottom-up strategy, based on isobaric tag labelling in conjunction with enzymatic digestion followed by nanoLC coupled off-line to MALDI TOF/TOF MS resulted in the identification of 78 proteins. Seven proteins were found to be upregulated in both fulminant MS samples but not in the relapsing–remitting case compared to the control. These proteins included Ig kappa and gamma-1 chain C region, complement C4-A, fibrinogen beta chain, serum amyloid A protein, neural cell adhesion molecule 1 and beta-2-glycoprotein 1. These proteins are involved in the immune response, blood coagulation, cell proliferation and cell adhesion.
Disease progression may be examined by analysing CSF samples from CIS patients who remain CIS and CIS patients who convert to clinically definite multiple sclerosis. Comabella et al. [19, 20] analysed pooled CSF samples with
isobaric labelling and mass spectrometry. They found that chitinase 3-like 1, ceruloplasmin and vitamin D-binding protein were upregulated in CSF of patients converted to clinically definite MS. In order to validate their results, the authors determined the levels of these selected proteins by enzyme-linked immunosorbent assay (ELISA) in individual CSF samples. Only chitinase 3-like 1 was validated. In a second validation step CSF chitinase 3-like 1 levels were measured in an independent CIS cohort and its level was again significantly increased in CIS patients who later converted to MS, compared to patients who remained as CIS. High CSF levels of this protein significantly correlated with the number of gadolinium enhancing and T2 lesions on baseline brain MRI scans and disability progression during follow-up.
The search for biomarkers that are able to identify patients at high risk of rapid progression becomes increasingly important with the appearance of more aggressive treatment possibilities. In another ongoing study we currently analyse LC-Fourier transform ion cyclotron resonance (FTICR) MS [20–22] data of a larger set of CSF samples from a variety of clinical forms of MS and matched controls.
Despite the increasing number of studies investigating potential biomarkers of MS disease progression and response to therapy, there is still no protein that is repeatedly identified and validated by different groups. This may be due to the relatively small sample sizes and the heterogeneity of the methods applied. Large scale multi-centre projects using standard methods for collecting, storing and analysing the samples are necessary to validate these preliminary results and integrate candidate biomarkers into the pathomechanism of the disease.
A great step in this direction is the BIOMS project, which aims a standardized sample collection, storage and processing during the preanalytical steps to rule out the differences occurred by sample preparation [23–25] and test the different methods and hypotheses on a great sample number in multiple centres to shed light on the sources of errors using different methods. One of these initiatives was the neurofilament validation study, which is a candidate biomarker in multiple sclerosis [26]. Another validation study tested two different methods of detecting the neutralizing antibodies against interferon-beta therapy, which is a biomarker of therapy in MS [27].
In the future multi-centre studies on standardized samples and methods can bring us closer to solve the questions regarding the pathological processes and the classification of patients to the most appropriate therapy.
Acknowledgement
TÁMOP-4.2.2.A-11/1KONV/-2012-0052 and The Swedish Research Council 621-2011-4423 are gratefully acknowledged for financial support.
References
1. Polman CH, et al. Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Ann Neurol 2011; 69: 292–302.
2. Freedman MS, et al. Recommended standard of cerebrospinal fluid analysis in the diagnosis of multiple sclerosis: a consensus statement. Arch Neurol 2005; 62: 865–870.
3. Karpievitch YV, et al. Liquid Chromatography Mass Spectrometry-Based Proteomics: Biological and Technological Aspects. Ann Appl Stat 2010; 4: 1797–1823.
4. Han X, et al. 3rd Mass spectrometry for proteomics. Curr Opin Chem Biol 2008; 12: 483–490.
5. Becker CH, Bern M. Recent developments in quantitative proteomics. Mutat Res 2011; 722: 171–182.
6. Ross PL, et al. Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents. Mol Cell Proteomics 2004; 3: 1154–1169.
7. Schutzer SE, et al. Establishing the proteome of normal human cerebrospinal fluid. PLoS One 2010; 5: e10980.
8. Hammack BN, et al. Proteomic analysis of multiple sclerosis cerebrospinal fluid. Mult Scler 2004; 10: 245–260.
9. Dumont D, et al. Proteomic analysis of cerebrospinal fluid from multiple sclerosis patients. Proteomics 2004; 4: 2117–2124.
10. Lehmensiek V, et al. Cerebrospinal fluid proteome profile in multiple sclerosis. Mult Scler 2007; 13: 840–849.
11. Stoop MP, et al. Multiple sclerosis-related proteins identified in cerebrospinal fluid by advanced mass spectrometry. Proteomics 2008; 8: 1576–1585.
12. Han MH, et al. Proteomic analysis of active multiple sclerosis lesions reveals therapeutic targets. Nature 2008; 451: 1076–1081.
13. Stoop MP, et al. Proteomics comparison of cerebrospinal fluid of relapsing remitting and primary progressive multiple sclerosis. PLoS One 2010; 5: e12442.
14. Ottervald J, et al. Multiple sclerosis: Identification and clinical evaluation of novel CSF biomarkers. J Proteomics 2010; 73: 1117–1132.
15. Cantorna MT, Mahon BD. Mounting evidence for vitamin D as an environmental factor affecting autoimmune disease prevalence. Exp Biol Med 2004; 229: 1136–1142.
16. Raghuwanshi A, et al. Vitamin D and multiple sclerosis. J Cell Biochem 2008; 105: 338–343.
17. §Myhr KM. Vitamin D treatment in multiple sclerosis. J Neurol Sci 2009; 286: 104–108.
18. Füvesi J, et al. Proteomic analysis of cerebrospinal fluid in a fulminant case of multiple sclerosis. Int J Mol Sci 2012; 13: 7676–7693.
19. Comabella M, et al. Cerebrospinal fluid chitinase 3-like 1 levels are associated with conversion to multiple sclerosis. Brain 2010; 133: 1082–1093.
20. Bergquist J. FTICR mass spectrometry in proteomics. Curr Opin Mol Ther 2003; 5: 310–314.
21. Ramstrom M, et al. Protein identification in cerebrospinal fluid using packed capillary liquid chromatography Fourier transform ion cyclotron resonance mass spectrometry. Proteomics 2003; 3: 184–190.
22. Ramstrom M, et al. Cerebrospinal fluid protein patterns in neurodegenerative disease revealed by liquid chromatography-Fourier transform ion cyclotron resonance mass spectrometry. Proteomics 2004; 4: 4010–4018.
23. Teunissen CE, et al. A consensus protocol for the standardization of cerebrospinal fluid collection and biobanking. Neurology 2009; 73: 1914–1922.
24. Teunissen CE, et al. Short commentary on ‘a consensus protocol for the standardization of cerebrospinal fluid collection and biobanking’. Mult Scler 2010; 16: 129–132.
25. Tumani H, et al. Cerebrospinal fluid biomarkers in multiple sclerosis. Neurobiol Dis 2009; 35: 117–127.
26. Petzold A, et al. Neurofilament ELISA validation. J Immunol Methods 2010; 352: 23–31.
27. Bertolotto A, et al. Development and validation of a real time PCR-based bioassay for quantification of neutralizing antibodies against human interferon-beta. J Immunol Methods 2007; 321: 19–31.
The authors
Judit Füvesi1 PhD, MD; Cecilia Rajda1 PhD, MD; Dénes Zádori1 PhD, MD; Krisztina Bencsik1 PhD, MD; László Vécsei1,2 PhD, MD; and Jonas Bergquist3,4* PhD, MD
1 Department of Neurology, Faculty of Medicine, Albert Szent-Györgyi Clinical Center, University of Szeged, Szeged, Hungary
2 Neuroscience Research Group of Hungarian Academy of Sciences and University of Szeged, Szeged, Hungary
3 Analytical Chemistry, Department of Chemistry-Biomedical Center, Uppsala University, Uppsala, Sweden
4 Science for Life Laboratory (SciLife Lab), Uppsala University, Uppsala, Sweden
*Corresponding author
E-mail: jonas.bergquist@kemi.uu.se
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, /in Featured Articles /by 3wmediaThe effects of tobacco smoke: first the bad news
, /in Featured Articles /by 3wmediaIt was over sixty years ago that Sir Richard Doll’s pioneering work first demonstrated a causal link between tobacco smoking and an increased risk of lung cancer. The lessons drawn from it have undoubtedly saved millions of lives over the years, but it is disappointing that according to the recently published European cancer mortality predictions for 2013, lung cancer remains the biggest cause of cancer death in male EU residents, and is predicted to become the biggest cause of cancer mortality in women in the near future, overtaking deaths from breast cancer.
The trend is similar in the US. A recently published paper in the New England Medical Journal, which involved data from more than two million women at three different time periods, showed that women who smoke currently are at a far greater risk of death from lung cancer than were women who smoked in the 1960s and the 1980s; the risk is now equal for both genders. While other factors that increase the risk of lung cancer, such as asbestos and radon gas exposure, have now been identified, tobacco smoke is still thought to be responsible for around 90% of lung cancer cases.
During the decades since Doll’s work it has, of course, been demonstrated that the risk of death from many other diseases, including other cancers, ischemic heart disease, stroke, chronic obstructive pulmonary disease and asthma, is augmented by smoking tobacco. More recently it has been recognized that passive smoking can also increase the risk of smoking-related diseases, and that prenatal exposure to tobacco smoke increases the risk of low birth weight and premature neonates, as well as SIDS and asthma in infancy. But in spite of the concerted efforts that have been made to educate the public about the dangers of tobacco smoke over more than half a century, a substantial minority of the population, including many physicians, still smokes.
Now for the good news. Several comparative studies indicate that public smoking bans now operating in much of the developed world are already affecting the rate of cardiovascular and respiratory disease. And a very recent robust study from Belgium, giving data from the three phases of the ban in that country, where smoking was first prohibited in the workplace (2006), then in restaurants (2007) and finally in bars serving food (2010), demonstrates a fall in the premature birth rate after each phase. So finally at least those of us who have heeded the oft-repeated health message may benefit fully from our prudence!
Detection of circulating tumour cells from peripheral blood of breast cancer patients via real-time PCR
, /in Featured Articles /by 3wmediaAs the appearance of circulating tumour cells in the peripheral blood of breast cancer patients is linked to a worse prognosis for overall survival and treatment efficiency, their detection and characterization will have a high impact on cancer therapy, opening roads to a more personalized treatment.
by Dr U. Andergassen, Dr A. C. Kölbl, Prof. K. Friese and Prof. U. Jeschke
Circulating tumour cells
Already in 1869 the occurrence of cancer cells in the peripheral blood of a metastatic cancer patient was described by Thomas Ashworth. Nowadays it is well known, that cells dissolve from primary epithelial tumours such as breast, lung, colon or prostate cancer, enter circulation and travel via the blood stream or lymphatic system throughout the whole body. If these cells [termed circulating tumour cells (CTCs)] leave circulation, they can settle at other sites in the body and are then considered to be the main reason for the generation of remote metastasis. Their appearance is linked to a poorer outcome of cancer therapy and to a worse prognosis for overall survival. Therefore, the detection of CTCs in peripheral blood [and of disseminated tumour cells (DTCs) in bone marrow] was already included into the international tumour staging systems.
Unfortunately the detection of CTCs is still a technical challenge, as the number of tumour cells in the blood stream is rather small (1 in 106–7 blood cells). To date, there is only one FDA-approved system for CTC detection, at least in the metastatic situation. This is the Cell Search® system (Veridex LLC.), which is based on immunomagnetic enrichment and simultaneous staining of tumour cells of epithelial surface markers, the cytokeratins. The huge disadvantage of this system is that it is rather expensive and, therefore, not yet routinely used in the clinic.
Real-time PCR in cancer cell detection
Another promising approach for CTC detection could be a real-time PCR-based method. The principle of this methodology is that breast-cancer CTCs are derived from an epithelial tumour, and, therefore, express a panel of epithelial cell genes. The surrounding blood cells in contrast are of mesenchymal origin, showing different gene expression profiles. Thus, it can be assumed that tumour cells are present in a given blood sample if the expression of epithelial genes is higher than in a negative control sample.
Real-time PCR measures gene expression levels by detecting an increase of fluorescence due to the incorporation of fluorescent reporter molecules into the newly synthesized DNA molecules during the PCR reaction. If a gene is highly expressed, a lot of mRNA of this gene is present, meaning plenty target for PCR reaction is available and thus influencing the fluorescence level measured at the end of each amplification cycle. The time point when fluorescence reaches a certain threshold is called the Ct-value, and this is the basis of the calculation of relative gene expression values by the 2-∆∆Ct-method [1]. In brief: the average Ct-value of a gene of interest is related to the average Ct-value of a reference gene. The resulting value is called the ΔCt-value. In the next step, this ΔCt-value is set in reference to the ΔCt-value of the same gene in the reference sample, rendering the so called ΔΔCt-value. The formula 2–ΔΔCt is then used to calculate relative quantification (RQ) values. RQ values >1 show an upregulation of the gene of interest, values <1 mean that the gene is downregulated. Spiking experiments
The first step towards a real time PCR based quantitative cancer diagnosis is to create calibration curves for the used marker genes to evaluate the number of cancer cells exhibited at a certain level of gene expression in a blood sample. Therefore, blood samples of healthy donors, to which a certain number of cells from a breast-cancer cell line were added, were used to create standard curves. For this evaluation different breast-cancer cell lines were used (Cama-1, MCF-7, MDA-MB231 and ZR-75-1), and real-time PCR was carried out for Cytokeratin 8, 18 and 19 as marker genes [2, 3]. Cancer cells were added in rising numbers and calibration curves could be drawn [Fig. 1], showing an increase in gene expression level from 10 cells added to a blood sample upwards, meaning that even a small number of cancer cells in the blood (resembling the ‘real’ conditions, with 1 CTC per106–7 surrounding blood cells) can be detected by this methodology.
PCR marker genes for CTC detection
As CTCs in the blood are rare, PCR marker genes have to be selected as accurately as possible. The first choice are the Cytokeratin (CK) genes 8, 18 and 19, as they are also used in the routinely applicated APAAP-staining, which is a histochemical detection method for CTCs. The cytokeratin family members are characteristic epithelial cell markers and only weakly expressed in blood cells, rendering them potentially useful for PCR-based detection of CTCs.
Three other genes (BCSP, MGL, Her2) were selected and used in an approach to detect differences in gene expression between normal individuals and adjuvant and metastatic breast-cancer patients [4]. Mammaglobin (MGL) is a gene which is only expressed in the adult mammary gland and is known to be upregulated in breast cancer [5]. Breast cancer specific protein (BCSP) is highly expressed in advanced infiltrating breast cancer and is a marker for recurrence of the disease and formation of metastases [6], and c-erbB2 (Her2) was used, because it is over-expressed in 20% of breast cancers and is also responsible for the aggressiveness of the tumour [7].
These markers were comparatively analysed in blood samples withdrawn from adjuvant and metastatic breast-cancer patients during surgery. The gene expression levels of adjuvant as well as metastatic breast-cancer patients were normalized to levels in blood samples from 20 healthy donors, considered as a negative control group. Differences in gene expression between the three sample groups were detected [Fig. 2] and it was attempted to find a signature of marker genes for CTCs in breast cancer by real-time PCR.
From the experiments, it could be concluded that cytokeratin genes seem to be the most promising markers for the detection of CTCs from peripheral blood of breast-cancer patients with reverse-transcription real-time PCR. The most suitable marker of the cytokeratin array is apparently CK8, rendering most expression values >1.
MGL, BCSP, and Her2 mRNA show few expression values >1 as well in adjuvant as in metastatic patients. Altogether, higher amplitudes for these three genes were detected in the adjuvant setting. CTCs can be detected from peripheral blood by real-time-PCR, using the cytokeratin markers, especially cytokeratin 8.
In contrast to these findings are the results published by Obermayr et al. 2010 [8], who found an overexpression of MGL/hMAM in 39% of the examined advanced breast cancer cases. But they also conclude that using more marker genes for CTC detection results in a higher percentage of detected cancer cases. The same findings were obtained by [9], who also used a real-time PCR-based approach for CTC detection. They used CK19, SCGB2A2, MUC1, EPCAM, BIRC5 and Her-2 as marker genes and found a high sensitivity and specificity (56.3% and 100% respectively).
Additionally CK20 was identified as a promising marker gene [10] and seems to be correlated with the aggressiveness of the tumour. To further improve the detection of CTCs by real-time-PCR, more marker genes need to be tested; promising candidates are, for example, MMP13 [11], UBE2Q2 [12],
Nectin-4 [13], and ALDH [14].
Future directions for cancer therapy
Real-time PCR-based techniques were already used for solid tumour profiling and are considered to be objective, robust and cost-effective molecular techniques that could be used in routine cancer diagnosis. In future, a real-time PCR assay for the detection of circulating tumour cells from peripheral blood could find its way into modern medicine. This would be advantageous for the patient by limiting the number of invasive procedures, such as biopsies or bone marrow aspirations, that have to be undertaken to produce samples for analysis.
Furthermore by implication of more marker genes a characterization of tumour cells could be pursued, which already gives hints towards a cancer prognosis, as for example Bölke et al. described, that the expression of certain genes is correlated to advanced breast cancer stages [15]. A better knowledge of cancer properties in turn will help to apply a more personalized therapy, side effects can be reduced and treatment efficiency will strongly increase.
References
1. Livak KJ, Schmittgen TD. Methods 2001; 25(4): 402–408.
2. Zebisch M, Kolbl AC, Schindlbeck C, Neugebauer J, Heublein S, Ilmer M, Rack B, Friese K, Jeschke U, Andergassen U. Anticancer Res 2012; 32(12): 5387–5391.
3. Zebisch M, Kölbl AC, Andergassen U, Hutter S, Neugebauer J, Engelstädter V, Günthner-Biller M, Jeschke U, Friese K. Biomedical reports; accepted for publication 2012.
4. Andergassen U, Hofmann S, Kolbl AC, Schindlbeck C, Neugebauer J, Hutter S, Engelstadter V, Ilmer M, Friese K, Jeschke U. Int J Mol Sci 2013; 14(1): 1093–1104.
5. Fleming TP, Watson MA. Ann N Y Acad Sci 2000; 923: 78–89.
6. Wu K, Weng Z, Tao Q, Lin G, et al. Cancer Epidemiol Biomarkers Prev 2003; 12(9): 920–925.
7. Kim YS, Konoplev SN, Montemurro F, Hoy E, Smith TL, et al. Clin Cancer Res 2001; 7(12):4008–4012.
8. Obermayr E, Sanchez-Cabo F, Tea MK, Singer CF, Krainer M, et al. BMC Cancer 2010; 10: 666.
9. de Albuquerque A, Kaul S, Breier G, Krabisch P, Fersis N. Breast Care (Basel) 2012; 7(1): 7–12.
10. Tunca B, Egeli U, Cecener G, Tezcan G, Gokgoz S, Tasdelen I, et al. Tumori 2012; 98(2): 243–251.
11. Chang HJ, Yang MJ, Yang YH, Hou MF, Hsueh EJ, Lin SR. Oncol Rep 2009; 22(5): 1119–1127.
12. Nikseresht M, Seghatoleslam A, Monabati A, et al. Cancer Genet Cytogenet 2010; 197(2): 101–106.
13. Fabre-Lafay S, Garrido-Urbani S, Reymond N, et al. J Biol Chem 2005; 280(20): 19543–19550.
14. Dontu G. Breast Cancer Res 2008; 10(5): 110.
15. Bolke E, Orth K, Gerber PA, Lammering G, Mota R, et al. Eur J Med Res 2009; 14(8): 359–363.
The authors
Ulrich Andergassen* MD, Alexandra C. Kölbl PhD, Klaus Friese MD, and Udo Jeschke PhD
Klinik und Poliklinik für Frauenheilkunde und Geburtshilfe, Ludwig Maximilian University of Munich, Munich, Germany
*Corresponding author
ulrich.andergassen@med.uni-muenchen.de
Tissue biomarkers of breast cancer: implications for prognosis
, /in Featured Articles /by 3wmediaBetter tissue biomarkers are needed to improve diagnosis and prognosis, guide molecularly targeted therapy, and monitor activity and therapeutic response across many cancers. Proteomics methods, based on mass spectrometry, hold great promise for the discovery of novel biomarkers that might form the foundation of a new clinical test. This review will focus on potential tissue biomarkers with utility for prognosis in breast cancer.
By Dr Liping Chung
Tissue biomarkers in breast cancer
Breast cancer is the leading cause of mortality among women worldwide. It is a complex and heterogeneous disease and includes several subtypes, which have different prognoses and responses to therapy. Recent molecular characterization of some breast cancer subtypes has led to the development of personalized options for treatment targeting [1].
One of the major advantages of biomarker research for individuals with cancer is the availability of tumour tissue for analysis and the possibility that potential tissue biomarkers can be detected in histological samples. In conjunction with tumour grading and measurement of lymphovascular invasion, several tissue biomarkers are now used with prognostic significance in daily practice including estrogen receptor (ER), progesterone receptor (PR), the type 2 epidermal growth factor receptor (HER2 or erbB-2), and Ki67 [1, 2].
The identification of protein biomarkers in easily accessible biological fluids has potential for the development of minimally invasive procedures for early diagnostics, but the analysis of body fluids such as plasma, serum and urine is complicated by their wide dynamic range of protein expression, the variation in their composition and their sensitivity to sample handling. Many serum biomarkers are not very specific or sensitive [1]. Analysis of tissue homogenates using the well-established and extremely powerful conventional techniques of differential proteomics has the advantage of covering the lower range of protein expression in such samples than in biological fluids [3].
Prognosis and response prediction
Different from diagnostic markers that detect the potential for developing a malignancy or test for the presence of a malignancy, biological markers that predict prognosis once a cancer has occurred are of great importance because they may influence major therapeutic recommendations. For breast cancer, these markers have become part of contemporary clinical practice. Among established tissue marker proteins in breast cancer, ER and HER2 are not diagnostic but have the greatest predictive utility [2]. It is generally accepted that estrogen receptor-positive (ER+) and ER-negative (ER−) breast cancers represent different disease entities. ER- tumours tend to be of high grade, have more frequent p53 mutations, and have worse prognosis compared with ER+ disease. Both ER+ and ER- tumours can be either HER2 positive or negative. Low-grade tumours are typically ER positive, and almost always HER2 non-amplified. The approximately 15% of patients with breast cancer who have HER2 overexpressing and amplified tumours are typically treated with a combination of trastuzumab, a monoclonal antibody targeting HER2, and adjuvant chemotherapy [4]. HER2 amplification and overexpression are generally associated with a poor prognosis. The prognostic significance of HER2 overexpression in tumour tissue has been evaluated in several clinical trials, suggesting that HER2 positivity is correlated with worse prognosis in untreated breast cancer patients, including node-negative populations [5].
The search for breast tissue biomarkers by mass spectrometry-based proteomics
Proteomic approaches, particularly those involving mass spectrometry (MS), have been widely used in breast cancer biomarker discovery, although to date no new markers based on proteomic discovery have been adopted for use in clinical practice. Using laser capture microdissection (LCM) for tissue samples, an extensive tissue study was performed by MALDI-MS (matrix-assisted laser desorption/ionization mass spectrometry) analysis on an average of 2000 cells from 122 invasive mammary carcinomas and 167 samples of normal breast epithelium [6]. Among clusters of protein/peptide peaks that were used to discriminate cancer from normal tissue with high sensitivity and specificity were ubiquitin, S100A6 (calcyclin) and S100A8 (calgranulin A). To confirm cDNA expression profiling of breast tissues, Brozkova et al. also analysed whole tissue lysates rather than serum of 105 breast carcinomas on IMAC30 protein chips by SELDI-TOF MS (surface-enhanced laser desorption/ionization, time-of-flight mass spectrometry) [7]. They compared this analysis to cDNA expression profiling of the same tumours and found similar clustering, providing supporting evidence for the effectiveness of this technique in identifying and classifying tumours.
Most clinical tissue samples are conserved as formalin-fixed paraffin-embedded (FFPE) samples. In particular, cancer tissues contain several different cell types at various developmental stages. It was generally believed that proteins in FFPE tissues were altered and inaccessible for analysis by mass spectrometry until recent developments have shown it is possible to access the protein in imaging mass spectrometry (IMS) experiments following antigen retrieval [8]. The direct analysis of cancer tissues by IMS preserves the spatial proteomic information. Consequently, it is holds great promise for the discovery of highly specific biomarkers. A recent study demonstrated the potential of MALDI-imaging MS for HER2 status of clinical parameters in cases of breast cancer based on protein patterns. This potentially allows the selection of patients likely to respond to trastuzumab treatment. Comparing the HER2-positive (HER2+) vs HER2-negative (HER2−) breast cancer protein profiles, the authors found a specific proteomic signature of seven species, able to accurately classify the HER2 status with a sensitivity of 83%, a specificity of 92% and an overall accuracy of 89% [9].
Protein biomarkers and conventional pathologic features
In a very recent study, using protein extracts of breast tissues (n=171), we have used SELDI-TOF MS to discover two proteins that, in combination, show high discrimination between breast cancer and healthy breast tissue samples [10]. These putative breast cancer biomarkers were verified on an independent sample set, and identified as ubiquitin and a novel truncated form of the S100 protein family member, S100P. Interestingly, the combined panel of two protein markers was significantly associated with tumour histologic grade, size, and lymphovascular invasion (LVI), and also with ER-positive (ER+) and PR-positive (PR+) status and HER2 overexpression. In particular, as shown in Figure 1, significant positive associations were seen between a previously unreported short form of S100P (9.2kDa) and tumour size, high grade, LVI and lymph node involvement (LN), and also associated with hormone receptor positive status and HER2 overexpression (unpublished data). These results implicate that a protein biomarker panel may indicate a HER2-enriched breast cancer subtype with poor prognosis, and that measurement of S100P may be valuable both in the classification of breast cancer and as a possible target for treatment. Furthermore, in another very recent study, the prognostic value of S100P was also tested for FFPE tissue obtained from 85 breast cancer patients with a median follow up of 17 years. High immunocytochemical staining of breast tumour sections for S100P has been associated with poor long-term patient survival [11].
Conclusion and future prospects
In this era of using new high-throughput methods, many new protein biomarkers have been reported for both prognostic and predictive purposes. However, none of these have been widely accepted in routine clinical practice, possibly due to a lack of sufficient validation to meet the criteria of the American Society of Clinical Oncology’s tumour marker utility grading system and guideline recommendations [1]. Identification of novel markers based on gene expression and proteomic profiling has led to more definitive insights into tumour biology. The accurate evaluation of the status of clinical parameters in cases of breast cancer is of primary importance for prognostic value and therapeutic decision. Different methodologies successfully used for breast cancer prognostic information and therapy outcome prediction may suggest that the future diagnostics and consequent individualization of therapy will become much more wide-ranging.
References
1. Harris L, Fritsche H, Mennel R, Norton L, Ravdin P, Taube S, Somerfield MR, Hayes DF, Bast RC, Jr. American Society of Clinical Oncology 2007 update of recommendations for the use of tumour markers in breast cancer. J Clin Oncol 2007; 25(33): 5287–5312.
2. Chung L, Baxter RC. Breast cancer biomarkers: proteomic discovery and translation to clinically relevant assays. Expert Rev Proteomics 2012; 9(6): 599–614.
3. Danova M, Delfanti S, Manzoni M, Mariucci S. Tissue and soluble biomarkers in breast cancer and their applications: ready to use? Journal of the National Cancer Institute Monographs 2011; 2011(43): 75–78.
4. Cheang MC, Chia SK, Voduc D, Gao D, Leung S, Snider J, Watson M, Davies S, Bernard PS, Parker JS, et al. Ki67 index, HER2 status, and prognosis of patients with luminal B breast cancer. Journal of the National Cancer Institute 2009; 101(10): 736–750.
5. Andrulis IL, Bull SB, Blackstein ME, Sutherland D, Mak C, Sidlofsky S, Pritzker KP, Hartwick RW, Hanna W, Lickley L, et al. neu/erbB-2 amplification identifies a poor-prognosis group of women with node-negative breast cancer. Toronto Breast Cancer Study Group. J Clin Oncol 1998; 16(4): 1340–1349.
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The author
Liping Chung PhD
Kolling Institute of Medical Research,
University of Sydney, Royal North Shore Hospital, NSW 2065, Australia
E-mail: liping.chung@sydney.edu.au